School of Economics and Finance QUT Business School Queensland University of Technology Gardens Point Campus Brisbane, Australia

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1 AGRICULTURAL BIODIVERSITY, FARM LEVEL TECHNICAL EFFICIENCY AND CONSERVATION BENEFITS: AN EMPIRICAL INVESTIGATION THIS DISSERTATION IS SUBMITTED TO THE FACULTY OF BUSINESS, QUEENSLAND UNIVERSITY OF TECHNOLOGY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY MAY 2012 K.M.R. Karunarathna B. A (Economcs) Hons., Unversty of Peradenya, Sr Lanka M.Sc. (Envronmental Economcs), Unversty of Peradenya, Sr Lanka School of Economcs and Fnance QUT Busness School Queensland Unversty of Technology Gardens Pont Campus Brsbane, Australa

2 Statement of Orgnal Authorshp The work contaned n ths thess has not been prevously submtted to meet requrements for an award at ths or any other hgher educaton nsttuton. To the best of my knowledge and belef, the thess contans no materal prevously publshed or wrtten by another person except where due reference s made.. K. M. R. Karunarathna 21 st May, 2012

3 Ths dssertaton s dedcated to: To my lovng husband, Wasantha son, Kavndu and daughter, Dsun To my mother, father and all who helped me to make t true

4 ACKNOWLEDGEMENTS I greatly acknowledge the assstance I receved from numerous ndvduals and nsttutons for completng ths research. Specal thanks should go to my advsers, Professor Clevo Wlson and Professor Tm Robnson, for ther constant support and gudance throughout my graduate program. Ther kndness, patence, and contnual coachng are greatly apprecated. They encouraged me to carry out ths nterestng dssertaton research and for ther nvaluable advce, gudance, endless encouragement and untrng efforts to make t a success. They provded a stmulatng envronment wth productve dscusson throughout the dssertaton research that helped make me a better researcher. I am grateful to them for ther support and wsdom, and the kndhearted assstance extended to me throughout the study perod. I am also thankful for the nvaluable help and encouragement I receved from my dssertaton commttee members Dr. Mark McGovern, Dr. Henr Burgers, Prof. Tm Robnson and Prof. Clevo Wlson. I also would lke to thank the panel members of my PhD confrmaton semnar, especally Dr. Lousa Coglan, for her constructve comments. People who are lvng n Anuradhapura, Kurunegala and Ampara dstrcts deserve my thanks for ther cooperaton n the data gatherng effort. I greatly apprecate the help gven by many ndvduals ncludng enumerators and government offcers durng the data collecton process. I thank the Unversty of Peradenya for grantng me study leave, staff members n the Department of Economcs and Statstcs who encouraged me to pursue my postgraduate studes at the Queensland Unversty of Technology n Australa. v

5 I must recognze the constant help gven by my colleagues at the School of Economcs and Fnance, for ther assstance and cooperaton throughout the course of study. I am also thankful for the nvaluable help and encouragement I receved durng my QUT lfe from Dr. Tony Sahama n the faculty of IT. I also should thank to Dr. Jeanette who helped me correct Englsh n ths dssertaton. I thank partcpants of local and nternatonal conferences for provdng useful feedback and facltatng dscusson on ths work that I have presented to them. I have benefted a lot from workng wth them. I gratefully acknowledge the role of Queensland Unversty of Technology for provdng fnancal support for my graduate studes. It s only wth the help of QUT s IPRS scholarshp, I was able to undertake ths study n Australa. I therefore acknowledge and thank QUT for awardng me ths scholarshp. Further, I gratefully acknowledge the role of Natonal Centre for Advanced Studes n Humantes and Socal Scences (NCAS) for provdng fnancal support for my PhD research. I am also thankful to Professor Tm Robnson, former head of the school, School of Economcs and Fnance, and all other admnstratve staff of the faculty of busness for ther nvaluable servce receved durng my study perod at QUT. Last but not least I wsh to express my deep grattude to my husband, Wasantha for hs understandng, patence and encouragement throughout my graduate studes. I am ndebted to my lovng son, Kavndu and daughter, Dsun. As I had to spend consderable tme on ths study, they mssed ther mum durng the tme n the frst few years n ther lfe. Fnally, I am deeply grateful to my beloved mother for her nvaluable contrbuton throughout my lfe. I also owe a debt of grattude to my late v

6 father. I also acknowledge my brother, ssters and ther famles, for ther uncondtonal love nspraton and encouragement throughout my lfe. v

7 TABLE OF CONTENTS STATEMENT OF ORIGINAL AUTHORSHIP DEDICATION. ACKNOWLEDGEMENTS... TABLE OF CONTENTS. LIST OF TABLES... LIST OF FIGURES. LIST OF ABBRIVIATION. ABSTRACT. v v x x x xv CHAPTER 1: INTRODUCTION Overvew Motvaton Expected contrbutons of the study Structure of the thess.. 18 CHAPTER 2: STATUS AND TRENDS OF BIODIVERSITY IN SRI LANKA Bodversty wlderness area n the world Bodversty n Sr Lanka Present status and future challenges of bodversty Agrcultural bodversty n the country.. 33 CHAPTER 3: DATA SOURCES AND DESCRIPTION Introducton Selectng approprate sample sze Selectng respondents for the survey Feld survey and ts content Desgn choce experment survey 49 v

8 CHAPTER 4: FARMERS VALUATION OF AGRICULTURAL BIODIVERSITY Introducton Lterature revew on valuaton of agrcultural bodversty Random utlty models Choce experment method Choce experment desgn and model selecton Emprcal approach to choce experments study Soco-economc profle of sample respondents Data cordng and estmaton procedure Result of the condtonal logt model (CLM) Result of the CLM ncludng attrbutes and socoeconomc varables Result of the random parameter logt model Estmatng welfare changes wth changng attrbutes and ther level Summary and key fndngs 116 CHAPTER 5: FACTORS INFLUENCING FARMERS DEMAND FOR AGRICULTURAL BIODIVERSITY Introducton Lterature revew on demand for agrcultural bodversty Dervaton of demand for agrcultural bodversty Emprcal model specfcaton and relevant varables Theoretcal approaches for the relevant models Posson regresson model Negatve bnomal (NB2) regresson model Emprcal tests for dfferent count data models Soco-economc characterstcs of the households Determnants of crops varety demand Determnants of lvestock varety demand Summary and key fndngs 169 v

9 CHAPTER 6: FARMERS PREFERENCES FOR DIFFERENT FARMING SYSTEMS Introducton Lterature revew on farmer s preference for dfferent farmng systems Methods of explanng farmer s preferences Factors nfluencng the selecton of landrace cultvaton Factors nfluencng the selecton of organc farmng Farmers demand for mx farmng system Summary and key fndngs CHAPTER 7: AGRICULTURAL BIODIVERSITY AND FARM LEVEL EFFICIENCY Introducton Lterature on agrcultural bodversty and farm level effcency Method of estmatng farm level techncal effcency Emprcal model of estmaton Estmates for parameters of stochastc fronter producton functon Estmatng margnal productvty and nput elastcty Varatons of techncal effcency Results of the neffcency model Summary and key fndngs. 238 CHAPTER 8: CONCLUSIONS AND POLICY IMPLICATIONS A summary of fndngs and dscusson Polcy mplcatons Lmtatons of the study and further research. 251 BIBLIOGRAPHY x

10 APPENDIX A (1): Defnng agrcultural bodversty APPENDIX A (2): TEV of agrcultural bodversty on small-scale farms APPENDIX A (3): Defnng TEV of agrcultural bodversty on farms. 290 APPENDIX B: Number of descrbed speces n the World APPENDIX C: Bodversty wlderness areas n the world APPENDIX D (1): Topography n Sr Lanka APPENDIX D (2): Major clmatc zones n Sr Lanka APPENDIX E: Protected areas under department of wldlfe n Sr Lanka APPENDIX F: Lst of protected areas of Sr Lanka APPENDIX G: Map showng survey areas n Sr Lanka APPENDIX H: Questonnare used n the survey APPENDIX I(1): A sample choce set s gven to the respondent APPENDIX I(2): Descrpton of 36 choce sets of the choce experment APPENDIX J: Descrptve statstcs of the sample respondents APPENDIX K: Zero nflated Posson / negatve bnomal regresson model APPENDIX L: MLE of parameters and pont estmates of TE APPENDIX M: Dervatves of elastctes usng translog producton functon 335 APPENDIX N(1): Lst of crops varetes on small-scale farms 336 APPENDIX N(2): Lst of lvestock breeds on small-scale farms x

11 LIST OF TABLES Tables Page Table 2.1: The lst of recorded speces n dfferent taxonomc groups Table 2.2: Estmated number of selected speces.. 29 Table 2.3: Natural ecosystem rchness Table 3.1: Estmatng mnmum sample sze for each dstrct Table 3.2: Detals of the survey areas Table 4.1: Classfcatons of small-scale farm attrbutes n the CE survey 85 Table 4.2: Attrbutes and ther levels.. 87 Table 4.3: Example of a choce set Table 4.4: Indvdual attrbutes for the estmaton of CL and RPL models Table 4.5: Regresson results of the CL model Table 4.6: Test of ndependence of rrelevance alternatves Table 4.7: CL model ncludng attrbutes and socoeconomc varables Table 4.8: Regresson results of the RPL model Table 4.9: Implct prce estmates for attrbutes Table 4.10: Estmates of WTA for varous scenaros: Ampara Table 4.11: Estmates of WTA for varous scenaros: Anuradhapura Table 4.12: Estmates of WTA for varous scenaros: Kurunegala Table 4.13: Smulaton total welfare gans to the dstrcts Table 5.1: Defnton of the agrcultural bodversty Table 5.2: Defnton of potental explanatory varables Table 5.3: Explanatory varables used n the demand model. 142 Table 5.4: Summary of the econometrc models to be used for the analyss Table 5.5: Posson regresson results for crops varety model Table 5.6: Posson regresson results for anmal varety model. 167 Table 6.1: Defnton dependent varables n dfferent models Table 6.2: Defnton of potental explanatory varables Table 6.3: Explanatory varables and ther expected sgns. 186 Table 6.4: Probt regresson results for landrace producton model Table 6.5: Probt regresson results for organc producton model. 191 Table 6.6: Probt regresson results for agro-dversty model. 195 Table 7.1: ML estmates for parameters of the producton functon x

12 Table 7.2: Estmated elastctes and margnal productvty of each nput. 227 Table 7.3: Frequency and percentage dstrbuton of the techncal effcences. 229 Table 7.4: Average TE, value of actual and potental output wth land sze Table 7.5: Average effcency wth farm type Table 7.6: ML estmates for parameters of the neffcency model LIST OF FIGURES Fgures Page Fgure 1.1: Summary of the three man sectons of the thess.. 10 Fgure 7.1: Stochastc fronter producton functon x

13 LIST OF ABBRIVIATION ASC BCAP CBD CS CEM CVM CL DSDs DFC EEZ EEPU EU FAO GDP GLR GM HYV IBEC IIA IID IFPRI IUCN LKR MLE MNL NB NBM NCS NEAP NGOs PGRC Alternatve Specfc Constant Bodversty Conservaton Acton Plan Conservaton on Bologcal Dversty Compensatng Surplus Choce Experment Method Contngent Valuaton Method Condtonal Logt Dvsonal Secretary Dvsons Department of Forest Conservaton Exclusve Economc Zone Envronmental Economc Polcy Unt European Unon Food and Agrculture Organzaton Gross Domestc Producton Generalsed Lkelhood Rato Genetcally Modfed Hgh Yeld Varetes Bodversty and Envronmental Conservaton Independence of Irrelevant Alternatves Independently and Identcally Dstrbuted Internatonal Food Polcy Research Insttute Internatonal Unon for Conservaton of Nature Sr Lankan Rupees Maxmum Lkelhood Estmator Multnomal Logt Negatve Bnomal Negatve Bnomal Model Natonal Conservaton Strategy Natonal Envronmental Acton Plan Non Government Organzatons Plant Genetc Resource Centre x

14 PM RPL RUM TE TEV TWTP TWTA UK USD VC WTA WTP ZIP ZINB Posson Model Random Parameter Logt Random Utlty Models Techncal Effcency Total Economc Values Total Wllngness to Pay Total Wllngness to Accept Unted Kngdom US Dollars Varance-covarance Wllngness to Accept Wllngness to Pay Zero-nflated Posson Zero-nflated Negatve Bnomal xv

15 ABSTRACT The ssues nvolved n agrcultural bodversty are mportant and nterestng areas for the applcaton of economc theory. However, very lttle theoretcal and emprcal work has been undertaken to understand the benefts of conservng agrcultural bodversty. Accordngly, the man objectves of ths PhD thess are to: (1) Investgate farmers valuaton of agrcultural bodversty; (2) Identfy factors nfluencng farmers demand for agrcultural bodversty; (3) Examne farmers demand for bodversty rch farmng systems; (4) Investgate the relatonshp between agrcultural bodversty and farm level techncal effcency. Ths PhD thess nvestgates these ssues by usng prmary data n small-scale farms, along wth secondary data from Sr Lanka. The overall fndngs of the thess can be summarzed as follows. Frstly, owng to educatonal and poverty ssues of those beng ntervewed, some polcy makers n developed countres queston whether non-market valuaton technques such as Choce Experment (CE) can be appled to developng countres such as Sr Lanka. The CE study n ths thess ndcates that carefully desgned and pre-tested nonmarket valuaton technques can be appled n developng countres wth a hgh level of relablty. The CE fndngs support the pror assumpton that small-scale farms and ther multple attrbutes contrbute postvely and sgnfcantly to the utlty of farm famles n Sr Lanka. Farmers have strong postve atttudes towards ncreasng agrcultural bodversty n rural areas. Ths suggests that these atttudes can be the bass on whch approprate polces can be ntroduced to mprove agrcultural bodversty. Secondly, the thess dentfes the factors whch nfluence farmers demand for agrcultural bodversty and farmers demands on bodversty rch farmng systems. As such they provde mportant tools for the mplementaton of polces desgned to avod the loss agrcultural bodversty whch s shown to be a major mpedment to agrcultural growth and sustanable development n a number of developng countres. The results llustrate that certan key household, market and other characterstcs (such as agrcultural subsdes, percentage of nvestment of owned money and farm sze) are the major determnants of demand for agrcultural bodversty on small-scale farms. The sgnfcant household characterstcs that determne crop and lvestock dversty nclude household member partcpaton on the farm, off-farm ncome, shared labour, market prce fluctuatons and household wealth. Furthermore, t s shown that all the ncluded market characterstcs as well as agrcultural subsdes are also mportant determnants of agrcultural bodversty. Thrdly, t s found that when the effcency of agrcultural producton s measured n practce, the role of agrcultural bodversty has rarely been nvestgated n the lterature. The results n the fnal secton of the thess show that crop dversty, lvestock dversty and mx farmng system are postvely related to farm level techncal effcency. In addton to these varables educaton level, number of separate plots, agrcultural extenson servce, credt access, membershp of farm organzaton and land ownershps are sgnfcant and drect polcy relevant varables n the neffcency model. The results of the study therefore have mportant polcy mplcatons for conservng agrcultural bodversty n Sr Lanka. xv

16 CHAPTER ONE INTRODUCTION 1.1 Overvew Bologcal dversty provdes all of manknd s food requrements, numerous medcnes and ndustral products. Agrcultural bodversty 1 (see Appendx A.1 for more detals) s a sub-set of general bodversty whch s essental for global food producton, lvelhood securty and sustanable agrcultural development (Brookfeld, 2001; Pascual and Perrngs, 2007). Agrcultural bodversty ncludes all forms of lfe drectly relevant to agrcultural producton. In addton to provdng drect benefts to farmers, agrcultural bodversty mproves ecologcal processes by regulatng clmate, mantanng sol qualty, provdng protecton from eroson, storng nutrents and breakng down polluton (Thrupp, 1988; FAO, 1999). Some socetes also value bodversty for cultural reasons as t mantans the aesthetc value of landscapes (Nagarajan et al., 2007). Despte all these benefts prevous experence has shown that populaton growth, nequty, nadequate economc polces and nsttutonal systems have manly contrbuted towards the ncreasng loss of agrcultural bodversty n the world (Ayyad, 2003; Ganesh and Bauer, 2006). Low levels of educaton and lack of ntegrated research on natural ecosystems and ther nnumerable components may exaggerate the process, 1 FAO, (1999a) defned agrcultural bodversty as the varety and varablty of anmals, plants and mcro-organsms that are used drectly or ndrectly for food and agrculture, ncludng crops, lvestock, forestry and fsheres. It comprses of the dversty of genetc resources (varetes, breeds) used for food, fodder, fbre, fuel and pharmaceutcals. It also ncludes the dversty of non-harvested varetes that support producton (sol mcro-organsms, predators, pollnators), and those n the wder envronment that support agro-ecosystems (agrcultural, pastoral, forest and aquatc) as well as the dversty of the agroecosystems. 1

17 especally n developng countres. Whle the loss of habtats may occur through clearng land for agrculture, specalsaton of agrcultural practces reduces farm level crops, genetc or lvestock dversty (Swanson, 1999). Neoclasscal economc theory predcts that specalsaton n one knd of varety or technology s the proft maxmsng soluton for a farmer and that t s costly to mantan a dverse portfolo of speces, varetes and management systems due to several reasons. These reasons nclude tme and management ntensty of dversty mantenance and hgh opportunty costs assocated wth not specalsng n partcular varetes wth the hghest current economc return (Brush et al., 1992; Smale et al., 2001; Gauchan and Smale, 2003). But n realty, t has been observed that contrary to economc theory, farmers, especally n developng countres often prefer to mantan a dverse portfolo of varetes and to contnue employng tradtonal agrcultural technologes, even when modern technologes and hgh yeldng varetes (HYVs) are avalable to them. Several explanatons have been found for ths persstence n management of agrcultural bodversty on farms. These nclude farmers atttudes towards rsk (n yeld, ncome, prce and consumpton) and ther need to compensate for market mperfectons n satsfyng household demands for dversty n consumpton. Many farmers manage hgh levels of agrcultural bodversty on farms to keep optons open for possble future benefts of dversty, such as beng sources of new varetes. Many farm famles use agrcultural bodversty as a way of spreadng out labour needs to ensure that lmted labour supples are used more effcently. There are also cultural benefts (e.g. cusne, rtual, prestge, payment, gft, socal tes) attached to agrcultural 2

18 bodversty. Equally, agrcultural bodversty s found to have postve mpacts on overall productvty and sol qualty. In recognton of agrcultural bodversty mportance, nternatonal agreements such as the Conventon of Bologcal Dversty (CBD) and the nternatonal nsttutes such as Internatonal Food Polcy Research Insttute (IFPRI) and Insttute of Bodversty and Envronmental Conservaton (IBEC) encourage the desgn of polces that convey economc ncentves for farmers to conserve agrcultural bodversty (CBD, 2002). The number of economc studes that have attempted to explan the reasons for on farm conservaton and the means by whch ths method of conservaton can be strengthened, are however small compared to the magntude of the problem of loss of agrcultural bodversty n farmers felds throughout the world. Modern agrcultural methods and technologes have brought spectacular ncreases n food producton (Tlman et al., 2002), but not wthout hgh envronmental costs. Efforts to boost food producton, for example, through drect expanson of cropland and pastures, have negatvely affected the capacty of ecosystems to support food producton and to provde other essental servces. Food producton wll undoubtedly be affected by external factors such as clmate change. But the producton and dstrbuton of food tself s also a major cause of clmate change. As food producton becomes ncreasngly ndustralsed, wth fewer nches avalable for varetes other than those targeted for producton, a rapd declne n the dversty of varetes used has been observed. These major changes n producton have lead to smplfed and less reslent agro-ecosystems, reducng not only the number of nches but also the range of products and ther dstrbuton over tme and space (FAO, 1999b). There s ample research whch ndcates 3

19 that modern agrcultural methods and technologes can generate large envronmental and socal costs. A substantal contrbuton to sustanng agrcultural bodversty can therefore be made through contnued support of producer organsatons workng wth small-scale farm producer groups to conserve, develop and use sustanably food and agrcultural genetc resources ncludng plant, anmal and aquatc. As mentoned above, agrcultural bodversty s erodng and resources avalable for conservaton are lmted, mplyng economc valuaton (especally estmaton of total economc value) can play an mportant role n ensurng an approprate focus for conservaton efforts (UNEP, 1995; Drucker et al., 2001). As Swanson et al. (1997) state, n order to desgn polces and programmes that both encourage mantenance of agrcultural bodversty on farm and ensure that economc and agrcultural development occur, t s necessary to establsh the value of what t s that needs to be conserved. The drect and ndrect benefts of conservng farm level bodversty can be numerous, especally n sem-subsstence economes. The measurement of economc values of servces provded by agrcultural bodversty can be done on the bass of total economc values (TEV). TEV conssts of use and non-use values. Dagrammatcally, the TEV framework can be expressed as shown n Appendces A.1 and A.2. Benefts obtaned by ndvduals usng agrcultural bodversty are defned as use values. Use values of agrcultural bodversty nclude, drect, ndrect, portfolo values and opton values 2 (Brown, 1990; Prmack, 1993; Swanson, 1996; Evenson et al., 1998). On the other hand, bequest values, altrustc values, exstence values and cultural values of agrcultural 2 Opton values can be placed under both use and non-use values. It ncludes future drect and ndrect use values. 4

20 bodversty are consdered under non-use values (Krutlla, 1967; Brown, 1990; Prmack, 1993; Evenson et al., 1998). In ths study, fve ndcators (components) are used to capture the use and non-use values of agrcultural bodversty. They are: crop dversty (number of crops varetes that are grown on the farm), lvestock dversty (number of lvestock varetes on the farm), mxed farmng systems (ntegraton of crop varetes and lvestock breeds), landrace cultvaton (whether a farm contans crop varetes that have been passed down from the prevous generaton and/or has not been purchased from a commercal seed suppler) and organc producton (when ndustrally produced and marketed chemcal nputs are not used n farm producton). Among these fve ndcators, the frst two represent agrcultural bodversty whle last three represent the dfferent farmng systems whch help mantan bodversty under rch farmng practces. More detals about usng these varables to capture farmers valuaton of agrcultural bodversty are found n studes conducted bybenn et al. (2003), Benn et al. (2004), Bellon (2004), Brol et al. (2006), Nagarajan et al. (2007), Brol et al. (2008) and Hadgu et al. (2009). It s evdent that economc values of conservng these components can only be calculated based on a comprehensve dentfcaton of the envronmental and socal values of the ecosystem servces that they provde. Commercal drect use value of agrcultural bodversty can be a relatvely small component of ther total use value n agrculture (Drucker et al., 2005). Many values are not captured well n market prces and hence nvestments n conservaton may not occur optmally (Swanson, 1996). Ths s one of the reasons why farmers actvtes gradually 5

21 reduce agrcultural bodversty. Some of the other possble reasons why farmers may tend to destroy agrcultural bodversty can be explaned as follows. Frstly, most benefts of conservng agrcultural bodversty are long-term (and nter-generatonal) and not traded n the market. For example, by cultvatng dfferent crops and lvestock, sol fertlty can be mproved. However, farmers may not take nto account these longterm benefts. Secondly, poor farmers wth lower levels of educaton may not be aware about the total benefts of conservng agrcultural bodversty. They may consder only the short-term drect use benefts and may select the specalsaton of cash crops as a mean of ncreasng ncome n the short term. However, sngle crops are more vulnerable to the rapd spread of dsease, ths greatly heghtens the vulnerablty of resource-poor farmers. Thrdly, sales promoton actvtes and credt facltes have promoted the cultvaton of modern crop varetes usng pestcdes and chemcal fertlsers. Such a system can ncrease short-term yelds whle destroyng the reslence of agro-ecosystems n the long-term. Fourthly, hgh dscount rates wll decrease the future value of agrcultural bodversty and provde some ncentves to ncrease present consumpton whch n turn can ncrease the degradaton of bodversty. These reasons show that as long as farmers underestmate the total benefts of conservng agrcultural bodversty, there wll be smplfed and less reslent agro-ecosystems, thus reducng the number of servces provded by them n the long-run. Although much theoretcal as well as emprcal work has nvestgated varous aspects of agrcultural bodversty there s stll a consderable lack of understandng of what socal benefts could be acheved from conservng agrcultural bodversty n developng countres. Economcs to some extent provdes us wth the analytcal tools to assst n 6

22 gudng towards socally desrable outcomes. However, lttle theoretcal and emprcal work has been undertaken n ths area of research. Ths means that there exsts a gap n the theoretcal and emprcal lterature, addressng practcal ssues utlsng correct economc nstruments n ths area. Ths thess examnes three man ssues that arse n the area of agrcultural bodversty n the context of Sr Lanka. The focus of the thess allows for the study of drect and tangble ssues facng polcy makers. After revewng a large number of studes, exstng models and emprcal work, the shortcomngs that exst n ther applcaton are dentfed. They are: (1) Farmers valuaton of agrcultural bodversty s not properly explaned. As a result socal welfare losses due to loss of agrcultural bodversty have not been adequately estmated. It s evdent that management of agrcultural bodversty requres measurement, and measures of dversty to some extent. It s thus necessary to measure and dsentangle some of the separate benefts of agrcultural bodversty n order to formulate approprate polces. However, many of the goods and servces provded by dfferent components of agrcultural bodversty are crucal, but not always quantfable n monetary terms. Many of these goods and servces are not traded n the market place and do not have an obvous prce or commercal value. The danger s that f these unprced values are not ncluded n the decson-makng process, the fnal decson may favour outcomes whch do have a commercal value and decson makers may not have full awareness of the consequences for bodversty conservaton. Therefore, t s of paramount mportance to understand the true value of agrcultural bodversty and to estmate the welfare change of the socety wth the change of agrcultural bodversty. The frst secton of ths thess attempts to capture farmers valuaton of agrcultural 7

23 bodversty. Ths objectve wll help to determne the economc value of conservng agrcultural bodversty to socety. (2) Factors affectng the conservaton of agrcultural bodversty are not adequately dentfed n the lterature. The lterature shows that, despte the emphass placed by polcy decson-makers on ncreasng the conservaton of bodversty n small scalefarms 3, t s ncreasngly becomng degraded n many agrcultural areas (see, for example, Matson et al., 1997; Perrngs, 2001; Brookfeld et al., 2002; Mattson and Norrs, 2005). Therefore, t s mportant to understand whch factors are contrbutng to decreasng agrcultural bodversty n small-scale farms. In the second secton of ths thess farmers demand for agrcultural bodversty and envronmentally rch farmng systems such as organc farmng and landrace cultvaton are estmated. Ths objectve wll help understand and dentfy factors nfluencng the degradaton of agrcultural bodversty n small-scale farms. (3) No prevous analyss has nvestgated the lnks between agrcultural bodversty and farm level techncal effcency. Some studes reveal that crop dversty s postvely related to agrcultural productvty of small-scale farms (see, for example, D Falco and Perrngs, 2003). They also fnd that nter-speces crop genetc dversty s postvely related to mean ncome and negatvely related to the varance of ncome. Whle ncreasng productvty on farms, dverse farmng systems help farmers manage some 3 A small-scale farm s defned as any farm whch s less than one hectare. We only concentrate on smallscale farms n ths study. Ths s due to three reasons. Frst, small-scale farms are the most common type of farms n rural areas n Sr Lanka. Second, mantanng dverse farmng systems wth the objectve of acqurng famly food consumpton s a common characterstc of small-scale farms rather than large-scale farms. Thrd, some ndcators of agrcultural bodversty that we consdered n ths study such as anmal dversty, landrace cultvaton and organc producton can commonly be seen n small-scale farms n the country. 8

24 resources, such as labour, optmally. It also helps to ncrease farm revenues by mnmsng market rsks whch s a common problem n developng countres. For example, n a partcular season prces of some crops or lvestock can decrease whle others can ncrease. Therefore, mantanng more dverse farmng systems help farmers manage unnecessary rsks n the markets. In the thrd secton of ths thess we nvestgate the relatonshp between agrcultural bodversty and farm level effcency.ths type of study allows us to analyse the effects of agrcultural bodversty on farm level techncal effcency. The overall objectve of ths thess s to address some of the ssues related to the above mentoned three sectons n the context of Sr Lanka s agrculture. Accordngly, the thess has three separate sectons. The structure of the three man sectons and subsequent studes are summarsed n Fgure 1.1. The frst secton of the thess analyses farmers valuaton of agrcultural bodversty. The choce experment (CE) method whch s one of the most wdely used and a preferred technque s used for ths purpose. The results are then used to estmate the lkely welfare gans under varous hypothetcal scenaros. The results of the study wll enable polcy decson-makers to better understand the relevant ssues and thereby take approprate acton to mtgate some of the adverse ssues n ths feld. The second secton of the thess examnes the demand for agrcultural bodversty n small-scale farms n Sr Lanka. Ths secton conssts of two studes. The frst study analyses farmers demand for crops and lvestock varetes respectvely whle the second 9

25 study examnes farmers demand for landrace cultvaton, mxed farmng and organc farmng systems. Ths secton attempts to dentfy the dfferent market and non-market Agrcultural Bodversty Conservaton Benefts Demand Estmaton Techncal Effcency Choce Experment Approach Agrcultural Household Model Stochastc Producton Fronter Approach Welfare change estmaton Agrcultural bodversty Dfferent farmng systems Effcency gans wth AB Prmary data: Three dstrcts Prmary data: Three dstrcts Prmary data: Three dstrcts Fgure 1.1: Summary of the three man sectons of the thess factors whch are mportant for ncreasng agrcultural bodversty on small-scale farms. An agrcultural farm household model s used for ths purpose. The motvatons of the second secton of ths thess are threefold. Frstly, ths study nvestgates whether farmers wthn a sem-subsstence economy allocate farm resources (e.g. land or household tme endowment) to the producton of food crops and thus have hgher levels 10

26 of agrcultural bodversty, or to cash crops 4, and have a subsequent loss of agrcultural bodversty. Secondly, the emprcal research that has nvestgated farmers preferences of envronmentally frendly farmng systems s lmted n the lterature (Van Dusen, 2000; Smale et al., 2001). Therefore, t s mportant to dentfy dfferent factors whch support an ncrease n landrace cultvaton, mxed farmng systems and organc farmng systems. Thrdly, a common fndng of prevous studes n ths area shows that market development s one of the causes of agrcultural bodversty loss on farms n most developng countres (Smale et al., 2001). Ths study attempts to nvestgate ths fndng usng sem-subsstence farm level data n Sr Lanka. The thrd secton of the thess nvestgates the relatonshp between agrcultural bodversty and farm level techncal effcency. The stochastc producton fronter approach s used to estmate farm level techncal effcency. There s ncreasng evdence (Adams et al., 2004; Agrawal and Redford, 2006) to show that agrcultural bodversty conservaton can n turn facltate ncreasng productvty and farm level effcency n small-scale farmng. However, the exstng scentfc knowledge regardng agrcultural bodversty and ts lnk wth farm level techncal effcency has not been fully examned. The exstng lterature does not assess the value of ecosystem servces to the poor and the mplcatons of these lnks for development polcy. As a result, the need for proper estmaton of costs and benefts of conservng agrcultural bodversty, as well as the demand for ntroducng approprate polcy regmes for managng them s ncreasng (Romstad et al., 2000). 4 Cash crops are those whch are produced for the purpose of generatng cash or money. The products are therefore ntended to be marketed for proft. A specalzed farmng system s the mostly preferred farmng system for the cultvaton of cash crops. 11

27 Among these sectons, the frst and the second sectons deal wth the mportant aspects of conservng agrcultural bodversty whle the thrd secton nvestgates the relatonshp between agrcultural bodversty and farm level techncal effcency. The mplcatons of these fndngs wll help llustrate the mportance of conservng natural resources n agrculture. Ths study wll help mplement polces to reduce degradaton of bodversty that can be hypotheszed to be ncreasngly posng a major mpedment to agrcultural growth and sustanable development n many developng countres. Therefore, the fndngs of the study wll provde useful polcy mplcatons. Sr Lanka s an deal representatve country for ths type of study. Ths s because the country, beng largely agrcultural, hstorcally has had phases of agrcultural polcy development on the bass that development of agrculture would lead to the overall development of the naton and would thus help to eradcate poverty. It has been later realzed that the ncreasng efforts to rase agrcultural growth has cost the country n terms of land as well as bodversty degradaton (Anon, 1999). Sr Lankan agrculture today has a dual structure consstng of large-scale, mechansed farms alongsde semsubsstence, small-scale farms managed wth famly labour and tradtonal practces. These Sr Lankan small-scale farms 5 have a range of local varetes of trees, crops and lvestock breeds, as well as sol mcro-organsms. Agrcultural scentsts descrbe small-scale farms as mcro-agro ecosystems that are rch n several components of agrcultural bodversty. Many expect that as a result of 5 Small-scale farms are sem-subsstence n nature and are the most common type of farms n rural area n Sr Lanka. These farms are prvately owned, labour ntensve and has a tradtonal producton system that mantans a hgh level of agrcultural bodversty n Sr Lanka. 12

28 contnued economc transton, the dual structure of Sr Lankan agrculture and the share of home-produced food wll eventually dsappear. So the prvate provson of publc goods generated by small-scale farms management cannot be sustaned n the long run.in addton to that, the dsappearance of the rural-based mult-crops farmng system has affected rural communtes n Sr Lanka n many ways (Anon, 1999). Therefore, t s necessary to mplements agr-envronmental schemes to advance the use of specfed farmng methods n rural areas, but so far the role of small-scale farms wthn these schemes has not been elucdated. Ths study dentfes the least-cost optons for ncludng farmng communtes n Sr Lankan s agr-envronmental schemes, by charactersng those who value agrcultural bodversty n ther small-scale farms most. The motvatons for undertakng ths type of study are explaned n the followng secton. 1.2 Motvaton The overall am of ths study s to estmate the conservaton benefts of agrcultural bodversty n small-scale farms wth specal reference to Sr Lanka. The results of the study can be used to develop/mplement economcally proftable and envronmentally feasble agro-ecosystems n any country. It s clear that understandng these ssues s crucal when formulatng polces to upgrade lvelhood of rural households and enhancng agrcultural bodversty. A study of ths nature also helps to develop a sustanable agrcultural system that mnmses the socal cost of usng natural resources. Lack of suffcent ncentves for managng farm level agrcultural bodversty could be one of the constrants n conservng bodversty n most developng countres. The frst 13

29 am of ths thess s to revew the current state of knowledge assocated wth agrcultural bodversty and to dentfy gaps n our knowledge base n ths area. Secondly, approprate economc methodologes are appled to analyse the three man research questons that have been hghlghted n the ntroducton. The overall objectve of the thess s to establsh a case for ncreasng the sustanable use of agrcultural bodversty n mprovng people s well-beng and food and nutrton securty. Agrcultural bodversty provdes a wde range of drect and ndrect benefts to the farmng communty (see, Appendces A.2 and A.3 for more detals). However, many human actvtes contrbute to unprecedented rates of bodversty loss, whch threaten the stablty and contnuty of ecosystems as well as ther provson of goods and servces. In ths context, several studes have been conducted to dentfy the possble monetary values based on farmers preference of agrcultural bodversty. However, most studes do not use a unform, clear measurement framework that enables the exploraton of the use of both market and non-market benefts. Moreover, exstng studes only analyse welfare changes wthout consderng crop heterogenety and regonal heterogenety smultaneously. The frst secton of ths research attempts to dentfy farmers valuaton of agrcultural bodversty usng the choce experment technque. Ths methodology helped estmate the welfare changes to socety due to changes n agrcultural bodversty. Heterogeneous farms from three dstrcts n Sr Lanka were selected. The mplcatons of these fndngs wll help llustrate the benefts of conservng dverse farmng systems n small-scale agrculture n developng countres. 14

30 Although the contrbuton of small-scale farms to household survval n developng countres s very mportant, only a few studes are avalable n ths feld. Moreover, exstng studes have taken nto account the market value of only crop or lvestock dversty. In ths study, the demand for crop dversty, lvestock dversty, mxed farmng systems, landrace cultvaton and organc producton are estmated usng farm household survey data. Ths wll help dentfy factors nfluencng the degradaton of agrcultural bodversty. The results wll provde nformaton for polcy makers to mplement a farmng system that provdes maxmum benefts to themselves and socety. The loss of bodversty may mpar ecosystem functons whle decreasng farm level productvty. A number of expermental studes have been performed or are emergng n ths area (see, for example, Johnson et al., 1996). However, most of these studes are restrcted to expermental work n the feld of scence rather than economc analyss. In ths context, the thrd secton of ths research attempts to nvestgate the relatonshp between agrcultural bodversty and effcency. To the best of my knowledge, no economcs study has attempted to examne ths relatonshp before. The results wll be a novel contrbuton to the exstng lterature. In ths study, t s expected to calculate farm level techncal effcency and nvestgate ts lnks wth mportant varables that are drectly lnked wth agrcultural bodversty. Stochastc producton fronter method s used for ths purpose. The results wll show a way of ncreasng farm level techncal effcency whch s a major challenge n developng countres, ncludng Sr Lanka. Rural people use and manage agrcultural bodversty n order to mprove ther lvelhoods. However, there s an ncreasng nterest n the opportuntes that 15

31 conservaton n a broader producton landscape could afford as a means to overcome poverty. Much has been wrtten on the loss of managed bodversty under threats from commercal and ntensfed agrcultural producton. However only a lmted amount of work has been conducted on how farm households manage ther resources so as to sustan and enhance them. The overall fndngs of ths study wll help conserve the agrcultural bodversty n small-scale farms whch can n turn help desgn poverty allevaton polces, especally n developng countres. In the next secton the expected contrbuton of ths thess wll be explaned. 1.3 Expected contrbutons of the study The strategc roles of agrcultural bodversty n food and nutrtonal securty and ncome generaton have been nsuffcently documented and understood. Systematc dentfcaton and nvestgaton of such roles are needed to buld on scattered research so far. Ths n turn requres the development, testng and dffuson of tools, methodologes and strateges that strengthen the mutually renforcng contrbuton of bodversty to lvelhoods and lvelhoods to bodversty conservaton. Most of the world s agrcultural bodversty s found n small-scale agrcultural areas n developng countres (Smale et al., 2001). Hence, an essental element of the research s to strengthen the benefts from agrcultural bodversty realsed by communtes n these areas. The key hypothess s that bodversty, gven certan nterventons and support, can be used to mprove nutrton and lvelhood optons, and n so dong creates ncentves for the conservaton of ts dversty n order to acheve a sustanable farmng system. 16

32 Ths research contrbutes to developng a bodversty rch agrcultural system across dfferent ecologcal and soco-economc contexts. It also evaluates the effects of dfferent farmng systems (wth dfferent bodversty levels) on farmers wellbeng. In the frst secton a stated preference method Choce modellng) s used to nvestgate farmers preferences for bodversty rch agrcultural systems. The second secton of the research attempts to dentfy the nfluencng factors for conservng agrcultural bodversty. The thrd secton contrbutes to the exstng lterature showng the agrcultural bodversty and ts lnk wth productvty and farm level effcency whch s a mssng part of the economcs lterature. Ths wll drectly help make sutable polces to mplement most approprate agrcultural systems for small-scale farms. In general, ths study wll show the mportance of the conservaton and sustanable use of agrcultural bodversty on farms. The overall polcy goal of the study s to ncrease awareness and generate support for nvestment n conservaton and development of agrcultural bodversty. The research ams at sharng nnovatve deas, research methods and fndngs n areas of agrcultural bodversty conservaton to the exstng lterature. It wll provde an opportunty to make necessary polces that provde ncentves to protect bodversty at farm level that generate regonal as well as global benefts n the future. Ths research wll also dentfy the weaknesses and the gaps that exst n ths feld. It wll help develop mechansms, approaches and pathways for strengthenng engagement on agrcultural bodversty for food and nutrton securty and envronment n the future. Ths wll nclude establshng a platform for actons for supportng and strengthenng research, development and polces n agrcultural bodversty. 17

33 The overall fndngs of ths PhD research wll help mplement polces to reduce degradaton of bodversty that s ncreasngly posng a major mpedment to agrcultural growth and sustanable development. The research fndngs could also be used to develop/mplement economcally proftable and envronmentally feasble agrecosystems n any country. Understandng the benefts of conservng bodversty and ts varatons are of paramount mportance when desgnng polces n ths feld. In general, the research contrbutes to the sustanable use of agrcultural bodversty to mprove farmers well-beng. For ths purpose, we frst attempt to explan some prevous economc models and dentfy the shortcomngs of prevous studes. Then we apply approprate economc models to analyse relevant ssues mentoned above, whch s an extenson of the conventonal work n ths feld. In ths context the results of the study help polcy makers understand the real ssues and come up wth approprate solutons. The way of carryng out ths task s explaned n the next secton. 1.4 Structure of the thess Ths PhD research addresses ssues related to agrcultural bodversty n small-scale farms whch are extremely mportant n the context of conservng agrcultural bodversty as well as mprovng the lvelhood of farmers n the agrcultural sector n Sr Lanka. The task of analysng these ssues s accomplshed n the followng manner. The thess s presented n eght chapters. Ths frst chapter defnes the research problem. Chapter two provdes background nformaton on the present status of bodversty n Sr Lanka, whch ncludes: bodversty wlderness area n the world; bodversty n Sr Lanka; present status and trends of bodversty and future challenges n agrcultural 18

34 bodversty. In Chapter three the conduct of the survey, the data collecton method and data sources are explaned. Chapter four nvestgates farmers preferences for dfferent attrbutes of agrcultural bodversty. It also analyses the welfare changes to socety due to changes n agrcultural bodversty. The ffth chapter estmates demand for agrcultural bodversty. It attempts to dentfy the determnants of crop dversty and lvestock dversty. Chapter sx focuses on the farmers preference for agrcultural bodversty rch farmng systems. Ths chapter nvestgates the mportant factors for selectng mxed farmng systems, landrace cultvaton and organc producton systems. Chapter seven focuses on nvestgatng the relatonshp between dfferent varables that represent agrcultural bodversty and farm level techncal effcency. The fnal Chapter provdes a bref summary of the thess wth a dscusson of the results wthn a polcy framework. Partcular attenton s pad to hghlghtng the key fndngs and polcy constrants. Ths Chapter attempts to clearly defne where the nformaton gathered from ths thess fts wthn the larger socal, poltcal and economc dscussons on agrcultural bodversty loss, economc growth and polcy falure. It presents some concludng remarks, whle hghlghtng obvous gaps n the lterature. The mportance of the analyss undertaken n ths study, along wth the lmtatons and remanng future research areas, are also hghlghted n the fnal Chapter. 19

35 CHAPTER TWO STATUS AND TRENDS OF BIODIVERSITY IN SRI LANKA 2.1 Bodversty wlderness area: a global prospectve Bodversty for food and agrculture ncludes the components of bologcal dversty that are essental for feedng human populatons and mprovng the qualty of lfe (Adams et al., 2004). It ncludes the varety and varablty of ecosystems, anmals, plants and mcro-organsms at the genetc, speces and ecosystem levels, whch are necessary to sustan human lfe as well as the key functons of ecosystems. Bodversty s usually explored at three levels; genetc dversty, speces dversty and ecosystem dversty (Brock and Xepapadeas, 2003). Genetc dversty s the varety of genes wthn a speces. Each speces s made up of ndvduals that have ther own partcular genetc composton. Ths means a speces may have dfferent populatons, each havng dfferent genetc compostons. To conserve genetc dversty, dfferent populatons of a speces must be conserved. Speces dversty s the varety of speces wthn a habtat or a regon. Speces are grouped together nto famles accordng to shared characterstcs. The number of globally dentfed speces under each category s gven n Appendx B. Ecosystem dversty s the varety of ecosystems n a gven place. An ecosystem s a communty of organsms and ther physcal envronment nteractng together (Brookfeld, 2001; Brock and Xepapadeas, 2003). An ecosystem can cover a large area, such as a whole forest, or a small area, such as an agrcultural farm. It s a communty of organsms and ther physcal envronment nteractng together. 20

36 Bodversty s crucal to the mantenance of many ecosystem servces such as regulaton of chemcal composton of the atmosphere, food producton, supply of raw materals, water provson, nutrents recyclng, bologcal control of populatons of flora and fauna, use of genetc resources and lesure actvtes (Brookfeld and Stockng, 1999; Brookfeld, 2001). Bodversty contnues to decrease at unprecedented rates as human development and expanson result n the fragmentaton and loss of habtat for flora and fauna (D Falco and Chavas, 2009). The loss of bodversty s expected to contnue at an unchanged ncreasng pace n the comng decades as well (Drucker et al., 2005). Key underlyng drvers for the loss of bodversty such as global populaton and economc actvty are expected to keep on growng. Between 2000 and 2050, the global populaton s projected to grow by 50 per cent and the global economy to quadruple (Slngenberg et al., 2009). The need for food, fodder, energy and wood wll unavodably lead to a decrease n and unsustanable use of natural resources. Bodversty s the bass of agrculture (see, Appendx A.1). As mentoned n the ntroducton, bodversty s the orgn of all speces of crops and domestcated lvestock and the varety wthn them. It s also the foundaton of ecosystem servces essental to sustan agrculture and human well-beng (Dwakar and Johnsen, 2009). Bodversty and agrculture are strongly nterrelated because whle bodversty s crtcal for agrculture, agrculture can also contrbute to conservaton and sustanable use of bodversty (Brookfeld, 2001). Indeed, sustanable agrculture both promotes and s enhanced by bodversty. Mantenance of ths bodversty s essental for the sustanable producton of food and other agrcultural products and the benefts these provde to humanty, ncludng food securty, nutrton and lvelhoods. As hghlghted 21

37 by Slngenberg et al. (2009) durng the last decades, worldwde bodversty has been lost at an unprecedented rate n all the ecosystems, ncludng agro-ecosystems. Accordng to the FAO (1999), t s estmated that about three-quarters of the genetc dversty found n agrcultural crops and lvestock has been lost over the last century, and ths genetc eroson wll further contnues n the future. Therefore, understandng the mportant causes of agrcultural bodversty loss s mportant for conservng bodversty n the world. A map showng the bodversty wlderness area n the world s gven n Appendx C. As can be seen, Sr Lanka s dentfed as a bodversty wlderness area. In ths context, the next secton provdes a bref overvew about the bodversty n Sr Lanka. 2.2 Bodversty n Sr Lanka Sr Lanka s an sland wth a total land area of 6,570,134 hectares, a coastlne of 1,600 km and an Exclusve Economc Zone (EEZ) that extends up to 320 km beyond the coastlne (Department of Census and Statstcs n Sr Lanka, 2010). Total cultvated land and forest cover comprse 39 per cent and 24 per cent, respectvely. The country s one of the smallest, but bologcally dverse countres n Asa (Sanjeeva, 2003). Consequently t s recognzed as a bodversty hotspot of global and natonal mportance. It s vared clmate and topography condtons have gven rse to rch speces dversty, beleved to be the hghest n Asa n terms of unt land area (Kotagama, 2002). Many of the speces are endemc, a reflecton of the sland's separaton from the Indan subcontnent. Ths s especally relevant for mammals, amphbans, reptles and flowerng plants. These speces are dstrbuted n a wde range of ecosystems whch can 22

38 be broadly categorzed nto forest, grassland, aquatc, coastal, marne and cultvated (Mnstry of Envronment and Natural Resource n Sr Lanka, 2007). The dversty of ecosystems n the country has therefore resulted n a host of habtats, whch contan hgh genetc dversty. In the broader context, bodversty n Sr Lanka ncludes speces dversty, genetc dversty and ecosystem dversty (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). An nterestng feature of ths speces dversty s ts hgh degree of endemsm, whch s observed n several taxonomc groups. A large proporton of these endemc speces s found n the wet zone n the south western regon of the sland. Genetc dversty s another component of bodversty that s mportant but not well nvestgated (Bellon, 2004). Almost all of the avalable nformaton s confned only to economcally mportant agrcultural crops. The Plant Genetc Resource Centre (PGRC) at Gannuoruwa, Peradenya, Sr Lanka has collected and preserved propagatve materal of a large number of speces from varous agro-clmatc zones of the country. For example, the PGRC has germoplasm materals of 3,194 tradtonal varetes and cultvars, and 17 wld relatves of rce (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). There s a wde range of ecosystem dversty under dfferent clmatc condtons n the sland. The topography n Sr Lanka and the major clmatc zones are shown on the maps n Appendx D.1 and D.2. The major natural ecosystems are forests, grasslands, nland wetlands, and coastal and marne ecosystems (Kotagama, 2002). There are also agrcultural ecosystems. Forests vary from wet evergreen forests (both lowland and 23

39 mountan), dry mxed evergreen forests to dry thorn forests. Grasslands are found n mountans and low country wetlands nclude a complex network of rvers and freshwater bodes. Marne ecosystems nclude sea-grass beds, coral reefs, estuares and lagoons and mangrove swamps. Contemporary ssues n relaton to the dversty of valuable ecosystems are: deforestaton, sol eroson, threatened wldlfe populatons (as a result of both poachng and urbansaton), coastal degradaton from mnng actvtes and ncreased polluton. Most of these ssues can be controlled by usng approprate polces. The Envronmental Economc Polcy Unt (EEPU) n Sr Lanka s responsble for the formulaton and deployment of polcy conservng and protectng Sr Lanka s natve natural captal. Although the EEPU s attemptng to address these ssues, the short term development goals that encourage economc growth over unsustanable resource use have generated a number of ssues. There are numerous polces, laws, acton plans and nsttutons nvolved n the conservaton of Sr Lanka s bodversty. Although most of the laws relate drectly or ndrectly to bodversty conservaton, mplementaton has been sluggsh (Sanjeeva, 2003). Therefore, adoptng sutable polces focusng on rural communtes, encompassng both economc development and ecologcal conservaton efforts would ad Sr Lanka n retanng long-term value n ts natural captal. There are many legslatve enactments that deal wth the protecton of bologcal resources n the country. In 1980, The Natonal Envronmental Act Consttuted the Central Envronmental authorty and establshed a Natonal Conservaton Strategy (NCS) to protect bodversty n the country. In 1988, the NCS was adopted to deal wth envronmental degradaton (Mnstry of Envronment and Natural Resource n Sr Lanka, 2007). In 1991, the Natonal Envronmental Acton Plan (NEAP) was adopted 24

40 for a four year perod. Based on the outcomes of ts mplementaton, t was revsed n 1994, for the perod Over the years these envronmental polcy frameworks have nfluenced and helped shape several sectoral and natonal development plans. The Natonal Conservaton Strategy, the Natonal Envronmental Acton Plan, the Forestry Sector Master Plan, the Natonal Coastal Zone Management Plan, and Coastal 2000, are some of the polcy documents that have addressed bodversty conservaton n the country (Mnstry of Envronment and Natural Resource n Sr Lanka, 2007). The Sr Lanka Bodversty Conservaton Acton Plan (BCAP) was adopted n The BCAP has dentfed four broad areas of ecosystem dversty, namely forests, wetlands, coastal and marne systems, and agrcultural systems. Under each ecosystem, the man ssues have been dentfed and the recommended actons and the mplementng nsttutons defned. At the regonal level, bodversty acton plans have been developed (Mnstry of Envronment and Natural Resource n Sr Lanka, 2007). The Internatonal Unon for Conservaton of Nature (IUCN) s currently workng on developng a legal framework to safeguard tradtonal knowledge relatng to the use of medcnal plants. However, shortages of traned manpower and fnancal assstance, and weak legslaton have affected the successful mplementaton of polces n ths feld. As a result the country s bodversty s contnung to decrease. Therefore, studes n ths area would provde enormous benefts for conservng bodversty n the future. The next secton provdes detals about the present status and future challenges of bodversty n Sr Lanka. 25

41 2.3 Present status and future challenges of bodversty Sr Lanka has the hghest bodversty per unt area of land among Asan countres n terms of flowerng plants and all vertebrate groups except brds (Kotagama, 2002). Accordng to the Mnstry of Envronment and Natural Resources n Sr Lanka (2007) the vegetaton of Sr Lanka supports over 3,350 speces of flowerng plants and 314 speces of ferns and fern alles. There s also consderable nvertebrate faunal dversty. The vertebrate fauna nclude 51 speces of teleost fshes, 39 speces of amphbans, over 125 speces of reptla, over 435 speces of brds, 96 speces of mammals ncludng 38 speces of marne mammals (IUCN, 2007). Among the vertebrates, there are 65 speces of freshwater fshes ndgenous to Sr Lanka, of whch about half s endemc. Many of these speces are rverne or marsh dwellng and occur manly n the wet zone streams. In addton, there are 22 speces of ntroduced fsh whch are consumed for food. There are about 350 speces of marne fsh whch nclude ornamental fshes and food speces such as seer, tuna and skpjack (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Table 2.1 summarses overvew of the status of some speces n Sr Lanka. 26

42 Table 2.1: The lst of recorded speces n dfferent taxonomc groups Taxonomc group No. of speces Percentage of world flora Sr Lanka World Angosperms 3, , Gymnosperms Pterdophytes , Bryophytes , Lverworts Lchens , Fung 1,920 46, Algae *2,260 70, Vrus/Bactera (NA) 8,050 - Source: Mnstry of Envronment and Natural Resources n Sr Lanka (2007) Note: *Fresh water In terms of speces, genes and ecosystems, Sr Lanka has a very hgh bodversty and s one of the 18 hot spots n the world (IUCN, 2007). The wet zone ranforests have nearly all of the country s woody endemc plants and about 75 per cent of the endemc anmals (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). The genetc dversty of agrcultural crops s qute remarkable, wth 3,000 accessons of rce beng recorded. The bodversty of coastal and marne ecosystems provde over 65 per cent of the anmal proten requrement of the country. The Mnstry of Envronment and Natural Resources n Sr Lanka (2007) provdes detaled nformaton about the dversty of dfferent speces. Accordngly, n terms of plant speces dversty, vegetaton supports over 3,368 speces of flowerng plants (of whch 26 per cent are endemc) and 314 speces of ferns and fern alles (of whch 57 are endemc). Speces dversty s also hgh among mosses (575), lverworts (190), algae (2,260) and fung (1,920). 27

43 The provsonal lst of threatened faunal speces of Sr Lanka ncludes over 550 speces, of whch over 50 per cent are endemc (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). The crop genetc dversty n the country s also hgh, especally for Oryza satva. In addton to the dversty seen n coarse grans, legumes, vegetables, roots and tubers and spce crops, there are over 170 speces of ornamental plants. In addton to that, domestcated anmals provde a large number of benefts to rural households. Among domestcated anmals of economc value are some ndgenous speces of buffalo, cattle, fowl and fsh. Table 2.2 provdes the status of estmated number of selected speces n Sr Lanka. The major threat to bodversty n Sr Lanka s the ever-ncreasng demand for land for human habtaton and related developmental actvtes. Poor land use plannng, ndscrmnate explotaton of bologcal resources, weak enforcement of legslaton and the absence of an ntegrated conservaton management approach are other threats to bodversty. Sr Lanka has establshed 501 protected areas, accountng for 26.5 per cent of the total land area of the country. Sr Lanka has also two Ramsar stes and two Bosphere Reserves. The bologcal resources of coastal and marne ecosystems provde nearly 70 per cent of the proten requrements of the country and generate employment for about 500,000 people (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Bodversty also contrbutes drectly to the natonal economy n the form of revenue from Natonal Parks and other wldlfe reserves, whle t s potental to promote eco-toursm could be a sgnfcant ncome generator n the future. 28

44 Table 2.2: Estmated number of selected speces Taxonomc group No. of speces (endemc) Percentage n Sr Lanka World Sr Lanka Vertebrate Fauna Psces 21, Amphba 5, Reptla 5, Aves 9, Mammala 4, Invertebrate Fauna Butterfles Dragonfles Freshwater Crabs Freshwater Shrmps Theraphosd spders Land molluscs Bees Aphds Ants Tcks Spders Marne Fauna Echnoderms Marne Molluscs Sharks Rays Marne Reptles Marne Mammals Source: Mnstry of Envronment and Natural Resources n Sr Lanka (2007) 29

45 Forests n Sr Lanka cover 1,933,000 hectares. The dense forest cover n Sr Lanka has decreased by 23 per cent, mostly n the dry zone durng the perod 1956 to The rate of deforestaton from 1960 to 1990 has been estmated at 42,000 hectares per year (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Between 1990 and 2000, Sr Lanka lost an average of 26,800 hectares of forests per year. Ths amounts to a rate of 1.14 per cent average annual deforestaton. Between 2000 and 2005 the rate accelerated to 1.43 per cent per annum. Threats to natural forest ecosystems n the wet zone are manly due to the expanson of tea, rubber, ol palm and other cash crops (Department of Census and Statstcs n Sr Lanka, 2007). In the dry zone the cultvaton of cash crops, large-scale development schemes lke the Accelerated Mahawel Development Project and shftng cultvaton have mpacted on natural forests. Mangrove ecosystems on the other hand, are threatened by the reclamaton of land, urbansaton and prawn culture. Dry zone ecosystems are also dsturbed by cyclones, whch fortunately are not frequent. The constructon of large reservors contnues to reduce the extent of natural ecosystems, partcularly n the lowland wet and ntermedate zones. Some of the most mportant wet zone forests n terms of bodversty are the Peak Wlderness Sanctuary (22,379 hectares), the Kannelya-Dedyagala-Nakyadenya Reserve (10,139 hectares), the Snharaja Forest (11,280 hectares), the Knuckles Range of Forests (21,650 hectares) and the Horton Plans Natonal Park (3,159 hectares). These forests are also mportant hydrologcally as they protect the headwaters of all of Sr Lanka's man rvers (Mnstry of Forestry and Envronment n Sr Lanka, 1999). 30

46 Most Sr Lankan habtats are offcally protected by the Department of Forest Conservaton (DFC) and the Department of Wldlfe Conservaton (DWLC). Protected areas under the DWLC are shown by the map gven n Appendx E. These areas nclude natonal parks, strct nature reserves, jungle corrdors, and sanctuares. Approxmately 30 percent of the naton s land area falls under some level of natural resource management. Protected areas of whch these are 501 n Sr Lanka are drectly admnstrated by DFC and DWLC. Among the world hertage stes, Snharaja Forest Reserve s an example of a natonal hertage forest. There are 32 forests categorzed as conservaton forests ncludng Knuckles Mountan Range. Total of all categores of areas protected s 1,767,000 hectares. Protected areas n Sr Lanka account for 26.5 percent of total areas (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Ths s a hgher percentage of protected areas than n all of Asa and much of the World. The natural ecosystem rchness n the country s shown by the Table 2.3. The lst of protected areas n Sr Lanka s gven n Appendx F. All stes contan endemc speces that are found nowhere else, and are therefore consdered rreplaceable, wth several stes havng more than 100 globally threatened speces. All of these stes techncally have some form of protecton, but there s an urgent need to strengthen the management and montorng of these areas. Addtonally, landscape-scale conservaton, partcularly reforestaton and conservaton of bologcal corrdors wll be requred for bodversty to persst n the severely fragmented regons, even n the short term. One of the most mportant reserves s the Snharaja Forest Reserve, whch encompasses 50 per cent of the remanng lowland ranforest vegetaton n Sr Lanka. Portons of the reserve have been protected snce 1875, and t was declared a World Hertage Ste n Sxty 31

47 fve per cent of Sr Lanka's 220 endemc tree and woody clmber speces and 270 speces of vertebrates have been recorded there (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Although publc awareness of Snharaja's bodversty s growng, the reserve stll faces threats. People from neghbourng vllages encroach on the reserve va loggng roads to collect non-tmber forest products. Table 2.3: Natural ecosystem rchness Types Categores Extent (hectares) Forests Tropcal lowland ranforests 141,506 Tropcal lower-montane forests 68,616 Tropcal upper-montane forests 3,108 Lawland dry-monsoon forests 243,886 Lawland sem-evergreen forests 1,090,981 Ard zone scrublands 464,076 Rverne forests 22,435 Grasslands Wet /Dry pathana grasslands 65,000 Savannahs - Freshwater wetlands Rver and streams 5,913,800 Thalawas, Damanas, Vllus 10,000 Marshes - Swamp forest - Bracksh water wetlands Salt marshes 23,819 Mangroves 12,500 Lagoons and Estuares 158,017 Coastal ecosystems Coral reefs - Sea grass beds 33,573 Sea shores/beaches 11,788 Mud flats 9,754 Sand dunes 7,606 Source: Mnstry of Envronment and Natural Resources n Sr Lanka (2007) 32

48 Bodversty s essental for ecosystem servces and hence for human well-beng. It goes beyond the provsonng for materal welfare and lvelhoods to nclude securty, reslency, socal relatons, health and choces. Therefore, durng the last few decades, the mportance of communty partcpaton n bodversty conservaton has ganed much recognton n the country. However, degradaton of bodversty s stll occurrng at an alarmng rate n the country. The threats to bodversty have several underlyng causes. They are populaton growth, trade pressures, poltcal nstablty, perverse ncentves, economc performance, poverty, lack of law enforcement, poor protecton standards, lack of awareness and lack of nformaton about the value of bodversty. Bodversty s ntegral to key development sectors such as agrculture and lvestock, forestry, and fshng or toursm. More than 8.5 mllon people depend on bodversty and on basc ecosystems goods and servces for ther lvelhoods (Sanjeeva, 2003). Snce the poor farmers are partcularly dependent on the goods and servces suppled by bodversty, development strateges that gnore ther protecton undermne poverty allevaton and are therefore counterproductve. For ths reason, t s crucal for development and poverty allevaton strateges and programs n the country to prortse bodversty, especally agrcultural bodversty whch s an mportant component of general bodversty. In the next secton we wll dscuss the present trend and ssues related to agrcultural bodversty n Sr Lanka. 2.4 Agrcultural bodversty n the country The conservaton of bologcal dversty s of specal sgnfcance to Sr Lanka n the context of ts predomnantly agrculture-based economy and the hgh dependence on 33

49 many plant speces for food, medcnes and domestc products (Jeremy Carew-Red, 2002). Over one thrd of the plant speces n the country are used n ndgenous medcal practce, and many of these speces are growng scarce due to habtat destructon and over-collecton. Sr Lanka has been an agraran-based socety. At present the agrcultural sector s gross domestc producton (GDP) contrbutes 20 per cent to the country's GDP, second only to the manufacturng sector (Central Bank n Sr Lanka, 2009). Currently, an estmated 8.9 mllon famles are engaged n farmng, and nearly 70 per cent of the country's labour force s dependent upon the agrcultural sector for ts ncome and sustenance (Department of Census and Statstcs n Sr Lanka, 2010). The Sr Lankan agrcultural sector s domnated by small-holders, and over 55 per cent of farmng famles n the country cultvate small holdngs of less than 0.44 hectares. The agrcultural landscape of the country conssts manly of rce paddes, coverng 780,000 hectares of cultvated land, and the plantaton sector amountng to about 772,000 hectares (Department of Census and Statstcs n Sr Lanka, 2010). The plantaton crops are tea, rubber, coconut and sugarcane, and on a smaller scale, coffee, cocoa, cnnamon, pepper, clove and other spces. Agrcultural crop bodversty n the sland ncludes Orza satva wth ts 2,800 accessons and seven wld relatves; seven coarse gran speces and ther tradtonal cultvars, maze and sorghum; 14 gran legumes speces; eght cucurbtaceous; two solonaceous and four other vegetable (bean, okra, amaranth, chll) speces; 17 root and tuber crop speces (Wjesnghe et al., 1993). The economcally useful spces are eght speces of cnnamon, elettara cardamomum, three pepper speces wth seven wld relatves, cloves, nutmeg, betel nut, vanlla, chll, and gnger. Others that are of 34

50 mportance nclude ctronella, three speces of ol crops and two fber crops. The hortcultural speces are banana wth nne cultvars and two wld relatves, ctrus, and over 15 other frut speces (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). The rch and dverse ecosystems of the country harbour many wld relatves of cultvated speces, and the gene pools represented by these wld plants are a resource of consderable potental value that could be used for the genetc mprovement of cultvated plants. Plant products such as fruts, fbre, spces, ktul sap, bamboo and rattan are used as raw materal for many small scale ndustres whch provde fnancal securty to rural populatons. Paddy cultvaton receves the hghest attenton n the agrcultural sector (Central Bank n Sr Lanka, 2009). Rce consttutes the staple food of the populaton and s the backbone of Sr Lanka's agrculture and ts ancent culture. There are varetes of rce whch are resstant to pests and adverse clmatc and sol condtons, exhbt varatons n gran sze and qualty, and show dfferences n rate of maturng. There s also sgnfcant crop genetc dversty among spces of commercal mportance. Other crops n ths sector nclude over 100 speces used as tems of food. Many of these, such as onon, potato and vegetables, reman a small farmer actvty, and most frut speces are grown n home gardens. Gran legumes and root and tuber crops also show a rch genetc varablty, as do frut crops such as banana, mango and ctrus. Smlarly, there are many varetes of vegetables such as cucurbts, tomato and eggplant. Out of 170 plant speces of ornamental value, 74 are endemc, and many speces of orchds and folage plants of commercal mportance occur naturally n forests (Department of Census and Statstcs n Sr Lanka, 2010). Gran legumes, such as cow pea, green gram, black gram, wnged 35

51 bean, and soya bean consttute an mportant source of proten for most Sr Lankans, partcularly n rural areas, and are ncreasngly used for crop dversfcaton. Wnged bean, n partcular, shows much genetc varablty as s evdent n the seed colour, pod sze and flower colour. A few crops, such as chll and cashew, are grown on a semcommercal scale (Department of Census and Statstcs n Sr Lanka, 2010). A good many feld crops also contnue to be harvested from shftng cultvaton plots n the dry zone. Ths method of agrculture has caused wdespread forest destructon n the dry zone where t has adversely affected overall bodversty n the country. Sr Lanka has a large number of vegetables, ncludng both temperate and tropcal speces, cultvated throughout the country. Among these, cucurbts, tomato and eggplant exhbt hgh genetc dversty. There are also a far number of root and tuber crops, of whch cassava, doscorea and nnala show consderable genetc varaton. Sweet potato, although ntroduced to ths country, s naturalzed and has hgh genetc varablty. There s also consderable genetc varaton among a wde range of frut crops, such as ctrus, mango, avocado and jak that are grown manly n home gardens. Other frut crops such as duran, pomegranate, rambutan, guava and papaw have also been n cultvaton for a long tme and exhbt a wde range of genetc dversty. Frut crops such as wood apple and velvet tamarnd are a source of ncome for the dry zone farmers, and are harvested from forests for sale. Of concern s the fact that harvestng of the latter speces from forests s destructve as t nvolves choppng down of large frut bearng branches to facltate collecton. 36

52 Among domestcated anmals of economc value are wld speces of buffalo, cattle and fowl (Department of Census and Statstcs n Sr Lanka, 2010). The local cattle show hgh resstance to dsease and tolerance of nternal parastes. Lkewse, the local breeds of poultry are resstant to tropcal dseases. In the lvestock ndustry, the anmals commonly reared comprse neat cattle (1,644,000), buffalo (760,900), goats (535,200), sheep (11,400), pgs (84,800) and poultry (9,136,600). The ndgenous cattle have a very low genetc potental for mlk producton, but are resstant to dseases and have the ablty to feed on coarse grasses. Several foregn breeds of cattle have been ntroduced to the country over the last four decades n an effort to boost mlk producton. The local backyard breed of scavengng poultry that are resstant to tropcal dseases and were commonly found n many vllage households pror to the 1960s are fast dsappearng due to the strong preference for mported germplasm (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). There are several reasons for the loss of agrcultural bodversty n rural areas n Sr Lanka. After the green revoluton, the adopton of modern varetes of seeds reached from 12 to 67 per cent (Mnstry of Envronment and Natural Resources n Sr Lanka, 2007). Access to, and use of, a wde range of agrcultural bodversty s threatened by ths smplfcaton of producton systems. Secondly, as food producton becomes ncreasngly ndustralsed, we are wtnessng a rapd declne n the dversty of varetes used. The FAO (2007) estmates show that more than 90 per cent of crop varetes have dsappeared from farmers felds n the past 100 years. Agrcultural plant varetes are contnung to dsappear at two per cent a year. Lvestock breeds are beng lost at fve per cent annually (FAO, 2007). The current extncton rate of speces ranges from 37

53 approxmately 1,000 to 10,000 tmes hgher than natural extncton rate (Benton, 2001). Ths s true for Sr Lanka as well. Thrdly, government ncentves to specalse crops (e.g. fertlser subsdes) have also badly affected the agrcultural bodversty. Sngle crops are more vulnerable to the rapd spread of dsease. As government ncentves encourage farmers to ncreasngly produce crops for the market to obtan ncome, ther mmedate dependence on agrcultural bodversty tends to dmnsh and they grow fewer crops and a lesser number of varetes. Hence, commercal food producton often goes hand-n-hand wth the reducton of cultvated crop or anmal dversty. Exstng agrcultural bodversty has to be conserved n order to ensure access to t now and n the future. Ths necessarly nvolves human nterventon. Small-scale farms make a substantal contrbuton to agrcultural producton, and t s estmated that there are now a total of around 1.33 mllon small-scale farms n Sr Lanka, accountng for about 367,800 hectares of cultvated land (Department of Census and Statstcs n Sr Lanka, 2010). Small-scale farms consttute a tradtonal system of perennal croppng for a wde range of valuable crops and are consdered mportant stes for n-stu conservaton of dfferent components of agrcultural bodversty. Ths thess wll focus attenton only on small-scale farms n the country as a means to conservng agrcultural bodversty n the future. 38

54 CHAPTER THREE DATA SOURCES AND DESCRIPTION 3.1 Introducton As mentoned n Chapter one, ths PhD thess conssts of three man sectons concerned wth dfferent aspects of agrcultural bodversty. We use prmary data along wth secondary data for the analyss. Secondary data were obtaned from the Mnstry of Agrculture n Sr Lanka, Department of Census and Statstcs, Central Bank of Sr Lanka (varous years), and varous publshed books and artcles. The Mnstry of Envronment and Natural Resources n Sr Lanka provded data related to bodversty degradaton n Sr Lanka. In addton to, data provded by the Internatonal Unon for Conservaton of Nature (IUCN) and Food and Agrcultural Organsaton (FAO) were used to explan the man ssues n ths area n the country. The farm household data from three agrcultural dstrcts (Anuradhapura, Ampara and Kurunegala) are used for the man analyss. A map showng n these three dstrcts s shown n Appendx G. There are at least three reasons for selectng farms n these dstrcts as representatve farms for ths study. Frstly, most of the farms n those dstrcts mantan a hgher dversty whch enables us to capture the market and non-market benefts. Secondly, the dversty between the dstrcts s sgnfcant. It wll help us to capture the benefts under heterogeneous systems. Thrdly, the loss of agrcultural bodversty s ncreasng rapdly n these dstrcts wth modern agrcultural practces. 39

55 Therefore, t s expected that farms n these dstrcts may be representatve farms whch wll assst n an understandng of the ssues n ths feld. The determnaton of sample sze s an mportant task for many researchers. Inapproprate, nadequate, or excessve sample szes contnue to nfluence the qualty and accuracy of research (Bartlett et al., 2001). Generally, the actual sample sze of a survey s a compromse between the desred level of precson, the survey budget and operatonal constrants such as budget and tme. Accordng to Wunsch (1986) two of the most consstent flaws n data collecton nclude (1) dsregard for samplng error when determnng sample sze and (2) dsregard for response and non-response bas. Ths clearly ndcates that n developng a quanttatve survey desgn, determnng sample sze and dealng wth non-response bas s essental. The followng secton wll dscuss the selecton of approprate sample sze n each dstrct for ths study. 3.2 Selectng approprate sample sze The choce of survey populaton obvously depends on the objectve of the survey (Lukas, 2007). Gven the survey populaton, a samplng strategy has to be determned. Possble strateges nclude a smple random sample, a stratfed random sample or a choce-based sample (Dattalo, 2008). A smple random sample s generally a reasonable choce. One reason for choosng a more specfc samplng method may be the exstence of a relatvely small but mportant sub-group whch s of partcular nterest to the study. Another reason may be to ncrease the precson of the estmates for a partcular sub- 40

56 group (Bartlett et al., 2001). In practce the selecton of sample strategy and sample sze s also largely dependent on the budget avalable for the survey. Louvere et al. (2000) provde a formula to calculate the mnmum sample sze. Equaton 3.1 provdes the sze of the sample, n, as determned by the desred level of accuracy of the estmated probabltes, pˆ. Let p be a true proporton of the relevant populaton, a s the percentage of devaton between pˆ and p that can be accepted and β s the confdence level of the estmatons such that: Pr( pˆ p ap) for a gven n. Gven ths, the mnmum sample sze s defned as: 1 p n 2 pa (3.1) 1 where 1 / 2 s the nverse cumulatve dstrbuton functon of a standard normal dstrbuton [N~(0,1)] taken at (1-α/2). Note that n refers to the sze of the sample and not the number of observatons. Snce each ndvdual makes R successon of choces n a choce experment, the number of observatons wll be much larger (a sample of 500 ndvduals answerng eght choce sets each wll result n 4,000 observatons). One of the advantages of choce experments s that the amount of nformaton extracted from a gven sample sze s much larger than, for example, usng referendum based methods and, hence, the effcency of the estmates s mproved. The formula above s only vald for a smple random sample and wth ndependency between the choces. A more detaled explanaton about ths ssue s found n studes carred out by Ben-Akva and Lerman (1985) and Louvere et al. (2000). 41

57 Plot survey nformaton was used to decde the mnmum sample sze n each dstrct. In agrcultural research, a 90 per cent confdence nterval s normally used (Bartlett et al., 2001). It gves the level of rsk the researcher s wllng to take that the true margn of error may exceed the acceptable margn of error. α s assumed to be 10 per cent. Based on the plot survey conducted n August 2010, estmaton for the true choce proporton of the relevant populaton s obtaned. The level of allowable devaton as a percentage between pˆ and p s assumed as 10 per cent (an equals 0.1). The parameters requred to estmate sample szes and ther calculatons are reported n Table 3.1. An estmate of the nverse cumulatve normal dstrbuton functon s obtaned usng a Mcrosoft Excel worksheet. It s clear that the cumulatve dstrbuton functon (CDF) of a normal dstrbuton s the probablty that a standard normal varable wll take a value less than or equal to z [P(Z z)] where z s some establshed numercal value of Z. Table 3.1 shows estmated true choce proporton of the populaton for each dstrct. These values are estmated usng nformaton provded by the plot survey n these dstrcts. For example, for Anuradhapura dstrct t s assumed that the researcher tolerates the sampled proporton of decson makers, pˆ beng wthn ± 10 per cent of the true populaton proportons, P, and that the estmated populaton proportons of selectng Farm A, Farm B and Nether Farm A or B are 0.41, 0.37 and 0.22 respectvely. For the plot survey the number of choce scenaros used was eght per household. The Z statstc was calculated usng NORMINV (1-α/2, 0,1) formula n an Excel worksheet. Ths formula can be used n Excel to calculate the nverse normal dstrbuton functon for dfferent normal dstrbuton functons wth varyng means, standard devatons and at varyng levels of α. We entered a mean of zero and a standard devaton of one nto the 42

58 NORMINV formula. Ths suggests that we are usng a standard normal dstrbuton such that Z ~ N(0,1). Table 3.1: Estmatng mnmum sample sze for each dstrct Anuradhapura P a^2 1-P R Z^2 Total Observaton n Farm A Farm B None Total Ampara P a^2 1-P R Z^2 Total Observaton n Farm A Farm B None Total Kurunegala P a^2 1-P R Z^2 Total Observaton n Farm A Farm B None , Total Note: Although optmal sample sze for Anuradhapura, Ampara and Kurunegala are 226, 222 and 239, we collected data coverng 251, 247 and 248 households n these dstrcts respectvely. Ths allows us to adjust the sample sze after removng erroneous and rratonal data ponts. 43

59 The total number of observaton column provdes the fnal number of observatons that s used to calculate the mnmum sample sze. The value of ths column should be dvded by the number of choce scenaros, n ths case, eght. Ths wll provde the mnmum sample sze for each study area. Usng ths nformaton we calculated a sample sze of 226, 223 and 239 for study areas n Anuradhapura, Ampara and Kurunegala respectvely. However, n the survey we used only sx choce sets as we found that answerng eght choce sets was dffcult for respondents. We obtaned data from 251, 248 and 247 farmers n Anuradhapura, Ampara and Kurunegala respectvely. The method of selectng respondents for the survey n each dstrct s explaned n the next secton. 3.3 Selectng respondents for the survey Several steps are nvolved n selectng farm households for the survey. Frstly, we dentfed dverse farms located at dvsonal secretarat (DS) level n each dstrct. Then we selected one dvsonal secretarat regme n each dstrct randomly. Secondly, four vllages n each dvsonal secretarat regme were selected randomly. Durng the thrd stage, selectons of households were done based on the name lst provded by vllage offcers of the representatve vllages. We assgned random numbers to represent each farm household address and used ths number to select the households for the ntervew (e.g. each thrd number). The survey was carred out durng a two month perod (Sept- Oct. 2010). 44

60 The procedure explaned n Secton 3.2 dentfes the mnmum requred sample sze. However, n practce the response rates are typcally well below 100 per cent. Bartlett (2001) recommends over samplng as a soluton. For example, f t s antcpated that a response rate of r per cent would be acheved based on pror research experence, the requred sample sze to be selected to the survey can be calculated as Sn n/ r where Sn = sample sze adjusted for response rate. Detals of survey areas, populaton and sample sze are provded n Table 3.2. Table 3.2: Detals of the survey areas Research Area (Dstrct and DS) Anuradhapura (Kahatagasdglya) Ampara (Uhana) Kurunegela (Paduvasnuwara) Vllages Pulyankadawela Kudapattya Kaneddawewa Kubukgollawa Veeragoda Udayagrya Hmdurawa Varankada Hathapola Veedyagala Kadavalagedara Hdagahawawa Populaton Sze Sample Survey Sample Sze Observatons 1, ,446 1, ,464 1, ,518 Note:. Ths s the number of people selected for the survey allowng for the non-respondent households.. Ths s the vald number of data ponts collected from the survey. A few survey questonnares n each dstrct were dropped due to ncomplete or erroneous reportng. These numbers were 8, 5 and 7 for Anuradhapura, Ampara and Kurunegala dstrcts respectvely.. Ths s the total number of possble observatons for the choce experment study. 45

61 The respondent rate s estmated based on plot survey nformaton. They were 94, 92 and 96 for Anuradhapura, Ampara and Kurunegala dstrcts. However, actual response rates for Anuradhapura, Ampara and Kurunegala dstrcts were 88, 92 and 87 per cent respectvely. Populaton sze n four selected vllages n Anuradhapura dstrct s 1,352. Of them, 288 households were selected usng the household lst provded by the vllage offcer. We ntervewed 255 households. However, only 247 survey forms could be used to analyse the data as a few survey forms had to be dropped due to ncomplete or erroneous recordng. Populaton sze for the selected four vllages n Ampara dstrct s 1,338. Only 273 households were selected for the ntervew n these vllages. After droppng a few ncomplete questonnares 248 households could be used n the analyss. Four selected vllages n Kurunegala dstrct have 1,442 households n total. Of them, 279 were selected for the survey. However, we used 251 households for the analyss. The total number of possble observatons n Anuradhapura, Ampara and Kurunegala dstrcts are 4,446, 4,464 and 4,518 respectvely 6. It s commonly accepted that no survey can acheve success wthout a well-desgned questonnare. It s a common challenge for many researchers wth napproprate, nadequate, or excessve questons that can nfluence the qualty and accuracy of research (Bartlett et al., 2001). On the other hand, collectng nadequate nformaton provdes data constrant n the analyss. Therefore, careful attenton s needed n each step of desgnng the questonnare. The next secton wll dscuss the process for desgnng the feld survey and ts content. 6 Ths number s estmated usng the number of respondents, number of optons and the number of choce scenaros. For example, n Anuradhapura number of respondents was 247. The number of optons was three whle the number of choce scenaros was sx. Hence the total number of observatons s 247*3*6 = 4,

62 3.4 Feld survey and ts content We use prmary data along wth secondary data for the analyss. Survey data were collected coverng approxmately 746 farmers n three agrcultural dstrcts n Sr Lanka. In August 2010, a plot survey was conducted to obtan the necessary nformaton for the man survey n certan randomly chosen areas of the Anuradhapura, Ampara and Kurunegala dstrcts. The man survey was started at the begnnng of September 2010 and completed at the end of October Surveys n all dstrcts were carred out by admnsterng a questonnare through a face-to-face ntervew wth the head or any other workng member of the households. A questonnare desgned to capture the varous aspects of agrcultural bodversty was valdated n a plot survey and n a number of focus group dscussons. The fnal questonnare was then adjusted. The gatherng of data was carred out carefully by a traned group of researchers under the close supervson of ther search team. The ntervews took place n the ntervewee s home. The partcpants were nformed about the purpose of the study and gave verbal consent. A feld supervsor revewed the qualty of the data gathered and entered t nto a database for analyss. It was confrmed that the survey questons were clearly understood by respondents and obtaned approprate nformaton regardng agrcultural bodversty, ts dfferent components and each farmer s atttudes towards conservng t. The questonnare used for the survey had sx man sectons. Secton A covered general nformaton about the small scale farm and the methods farmers use to cultvate t. Ths 47

63 secton had 15 man questons followng a few other sub questons. Secton B sought detals of the dfferent components of agrcultural bodversty and the level of effcency on the farm. At the begnnng of ths secton the enumerator gave a broad ntroducton on dverse farmng systems, practced n dfferent areas n Sr Lanka and then narrowed attenton to the farmng system n the survey areas. Then detals about dfferent types of benefts that farmers can obtan by havng a dverse farmng system are gathered. In addton to that farmng practces, dfferent crops varetes and lvestock breeds, cost and producton data were also collected. Further, data on nputs as well as outputs were obtaned n detal for estmatng farm level effcency. Secton C dealt wth evaluatng poverty, ncome and expendture. Household ncome and expendture, food avalablty, ther health stuaton and detals of agrcultural as well as non-agrcultural debt were obtaned n ths secton. Secton D collected nformaton about farmers preference for agrcultural bodversty on farms. Ths s the CE part of the questonnare. More detals about ths secton are provded n Secton 3.5. Secton E measured the farmer s atttudes towards dfferent components of agrcultural bodversty on farms whle Secton F covered varous soco-economc and demographc features such as age, gender, level of educaton, martal status, occupaton and the sze of the dwellngs and total famly ncome. The questonnare used for ths survey s shown n Appendx H. Pror to conductng the survey, the enumerators attended tranng conducted by the researcher. They were brefed on the CE procedure, the dea of economc valuaton, the background of the study. Role-play exercses were used to expose the enumerators to the ways of obtanng cooperaton from the respondents. They were also made aware of possble bases (lke strategc and startng-pont bas) durng ntervews and ways to 48

64 mnmse these. The enumerators were taken for a bref tour to famlarse them wth the areas of the study stes and also to meet the vllage heads to seek ther help n gettng respondents to cooperate n the survey. The CE part s the most mportant secton of the questonnare and t needs expert knowledge and careful attenton. In a CE, ndvduals are presented wth a choce set or seres of choce sets that are framed wth varous attrbutes and attrbute levels and are asked to choose one bundle at a vared set of prce and attrbute levels. Consumers wllngness to accept (WTA) compensaton payment for each attrbute s then computed from estmates of econometrc models. An ntrnsc problem that all researchers face n desgnng a survey questonnare s how much nformaton or complexty to ncorporate. Specfcally, these ssues may nclude whch attrbutes should be used, how many levels of each attrbute need to be consdered, how many alternatves need to be presented n each choce set, and how many choce sets should be ncluded n each questonnare. More detal about the way of addressng these ssues s explaned n Chapter Four. The process for desgnng CE questons for ths survey s brefly explaned n the next secton. 3.5 Desgn choce experment (CE) survey The overall objectve of the CE part of the study s to estmate the possble prvate benefts that could be acheved from conservng agrcultural bodversty. Under ths method a sample of people s asked to choose ther most preferred alternatves from a sequence of grouped optons that relate to dfferent agrcultural bodversty 49

65 management strateges. Each opton s descrbed n terms of ts agrcultural bodversty outcomes and a monetary cost to be borne personally by the respondent. By analysng the choces made by respondents t s possble to nfer the tradeoffs that people are wllng to make between money and greater benefts of agrcultural bodversty. Ths n turn allows the estmaton of changes of prvate benefts wth changng levels of agrcultural bodversty. Expermental levels of the sx agrcultural bodversty attrbutes descrbed below were dentfed through pror knowledge and lterature n ths feld. A monetary attrbute n terms of requred addtonal labour days s ncluded n order to estmate welfare changes 7. The monetary attrbute n ths CE s a proxy, measurng the labour costs that farmers have to allocate for recevng the benefts of agrcultural bodversty. Ths attrbute represents WTA compensaton whch s measured as a cost rather than a beneft. Farm attrbutes and ther levels used n ths study are explaned below. Farm attrbutes and ther levels nclude: 1. Crop speces dversty. Ths s measured by the total number of crop speces that are grown n the small-scale farm n a gven season. For example, a farm wth tomatoes, beans and carrots has n total 3 dfferent crops. We present ths wth four levels of crop dversty: 3, 7, 10, and 15 varetes. 7 Ths ndrect measure s preferred over a drect monetary attrbute because most (f not all) of the outputs and functons of farms that result n agrcultural bodversty are not traded n the markets, but consumed by the farm famles themselves. Hence, they are not lkely to be famlar wth a drect monetary measure. The proxy monetary attrbute can easly be converted nto actual monetary unts by usng secondary data on labour costs. 50

66 2. Mxed crop and lvestock dversty. Ths attrbute nvestgates whether a farmer prefers an ntegrated crop and lvestock producton system over a system that s specalsed n crops or lvestock. 3. Organc producton. Ths attrbute nvestgates whether a farmer prefers organc methods of producton over a system usng chemcal fertlser and pestcdes. For example, when a farmer sells small-scale farm crops that are produced entrely wth organc methods, these products are certfed as organc. We asked farmers to thnk about ther magnary farm and decde whether or not they prefer a farm n whch they produce crops wth entrely organc methods. 4. Landrace cultvaton. Ths attrbute nvestgates whether a farmer prefers to have a farm n whch a landrace s grown as opposed to none. A landrace cultvaton s defned as a crop varety that was passed down from ther ancestors and s very resstant to any dsease. In general these varetes are called tradtonal varetes n rural agrcultural areas n Sr Lanka. Varetes that were ntroduced after the agrcultural modernsaton programs, took place durng the 1960s are called modern varetes. 5. Estmated benefts n terms of decreasng household s food expendture. Ths s defned as a percentage of decreasng household food expendture under dfferent polcy optons. Farmers receve these benefts as the dverse farmng system ncreases ther self-suffcency level. It ndcates the addtonal benefts that farmers are gong to receve when they are acceptng a new polcy. We present ths attrbute wth three levels of percentages: 5, 10, and

67 6. Estmated costs n terms of addtonal labour days. Ths s defned as a percentage of addtonal labour requrements under dfferent polcy optons. It ndcates the addtonal costs that farmers have to bear when they are acceptng a new polcy. The percentages that are presented to them are 10, 20 and 30. The frst four attrbutes reflect the varous attrbutes of agrcultural bodversty found n the small-scale farms n Sr Lanka. The ffth factor represents benefts that farmers can receve n terms of recevng foods from ther farms under dfferent polcy optons. The last factor s the monetary attrbute n terms of addtonal labour costs that farmers have under dfferent polcy optons. As compared to wllngness to pay (WTP), wllngness to accept s measured as a beneft rather than a cost (Freeman, 2003). In order to estmate ths beneft, a monetary attrbute n terms of addtonal labour costs that farmers are wllng to offer s ncluded. The sze of the hypothetcal small-scale farm s fxed as one acre 8 n area n each case (ths s the average small-scale farm sze n Sr Lanka). There are several dfferent desgn types n the lterature to obtan a choce set. One s a full factoral desgn whch conssts of all possble choce stuatons (Bennett and Blamey, 2001). Wth ths desgn all possble effects (man and nteracton effects) can be estmated. However, for a practcal study the number of choce stuatons n a full factoral desgn s too large. Therefore, most people rely on fractonal factoral desgns. However, wthn ths class there exst many dfferent types of desgns. One could randomly select choce stuatons from the full factoral, but clearly ths s not the best 8 Ths small-scale farm sze was chosen from the agrcultural census survey conducted n 2002 (Census of Agrculture, 2002). 52

68 way of dong t. Another way to select choce stuatons n a structured manner, such that the best data from the stated CE wll be produced n estmatng the model (Hensher et al., 2005). A fractonal factoral desgn conssts of a subset of choce stuatons from the full factoral. The most well-known fractonal factoral desgn type s the orthogonal desgn, whch ams to mnmse the correlaton between the attrbute levels n the choce stuatons (Kuhfeld, 2005). However, these orthogonal desgns have lmtatons and cannot avod choce stuatons n whch a certan alternatve s clearly more preferred over the others (hence not provdng much nformaton). More recently, several researchers have suggested another type of fractonal factoral desgns, so-called effcent desgns (Hensher et al., 2005; Scarpa and Rose, 2008). Instead of merely lookng at the correlaton between the attrbute levels, effcent desgns am to fnd desgns that are statstcally as effcent as possble n terms of predcted standard errors of the parameter estmates. Essentally, these desgns attempt to maxmse the nformaton from each choce stuaton. In case any nformaton about the parameters s avalable, then effcent desgns wll always outperform orthogonal desgns (Kessels et al., 2006). Ths s due to the fact that effcent desgns use the knowledge of the pror parameters to optmse the desgn n whch the most nformaton s ganed from each choce stuaton (e.g. domnant alternatves can be avoded as the utltes can be computed). Whle effcent desgns outperform the orthogonal desgns, pror parameter estmates need to be avalable (Hensher et al., 2005). Therefore, effcent desgns rely on the accuracy of the pror parameter estmates. As we do not have the pror parameter values for our estmaton n ths study we used orthogonal desgn to generate the number of choce stuaton n ths study. 53

69 Three reasons can be gven to justfy usng orthogonal desgn n ths study. Frstly, t allows for an ndependent estmaton of the nfluence of each desgn attrbute on choce. Secondly, wth the absence of pror parameter, there s no way to apply effcent desgn n ths study. Thrdly, the common use of orthogonal desgns n stated choce studes s largely a result of hstorcal mpetus. In the past, the expermental desgn lterature has been prmarly concerned wth lnear models (such as lnear regresson models), where the orthogonalty of data s consdered mportant (Scarpa and Rose, 2008). In lnear regresson models, ths s because (a) orthogonalty ensures that the model wll not suffer from multcollnearty, and (b) orthogonalty s thought to mnmse the varances of the parameter estmates, whch are taken from the varance-covarance (VC) matrx of the model (Hensher et al., 2005). The VC matrx of a lnear regresson model s gven n Equaton 3.2. VC ' X X 1 2 (3.2) where 2 s the model varance, and X s the matrx of attrbute levels n the desgn or n the data to be used n estmaton. Fxng the model varance, the elements of the VC matrx for lnear regresson models are mnmsed when the X matrx s orthogonal. A desgn that results n a model where the elements contaned wthn the VC matrx are mnmsed s preferable, for two reasons (Hensher et al., 2005). Frstly, such a desgn wll produce the smallest possble standard errors, and hence maxmse the t-ratos produced from that model. Secondly, an orthogonal desgn wll produce zero-off dagonals n the models VC matrx, thus ensurng that the parameter estmates are 54

70 unconfounded wth one another (or no multcollnearty problem). As such, orthogonal desgns, at least n relaton to lnear models, meet the two crtera for a good desgn (Scarpa and Rose, 2008). They allow for an ndependent determnaton of each attrbutes contrbuton on the dependent varable, and they maxmse the power of the desgn to detect statstcally sgnfcant relatonshps (e.g. maxmse the t-ratos at any gven sample sze). In ths study orthogonal desgn s used to generate the number of choce stuatons. A large number of unque farm profles can be constructed from the sx attrbutes and ther levels. An orthogonalsaton procedure was used to recover only the man effects, consstng of 36 par-wse comparsons of dfferent farm profles. These were randomly blocked to sx dfferent versons, wth sx choce sets. In face-to-face ntervews, each farmer was presented wth sx choce sets. The questonnare used for ths survey s shown n Appendx H. More detals about the mplementaton of the choce experment study are gven n Chapter Four. Hypothetcal farms n pars on a seres of cards were generated and then farmers were asked- to ndcate out of the par, whch type of farm they preferred for each card. Each set contaned two farm profles and an opton to select nether. The farmers who took part n the choce experment were by and large those responsble for makng decsons n the farms. Enumerators explaned the context n whch choces were to be made; (a) farm sze s an acre; and (b) that attrbutes of farms had been selected as a result of pror research and were combned artfcally. More detals about the CE survey are gven n Chapter Four. 55

71 The next four chapters provde methodology, lterature and results of the analyss related to the three man sectons n ths thess. Each study s carred out as a separate study and presented as a separate chapter n the thess. Chapters Fve, Sx and Seven used a total of 746 observatons for ther man analyss whle Chapter Four used 12,006 observatons for ts pool data analyss. In most of the cases, the analyss s carred out usng dstrct wse data and pool data separately. Ths type of analyss wll help to understand the heterogenety across dfferent dstrcts. 56

72 CHAPTER FOUR FARMERS VALUATION OF AGRICULTURAL BIODIVERSITY 4.1 Introducton The valuaton of nonmarket goods s one of the prncpal ssues addressed by envronmental economcs research (Bshop and Romano, 1998; Champ et al., 2004). When compettve markets exst, market prces are the approprate measure of socal valuaton. However, n practce, all markets do not functon exactly n the manner assumed by economc theory. In such cases market prces are not the best avalable approxmate measure of socal values of goods and servces (Portney, 1994; Freeman, 2003). For example, all benefts of dverse farmng practce provded by small-scale farms are not marketed n rural areas. However, t s extremely mportant to analyse the role of subjectve well-beng receved from these farms for nformng polcy decsons. The value of agrcultural bodversty can be measured n a varety of ways. However, the range of agrcultural bodversty valuaton technques can be consdered under two headngs that reflect the contnuum from pure market to pure non-market technques (Freeman, 2003). The frst method uses revealed preference technques because people s preferences for agrcultural bodversty protecton are revealed through ther actons n related markets. The second method uses stated preference technques. These are valuaton technques that requre people to state the strength of ther preferences and 57

73 hence reveal the values they enjoy through structured questonnares (Bshop and Romano, 1998). Ths method does not nvolve any relance on market data. For market based valuaton technques, the beneft generated by agrcultural bodversty must be bought and sold n markets. The technques are most sutable for applcatons where drect use benefts are nvolved. As both consumer and producer receve the benefts, consumer surplus and producer surplus can be used to measure the total benefts receved from use value of agrcultural bodversty 9. Therefore, t s clear that f there are suffcent observatons of trade, t s possble to use standard economc technques to estmate values for both buyers and sellers (Freeman, 2003). For example, f a speces s under threat of extncton, the cost of a captve breedng program may be used to estmate the beneft beng provded by ts contnued survval. Another approach nvolves the estmaton of how much t would cost to replace the lost agrcultural bodversty beneft wth a substtute. Ths replacement cost technque s wdely used n varous analyses because of ts relablty as well as the smplcty of capturng the relevant cost. Lmtatons n the range of agrcultural bodversty value types that can be estmated usng ether the market based or revealed preference technques, led to the development of stated preference technques (Champ et al., 2004). In ths type of technque a sample of people are asked about ther preferences for a bodversty senstve asset under a 9 Observatons of market supply (the margnal costs of supplers) and prces receved through transactons recorded n markets allow the estmaton of profts enjoyed by producers (known techncally as the producers surplus). Observatons of market demand (the margnal values of consumers) and prce pad allow the estmaton of the net beneft receved by consumers when they purchase the bodversty derved goods or servce nvolved. Ths s known as the consumers surplus. 58

74 hypothetcal set of crcumstances. A number of dfferent methods have been developed to nqure about peoples preferences. The frst stated preference technque to be developed was the contngent valuaton method (CVM) 10. Orgnally, ths method requred that a sample of people be asked the amount they would be wllng to pay to secure an mprovement n a partcular aspect of agrcultural bodversty. More recently, ths technque has been refned to accommodate a dchotomous choce verson that nvolves people beng asked f they would or would not support a proposal to mprove agrcultural bodversty gven some personal monetary cost. A wdely used stated preference technque s the CE method 11. Under ths method a sample of people s asked to choose ther most preferred alternatves from a sequence of grouped optons that, n the case of ths study relate to dfferent bodversty management strateges. Each opton s descrbed n terms of ts agrcultural bodversty outcomes and a personal monetary cost to be borne personally by the respondent. By analysng the choces made by respondents t s possble to nfer the tradeoff that people are wllng to make between money and greater bodversty benefts. Ths n turn allows the estmaton of values for agrcultural bodversty changes. In ths study, partcular effort s gven to usng the CE method for valuaton of dfferent attrbutes of agrcultural bodversty. The next secton crtcally looks at the exstng research that s drectly lnked to valuaton of agrcultural bodversty n dfferent countres. It provdes the context for 10 The dea of CVM was frst suggested by Cracy-Wantrup (1947), and the frst study ever done was n 1961 by Davs (1963). 11 For a detaled explanaton of choce experment desgn technques, please see Louvere et al., 2000; Bennett and Blamey, 2001; Bateman et al., 2003; Drucker et al., 2005; Hensher, et al.,

75 the present research by lookng at what work has already been done n ths feld. It also dentfes the shortcomngs of exstng work and hghlghts the mportance of carryng out the present work. 4.2 Lterature revew on valuaton of agrcultural bodversty There have been some studes that have employed CE method or CVM to value crop dverstes, lvestock dverstes and other types of farmng practces n dfferent countres. Hanley et al. (1998) employed the CE method to ad the desgn of agrenvronmental programs that yeld the hghest beneftsn Scotland. They also valued the components of a Scottsh agr-envronmental scheme, whch offers payments to farmers n return for adopton of conservaton practces. Scarpa et al. (2003) estmated the value of anmal genetc resources to farm famles, who produce and consume them, by comparng the value of attrbutes of creole pgs to those of more productve, but less well adapted exotc breeds n Yucatan, Mexco. Kontoleon (2003) nvestgated consumers perceptons of genetcally modfed (GM) food and found that consumers across the European Unon (EU) were wllng to pay more to obtan nformaton on the GM content n ther food supples. Usng the CE method, Lusk et al. (2003) nvestgated consumers preferences for beef produced wth hormones n the Unted States. Ndjeunga and Nelson (2005) estmated farmer valuaton of crop varetes, whereas Brol (2004) estmated farmer valuaton of several components of agrcultural bodversty n Hungaran home gardens. In ths study she appled the CE method to estmate farmers valuaton of agrcultural 60

76 bodversty usng prmary data collected n three envronmentally senstve areas of Hungary. Her fndngs show the varaton n values farmers assgn to home gardens across regons and households. The CE method was used to nvestgate farmers valuaton of agrcultural bodversty of maze varetes, usng 414 farm households from three states of Mexco by Brol et al. (2006). The results revealed that there s a consderable heterogenety n farmers preferences for Mlpa dversty and GM maze across and wthn the three states. Ouma et al. (2007) used mxed logt and latent class models to examne preferences for cattle trats wth a focus on heterogenety among cattle keepers, usng CE data of 506 cattle-keepng households n Kenya and Ethopa. The fndngs ndcated the exstence of preference heterogenety based on cattle producton. Ruto et al. (2008) nvestgated buyers preference for ndgenous breads and Roessler et al. (2008) assessed farmers preferences and trade-offs for pg breedng for a lst of adaptve and productve trats usng the CE method. Further, Zander and Drucker (2008) provded emprcal evdence for the hgh economc value of the Borana breeds usng CE surveys. A CE method was employed to elct the preferences and a random parameter logt (RPL) model was used to estmate the relatve mportance of the preferred attrbutes of ndgenous cows n Central Ethopa by Kasse et al. (2009). They dentfed the relatve weghts assgned to the preferred trats of the ndgenous cow populaton n the most domnant crop-lvestock mxed producton system. The results show that fertlty, dsease resstance and calf vgour trats are at least as mportant as mlk provded by cows. The locaton the cows are brought from s an mportant attrbute for buyers. The 61

77 fndngs suggest that the smallholder communty n ths part of Ethopa depends on sem-subsstence agrculture and so lvestock development nterventons should focus on a multtude of reproductve and adaptve trats that stablse the herd structure rather than focusng on trats that are only mportant for commercal purposes. Poudel and Johnsen (2009) soughtto advance the applcaton of CVM to document the economc value of crop genetc resources based on farmers wllngness to pay for conservaton. Accordng to them landholdng sze, household sze, educaton level, soco-economc status, gender of respondent, number of crop landraces grown, and knowledge of bodversty nfluence the wllngness to pay for n stu conservaton, whereas only landholdng sze and household sze nfluence the wllngness to pay for ex stu conservaton. The CE approach was employed to nvestgate Ethopan farmers crop varety preferences and estmate the mean wllngness to pay for each crop varety attrbutes by Asrat et al. (2009). They also dentfed household-specfc and nsttutonal factors that governed the preferences. However, the costs and benefts estmated from these studes cannot be generalzed for all countres. The range n benefts s extremely senstve to assumptons concernng socoeconomc characterstcs and the dscount rate. Recently, a choce experment method was used by Kkulwe et al. (2011) to estmate farmers valuaton of agrcultural bodversty n the mlpa system, and examned ther nterest n cultvatng genetcally modfed (GM) maze. Although these studes dentfed the mportance of small-scale farms for conservng agrcultural bodversty, lterature on economc valuaton of both crop and lvestock resources n small-scale farms are very lmted n developng countres. Ths s because 62

78 assgnng monetary values to crop and lvestock resources are complcated n subsstence farmng systems (Gauchan, 2004) and, therefore, a challengng area of study. Furthermore, the above revew has demonstrated that most studes have tended to smply value a partcular bologcal resource such as speces, habtat or ecosystem servce n agrculture. As a result these studes have only provded lmted nformaton on the value of the dfferent attrbutes of agrcultural bologcal dversty. Accordngly, t s obvous that more conceptual and theoretcal work s needed to develop a better understandng of feasble, cost-effectve approaches to valung multple attrbutes of agrcultural bodversty n developng countres. Among the envronmental valuaton methods, the CE method s consdered to be the most approprate method for valung the multple benefts of small-scale farms attrbutes. Ths s because the CE method allows for estmaton not only of the value of the envronmental good as a whole, but also of the mplct values of ts attrbutes (Hanley et al., 1998; Bateman et al., 2003). Ths approach has a theoretcal groundng n Lancaster s attrbute theory of consumer choce (Lancaster, 1966) and an econometrc bass n models of random utlty (Luce, 1959; McFadden, 1974). Therefore, n the next secton, the theoretcal explanaton for the random utlty model (RUM) s provded. 63

79 4.3 Random Utlty Models (RUM) The CE model s of the class of multnomal choce models used to analyse the dscrete response data produced by the survey nstrument 12. The CE methods rely on the random utlty model framework to provde a utlty theoretcal nterpretaton of the dscrete responses observed from the respondents. Garber-Yonts (2001) provded the basc steps of the RUM and a dervaton of WTP compensaton that s explaned below. Gven a set of alternatves A n, presented to an ndvdual n, the probablty that any one alternatve s chosen s gven by: P( / An ) Pr( Un U jn, Vj An ) (4.1) where U n s the utlty that ndvdual n acheves by choosng alternatve. Accordng to the random utlty theory, the utlty whch s not drectly observable can be parttoned nto a determnstc component and a random component (Hanemann, 1984; Ben-Akva and Lerman 1985; Garber-Yonts, 2001). The accompanyng assumpton s that the ndvdual knows ther utlty functon wth certanty, however wth other measurement errors, utlty can be stochastc: U n V n n (4.2) 12 The prncpal alternatve method of WTP elctaton s usng open ended questons to whch the respondent provdes a drect statement of the amount they would pay to gan an economc beneft, or alternatvely, accept n compensaton or forego. Although ths elctaton method s much smpler to analyse from a statstcal perspectve, t has been shown to be problematc n elctng accurate responses (Arrow et al., 1992). The advantage of closed-ended, dscrete response elctaton questons wth respect to realsm and ncentve compatblty are purchased at the cost of greater statstcal complexty. 64

80 where V n s the mean and the random dsturbance of the stochastc random utlty functon. The specfcaton of V n ncludes a vector of attrbute of alternatve, X n, whch ncludes a prce or bd varable, and a vector of characterstcs of the respondent, H n, ncludng ncome (Garber-Yonts, 2001). Thus model can be wrtten as Equaton 4.3: U n ' f ( X, H ) n n n (4.3) where the determnstc component s here specfed as lnear n parameters, though the functon f(.) can be nonlnear. However, when choosng the functonal form, there s a trade-off between the benefts of assumng a less restrctve formulaton and the complcatons that arse from dong so. Ths s especally relevant for the way ncome enters the utlty functon (Garber-Yonts, 2001). A smpler functonal form (e.g. lnear n ncome) makes estmaton of the parameters and calculaton of welfare effects easer, but the estmates are based on restrctve assumptons (Ben-Akva and Lerman, 1985). Most often researchers have been nclned to use a smpler lnear n the parameters utlty functon. Another mportant thng s that the error term enters the utlty functon as an addtve term. Ths assumpton, although restrctve, greatly smplfes the computaton of the results and the estmaton of welfare measures. Wth the ndrect utlty specfed as above, the ndvdual seeks to maxmse utlty such that: ' ' P ( / A ) P( f ( X, H ) f ( X, H ) n n n n n jn n jn ' ' P ( / A ) P( f ( X, H ) f ( X, H ) ( ));, j A, n n n n jn n jn n n j (4.4) 65

81 It becomes clear that unless H n enters the functon f(.) nonaddtvely, t appears dentcally on both sdes of the nequalty and cancels out of the functon. Thus, H n must enter nonaddtvely f the effects of respondent characterstcs on choce are to be measured (Garber-Yonts, 2001). If ε n and ε jn are assumed to be extreme value ndependently and dentcally dstrbuted (IID) wth scale parameter µ, then ε * =ε jn - ε n s logstcally dstrbuted (Ben-Akva and Lerman, 1985). Ths dstrbutonal assumpton approxmates the normal dstrbuton whch leads to the multnomal logt (MNL) model for the choce probabltes (McFadden, 1974; Ben-Akva and Lerman, 1985). Ths s the smplest verson of the analyss of multnomal outcomes when comparng wth condtonal logt (CL) model and RPL model. MNL model can be gven as Equaton 4.5: P ( / A ) e n n V jn / ja n e V jn e ' f ( X n, H ) n / ja n e V jn e ' f ( X jn, H ) n (4.5) Snce µappears as a multplcatve constant on every parameter of the model, t s not dentfable. A common assumpton employed by users of MNL models s that the scale parameter, µ, s equal to one, whch has a homoscedastc dsturbances (Garber-Yonts, 2001). Emprcal observatons about ths assumpton found that t was not sgnfcantly dfferent that one (Xu, 1997; Adamowcz et al., 1998). Therefore, we adhere to ths assumpton n ths study. The log lkelhood functon for the MNL model can be wrtten as Equaton 4.6: ln L n ' ' s ( / ) [ (, ) ln ( jn, n)] A np An s n A n f X n Hn f X H n n jan (4.6) 66

82 where sn=1f alternatve s chosen by ndvdual n, otherwse sn= 0. Garber-Yonts (2001) provdes the detals explanaton about the dervatves of all Equatons related to MNL. The necessary frst order condtons to maxmse the lkelhood functon are obtaned by settng the frst dervatve of Equaton 4.6 wth respect to the parameter vector equal to zero: (4.7) Estmaton of the parameters of ths model can be done by usng maxmsaton of the multnomal lkelhood. Ths usually requres numercal procedures, and Fsher scorng or Newton-Raphson often work rather well. McFadden (1974) argues that, under certan condtons, ln L n Equaton 4.6 s globally concave so that a soluton to Equaton 4.7 exsts and s unque. Thus the maxmum lkelhood estmator of β s consstent, asymptotcally normal, and asymptotcally effcent. Estmaton of Hcksan welfare effects from the MNL choce probabltes follows the method outlned by Hanemann (1984) and Hanemann and Kannnen (1999). Gven a quantty change n the level of a publc good from to, the compensatng surplus whch exactly offsets the utlty gan of the change s the level of B whch provdes the equalty: (4.8) 67

83 where v s ndrect utlty, p s the vector of market prces, a X s vector of attrbutes other than the bd level B, y s ncome, H s a vector of the soco-demographc characterstcs, and s a random error term. The objectve s to obtan the soluton for the expected value of whch s the maxmum WTP for the change from to Assumng the addtve separablty of the cost attrbute of the ndvdual s ndrect utlty functon, we can express the determnstc part of utlty as shown n Equaton 4.9: (4.9) where B s the specfed bd level alternatve, and s assocate parameter. The followng measures Total WTP/Total WTA (TWTP/TWTA) for a change n the attrbutes of a good from state to state j aggregated over all observatons (Hanemann, 1984; Adamowcz et al.,1994; Xu, 1997; Garber-Yonts, 2001 ): (4.10) If the mean value of TWTP/TWTA for the change n all attrbutes from state to state j s for nterest, Equaton 4.10 smplfes to: (4.11) 68

84 where f(x,h) s evaluated at the sample mean value of H, recallng that H drops out of the Equaton f t enters f(.) addtvely. The TWTP/TWTA for the part-worth of the change of an ndvdual attrbute k from state to state j, holdng other attrbutes constant, further smplfes to Equaton 4.12: (4.12) Fnally, as adopted by Hanemann et al. (1991); Xu (1997) and Garber-Yonts, (2001) the Hcksan compensated demand curve, depctng margnal WTP/WTA for attrbute k at level, s gven as Equaton 4.13: (4.13) In choce modellng applcatons to agrcultural bodversty, dfferent components of agrcultural bodversty as well as monetary factors should be ncluded as attrbutes of the optons n a choce set. Thus, choce modellng allows one to obtan compensatng surplus estmates so that one can account for the welfare change generated by a bundle of changes n relevant attrbutes. It s also possble to determne the relatve mportance of these attrbutes to people n makng ther choces. Haneman and Kannnen (1999) make an mportant dstncton between the conventonal regresson technques used n analyss of open ended WTP data and the lmted dependent varable models used n conjuncton wth dscrete choce elctaton methods. Wth the former, the nvestgator obtans an estmate of the mean WTP condtonal on the regressors. The later estmates the entre condtonal cumulatve dstrbuton functon 69

85 (cdf) of the dependent varable. The preferred measure of central tendency by whch to summarse the estmated cdf s therefore at the dscreton of the nvestgator, and ts selecton can sgnfcantly alter the results of the analyss (Garber-Yonts, 2001). It s clear that the choce experment technque s an applcaton of the characterstcs theory of value combned wth random utlty theory (see, for example, Thurstone, 1927; Lancaster, 1966; Mansk, 1977). In ths method, respondents are asked to choose between dfferent bundles of (envronmental) goods, whch are descrbed n terms of ther attrbutes, or characterstcs, and the levels that these take. The CE approach s essentally a structured method of data generaton. It reles on carefully desgned choce tasks that help reveal the factors nfluencng choce. Desgnng a CE technque also requres careful defnton of the attrbute levels and ranges. Furthermore, the choce experment approach nvolves the use of statstcal desgn theory to construct choce scenaros whch can yeld parameter estmates that are not confounded by other factors. In the next secton, we dscuss the man steps to be followed when applyng CE method for envronment valuaton. 4.4 Choce experment method Snce the CE method paves the way to estmate farmers preferences for agrcultural bodversty n small-scale farms, ths method s used to analyse the data gathered from personal ntervews wth farmers. It s the most approprate for valung attrbutes of small-scale farms, consderng ther multple benefts and functons. Ths method, whch s based on farmers choosng between hypothetcal (bodversty enhanced agrcultural 70

86 system) farms, enables estmaton of the value of new small-scale farm attrbutes, whch are outsde farmers current set of experences (Adamowcz et al., 1994). As mentoned n the prevous secton, the CE method has ts theoretcal groundng n Lancaster s model of consumer choce (Lancaster, 1966). Lancaster proposed that consumers derve satsfacton not from goods themselves, but from the attrbutes they provde. To llustrate the basc model behnd choce experments, assume that farm famles have a utlty functon of the form: U U j ( X j, Z ) (4.14) where for any farm famly, a gven level of utlty wll be assocated wth any alternatve small-scale farm j. Utlty derved from any of the small-scale farm alternatves depend on the attrbutes of the small-scale farm X j and the socal and economc characterstcs of the farm famly, snce dfferent famles may receve dfferent levels of utlty from these attrbutes. Accordng to the random utlty model, Z the utlty of a choce comprses of a systematc (determnstc) component, T j and an error (random) component, e j, whch s ndependent of the determnstc part and follows a predetermned dstrbuton (Hanemann et al., 1991): U j T j e j (4.15) The systematc component can be explaned as a functon of the characterstcs of the small-scale farm and of the socal and economc characterstcs of the farm famly. Accordngly, Equaton 4.15 can be expressed as U T( X, Z ) e. 71 j j

87 Gven an error part n the utlty functon, predctons cannot be made wth certanty and the analyss becomes one of probablstc choce (Bateman et al., 2003). Consequently, choces made between alternatve small-scale farms wll be a functon of the probablty that the utlty assocated wth a partcular small scale-farm opton ( j) s hgher than that for other alternatve small scale-farm. Hence, the probablty that farm famly choose small-scale farm j over all other optons n s gven by: wll P j prob T j e j T n e n where j n. We assume that the relatonshp between utlty and attrbutes follows a lnear path n the parameters and varables. We further assume that the error terms are dentcally and ndependently dstrbuted wth a Webull dstrbuton 13 (Greene, 1997). These assumptons ensure that the probablty of any partcular alternatve j beng chosen can be expressed n terms of logstc dstrbuton. Ths specfcaton s known as the CL model (McFadden, 1974; Greene, 1997; Maddala, 1999) whch has the followng general form: P j ' ' exp( X Z ) J j 1 j ' exp( X Z ) ' j (4.16) The components of X j are typcally called the attrbute of the choces. However, Z contans characterstcs of the ndvdual and s, therefore, the same for all choces. Equaton 4.16 s the probablstc response functon and t shows that, gven all other 13 Webull dstrbuton s a contnuous probablty dstrbuton. For further detals about the basc propertes of ths dstrbuton, please see Greene (1997). 72

88 optons the probablty that farmers selectng the opton j type small-scale farm. The CL model generates results for a condtonal ndrect utlty functon of the form: T j X X... Z m X m 1Z1 2Z 2 k k (4.17) where s the alternatve specfc constant (ASC), that captures the effects n utlty from any attrbutes not ncluded n choce specfc attrbutes (Rolfe et al., 2000). The number of small-scale farm attrbutes consdered s m and the number of socal and economc characterstcs of the farm famly employed to explan the choce of the smallscale farm s k. The vectors of coeffcents are attached to the vector of attrbutes (X ) and to a vector of soco-economc factors (Z ) that nfluence utlty, respectvely. The CE method s consstent wth utlty maxmsaton and demand theory (Bateman et al., 2003). When parameter estmates are obtaned, welfare measures can be estmated from the CL model usng the followng formula: ln exp( T ) ln exp( T 0) CS 1 (4.18) wherecs s the compensatng surplus welfare measure, s the margnal utlty of ncome (generally represented by the coeffcent of the monetary attrbute n the CE) and and T0 T 1 represent ndrect utlty functons of alternatve (wth subscrpt 0 ndcatng the base stuaton and 1 ndcate the changed stuaton) before and after the change under consderaton. For the lnear utlty ndex the margnal value of change wthn a sngle attrbute can be represented as a rato of coeffcents, reducng Equaton 4.18 to 4.19: 73

89 W attrbute monetary_ varable (4.19) Equaton 4.19, the mplct prces (W) for the varous small-scale farm attrbute can be calculated. These demonstrate the margnal rate of substtuton between cost and the attrbute n queston. Ths s the same as the margnal welfare measure (WTP or WTA) for a change n any of the attrbutes. An alternatve model specfcaton to the CL model s random parameter logt (RPL) model whch s ncreasngly becomng popular n CE studes. The advantage of RPL model s that t accounts for consumers taste heterogenetes and also relaxes the Independence of Irrelevant Alternatves (IIA) assumpton of the CL model. It also provdes a flexble and computatonally practcal econometrc method for any dscrete choce model derved from random utlty maxmsaton (McFadden and Tran, 2000). More mportantly preferences are n fact heterogeneous and accountng for ths heterogenety enables estmaton of unbased estmates of ndvdual preferences and enhances the accuracy and relablty of estmates of parameters of the model and total welfare (Greene, 1997). Furthermore, accountng for heterogenety enables prescrpton of polces that take equty concerns nto account. Ths s because an understandng of who wll be affected by a polcy change n addton to understandng the aggregate economc value assocated wth such changes s necessary (Boxall and Adamowcz, 2002). Formally, the random utlty functon n the RPL model s gven by: U j U[ X j ( ), Z)] (4.20) 74

90 As wth the CL model, ndrect utlty s assumed to be a functon of the choce attrbutes (X j ), wth parameters β, whch, due to preference heterogenety, may vary across respondents by a random component µ, and by the socal, economc and atttudnal characterstcs (Z ), namely ncome, educaton, household sze and farmers atttudes to agrcultural bodversty. By accountng for unobserved heterogenety, Equaton 4.16 now becomes: P j exp[ X J j 1 ' j exp[ X ' ( ) Z ] ' j ' ( ) Z ] (4.21) Snce ths model s not restrcted by the IIA assumpton, the stochastc part of utlty may be correlated among alternatves and across the sequence of choces va the common nfluence of µ. Treatng preference parameters as random varables requres estmaton by smulated maxmum lkelhood (Kkulwe et al., 2011). In general, the maxmum lkelhood algorthm searches for a soluton by smulatng n draws from dstrbutons wth gven means and standard devatons. Probabltes are calculated by ntegratng the jont smulated dstrbuton. Recent applcatons of the RPL model have shown that ths model s superor to the CL model n terms of overall ft and welfare estmates (Breffle and Morey, 2000; Layton and Brown, 2000; Carlsson et al., 2003; Kontoleon, 2003; Lusk et al., 2003; Morey and Rossmann, 2003). Even f unobserved heterogenety can be accounted for n the RPL model, the model fals to explan the sources of heterogenety (Boxall and Adamowcz, 2002). Ths can be done by ncludng nteractons of respondent-specfc socal, economc and atttudnal 75

91 characterstcs wth choce specfc attrbutes and/or wth ASC n the utlty functon. Ths enables the RPL model to pck up preference varaton n terms of both uncondtonal taste heterogenety (random heterogenety) and ndvdual characterstcs (condtonal heterogenety), and hence mprove model ft (e.g. Revelt and Tran, 1998; Morey and Rossmann, 2003; Kontoleon, 2003). In the context of emprcal applcaton of choce experment model, choce experment desgn as well as model selecton steps are extremely mportant. Therefore, the next secton dscusses basc steps of choce experment desgn and selectng the approprate model for econometrc estmaton. 4.5 Choce experment desgn and model selecton A choce experment s a hghly structured method of data generaton, relyng on carefully desgned tasks (experment) to reveal the factors that nfluence choces (Hanley et al., 1998). Expermental desgn theory s used to construct profles of the envronmental good n terms of ts attrbutes and levels of these attrbutes. Profles are assembled n choce sets, whch are n turn presented to the respondents, who are asked to state ther preferences 14. In the CE method, respondents are presented wth panels of choces wth two or more alternatves each, where each alternatve s a bundle of attrbutes whch are specfed at dfferent levels n each alternatve (Louvere et al., 2000). The ncluson of a prce or cost attrbutes permts estmatng the effect of cost on the respondents choce. For example a farmer may choose from a number of dfferent farm scenaros n her choce 14 For a detaled explanaton of choce experment desgn technques, please see Louvere et al. (2000); Bennet and Blamey (2001);Bateman et al. (2002) and Hensher, et al. (2005), 76

92 set, each of whch exhbts varaton n an array of attrbutes such as crops dversty, lvestock dversty, mxed farmng system, landrace cultvaton and organc producton. A farmer chooses the type of farm n a gven season dependng on the balance of preferences for dfferent attrbutes and the degree to whch they are represented at a gven farm. In a survey context, the researcher should dentfy the essental attrbutes and levels of the envronmental goods n queston and desgns the choce queston to reveal the structure of the respondents preferences (Bateman et al., 2002). Adamowcz et al. (1999) provded several stages of desgnng a CE study. They are as follows: 1. Identfcaton of relevant attrbutes 2. Selecton of measurement unt for each attrbute 3. Specfcaton of the number and magntude of the attrbute levels 4. Expermental desgn 5. Model estmaton 6. Use of parameters to smulate choce The frst three steps are nvolved n developng a concse and suffcently complete representaton of the valuaton scenaro whch wll provde the survey respondent wth an approprate nformaton set on whch to base statements of preference. Ths phase uses nformaton obtaned from secondary sources, experts n the feld, focus groups and personal ntervews n order to refne the nformatonal content of the survey nstrument. The selecton of attrbutes n relaton to the choces of nterest s very mportant n framng a CEexercse. Accordng to Blamey et al. (2000) attrbute selecton needs to 77

93 take place from both the perspectves of the end-user (the populaton of nterest) and the decson-makers/resource managers to ensure that the attrbutes are not only easly dentfable, but produce polcy-relevant nformaton. Another goal of the attrbute selecton process s to mnmse the number of attrbutes as the use of a large number of attrbutes s lkely to lead to lower data relablty due to the excessve cogntve burden t would place on respondents (Mogas et al., 2002). Identfcaton of approprate attrbute ranges s another basc framng task n choce experment, as a falure to accept trade-offs ndcates that the range of attrbute levels offered s not salent (Johnson et al., 2000). In determnng how many attrbutes to nclude n a study desgn, there s often a trade-off between descrbng tradeoffs accurately (requrng more attrbutes) and mnmsng choce and expermental desgn complexty (requrng fewer attrbutes). Louvere et al. (1993) clam to have successfully admnstered surveys wth up to 32 choce tasks, though ths requres scalng down the number of alternatves and attrbute levels. Boxall et al. (2002) suggests that respondents can endure large numbers of choce sets but sets wth more than sx alternatves tend to exceed cogntve lmts. Louvere et al. (1993) suggest that the average choce experment survey employs seven attrbutes, four choce sets and four alternatves per set, though they note that there s a great deal of varablty and ths average does not consttute a best practce. 78

94 Expermental desgn 15 s the next mportant aspect of choce modellng and t s concerned wth how to create the choce sets n an effcent way or how to combne attrbute levels nto profles of alternatves and profles nto choce sets. In practce, a desgn s developed n two steps: () obtanng the optmal combnatons of attrbutes and attrbute levels to be ncluded n the experment and () combnng those profles nto choce sets. A startng pont s a full factoral desgn, whch s a desgn that contans all possble combnatons of the attrbute levels that characterse the dfferent alternatves. A full factoral desgn s, n general, very large and not tractable n a choce experment (Louvere et al., 2000). Therefore, we need to choose a subset of all possble combnatons, whle followng some crtera for optmalty and then construct the choce sets. The standard approach used n most research has been to use orthogonal desgns, where the varatons of the attrbutes of the alternatves are uncorrelated n all choce sets. More recently researchers n marketng have developed desgn technques based on the Doptmal crtera for non-lnear models n a choce experment context. However, there can be some problems wth these more advanced desgn strateges due to ther complexty, and t s not clear whether the advantages of beng more statstcally effcent outwegh the problems (Scarpa and Rose, 2008) 16. The next step of choce experment nvolves econometrc model selecton and estmaton. The most common model estmated n economcs lterature has been the MNL model, and the most common estmaton crteron s maxmum lkelhood. The 15 Ths step s much more complex n choce experments n that the expermental desgn s crtcal to producng a dataset that wll yeld estmable parameters for the attrbutes n an econometrc model of preferences. 16 For example, utlty balance n more advanced desgn makes the choce harder for the respondents, snce they have to choose from alternatves that are very close n terms of utlty. 79

95 MNL model s easy to estmate, and nterpretaton s straghtforward. However, there are also examples of other choce model specfcatons such as the CL model and RPL model. Selecton between the MNL and CL depends on whether the researcher s nterested n ncludng socoeconomcs varables n addton to the choce attrbute nto the model. If researcher uses only choce attrbutes, the MNL model can gve hgher accuracy of the model fts. However, f the researcher uses choce attrbutes as well as socoeconomc varables n the model, the CL model provdes more accurate results (Rolfe et al., 2000). In emprcal settngs, ncluson of socal and economc characterstcs s also benefcal n avodng IIA volatons, snce socal and economc characterstcs relevant to preferences of the respondents can ncrease the systematc component of utlty whle decreasng the random error (Rolfe et al., 2000; Bateman et al., 2003). The MNL model reles on the assumpton of the ndependence of rrelevant alternatves 17. The IIA arses from the assumpton about the IID of the error term. IID of error term means that t has an extreme value error dstrbuton. The IIA means that the probablty of choosng an alternatve s dependent only on the optons from whch a choce s made, and not on any other optons that may exst. If the IIA/IID s volated, the estmates derved from the model could be based and not generate accurate values for ncluson n cost beneft analyss (Ben-Akva and Lerman, 1985). The IIA property allows the addton or removal of an alternatve from the choce set wthout affectng the structure or parameters of the model. Ths assumpton has three man advantages. 17 The ndependence of rrelevant alternatves means that, all else beng equal, a person s choce between two alternatve outcomes s unaffected by what other choces are avalable. 80

96 Frstly, the model can be estmated and appled n cases where dfferent members of the populaton face dfferent sets of alternatves. For example, n the case of the farm choce model, households lvng n one area may not have one component of agrcultural bodversty. Secondly, ths property smplfes the estmaton of the parameters n the MNL and CL models. Thrd, ths property s advantageous when applyng a model to the predcton of choce probabltes for a new alternatve. On the other hand, the IIA property may not properly reflect the behavoral relatonshps among groups of alternatves (Hensher et al., 2005). That s, other alternatves may not be rrelevant to the rato of probabltes between a par of alternatves. In some cases, ths wll result n erroneous predctons of choce probabltes. There are varous reasons why IIA/IID volaton could occur. One possblty s the exstence of random taste varatons (that s heterogenety). To account for ths, a model whch ncludes socoeconomc varables n addton to the attrbutes n the choce sets can be estmated (Bennett and Blamey, 2001). The soco-economc nformaton could be ncluded n two dfferent ways. The frst s by nteractons wth the attrbutes n the choce sets. The second method ncludes the soco-economc nformaton through nteractons wth the alternatve specfc constants. These nteractons show the effect of varous soco-economc characterstcs on the probablty that a respondent wll choose partcular optons. Alternatve model specfcatons to the MNL models are the CL and RPL. The CL model allows us to estmate the effect of choce-specfc varables on the probablty of choosng a partcular alternatve. The CL model also assumes the IIA property, whch 81

97 states that the relatve probabltes of two optons beng chosen are unaffected by ntroducton or removal of other alternatves. In other words, the probablty of a partcular alternatve beng chosen s ndependent of other alternatves. If the IIA property s volated then the CL model results wll be based and hence a dscrete choce model that does not requre the IIA property, such as the RPL model, should be used. To test whether the CL model s approprate, the Hausman and McFadden (1984) test for the IIA property can be employed. In ths case, whether or not IIA property holds can be tested by droppng an alternatve from the choce set and comparng parameter vectors for sgnfcant dfferences. A RPL model s a generalsaton of a standard multnomal logt model. The advantages of a RPL model are that () the alternatves are not ndependent (the model does not exhbt the ndependence of rrelevant alternatves property) and () there s an explct account for unobserved heterogenety. In ths study we followed all these steps n order to ncrease the accuracy as well as relablty of the results of ths study. We carefully desgned the CE survey and used approprate econometrc technques for the analyss. The way of approachng each step of the choce experment study s explaned n the next secton. 4.6 Emprcal approach to choce experments study As dscussed n the prevous secton, a startng pont of CE study nvolves studyng the attrbutes and attrbute levels used n prevous studes and ther mportance n the choce decsons (Green and Srnvasan, 1990). The selecton of attrbutes should be guded by the attrbutes that are expected to affect respondents' choces on agrcultural bodversty, 82

98 as well as those attrbutes that are polcy relevant n ths feld. Informaton obtaned from prevous studes was used as the base for selectng the attrbutes and relevant attrbute levels to nclude n the frst round of focus group dscusson n ths study 18. The focus group dscusson can provde nformaton about credble mnmum and maxmum attrbute levels. It was found that crop dversty, mxed farmng systems, organc producton and landrace cultvaton were the most mportant attrbutes of agrcultural bodversty used n prevous studes. In addton to that t s necessary to nclude a monetary attrbute for calculatng welfare measures (Rolfe et al., 2000). In ths study frst we attempted to defne the bodversty rch farms n terms of ther attrbutes and the levels of these attrbutes n study areas. The most mportant attrbutes and ther levels were dentfed n consultaton wth experts from the Mnstry of Envronment n Sr Lanka, drawng on the results of nformal ntervews and workshops wth tradtonal small-scale farmers n the study stes, focus group dscussons and a thorough revew of prevous research n ths area n the country. The chosen small-scale farm attrbutes used n ths study are reported n Table 4.1. The attrbutes shown n Table 4.1 were found to be of the most nterest to both potental respondents and agrcultural offcers n tradtonal agrcultural dstrcts n Sr Lanka. 18 The task n a focus group s to determne the number of attrbutes and attrbute levels, and the actual values of the attrbutes. Attrbutes are dentfed from pror experence, secondary research and/or prmary, exploratory research. It s also mportant to dentfy any possble nteracton effect between the attrbutes. 83

99 Ths monetary attrbute s specfed n terms of requred addtonal labour days s ncluded n order to estmate welfare changes 19. The monetary attrbute n ths CE s a proxy, measurng the labour costs that farmers have to allocate for recevng the benefts of agrcultural bodversty. Ths attrbute represents WTA compensaton whch s measured as a beneft rather than a cost. It s clear that the frst fve attrbutes reflect the varous attrbutes of agrcultural bodversty found n the farms n Sr Lanka. The sxth factor represents benefts that farmers can receve n terms of reducng famly food expendture under dfferent polcy optons. The last factor s the monetary attrbute n terms of addtonal labour costs that farmers have to use under dfferent polcy optons. 19 Ths ndrect measure s preferred over a drect monetary attrbute because most (f not all) of the outputs and functons of farms that result n agrcultural bodversty are not traded n the markets, but consumed by the farm famles themselves. Hence, they are not lkely to be famlar wth a drect monetary measure. The proxy monetary attrbute can easly be converted nto actual monetary unts by usng secondary data on labour costs. 84

100 Table 4.1: Classfcatons of small-scale farm attrbutes n the CE survey Farm attrbutes Crop speces dversty Lvestock dversty Mxed farmng system Defntons The total number of crops that are grown n the farm The total number of anmal speces on the farm Mxed crop and lvestock producton, representng dversty n agrcultural management system Landrace cultvaton Whether or not the farm contans a crop varety that has been passed down from the prevous generaton and/or has not been purchased from a commercal seed suppler. Organc producton Whether or not ndustrally produced and marketed chemcal nputs are appled n farm producton Expendture Estmated labour cost Own farms contrbuton to reduce famly food expendture Estmated cost n terms of addtonal labour requrement Notes: These attrbutes are common to most agrcultural dstrcts n Sr Lanka. However, the mportance of dfferent attrbutes can be dfferent n dfferent areas. More detals of all attrbutes are gven under secton 3.5 n Chapter three. After dentfyng the attrbutes for a partcular experment, the analyst must assgn values or levels to each attrbute. These levels should be chosen to represent the relevant range of varaton n the present or future nterest of respondents. In general, focus group dscussons wll ndcate the level of the attrbutes as well as the best way to present them. Though commonly presented n words and numbers, attrbute levels may be presented usng pctures. To the extent that vsual representatons of attrbute levels are utlsed, t s lkely that respondents wll perceve levels more homogeneously, lkely leadng to more precse parameter estmates n the modellng stage (Alpzar et al., 2001). 85

101 We presented choce set usng pctures of the dfferent attrbutes and ther levels. A sample choce set s gven n Appendx I.1. In ths study crop speces dversty s explaned as dfferent levels, whle mxed farmng system, landrace cultvaton and organc producton varable are gven as bnary varables. The anmal dversty varable was dropped from the choce set as ncludng ths varable could provde choce sets whch cannot be nterpreted. Ths s because t s drectly lnked wth mxed farmng systems. The levels of relevant varables were dentfed through the plot survey that conducted n August Attrbute levels used n ths study are gven n Table 4.2. As credblty plays a crucal role n choce modellng, the researcher must ensure that the attrbutes selected and ther levels can be combned n a credble manner (Layton and Brown, 1998; Alpzar et al., 2001). Therefore, expermental desgn, where dfferent types of hypothetcal farms are created plays an mportant role n choce modellng. A large number of dfferent types of farms (combnatons of attrbutes) could be constructed from ths number of attrbutes and levels. The number of farms that can be generated from sx attrbutes, 1 wth 4 levels, 2 wth 3 levels and remanng 3 wth 2 levels s 288. Ths means that t would be possble to generate 4 1 *3 2 *2 3 =288 alternatves from these, smply by consderng all the possble combnatons or complete factoral desgn. Clearly t would not be practcal to ask respondents to consder smultaneously 288 possble alternatves. It s not necessary to do so. The answer les n the use of statstcal expermental desgns. Therefore, the fractonal factoral desgn s used to create an optmal number of choce optons for the survey. In our case the mnmum number of choce optons whch could be used n the survey was 12. However 36 choce optons were used and randomly blocked them nto sx dfferent versons (each has sx 86

102 optons). The 36 choce optons are gven n Appendx I.2. Usng the Dptmal procedure n Engne an expermental desgn was undertaken to recover only the man effects, consstng of 36 par wse comparsons of farms profles. Table 4.2: Attrbutes and ther levels Attrbutes Levels Crop speces dversty 3, 7,10 and 15 Mxed farmng system Mxed crop and lvestock producton vs. specalzed crop or lvestock producton (If Yes they mantan mxed crop and lvestock otherwse No) Organc producton Organc producton vs non-organc producton (If Yes organc producton, otherwse No) Landrace cultvaton Whether farm contans landraces or not (If Yes farm contans landraces otherwse No) Decrease n food expendture 5 %, 10% and 15% (n percentage) Estmated cost n terms of 10%, 20% and 30% addtonal labour days Note:. Upper and lower bound of the crop speces dversty, food expendture change and addtonal labour requrements are estmated usng plot survey nformaton.. These attrbutes are common to most agrcultural dstrcts n Sr Lanka. However, t s mportant to note that attrbutes can be dfferent n dfferent areas. The questonnare s usually a paper and pencl task that s presented through an ntervewer. Whle ts man content wll be sx choce scenaros through whch the respondent wll be guded, t may also nclude sectons requestng soco-demographc, economcs, atttudnal and past behavour data. The questonnare used for ths study was developed usng the results from nne focus groups dscussons and a pre-test. A 87

103 pre-test of 30 respondents was undertaken n August 2010 n three dstrcts. On the bass of the pre-test, only mnor modfcatons to the questonnare were requred. In the questonnare, respondents were told that the development of the choce experment questonnare was based on focus group studes. Nne focus groups dscusson were conducted for both potental respondents (6) and agrcultural offcers (3) to ensure that nputs for choce sets were correctly specfed. The purposes of the focus group studes were to determne attrbutes relevant to respondents and agrcultural managers and test a draft questonnare. More detals about selectng sample sze and the content of the questonnare are provded n Secton 3.2 n Chapter three. Before the ntervew t was confrmed whether the respondents were generally those responsble for farm producton decson makng. An ntroductory secton explaned to the respondents the context n whch choces were to be made, descrbed each attrbute and explaned that the key attrbutes of farms had been selected as a result of pror research and were combned artfcally n the choce sets. Respondents were told that ther names and ndvdual choces were confdental and that completon of the exercse would provde nformaton to agrcultural polcy makers n summary form. In face-toface ntervews, each respondent was presented wth sx choce sets showng varous optons for the dfferent farms, the one of whch was an example gven n Table 4.3.Respondents were told that three sets of possble optons had been prepared and were then asked for ther preferred choce from each set of optons. Before answerng the choce sets, respondents were requested to keep n mnd ther avalable ncome, food consumpton expendture, avalable labour, sze of the land and other thngs on whch they may consder when makng a decson. They were also remnded that dfferent 88

104 types of farms may have cost and benefts for them n the future. There was not any tem non-response, n other words all the choce sets were answered due to the advantage of the n person ntervewng. Therefore, a total of 4,488 (748*6) choces could be elcted from a total of 746 farm famles. Table 4.3 shows an example of a choce set used for a choce experment. In the survey, the enumerator asked: Assumng that the followng farms were the ONLY choces you have, whch one would you prefer to cultvate? Each choce set conssts of two dfferent profles and one common profle. We presented dfferent optons to the respondents sx tmes and asked them to select only one opton each tme. Nether s a status quo alternatve and t s common to all choce sets. The sample populaton n each area was randomly dvded nto sx, each sub-sample recevng one of the sx versons of the choce experment. Table 4.3: An example of a choce set Farm Characterstcs Farm Farm (A) (B) Total number of crop varetes grown on a farm 10 7 Nether Crops s combned wth lvestock/poultry producton Yes No Farm crops produced entrely usng organc methods Yes Yes Farm has a landrace cultvaton No No Expendture reducton (n percentage) 15% 10% Small-scale farm (A) nor Small-scale farm (B): Estmated cost n terms of addtonal labour requrement (n percentage) 20% 10% I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) 89

105 In addton to the man attrbutes, t s requred to obtan some soco-economc and household characterstcs whch can be used as nteracton term for the estmaton of the CL and RPL models. Some of the ndvdual specfc attrbutes that can be used n the estmaton of the CL and RPL models are reported n Table 4.4. These types of characterstcs have been commonly used by prevous studes n choce experment. More detals about ncludng soco-economc and household characterstcs are gven by Brol (2005) and Rolf et al. (2000). Table 4.4: Indvdual attrbutes for the estmaton of CL and RPL models Indvdual attrbutes Age Famly Sze Farm ownershp Busness vehcle Experence Off farm employment Educaton level Atttudes Number of plots Defntons Age of the small-scale farm decson maker Total number of famly members n the farm famly Type of ownershp of the land Household owns a busness vehcle or not Experence of small-scale farm decson maker n years Number of famly members employed off-farm Educaton of the small-scale farm decson maker Farmers atttudes towards to agrcultural bodversty Numbers of plots own to farmer Note: These attrbutes were selected to be used n the CL model as nteracton terms wth the man attrbutes. After a prelmnary run of the model, the most mportant fve varables were selected n the fnal model. Under the CE method a sample of people s asked to choose ther most preferred alternatves from a sequence of grouped optons that relate to dfferent agrcultural 90

106 bodversty management strateges. Each opton s descrbed n terms of ts agrcultural bodversty outcomes and a personal monetary cost to be borne personally by the respondent. By analysng the choces made by respondents t s possble to nfer the tradeoffs that people are wllng to make between money and greater benefts of agrcultural bodversty. Ths n turn allows the estmaton of changes of prvate benefts wth changng agrcultural bodversty. Soco economc aspects such as communty, gender, age, martal status, lteracy level, ncome, expendture, savngs and ndebtedness provde a base for studyng the mpact of any program. Therefore, before estmatng the models t s mportant to know the basc soco-economc profle of the respondents n each dstrct. The most mportant socoeconomc varables are explaned n the next secton wth reference to the respondents and ther famles. 4.7 Soco-economc profle of sample respondents In Appendx J.1, J.2 and J.3 some descrptve statstcs of the respondents are presented. The mean value of age was slghtly hgher n Anuradhapura samples and men dsplayed a hgher response-rate n all three dstrcts. The average number of persons n the household was 5, 4 and 5 n Ampara, Anuradhapura and Kurunegala samples respectvely. Although agrculture was the domnant source of household ncome, monthly ncome from non-farm actvtes was approxmately Rs. 1,750, Rs. 2,300 and Rs. 2,200 per household, whch accounted for almost 7, 8 and 10 per cent of the total household ncome n Ampara, Anuradhapura and Kurunegala samples respectvely. The 91

107 mean labour usage per season was 96 man-days for the three samples. Ths s expected, gven the tedous labour ntensty for all agrcultural work n sem-subsstence economy. There was low usage of external nput (Rs. 2,110 n captal) as a result of the small sze of farms n the study areas. Rce was cultvated by the most number of households (532), followed by varous types of vegetables and cash crops. The maxmum number of crop varetes cultvated by any household was nne. Percentage of households that have cultvated between one and nne crop varetes s as follows. Approxmately 14 per cent cultvated one varety only, whle 18, 22, 31 and 15 per cent cultvated two, three, four and more than fve varetes, respectvely n all dstrcts. Only 54, 46 and 72 households used a modern varety of seeds n Ampara, Anuradhapura and Kurunegala samples respectvely. Approxmately 68, 72 and 66 per cent of households n respectve samples used mxed farmng systems where they have crops as well as lvestock. The average number of years of educaton s 8, 9 and 8 n Ampara, Anuradhapura and Kurunegala samples respectvely. In the survey t was found that a few farmers have not attended any schools. Any ntervewee whose educaton level s less than three years was not ncluded nto the choce experment study. Ths s to make sure that everyone could understand the trade-offs between dfferent alternatves. Approxmately 1 per cent of the respondents had a dploma or degree and 26 per cent of the ntervewees have passed the ordnary level examnaton. Such relatvely hgh educaton levels may be attrbuted to the relable results of the choce experment part of ths study. 92

108 About 22 per cent of the respondents were wthn the range years old. The most frequent age class was years (65 per cent). Around 23 per cent and 6.2 per cent of the cases fell wthn the age-ranges and more than 55 years, respectvely. The average age of respondents was approxmately 41 years for the three samples. The man ncome source of famles was agrcultural ncome. Average monthly agrcultural ncome was Rs. 22,844, Rs. 26,109 and Rs. 29,074 for Ampara, Anuradhapura and Kurunegala samples respectvely. The majorty of household expendture was spent on food, followed by health and personal care, and transport. Approxmately 51, 62 and 39 per cent of the farm outputs were used for famly consumpton n Ampara, Anuradhapura and Kurunegala samples respectvely. We compared the above mentoned sample averages wth dstrct averages for smallscale farmers whch were provded by the Department of Census and Statstcs n Sr Lanka. In most of the cases, sample averages are smlar to the populaton averages for these dstrcts and hence the results reported n ths chapter of the thess could be generalzed for the entre populaton of these dstrcts. Gven ths general nformaton about the respondents, next, the results of ths analyss should be nvestgated. Before explanng the results, t s mportant to know the way of codng the data and the estmatng procedure n ths analyss. Therefore, data codng s explaned n the next secton. 93

109 4.8 Data codng and estmaton procedure Data codng s one of the mportant parts of the choce experment model. In ths study the data were coded accordng to the levels of the attrbutes. Attrbutes wth 2 levels entered the utlty functon as bnary varables that were effects coded (Louvere et al. 2000). Crops dversty varable s used as a contnuous varable. Consequently the crop speces dversty attrbute took levels 3, 7, 10 and 15. For the mxed farmng system, landrace cultvaton and organc farmng method were coded as effect codng method. For example, f a farm famly selected the mxed farmng system, t was entered as 1 and f they selected specalsed crop or lvestock producton, t was entered as 1. For the organc producton attrbute, organcally produced farms were entered as 1 and those farms that were not produced organcally were entered as 1. For the landrace attrbute, those farms that contaned a landrace were entered as 1 and those wthout were effects coded as 1. Farm contrbuton to famly expendture reducton and labour requrement are transformed nto monetary values when estmatng the models. The percentage values of the levels gven to farmers of possble famly food expendture reducton due to own farm consumpton s 5, 10 and 15. On average farm famles spend approxmately Rs. 12, for ther monthly food consumpton. Accordngly, the value of net expendture reducton due to own farm food consumpton can be represented as Rs. 600, Rs. 1,200 and Rs. 1,800 for the three levels respectvely. The percentage values of addtonal labour requrement were gven as 10, 20 and 30. On average, farm famles need approxmately 16 labour days per month for ther farm cultvaton. Daly average 20 Exchange rate at the tme of survey was 1 US$ = LKR 115(approxmately). 94

110 wage rate per person per day was Rs Accordngly, value of the cost of acceptng alternatve farms can be represented as Rs. 720, Rs. 1,440 and Rs. 2,160 for the three levels respectvely. In ths way the levels used for expendture reducton and labour requrement varables were entered n a cardnal-lnear form. The attrbutes for the nether farm opton were coded wth zero values for all attrbutes. The alternatve specfc constants were equalled to 1 when ether farm A or B was chosen and to 0 when nether farm alternatve was chosen. In other words, n ths model the ASC s specfed to account for the proporton of choce of partcpaton n small-scale farm producton. Choce data were converted from wde to long format wth a program coded n LIMDEP 9.0 NLOGIT 4.0. Ths data converson step was necessary to estmate models wth multple responses from each respondent, a format smlar to panel data. Frst, we estmated the CLM. The IIA property of ths model s tested usng a procedure suggested by Hausman and McFadden (1984). Ths test nvolves constructng a lkelhood rato test around dfferent versons of the model where choce alternatves are excluded. If IIA holds then the model estmated on all choces (the full choce set) should be the same as that estmated for a sub-set of alternatves (Bateman et al., 2003). It s found that the IIA condtons are not volated any of the case. Therefore, the IIA tests performed ndcate that the model fully conform to the underlyng IIA condtons. Then socal and economc characterstcs were we ncluded as nteracton terms and test 21 Ths vares between Rs. 500 and Rs. 400 dependng on varous factors (gender, perod and area). For example, men s wage rate s slghtly hgher than female. Wage rate n the harvestng perod s greater than other perod. 95

111 whether there was an mprovement of the results. It was found that there was no mprovement by ncludng any socal-economc characterstcs as the nteracton term. As the next step of the analyss, the RPL model was used n order to take nto account preference heterogenety. We estmated basc the RPL model whch ncludes only attrbutes as well as the extended RPL model that ncludes some soco-economc varables. When comparng wth the RPL results wth the CL results t was found that basc CL results were better n term of overall ft of the model and number of sgnfcant varables. Therefore, the results of the basc CL model could be used to smulate welfare change of the socety when changng dfferent attrbutes and ther level of agrcultural bodversty. The results of the CL model are dscussed n the next secton. 4.9 Results of the CL model It s well known that the choce experment s desgned wth the assumpton that the observable utlty functon would follow a strctly addtve form. Ths study explored a varety of dfferent specfcatons of the utlty functons to dentfy the best specfcaton of the data. These tests nclude both formal statstcal tests and nformal judgments about the sgns, magntudes, or relatve magntudes of parameters based on our knowledge about the underlyng behavoral relatonshps that nfluence dfferent farms choce. Dfferent researchers have dfferent styles and approaches to the model development process. One of the most common approaches s to start wth a mnmal specfcaton whch ncludes those varables that are consdered essental to any reasonable model (Hensher et al., 2005). Workng from ths mnmal specfcaton, ncremental changes 96

112 are proposed and tested n an effort to mprove the model n terms of ts emprcal ft to the data whle avodng excessve complexty of the model. Another common approach s to start wth a rcher specfcaton whch represents the model developer s judgment about the set of varables that s lkely to be ncluded n the fnal model specfcaton. The frst of these methods were adopted n ths study for the specfcaton of a model choce as t was the most approprate approach for those new to dscrete choce modellng. As a formal statstcal process, dfferent model specfcatons were compared accordng to hgher log-lkelhood value crteron n ths study. Most approprate specfcaton was found to be the model wth the lnear verson of the sx attrbutes of the study. Accordngly, the CL model was specfed so that the probablty of selectng a partcular alternatve s a functon of attrbutes of the alternatves and of the alternatve specfc constant. Indrect utlty receved by the farm attrbutes take the form: T j 1( X crop _ dversty ) 2 ( X mx _ farm ) 3( X organc _ farm ) 4 ( X 0 landrace ) 5 ( X expendture ) 6 ( X labour) (4.22) where β 0 refers to the alternatve specfc constant and β 1-6 refers to the vector of coeffcents assocated wth the vector of attrbutes descrbng farms characterstcs. The results of the estmated basc CL model for the separate dstrct and pool data set are presented n Table 4.5. All attrbutes n the model were statstcally sgnfcant at conventonal levels, and ther sgns were as expected. The overall ft of the model as measured by McFadden s R 2 was also good by conventonal standards used to descrbe 97

113 probablstc dscrete choce models (Ben-Akva and Lerman, 1985). The results ndcate that the ndrect utlty functon takes the followng form: T ( X crop _ dversty ) 0.119( X mx_ farm ) 0.096( X organc_ ) 0.090( X j( Ampara) farm landrace ( X exp endture) ( X labour) (4.23) ) T ( X crop _ dversty ) 0.095( X mx_ farm ) 0.077( X organc_ ) 0.112( X j ( Anuradhapura) farm landrace ( X expendture) ( X labour) (4.24) ) T ( X crop _ dversty ) 0.092( X mx_ farm ) 0.064( X organc_ ) 0.243( X j( Kurunagala) farm landrace ( X exp endture) ( X labour) (4.25) ) T ( X crop _ dversty ) 0.077( X mx_ farm ) 0.079( X organc_ ) 0.144( X j( Pool_ data) farm landrace ( X exp endture) ( X labour) (4.26) ) As shown n Table 4.5 we estmated models for three samples separately. In addton to that pool data model was estmated. Ths type of estmaton allowed us to compare relatve values of attrbutes n dfferent regme. It also helped n understandng the heterogenety of the results among dfferent dstrcts. All of the farm attrbutes are statstcally sgnfcant at 10 per cent level mplyng that any sngle attrbute ncreases the probablty that a farm s selected, other thngs remanng equal. Snce the underlyng sample s statstcally sgnfcant, these 98

114 Table 4.5: Regresson results of the CL model for separate dstrcts and pool data Varables Ampara Anuradhapura Kurunegala Pool data ASC 1.832(0.199)* 5.028(0.445)* 2.984(0.219)* 2.711(0.123)* Crop dversty 0.028(0.009)* 0.019(0.008)** 0.018(0.009)** 0.021(0.005)* Mxed system 0.119(0.041)* 0.096(0.041)** 0.092(0.042)** 0.077(0.021)* Organc farms 0.096(0.041)** 0.077(0.040)*** 0.064(0.041)*** 0.079(0.023)* Landrace cultvaton 0.090(0.041)** 0.112(0.042)* 0.243(0.043)* 0.145(0.024)* Expendture reducton 2.3E-04(8.4E-05)* 1.8E-04(8.4E-05)* 3.4E-04(8.9E-05)* 2.5E-04(4.9E-05)* Labour requrement -4.5E-04(6.9E-05)* -2.4E-04(6.8E-05)* -6.9E-04(7.2E-05)* -4.5E-04(4.0E-05)* LR ch2(7) Prob > ch Pseudo R N 4,032 4,032 3,942 12,006 Note:. Standard errors are shown n brackets.. *denotes sgnfcant at 1% level whle ** and *** ndcates sgnfcant varables at 5% and 10% level respectvely. 99

115 parameters represent preference estmates of farm famles for farms attrbutes n 3 envronmentally dfferent areas of Sr Lanka. In the Ampara dstrct organc farm and landrace cultvaton varables are sgnfcant under fve per cent level whle all other attrbutes are sgnfcant at one per cent level. In Anuradhapura dstrct crop dversty and mxed farm varables are sgnfcant under fve per cent level whle all other attrbutes except the organc farms attrbute are sgnfcant at one per cent level. Ths s smlar to the results of Kurunegala sample. However, the organc farm attrbute of Anuradhapura and Kurunegala dstrcts are sgnfcant at 10 per cent level. Interestngly, all varables n the pool data model are sgnfcant at one per cent level. When the addtonal labour requrement attrbute s used as the normalsng varable, t can be seen that the almost all attrbutes are sgnfcantly contrbutng towards the welfare n rural agrcultural socety n Sr Lanka. The postve sgn on the ASC coeffcent mples that respondents are hghly responsve to changes n exstng farms attrbutes level and they make decsons that are closer both to ratonal choce theory and the behavour observed n realty (Hensher et al., 2005). Investgaton of the results n each regme reveals that the fndngs of the study are strkngly n lne wth those as predcted by economc theory. It s obvous that regons where food markets as well as road nfrastructure are fully developed, farmers demand for crop speces dversty and mxed farmng s hghly sgnfcant and organc farm and landraces are relatvely less sgnfcant. In contrast to that, n the relatvely solated regon where communty level markets are lackng and dstance to the nearest towns are large, organc farmng method and landrace cultvaton are sgnfcantly and postvely demanded by the farmers. However, n contrast to our fndngs, Brol (2004) found that farmers 100

116 demand for crop speces dversty n home gardens was postve and sgnfcant n rural solated areas, more so than n areas where market as well as transport facltes were easly accessble. The overall ft of all models can be measured by Pseudo R 2 and t s reasonable when consderng probablstc dscrete choce models (Hensher et al., 2005). We used Swat- Louvere log lkelhood rato test n order to test whether there s a sgnfcant regonal heterogenety of the farm famles utlty for dfferent attrbutes. The rejecton of the nullhypothess would mply that farmers n dfferent dstrcts have dfferent preferences for farms and ther attrbutes. It s found that Swat-Louvere log lkelhood rato test rejects the null hypothess that the regresson parameters are equal at fve per cent sgnfcance level. Ths mples that farm famles n each of the three regons have dstnct preferences for dfferent farms and ther attrbutes. As the next step of the analyss, the IIA property of all models s tested usng a procedure suggested by Hausman and McFadden (1984) and contaned wthn NLOGIT 4.0. Ths test nvolves constructng a lkelhood rato test around dfferent versons of the model where choce alternatves are excluded 22. If IIA holds then the model estmated on all choces (the full choce set) should be the same as that estmated for a sub-set of alternatves (Bateman et al. 2003). It was found that the IIA property s not volated mplyng that the condtonal logt estmates do not hold any bas that could have resulted from ncluson of the nether opton. The test results are reported n Table 4.6 for all versons ncludng pooled model 22 It s evdent that maxmum lkelhood of condtonal logt s consstent and effcent f the model s correctly specfed. A consstent but neffcent estmator s obtaned by estmatng the model on a restrcted set of outcomes. If IIA holds and the dropped choces are rrelevant, the estmates of the parameters should be the same. 101

117 wthout the constant. The results of Hausman-McFadden test reported n Table 4.6 strongly provde the evdence of holdng IIA assumpton for each sample n our data set. However, as mentoned prevously, CL model assumes homogeneous preferences across farm famles n each dstrct. Table 4.6: Test of ndependence of rrelevance alternatves Ampara χ 2 D.O.F Probablty Scenaro A Scenaro B Scenaro C Anuradhapura Scenaro A Scenaro B Scenaro C Kurunegala Scenaro A Scenaro B Scenaro C Pool data Scenaro A Scenaro B Scenaro C Note: The Hausman-McFadden test s based on the comparson of two estmators of the same parameters. One estmator s consstent and effcent f the null hypothess s true (IIA holds), whle the second estmator s consstent but neffcent. 102

118 In general preferences across famles are n fact heterogeneous. Accountng for ths heterogenety enables estmaton of unbased estmates of ndvdual preferences and enhances the accuracy and relablty of parameter estmates and hence total welfare (Rolfe et al., 2000; Bateman et al., 2003). Furthermore, accountng for heterogenety enables prescrpton of polces that take equty concerns nto account (Brol, 2004). There are two standard ways of accountng for preference heterogenety. Frst, t can be done by separatng the respondents nto varous groups (segments) and estmatng the basc model for each group separately. Estmatng the CL model for each dstrct separately s one way of dong ths. Second, t s possble to accountng for preference heterogenety by usng household and decson-maker level characterstcs drectly as nteracton terms. Interacton of ndvdual-specfc socal and economc characterstcs wth choce specfc attrbutes or wth ASC of the ndrect utlty functon s a common soluton to dealng wth the heterogenety. However, the man problem wth ths method s multcollnearty, whch occurs when too many nteractons are ncluded n the estmaton. In ths context, the model needs to be tested down, usng the hgher log-lkelhood crtera (Bateman et al., 2003; Brol, 2004). Therefore, as the next step of the analyss, CL model s estmated usng fve socoeconomc varables as nteracton terms Results of the CL model ncludng attrbutes and socoeconomc varables In order to account for heterogenety of preferences across farm famles, nteractons of household-specfc socoeconomc characterstcs wth choce-specfc attrbutes were 103

119 ncluded n the utlty functon. The use of socoeconomc varables as ndependent varables s justfed under the hypothess that socoeconomc characterstcs are separate factors nfluencng behavoural ntentons and behavour (Lynne et al., 1988; Rolfe et al., 2000; Bateman et al., 2003). As dscussed n secton 4.2, n random utlty models the effects of socal and economc characterstcs on choce cannot be examned n solaton but as nteracton terms wth choce attrbutes. It s not possble to nclude nteractons between many household specfc characterstcs and the sx farm attrbutes when estmatng the CL models due to possble multcollnearty problems (Hensher et al., 2005). Therefore, only fve mportant household specfc characterstcs are selected. They are; age of the respondent (age), whether farmer owned a farm or not (landownershp), educaton level of the respondent (educaton), household sze (hhs) and number of famly members who have off farm employment (offfarm). Accordngly, ndrect utlty receved by the farm attrbutes and nteracton wth socoeconomc characterstcs can be respecfed as follows: T j ( X 0 1 crop_ dversty ) X 2 mx _ farm ) ( X 3 organc_ farm ) X 4 landrace ) ( X 5 expendture ) X 6 labour ) ( X 1 ( X 11 ( X 21 crop_ dversty organc_ farm expendture Z Z Z age age age )... ( X 5 )... ( X )... ( X crop_ dversty organc_ farm expendture Z Z Z offfarm offfarm offfarm ) ( X ) ( X ) ( X mxed _ farm labour landrace Z Z Z age age age )... ( X )... ( X )... ( X labour mxed _ farm landrace Z Z offfarm Z offfarm )) offfarm ) ) (4.27) It s clear that n model 4.27 fve socoeconomc varables are ncluded n addton to the attrbutes from the choce sets. The total number of coeffcents n the full model s 36. We tested varous nteractons of the sx farm attrbutes wth the household-level characterstcs mentoned above. An ntal run of the model wth all nteracton terms reveal that a large 104

120 number of varables are nsgnfcant for all three models. Then we estmated the correlaton matrx and t was revealed that there was a hgher level of correlaton and multcollnearty among these household-level varables. Estmaton of varance nflaton factor further provded the evdence about hgher correlaton among household level varables. To address ths lmtaton, ndependent varables were elmnated based on varance nflaton factors, whch were calculated by runnng ordnary least square regressons between each ndependent varable 23. Then the results of the correlaton matrx were also used for further elmnatng some of the nteracton terms. The estmated results of the fnal models are reported n Table 4.7. Ths specfcaton of the model was not sgnfcantly dfferent from the prevous specfcaton. In partcular, the model dd not reveal a hgher level of parametrc ft compared wth the frst model. Most of the nteracton terms of all three models are not sgnfcant. Further, ncludng the nteracton terms has reduced the sgnfcance of some of the attrbutes of the models. Therefore, t can be concluded that the mprovement n model ft was not sgnfcant. The Hausman-McFadden test also revealed that the CL model wthout nteractons s a better ft for the data than the CL model wth nteracton. Among the sgnfcant nteractons, households wth hgher ages n Anuradhapura and Kurunegala had a hgher preference for crop varety dversty. Hgher age households n Kurunegala dstrct had hgher preferences for mxed farmng systems. Demand for a 23 Those ndependent varables for whch VIFj > 5 ndcate clear evdence that the estmaton of the characterstc s beng affected by multcollnearty (Maddala, 2000). 105

121 landrace cultvaton n the small-scale farm also ncreased wth land ownershp. Ths mples that farmers who have ther own land are lkely to select tradtonal varetes for ther cultvaton. Farmers who have the ownershp of the land have hgher probablty of usng organc farmng methods as well. More educated farmers were more lkely to select organc farmng methods n Ampara sample. Ths mples that land ownershp as well as educaton has a sgnfcant mpact on agrcultural bodversty n all regmes. As expected, off farm employment has sgnfcant negatve mpact on bodversty mprovement n these areas. Preferences of farm famles for small-scale farms wthout land race cultvaton may reflect the effect of government subsdes for purchasng the seed of modern varetes on agrcultural bodversty mantaned n farms. The nteracton between the demand for crop varetes and the number of members n the famly s postve and hghly sgnfcant n all models. We ncluded nteracton between organc producton and the number of members n off farm employment n the famly to see whether ths varable provded good results n ths analyss. However, ths varable was hghly correlated wth other varables. As a result ths varable was removed from the fnal verson of the model. The demand for crop speces dversty decreased wth the number of household members employed off farm. It was found that households wth hgher number of members n the famly were more lkely to choose more mxed farmng systems that would provde more foods for household consumpton. The overall model s sgnfcant at the one per cent level. Compared to basc CL model, the explanatory power of the model has not changed sgnfcantly. 106

122 Table 4.7: CL model ncludng attrbutes and socoeconomc varables Varables Ampara Anuradhapura Kurunegala ASC 1.65(7.07)* 5.60(10.98)* 2.42(9.66)* Crop dversty 0.27(7.22)* 0.35(2.96)* 0.06(1.63)*** Mxed system 0.33(1.92)*** 0.83(1.96)*** 0.34(1.74)*** Organc farms 0.59(4.21)* 0.51(1.42) 0.23(1.14) Land race cultvaton 0.22(1.53) 1.19(1.42) 0.31(1.50) Expendture 2.1E-04(0.90) 4.3E-02(2.91)* 1.1E-02(2.19)** Labour -6.6E-04(-3.11)* -5.5E-04(-2.54)** -1.9E-02(-3.30)* Crops_age 3.2E-04(0.73) 1.6E-02(2.49)** 1.9E-02(2.20)** Mxed_Age 7.6E-04(0.22) 2.1E-03(0.60) 9.6E-02(2.14)** Crops_ownershp 0.29(22.62)* 5.1E-03(1.47) 8.5E-02(1.87)*** Organc_ownershp 0.50(5.32)* 1.2E-04(0.04) 8.3E-02(1.80)*** Landrace_ ownershp 0.31(3.31)* 1.2E-05(1.39)*** 7.1E-05(5.84)* Crops_educaton 0.012(4.39)* 5.4E-07(0.57) 2.7E-06(2.06)* Mxed_educaton 0.03(2.56)** 0.29(2.61)* 0.03(1.81)*** Organc_educaton 0.04(2.58)** 0.98(1.20) 0.14(1.33) Landrace_educaton 0.01(0.55) 0.51(1.11) 0.14(1.29) Expendture_educaton 2.9E-06(0.13) 0.98(1.19) 0.36(3.31) Labour_educaton 4.6E-06(2.26)** 4.5E-03(3.14)* 2.7E-04(0.96) Crops_hhs 5.2E-02(2.22)* 4.9E-03(2.28)** 7.8E-06(2.26)** Mxed_hhs 1.6E-02(0.08) 0.11(2.08)* 0.05(3.25)* Landrace_hhs -2.6E-05(-1.09) -0.02(-0.04) -0.36(4.47)* Crops_offfarm -0.03(-2.49)** -0.13(-2.40)** -0.23(-2.84)* Mxed_offfarm 0.05(0.79) 5.5E-04(3.16)* 1.2E-03(5.89)* Labour_Offfarm 8.3E-05(0.96) 3.4E-05(1.74)*** 7.2E-05(3.09)* LR ch2(25) Prob > ch Pseudo R N Note:. *denotes sgnfcant at 1% level whle ** and *** ndcates sgnfcant varables at 5% and 10 % level.. t values are n parenthess. 107

123 An alternatve method to account for preference heterogenety s the use of the RPL model. We next estmate the results usng the RPL model to nvestgate whether there s an observable mprovement of the results. The RPL model s one of the fully flexble versons of the dscrete choce models because ts unobserved utlty s not lmted to the normal dstrbuton. It decomposes the random parts of utlty nto two parts. One has the ndependent, dentcal type 1 extreme value dstrbuton, and the other representng ndvdual tastes can be any dstrbuton. It s also charactersed by accommodatng heterogenety as a contnuous functon of the parameters. Therefore, as the next step of the analyss, we ran the RPL model and the results of t are explaned n the next secton Results of the RPL model Runnng the RPL model requres an assumpton to be made about the dstrbuton of preferences for each attrbute. The man canddate dstrbutons are normal and log normal. The former allows preferences to range between postve and negatve for a gven attrbute, the latter restrcts the range to beng of one sgn only. Further, treatng preference parameters as random varables requres estmaton by smulated maxmum lkelhood. Ths means that the maxmum lkelhood algorthm searches for a soluton by smulatng m draws from dstrbutons wth gven means and standard devatons. Probabltes can be calculated by ntegratng the jont smulated dstrbuton. In ths study the RPL model was estmated usng NLOGIT 4.0. All the parameters were specfed to be ndependently normally dstrbuted and dstrbuton smulatons were based on 500 draws. The results of the RPL estmatons for the separate dstrcts are reported n Table

124 Table 4.8: Regresson results of the RPL model for separate dstrcts and pool data Varables Ampara Anuradhapura Kurunegala Pool data ASC (0.191)* 4.743(0.477)* 2.468(0.213)* 2.304(1.121)* Crop dversty 0.024(0.009)* 0.020(0.009)** 0.015(0.008)*** 0.018(0.005)* Mxed system 0.076(0.041)*** 0.557(0.041) 0.135(0.041)* 0.059(0.021)** Organc farms 0.157(0.044)* 0.092(0.044)** 0.154(0.045)* 0.136(0.025)* Landrace cultvaton 0.048(0.042) 0.090(0.043)** 0.206(0.044)* 0.107(0.024)* Expendture 2.1E-04(9.7E-05)** 8.9E-05(1.0E-04) 0.5.4E-04(1.1E-04)* 2.6E-04(5.5E-05)* Labour -6.1E-0.4(7.8E-05)* -2.7E-04(7.8E-05)* -8.4E-04(8.4E-05)* -5.6E-04(4.6E-05)* Log lkelhood Smulaton ρ N Note:. Standard errors are shown n brackets.. *denotes sgnfcant at 1% level whle ** and *** ndcates sgnfcant varables at 5% and 10 % level respectvely. 109

125 The results of the RPL model are qute smlar n sgn and magntude to the CL model where preferences are assumed to be homogenous. The crop dversty coeffcent for the standard CL model s whereas t s for the RPL for pool data model. Pool data coeffcents of the mxed farmng systems are and for the CL model and RPL model respectvely. The CL model contans all postve and sgnfcant choce attrbutes except landrace cultvaton n Ampara dstrct and mxed farmng n Anuradhapura dstrct wth smlar magntudes to the RPL results. The major dfference between the two models s wth regard to the mxed farmng system and landrace coeffcents. The landrace cultvaton varable was not sgnfcant n Ampara sample whle mxed farmng system varable was not sgnfcant for Anuradhapura sample for RPL model whle these varables are hghly sgnfcant n the CL model. The CL model, unlke the RPL model, dsplays the sgnfcant results for all varables. The log lkelhoods are almost the same for the all three models the CL model and wth RPL model. Therefore, the Swat Louvere Log Lkelhood rato test results of the test cannot reject the null hypothess that the RPL model and CL model estmates are equal. Hence no mprovement n the model ft can be acheved wth the use of a RPL model. Accordngly, t can be concluded that the CL model s suffcent for analyss of the data set presented n ths study Estmatng welfare changes wth changng attrbutes and ther level Comparng the results of dfferent models reveals that the basc CL model provdes the most sgnfcant results of the data. Therefore, the results of the CL model reported n Table 4.5 can be used to calculate the value assgned by the farm famles to farm attrbutes. Pont estmates of the WTA a change n one of the attrbutes n the 110

126 choce sets can be found by estmatng mplct prces. Implct prces are the margnal rates of substtuton between the attrbute of nterest and the monetary attrbute. Ths s equal to the rato of the coeffcent of one of the non-monetary attrbutes and the monetary attrbutes. Equaton 4.19 s used to estmate the mplct prces for each attrbute. Estmates of mplct prces for each of the non-monetary attrbutes n the choce sets are reported n Table 4.9. Table 4.9: Implct prce estmates for attrbutes Varables Ampara Anuradhapura Kurunegala Crop dversty Mxed system Organc farms Landrace cultvaton Note: all mplct prces are estmated usng the result of the basc CLM. These estmates ndcate that, for example, farmers valuaton of the addtonal benefts that farmers could obtan per month n ncreasng crop dversty by one s Rs. 60, 81 and 27 n Ampara, Anuradhapura and Kurunegala farmers. It s clear that farmers n Anuradhapura have placed relatvely hgh values on organc farms and landrace cultvaton. Ths s expected as most farmers n these dstrcts use ther farm products for ther own consumpton. However, these estmates are based on a ceters parbus assumpton where we assume that all other parameters are held constant except the attrbute for whch the mplct prce s beng calculated. Implct prces, however, do not provde estmates of compensatng surplus. Estmatng the overall WTA for a change from the current stuaton requres more substantal calculatons. 111

127 Ths s because the attrbutes n the choce sets do not capture all of the reasons why respondents mght choose to ncrease agrcultural bodversty. To estmate overall WTA t s necessary to nclude the alternatve specfc constant. As dscussed n Secton 4.2, the alternatve specfc constant captures systematc but unobserved nformaton about why respondents chose a partcular opton (unrelated to the choce set attrbutes). Therefore, followng Equaton was used to estmate the consumer surplus n dfferent areas: 1 CS labour crop _ dversty mx_ farm organc _ farm landrace expendture ASC (4.28) To llustrate ths process, estmates are provded for sx alternatve scenaros. The current stuaton and sx scenaros are provded. These sx household profles were generated to descrbe the varaton n WTA wthn the sample, based on the farm characterstcs that were found to affect the households preferences for dfferent compensaton plan attrbutes. The current stuaton s dentfed as a farm wth three crop varetes and no mxed farmng as well as not organc farms or landrace cultvaton. We also assume contrbuton of farms to reduce household expendture s fve per cent. We changed these characterstcs for the rest of the profle gradually and estmated change of the CS under each profle. Estmated change of CS n each dstrct s gven n Table 4.10, 4.11 and Estmates of WTA the sx scenaros n Ampara dstrct are presented n Table These are margnal estmates, showng wllngness to accept a change from the current stuaton. Compared to the average household profle, household profles 112

128 three, fve and sx were WTA sgnfcantly hgher amounts. The CS values ndcate that the value attached to scenaro one was Rs. 4,802, 5,382 and 4,865 n Ampara, Anuradhapura and Kurunegala sample respectvely. That s, the average benefts that each household can obtan by ncreasng crops dversty from three varetes to seven varetes wth havng a mxed farmng system. Ths shows that farmer welfare could be easly ncreased by shftng farmng practce to more dverse systems n rural areas n Sr Lanka. WTA value estmates for the sx household profles n the three regons dsclose a few man nterestng fndngs. Frst, all attrbutes have postve use value n all samples areas. Ths result shows that farm famles n study area have strong preference to ncrease agrcultural bodversty. It s clear that all dversty components are valued hghly by all types of households n study area. Second, farmers valuaton of dfferent attrbutes s dfferent n dfferent areas. Table 4.10: Estmates of WTA for varous scenaros: Ampara Crops dversty Mxed farm LR OP Consumpton (%) Status quo CS (Rs.) As a percentage of average ncome Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Note: Crops dversty represent the number of crops n the farm. Mxed farm, landrace cultvaton and organc farm varables are dummy varables whle the expendture varable provdes a percentage of the farms contrbuton to reduce famly expendture. 113

129 Table 4.11: Estmates of WTA for varous scenaros: Anuradhapura Crops Mxed LR OP Consumpton CS dversty farm (%) (Rs.) Status quo As a percentage of ncome Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Note: Crops dversty represent the number of crops n the farm. Mxed farm, landrace cultvaton and organc farm varables are dummy varables whle the expendture varable provdes a percentage of the farms contrbuton to reduce famly expendture. Table 4.12: Estmates of WTA for varous scenaros: Kurunegala Crops Mxed LR OP Consumpton CS dversty farm (%) (Rs.) Status quo As a percentage of ncome Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Scenaro , Note: Crops dversty represent the number of crops n the farm. Mxed farm, landrace cultvaton and organc farm varables are dummy varables whle the expendture varable provdes a percentage of the farms contrbuton to reduce famly expendture. Results show that most of the attrbutes are hghly valued by Anuradhapura farmers. For example, crops dversty s relatvely valued hghly by Anuradhapura farmers than farmers n other two dstrcts. Thrd, the demand for the farms wth organcally produced products as well as landrace cultvaton s relatvely hgher than that of 114

130 other attrbutes. Ths s revealed by relatve hgh value of these two when comparng wth other attrbutes. These per household estmates can be extrapolated to estmate the total beneft that could be acheved for the total dstrct. Ths type of analyss can provde the possble socal welfare estmates whch can be used to nform approprate polces n the future. Accordng to the Census and Statstcs of Sr Lanka, the number of farmers who cultvate less than 0.25 acre n Ampara, Anuradhapura and Kurunegala dstrcts are 67,778, 26,351 and 90,104 respectvely. The total number of farmers who cultvate less than 1 acre for the same dstrcts s 80,778, 76,823 and 152,042 respectvely. Usng ths secondary nformaton, we estmated possble socal welfare gans under dfferent profles for dfferent dstrcts. Table 4.13 reports the results. Table 4.13: Smulaton total welfare gans to the dstrcts (Rs. mllon / per season) Total WTA Scenaro Scenaro Scenaro Scenaro Scenaro Scenaro Ampara Anuradhapura Kurunegala As a percentage of average ncome Total WTA As a percentage of average ncome Total WTA As a percentage of average ncome Note: Total welfare gan s estmated usng the total number of small-scale farm n these three dstrcts Results n Table 4.13 clearly show that mprovng agrcultural bodversty n rural areas n Sr Lanka enables sgnfcantly ncreased socal welfare. That s, the average 115

131 benefts that can be obtaned by ncreasng crop dversty from three varetes to seven varetes through havng a mxed farmng system are Rs. 387 Rs. 413 and Rs.739 mllon per season n Ampara, Anuradhapura and Kurunegala sample respectvely. The results of ths type of analyss can also be used to estmate values assocated wth a range of scenaros resultng from dfferent ecosystem management practces. Government polcy makers can use these value estmates, and estmates of the value of any change n Sr Lanka to determne whch scenaros are lkely to have the greatest net beneft for the communty. From the emprcal analyss, scenaro sx produced the hghest wllngness to accept. Ths type of aggregate WTA can be compared to aggregate costs n a cost-beneft analyss framework to assess net welfare change n the socety when ntroducng new polces to ncrease agrcultural bodversty Summary and key fndngs The research reported n ths chapter of the thess represents one of the frst attempts to use choce modellng to nvestgate farmers preference for dfferent attrbutes of agrcultural bodversty that can be seen n small-scale farm n Sr Lanka. We appled the choce modelng approach to dentfy the possble benefts of conservng agrcultural bodversty n the country. The frst of the two CL models presented here was found to be robust, beng statstcally sgnfcant, havng relatvely hgh explanatory power and havng dentcally and ndependently dstrbuted error terms. Therefore, the result of that model s used to analyse the welfare changes n the socety. The study provdes mportant nformaton for polcy-makers consderng the 116

132 consequences of changes n the condton or qualty of an ecosystem n small-scale farms n rural agrcultural areas. Four conclusons can be drawn from ths study. Frstly, owng to educatonal and poverty ssues, some polcy makers n developed countres are suspcous of whether non-market valuaton technques lke CVM and CE method can be appled n developng countres lke Sr Lanka. Ths CE study has demonstrated that carefully desgned and pre-tested nonmarket valuaton technques can be appled n developng countres wthout any doubt. Secondly, farmers have strong postve atttudes towards ncreasng agrcultural bodversty n rural areas. Ths s evdent from the results obtaned from CL model. Thrdly, the study llustrates that there s a possblty to mprove agrcultural bodversty usng approprate polces n the country. Fnally, the applcaton of CE study appears promsng by ts potental to model complex and smultaneous trade-offs n the feld of ecologcal management. The choce experment technque can be used to model a varety of smultaneous trade-offs whch nvolve a mxture of envronmental and soco-economc factors. The results provde a tool for decson makers to use n prortsng ecosystem management optons n the agrcultural sector. In general, the fndngs of the choce experment support the pror assumpton that small-scale farms and ther multple attrbutes contrbute postvely and sgnfcantly to the utlty of farm famles n Sr Lanka. To the extent that the fndngs are representatve of other rural areas n the country as they confrm that small-scale farms contnue to be a vtal for that naton snce the benefts to farms are overall postve and hgh. The value estmates reported n ths chapter represent lower 117

133 bounds snce only the prvate, use values of small-scale farms were estmated. The results reveal that dfferences between regons, n terms of market ntegraton, nfrastructure qualty and agro-ecologcal condton, affect small-scale farmers prvate valuaton. Our results ndcate that n solated regons farmers hghly value organc farmng methods and landrace cultvaton practces. The CE study dscloses the farm famly and regonal characterstcs that are mportant to consder n desgnng programs or polces to conserve or enhance the agrcultural bodversty and other attrbutes of Sr Lankan farms. It s clear that varous attrbutes of agrcultural bodversty provde drect and ndrect benefts and advantages whch meet human needs n dfferent ways. Puttng a value on these benefts s extremely dffcult, but decson makers often call for them to be expressed n monetary terms. To ths end, n ths study we present the results of a CE study desgned to shed lght on poor farm households preferences for varous farm attrbutes and these households trade-offs among these attrbutes. The fndngs presented here are, therefore, expected to nform the desgn of effcent, effectve, equtable, and targeted compensaton and lvelhood dversfcaton polces n the country. The results of ths study wll suggest how economc polces may be desgned and approprately mplemented n the future n Sr Lanka. 118

134 CHAPTER FIVE FACTORS INFLUENCING FARMERS DEMAND FOR AGRICULTURAL BIODIVERSITY 5.1 Introducton Agrcultural bodversty s of fundamental sgnfcance to human socetes, provdng soco-cultural, economc and envronmental benefts (Mozumder and Berren, 2007). It s essental to food securty and poverty allevaton n rural economes. The conservaton and sustanable use of all aspects of agrcultural bodversty may presents opportuntes for enhancng sol fertlty, naturally controllng pests, reducng the use of pestcdes whle ncreasng yelds and ncomes (Brock and Xepapadeas, 2003). Dversfed agrcultural producton also offers opportuntes to expand new markets and further ncrease the level of food securty for rural households (Brol, 2004; Ceron et al., 2005). The underlyng causes for the loss of agrcultural bodversty are extremely complex. They are closely related to the needs of ncreasng food demands, growng market pressure, agrcultural development polces, demographc, economc and socal factors (Mozumder and Berren, 2007). Many agrcultural practces such as relance on monoculture, exotc/cross breeds, hgh yeldng varetes, mechanzaton, and msuse of agrcultural chemcals have caused negatve mpacts on agrcultural bodversty at all levels n the long term. Such loss of bodversty may be accompaned by the loss of cultural dversty of tradtonal communtes (see Appendx A.1), and ther mpovershment (Franks, 1999). 119

135 Conservng and sustanable use of agrcultural bodversty may provde local, natonal and global benefts (Bardsley, 2003). The global nterest n mantanng agrcultural bodversty s lnked to the fact that most speces mportant to agrculture may be of beneft not only to the regon of ther orgn, but other regons of the globe as well. Addtonally the conservaton and sustanable use of assocated agrcultural bodversty can contrbute to mantanng the health and qualty of the global envronment, by, for example, provdng habtats for wldlfe, protectng watersheds, and reducng the use of harmful chemcals (Gauchan et al., 2005). Consequently, usng agrcultural bodversty sustanably may provde envronmental, economc and soco-cultural benefts on natonal, regonal and global scales (Hengsdjk et al., 2007). Therefore, understandng the underlyng causes of degradaton of agrcultural bodversty would help to ntegrate global envronmental mperatves nto exstng sustanable development efforts n the approprate regons and countres. Ths chapter ams at dentfyng the determnant factors of conservng crop varety dversty (rchness n crop varetes) and lvestock varety dversty (rchness n anmal breeds) whch are mportant parts of agrcultural bodversty. A farm household model s used to predct farmer demand for crop varety and lvestock varety usng small-scale farms data n Sr Lanka. Farm households who are most lkely to sustan observed levels of agrcultural bodversty are descrbed statstcally. Fndngs can assst those who formulate agr-envronmental polcy n Sr Lanka to desgn effcent programs that ncorporate famly farm management. The next secton provdes the context for the present research by lookng at what work 120

136 has already been done n the feld of agrcultural bodversty. It crtcally looks at the exstng research that s sgnfcant to the work carred out n ths study. 5.2 Lterature revew on demand for agrcultural bodversty Several studes have used econometrc models to dentfy the determnants of dversty n lvestock and crops n developng or transtonal economes. Detaled case studes, conducted n Peru (potato), Turkey (wheat), and Mexco (maze), have sought to dentfy some of the mportant factors that postvely and negatvely affect the conservaton of agrcultural bodversty (Brush et al., 1992; Meng, 1997; Van Dusen, 2000; Smale et al., 2002). However, most of these studes (Brush et al., 1992; Franks, 1999) on n stu conservaton of agrcultural bodversty on farms concentrate on dversty wthn a sngle crop or anmal bread. When analysng the multple benefts of the farms under sem-subsstent rural areas, concentraton on varety dversty s more mportant than consderng a sngle crop. Accordng to Fafchamps (1992) crop dversty may be partcularly mportant for farmers wth lmted opportuntes to trade and partcpate n markets. He dentfed agro-ecologcal heterogenety and mperfect markets wth hgh transacton costs n rural areas as contrbutng factors to the demand for agrcultural bodversty. Brock and Xepapadeas (2003) develop a conceptual framework for valung bodversty from an economc perspectve. They consder bodversty mportant because of a number of characterstcs or servces that t provdes or enhances. Ths study shows that a more dverse system could attan a hgher value even though the genetc dstance of the speces n the more dverse system could be almost zero. Maurco (2004) argues that crop dversty mantaned by farmng household s results from the 121

137 nterplay between a demand and a supply for ths dversty. Accordng to them nterventons to support on farm conservaton can be conceptualsed by the way they nfluence these two factors. Demand nterventons should ncrease the value of crop dversty for farmers or decrease the farm-level opportunty costs of mantanng t, whle supply nterventons should decrease the costs of accessng dversty. Bunnng and Hll (1996) present a gender perspectve on farmers' rghts and llustrate wth several case studes that attempt to dentfy the dfferent roles and responsbltes of women on conservng crop dversty. Ths study explans women s role n the conservaton, development and utlsaton of less common crops and varetes, and n the management of hgh-dversty home gardens. The theory of mpure publc goods was used by Hesey et al. (1997) to demonstrate why farmers may not grow wheat cultvars wth the socally desrable level of rust resstance. They argue that farmers may grow cultvars that are hgh yeldng though susceptble to rust. Furthermore, many farmers may grow cultvars wth a smlar genetc bass of resstance. Ths study shows three ways of reducng expected rust losses. They are (a) more dversfed genetc background n released wheat cultvars; (b) greater spatal dversty n planted cultvars; or (c) use of temporally changng lst of cultvars known to be rust resstant. Yeld trade-offs assocated wth these polces llustrate potental costs of ncreasng genetc dversty. Meng (1997) nvestgated the dversty of tradtonal varetes of wheat on Turksh farms. He analyzed the mpacts of a combnaton of factors, ncludng mssng markets, farmers atttudes towards rsk and envronmental constrants on wheat 122

138 dversty outcomes. Accordng to ths study, regonal effects, off-farm ncome and dstance from markets sgnfcantly explan dversty of tradtonal varetes of wheat on Turksh farms. Franks (1999) dscussed the value of plant genetc resources for food and agrculture n the Unted Kngdom (UK). Accordng to hm the UK s agrenvronmental conservaton schemes do not prortze the conservaton of genetc dversty of agrcultural crops. Accordng to Van Dusen (2000) agro-ecologcal and market characterstcs sgnfcantly affect the levels of dversty mantaned by households. He developed a theoretcal model n whch a household's decson to plant a mlpa varety s lnked to household specfc, agro-ecologcal, and market varables. The emprcal methodology n ths study uses a Posson regresson. The results from the regressons of household level dversty showed that a range of household, vllage, envronmental, and market condtons affect the dversty outcomes. Market ntegraton, measured by dstance to a regonal market, use of hred labour, and nternatonal mgraton, were found to negatvely affect dversty outcomes. Agroecologcal condtons, measured by the number of plots, plots wth dfferent slopes, and the hgh alttude regon, were all found to postvely ncrease agrcultural bodversty n the study area. Accordng to Makhur et al. (2001) envronmental, bologcal, soco-cultural and economc varatons n the Hmalayas have led to the evoluton of dverse and unque tradtonal agro ecosystems, crop speces, and lvestock, whch help the tradtonal mountan farmng socetes to sustan themselves. It was found that the loss of agrcultural bodversty and the changng soco-cultural and economc dmensons 123

139 and ther mpacts on the sustanablty of Hmalayan agro ecosystems are emergng as major causes of concern at local/regonal/natonal levels. Ths study also dscusses the approprate optons to meet these challenges. D Falco and Perrngs (2003) nvestgated the mpact of provdng fnancal assstance to farmers n mantanng crop bodversty n an uncertan settng. The fndngs reveal that rsk averson s an mportant drvng force for crop bodversty conservaton 24. L-zh Gao (2003) nvestgated genetc eroson of rce and ts possble mpacts on the Chnese economy. The result of ths study fnds that genetc eroson can sgnfcantly affect the future yeld of any crop n Chna. Meanwhle Scarpa et al. (2003) show that for Creole pgs n Mexco, the respondent s age, years of schoolng, sze of the household and the number of economcally actve members of the household were mportant factors n explanng breed trat preferences. Accordngly younger, less educated and lower ncome households placed relatvely hgher values on the attrbutes of ndgenous pglets compared to exotcs and ther crosses. A farm household model was used to dentfy the factors affectng nter and ntra crop speces dversty of cereal crops n the northern Ethopan hghlands by Benn et al. (2003). They compared factors explanng the nter-specfc dversty and nfraspecfc dversty. Ths study found that a combnaton of factors related to the agroecology of a communty, ts access to markets, and the characterstcs of ts households and farms sgnfcantly affect both nter-specfc and ntra-specfc dversty of cereal crops. Ther fndngs showed that agro-ecologcal, market, 24 Rsk averse farmers can hedge aganst uncertanty they face by allocatng land to dfferent crop speces. 124

140 household and communty level characterstcs affect ncreasng agrcultural bodversty at the farm level. An emprcal approach was employed to understandng the determnants of farmers' access to and use of, crop genetc resources by Van Dusen (2005). He also nvestgated the mpacts of farmer behavour on crop populatons. In the same year Van Dusen et al. (2005) carred out an emprcal case study about farmer management of rce genetc resources n two communtes of Nepal. The decsonmakng process of farm households s modelled and estmated n order to provde nformaton for the desgn of communty-based conservaton programs. Gauchan et al. (2005) nvestgated the socoeconomc, market and agro ecologcal determnants of farmers mantenance of rce dversty at the household level. They assessed spatal rce dversty at the farm level usng household survey data. Fndngs of ths study are useful for desgnng polces for farm conservaton programs. Wnters et al. (2005) studed potato dversty managed on farms n Peru. Ther fndngs showed that the dversty of potato varetes managed on farms ncreases wth the sze of the land owned, number of dfferent plots cultvated, dstance to the nearest market and wealth ndcators at a dmnshng rate. The two-stage tobt procedure was used to dentfy the determnants of on-farm varety dversty n a ran fed ecosystem n Nepal by Ganesh and Bauer (2006). The results dentfed motvatng factors for varety dversfcaton such as heterogeneous producton envronments, rsk consderaton and farmers partcpaton n the markets. Wlson and Tsdell (2006) nvestgated how specalsaton of producton of commodtes n the agrculture sector leads to the concentraton of genetc materals. 125

141 Isakson (2007) nvestgated how the partcpaton of Guatemalan peasants n the market economy s related to on-farm conservaton of crop genetc dversty n three crops: maze, legumes, and squash. He found that partcpaton n markets s not nherently detrmental to the provsonng of crop genetc resources. However, wthout proper protectons n place market partcpaton may unleash processes that contrbute to genetc eroson over tme. Nagarajan et al. (2007) nvestgated the determnants of bologcal dversty of mllet crops n the sem-ard regons n Inda. Ths analyss s based on data collected through sample surveys of farmers and traders n selected stes of Karnataka and Andhra Pradesh, combned wth cultvar taxonomes developed wth genetcsts and appled to seed samples. Fndngs n ths study demonstrate that mllet crop dversty levels at both scales of analyss are sgnfcantly nfluenced by seed system parameters, factors whch related studes have omtted. In partcular, the presence of actve local (formal and nformal) seed markets enhances mllet rchness among and wthn farmng communtes. Accordngly, crop mprovement strateges orented toward local seed markets could provde mportant benefts and ncentves to farm households lvng n these areas. An agrcultural household model was developed wth mssng market for a subsstence crop that arses from non-market values of the crops by Arslan (2007). Ths study theoretcally derved household-specfc shadow prces for maze and emprcally estmated these shadow prces for rural farmers n Mexco. The results suggest that the value of tradtonal maze varetes for subsstence farmers s sgnfcantly hgher than market prces for maze. Pascual and Perrngs (2007) dstngushed between the proxmate and fundamental causes of bodversty loss n terms of decentralzed behavour of farmng households. Specal attenton s pad to 126

142 the nterplay between mcro-economc decsons and the macro-economc factors (nsttutonal and market condtons) that determne the effects of government polces. Accordng to the above lterature revew t s clear that a large number of studes have been conducted n the area of agrcultural bodversty. They have addressed varous ssues n ths feld. However, t s obvous that more conceptual and theoretcal work s needed to understand the factors nfluencng farmers demand for agrcultural bodversty n developng countres. For example, analyss ncludng drect polcy relevant varables to demand for crop varety dversty and anmal varety dversty s not properly explaned n the lterature. Moreover, although a wder cross-secton of case studes has been conducted n commercally-orented farmng systems, an analyss of subsstence orented farmng systems s requred n order to generalse and valdate the emprcal fndngs (Ceron et al., 2005). In the next secton, the theoretcal model that s used to analyze the demand for agrcultural dversty s explaned. The behavoural model employed to explan the farm households producton and consumpton decsons s based on the semsubsstence model of the farm household n rural economy (Sngh et al., 1986; de Janvry et al., 1991; Taylor and Adelman, 2003; Brol et al., 2005). Frstly, we explan the way of dervng demand for agrcultural bodversty usng basc farm household model. Second, the emprcal approach of dfferent model estmaton s dscussed. The background to the general model s provded n the next secton. 127

143 5.3 Dervaton of demand for agrcultural bodversty In order to estmate demand for agrcultural bodversty we use a basc model developed by Sngh et al. (1986); Taylor and Adelman (2003) and Van Dusen and Taylor (2005). A smlar model was used by Brol et al. (2005) to analyse four components of agrcultural bodversty found on famly farms n Hungary. The utlty a household derves from varous consumpton combnatons and levels depends on the preferences of ts members. Preferences are n turn shaped by the characterstcs of the household, such as the age or educaton of ts members, and wealth (Brol, 2004). Choces among goods are constraned by the full ncome of the household, total tme (T) allocated to farm producton (F) and lesure (l), and a fxed producton technology represented by G(.). Suppose a farm famly maxmses hs/her utlty over consumpton of market purchased goods, C m, lesure, C l, and owned farm outputs, C f. The utlty s maxmsed subject to budget, tme, and producton technology constrants respectvely. Household utlty s nfluenced by a vector of household characterstcs h. The utlty functon s assumed to be quas-concave wth postve partal dervatves (Brol, 2004; Van Dusen and Taylor, 2005). The prces of all market purchased goods, nputs and wages are exogenous, and producton s assumed to be rskless. The model can be wrtten as follows: U U C, C, C ; ) (5.1) ( m l f h Constrants: I wt I wf p X p C (ncome constrant) (5.2) e x m m G( Q, F, X; ) 0 (technology constrant) (5.3) f 128

144 F L C T (tme constrant) (5.4) d l Equaton 5.1 gves the utlty functon of a representatve household, whle Equaton 5.2 gves the full ncome constrant. Full ncome s composed of value of stock of total tme owned by the household T, exogenous ncome I e, the values of household management nput used n the small-scale farm producton F, other varable nputs requred for producton of small-scale farm outputs, X and market commodtes consumed by the farm famly, C m. The household faces a producton constrant for the producton technology on the small-scale farm (Equaton 5.3). It gves the relatonshp between farm nputs F, X and all outputs Q, and has the propertes of quas-convexty, ncreasng n outputs and decreasng n nputs (Taylor and Adelman, 2003). The vector, represents the fxed agro-ecologcal features of the f small-scale farm, such as sol qualty and land shape. The household also faces a tme constrant. Labour use n small-scale farm cultvaton F s one use of labour whch competes wth other uses, ncludng off farm employment L d and lesure C l. The household s drven toward the goal of ncreasng dverse farmng wthn the famly farm because of uncertanty, unrelable or mssng markets, as well as the desre to consume fresh food. Ths phenomenon brngs about an addtonal constrant that nduces the household to equate small-scale farm output demand and supply, resultng n an endogenous, shadow prce for small-scale farm outputs (Sngh et al., 1986; Brol, 2004). Ths can be wrtten as follows: Qf Cf (Z) (5.5) 129

145 Q f and C f denote the quantty suppled and consumed of small-scale farm produce, and Z s a vector of exogenous characterstcs related to avalablty and access to markets. Ths equalty condton mplctly defnes the shadow prces for small-scale farm outputs under mssng market, whch gudes producton decsons (Brol, 2004). The producton and consumpton decsons of the household cannot be separated when labor markets, markets for other nputs, or product markets are mperfect. Then, prces are endogenous to the farm household and affected by the costs of transactng n the markets (Taylor and Adelman, 2003). The specfc characterstcs of farm households (represented by vector h ) and physcal access to markets (represented by vector Z) nfluence the magntude of transacton costs and hence, the effectve prce governng the household s choces (Van Dusen and Taylor, 2005). The household maxmses utlty subject to constrants explaned n Equatons 5.2, 5.3, 5.4 and 5.5. Ths maxmsaton results n the followng Lagrangan Equaton 5.6: L U ( C, C, C ; ) ( wt I wc p C wf p X ) m l f h e l m m x [ Q f C f ( Z)] G( Q f, F, X ; f ) (5.6) However, when all relevant markets functon perfectly, farm producton decsons are made separately from consumpton decsons (Brol, 2004). In ths context, full ncome n a sngle decson-makng perod s composed of the net farm earnngs (profts) from crop or lvestock producton (Q f ), of whch some may be consumed on farm and the surplus sold, and ncome that s exogenous to the season s crop/anmal breads and varety choces, such as stocks carred over, remttances, pensons, and other transfers from the prevous season (I e ). The household maxmses the net farm 130

146 earnngs subject to constrants and then allocates these wth other ncome among consumpton goods (Smale et al., 2001). Farm producton decsons, such as crop/anmal breeds and varety choces, are drven by net returns, whch are determned only by wage, nput and output prces (w, p x and p o ) and farm physcal characterstcs (represented by vector β f ) 25. Ths wll only change the full ncome budget constrant addng farm proft as an ncome and market prces have some role to play n decson makng (Sngh et al., 1986; Meng et al., 1998 and Smale et al., 2001). Accordngly, Q C ( Z) 0 f f and addtonal ncome constrant can be added to the Equaton 5.6. It can be gven as [ Q C ( Z)] p f f 0 where p 0 s the output prces of the commodtes that are produced by the small-scale farms and has a market. Assumng nteror solutons exst, the optmal set of choce varables are gven by the solutons to the frst order condtons. The frst order necessary condtons wth respect to decson varables are: L / C U / C p 0 (5.7) m m m L / C U / C w 0 l l L / w( T F C ) I p X p C 0 L / F w G 0 f l e x m m (5.8) (5.9) (5.10) 25 When comparng farmers among communtes located n a broader geographcal area, one can see that ther decsons are also affected by factors that vary at a regonal level but that they themselves cannot nfluence. These nclude several fxed factors hypotheszed to affect varaton n the dversty mantaned among regons, such as agro-ecologcal condtons or nfrastructural development, or the rato of labor to land. 131

147 L / X p x G 0 x (5.11) L / G( Q, F, X ; ) 0 (5.12) f L / C U / 0 f C f L / Q f G 0 f (5.13) (5.14) Equatons 5.7 and 5.8 mply the optmal demand for market purchased goods and lesure respectvely. These equatons show that the margnal utlty the household receves from each commodty equals to Lagrange multpler,, tmes ts market prce, p m and w respectvely. Equaton 5.9 s the full ncome constrant, whch ensures that the net full ncome receved s spent. Equaton 5.10 and 5.11 represent the optmal amount of each nput requred n the small-scale farm, determned by the equalty between the Lagrange multpler,, tmes the prce of the nput and ts margnal product. Equaton 5.12 ensures beng on the transformaton functon. The optmal demand for small-scale farm output s gven by Equaton Ths condton mples that the margnal utlty obtaned from consumng small-scale farm products s equal to ts shadow prce,. The supply of the small-scale farm output s gven by Equaton Ths mples that the margnal cost of producng small-scale farm products equals to ts shadow prce. Substtutng for the shadow prce n 5.13 and 5.14, t can be shown that the margnal utlty of small-scale farm outputs s equal to the margnal cost of small-scale farm outputs and to the shadow prce (Brol, 2004). Smlar dervaton could be found n the study carred out by Brol (2004) n order to estmate the demand for attrbutes n home garden n Hungary: 132

148 U C f G f (5.15) The endogenous shadow prce s household-specfc, dependng on the household characterstcs that affect access to markets and consumpton demand, such as wealth, educaton, age, household composton. Agro-ecologcal features of the small-scale farm such as sol qualty or rrgaton enter the equaton through ther effect on supply. Fxed factors related to market transactons costs and observed market prces also nfluence the shadow prces of small-scale farm outputs (Feder and Umal, 1993). The shadow prce,, can therefore be expressed as a functon of all exogenous prces and household, agro-ecologcal and market characterstcs: * ( P, P, w;,, Z) (5.16) m x h f The general soluton to the household maxmsaton problem yelds a set of optmal choces for producton, nputs demand and consumpton demand as gven n followng Equatons: Q f * Q (, p, w : ) (5.17) f x f F F * (, p x, w : ) (5.18) f X C X * (, p x, w : ) (5.19) f * C (, p, w: ) I =m, l, f (5.20) m h Equaton 5.17 s the optmal supply of small-scale farm outputs whle Equaton 5.18 provdes the expresson for optmal demand of household labour n small-scale farm 133

149 producton. Equaton 5.19 gves the optmal demand for all other nputs to smallscale farm producton and Equaton 5.20 s the optmal demand for market purchased goods (m), household produced goods (f) and lesure (l). Substtutng these solutons for the shadow prce (Equaton 5.16) nto small-scale farm output producton and consumpton solutons (Equatons 5.17 and 5.20), the optmal producton of small-scale farm outputs s seen to be a functon of all exogenous varables: Q f * Q ( P, P, w;,, Z) (5.21) f m x h f We assume that the household does not value dversty tself rather than the drect benefts of t. Therefore, dversty s not explctly n the utlty functon. The dversty wthn a gven household s the result of the choce of whch crops to produce, subject to constrants. Ths dversty outcome n the constraned case takes the form of a derved demand for number of varetes resultng from the farmer s utlty maxmsaton subject to ncome, producton, and market constrants. Followng Van Dusen and Taylor (2005) the level of agrcultural bodversty mantaned on the small-scale farms, whch s a drect outcome of the producton and consumpton choces of the farm household, s a functon of all prces, and characterstcs of the households, markets, and of the small-scale farm plots. Ths relatonshp can be gven as shown n Equaton 5.22: BD BD{ Q * ( P, P, w;,, Z)} (5.22) f m x h f It becomes clear that conceptual approach used n ths study to analyse the demand for agrcultural bodversty s based on the theory of the farm household model 134

150 developed by Sngh et al. (1986); Taylor and Adelman (2003) and Van Dusen and Taylor (2005). Some of the nterestng appled economc analyses of agrcultural bodversty based ether on the farm household model or a model of varety choce are Brush et al. (1992); Meng (1997); Smale et al. (2001) and Brol (2004). Studes n ths area commonly use count data analyss or Logt/Probt model for emprcal estmaton. In ths study crop or lvestock dversty was taken as count number. Ths s a dscrete varable rangng between zero and nne n our sample. It s preferred n ths study as a measure of agrcultural bodversty because t s smple to construct and yet elaborate enough to descrbe the rchness of speces. The emprcal model specfcaton, relevant varables and theoretcal background behnd each model are explaned n subsequent sectons below. 5.4 Emprcal model specfcaton and relevant varables In ths study agrcultural bodversty s nvestgated n terms of crop dversty and lvestock dversty. The defntons of these varables are gven n Table 5.1. Table 5.1: Defnton of the agrcultural bodversty Components CD AD Defntons The total number of crops that are grown n the farm The total number of anmal speces n the farm Note: In ths analyss we nvestgate the nfluencng factors for crop varety and lvestock varety selectons. Mult-crops and mult-lvestock practces are the most mportant farmng practces that can be seen on small-scale farms n Sr Lanka. 135

151 In order to understand the mportant determnants of varety demands, dfferent types of polcy relevant varables are selected. Importance of these varables were understood by the nformaton gathered from the plot survey as well as nformaton provded by the agrcultural specalst n ths area. All collected varables are dvded nto three man categores namely household characterstcs, market characterstcs and other characterstcs. Table 5.2 provdes the defnton of all varables used n the regresson analyss. Table 5.2: Defnton of potental explanatory varables Varables Defnton Household characterstcs EXP Experence of farm decson maker (number of years) OWN Household owns a busness vehcle or not: dummy- 1 f Yes, Otherwse 0 HMP Household member s partcpaton n agrcultural actvtes (%) GEN Decson maker, male or female: dummy- 1 f Male, Otherwse 0 INC Off farm ncome of the famly (Rs. 000) SHL Shared labour (number n the last season) WLH Household wealth: dummy- 1 f wealther, Otherwse 0 Market characterstcs NMA Number of market access days per week (number) DIMK Dstance to the nearest market (KM) DSN Drect sales or not (ntermedary) : dummy- 1 f Yes, Otherwse 0 PRIF Prce fluctuaton of the output(ndex) Other characterstcs AS Recevng agrcultural subsdze: dummy- 1 f Yes, Otherwse 0 IOM Percentage of nvestment of owned money Note:. Prce fluctuaton ndexes were constructed usng average unt prce changes over the last two seasons for crops and lvestock outputs.. Ths varable s created by takng the percentage value of own money nvested to total farm nvestment n the last season. Total farm nvestment ncludes own money plus borrowng for the last season. 136

152 All these ndependent varables are based drectly on the questonnare responses. Durng the survey we collected some varables related to farm specfc characterstc such as rrgaton water avalablty, sol fertlty and land shape. However, these varables were dropped from the analyss due to three reasons. Frstly, these varables are not mportant for determnng anmal dversty. Secondly, most of these varables are relatvely less polcy relevant and beyond the farmers control. Thrdly, n order to avod the over dentfcaton problem, some of the varables had to be dropped from the analyss. It s clear that some varables are defned as numbers (such as number of years n experence n farmng) whle other varables are defned as dummy varables. Experence n farmng s one of the mportant varables used n the analyss. Experence of household head n agrcultural actvtes s expected to have a quadratc relatonshp n selectng a dverse farmng system (Van Dusen, 2000), as younger households may be more wllng to try out dfferent crops and varetes, whle older households wth more experence n farmng may be more set n ther producton actvtes and are less lkely to try new crops and varetes. Therefore, t s hypothessed that demand for agrcultural bodversty wll decrease wth experence n farmng. Ownng a busness vehcle can have a postve correlaton wth agrcultural bodversty. Ths s because a busness vehcle can help farmers to take dfferent products nto dfferent markets. Gven the lmted market places as well as market access days n rural areas n developng countres, busness vehcles can be used to sell farm products drectly n the market. Ths wll avod an ntermedary transacton. 137

153 A household member s partcpaton n agrcultural actvtes s one of the mportant varables used n ths analyss. Ths varable shows the number of mandates receved from members of the famly (except household head) for agrcultural work durng the last season. Partcpaton rate captures the famly labour avalablty for farmng actvtes. In general, the number of members n the famly s expected to have a postve effect on dversty through ts effects on preferences and overall labour capacty. Consderng the household preferences, t s clear that when the famly sze ncreases, expendture on food consumpton 26 also ncreases. Dverse or more productve farmng systems can help mnmse household expendture on food consumpton. However, dverse farmng systems mean that the labour requrement s also hgher. Therefore, large famles wth hgher partcpaton rates may not face any labour constrants for mantanng dverse farmng systems. Gender varable can gve dfferent results snce t depends on ther preference. Women household heads are thought to nfluence dversty n postve as well s n negatve ways. It s expected that a women s knowledge n seed selecton and management would contrbute towards ncreased rchness. On the other hand, ther low economc poston such as lack of sklls n ploughng may nfluence ther decsons to grow hgh number of varetes. Off farm ncome s expected to have a negatve correlaton wth agrcultural bodversty. The reason s that farmers who have other types of ncome sources are less lkely to mantan dverse farmng practces due to manageral mpossblty and labour constrants. Shared labour shows the strength of socal captal n rural area. 26 A postve correlaton can be expected between agrcultural bodversty and ncome spent on food consumpton as well. 138

154 Ths varable shows the exchange labour quanttes n a gven cultvatng season. As ths helps reducng labour constrants, t s expected to have a postve correlaton wth dverse farmng systems. We created dummy varables to dfferentate whether households are wealther or not. Three thngs were consdered for makng ths decson. Frstly, we classfed houses as luxury/ upper mddle class, ordnary and small house/cottage. Secondly, the facltes avalable to ther house are nvestgated. Under ths category, telephone, electrcty, ppe water, vehcle road to the house, water sealed tolet and attached bathrooms were consdered. Thrdly, durable assets are consdered. They nclude vehcles, threshng machnes, water pumps and motorcycles. If a household belongs to a luxury/ upper mddle class or ordnary house and has at least four of the afore mentoned facltes wth at least two of the asset varetes, that household s dentfed as a wealther household. It s hypothessed that wealth s negatvely correlated wth agrcultural bodversty. Ths s because wealth helps reduce the rsk of havng famly household needs for poor farmers n rural area. A few nterestng market characterstcs as explaned n the Table 5.2 were used to see whether these varables are mportant determnants of agrcultural bodversty. Market nfrastructure operates n several ways that may not be dssocable n a gven locaton at one pont n tme. For example, the more removed a household s from a major market centre, the hgher the costs of buyng and sellng on the market and the more lkely that the household reles prmarly on ts own producton for subsstence. Ths mples that the more physcally solated a communty or household, the less specalsed ts producton actvtes. On the other hand, as market nfrastructure reaches a vllage, new trade possbltes may emerge, addng crops and producton 139

155 actvtes to the portfolo of economc actvtes undertaken by ts members. The theory of the household farm predcts that the hgher the transactons costs faced by ndvdual households wthn communtes, the more we would expect them to rely on the dversty of ther crop and varety choce to provde the goods they consume. Consstent wth ths hypothess, Van Dusen (2000) found that the more dstant the market, the greater the number of maze, beans, and squash varetes grown by farmers. In Andean potato agrculture, Brush et al. (1992) found proxmty to markets to be postvely assocated wth the adopton of modern varetes, but ths adopton dd not necessarly decrease the numbers of potato types grown. We hypothessed that the number of market access days s expected to have a postve correlaton wth agrcultural bodversty as t helps mnmse the rsk of sellng the surplus. Dstance to the nearest market s one of the mportant varables used n ths study. It s hypothessed that when the dstance to the nearest market s hgher, farmers are less lkely to mantan a dverse system 27. Ths s because whenever farmers face market constrant, they are less lkely to have dverse output for market. A drect sale varable s ncluded to see whether t has some mpact on selectng a dverse farmng system. It s expected that farmers who sell ther output to market drectly are more lkely to mantan a dverse farmng system. A varable to capture prce fluctuaton on agrcultural bodversty s used n ths analyss. Ths varable s created for average output prce changes for crops and lvestock over the last two cultvaton seasons. It s expected that the coeffcent of ths varable has a postve correlaton wth agrcultural bodversty. 27 Ths may not be a reasonable hypothess for rural subsstence area. Ths s because ther man purpose of producton s the consumpton. However, farmers n sem-subsstence area have two man objectves of ther farmng. One s consumpton whle other s revenue purpose by sellng the surplus to the market. 140

156 Among the other characterstcs, recevng agrcultural subsdy and own money nvestment n the farm are mportant polcy relevant varables. Recevng agrcultural subsdy helps reduce fnancal constrant of rural farmers. It s expected that ths varable has a postve mpact on selectng more a dverse farmng system. Farmers can fnance ther expendture for the agrculture n dfferent ways. Some farmers use ther own savng whle others borrow money from formal or nformal sources. Borrowng agrcultural nputs from nformal sources s also common practce n rural areas n Sr Lanka. For example, some farmers borrow seeds or pestcdes or fertlser from vllage shops wth the promse of payng after sellng ther product 28. We ncluded a varable to understand ths behavour and agrcultural bodversty. We hypothessed that the percentage of own money contrbuton to total farm expendture has a postve correlaton wth agrcultural bodversty. Ths s because farmers often borrow money n order to mantan a specalsaton system wth a marketng purpose. It s clear that the relevance of these varables for the dfferent models can be dfferent. For example, although agrcultural subsdy s mportant for determnng crop varetes, t s not an mportant determnant for anmal varetes. Ths s because agrcultural subsdy polces n the country only focus on the crop sector. Therefore, the subsdy varable s not ncluded for the anmal varety model. Theoretcally, possble sgns n dfferent varables are gven n Table In ths case nterest pad s very hgh. It s around 20 per cent per month n most rural areas. 141

157 Table 5.3: Explanatory varables used n the demand model Varable Defntons CD AD Household characterstcs EXP Experence of farm decson maker - - OWN Household owns a busness vehcle or not + + HMP Household member s partcpaton + + GEN Decson maker, male or female +/- +/- INC Off farm ncome of the famly - - SHL Shared labour + + WLH Household wealth - - Market characterstcs NMA Number of market access days per week +/- +/- DIMK Dstance to the nearest market +/- +/- DSN Drect sales or not (ntermedary) +/- +/- PRIF Prce fluctuaton of the output + + Other characterstcs AS Recevng agrcultural subsdze - NA IOM Percentage of nvestment of owned money + + Note: Defntons of all varables are gven n Table 5.2. Expected sgns n each varable are provded n ths Table. As shown n the Table, some varables can take postve or negatve dependng on the stuaton. A summary of the models to be used for the emprcal estmaton s provded n Table 5.4. The Posson model (PM) or Negatve bnomal model (NBM) for count data may be the sutable model for estmatng the determnants of the farm famly s decson about how many crop and lvestock speces to cultvate on the farm (see, for example, Greene, 1997). Negatve bnomal regresson s used to estmate count models when the Posson estmaton s napproprate due to overdsperson. In a Posson dstrbuton the mean and varance are assumed to be equal (Wnkelmann, 2008). When the varance s greater than the mean the dstrbuton s sad to dsplay over dsperson. When over dsperson s an ssue n the data, the negatve bnomal model should be used (Hlbe, 2011). 142

158 Table 5.4: Summary of the econometrc models to be used for the analyss Dfferent components of agrcultural bodversty Econometrc model Defnton Posson model Sutable model for estmaton of count data, based on Posson Crop speces dversty and Lvestock dversty dstrbuton, but restrcted by the assumpton that the sample mean equals sample dstrbuton Negatve Sutable model for estmaton of bnomal count data, based on Posson model dstrbuton, however, unlke the Posson model, t s not based on the assumpton that the sample mean equals sample dstrbuton Note: Theoretcal explanatons about these models are gven n Secton 5.5. Before estmatng the fnal models, dfferent tests were performed to fnd most approprate model for the each estmaton. In the next secton log-lnear models for count data under the assumpton of a Posson error structure are explaned. These models have many economc applcatons, not only to the analyss of counts of events, but also n the context of models for contngency tables and the analyss of varous ncdents. We ntroduce the Posson regresson model and dscuss the ratonale for modellng the logarthm of the mean as a lnear functon of observed covarates. Then the negatve bnomal model s dscussed. As an extenson, zero-nflated Posson and negatve bnomal models are explaned n the Appendx K. 5.5 Theoretcal approaches for the relevant models A count varable s a varable that takes on nonnegatve nteger values. Both varables that are of nterest n ths study come as counts. For example, crop 143

159 dversty and anmal dversty. These varables have two mportant characterstcs n common: there s a natural upper bound, and the outcome wll be zero for at least some members of the populaton. In order to analyse ths type of varable, the Posson or negatve bnomal model can be used. The theoretcal approaches for all these models are explaned below Posson regresson model Posson dstrbuton s a dscrete probablty dstrbuton that expresses the probablty of a number of events occurrng n a fxed perod of tme f these events occur wth a known average rate and ndependently of the tme snce the last event (Greene, 1997). In other words, t s used to model the number of events occurrng wthn a gven tme nterval.the theoretcal bass for usng ths type of count data models s very mportant for nterpretaton of estmaton results. Posson model expresses the natural logarthm of the event or outcome of nterest as a lnear functon of a set of predctors. The dependent varable s a count of the occurrences of nterest varables. Typcally, one can estmate a rate rato assocated wth a gven predctor or exposure. In other words, the typcal Posson regresson model expresses the log outcome rate as a lnear functon of a set of predctors (Wnkelmann, 2008). For the th observaton, = 1 to n, let denote the mean value of y gven x. 1 Suppose e x 0 (ths nsures that s postve) and y = +, where s a random error term. Then ln( ) 0 1 x. Thus, there s a log-lnear relatonshp between y and x. Snce each y has a Posson dstrbuton wth mean, the probablty of y gven x s: 144

160 e y P( y ) e = y! ( 01x ) ( 1 0 x ) y! y (5.23) where y s a non-negatve nteger valued random varable. Estmates of the coeffcents 0 and 1 are obtaned by formng the lkelhood functon and choosng values of 0 and 1 that maxmse the lkelhood (that maxmse the log-lkelhood). That s, ˆ 0 and ˆ 1 are maxmum lkelhood estmates. In Posson regressons, as n logstc regresson, the model devance s used to measure the goodness of ft of the Posson regresson model, and the change n devance s used to test whether 1 ˆ s sgnfcantly dfferent from zero (Greene, 1997; Wnkelmann, 2008). The functonal form of the parametersaton for the condtonal mean can be gven as followng Equaton 5.24: E( y / x) exp( ' ) x (5.24) The Posson model assumes that the condtonal mean,, s equal to the condtonal varance. Overdsperson s when the condtonal varance exceeds the condtonal mean and s consdered to be heteroskedastc (Wooldrdge, 2002). The standard approach of estmatng the model s usng a form of maxmum lkelhood estmaton, ether usng a Newton-Ralphson algorthm or the teratve reweghted least squares, whch s used by the generalzed lnear model approach (Wooldrdge, 2002; Hlbe, 2005). 145

161 The maxmum lkelhood estmator (MLE) of the parameter s obtaned by maxmsng the log lkelhood functon 29. The Posson log-lkelhood functon may then be derved as follows: n l( ; y) [ y ln( ) ln( y!) 1 (5.25) As exp( x ' ), t can be substtuted nto above equaton. l( ; y) [ y n 1 exp( x' ) exp( x' ) ln( y!) (5.26) Equaton 4.26 can be expresses n terms of the log-gamma functon as ln( y!) ln ( y 1) l( ; y) [ y n 1 exp( x' ) exp( x' ) ln ( y 1) (5.27) The frst dervatve of the Posson log-lkelhood functon, n terms of ts coeffcent value can be derved as follows: l n 1 [ y x ' x exp( x )] l n 1 {[ y exp( x' )] x } (5.28) Solvng for parameter estmates entals settng Equaton 5.28 to zero and solvng t. Resultng soluton determne the parameter estmates for β. In the estmated model, the condtonal mean functon s assumed to be correctly specfed and the MLE s consstent, effcent, and asymptotcally normally dstrbuted. Snce the mean s equal to the varance, any factor that affects one wll also affect the other. Thus, the usual assumpton of homoscedastcty would not be approprate for Posson data. 29 The lkelhood functon s a transformaton of the probablty functon for whch the parameters are estmated to make the gven data most lkely. 146

162 The Posson regresson model s also consdered as a non-lnear regresson to be estmated usng maxmum lkelhood methods. In the emprcal settng, ths model s typcally used ether to summarse predcted counts based on a set of explanatory predctors, or for the nterpretaton of exponentated estmated slopes, ndcatng the expected changes or dfference n the ncdence rate rato of the outcome based on changes n one or more explanatory predctors (Wooldrdge, 2002). In ths context, emprcal model specfcaton of the Posson model can be gven as follows: Y EXP OWN HMP GEN INC SHL WLH NMA DIMK DSN PRIF AS IOM U (5.29) 9 where Y s a count dependent varable that represents the dversty ndces, namely crops or lvestock, and all other ndependent varables are as explaned n Table 5.3. Sgnfcant varables n ths model wll provde mportant nsghts nto the parameters that must be taken nto account n order to desgn polces n ths feld. The predctons based on ths econometrc model enable us to profle households that are most lkely to sustan current levels of crops dversty and anmal dversty because they reveal the greatest preference for them. The regresson explanng the rchness of all crop varetes grown and anmal varetes mantaned n ther farms can be estmated usng a Posson regresson wth the assumpton of mean equals varance. However, f the statstcal tests for sample data reveal overdsperson, a negatve bnomal model, an extenson of the Posson regresson model that allows the dstrbuton of the varance to dffer from the dstrbuton of the sample mean has to be used (Greene, 1997). Therefore, the theoretcal explanaton of negatve bnomal model s explaned n the next secton. 147

163 5.5.2 Negatve bnomal (NB2) regresson model The assumed equalty of the condtonal mean and varance functons s typcally taken to be the major shortcomng of the Posson regresson model 30. Many alternatves have been suggested by dfferent authors (Cameron and Trved, 1986). The most common s the negatve bnomal model, whch arses from a natural formulaton of cross-secton heterogenety. It s clear that the negatve bnomal model s employed as a functonal form that relaxes the equdsperson restrcton of the Posson model. Therefore, negatve bnomal regresson s used to estmate count models when the Posson estmaton s napproprate due to overdsperson (Hlbe, 2005). It s possble to generalse the Posson model by ntroducng an ndvdual, unobserved effect nto the condtonal mean as follows 31 : log ' x log log log u (5.30) where the dsturbance ω reflects ether specfcaton error as n the classcal regresson model or the knd of cross-sectonal heterogenety that normally characterses mcro-economc data. Then, the dstrbuton of y condtoned on x and u remans Posson wth condtonal mean and varance : e f ( y;, u) u ( u ) y! y (5.31) 30 In a Posson dstrbuton the mean and varance are equal. When the varance s greater than the mean the dstrbuton s sad to dsplay over dsperson. Although econometrcans have modfed the Posson regresson model to deal wth over dsperson, a popular alternatve has been the use of the negatve bnomal regresson model. 31 Ths s known as the NB2 model because t has a quadratc varance functon. The error term reflects unobserved heterogenety and s dstrbuted gamma. 148

164 149 Ths can be assumed as a Posson model wth gamma heterogenety where the gamma nose has a mean of one (Greene, 2000). The condtonal mean of y under gamma heterogenety s thereby expressed as µu rather than as only µ. As a result, the uncondtonal dstrbuton ) / ( y x f can be derved from the followng expresson: y u u u g y u e u x y f ) (! ) ( ), ; ( 0 (5.32) The uncondtonal dstrbuton of y s specfed by the defnton of g(u). For ths model a gamma dstrbuton s gven u = exp(ε) where (Wnkelmann, 2008). Assumng a mean of 1 to the gamma dstrbuton, t s possble to have the followng Equaton 5.33: u y u du e u y u e u x y f 1 0 ) (! ) ( ), ; ( (5.33) The gamma nature of u s evdent n the dervaton from above Equaton 5.33 to followng Equaton 5.34: y u y du u e y u x y f 1 ) ( 0 ) ( ) ( 1) ( ), ; ( (5.34) We can contnue the dervaton further by movng to the left of the ntegral, wth the remanng terms under the ntegral equatng one. More detals about the dervaton of these Equatons can be found n Hlbe (2011): 0 ln x

165 150 y y y y ) ( ) ( ) ( 1) ( (5.35) Further soluton of ths ntegraton can be contnued as follows: y y y y y u x y f 1 1 ) ( ) ( 1) ( ), ; ( y y y u x y f 1 ) ( 1) ( ) ( ), ; ( y y y u x y f / / 1 1 ) ( 1) ( ) ( ), ; ( (5.36) As we derves of the NB2 model, nvertng the gamma scale parameter (θ) yelds the negatve bnomal heterogenety or over dsperson parameter (α). Accordngly, the resultng negatve bnomal probablty mass functon can be wrtten as follows: y y y u x y f ) (1/ 1) ( ) 1/ ( ), ; ( 1/ (5.37) In ths form the heterogenety parameter s nversely related to the amount of Posson over dsperson n the data (Hlbe, 2005). When we have n dervng the parametersaton of the negatve bnomal, y and α are assumed to be ntegers. However, ths assumpton does not have to obtan when t s used as the dstrbutonal bass of a regresson model. As a count data model, the negatve bnomal response y should conssts of non-negatve nteger values whle α should take postve ratonal values (Wnkelmann, 2008).

166 The negatve bnomal model can be estmated by usng maxmum lkelhood method. The lkelhood functon for the negatve bnomal probablty functon can be gven as follows: n L( ; y, ) exp y 1 1 ln ln(1 ) ln y ln ( y 1) ln (5.38) The log-lkelhood s obtaned by takng the natural log of both sdes of the Equaton As wth the Posson models, the functon becomes addtve rather than multplcatve. Therefore, log-lkelhood functon can be wrtten as follows: l( ; y, ) n y ln ln(1 ) ln y ln ( y 1) ln 1 (5.39) The negatve bnomal log-lkelhood, parametersed n terms of β (model coeffcents) can be expressed as follows: n exp( x ' ) 1 1 l( j; y, ) y ln ln[1 exp( x ' )] ln y 1 1 exp( x ' ) 1 ln ( y 1) ln (5.40) Maxmum lkelhood prncples defne estmatng Equatons as the dervatves of the log-lkelhood functon. It s clear that ML estmates of the model parameters are determned by settng the frst dervatve of the log-lkelhood wth respect to model parameters (β) to zero and solvng the resultng Equaton. As Posson model s a varety of the negatve bnomal model, a test of the dstrbuton s often carred out 151

167 by testng the hypothess θ = 0 usng the Wald or lkelhood rato test. In the present study, emprcal model specfcaton of the negatve bnomal model can be gven as follows: Y * EXP OWN HMP GEN INC SHL WLH NMA DIMK DSN PRIF AS IOM U (5.41) 9 where Y * s a count dependent varable that represent the dversty ndces such as crop dversty or lvestock dversty and all other ndependent varables are as explaned n Table 5.3. Sgnfcant varables n ths model wll provde mportant nsghts nto the parameters that must be taken nto account n order to desgn polces n ths feld. As noted n the prevous secton, the Posson model mposes the transparently restrctve assumpton that the condtonal varance equals the condtonal mean. The typcal alternatve s the negatve bnomal model. The model can be motvated as an attractve functonal form smply n ts own rght that allows over dsperson. However, n the emprcal context, model selecton should be done usng some statstcal test (Wnkelmann, 2008). Therefore, the next secton dscusses the way of selectng an approprate model for the data used n ths study Emprcal tests for dfferent count data models Count outcomes are commonly encountered n many economc applcatons, and are often charactersed by a large proporton of zeros. Although Posson or negatve bnomal regresson models have often been used to analyse count outcomes, the 152

168 resultng estmates are lkely to be neffcent, nconsstent or based wth the presence of excess zeros (Hlbe, 2005). Several models belongng to the famly of generalsed lnear models are avalable for performng regressons wth excess zeros and dsperson 32 (Wnkelmann, 2008). For example, zero-nflated Posson (ZIP) and zero-nflated negatve bnomal (ZINB) are specfcally developed for count outcomes wth excess zeros and dsperson. Theoretcal aspects of usng these types of zero-nflated models are dscussed n the Appendx K. In the emprcal model the phenomenon of havng zeros can be a concern n ths study. Ths s because farmers who do not cultvate any crops (only lvestock) and farmers who do not have lvestock (only crops) provde zero outcomes for crops and lvestock varetes dversty models respectvely. When the farmers have only lvestock, the crop varety ndex becomes zero whle when they have crops only, the lvestock dversty ndex becomes zero. As dscussed n Appendx K, the ssue of excess zeros can be dealt wth through the applcaton of ZIP / ZINB regresson models. Besdes ZIP or ZINB models, two-part or hurdle models are commonly appled n count data wth excess zeros (Hlbe, 2005). However, from the prelmnary nvestgaton, t was found that farmers who have only lvestock varetes are very few n our samples. It s 7, 10 and 9 per cent of total samples n Ampara, Anuradhapura and Kurunegala respectvely 33. As a result the excess zero ssue was not a problem when estmatng crops varety dversty. When estmatng the determnants of anmal varety dversty, mxed farmng system (both 32 Method of addressng excessve zero counts were frst ntroduced by Lambert (1992). Zero-nflated models are two-part models, consstng of both bnary and count model sectons. 33 When estmatng crop varety dversty we have ncluded two categores, farmers who cultvate crops only and farmers who mantan a mxed farmng system (crops and lvestock). 153

169 lvestock and crops) and farms that have only anmals are ncluded. Farms that have only crops were recorded as zero dversty farms here. The percentage of zero values n samples of Ampara, Anuradhapura and Kurunegala were 19, 16 and 22. It clearly shows that there s not an excess zero ssue here too. Therefore, for both analyses, ether Posson or negatve bnomal model could be used. In addton to ths prelmnary observaton, one can use the asymptotcally normal Wald type t statstc defned as the rato of the estmate of α to ts standard error. If the t statstc falls outsde ( 1.96, 1.96) nterval, we reject the null hypothess that α equals zero (reject the Posson model at fve per cent sgnfcance level). Another way to test the null hypothess of α equals zero s to use the lkelhood rato statstc, whch s approxmately ch-square dstrbuted wth one degree of freedom when the null hypothess s true (Hlbe, 2005). Both the lkelhood rato test and the Wald type t test are asymptotcally equvalent (Wnkelmann, 2008). In emprcal context, both provde the smlar conclusons about selectng the approprate model. Grootendorst (1995) ntroduced steps to choose the best model among the ZINB, ZIP, NB, and Posson models. If the Vuong test shows that the ZINB model s rejected n favour of the NB model, the splttng mechansm wth excess zero s rejected. In ths case, we wll estmate the NB model and test f the heterogenety parameter α s sgnfcant by usng the t-test; a sgnfcant α suggests that unobservable heterogenety accounts for dsperson. On the other hand, f the Vuong test shows that the NB s rejected n favor of the ZINB model, we wll test f the parameter α n the ZINB model s sgnfcant. If the 154

170 estmate of α s also sgnfcant, both the splttng mechansm and ndvdual heterogenety account for dsperson (Hlbe, 2005). Prelmnary test for over dsperson shows that t s not a problem n dstrct sample data or pool data. Ths type of result can be expected due to two reasons. Frstly, the range of crops varety varaton for all data s between zero and nne whle anmal varety t s zero and fve. It shows low level of varaton of our count varables. Secondly, a majorty (72 per cent) of farmers have cultvated three to sx crops and two to three anmal varetes (63 per cent). Ths type of result helps conclude that there s no overdsperson ssue n our data. Therefore, we selected the Posson model as the best model for nterpretng the results. The STATA verson 11.0 as well as Nlogt 4.0 verson of the program was used for the emprcal analyses. 5.6 Soco-economc characterstcs of the households We estmated the dversty regresson equatons for selectng crops varetes and anmal varetes. Most farmers n a gven dstrct cultvate or mantan approxmately smlar crops or lvestock. Rce, dfferent types of vegetables and cash crops are found to be the common type of crops that farmers cultvate 34. Anmal breeds nclude manly cattle, chckens, goat, pgs and buffalos. Most households cultvated between two and sx types of crop varetes. In the case of anmals, most households mantaned two to three anmal varetes n the study areas. On average, approxmately 92 per cent of the sample respondents man occupaton s farmng. Approxmately eght per cent of respondents are employed n the government or prvate sector. Ther man ncome source s the salary from the job whle an 34 We only ncluded seasonal crops n ths analyss. Ths mples that any varety that takes more than 6 months to harvest s excluded from the survey. Appendx N.1 and N.2 provde the lst of crop varetes and anmal breeds whch were found n small-scale farms n the study area). 155

171 agrcultural practce s a secondary actvty to them. However, some households (32 per cent) have some other source of secondary ncome n addton to ther farm ncome. Some farmers (approxmately 23 per cent) work as waged labourers on some days n the month and ths provdes some addtonal ncome for poor farmers to meet day to day expenses. Soco-demographc characterstcs of the household such as the age, the educaton of ts members, and household sze could be some of the sgnfcant factors that determne the dversty of crops and lvestock they grow. However, n the present study we only use drectly polcy relevant varables. The average experence of farmng s 24, 19 and 26 years n Ampara, Anuradhapura and Kurunegala samples respectvely. Approxmately 78 per cent of respondents are male whle 22 per cent are female. Household members partcpaton n agrcultural actvtes s very hgh n rural communtes n Sr Lanka. Average partcpatng rates were 87, 92 and 96 per cent of the total number of households (greater than 14 years old) n Ampara, Anuradhapura and Kurunegala dstrcts respectvely. Off farm ncome s not sgnfcant for most households as ther man ncome source s determned by the farm output. Earnngs as a waged labourer, small-scale busness and government famly allowance (Samurd allowance) are among the most common off farm ncome sources n rural areas. One of the nterestng aspects of rural households s explaned by shared labour. Ths varable represents the magntude of socal captal. Average number of shared labour per season s 12, 21 and 18 Ampara, Anuradhapura and Kurunegala respectvely. On average t s approxmately 17 per cent of ther labour usage n a gven season. 156

172 Accordng to the crtera that we used to solate wealther famles from others, a sgnfcant percentage of famles belong to other category. For example only 31, 18 and 22 per cent of the respondents were dentfed as wealthy famles n Ampara, Anuradhapura and Kurunegala dstrct respectvely. A sgnfcant dfference could be observed n the number of market access days n dfferent dstrcts. It vares 1 to 7 days per week n dfferent dstrcts. There are dfferent types of markets where farmers could sell ther products. One type of market whch s commonly called a weekly far could be functonng properly n some vllages. In ths case farmers could drectly sell ther products. However, ntermedary traders also come to the vllage and purchase varous tems. Some farmers sell ther product to ntermedary traders. In general, nformal dscussons wth farmers reveal that marketng s the bggest problem for all areas. Ths s because n some seasons there s no demand for ther product whle n other seasons they do not get an expected prce. It was revealed that one of the man objectves of ther agrcultural actvtes s to meet the famly food requrement. The marketable surplus of small-scale farms n rural areas s relatvely small. After the harvestng most households mantan a stock of foods untl the next harvestng season approaches. It was found that the consumpton rate of some of the crop and lvestock products are as hgh as 98 per cent of ther output. Average famly consumpton rate of rce was approxmately 73 per cent whle the consumpton rate of some vegetable varetes was approxmately 95 per cent. Some farmers cultvate cash crops for marketng purposes n small-scale farms. The average marketable surplus of cash crops such as Chls and Onons were 157

173 approxmately 79 and 86 per cent respectvely n the study areas. The dstance to the nearest market s relatvely hgher n the Anuradhapura dstrct sample. However, average prce fluctuatons are smlar n all three dstrcts. Moreover, a sgnfcant dfferent could not be observed for recevng subsdes for cultvatng crops n dfferent dstrcts. Ths s expected as nput or output subsdy polces were handled by the government n Sr Lanka. For example, any farmer who has hs own land s elgble for recevng fertlser subsdes n any gven season. Gven ths general nformaton about the respondents, t s nterestng to nvestgate the results of ths analyss. Estmated results are reported wth ther nterpretaton n the next sectons. As we were coverng three separate dstrcts, data were analysed n two ways for each model that represents agrcultural bodversty. Frstly, separate regressons were run for dstrct wse data separately. Secondly, the pool data model was run after combnng three data sets together. A dummy varable s ncluded n the pool data model to capture the effects of regonal fxed factors for Anuradhapura and Kurunegala, as compared to Ampara. The next secton dscusses the determnants of crops varety dversty n separate dstrct data and pool data models. 5.7 Determnants of crops varety demand As explaned n the prevous secton, we use smple rchness measures or counts of the number of crop varetes the household plants as our basc measures of speces dversty at the household level. In order to model crop speces dversty, we use a Posson regresson because of the dscrete, count nature of the dependent varable. Ths econometrc approach can be lnked to the theoretcal model through a random- 158

174 utlty framework nvolvng a seres of dscrete decsons of whether to plant ndvdual crops (Wooldrdge, 2002). In order to check for over or under-dsperson, the estmated Posson model was tested aganst the negatve bnomal regresson models, resultng n falure to reject the Posson model. Therefore, we used the Posson regresson for nterpretng fnal results. The more detaled explanaton about the way of selectng the approprate model usng dfferent crtera was gven n Secton Margnal effects provde a way to measure the effect of each covarate on the dependent varable. The margnal effect of one covarate s the expected nstantaneous rate of change n the dependent varable as a functon of the change n that covarate, whle keepng all other covarates constant. We reported only margnal effects for all regresson models.these coeffcents ndcate how a one unt change n an ndependent varable alters the count dependent varable. For the crops varety dversty, four Posson regresson models were estmated: three for separate dstrcts data and one for the pool data for all dstrcts. The estmated results of the four regresson models are reported n Table 5.5. The results show that experence n farmng s hghly sgnfcant n all models and has shown a postve coeffcent value. Ths result s not consstent wth our ntal hypothess. We expected that younger households may be more wllng to try out dfferent crops and varetes, whle older households wth more experence n farmng may be more set n ther producton actvtes and less lkely to try new crops and varetes. However, ths type of hypothess can be expected n a more commercalzed farmng system. In a sem-subsstence farmng system, we found 159

175 that the farmers who have more experence n farmng are lkely to mantan a more dverse farmng system. Ths s because more experenced farmers may have a better understandng about the benefts of havng a dverse farmng system than less experenced farmers. Further, ths mples that the human captal and access to nformaton are favourable for growng a wder range of crop varetes n rural areas n Sr Lanka. It s also obvous that farmers experence s hghly correlated wth ther age. Therefore, ths varable can serve as a proxy for farmers age. Ownng a busness vehcle s not sgnfcant n Anuradhapura sample 35. However, t s a sgnfcant varable for the other three models. The possble mplcaton s that farmers who have a busness vehcle are more lkely to mantan a dverse farmng system. Ths s because havng a busness vehcle may help reduce market transacton costs for sellng any surplus of ther farm. Household members partcpaton varable s hghly sgnfcant n all models. Ths varable shows labour support provded by famly members for ther farmng. It s clear that more actve household labour n agrculture generally contrbutes postvely to crop dversty. A dverse farmng system requres more labour tme and results are consstent wth the theory. As hypothessed, households headed by men grow more dverse varetes. Ths mght be assocated wth the skll or requrement for frequent manual work for cultvatng more varetes. The nfluence of ths varable s unform and sgnfcant across all models. 35 Ownershp of busness vehcle n Anuradhapura sample s relatvely smaller than other two samples. It s 8 per cent n Anuradhapura sample whle 21 and 26 per cent n Ampara and Kurunegala samples. 160

176 Table 5.5: Posson regresson results for crops varety model Varables Ampara Anuradhapura Kurunegala Pool data EXP 0.022(0.004)* 0.016(0.003)* 0.018(0.004)* 0.011(0.002)* OWN 0.325(0.118)* 0.073(0.129) 0.267(0.118)** 0.208(0.096)** HMP 0.009(0.002)* 0.006(0.001)* 0.007(0.002)* 0.008(0.001)* GEN 0.181(0.131)**** 0.456(0.121)* 0.235(0.106)** 0.263(0.078)* INC (0.006)** (0.005) (0.001)** (0.002)*** SHL 0.033(0.007)* 0.029(0.008)* 0.019(0.007)* 0.037(0.005)* WLH (0.115)* (0.111)** (0.068) (0.056)** NMA 0.152(0.036)* 0.086(0.020)* 0.066(0.023)* 0.154(0.019)* DIMK (0.032)* (0.022)* (0.025)* (0.015)* DSN 0.350(0.112)* 0.647(0.129)* 0.495(0.110)* 0.387(0.068)* PRIF 0.008(0.002)* 0.002(0.001)*** 0.007(0.001)* 0.004(0.001)* AS (0.153)**** (0.162)* )* (0.094)* IOM 0.021(0.004)* 0.003(0.001)*** 0.009(0.002)* 0.010(0.001)* Anuradhapura (0.141)* Kurunagala (0.114)** N Pseudo R Wald ch 2 (13) Note:. Defntons of the varables used n the regresson analyss are shown n the Table 5.3. In the pool data analyss, Ampara s used as the base dstrct when creatng dummy varables.. Standard errors are shown n brackets. *, **, *** and **** denotes the sgnfcant varables at 1%, 5%, 10% and 20% level of sgnfcance respectvely.. Margnal effects on the count dependent varable are reported n ths Table. These coeffcents ndcate how a one unt change n an ndependent varable alters the count dependent varable. Off-farm ncome of the household has been ncluded, and s measured as the sum of (the value of) remttances, penson and salary from other employment. Ths type of exogenous ncome can be used to hre labour and purchase other nputs (e.g., mproved seed) for ther cultvaton. Off-farm ncome can release the cash ncome constrant faced by some farmers, enablng them to shft ther focus from growng varetes for sale to growng the varetes they may prefer to consume. Moreover, 161

177 hgher off farm ncome mples that more members of the famly are nvolved n economc actvtes other than agrculture. Ths means less labour avalablty to mantan a dverse farmng system. In ths context, off-farm ncome may enable them to specalse n the most proftable crops and varetes. However, lterature related to off-farm ncome and crop dversty shows ambguous results. In Mexco, Bellon and Taylor (1993) found that off-farm employment was assocated wth hgher levels of maze dversty. Meng (1997) found the exstence of off-farm labour opportuntes to have no statstcally sgnfcant effect on the lkelhood of growng wheat landraces n Turkey. The result of ths study shows that off-farm ncome has sgnfcant negatve effect on crop varety dversty. One of the possble reasons s that when the off-farm ncome s hgher farmers attempt to purchase most of the food they need for consumpton from the market. Therefore, the ncentve for havng dverse system, manly focusng on famly consumpton, s less. Another reason can be the labour constrant. A sgnfcant porton of off-farm ncome comes as off-farm employment. If farmers are employed n other places, the ncentve to mantan a dverse farmng system s less as t needs a relatvely hgher amount of labour. Shared labour s another nterestng varable used n ths analyss. Ths varable shows the number of mandates a partcular household exchange wth other households durng the last crop season. Shared labour s one of the mportant socal captals n rural areas n Sr Lanka. Ths varable shows a sgnfcant postve correlaton wth crop varety dversty. Shared labour helps reduce the drect cost of hrng people for agrcultural actvtes. 162

178 The coeffcent on household wealth s negatve and sgnfcant. The greater the wealth of the household, the less lkely the household s to plant a dverse set of crops. Ths fndng s consstent wth a rsk motvaton for nvestng n dversty. Decreasng rsk averson and greater ablty to self-nsure gves wealthy households less ncentve to nvest n a portfolo of crop varetes. The wealth effect s not necessarly lmted to rsk. Wealth may be a proxy for networks, nformaton, and access to outsde market opportuntes n the presence of varous knds of market mperfectons. In the state of Puebla, Mexco, Van Dusen (2000) found that the greater the wealth of the household, as measured by house constructon and ownershp of durable goods, the less lkely the household s to plant a dverse set of maze, beans, and squash varetes. The relatonshp between markets and the conservaton of agrcultural bodversty s complex. As the analyss n ths study has shown, hgher rates of market partcpaton are not necessarly assocated wth hgher measures of crop dversty. Sometmes, hgher market partcpaton can contrbute to the eroson of crop dversty over tme. Market nfrastructure operates n several ways that may not be dssocable n a gven locaton at one pont n tme. For example, the more removed a household or communty s from a major market centre, the hgher the costs of buyng and sellng on the market and the more lkely that t reles prmarly on ts own producton for subsstence. Ths mples that the more physcally solated a communty or household, s the less specalsed ts producton actvtes 36. On the other hand, as 36 The theory of the household farm predcts that the hgher the transacton costs faced by ndvdual households wthn communtes as a functon of ther specfc socal and economc characterstcs, the more we would expect them to rely on the dversty of ther crop and varety choce to provde the goods they consume. Consstent wth ths hypothess, Van Dusen (2000) found that the more dstant the market, the greater the number of maze, beans, and squash varetes grown by farmers. 163

179 market nfrastructure reaches a vllage, new trade possbltes may emerge, addng crops and producton actvtes to the portfolo of economc actvtes undertaken by ts members. We have ncluded four market related varables n ths study. They are the number of market access days per week, dstance to the nearest market, drect sales or not (ntermedary) and prce fluctuaton of the output n the prevous season. An ncrease n the level of market access can ncrease level of total dversty n a farmer s feld. Ths s because, farmers could maxmse ther return from dverse output f they can easly access the market. As expected, the coeffcent of ths varable s sgnfcant n all four models and has a postve sgn. The dstance to the nearest market s another nterestng varable used n the analyss. The dstance of the household farm to the nearest market, whch s a major component of the cost of engagng n market transactons related to seed, labour, other nputs, and farm produce, s hypothessed to affect negatvely crop dversty. Ths means that households further from markets are less lkely to produce a dverse farmng system n a sem-subsstence area. Households further from markets are less responsve to dversty selecton due to the hgher transacton cost of market access, whch lmts nteracton wth the market and results n more autarkc behavour. Households closer to the market wll select more crops as expected, provdng evdence of market partcpaton when transacton costs are low. Ths s what the result of ths study has shown. Prce fluctuaton of output s another nterestng market characterstcs used n ths analyss. Ths varable s a proxy for rsk of future return of farm output. Interestngly, market prce fluctuaton s, as expected, postvely related to varety 164

180 demand. The hgher the market prce fluctuaton, the hgher the lkelhood that a household s to cultvate more crops on ther farms. Ths s because, ths could help farmers to mnmse the rsk of ther return. Recevng agrcultural subsdes s another nterestng varable used n the analyss. Ths varable s sgnfcant n all models and has taken negatve coeffcent value. Ths mples that agrcultural subsdes are lkely to reduce crop dversty n rural areas. Ths s because most of the agrcultural subsdy scheme n the country focuses on specalsaton crops. As a result, f farmers receve subsdes they have to mantan a sngle varety system or specalsed system. The last varable that we ncluded n ths model s the percentage of own money nvested for agrcultural actvtes over the last season. As hypothessed, when the percentage of own money expendture s hgher, ther varety selecton s also hgher. Ths coeffcent s sgnfcant n all models n the analyss. In addton to these fndngs, the pool data results show that heterogenety among dstrcts s sgnfcant. Ths s expected as we have selected three dstrcts to represent dfferent aspects of agrcultural bodversty n the country. In general, the fndngs suggest that some farm households, market and other characterstcs have a greater mpact on varaton n crops dversty levels across small-scale farms n Sr Lanka. Farmers choces and cultvaton of dfferent crops dversty and ther possble mplcatons for conservaton polcy are ndcated by the sgnfcance of margnal probabltes of the explanatory varables n ths analyss. In the next secton, we wll nvestgate mportant varables for determnng anmal varety dversty. 165

181 5.8 Determnants of lvestock varety demand The development of hgh-performng lvestock and poultry breeds has greatly contrbuted to ncrease food producton. Wthn the agrcultural context, anmal bodversty s the genetc varablty (or dversty) between breeds and wthn breeds of the same speces. However, n ths study we only focus genetc varablty between breeds as the varablty of the breeds n the same speces s not sgnfcant n Sr Lanka. Therefore, as the next step of ths analyss we nvestgate the determnants of lvestock varety demand n separate dstrct data and pool data. We ncluded all varables whch were ncluded n the crop varety model except agrcultural subsdy nto ths model. The estmated result of the Posson regresson model s gven n Table 5.6. The results n Table 5.6 show that experence n agrcultural actvtes s hghly sgnfcant n Anuradhapura and the pool data model. Ths varable s sgnfcant under 5 per cent level of sgnfcance for samples n Ampara and Kurunegala. All models show a postve coeffcent value mplyng that farmers who have more experence n farmng are lkely to mantan a dverse lvestock farmng system. Ths s expected as lvestock farmers need specal knowledge to mantan them. Ownng a busness vehcle s not a sgnfcant determnant of lvestock varetes as the coeffcents are not sgnfcant n Ampara and Anuradhapura samples whle t s weakly sgnfcant n the Kurunegala sample. Ths s because lvestock farms are manly mantaned for the famly consumpton purpose n rural areas n Sr Lanka. Household members partcpaton varable s hghly sgnfcant n all models. Ths varable shows labour support provded by famly members for ther farmng. It s 166

182 clear that more actve household labour partcpaton generally contrbutes postvely to varety dversty. The gender varable s not sgnfcant n Ampara and Kurunegala sample. However, t s sgnfcant at 20 per cent and 5 per cent level of sgnfcance for Anuradhapura and the pool data model. The negatve coeffcent mples that households headed by women grow more dverse anmal varetes. Lvestock dversty s a small-scale busness n most areas n the country. Women can easly manage t from home as t does not need as much attenton as crops. Table 5.6: Posson regresson results for anmal varety model Varables Ampara Anuradhapura Kurunegala Pool data EXP 0.003(0.001)** 0.018(0.003)* 0.008(0.003)** 0.010(0.001)* OWN 0.023(0.035) 0.079(0.155) 0.195(0.124)**** 0.085(0.054)**** HMP 0.004(0.001)* 0.003(0.002)**** 0.004(0.001)* 0.004(0.001)* GEN (0.035) (0.118)**** (0.103) (0.047)** INC (0.001)* (0.003)* (0.002)* (0.001)* SHL 0.008(0.003)** 0.019(0.010)** 0.026(0.006)* 0.021(0.004)* WLH (0.054)*** (0.133)* (0.129)* (0.055)* NMA (0.022)* (0.021)* (0.025) (0.011)* DIMK 0.017(0.001)** 0.032(0.024)**** 0.042(0.016)** 0.029(0.012)* DSN 0.103(0.037)* 0.162(0.120)**** 0.131(0.123) 0.082(0.051)*** PRIF 0.001(0.000)** 0.003(0.001)* 0.002(0.001)*** 0.002(0.000)* IOM 0.002(0.000)* 0.009(0.001)* 0.001(0.001)* 0.004(0.001)* Anuradhapura (0.091)* Kurunagala (0.080)* N Pseudo R Wald ch 2 (12) Note:. Defntons of the varables used n the regresson analyss are shown n the Table 5.3. In the pool data analyss, Ampara s used as the base dstrct when creatng dummy varables.. Standard errors are shown n brackets. *, **, *** and **** denotes the sgnfcant varables at 1%, 5%, 10% and 20% level of sgnfcance respectvely.. Margnal effects on the count dependent varable are reported n the table. These coeffcents ndcate how a one unt change n an ndependent varable alters the count dependent varable. 167

183 The results show that off-farm ncome has a sgnfcant negatve effect on anmal varety demand. One of the possble reasons s that when the off-farm ncome s hgher farmers attempt to purchase most of the food they need for consumpton from the market. Therefore, the ncentve for havng a dverse system, manly focusng on famly consumpton s less. Another reason can be the labour constrant. A sgnfcant porton of off-farm ncome comes as off-farm employment. If farmers are employed n other places, an ncentve to mantan a dverse farmng system s less as t needs a relatvely hgher amount of labour. Shared labour s one of the mportant socal captals n rural areas n Sr Lanka. Ths varable shows a sgnfcant postve correlaton wth anmal varety dversty. The coeffcent for household wealth s negatve and sgnfcant. The greater the wealth of the household, the less lkely the household s to have a dverse set of anmals. The coeffcent for the number of market access day s varable s sgnfcant at one per cent n Anuradhapura and has shown a postve sgn. It s less sgnfcant n Ampara and Anuradhapura whle no sgnfcant result s found n Kurunegala model. The dstance to the nearest market s another varable used n the analyss. Households further from markets are less responsve to dversty selecton due to the hgher transacton cost. Households closer to the market wll select more marketed tems, provdng evdence of market partcpaton. However, the results show that households who are lvng far away from the market are more lkely to mantan a dverse farmng system. Ths shows the subsstence nature of the lvestock farmng system n these areas. When the households are away from the market, they are more lkely to mantan a dverse lvestock system for ther own consumpton. In general, ths varable s less sgnfcant n ths analyss. 168

184 The varable representng the drect sales or not s weakly sgnfcant n the Anuradhapura sample and not sgnfcant n the Kurunegala sample. Prce fluctuaton of output s another varable used n ths analyss. Ths varable s a proxy for rsk of future return of output. Interestngly market prce fluctuaton s, as expected, postvely related to varety demand. The hgher the market prce fluctuaton, the hgher lkelhood a household s to mantan dverse lvestock system. Ths s because ths could help farmers to mnmse the rsk of ther return. The last varable that we ncluded n ths model s the percentage of own money nvested for farm actvtes over the last season. As hypothessed, when the percentage of own money expendture s hgher, varety selecton s also hgher. Ths coeffcent s sgnfcant n all models n the analyss. In addton to these fndngs, pool data results show that heterogenety of anmal varetes among dstrcts s sgnfcant. The results show that some households, market and other characterstcs have a greater mpact on varaton n lvestock dversty levels across small-scale farms n Sr Lanka. In the next secton the man conclusons drawn from ths study s explaned. 5.9 Summary and key fndngs A study on the current status of agrcultural bodversty and ts determnants s useful for polcy decson makers n order to conserve agrcultural bodversty n rural areas n the country and hence mprove farmer lvelhoods. In ths context, t s mportant to know f farmers promote dversty and what are the determnants of t. Ths study nvestgated ths ssue usng farmers demand for crop and lvestock 169

185 varetes. It s found that mantanng on-farm dversty has receved ncreasng attenton as a strategy for mtgatng producton rsk and protectng food securty n rural areas n Sr Lanka. For poorer farmers wth small land holdngs, crop and anmal varety dversfcaton ncreases optons for copng wth varable envronmental and market condtons. Also due to the exstence of mperfect markets, farmers may grow dfferent varetes to meet ther consumpton requrement. On the one hand ths practce ncreases ther food securty. On the other hand, t provdes more fresh food wth hgh nutrton content. Farmers may also sell some of the surplus to the market so as to buy ther famly needs (clothes and other goods/commodtes). Ths may motvate farmers to grow the varetes that can be sold n the market for cash. We fnd that the key varables promotng dversty are household characterstcs, market characterstcs, and some of the other characterstcs such as percentage of own savngs nvested for agrculture. One of the man conclusons drawn from ths study s that the centralty of markets n shapng dversty does not suggest a tradeoff between development and dversty. Ths s because as ntegraton wth outsde markets ncreases, the level of dversty on farms can also be ncreased for crops. Further, we found that households wth more experence, more labour avalablty and more foods requred for consumpton can grow more dverse crops or lvestock because they have the resources to do so. Greater total crops or lvestock assets are assocated wth greater experence. In rural farms n Sr Lanka, wealth n lvestock can ensure aganst any crop producton rsks that mght arse when fewer crops are grown. Households lvng 170

186 further away from markets could demand fewer crops or hgher lvestock breeds. Access to roads and markets were nsgnfcant factors. Locaton of farm contrbutes to hgher levels of crop dversty. However, off-farm ncome, wealth and agrcultural subsdes were shown to be negatvely related wth agrcultural bodversty n smallscale farms n Sr Lanka. Furthermore, output prce fluctuatons s one of the mportant varables that provded sgnfcant results n all the models. Despte the rch agrcultural bodversty n Sr Lanka, the mpacts of soco-economc change upon dverse farmng systems n the country has receved lttle attenton. Ths research has helped to fll the gap by nvestgatng how dfferent forms of market provsonng and other varables shape the on-farm conservaton of agrcultural farm bodversty n Sr Lanka. It s clear that polces that affect a household s labour supply and ts composton are therefore lkely to have a major mpact on most components of agrcultural bodversty n the country. Educatonal campagns, and recognsng the possble mportance of women n varety choce and seed management are also relevant. The nformaton provded by analyss of all models s drectly polcy relevant and approprate polces can be desgned to control the dentfed factors. The predctons from the models estmated above enable us to dentfy the types of famles that are most lkely to sustan the agrcultural bodversty. Profles can be used to desgn targeted, least cost, ncentve mechansms to support conservaton as part of natonal envronmental programs. In each statstcal analyss conducted, whether descrptve or econometrc, the regonal heterogenety has emerged. Hence, any agr-envronmental polcy or programs that am to support the management of current levels of agrcultural 171

187 bodversty n rural areas n Sr Lanka wll need to recognse the heterogenety of these tradtonal farms and ther context. Furthermore, any polcy or program that affects the wealth, educaton or labour partcpaton of famly members, or the formaton of food markets wthn settlements, wll nfluence ther choces. As we argued n Chapters sx and seven, farmers mantan dversty for many reasons other than those explaned n ths chapter. There are a number of other reasons and aspects that we should consder when desgnng polces n ths feld. More detals and dfferent aspects of these ssues are dscussed n Chapters sx and seven. In the next chapter we dscuss the farmers preference for dfferent farmng system such as organc farmng, landrace varetes and mxed farmng practces whle the effcency aspects of small-scale farms are dscussed n Chapter seven. 172

188 CHAPTER SIX FARMERS PREFERENCES FOR DIFFERENT FARMING SYSTEMS 6.1 Introducton Organc farmng and landrace cultvaton are ncreasngly dsappearng n most rural areas n developng countres. Contnued landrace loss and dsappearng organc farmng methods n developng countres can be attrbuted to several factors. Frstly, the dffuson of modern cultvars whch, beng more productve, under hgh nputs at least, rapdly substtuted landraces when agrculture became a market-orented actvty. Secondly, socal-economc and cultural transformaton of the socety has ncreased demand for more commercalsed farmng practce. Thrdly, some of the other factors nclude the constant reducton n rural populatons, the constant smplfcaton of productve processes due to hgh manpower costs and problems wth passng nformaton from one generaton to the next are serous threats for the on-farm mantenance of landraces or exstng organc farmng methods n rural areas (Negr, 2003). These factors have sgnfcantly changed the tradtonal mxed farmng system as well. It s well known that landraces possess a wde range of genes useful for qualty breedng, specalty uses, and ther varablty of characterstcs. The best means of ther conservaton s f the materals are stll avalable wthn the farmng system. However, except for rare cases, there are only several remanng tradtonal landraces presently n agrculture. The economc envronment of the farm household sgnfcantly determnes the extent of genetc dversty n agrculture, selectng 173

189 organc or mxed farmng system. Economc development predomnantly had a negatve mpact on agrcultural bodversty due to escalatng norganc farmng as well as usng modern varetes n specalsed farmng systems. Snce the long term costs of losng bodversty rch farmng practces s sgnfcant, t s mportant to understand the nfluencng factors for selectng landrace, organc and mxed farmng systems n small-scale farms n developng countres. In some rural areas n Sr Lanka, landraces are stll cultvated, manly wth tradtonal methods. Compared to commercal varetes, these landraces may be less productve and more varable, but better adapted to the specfc clmatc condtons. Moreover, ther product has market desrable qualty trats (easy cookng, tasteful). Organc farmers can proft from the physologcal and qualtatve characterstcs of such genetc materal adapted to local condtons wth possble tolerance to dseases and weed competton. Consumer preferences of hgh qualty product wth good physcochemcal characterstcs are also an mportant factor when selectng cultvars adapted to organc farmng (Ghaout et al., 2008). In ths context, the objectve of ths chapter of the thess was to nvestgate the determnant factors of selectng organc farmng method, landrace cultvaton and mxed farmng system n smallscale farms n Sr Lanka. The results wll contrbute to the better explotaton of local plant materal and gve us mportant nformaton about conservaton of landrace cultvaton, organc farmng and mxed farmng systems whch are drectly related to mprovng agrcultural bodversty n small scale-farms n Sr Lanka. Farm households who are most lkely to mantan farms wth landrace cultvaton, organc farmng systems and mxed farmng systems are descrbed statstcally n ths 174

190 study. Fndngs can assst those who formulate agr-envronmental polcy n Sr Lanka to desgn effcent program that ncorporate small-scale famly farm management. The next secton provdes the context for the present research by lookng at what work has already been done n ths feld. It crtcally looks at the exstng research that s sgnfcant to the work carred out n ths study. 6.2 Lterature revew on farmers preference for dfferent farmng systems There are a number of studes that have analysed farmers preferences for landrace cultvaton, organc farmng and mxed farmng systems n dfferent countres. Brush et al. (1992) nvestgated the effects of the adopton of modern varetes of potato on the dversty of potato varetes on Andean farms. They found that adopton of modern varetes to be one of the prncpal causes of agrcultural bodversty loss. Ther fndngs reveal that farmers adapted only partally to modern varetes of potato and they contnue to employ tradtonal technologes and to mantan crop dversty on farms. Accordng to Brush et al. (1992) the loss of bologcal resources n agrcultural systems due to the ntroducton of hgh-yeldng varetes s a potental cost of agrcultural development. Ther econometrc analyss usng data from Peru ndcates that the adopton of hgh-yeldng potato varetes results n a reducton but not a complete loss of bologcal dversty on ndvdual farms and a possble loss n aggregate dversty. They concluded that on-ste conservaton of seed resources may be a vable complement to the off-ste methods now n place. A study conducted by Brush (1995) presented three cases of on-gong mantenance of landraces by farmers who have also adopted hgh-nput technology, ncludng hgh 175

191 yeldng crop cultvars. These cases are potatoes n the Andes of Peru, maze n southern Mexco, and wheat n western Turkey. These cases suggest that on-farm conservaton of landraces can be decoupled from tradtonal farmng practces. Factors that promote n stu conservaton are the fragmentaton of land holdngs, margnal agrcultural condtons assocated wth hll lands and heterogeneous sols, economc solaton, and cultural values and preference for dversty. Landraces are lkely to persst n patches and slands of farmng systems n regons of crop domestcaton and dversty, and these patches provde potental stes for conservaton programs. Conventonal and ecologcally sound agrculture were compared for the specfc case of corn producton by Pmentel (1997). As opposed to conventonal agrculture, the ecologcal agrcultural system used manure as a substtute of norganc fertlser to provde sol nutrents. Ths modfed system also adopted tllage to substtute herbcdes and used crop rotaton for nsect control and no pestcdes. In addton to envronmental benefts (e.g. reduced sol eroson and reduced fossl energy consumpton), the modfed ecologcally-sound system produced hgher corn yeld (15.7 per cent more) at a reduced cost (36 per cent less). Hesey et al. (1997) demonstrated that hgher levels of latent genetc dversty n modern wheat varetes would have generated costs n terms of yeld losses n some years n the Punjab of Pakstan. In other years, the mxed of varetes and ther spatal dstrbuton across the regon generated both lower overall yelds and less dversty than was feasble. Tsegaye (1997) looked at crop dversty n Ethopa and the role that women play n the development and conservaton of crop genetc resources. 176

192 Howard-Borjas (1999) examned the role of women n plant genetc resource management and concludes that ntegraton of gender perspectves n plant genetc resource management programs s necessary f such ntatves are not to fal. Smale et al. (2001) studed farmers demand for tradtonal varetes of maze n a regon of Mexco where cultvaton of modern varetes of the crop s neglgble. They found that farmers contnue to cultvate tradtonal varetes of maze because they receve prvate benefts. Mulatu and Belete (2001) studed the effectveness of farmers' partcpatory varetal evaluaton on sorghum crops n the Kle-Bsdmo plans of eastern Ethopa for three consecutve years, The study amed at provdng farmers wth alternatves to ther landrace to enable them to overcome crop losses and to dentfy farmers' varetal selecton crtera for ncluson n future breedng work. The study also confrmed that ncreasng farmers' access to ther preferred varetes would result n a faster rate of dffuson through farmer-to-farmer seed exchange. Benn et al. (2004) ponted out that n less favoured areas such as the hghlands of Ethopa, farmers manage rsk through land allocaton to crops and varetes snce they cannot depend on market mechansms to cope. Farmers also grow tradtonal varetes that are genetcally dverse and have potental socal value. Accordng to them supportng the mantenance of crop and varety dversty n such locatons can address both the current needs of farmers and future needs of socety, though t entals numerous polcy challenges. The result of ths study shows that growng modern varetes of maze or wheat does not detract from the rchness or evenness of these cereals on household farms. A survey was conducted coverng 408 households to understand the role of socoeconomc, cultural and envronmental factors n determnng the rce varetal 177

193 dversty n two contrastng eco stes n Nepal by Rana et al. (2005). The results of ths study suggested that land, lvestock number and use of chemcal fertlser have sgnfcant postve nfluence on landraces dversty on-farm. Other factors lke total land area and membershp n farmers groups have sgnfcant, but negatve nfluence on landrace dversty. Accordng to them resource-rch households mantan sgnfcantly hgher varetal dversty on-farm than that of the resource-poor households. Revewng the conservaton bology lterature, Hole et al. (2005) conclude that organc farmng ncreases bodversty at every level of the food chan. Degraded sol also could be restored through mproved agrcultural practces. Such evdence supports the promoton of alternatve agrcultural practces to acheve sustanable food supples. The degree of urbansaton and the avalablty of nfrastructure contrbuted more strongly to genetc eroson as compared to clmatc condtons (Keller et al., (2006)). Farmers tranng encouraged exotc vegetable cultvaton and reduced tradtonal vegetable dversty. At the same tme, ndgenous knowledge on how and where to collect, cultvate and prepare tradtonal vegetables was dsappearng. Mozumder and Berrens (2007) nvestgated the emprcal relatonshp between the ntensty of norganc fertlser use and bodversty rsk. Usng cross-country bodversty rsk ndces, ther statstcal estmates ndcate that the amount of norganc fertlser use per hectare of arable land s sgnfcantly related to ncreasng bodversty rsk. Robust fndngs across varous specfcatons hold after controllng for heterogenety across countres, ncludng the scale of agrcultural producton. 178

194 Sharma et al. (2007) nvestgated the relatonshp between landraces and rce dversty usng 183 landraces of rce adapted to the lowlands and the hlls n Nepal. Abdelal-Martn et al. (2008) assessed gender roles as a determnant factor of managng agrcultural bodversty. Accordng to them ncreased empowerment actons of women through alternatve sources of ncome optons are needed to enhance ther role n conservaton and sustanable use of agrcultural bodversty. Arslan and Taylor (2008) nvestgated how shadow prces gude farmers' resource allocatons. They estmated the shadow prces of maze usng data from a natonally representatve survey of rural households n Mexco. Accordng to them shadow prces were sgnfcantly hgher than the market prce for tradtonal, but not mproved maze varetes. The CVM was used to document the economc value of crop genetc resources based on the farmers' wllngness to pay for conservaton by Dwakar and Johnsen (2009). A total of 107 households n Kask, Nepal were surveyed n November Ther mean wllngness to pay was USD 4.18 for n stu and USD 2.20 for ex stu conservaton per annum. Landholdng sze, household sze, educaton level, socoeconomc status, sex of respondent, number of crop landraces grown, and knowledge on bodversty nfluenced the wllngness to pay for n stu conservaton, whereas only landholdng sze and household sze nfluenced the wllngness to pay for ex stu conservaton. The respondents were wllng to contrbute more for n stu than ex stu conservaton because of the addtonal effect of drect use and drect nvolvement of the farmers n n-stu conservaton. 179

195 Accordng to the above lterature revew t s clear that a large number of studes have been conducted to show the benefts of landrace cultvaton, organc farmng method and mxed farmng system. They have addressed varous ssues n ths feld. However, t s obvous that more emprcal work s needed to understand the determnants of the farmers demand for landrace cultvaton, organc farmng method and mxed farmng system n developng countres. As these farmng practces enhance the agrcultural bodversty, any conservaton program that s targeted to ncrease the farm level bodversty should take nto account the nfluencng factors for mantanng these farmng practces. Avalable studes n ths area also have focused n commercally-orented farmng systems. Therefore, an analyss of sem-subsstence orented farmng systems s requred n order to generalse and valdate the emprcal fndngs. In the next secton, the method of explanng farmers preferences s dscussed. 6.3 Methods of explanng farmers preferences When economc behavour s expressed as a contnuous varable, a lnear regresson model s often adequate to descrbe the mpact of economc factors on ths behavour. However, there are a varety of economc behavours where the contnuous approxmaton s not possble. In such cases bnary dependent varable method can be used to estmates the parameters (Wooldrdge, 2002). Probt and logt models are among the most wdely used members of the famly of generalsed lnear models n the case of bnary dependent varables. In probt models, the lnk functon relatng the lnear predctor µ= xβ to the expected value µ s the nverse normal cumulatve dstrbuton functon, Φ -1 (λ) = µ. In the logt model the lnk functon s the logt 180

196 transform, ln (λ/1- λ) = µ. Gven the smlartes between the two types of models, ether model wll gve dentcal substantatve conclusons n most applcaton 37. As sample sze n ths study s relatvely large, we use Probt regresson model to analyse the dummy dependent varables that represent agrcultural bodversty rch farmng systems namely landrace, organc farmng method and mxed farmng system. Bernoull random varable s the bass of bnary choce model (Wooldrdge, 2002). If N observatons are avalable, then the lkelhood functon of bnary dependent varable can be wrtten as followng Equaton 6.1: L N y 1 P (1 P ) 1 y (6.1) The Probt model arses when P s specfed to be gven by normal cumulatve dstrbuton functon evaluated at x'. Let F( x' ) denote the cumulatve dstrbuton functon. Then, the lkelhood functon of Probt models can be gven as followng Equaton 6.2: N L F( x' ) {1 F( x' )} 1 y 1 y (6.2) Then, the log-lkelhood functon s gven by Equaton 6.3: N ln L y ln F( x' ) (1 y)ln(1 F( x' )) 1 (6.3) The frst order condtons arsng from Equaton 6.3 are nonlnear functon. Therefore, we have to obtan the ML estmates usng numercal optmsaton 37 If one multples a Probt estmate by a factor, one gets an approxmate value of the correspondng Logt estmates. Emprcal support for the recommendatons regardng both the smlartes and dfferences between the probt and logt models can be traced back to results obtaned by Chambers and Cox (1967). They found that t was only possble to dscrmnate between the two models when sample szes were large and certan extreme patterns were observed n the data. 181

197 methods. The maxmum of lkelhood s solved by dfferentatng the functon wth respect to each of the β and settng the partal dervatves equal to zero. Followng Greene (2000) and Gujarat (2003), the emprcal model can be generally expressed as follows: * Z X ' (6.4) Accordng to the Equaton 6.4, the decson of the th farmers to select landrace cultvaton method or organc farmng method depends on household, market and other characterstcs. In ths model the dependent varables represent whether farmer selects landrace cultvaton (LR), organc producton (OP) and mxed farmng system (MIX). Emprcal model specfcaton s gven n Equaton 6.5: * Z EXP OWN HMP GEN INC SHL WLH FAT NMA DIMK DSN PRIF AS IOM SF (6.5) 8 where Z * s a dummy dependent varable that represent that represent farmer s preference on dfferent farm type. We used eght ndependent varables n landrace cultvaton and organc farmng models and 13 ndependent varables for mxed farmng model. Sgnfcant varables n these models wll provde mportant nsghts nto the parameters that must be taken nto account n order to desgn polces n ths feld. The defntons of the dependent varables are gven n Table 6.1. In order to understand the mportant determnants of these farmng practces, dfferent types of polcy relevant varables are selected. Importance of these varables were understood by the nformaton gathered from the plot survey as well as nformaton provded by the specalst n ths area. 182

198 Table 6.1: Defnton of dependent varables n dfferent models Varables Defntons LR Whether or not the farm contans a crop varety that has been passed down from the prevous generaton and/or has not been purchased from a commercal seed suppler. Farm contans a landrace vs. farm does not contan a landrace OP Whether or not ndustrally produced and marketed chemcal nputs are appled n farm producton MIX Mxed farms that nclude crop and lvestock producton, representng dversty n agrcultural management system Note: As mentoned prevously, these farmng systems are common n small-scale farms n Sr Lanka. All collected varables are dvded nto three man categores namely household characterstcs, market characterstcs and other characterstcs. Table 6.2 provdes the defnton of all ndependent varables used n the regresson analyss. All these ndependent varables are based drectly on the questonnare responses. It s clear that some varables are taken numbers whle other varables are defned as dummy varables. Experence n farmng s one of the mportant varables used n the analyss. Experence of household head n agrcultural actvtes s expected to have a postve relatonshp wth landrace, organc and mxed farmng system. Ths s because younger households may be more wllng to try out modern varetes and modern farmng practce, whle older households wth more experence n farmng may be more set n ther producton actvtes and less lkely to try modern farmng practces. Therefore, we hypothessed that demand for the organc farmng method, landrace and mxed farmng system would ncrease wth experence n farmng. 183

199 Table 6.2: Defnton of potental explanatory varables Varables Defnton Household characterstcs EXP Experence of farm decson maker (number of years) OWN Household owns a busness vehcle or not: dummy- 1 f Yes, Otherwse 0 HMP Household member s partcpaton n agrcultural actvtes (%) GEN Decson maker, male or female: dummy- 1 f Male, Otherwse 0 INC Off farm ncome of the famly (Rs. 000) SHL Shared labour (number n the last season) WLH Household wealth: dummy- 1 f wealther, Otherwse 0 FAT Farmers atttudes towards to AB : dummy- 1 f Postve, Otherwse 0 Market characterstcs NMA Number of market access days per week (number) DIMK Dstance to the nearest market (KM) DSN Drect sales or not (ntermedary) : dummy- 1 f Yes, Otherwse 0 PRIF Prce fluctuaton of the nput(ndex) Other characterstcs AS Recevng agrcultural subsdze: dummy- 1 f Yes, Otherwse 0 IOM Percentage of nvestment of owned money SF Sze of the farm (hectare) Note:. In the questonnare we asked, what s your general atttude towards agrcultural bodversty and possble answer were; very postve, postve, normal, negatve and strongly negatve. Frst three answers were corded as postve whle other two were corded as negatve when creatng dummy varable.. Prce fluctuaton ndexes were constructed usng average unt prce changes over the last two seasons for crops and lvestock outputs and nputs. Gender can gve dfferent results as t depends on ther preference. Women household heads are thought to nfluence selectng landrace and organc farmng method n postve and negatve ways. It s expected that women s conservatve atttudes would contrbute towards selectng landrace and organc farmng method. On the other hand, ther lack of ablty to undertake more labour ntensve work may nfluence ther decsons to grow modern varetes. Farmers atttudes towards 184

200 agrcultural bodversty s an mportant polcy varable used n the analyss. Before ncludng ths varable n the models, a correlaton matrx was obtaned to test whether ths varable s correlated wth other ndependent varables. It was found that the correlaton coeffcents are less than Therefore, ths varable s ncluded n the emprcal models n order to nvestgate whether there s an mpact of farmers atttudes on the conservaton of agrcultural bodversty. Four nterestng market characterstcs as explaned n the Table 6.2 were used to see whether these varables are mportant determnants of selectng mxed farmng systems. However, two varables that represent market characterstcs are used to see whether these varables are mportant for selectng landrace and organc farmng method. It s hypothessed that farmers who are more solated from markets are more lkely to select organc farmng methods and landrace cultvaton. In ths context, as the dstance to the nearest market s hgher, farmers are more lkely to mantan landrace and organc farmng methods. Input prce fluctuaton s an mportant varable used n ths analyss. Ths varable was created usng average nput prce changes (by takng the dfferent between maxmum and mnmum unt prces) over the prevous two cultvaton seasons. It s expected that the coeffcent of ths varable has a postve correlaton wth selectng landrace and organc farmng method. Explanatory varables and ther expected sgns for dfferent models are gven n Table

201 Table 6.3: Explanatory varables and ther expected sgns Varable Defntons LR OP MIX Household characterstcs EXP Experence of farm decson maker OWN Household owns a busness vehcle or not NA NA + HMP Household member s partcpaton NA NA + GEN Decson maker, male or female INC Off farm ncome of the famly NA NA - SHL Shared labour NA NA + WLH Household wealth NA NA - FAT Farmers atttudes towards to AB + + NA Market characterstcs NMA Number of market access days per week NA NA + DIMK Dstance to the nearest market DSN Drect sales or not (ntermedary) NA NA + PRIF Prce fluctuaton of the nput/output Other characterstcs AS Recevng agrcultural subsdze IOM Percentage of nvestment of owned money SF Sze of the farm (hectare) - - NA Note:. Only relevant varable are ncluded n each model n order to mnmse over dentfcaton problem. Prce fluctuaton of nput s used as explanatory varable n models of landrace cultvaton and organc producton decson as well. Ths varable s created by takng average unt prce changes over the last two seasons for crops and lvestock outputs and nputs. However, prce fluctuaton of output s used n mxed farmng model. Among the other characterstcs, recevng agrcultural subsdes, farm s own nvestment n ther farm and farm sze are mportant polcy relevant varables n the model. Agrcultural nput subsdes were crucal nstruments n the green revoluaton strategy ntroduced n the 1960s to ncrease output and productvty. Agrcultural nput subsdes that are known to have an adverse effect on the envronment nclude pestcdes, fertlzers and rrgaton. These subsdes provde ncentve for farmers to select specalsed crops whch are dependent on chemcal nputs and rrgaton. Moreover, heavy subsdes on nputs potentally dstort the relatve costs of factors of producton leadng to neffcent allocaton of nputs. Ths apples partcularly where 186

202 nputs are substtutes, rather than cases where they are complementary. Therefore, recevng agrcultural subsdes s used as an mportant polcy varable n ths study. It s expected that recevng agrcultural subsdy varable wll have a postve mpact on selectng landrace and organc farmng method. Farmers who borrowed money for ther cultvaton are less lkely to select organc and landrace varetes. Ths s because farmers often borrow money n order to mantan a specalsaton system wth marketng purpose. Sze of the farm s expected to have negatve mpact on selectng landrace and organc farmng method. Ths s because when the farm sze s larger, farmers are more lkely to mantan a specalsaton farmng system wth modern varetes. It s clear that the relevance of these varables for selectng landrace cultvaton, organc farmng and mxed farmng system can be dfferent. As shown n Table 6.3 we used eght ndependent varables to estmate landrace cultvaton and organc producton regresson model. However, 13 ndependent varables are used n the mxed farm model. Sgnfcant varables n these models wll provde mportant nsghts nto the parameters that must be taken nto account n order to desgn polces n ths feld. In the next secton, we wll nvestgate the emprcal results of the analyss. 6.4 Factors nfluencng the selecton of landrace cultvaton The loss of dversty n plantng materals threatens the lvelhoods of mllons of small holders who have local seeds as ther major source of plantng materals. Ths 187

203 s because the loss n dversty weakens the possblty to combne complementary plantng materals whch are adaptable to mosture, temperature, and sol type varablty (Chavas and Holt, 1996). It would also reduce the avalable pool of genetc materals for breedng to enhance productvty and ensure envronmental stablty (Salvatore et al., 2010). Therefore, t s mportant to understand the man varables that affect farmers decsons for selectng landrace cultvaton n rural areas n Sr Lanka. Ths secton of ths analyss uses Probt models to determne whch factors are more lkely to contrbute to farmers decsons on selecton landrace cultvaton n ther farms. We have ncluded eght mportant varables whch were explaned n Table 6.3 for ths purpose. The results of the model estmatons are shown n Tables 6.4. The results n Table 6.4 show that experence n agrcultural actvtes s sgnfcant for Ampara and pool data models for selectng landrace cultvaton. However, ths varable s not sgnfcant for Anuradhapura and Kurunagala samples. The gender varable s sgnfcant n all models. The negatve coeffcent mples that households headed by women are more lkely to use landrace cultvaton n ther farm. Ths shows the conservatve nature of women. Household atttude toward the conservaton of agrcultural bodversty s one of the nterestng varables used n ths analyss. The estmaton results clearly show that household postve atttude toward agrcultural bodversty s more lkely to contnue wth landrace cultvaton. The coeffcent of ths varable s hghly sgnfcant n all models and provdes expected sgn. Dstance to the nearest market varable s sgnfcant n Anuradhapura, Kurunegala and pool data models under 10 per cent and 1 per cent respectvely. The 188

204 mplcaton s that when the dstance to the nearest market s hgher, probablty of cultvatng landraces s also hgher. Meng (1997) also found that cultvaton of wheat landraces was postvely assocated wth ther relatve solaton from markets n Turkey. In Andean potato agrculture, Brush et al. (1992) found proxmty to markets to be postvely assocated wth the adopton of modern varetes. In southeast Guajanuato, Mexco, Smale et al. (2001) found that the better the market nfrastructure n a regon the greater the area households allocated to any sngle maze landrace but the greater the evenness n the dstrbuton of landraces across the regon. It s clear that the result of ths study s consstent wth these prevous fndngs. Table 6.4: Probt regresson results for landrace producton model Varables Ampara Anuradhapura Kurunegala Pool data EXP 0.017(0.002)* 0.010(0.008) 0.006(0.005) 0.023(0.003)* GEN (0.078)* (0.131)**** (0.124)* (0.069)* FAT 0.174(0.061)* 0.738(0.176)* 0.372(0.109)* 0.338(0.062)* DIMK 0.002(0.016) 0.012(0.063)*** 0.109(0.033)* 0.065(0.018)* PRIF 0.002(0.001)*** 0.006(0.003)** 0.017(0.003)* 0.006(0.001)* AS (0.065)* (0.188)* (0.105)* (0.050)* IOM 0.002(0.001)*** 0.004(0.003)**** 0.011(0.003)* 0.005(0.001)* SF (0.031)* (0.215)*** (0.143)* (0.041)* Anuradhapura (0.082)* Kurunagala (0.004)* N Pseudo R LR ch2(8) Note:. In the pool data analyss, Ampara s used as the base dstrct when creatng dummy varables.. Standard errors are shown n brackets. *, **, *** and **** denotes the sgnfcant varables at 1%, 5%, 10% and 20% level of sgnfcance respectvely.. Margnal effects of probt models are reported n the table. 189

205 Prce fluctuaton of nput s another nterestng varable used n ths analyss. The results show that when the market prce fluctuaton of nputs s hgher, the probablty of selectng landrace cultvaton s hgher. Ths s expected as nput prce fluctuaton can ncrease rsk n farmng by addng an addtonal cost component to farmers. Recevng agrcultural subsdes s another nterestng varable used n the analyss. Ths varable s sgnfcant n all models and has taken a negatve coeffcent value. Ths mples that agrcultural subsdes are lkely to reduce the probablty of havng a landrace cultvatng system n rural areas. We also ncluded the percentage of the farm s own money nvested on farm actvtes over the last season as an ndependent varable. Ths coeffcent s sgnfcant n all models n the analyss and has taken the expected sgn. It mples that when the percentage of own money expendture s hgher farmers are more lkely to use landrace systems. The sze of the farm s an mportant varable used n ths model. The coeffcent of ths varable shows that relatvely small farms are more lkely to use landrace cultvaton. In addton to these fndngs, pool data results show that heterogenety among dstrcts s sgnfcant. In general, the fndngs suggest these varables have a greater mpact on landrace cultvaton across households n Sr Lanka. 6.5 Factors nfluencng the selecton of organc farmng In ths secton we nvestgated the mportant varables for determnng the decson of havng an organc farmng system. The econometrc results for ths model are weaker statstcally because of the smaller percentages of farmers engaged n organc 190

206 producton relatve to other models explaned n the prevous chapters, though they are consstent wth hypotheses based on economc theory. The results of the Probt models for organc farmng method are gven by Table 6.5. Table 6.5: Probt regresson results for organc producton model Varables Ampara Anuradhapura Kurunegala Pool data EXP 0.009(0.002)* 0.033(0.006)* 0.012(0.005)* 0.014(0.002)* GEN (0.066)** (0.129)** (0.119)* (0.053)* FAT 0.115(0.050)** 0.232(0.127)*** 0.278(0.163)*** 0.182(0.054)* DIMK 0.015(0.012) 0.119(0.026)* 0.111(0.019)* 0.067(0.011)* PRIF 0.006(0.001)* 0.003(0.001)* 0.009(0.003)** 0.006(0.001)* AS (0.102)* (0.126)** (0.168)*** (0.060)* IOM 0.005(0.001)* 0.005(0.002)* 0.009(0.002)* 0.006(0.001)* SF (0.031)*** (0.083)** (0.080) (0.035)** Anuradhapura (0.082)*** Kurunagala (0.027)* N Pseudo R LR ch2(8) Note:. In the pool data analyss, Ampara s used as the base dstrct when creatng dummy varables. Standard errors are shown n brackets. *, ** and *** denote the sgnfcant varables at 1%, 5% and 10% level of sgnfcance respectvely.. Margnal effects of probt models are reported n the table. It s clear that experence n farmng s sgnfcant for all models whle the gender varable s hghly sgnfcant for the Kurunegala and pool data models. Ths mples that more experenced farmers are more lkely to mantan organc farmng systems. Household atttude toward the conservaton of agrcultural bodversty s one of the nterestng varables used n ths analyss. The estmaton results clearly show that households wth postve atttudes towards agrcultural bodversty are more lkely to contnue wth organc farmng. The coeffcent of ths varable s hghly sgnfcant n 191

207 all models and provdes the expected sgn. Dstance to nearest market varable s sgnfcant for Anradhapura, Kurunegala and pool data models. However, prce fluctuaton of nput s sgnfcant n all models. Results show that when the market prce fluctuaton s hgher, the probablty of selectng organc systems s hgher. Recevng agrcultural subsdes s another varable used n the analyss. Ths varable s sgnfcant n all models and has taken a negatve coeffcent value. Ths mples that agrcultural subsdes are lkely to reduce the probablty of havng organc farms n rural areas. The percentage of own money nvested on farm actvtes varable was used n ths model. Ths coeffcent s sgnfcant n all models n the analyss and has taken the expected sgn. The last varable that we used n ths analyss s the sze of the farm. The coeffcent of ths varable shows that small farms are more lkely to use organc farmng system. Snce organc technques requre labour to substtute for chemcals n pest and dsease control, larger farms reduce the lkelhood that they are used. In addton to these fndngs, pool data results show that heterogenety among dstrcts s sgnfcant. Organc farmng has proved to be more cost-effectve and eco-frendly than conventonal farmng. The nutrtonal value of food s largely a functon of ts vtamn and mneral content. In ths regard, organcally grown food s dramatcally superor n mneral content to that grown by modern conventonal methods. A major beneft to consumers of organc food s that t s free of contamnaton wth health harmng chemcals such as pestcdes. It s also known that organcally grown food tastes better than conventonally grown food. Furthermore, organcally grown foods 192

208 can be stored longer and do not show the latter s susceptblty to rapd mold and rottng. The survey results n ths study show that organc farmng reduces the producton cost by about per cent snce t does not nvolve the use of chemcal fertlsers and pestcdes, whch thus makes organc farmng very cost-effectve. There s a dscernng market of consumers who recognse the greater food value of organc produce and are wllng to pay premum prces for t. However, the exstence of a prce premum for organc products are not sgnfcant n Sr Lanka. Although there are some sgnfcant benefts of organc farmng, t has a cost premum as well. Organc farmng requres greater nteracton between a farmer and hs/her crop for observaton, tmely nterventon and weed control for nstance. It s nherently more labor ntensve than chemcal/mechancal agrculture so that, naturally a sngle farmer can produce more crops usng ndustral methods than he/she could by solely employng organc methods. Organc farmers do not have a convenent chemcal fx on the shelf for every problem they encounter. A detaled analyss of these costs and benefts are beyond the scope of ths study. In general, the fndngs of the analyss n Secton 6.4 and 6.5 suggest that these varables have a great mpact on selectng landrace system and organc farmng system across small-scale farms n Sr Lanka. Farmers choces for landrace cultvaton as well as organc farmng systems and ther possble mplcatons on conservaton polcy are ndcated by the sgnfcance of margnal probabltes of the explanatory varables n the models. It s clear that these fndngs can assst those who formulate agr-envronmental polces n Sr Lanka to desgn effcent program 193

209 that ncorporate small-scale farm management. In the next secton farmer s demand for mxed farmng system s explaned. 6.6 Farmers demand for mxed farmng system Rsk exposure and rsk management are nherent components of agrcultural actvtes. Farmers face varous forms of rsks, rangng from vagarous clmatc condtons, pests and pathogens, and prce volatlty. In the presence of effcent nsurance markets, farmers may nsure themselves effectvely to manage these rsks. However, n the absence of perfect nsurance markets, as s often the case n developng countres, exposure to such rsks s lkely to affect the ex-ante producton choces (Fafchamps, 1992; Chavas and Holt, 1996; Kurosak and Fafchamps, 2002). In developng countres, farmers' choce for farm dversfcaton may reflect an nsurance mechansm desgned to reduce producton rsk. A growng body of research suggests that mxed farmng system contrbutes to ncrease agrcultural crop yeld, and to reduce producton rsk (Smale et al., 1998; D Falco and Chavas, 2009; Salvatore et al., 2010). In ths secton we nvestgate the determnants of mxed farms n separate dstrct data and pool data. The dchotomous choce of whether or not to rase crops together wth lvestock n the farm s estmated wth a unvarate probt model. Table 6.6 presents the results of the mxed farms regresson model. The decson to mantan a mxed farmng system s assumed to be a functon of household characterstcs, market characterstcs and some of the other characterstc. Results show that most of the ncluded varables are sgnfcant for determnng mxed 194

210 farmng systems. It s also evdent that for all regons taken together, household characterstcs as a set are hghly sgnfcant determnants of the decson to rase both crops and lvestock when comparng wth other characterstcs. Table 6.6: Probt regresson results for mxed farm model Varables Ampara Anuradhapura Kurunegala Pool data EXP 0.007(0.003)*** 0.018(0.009)*** 0.022(0.008)* 0.009(0.003)** OWN 0.106(0.080)**** 0.121(0.175) 0.165(0.156) 0.187(0.067)* HMP 0.006(0.002)** 0.017(0.005)* 0.006(0.003)*** 0.008(0.001)* GEN 0.122(0.090)**** 0.280(0.179)**** 0.571(0.151)* 0.153(0.081)*** INC (0.003)** (0.004)* (0.006) (0.002)* SHL 0.014(0.006)** 0.148(0.035)* 0.060(0.013)* 0.043(0.007)* WLH (0.065) (0.162)* (0.145) (0.064)** NMA 0.130(0.030)* 0.111(0.049)** 0.065(0.035)** 0.058(0.024)** DIMK (0.025)** (0.069)* (0.027)* (0.004)* DSN 0.276(0.177)**** 0.004(0.245) 0.099(0.136) 0.152(0.076)** PRIF 0.004(0.001)*** 0.007(0.002)* 0.014(0.003)* 0.005(0.001)* AS (0.112)** (0.194)** (0.128)* (0.064)* IOM 0.004(0.001)** 0.015(0.003)* 0.004(0.002)**** 0.007(0.001)* Anuradhapura (0.091)**** Kurunegala (0.072)** N Pseudo R LR ch2(13) Note:. In the pool data analyss, Ampara s used as the base dstrct when creatng dummy varables.. Standard errors are shown n brackets. *, **, *** and **** denotes the sgnfcant varables at 1%, 5 %, 10% and 20% level of sgnfcance respectvely.. Margnal effects of probt models are reported n the table. The results n Table 6.6 show that experence n agrcultural actvtes s hghly sgnfcant n all models and shows a postve coeffcent value mplyng that farmers who have more experence n farmng are lkely to mantan mxed farm. The reason 195

211 may be, wth the experence, they can understand the possble benefts of havng a mxed farmng system. Ownng a busness vehcle s weakly sgnfcant n Ampara sample whle t s hghly sgnfcant for pool regresson model. It s clear that busness vehcles help farmers reduce the transacton costs for marketng output. Household members partcpaton varable s hghly sgnfcant n all models. It s clear that more actve household labour partcpaton generally contrbutes postvely to mantan mxed farmng systems. The gender varable s sgnfcant n all models. The postve coeffcent mples that, households headed by men mantan more dverse or mxed farmng systems. The results show that off-farm ncome has a sgnfcant negatve effect on mxed farms. Ths s expected when consderng famly food requrement as well as labour requrements. It s clear that a sgnfcant porton of off-farm ncome comes as offfarm employment. If they are employed n other places, the ncentve to mantan a dverse farmng system s less as t needs a relatvely hgher amount of labour. As mentoned prevously, shared labour s one of the mportant socal captals n rural areas. Ths varable shows a sgnfcant postve correlaton wth mxed farmng systems. The coeffcent for household wealth s negatve and sgnfcant. The greater the wealth of the household, the less lkely the household s to have a mxed farmng system. The coeffcent for the number of market access day s varable s sgnfcant n all models and has shown a postve sgn. The dstance to the nearest market s another nterestng varable used n the analyss. The results show that households who are close to the market are more lkely to mantan mxed farmng systems. Ths s because ther transacton costs are lkely to be less. When the households are away 196

212 from the market, they are less lkely to mantan dverse farmng systems as ther market transacton cost s expected to be hgh. The varable representng drect sales or not s sgnfcant only Ampara and pool data models. Ths mples that farmers who can drectly sell ther output are more lkely to mantan a dverse farmng system. Prce fluctuaton of output s another nterestng varable used n ths analyss. Ths varable s a proxy for rsk of future return of farm output. Results show that when the market prce fluctuaton s hgher, the probablty of selectng mxed farmng systems s hgher. Ths s expected as t shows the way of managng rsk of the farm. Ths could help farmers to mnmse the rsk of ther return. Recevng agrcultural subsdes, another varable used n the analyss, s sgnfcant n all models and has taken a negatve coeffcent value. Ths mples that agrcultural subsdes are lkely to reduce the probablty of havng a mxed farmng system n rural areas. The last varable that we ncluded n ths model s the percentage of own money nvested for farm actvtes over the last season. As hypothessed, when the percentage of own money expendture s hgher, the probablty of selecton of a mxed farmng system s also hgher. Ths coeffcent s sgnfcant n all models n the analyss. In addton to these fndngs, pool data results show that heterogenety among dstrcts s sgnfcant. In general, the fndngs of ths analyss suggest that household, market and other characterstcs have a great mpact on determnng mxed farms levels across smallscale farms n Sr Lanka. Farmers choces on selecton of mxed farmng systems and ther possble mplcatons for conservaton polcy are ndcated by the 197

213 sgnfcance of margnal probabltes of the explanatory varables n ths analyss. In the next secton the man conclusons drawn from ths chapter are explaned. 6.7 Summary and key fndngs Although the benefts of envronmentally rch farmng systems n Sr Lanka are clear, the mpacts of soco-economc change upon agrcultural bodversty n the country have receved lttle attenton. A study on the current status of agrcultural bodversty s useful for polcy decson makers n order to make polces for conservaton n rural areas n the country. It s clear that the dfferent farmng practces that farmers use s drectly related wth agrcultural bodversty. Therefore, t s mportant to know the determnant factors for selectng landrace cultvaton, organc farmng and mxed farmng systems. Ths chapter of the thess nvestgated ths ssue usng small-scale farms n Sr Lanka. We found that the key varables promotng landrace cultvaton, organc farmng and mxed farmng systems are household characterstcs, market characterstcs, and some of the other characterstcs such as percentage of farmers own money spent for agrculture. The results show that gender s an mportant varable to determne the landrace cultvaton. It shows that female domnant farms are more lkely to select landrace varetes. Farmers postve atttudes towards agrcultural bodversty have a sgnfcant mpact on selectng landrace varetes. In addton to that farms sze, nput prce fluctuatons, agrcultural subsdes and percentage of own money nvestment are found to be among mportant factors when takng decsons related to mantanng landrace cultvaton. An nterestngly agrcultural subsdy s one of the 198

214 mportant varables that provded sgnfcant results n all models. It mples that the exstng subsdy program n Sr Lanka has negatvely affected choces about cultvatng landrace varetes. Investgaton of profles of farm famles that are most lkely to cultvate landraces and use organc farmng reveals that they have less ncome compared to those farm famles who are not lkely to cultvate landraces. They are more agrculturally-based, wth less off-farm employment and are more solated from the markets. Among the mportant varables n the organc farm model, farmers atttudes towards agrcultural bodversty, nput prce fluctuatons, agrcultural subsdes and farm sze are found to be the most sgnfcant varables. It s clear that most of the varables used n the mxed farm model are sgnfcant and have taken expected sgns. We found that households wth more experence, more labour avalablty and less off farm ncome are more lkely to have a mxed farmng system. The results also show that the market characterstcs as well as agrcultural subsdes are mportant determnants for selectng mxed farmng systems. Off-farm ncome, wealth and agrcultural subsdes have been shown to be negatvely related to mxed farmng systems n small-scale farms n Sr Lanka. The nformaton provded by analyss of all models s drectly polcy relevant and approprate polces can be desgned to control them. The predctons from the models estmated above enable us to dentfy the types of famles that are most lkely to ncrease the agrcultural bodversty n Sr Lanka. Accordngly, household profles can be used to desgn targeted, least cost ncentve mechansms to support conservaton as part of the natonal envronmental program. Ths study contrbutes to the lterature by provdng nsghts nto farmers landrace cultvaton, organc 199

215 farmng and mxed farmng preferences, usng small-scale farm household data n a typcal developng country settng. It also dentfes the household contextual factors that govern these decsons. 200

216 CHAPTER SEVEN AGRICULTURAL BIODIVERSITY AND FARM LEVEL EFFICIENCY 7.1 Introducton Technologcal nnovaton and the more effcent use of producton technologes are the man strateges of achevng productvty growth n agrculture (Hoang and Coell, 2009). However, n developng countres most new agrcultural technologes have only been partally successful n mprovng productvty. Ths s often due to a lack of ablty or desre to adjust nput levels by the producers because of ther famlarty wth tradtonal agrcultural systems or because of nsttutonal constrants (Bnam et al., 2004). These consderatons suggest that the best opton to assst developng countres to rase productvty s ncreasng effcency. If farmers are not effectvely usng exstng technology, then efforts desgned to mprove effcency may be more cost-effectve than ntroducng new technologes (Belbase and Grabowsk, 1985). The presence of shortfalls n effcency means that output can be ncreased wthout requrng addtonal conventonal nputs and wthout the need for new technology. If ths s the case, emprcal measures of effcency are needed to determne the magntude of the gans that could be obtaned by mprovng performance n agrcultural producton wth a gven technology. In ths chapter of the thess farmers ablty to select a producton system and ts relatonshp wth farm level techncal effcency s nvestgated. 201

217 There are several mportant reasons for measurng the farm level techncal effcency of agrcultural producton. Frstly, f farmers are not makng effcent use of exstng technologes, then efforts desgned to mprove effcency would be more cost effectve than ntroducng a new technology as a means of ncreasng output (Shapro, 1983). Secondly, measurng effcency leads to sustanable resource savngs, whch has mportant mplcatons for both polcy formulatons and farm management (Bravo-Ureta and Evenson, 1994). Thrdly, t s only through measurng effcency and separatng ts effects from the effects of the producton envronment that one can explore hypotheses concernng the sources of effcency dfferental. Fourthly, dentfcaton of sources of neffcency s mportant to the nsttuton of publc and prvate polces desgned to mprove performance of agrculture (Bozoglu et al., 2007). Bodversty conservaton of agrcultural land s an objectve that has receved a consderable attenton from polcy makers n recent years (Wdawsky and Rozelle, 1998; Wnters et al., 2005). Ths s because agrcultural producton can play an mportant role on mantanng envronmental frendly farmng system n the long run. Moreover, experence shows that producton can be ntensfed (more producton per unt of area) whle reducng nputs and lowerng the envronmental degradaton n agrculture through mprovng bodversty n the agrcultural sector (Wnters et al., 2005). However, enhancement of bodversty appears not to be explctly recognsed as a proper target or a postve output when producton effcency s measured n practce. We hypothesse that ths gnorance may cause bases n tradtonal effcency calculatons and such ncomplete measures may therefore dscrmnate aganst envronmentally bengn technologes. 202

218 Ths Chapter of the thess ams at delverng emprcal evdence on the lnks between techncal effcency and agrcultural farm bodversty by analysng farm level data collected from 746 small-scale farmers n Sr Lanka. To the best of our knowledge, ths s the frst attempt that nvestgates farm level techncal effcency and bodversty n Sr Lanka or any other country. It s beleved that agrcultural bodversty ncreases farm level techncal effcency due to three reasons. Frst, farmers beleve that they can utlse famly labour optmally when they mantan a dverse agrcultural system (Brookfeld et al., 2002). For example, dfferent crops may requre labour n dfferent tme perods and famly labour can easly be dstrbuted among dfferent crops and/or lvestock n order to obtan maxmum benefts. Second, a dverse farmng system mnmses external rsk that farmers often face. For example, f a farmer has both crops and lvestock ths wll mnmse the rsk from drought or water shortage. That s, whle crops can be devastated, the farmer stll can derve an ncome from lvestock. Thrd, a bologcally rch farmng system can mprove sol fertlty and mnmse nput costs n the long run. The next secton wll summarse the exstng emprcal studes of agrcultural bodversty and farm level techncal effcency. Ths type of analyss helps to dentfy what work has already been undertaken n ths feld. It also helps n understandng the shortcomngs of exstng work and hghlghts the mportance of conductng the present research. As shown n the lterature revew, no studes n ths area analyses the relatonshp between agrcultural bodversty and farm level techncal effcency usng small-scale farms data n developng countres. Therefore, the results of the study wll be a novel contrbuton to the lterature n ths area. 203

219 7.2 Lterature on agrcultural bodversty and farm level effcency Agrcultural bodversty s found to have some postve mpacts on overall productvty and sol qualty (Hesey et al., 1997; Wdawsky and Rozelle, 1998; Meng et al., 2003). It also can affect farm level effcency through the management of scare resources n a dverse farmng system. Belbase and Grabowsk (1985) estmated a determnstc Cobb-Douglas producton fronter model to nvestgate effcency n Nepalese agrculture. Accordng to ths study, average techncal effcency level of manstream agrcultural crops s found to be 80 per cent. Based on the effcency measures obtaned from all crops, correlaton analyss showed that nutrtonal levels, ncome, and educaton were sgnfcantly related to techncal effcency, whle no relatonshp was found for farmng experence. Parkh and Shah (1995) presented a revew of the varous approaches to effcency measurement and conducted emprcal analyses of cross-sectonal data from 397 sample farmers n the North-West Fronter Provnce of Pakstan. Ther results show that small farms were relatvely more effcent than large farms n the study area. The techncal effcency and productvty of maze producers n Ethopa were analysed by Seyoum et al. (1998). Ths study compared the performance of farmers wthn and outsde the program of technology demonstraton. Usng Cobb-Douglas stochastc producton functons, ther emprcal results showed that farmers who partcpate n the program are more techncally effcent wth a mean techncal effcency equal to 94 per cent compared wth those outsde the project whose mean effcency was equal to 79 per cent. Smale et al. (1998) found that the producton envronment determnes the sgn of the relatonshp between dversty and 204

220 productvty for wheat varetes n the Punjab of Pakstan. For nstance, among ranfed dstrcts, genealogcal dstance and a greater number of dfferent varetes grown of smaller areas were assocated wth both hgher mean yelds and more yeld stablty. New evdence on techncal effcency and ts sources were presented by examnng the cost behavour of 387 farms n fve rrgated dstrcts of Punjab by Burk and Shah (1998). They concluded that farm effcency s postvely related to formal schoolng of farm operators and negatvely related to farm sze. The age of farm operators s shown to have no effect on effcency. Dary farms are also the subject of a paper by Hadr and Whttaker (1999) where the effcency of a small panel of dary farms n the south-west of England was consdered n the context of ther use of potentally pollutng agrochemcals. Ths study showed a postve relatonshp between techncal effcency and farm sze. However, there s a neglgble negatve relatonshp between farm sze and use of contamnants n farms. A stochastc producton fronter methodology was used to nvestgate the techncal effcency of organc and conventonal olve-growng farms by Vangels et al. (2001). Fndngs ndcated that the organc olve-growng farms examned exhbt a hgher degree of techncal effcency (relatve to ther producton fronter) than do conventonal olve-growng farms. Reasons may nclude lower proft margns and restrctons on nputs permtted, thus forcng organc farmers to be more cautous wth nput use. However, both nput and output-orented techncal effcency scores were stll relatvely low for both types of olve-farmng. Wlson et al. (2001) examned the techncal effcency of a cross-secton of cereal farmers n Eastern countes. Accordng to them, the techncal effcency ndex across producton unts ranged from 62 to 98 per cent. The objectves of maxmsng annual profts and 205

221 mantanng the envronment are postvely correlated wth, and have the largest nfluence on, techncal effcency. Moreover, those farmers who seek nformaton, have more years of manageral experence, and have a large farm, are also assocated wth hgher levels of techncal effcency. The effcency of smallholder rce farmers were nvestgated by Sherlund et al. (2002) n Côte d Ivore whle controllng for envronmental factors that affect the producton process. Apart from dentfyng factors that nfluence techncal effcences, the study found that the ncluson of envronmental varables n the producton functon sgnfcantly changed the results: the estmated mean techncal effcences ncreased from 36 per cent to 76 per cent. Karaganns et al. (2002) also analysed the effcency of dary farms n England and Wales. Bnam et al. (2004) examned factors nfluencng techncal effcency of groundnut and maze farmers n Cameroon. Usng a Cobb-Douglas producton functon they fnd mean techncal effcences to be n the regon of 73 per cent and 77 per cent. They also concluded that access to credt, socal captal, and dstance from the road and extenson servces are mportant factors explanng the varatons n techncal effcences. Testng the relatonshp of wheat varety dversty to productvty and economc effcency n Chna, Meng et al. (2003) found that although evenness n morphologcal groups contrbuted to hgher per hectare costs of wheat produced, potentally mportant cost savngs were apparent for some nputs, such as pestcdes. A greater concentraton of cooperatve market assocatons n regons of southern Italy contrbuted to greater dversty of durum wheat varetes, wth postve effects on productvty (D Falco, 2003). 206

222 Hadley (2006) estmated stochastc fronter producton functons for eght dfferent farm types (cereal, dary, sheep, beef, poultry, pgs, croppng and mxed) for the perod 1982 to Dfferences n the relatve effcency of farms were explored by the smultaneous estmaton of a model of techncal neffcency effects. The results showed that factors such as farm or herd sze, farm debt ratos, farmer age, levels of specalsaton and ownershp status are sgnfcant varables n the effcency functon. Idong (2007) provded estmates of techncal effcency and ts determnants usng data obtaned from 112 small scale rce farmers. The results ndcated that rce farmers were not fully techncally effcent. The mean effcency obtaned was 77 per cent ndcatng there was a 23 per cent allowance for mprovng effcency. The results also showed that farmers educatonal level, membershp of cooperatve/farmer assocaton and access to credt sgnfcantly and postvely nfluenced the farmers effcency. A study conducted by Bozoglu and Ceyhan (2007) explored determnants of techncal neffcency n the Samsun provnce of Turkey. Farm managers from 75 randomly selected farms were ntervewed for farm level data n the producton perods. Research results revealed that the average output of vegetable farms n Samsun could ncrease by 18 per cent under prevalng technology. The techncal effcency of the sample vegetable farms ranged from 0.56 to 0.95 (0.82 average). The varables of schoolng, experence, credt use and partcpaton by women negatvely affected techncal neffcency. However, age, famly sze, offfarm ncome and farm sze showed a postve relatonshp wth neffcency. 207

223 There are a few studes that ndrectly concentrate on agrcultural bodversty and farm level effcency. Czech (2003) nvestgated the role of technology n agrculture n conservng bodversty. Latruffe et al. (2004) analysed techncal effcency and ts determnants for a panel of ndvdual farms n Poland specalsed n crop and lvestock producton n Techncal effcency s estmated usng stochastc fronter analyss and the determnants of neffcency are also evaluated. Latruffe et al. (2005) analysed the techncal and scale effcency of Polsh farms usng data envelopment method. Effcency dfferences are measured accordng to farm specalsaton, n crop or lvestock, at two ponts n tme durng transton, 1996 and Ther fndngs ndcate that lvestock farms are on average, more techncally and scale effcent than crop farms. Scale effcency s hgh for both specalsatons. Haj (2006) estmated techncal, allocatve and economc effcences and dentfes ther determnants n smallholders vegetable-domnated mxed farmng system of eastern Ethopa. An econometrc analyss usng a Tobt model ndcates that asset, off-farm ncome, farm sze, extenson vsts and famly sze were the sgnfcant determnants of techncal effcency. On the other hand assets, crop dversfcaton, consumpton expendtures and farm sze had a sgnfcant mpact on allocatve and economc effcences. Accordng to the above revew of prevous studes, t becomes clear that a large volume of lterature deals wth farm level techncal effcency n varous contexts. However, none of the studes consder the causal relatonshp between agrcultural bodversty and farm level techncal effcency n a sem-subsstence economy. As a result, there s a need to focus on small farms, the prmary farmng system n Asa, Afrca and Latn Amerca. Ths study attempts to fll these gaps n the lterature. The 208

224 prmary focus of the chapter s to nvestgate the lnk between agrcultural farm bodversty and farm level techncal effcency. It s also expected to dentfy some of the other factors that affect neffcency n small-scale farms n rural areas n Sr Lanka. The results of ths study wll provde the necessary nformaton for polcymakers to evaluate the socal benefts of conservaton of agrcultural bodversty n rural areas n developng countres. The relevant theoretcal and emprcal approaches are explaned n the followng secton. 7.3 Method of estmatng farm level techncal effcency The term effcency of a farm can be defned as ts ablty to provde the largest possble quantty of output from a gven set of nputs. The modern theory of effcency dates back to the poneerng work of Farell (1957) who proposed that the effcency of a farm conssts of techncal and allocatve components, and the combnaton of these two components provdes a measure of total economc effcency. Techncal effcency measures how well the ndvdual farm transforms nputs nto a set of outputs based on a gven set of technology and economc factors (Agner et al., 1977; Kumbhakar and Lovell, 2000). It s measured ether as nput conservng orented or output-expandng orentaton (Jondrow et al.,1982; Coell, 1995). Accordngly, ths secton begns wth a descrpton of the basc stochastc producton fronter model, where output s specfed as a functon of a non-negatve random error whch represents techncal neffcency, and a symmetrc random error whch accounts for statstcal nose. It also shows how the estmated parameters of the model can be used to predct the techncal neffcences of farms. 209

225 We use the stochastc fronter producton functon approach to estmate farm level techncal effcency n small-scale farms n Sr Lanka 38. The advantage of usng stochastc fronter models are: (1) It ntroduces a dsturbance term representng statstcal nose, measurement error and exogenous shocks beyond the control of producton unts whch would other-wse be attrbuted to techncal neffcency, (2) It provdes the bass for conductng statstcal tests of hypothess regardng the producton structure and the degree of neffcency. The estmaton of fronter functon and effcency can be completed ether n one stage or n two stages. Ths method has been used extensvely n the past two decades to analyse techncal effcency. In ths study, the model of Battese and Coell (1995) s used n accordance wth the orgnal models of Agner et al. (1977) and Meeusen and van den Broeck (1977). The general form of the stochastc fronter producton can be defned by: Y f x exp V U, = 1,2...N (7.1) Y refers to the output obtaned by farm, x s the vector of dfferent nputs used and β s a vector of parameters to be estmated. The model s such that the possble producton, Y, s bounded above by the stochastc quantty, f x, )exp( V ). ( Therefore,the term stochastc fronter s used. The error components V are assumed 2 to be ndependently and dentcally dstrbuted as N(0, v ). Ths s assocated wth random factors such as random errors, errors n the observaton and measurng of data, whch are not under the control of the farm (Coell et al., 2005). The error 2 components, U are non-negatve truncatons of the N(0, u ) dstrbuton that can be 38 Coell (1996) observed that 30 out of 40 studes on applcaton of fronter models to agrculture have used stochastc fronter producton functons. 210

226 half normal, truncated normal, exponental dstrbuton or gamma dstrbuton. The truncated normal fronter model s due to Stevenson (1980) whle the gamma model s due to Green (1990). The log-lkelhood functons for these dfferent models can be found n Kumbhakar and Lovell (2000). The model explaned by Equaton 7.1 can be expressed as follows: ln Y 0 1 ln X V U (7.2) Y Y exp 0 1 ln X V U (7.3) ln X exp V exp U exp 0 1 (7.4) Frst component of the rght hand sde of Equaton 7.4 gves the determnstc component whle second and thrd components gve nose and neffcency parts. The basc structure of the stochastc fronter model s explaned n Fgure 7.1 n whch the productve actvtes of two farms, represented by and j, are consdered. Output Y Fronter output, * Y fv 0 Determnstc producton functon, Y = f(x;β) Y f(, ) x Y f (, ) x j Fronter output, * Y j, fv 0 j Observed output, Y Observed output, Y j Source: Coell et al. (2005). x x j Inputs X Fgure 7.1: Stochastc fronter producton functon 211

227 In ths case, the determnstc component of the fronter model has been drawn to reflect the exstence of dmnshng returns to scale. Values of the nput are measured along the horzontal axs and outputs are measured on the vertcal axs. Farm uses nputs wth values gven by the vector of x and producers output Y, but the fronter output Y *, exceeds the value on the determnstc producton functon, f (, ), because ts productvty s assocated wth favourable condtons for whch the random error, V s postve. However, farm j uses nputs wth values gven by the vector x j and producers output, Y j, whch has correspondng fronter output, Y j *, whch s less than the value on the determnstc producton functon, f (, ), because ts productve actvty s assocated wth unfavourable condtons for whch the random error V j s negatve. In both cases the observed producton values are less than the correspondng fronter values, however, the (unobservable) fronter producton values le above or below the determnstc producton functon dependng on the exstence of favourable or unfavourable condtons beyond the farms control (Coell et al., 2005). x x Accordngly, random varables U are assumed n capturng techncal neffcency. Gven the assumptons of the stochastc fronter model, nference about the parameters of the model can be based on the maxmum lkelhood estmators (Agner et al., 1977). The parameter γ can be calculated usng nformaton of the varance of two error terms ( and ). More detals about the method of obtanng parameters 2 u 2 v are gven by Coell et al. (2005). Gven the assumptons of the stochastc fronter model, nference about the parameters of the model can be based on the maxmum lkelhood estmators (Agner et al., 1977). Battese and Corra (1977) consdered the 212

228 2 2 parameter, u v, /( ) whch s bounded between zero and one. u u v 2 2 It s clear that u v, the coeffcent of /( ) s bounded between zero and one. If the γ equals zero, the dfference between farmers yeld and effcent yeld s entrely due to statstcal nose. On the other hand γ = 1 ndcate the dfference s entrely due to less than effcent use of technology (Coell et al., 2005). u u v 2 2 If u v, then the more δ s greater than one, the more producton s domnated by techncal neffcency. The closer t s to zero, the more the dscrepancy between the observed and fronter output s domnated by random factors beyond the control of the farmer (Coell, 1995). The techncal effcency of ndvdual farms can be estmated by usng the condtonal dstrbuton of U gven the ftted values of error term and the respectve parameters. The techncal effcency of an ndvdual farm s defned n terms of the rato of the observed output to the correspondng fronter output, condtonal on the levels of nputs used by that farm (Coell and Battese, 1996). It s the factor by whch the level of producton for the farm s less than ts fronter output. The techncal effcency of farm n the context of the stochastc fronter producton functon s the same expresson as for the determnstc fronter model (Coell et al., 2005). Although the techncal effcency of a farm assocated wth the determnstc and stochastc fronter models are the same, they have dfferent values for the two models (Battese, 1992). As shown n fgure 7.1, techncal effcency of farm j s greater under the stochastc fronter model than for the determnstc fronter. However, for a gven set of data, the estmated techncal effcences obtaned by fttng a determnstc fronter wll be less than those obtaned by fttng a stochastc fronter, because the determnstc 213

229 fronter wll be estmated such that no output values wll exceed t (Battese, 1992). Gven the determnstc fronter model, the fronter output for the th farm s Y * f ( x, and the techncal effcency for the th ; )exp( V ) farm, denoted by TE s that: Y TE * (7.5) Y f ( x, )exp( V U ) TE f ( x, )exp( V ) TE exp( U ) (7.6) (7.7) Techncal effcences for ndvdual farms are predcted by obtanng the rato of the observed producton values to the correspondng estmated fronter values. TE Y f ( x, ) where s ether the maxmum lkelhood estmator or the corrected Ordnary Least Squares estmator for β. It measures the output of the th farm relatve to the output that could be produced by a fully-effcent frm usng the same nput vector. Once the neffcency component of the producton functon s separated, ts determnant should be dentfed. For ths purpose t s assumed that the average level of techncal neffcency s a functon of factors beleved to affect techncal neffcency as shown below: U Z g (7.8) 214

230 where g s a random varable dstrbuted wth mean value of zero and varance 2 That s, ~ N0, g 2 g. g. The random varable U s defned by the truncaton of the normal dstrbuton. In ths study, we propose the use of the more flexble truncated normal dstrbuton that allows for a wder range of dstrbutonal shape (Coell et al., 2005). The assumpton of truncated normal dstrbuton for the U s s an approach that was suggested by Stevenson (1980) by generalsng the assumpton of halfnormal dstrbuton. In the half normal dstrbuton U are assumed to be the postve half of a normally dstrbuted varable wth mean zero U ~ N 2 (0, ). Kumbhakar u and Lovell (2000) state that ndvdual effcency scores, as well as the composton of the top and bottom effcency scoredecles, are not affected by the dstrbutonal assumptons of the neffcency component, U, and suggest the use of relatvely smple dstrbutons such as a half normal or an exponental dstrbuton. Complete detals of the MLE dervatves are shown n Appendx L. 7.4 Emprcal model of estmaton Ths secton explans the emprcal method of estmatng agrcultural bodversty and farm level techncal effcency. As explaned n the prevous secton, snce the basc stochastc fronter model was frst proposed by Agner et al. (1977) and Mueeusen and Van den Broeck (1977), varous other models have been suggested and appled n the analyss of cross sectonal and panel data. However, the emprcal model of techncal effcency n ths study was based on the stochastc producton functon proposed by Battese and Coell (1995). In the frst phase of the emprcal analyss, techncal effcency effects for a cross secton of farmers s modeled n terms of nput varables n the producton process. Rural agrcultural households 215

231 generally cultvate dfferent crops on ther farms. Therefore, ths practce renders the sngle crop producton model to be nfeasble. In the case of multple outputs, the dependent varable n the producton model s measured n terms of the total value of agrcultural outputs or producton. Inputs can be categorsed nto four groups: land, labour, captal and other nputs. It s assumed that the captal use n agrculture s homogenous across the households. The translog producton functon s used snce t captures the nteracton effects of the varables 39. Estmaton of the translog producton functon was performed usng Fronter verson 4.1 (Coell, 1996).Accordngly, the stochastc fronter model to be estmated s defned by: ln Y 4 0 j ln X j jk ln j1 4 4 jk k X j X k V U (7.9) where ln represent the natural logarthm. The subscrpt, ndcates the th farmer n the sample ( = 1,2..,n). lny represents the natural logarthm of the value of farm output (VFOUT) ln X 1represents the natural logarthm of the total area of land (n acres) under cultvaton (LAND). 39 The translog producton functon developed by Chrstansen et al. (1973) s the most prevalent functonal form used n stochastc fronter analyss lterature for a number of reasons. Frst, t provdes some degree of generalty as t s a second order approxmaton to an arbtrary functonal form. Other famlar functonal forms such as the Cobb Douglas and CES are specal cases of the translog functon so these common forms are encompassed by the translog producton functon. Second, the translog functon allows for varyng returns to scale and for technologcal progress to be both neutral and factor augmentng. Addtonally, partal elastctes of substtuton are allowed to vary and elastcty of scale can vary wth output and nput proportons. 216

232 ln X 2 represents the natural logarthm of labour n man dates 40 (LAB) ln X3 represents the natural logarthm of captal expendture (CAP) ln X 4 represents the natural logarthm of other cost: raw materals (COS) j s are unknown parameters to be estmated V s are assumed to be ndependent and dentcally dstrbuted normal random errors 2 havng zero mean and unknown varance; ; U s are non-negatve random varables, called techncal neffcency effects, whch are assumed to be ndependently dstrbuted such that v U s defned by the truncaton (at zero) of the normal dstrbuton wth mean, 2 and varance u. The model for the techncal neffcency effects specfes that the techncal neffcency effects of the stochastc fronter are a functon of the age, educaton, household sze, number of separate plots, agrcultural extenson servces, credt access, membershp of a farm organsaton, land ownershp and dfferent varables that represent agrcultural farm bodversty. Some of these varables are assumed to be drectly related to farmers management sklls, whle the others could mpact on ther techncal effcency through avalablty of labour for tmely management of farmng actvtes or ncentves for ncreasng effcency n farms. Older farmers are expected to ncrease techncal neffcency (Battese and Coell, 1992; Burk and Terrell, 1998) partly because older farmers tend to be less adaptable to new techncal developments. It s hypothessed that ncreased formal educaton, 40 Labour s measured by the number of adult famly members workng (greater than 14 years old). Ths ncludes famly labour as well as hred labour. However, there s no measure of ndvdual ntensty of work such as number of hours per week. Snce farmers cannot exactly remember the number of hours worked each day, t was not possble to obtan ths nformaton. 217

233 ceters parbus, s expected to reduce techncal neffcency. Expected sgn of the household sze s negatve. Ths s because when the household sze ncreases, avalable labour for agrcultural actvtes s hgher and farmers wll not face any labour constrant n ther farmng. The number of separate plots may ncrease neffcency f farmers cannot manage them well. More advce from extenson workers, ceters parbus, s expected to reduce techncal neffcency effects, whch can be categorsed as nsttutonal characterstcs. Agrcultural credt access and beng a member of farmer organsaton could ncrease techncal effcency whle land ownershp wll have negatve sgn as t affects the farmer manageral power of the farm. We ncluded three varables to capture effect of agrcultural farm bodversty on farm level techncal effcency. They are crop dversty, lvestock dversty and mxed farmng system. It s hypothessed that all these varables result n contrbuton to decrease farm level techncal neffcency n small-scale farms. Accordngly the emprcal neffcency model can be set out as shown n Equaton 7.10: U 0 1Z1 2Z2 3Z3 4Z4 5Z5 6Z6 7Z7 8Z8 9Z9 10Z10 11Z11 g (7.10) Z 1 s the age of the responded n years (AGE) Z 2 s the formal educaton of the responded n years (EDU) Z 3 s the household sze (HS) Z 4 s number of separate plots (FS) Z 5 s agrcultural extenson servces contacts (AEC):Dummy varables f Yes 1, otherwse 0 218

234 Z 6 s credt access: Dummy varables f Yes 1, otherwse 0 Z 7 s member of a farm organzaton: Dummy varables f Yes 1, otherwse 0 Z 8 s the land ownershp (LO): Dummy varable f owned 1, otherwse 0 Z 9 s crop speces dversty (CSD): Dummy varable f mult-crops farm 1, otherwse 0 Z 10 s lvestock dversty (LD): Dummy varable f mult-lvestock farm 1, otherwse 0 Z 11 s mxed farm (AD): Dummy varable f mxed farm 1, otherwse 0 The econometrc estmaton strategy requres some of the assumpton about functonal forms and dstrbuton of error components. Gven functonal and dstrbutonal assumptons, maxmum-lkelhood estmates (MLE) for all parameters of the stochastc fronter producton and neffcency model defned by Equatons 7.9 and 7.10 s smultaneously estmated usng the program, FRONTIER 4.1 (Coell, 1996). The techncal effcency of a farmer s between 0 and 1 and s nversely related to the level of the techncal neffcency effects (Battese and Coell, 1995). Techncal effcency can also be predcted usng the FRONTIER program, whch calculates the maxmum-lkelhood estmator of the predctor for Equaton 7.6 that s based on ts condtonal expectaton, gven the observed value of (V -U ) (Battese and Coell, 1988). More detals about obtanng maxmum-lkelhood estmator s gven by Coell et al. (2005). The next secton statstcally evaluates the predctons of the model on agrculture bodversty and farm level techncally effcency. The man nterest les n quantfyng the effect of techncal neffcency n rural agrcultural areas n Sr Lanka. A seres of statstcal tests were performed to decde the functonal form and presence of neffcency effects. Then the frst stage of the estmaton was done by 219

235 usng the translog producton functon followed by the fndng of factors assocated wth techncal neffcency. In the second stage, predcton of techncal effcency was used to analyse the dstrbuton of techncal effcency among dfferent farmers. A comparson of the results among dfferent dstrcts s made. 7.5 Estmates for parameters of stochastc fronter producton functon As explaned n the prevous secton, the econometrc method usng the stochastc fronter producton functon was used to estmate the techncal effcency of the farmers and the factors that nfluence neffcency. The stochastc fronter producton functon model has the advantage of allowng smultaneous estmaton of ndvdual TE as well as ts determnants.followng Battese and Coell (1995), maxmum lkelhood estmaton s used to smultaneously estmate the parameters of stochastc producton fronter and the factors contrbutng to neffcency. The software program FRONTIER 4.1 s used for estmaton. The total value of output of the farm was modelled n terms of four nput varables, namely, sze of the land (plot), labour, captal expendture and expendture on row materals. Last varable manly ncludes the expendture on seeds, pestcdes and fertlser. Varous tests of null hypotheses for the parameters n the fronter producton functons and n the neffcency models are performed at the begnnng of the emprcal estmaton. Frst, Fronter 4.1 allows varous choces n relaton to the model s functonal form and neffcency dstrbuton. In ths study, hypothess tests based on the Generalsed Lkelhood Rato (GLR) test were conducted to select the functonal form. The null hypothess here s that Cobb-Douglas s an adequate 220

236 representaton of the data. The lkelhood rato test statstc λ = -2{ln[L(H 0 )] - ln[l(h 1 )]} where ln[l(h 0 )] and ln[l(h 1 )] represent the values of the log-lkelhood functon under the null (H 0 ) and alternatve hypothess (H 1 ). The lkelhood-rato statstc, λ = -2{log[Lkelhood (H 0 )] log[lkelhood (H 1 )]} has approxmately χ 2 ρ dstrbuton wth ρ equal to the number of parameters assumed to be zero n the null hypothess (Battese and Coell, 1992; Coell, 1995). The LR test shows that the Cobb-Douglas s rejected; ndcatng that the more general form of the translog model fts ths data better for all models. The LR test shows that some combnaton of the squared and cross product terms n the translog model mprove the ft of the model. Second, the dstrbutonal assumptons were tested based on the prevously explaned lkelhood rato test statstc. The truncatednormal assumpton s strongly accepted. Thrd, the truncated-normal translog specfcaton was tested for the exstence of a fronter. The test result rejects the H 0 : 2 0 (.e. u = 0 and therefore no neffcency exsts), at the 1 per cent level for Ampara, Anuradhapura and Kurunegala survey data usng the approprate tables derved by Kodde and Palm (1986). As explaned n Chapter three, we dentfed 248, 247 and 251 observatons as completed survey questonnares n Ampara, Anuradhapura and Kurunegala dstrct respectvely. However, when estmatng the effcency model, we had to drop some observatons as a few respondents had not answered all the questons related to the creaton of the requred varables n the effcency model. For example, a few households had not answered the queston related to organc farm methods and landrace cultvaton or some had mentoned that they use both methods, that s, 221

237 landrace varetes as well as modern varetes. Furthermore, some farmers used chemcal as well as organc fertlsers. In such cases we removed these observatons from the models. After removng nconsstent observatons, 238, 242 and 243 household level observatons n Ampara, Anuradhapura and Kurunagala dstrcts could be used to estmate the effcency model. The soco-economc characterstcs of the respondents are presented n Table n J.1, J.2 and J.3 n the Appendx J. The study revealed that a majorty of household heads (94 per cent) were males on average. The age of the farmers ranged between 16 and 64 years. A majorty of the respondents (65 per cent) were between the age of 30 and 55 years. The mean age was 41 years. Ths mples that the majorty of the farmers were at an economcally actve age and could therefore make a postve contrbuton to farm producton. Most respondents (98 per cent) were marred. Ths contrbuted wdely to the use of famly labour by the households as the wves and chldren consttuted the labour force. The lteracy level among the farmers n the study area was hgh. Chemcal fertlsers were appled to 52 per cent of the plots whle hybrd varetes were the type of seed used on 49 per cent of the plots. In the study areas, 58 per cent of respondents had secondary educaton. A majorty of the respondents (68 per cent) had more than 10 years of farmng experence, whch ndcated the manageral ablty of the farmers could be assumed to be reasonably good. The study also revealed that a large proporton of the respondents (67 per cent) were members of a farmers organsaton. As well, most farmers had used the servces of agrcultural extenson offcers. Around 42 per cent had obtaned credt for ther farms. The household sze of most respondents (88 per 222

238 cent) ranged between 2 and 5 members. Gven that a large household sze also means more mouths to feed, large households generally produce a smaller market surplus (Mnot et al., 2006). However, n tradtonal agrculture, the larger the household sze the more labour force s avalable for farm actvtes. Crop dversty vares between one and nne whle lvestock dversty vares between one and fve. The sze of the farm can affect the dversfcaton decson n both ways. In some areas, the larger the farm sze, the hgher the tendency of dversfcaton of crop producton thus leadng to producton for home consumpton and for sale (Brol, 2004). However, heterogenety of the farm should be mportant n ths case. For example, suppose only part of the large farm has receved rrgaton water, then farmers wll attempt to dversfy farmng accordng to the water requrement. Some farmers dversfy ther farms accordng to the sol qualty or shape of the land. On the other hand f the physcal characterstc of the farm s homogeneous, there s a hgher probablty to select a specalzaton system. On average 68 per cent of farmers had mxed farmng systems ncludng both crops and lvestock. A relatvely hgher percentage of farmers n Ampara and Kurunegala mantan mxed farmng systems. The maxmum-lkelhood estmates for the parameters of the translog producton functon defned by Equaton 7.9 are presented n Table 7.1. From the results all except a few nteracton varables had the expected postve sgns suggestng that more output would be obtaned from the use of addtonal quanttes of these varables, ceters parbus. The coeffcents of the land varable were postve and statstcally sgnfcant at one per cent level n all models. The coeffcents of labour 223

239 nputs were postve and hghly sgnfcant ndcatng ts mportance n agrcultural producton. The captal varable had a postve sgn, whch conforms to a pror expectatons. Ths ndcated that hgher captal use would result n hgher crop yeld. The coeffcent of the raw materal nput was postve as expected and statstcally sgnfcant at one per cent level. The sgnfcance of the varables derves from the fact that they are major land augmentng nputs n the sense that they mprove the productvty of land thus leadng to ncreased yeld. In addton to ths most of the square varables and nteracton terms provde expected sgns and are statstcally sgnfcant. The producton functon estmates ndcate the relatve mportance of factor nputs n agrcultural producton. The coeffcents of all factors have the expected sgns and magntudes. Land appears to be the most mportant factor of producton wth the coeffcent values of 0.39, 0.39 and 0.25 n Ampara, Anuradhapura and Kurunegala dstrcts respectvely. Labour appears as the second most mportant factor for Anuradhapura whle row materal s the second most mportant factor for farms n Kurunegala dstrct. The role of raw materal n Ampara dstrct s relatvely less mportant as a sgnfcant number of farmers were usng organc methods and landrace cultvaton n ths dstrct. In addton to these varables, results show that most of the nteracton terms are sgnfcant at an acceptable margn and have expected sgns. 224

240 Table 7.1: Maxmum-lkelhood estmates for parameters of the producton functon Ampara Anuradhapura Kurunegala Varable Coeffcent Coeffcent Coeffcent Constant (8.28)* (3.42)* (1.20) Land (22.83)* (23.99)* (12.29)* Labour (2.67)* (2.25)* (8.37)* Captal (6.78)* (9.58)* (18.55)* Raw Materal (2.18)* (11.72)* (2.39)* Land*Land (1.85)** (6.09)* (10.36)* Labour*Labour (1.38) (8.38)* (1.88)** Captal* Captal (4.12)* (1.77)** (1.91)** Raw materal* Raw mate (2.69)* (7.17)* (10.82)* Land*Labour (-0.74) (5.83)* (-0.24) Land* Captal (4.36)* (6.75)* (3.29)* Land* Raw Materal (2.58)* (1.75)** (8.76)* Labour*Captal (1.12) (3.05)* (11.11)* Labour*Raw Materal (-3.45)* (-2.11)* (4.56)* Captal*Raw Materal (-0.10) (-2.52)* (-0.59) Model Varance (9.02)* (11.21)* (11.08)* Varance Rato (11.01)* (3.59)* (2.05)* Log Lkelhood functon Number of observaton Note:. t ratos are gven n the parenthess. * denotes sgnfcant varables at 1% level whle ** ndcates sgnfcant at 5% level of sgnfcant.. All estmated frst order coeffcents n the translog model fall between zero and one, satsfyng the monotoncty condton that all margnal products are postve and dmnshng at the mean of nputs. The parameter γ = σ 2 u /σ 2 les between zero and one wth a value equal to zero mplyng that techncal neffcency s not present and the ordnary least square estmaton would be an adequate representaton and a value close or equal to one mplyng that the fronter model s approprate. The values of γ are 0.71, 0.62 and 0.67 for Ampara, Anuradapura and Kurunegala dstrcts and they are statstcally 225

241 sgnfcant at the one per cent level whch mples that more than half of the resdual varaton s due to the neffcency effect. Ths also mples that systematc nfluences that are unexplaned by the producton functon were the domnant sources of random errors. In other words, the shortfall of observed output from the fronter output s prmarly due to factors whch are wthn the control of the small-scale farmers n the sample (Amos et al. 2004). 7.6 Estmatng margnal productvty and nput elastcty As the second step we estmated output elastctes of each nput. Ths s gven by the frst dervatve of the translog producton functon wth respect to each varable. The values of explanatory varables n the translog stochastc fronter model were meancorrected by subtractng the means of the varables so that ther averages were zero. Therefore, the frst order parameters provde drect output elastctes for the ndvdual nputs at the mean values. Estmates of elastctes and margnal productvty are gven n Table 7.2. These coeffcents can be nterpreted as elastctes of real output wth respect to nputs (land, labour, captal and raw materal). The land sze and labour provde relatvely hgher output elastctes. Ths s because land and labour are the most mportant producton nputs for semsubsstence agrcultural areas. 226

242 Table 7.2: Estmated elastctes and margnal productvty of each nput Ampara Anuradhapura Kurunegala Margnal Margnal Margnal Elastctes Productvty Elastctes Productvty Elastctes Productvty Land , , ,236 Labour Captal Row materal Note: All equaton for estmatng output elastctes and margnal products for translog stochastc fronter model s gven n Appendx M. All elastctes are estmated usng mean values of respectve varables. Table 7.2 dsplays the mean estmates of nput elastcty for each area as calculated usng Equatons M.9, M.10, M.11 and M.12 n Appendx M. It becomes clear that the average value across the sample for output elastcty of land s whle that for labour s Average output elastctes of captal and raw materals are and respectvely. All elastctes are postve ndcatng that, as these nputs are ncreased, output ncreases. Returns to scale are determned by summng all values of elastctes. If the sum s less than one decreasng returns are ndcated; f greater than one ncreasng returns to scale are ndcated. By addng coeffcents of elastctes together the returns to scale for Ampara, Anuradhapura and Kurunegala dstrcts are shown to be 0.75, 1.01 and 0.83 respectvely. Ths mples there are decreasng returns to scale for at least Ampara and Kurunegala dstrcts farms. Constant returns to scale hold for farms n the Anuradhapura dstrct. Table 7.2 also provdes the estmated margnal productvty for each nput. Margnal productvty of land per acre s Rs. 11,349, 9,114 and 9,236 for Ampara, 227

243 Anuradhapura and Kurunegala dstrcts respectvely. Ths provdes the value of usng addtonal acre of land for the farmng practce n dfferent dstrcts. Results clearly show that margnal productvty of the land n Ampara dstrct s relatvely hgher than that of other dstrct. One of the possble reasons could be that relatvely larger number of small-scale farmers n Ampara dstrct use landrace cultvaton and organc farmng method whch could help them mantans hgher sol fertlty n the long run. Interestngly, estmated margnal productvty of labour s relatvely lower than the exstng wage rate n rural areas n Sr Lanka. Average wage rate vares between Rs. 400 and Rs. 450 dependng on peak or off-peak tme. Also n some areas there s a margnal dfferent of the daly wage between women and men (t s Rs. 450 for men whle Rs. 400 for women). However, estmated margnal productvty of labour s found of the range between Rs. 350 and Rs Margnal productvty of captal s Rs. 0.97, Rs and Rs for Ampara, Anuradhapura and Kurunegala dstrcts respectvely. Margnal productvty of raw materal s relatvely lower n Anuradhapura dstrct when compared wth the other two dstrcts. 7.7 Varatons of techncal effcency As the thrd step of the analyss, we examne the dstrbuton of techncal effcency of farmers n dfferent regons. The results are presented n Table 7.3. The average resource-use effcency n the sample for Ampara, Anuradhapura and Kurunegala are 0.692, 0.511, and respectvely. Ths mples that about 30.8, 48.9 and 31.5 per cent hgher levels of producton could be acheved wthout addtonal resource for Ampara, Anuradhapura and Kurunegala dstrcts respectvely. From the dstrbuton, 228

244 the most effcent farmers n terms of resource use n Ampara dstrct sample have an ndex of per cent and the least effcent farmers n the same dstrct have a Table 7.3: Frequency and percentage dstrbuton of the techncal effcences Ampara Anuradhapura Kurunegala Effcencyrange Number of farms Percentage (%) Number of farms Percentage (%) Number of farms Percentage (%) Note: Number of farms used for ths analyss are 238, 242 and 243 Ampara, Anuradhapura and Kurunegala dstrct respectvely. Descrptve statstcs shows that the average farm sze n Anuradhapura farms s relatvely hgher that of other two dstrcts. resource use effcency of per cent. However, the most effcent farmer n Anuradhapura sample have ndex of per cent and the least effcent ones have a resource use effcency of per cent n the same dstrct. The hghest effcency level of the Kurunegala sample s recorded as per cent whle mnmum s per cent. A wde varaton of the techncal effcency level among farmers n dfferent dstrcts s evdent by these fgures. 229

245 The nablty of any of the farmers to operate on the fronter could be attrbuted to certan factors rangng from techncal constrant, socoeconomc factors and envronmental factors. Specfcally, scare nputs may be allocated to varous users on the bass of ther margnal shadow values thereby preventng farmers from reachng the effcency fronter. The dstrbuton of the neffcency estmates shown n ths study agree wth prevous work carred out n other peasant farmng settngs n ths area (Burk and Shah, 1998; Coell and Battesse, 1996). In the present study, approxmately 10 per cent of sample farmers n Ampara and Kurunegala had a mean techncal effcency of less than 0.50 and approxmately 70 per cent had a mean techncal effcency n the range of for the same dstrcts. On average the predcted TEs for the farmers n all dstrcts ranged from 0.16 to The mean TE of 0.63 ndcated that the average farmer produced about 63 per cent of maxmum attanable output for gven nput levels n the study area. Next we estmated average effcency levels for dfferent land sze. The purpose of ths analyss s to nvestgate whether there s a drect lnk between farm level effcency and farm sze. The average estmates of techncal effcences by farm-sze categores are presented n Table 7.4. It s clear that producers n relatvely larger farms are as effcent as the producers n relatvely smaller farms. Ths mples that there s no dfference of mean techncal effcency between dfferent farm szes. We also estmated actual output as well as potental output under each land sze category. It s clear that actual output and potental output ncrease wth land sze holdng mean techncal effcency as the same level. As the average techncal effcency level n each land sze s almost the 230

246 same n all dstrcts, t can be concluded that land sze does not change the farm level techncal effcency among small-scale farms n Sr Lanka. Table 7.4: Average TE, value of actual and potental output (Rs.) wth land sze Ampara Farm sze (acres) Number Effcency average Actual output Potental output , , , , , , , , , , Anuradhapura Farm sze (acres) Number Effcency average Actual output Potental output , , , , , , , , , , Kurunegala Farm sze (acres) Number Effcency average Actual output Potental output , , , , , , , , , , Note: Potental output represents the value of actual output plus output loss due to neffcency. The value of neffcency s estmated usng coeffcents of neffcency n each category. In the next step we calculated the average effcency level wth farm type. We dvded farms nto sngle varety, multple varety and mxed system. Sngle varety ncludes farms that have only one crop varety or one lvestock varety. Multple 231

247 varetes nclude farms that have more than one crop varety or more than one lvestock varety. Mxed system ncludes farms that have both crops and lvestock. Ths type of analyss provdes us evdence about the farm level techncal effcency and dversty n the farm. The result of the average effcency under dfferent farmng systems s gven n Table 7.5. Table 7.5: Average effcency wth farm type Ampara Anuradhapura Kurunegala Category Farms Effcency Farms Effcency Farms Effcency Sngle varety only More than one varety only Mxed(crops and lvestock) Total Note: Sngle varety and more than one varety nclude only sngle and multple varety crops or lvestock. The mxed category nclude sngle varety or/and multple varety crops and sngle or/and multple varety lvestock. The average effcency level for sngle varety s 0.58, 0.31 and 0.49 for farms n Ampara, Anuradhapura and Kurunegala dstrcts respectvely. However, these numbers ncreased to 0.68, 0.51 and 0.69 n the same dstrcts for farms whch have more than one varety. The hghest average effcency s recorded for farms whch have a mxed farmng system. Ths means that the techncal effcency level of farmers who have both crops and lvestock s relatvely hgher than other categores for all dstrcts. For examples, average techncal effcency of mxed system farms n Ampara, Anuradhapura and Kurunegala s 0.81, 0.81 and 0.86 respectvely. It s therefore clear the mxed farmng system s more effcent than other farm systems n each dstrct. Ths evdence encourages us to nvestgate the agrcultural bodversty and farm level techncal effcency n the formal effcency analyss. 232

248 Informaton gven n Table 7.5 shows those farms wth sngle varetes s relatvely hgher n Anuradhapura dstrct. It was observed that most farms of ths category n Anuradhapura dstrct had cultvated rce as the sngle crop. More than one varety farms n Ampara, Anuradhapura and Kurunegala dstrcts were 26, 23 and 28 per cent. However, ths category only ncludes farms that have more than one varety of crops or lvestock. Mxed farms are relatvely hgher n all three dstrcts. Ths s because the mxed farmng system s the most common farmng system n most rural small-scale farms n Sr Lanka. Results of ths study clearly show that techncal effcency level of ths type of farmng system s relatvely hgher. 7.8 Results of the neffcency model As the fnal step of the analyss, the varables of the neffcency model were modeled to explan the determnants of neffcency of producton among farmers n three dstrcts. The TE dfference between farmers could be due to farm-specfc or farmer-specfc varables. The sgn of the varables n the neffcency model s very mportant n explanng the observed level of TE of the farmers. A negatve sgn would mply that the varable had the effect of reducng techncal neffcency, whle a postve coeffcent would ndcate ncreasng neffcency. The results are presented n Table 7.6 and ndcate that all the ncluded varables except age had the expected sgn. 233

249 Table 7.6: Maxmum-lkelhood estmates for parameters of the neffcency model Ampara Anuradhapura Kurunegala Varable Coeffcent Coeffcent Coeffcent Constant (1.96) (5.40)* (1.99)** Age (3.05)* (5.74)* (4.20)* Educaton (-5.29)* (-5.40)* (-3.74)* HH sze (-4.77)* (-5.14)* (-6.36)* Number of plots (2.57)* (6.93)* (5.16)* Extenson servces (-1.76)** (-1.74)** (-2.62)* Credt (-3.14)* (-1.64)*** (-5.77)* MFO (-1.61)** (-2.06)* (-6.99)* Land ownershp (-3.77)* (-2.41)* (-1.92)** Crop dversty (-1.62)*** (-1.72)** (-7.48)* Anmal dversty (-2.24)* (-2.11)* (-8.01)* Mxed farm (-2.13)* (-2.25)* (-13.19)* Note:. mxed farm varable show the farm has a mxed system or not. A mxed system ncludes sngle varety or/and multple varety crops and sngle or/and multple varety lvestock.. t ratos are gven n the parenthess. * denotes sgnfcant varables at 1% level and ** ndcates sgnfcant at 5% level whle *** denotes sgnfcant varables at 10% level of sgnfcant. The estmated coeffcents n the neffcency model are of partcular nterest to ths study. Ths s because these estmated coeffcents of the neffcency functon provde explanatons for the relatve techncal effcency levels among ndvdual farms. Most of the coeffcents of the explanatory varables n the neffcency model are found to have expected sgns. The age coeffcent s postve n all three models, whch ndcates that the older farmers are more neffcent than the younger ones. Ths varable s sgnfcant at one per cent level for Ampara and Anuradhapura dstrct whle t s sgnfcant at fve per cent level for Kurunegala sample. The postve coeffcent of age suggests that age led to techncal neffcency of the farmers (Seyoum et al., 1998; Amos et al., 2004; Ogunynka and Ajbefun, 2004). A 234

250 possble explanaton could be that the general ablty to supervse farmng actvtes decreases as farmers advanced n age. The negatve estmate for educaton varable mples that farmers wth greater years of schoolng tend to be less neffcent. The relatonshp s relatvely strong, because the coeffcent s very hgh relatve to ts estmated standard error n all three models. The coeffcent of educaton s sgnfcant at one per cent level. It can therefore be assumed that farmers wth greater years of formal schoolng tend to be more techncally effcent. Ths agrees wth the fndngs of Ajbefun and Adernola (2003) who reported that farmers n Southwestern Ngera become more techncally effcent wth more years of formal schoolng. These data asserted that more years of formal educaton and new technologes were mperatve to better understand and adapt the technologes, whch subsequently make t possble to move close to the fronter. The predcted coeffcent of household sze was negatve and sgnfcant at one per cent for sample of Ampara farmers whle t was sgnfcant at fve per cent for Anuradhapura and Kurunegala farmers. The negatve coeffcent s n agreement wth the hypothessed expected sgn and mples that as the number of persons (adult) n a household ncreases, effcency also ncreases. Ths s because more adult members n a household mean that addtonal qualty labour would be avalable for carryng out farmng actvtes n a tmely fashon, thus makng the producton process more effcent (Vllano and Flemng, 2006; Shehu et al., 2007). The number of separate plots may ncrease neffcency f farmers cannot manage them well. The result of ths study shows that the greater the number of plots grown by each household, the lower the farm level techncal effcency (see, Table 7.6). 235

251 Ths varable s sgnfcant at one per cent level n all three models. The probable reason s that the separate plots can affect farmer manageral ablty. When the number of plots s hgher, farmers need addtonal tme to look after them whch can lead ncreasng farm level techncal neffcency. One of the man ssues faced by rural farmers s that they had to protect ther crops or lvestock from predators. The locaton of dfferent plots n dfferent places means that farmer ablty to overcome ths problem s less. Gven ths varable s hghly sgnfcant n all three models ths appears to confrm our assumpton. The coeffcent of extenson contact s negatve and sgnfcant, suggestng that such contact ncreases farm level techncal effcency because farmers are able to use modern technques of farmng nvolvng land preparaton, plantng, applcaton of agro-chemcals (for example, fertlser) and harvestng. Ths fndng confrms the results of Xu and Jeffrey (1998) that extenson vsts to farmers are mportant n reducng farm neffcency. The coeffcents of avalablty of agrcultural credt and becomng a member of a farm organsaton were also statstcally sgnfcant for all three models and had the expected sgns. Credt access can remove farmers fnancal constrants and thereby ncrease the farm level effcency. It can also be assumed that beng a member of a farm organsaton helps farmers mprove manageral sklls as t provdes tranng programs wth necessary nformaton durng the crop season. The results also show that land ownershp has a negatve mpact on neffcency. A smlar concluson was drawn by Ajbefun and Adernola (2003) and Amos et al. (2004) n ther analyss. Ths mples that farmers who cultvate ther own land are more effcent than those who cultvate land that s leased. Ths s because farmers 236

252 who own land have added motvaton to cultvate more effcently as they have an ncentve to mantan ther land for long-term benefts. In general, agrcultural land market does not functon well n Sr Lanka. A number of market dstortons could be observed of the land market n rural areas. The possble polcy mplcaton s that steps should be taken to reduce mperfectons that exst n the agrcultural land market. The estmated coeffcents of the varables that represent agrcultural bodversty are of central nterest to ths study. Ths s because the estmated coeffcents of the neffcency functon provde an explanaton of the way n whch they contrbute to farm level techncal effcency n small-scale farms n a sem-subsstence economy. The results show that crop dversty s sgnfcant at one per cent level for Anuradhapura sample whle t s sgnfcant at fve per cent level for the other two dstrcts. The anmal dversty varable s sgnfcant at one per cent level for Anuradhapura and Kurunegala sample whle t s sgnfcant at fve per cent level for Ampara sample wth expected sgns. Ths mples that dverse farms are more effcent than the other farms. Accordngly, we fnd that the hgher the crops or lvestock dversfcaton, the hgher the farm level techncal effcency. Possble reasons nclude: farmers can utlse famly labour optmally when they mantan a dverse agrcultural system; a dverse farmng system mnmses external rsks that farmers often face and a bologcally rch farmng system can mprove sol fertlty and mnmse nput costs n the long run. Varable that captures the mxed farmng system s hghly sgnfcant n all models. Ths mples that the effcency level of the farms whch mantan mxed farmng 237

253 systems s hgher than that of other farms. Ths result s consstent wth our hypothess that gven the sem-subsstence nature of the rural famng system, farmers can mprove ther effcency level sgnfcantly by adoptng mxed farmng systems for ther farms. The results ndcated n Table 7.6 show a sgnfcant decrease n farm household neffcency wth the mxed farmng system. 7.9 Summary and key fndngs Ths study provdes an economc analyss of farm household effcency among rural households n Sr Lanka, where crop and lvestock farmng generate a large part of household ncome. Usng stochastc fronter analyss, the results show the potental of encouragng mxed farmng systems as a drvng force for output growth. Econometrc analyss of survey data shows that land sze, labour, captal expendture and expendture on raw materals are mportant nputs and are strongly assocated wth the total output. The analyss reports evdence of farm level techncal neffcency and ts determnants. Results of ths study show the potental for large gans n real output f techncal effcency s ncreased. The results depct a wde gap between farmers who are relatvely poor n ther effcency performance (20 per cent) and those who are hghly effcent (more than 90 per cent). In partcular ths study shows that the output value of farms n the study area can be ncreased wth the current levels of nputs and technology f less effcent farmers are encouraged to follow the resource utlsaton pattern as well as farm types that have already been adopted by the most effcent farmers. 238

254 Among the sgnfcant varables n the neffcency model, level of educaton, number of separate plots, agrcultural extenson servce, credt access, membershp of farm organsaton and land ownershp are drect polcy relevant varables. Ths means that all these varables can be controlled by usng approprate polces n the country. More farmers n rural areas are not aware about the possble benefts that they could gan by followng ther more effcent peers. It s also found that the varables that were used to represent agrcultural farm bodversty (crop dversty, anmal dversty, mxed farmng) are sgnfcant determnants of farm level techncal effcency n rural small-scale farmng n Sr Lanka. In general, the analyses of determnants of neffcency clearly ndcated that households whch have access to agrcultural extenson servces, credt facltes and those who mantan more dverse or a mxed farmng system wth hgher levels of dversfcaton are more lkely to be more effcent than the other households. What polcy nterventons would be approprate to ncrease effcency at rural household level? The results suggest that polcy makers could place more emphass on rural agrcultural extenson servces to ncrease the probablty that farmers wll adopt mxed farmng system wth more dversfcaton. The analyss of farm level techncal effcency ndcates that mantanng more dverse farmng systems s crucal to reducng neffcency and mproves the welfare of rural households n Sr Lanka. Ths fact has partcular mplcatons for polces requred to sustan gans n agrcultural productvty and effcency. Agrcultural advsory servces, rural credt organsatons and other stakeholders workng for rural development should clearly talor ther messages and servces to meet the dentfed needs of rural farmers. 239

255 Desgnng formal and nformal educaton programs that wll mprove farmers abltes to mprove effcency s extremely mportant. The emphass should be on provdng educaton that wll help farmers understand the socoeconomc and polcy condtons governng ther farmng actvtes. A further ntatve would be taken to strengthen the capacty of farmers through farmer centred tranng workshops geared towards manageral effcency as well as resource use effcency. Ths could be done n a collaboratve manner nvolvng the government, dstrct assembles and NGOs. Government also need to ntensfy ts agrcultural extenson servces program by tranng and deployng qualfed extenson offcers. The offcers, n turn, should ntensfy farmer educaton on nput use. It s notable that the agrcultural extenson offcer-farmer ratos, as well as extenson contact wth farmers n the study area, are low. There s, therefore, a need to motvate and tran the exstng extenson offcers to work more effectvely and to tran more offcers. It s also suggested that () an approprate polcy or regulaton that recognses and encourages the effectve use of agrcultural land be formulated by state authortes at varous levels; () farmers should be encouraged to move to more dverse farmng practce and () the role of educatonal programs n mprovng effcency should be hghlghted. It s clear that the neffcency effects n ths partcular nstance renforce other emprcal evdence from other developng countres (Al and Chaudry, 1990; Parkh and Shah, 1995; Shehu et al., 2007). In general, the study has revealed that most of the farmers n Sr Lanka are not fully techncally effcent and, therefore, there s capacty to mprove effcency by addressng some mportant polcy varables that negatvely and postvely nfluence farmers levels of techncal effcency. 240

256 CHAPTER EIGHT CONCLUSIONS AND POLICY IMPLICATIONS 8.1 A summary of fndngs and dscusson Sustanable agrcultural development s wdely acknowledged as a crtcal component n a strategy to combat both poverty and envronmental degradaton. Yet, sustanable agrcultural development remans an elusve goal, partcularly n many of the poorest regons of the world. Bodversty degradaton contnues to be a key factor n unsustanable agrcultural systems, despte decades of research focus on dfferent ssues related to agrcultural bodversty conservaton (Brush et al., 1992; Ceron et al., 2005). The prevalng economc explanaton for the contnung trend toward agrcultural bodversty degradaton n many parts of the world s that economc ncentves often encourage degradaton and dscourage conservaton. These ncentve problems have been attrbuted to poor farmers hgh dscount rates, lack of markets, hgh transport costs and other market mperfectons, adverse government polces and nsecure property rghts (D Falco and Perrngs, 2003). From ths perspectve, the challenge facng researchers and polcy analysts s to understand the factors causng agrcultural bodversty degradaton and desgn mechansms that wll provde farmers n developng countres wth the economc ncentves needed to adopt more sustanable land use and management practces wth envronmental rch farmng 241

257 systems. Ths research analysed these ssues usng small-scale farm data n Sr Lanka. The man fndngs of the study are summarsed below. Frst, the research reported n Chapter four of the thess represents one of the frst attempts to use the CE approach to nvestgate farmers preference for dfferent attrbutes of agrcultural bodversty that s present n small-scale farms n developng countres. We appled the CE approach to dentfy the potental benefts of conservng agrcultural bodversty n Sr Lanka. Four conclusons can be drawn from ths chapter. Frstly, owng to educatonal and poverty ssues, some polcy makers n developed countres are suspcous of whether non-market valuaton technques lke CVM and CE can be appled n developng countres such as Sr Lanka. Ths CE study has demonstrated that carefully desgned and pre-tested nonmarket valuaton technques can valdly be appled n developng countres Secondly, farmers have strong postve atttudes towards ncreasng agrcultural bodversty n rural areas. Ths s evdent from the results obtaned from the CLM. Thrdly, the study llustrates that n Sr Lanka t s possble to mprove agrcultural bodversty usng approprate polces n whch draw on the fndng of ths study. Fnally, the applcaton of the CE approach appears promsng, gven ts capacty to model complex, smultaneous tradeoffs nvolved n ecologcal management. The CE technque can be used to model a varety of smultaneous tradeoffs whch nvolve a mxture of envronmental and soco-economc factors. The results provde a tool for decson makers to use n prortsng ecosystem management optons n rural agrcultural areas. 242

258 Secondly, a study on the current status of agrcultural bodversty and ts determnants s shown to be a useful nput for polcy decsons makers concerned wth conservng agrcultural bodversty n rural areas and hence mprovement of farmer lvelhoods. In ths context, t s mportant to know whch farmers are promotng dversty and what the determnants are. Chapter fve of ths thess nvestgated ths ssue usng nformaton derved from farmers demand for crop and lvestock varetes. It s found that mantanng on-farm dversty has receved ncreasng attenton as a strategy for mtgatng producton rsk and protectng food securty n rural areas of Sr Lanka. For poorer farmer s small land sze, crop and anmal varety dversfcaton ncreases the optons for copng wth varable envronmental and market condtons. As well, due to the exstence of mperfect markets, farmers grow dfferent varetes to meet ther consumpton requrements. On the one hand, ths practce ncreases ther food securty. On the other hand, t provdes more fresh food wth hgh nutrton content. Farmers may also sell some of the surplus to the market so as to buy ther famly needs (clothes and other goods/commodtes). Ths may motvate farmers to grow the varetes that can be sold n the market for cash. We therefore fnd that the key varables promotng dversty are household characterstcs, market characterstcs, and some of the other characterstcs such as percentage of ther own money spent for agrculture. One of the man conclusons drawn from ths study s that the centralty of markets n shapng dversty does not suggest a trade-off between development and dversty. Ths s because as ntegraton wth outsde markets ncreases, the level of crop dversty on farms can also be ncreased. 243

259 Thrdly, although the benefts of envronmentally rch farmng systems n Sr Lanka are clear, the mpacts of soco-economc change upon agrcultural bodversty n the country have receved lttle attenton. Chapter sx of the thess nvestgated the farmers preferences for dfferent farmng systems such as landrace cultvaton, organc and mxed farmng practces. We fnd that the key varables promotng landrace cultvaton, organc farmng and mxed farmng systems are household characterstcs, market characterstcs, and some of the other characterstcs such as percentage of ther own money spent for agrculture. The results show that gender, farmers postve atttudes towards agrcultural bodversty, farms sze, nput prce fluctuatons, agrcultural subsdes and percentage of own money nvestment are found to be mportant factors when takng decsons to mantan landrace cultvaton. Investgaton of profles of farm famles that are most lkely to cultvate landraces and organc farmng reveals they have less ncome compared to those farm famles that are not lkely to cultvate landraces. They are more agrculturally-based, wth less off-farm employment and more solated from the markets. Among the mportant varables n organc farmng models, farmers atttudes towards agrcultural bodversty, nput prce fluctuatons, agrcultural subsdes and farm sze are found to be the most sgnfcant varables. Organc farmng has proven benefcal for many farmers, but the yeld of organc farmng has not been substantal. Many farmers can be encouraged to undertake organc farmng f the benefts could be shown to them. There have also been nstances where farmers have opted for organc farmng on account of reduced producton costs compared to 244

260 conventonal farmng. Low productvty, ncreased tme requred to yeld, and the requrement of specalsed sklls have been some of the dsadvantages of organc farmng. However, organc farmng contrbutes towards provdng qualty food and also protectng agrcultural sols. It s clear that most of the varables used n the mxed farmng model are sgnfcant and have taken expected sgns. We found that households wth more experence, more labour avalablty and less off farm ncome are more lkely to have mxed farmng systems. The results also show that the market characterstcs as well as agrcultural subsdes are mportant determnants for selectng mxed farmng systems. Off-farm ncome, wealth and agrcultural subsdes have been shown to be negatvely related wth mxed farms n small-scale farms n Sr Lanka. Possble polcy mplcatons related to agrcultural subsdes s that gven the government's lmted resources and competng demands, the best use of funds whch are allocated for agrcultural development s to mprove rural nfrastructure/technology and to buld market lnkages rather than usng them for wasteful subsdes whch have no long-term development mpacts. Fourthly, Chapter seven of ths thess provdes an economc analyss of farm household effcency among rural households n Sr Lanka, where crop and lvestock actvtes generate a large part of household ncome. Usng stochastc fronter analyss, the results show the potental of encouragng mxed farmng systems as a drvng force of output growth. Econometrc analyss of survey data shows that land sze, labour, captal expendture and expendture on raw materals are mportant 245

261 nputs and are strongly assocated wth the total output. Results of ths study show the potental for large gans n real output f techncal effcency s ncreased. The results depct a wde gap between farmers who are relatvely poor n ther effcency performance and those who are hghly effcent. In partcular ths study shows that the output value of farms n the study area can be ncreased wth the current levels of nputs and technology f less effcent farmers are encouraged to follow the resource utlsaton patterns and farm types that have already been adopted by the most effcent farmers. Among the sgnfcant varables n the neffcency model educaton level, number of separate plots, agrcultural extenson servce, credt access, membershp of farm organsaton and land ownershps are drect polcy relevant varables. Ths means that all these varables can be controlled by usng approprate polces n the country. More farmers n rural areas are not aware about the possble benefts to be ganed by followng ther more effcent peers. It s also found that crop dversty, anmal dversty and mxed farmng systems are sgnfcant determnants of farm level techncal effcency n rural small-scale farms n Sr Lanka. In general, the analyss of determnants of neffcency clearly ndcated that households whch have access to agrcultural extenson servces, credt facltes and those who mantan more dverse or a mxed farmng system wth hgher levels of dversfcaton are more lkely to be more effcent than those who are not. 246

262 8.2 Polcy mplcatons There are number of mportant polcy mplcatons that arse from the fndngs of the thess. Some of the major mplcatons are dscussed as follows. Frst, the fndngs of the choce experment whch support the assumpton that small-scale farms and ther multple attrbutes contrbute postvely and sgnfcantly to the utlty of farm famles n Sr Lanka. To the extent that the fndngs are representatve of other rural areas n the country they confrm that small-scale farms contnue to be a vtal for that naton snce the benefts to farms are overall postve and hgh. The value estmates reported n ths analyss represent lower bounds snce only the prvate use values of small-scale farms were estmated. The results reveal that dfferences between regons, n terms of market ntegraton, nfrastructure qualty and agro-ecologcal condton, affect small-scale farmers prvate valuaton. The CE study dscloses the farm famly and regonal characterstcs that are mportant to consder n desgnng program or polces to conserve or enhance the agrcultural bodversty and other attrbutes of Sr Lankan farms. Second, t s clear that varous attrbutes of agrcultural bodversty provde drect and ndrect benefts and advantages whch meet human needs n dfferent ways. Puttng a value on these benefts s dffcult, but decson makers often call for them to be expressed n monetary terms. To ths end, n ths study we present the results of a CE study desgned to shed lght on subsstence farm households preferences for varous farm attrbutes and these households trade-offs among these attrbutes. The fndngs presented here are therefore expected to nform the desgn of effcent, effectve, equtable, and targeted compensaton and lvelhood dversfcaton 247

263 polces n the country. Such economc polces would be desgned and approprately target the future conservaton of agrcultural bodversty n Sr Lanka. Thrd, analyss n Chapter fve has attempted to fll the gap by nvestgatng how dfferent forms of market provsonng and other varables shape the on-farm conservaton of agrcultural farm bodversty n Sr Lanka. It s clear that polces that affect household labour supply and ts composton are therefore lkely to have a major mpact on most components of agrcultural farm bodversty n the country. Educatonal campagns on varety choce and seed management are also relevant. The nformaton provded by analyss of all models s drectly polcy relevant and approprate polces can be desgned to control them. The predctons from the models estmated above enable us to dentfy the types of famles that are most lkely to sustan the agrcultural bodversty. Profles can be used to desgn targeted, least cost ncentve mechansms to support conservaton as part of natonal envronmental and agrcultural programs. Fourth, n each statstcal analyss conducted, whether descrptve or econometrc, regonal heterogenety s observed. Hence, any agr-envronmental polcy or program that ams to support the management of current levels of agrcultural bodversty n rural areas n Sr Lanka wll need to recognse the heterogenety of these tradtonal farms and ther context. Furthermore, any polcy or program that affects the wealth, educaton or labour partcpaton of famly members, or the formaton of food markets wthn settlements, wll nfluence ther choces. It s hoped that these analyses wll contrbute to advancng the economcs methods used to analyse the prospects for on farm conservaton, where evdence demonstrates that the expected 248

264 socal beneft-cost rato of on farm conservaton s hgh. The relatonshp between the dversty mantaned by ndvdual household farms and the dversty mantaned from the perspectve of the communty as a whole wll also be essental for the desgn of polcy nstruments. Ffth, the nformaton provded by the analyss of all models n Chapter fve s shown to be of hgh polcy relevance. Specfcally the predctons from these models enable us to dentfy the types of farm famles that are most lkely to ncrease the agrcultural bodversty n Sr Lanka. Accordngly, household profles can be used to desgn targeted, least cost ncentve mechansms to support conservaton dfferent tradtonal farmng system n the country. Ths study contrbutes to the lterature by provdng nsghts nto farmers landrace cultvaton, organc farmng and mxed farmng preferences, usng small-scale farm household data n a typcal developng country settng. For example, agrcultural subsdes varable s sgnfcant n all the models. It mples that the exstng subsdy program n Sr Lanka has negatvely affected choces about cultvatng landrace varetes and organc farmng systems. Therefore, steps should be taken to rethnk the exstng subsdy program n the country. Furthermore, the results of the study also dentfy the household contextual factors that govern these decsons. Sxth, on farm conservaton of crop dversty poses obvous polcy challenges n the desgn of approprate ncentve mechansms and n terms of possble trade-offs between conservaton and productvty or other socal objectves. It s clear that sales promoton actvtes and credt facltes have promoted the cultvaton of modern crop varetes usng pestcdes and chemcal fertlsers. Such a system can ncrease 249

265 short-term yelds whle destroyng the reslence of agro-ecosystems n the long-term. Polcy decson-makers should take necessary acton to mnmse the mpacts of such programs whle showng the benefts of agrcultural bodversty. Progress has also been hampered both by deologcal debates that are based on lmted nformaton, and by the hgh cost nvolved n assemblng the sort of large-scale scentfc databases that would be necessary to mprove the qualty of that nformaton. Furthermore, bologcal dversty has many components that are nterrelated wthn a contnually evolvng agro-ecosystem, and analysng causal relatonshps n any component over a bref tme horzon obvously leads to partal, statc conclusons. Seventh, desgnng formal and nformal educaton programs that wll mprove farmers effcency should be a hgh prorty. The emphass should be on provdng educaton that wll help farmers understand the socoeconomc and polcy condtons governng ther farmng actvtes. A further ntatve would be to strengthen the capacty of farmers through farmer centered tranng workshops geared towards manageral effcency as well as resource use effcency. Ths could be done n a collaboratve manner nvolvng the government, dstrct assembles and NGOs. Government also needs to ntensfy ts agrcultural extenson servces program by tranng and deployng qualfed extenson offcers. The offcers, n turn, should ntensfy farmer educaton on nput use. Eght, t s notable that the agrcultural extenson offcers-farmer ratos, as well as agrcultural extenson contact wth farmers n the study area, are low. There s therefore a need to motvate and tran the exstng extenson offcers to work more effectvely and to tran more offcers. It s also suggested that () an approprate 250

266 polcy or regulaton that recognses and encourages the effectve use of agrcultural land be formulated by state authortes at varous levels; () farmers should be encouraged to move to more dverse farmng practce and () the role of educatonal program n mprovng effcency should be hghlghted. There s, therefore, a need to desgn approprate polces focusng on rural small-scale farms n Sr Lanka. Nne, the results suggest that polcy makers could frutfully place more emphass on rural agrcultural extenson servces to ncrease the probablty that farmers wll adopt mxed farmng systems wth more dversfcaton. The analyss of farm level techncal effcency ndcates that mantanng more dverse farmng systems s crucal to reducng neffcency and mprovng the welfare of rural households n Sr Lanka. Ths fact has partcular mplcatons for polces requred to sustan gans n agrcultural productvty and effcency. Agrcultural advsory servces, rural credt organsatons and other stakeholders workng for rural development should clearly talor ther messages and servces to meet the dentfed needs of rural farmers. 8.3 Lmtatons of the study and further research It s mportant to be conscous of the possble lmtatons of the study. It s also mportant to consder some of extensons to ths study. These are explaned below. Frst, all data used n ths thess are prmary data collected through a feld and CE survey and should be consdered farly relable. However, there s the possblty that durng ntervews the ntervewer asks the specfc questons n a based way. In order to reduce ths problem the survey was pre-tested on focus group dscusson. 251

267 From the feed-back of the focus group dscusson t was understood that the questons were seen as unproblematc and n that sense the data collected s judged to be relable. However, t also noted that the answers from respondents n the survey may be based towards ther own ndvdual preferences. Ths means that the respondent n the choce experment may answer n a way that does not concde wth hs behavour n realty. Second, sample data used n ths thess are not representatve samples of all Sr Lankan farmers. We only selected the more dverse farmng areas for ths study. Therefore, further research coverng dfferent clmatc and socal groups n ths area s needed to generalse the results of ths research to Sr Lanka. Ths s another area for future research. Moreover, obtanng accurate nformaton from farmers was a major challenge that was faced when collectng data. However, the data s as accurate as possble snce the traned research team was observng ther behavour for at least a two month perod. The valdty of the data ganed through ntervewng vllage level offcers, agrcultural offcers as well as leaders of farmers organsatons was constantly valdated durng the data collecton perod. Thrd, some of the mportant varables such as nfluences of agrbusness n promotng chemcal, seed and other products 41 were not used n the analyss n Chapters fve and sx. Durng the survey we collected some varables related to farm specfc characterstcs such as rrgaton water avalablty, sol fertlty and land shape. However, these varables were dropped from the analyss n order to avod the over dentfcaton problem. Comprehensve analyss coverng all these varables 41 However, most of these varables do not play a sgnfcant role n small-scale farms n the country. 252

268 wth a large sample could provde more accurate and relevant nformaton n order to desgn polces n ths feld. Furthermore, the results of the demand for agrcultural bodversty show that all are postvely valued n terms of extra labour requred. However, some farmers are lkely to have nadequate knowledge of the long run health effects and sustanablty benefts from these possble changes, whch wll bas ther valuatons downwards. Methodologcal advances may be requred to relate polces to dversty outcomes measured at varous geographcal scales or levels of aggregaton n the same farmng system. Specfc ssues for further socal scence research nclude the relatonshp of seed management practces, seed markets, tenure and sol conservaton practces to dversty conservaton, and the possble applcaton of bo-economc models to the analyss of speces and genetc dversty nteractons wth farmng systems also requre study. For polcy purposes, t wll be mportant to better understand the partcular nsttutonal and socal elements that cause communtes to behave dfferently n terms of conservaton agrcultural bodversty n small-scale farms n Sr Lanka n the future. Fourth, t s clear that agrcultural bodversty s strongly determned by spatal heterogenety and temporal varablty of the envronment. Spatal heterogenety at the habtat, landscape and country levels play an mportant role n controllng agrcultural bodversty dynamcs. Dynamc botc processes such as nterspecfc competton and mutualstc nteractons are mportant for the generaton and varaton of agrcultural bodversty. Lack of knowledge about central processes determnng the spatal dstrbuton of speces n communtes and ecosystems s a 253

269 serous problem for plannng conservaton measures. However, ths study does not focus on the mplementaton of management practces adapted to dynamc n stu preservaton of genetc resources. It does not am at dentfyng new practces of managng varetal dversty based on nteracton at dfferent levels of farmer, commercal, and nsttutonal seed systems. Ffth, the smplest measure of dversty we use s a count of varetes. Whle counts of varetes provde a relatvely straghtforward measure of rchness, they suffer two mportant lmtatons. One shortfall s that the count measures are not weghted accordng to the area cultvated by a partcular household. Thus, a household that cultvates three seed lots on three hectares of land has the same dversty score as a household that cultvates three seed lots on one hectare of land, even though the former manages less dversty per unt of land. A second lmtaton of count measures s that they do not capture the evenness of a dstrbuton. Ths s another area for future research Sxth, n Chapter seven, some of the mportant factors that could play a major role n the neffcency functon were not analysed. For example, the roles of the socal nsttutons and government agrcultural polces can emerge as sgnfcant factors behnd techncal effcency of farmers. These factors were not targeted snce the prmary purpose of ths study was to nvestgate agrcultural bodversty and farm level effcency. Furthermore, some areas of further research under effcency measurements should be consdered. These nclude: comparng stochastc and DEA fronter analyses; nvestgatng dstrct or regonal varatons of techncal effcency and nvestgatng techncal effcency and productvty changes n usng panel data. 254

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286 Hlbe, J. M Negatve Bnomal Regresson.(2 nd Eds.), New York: Cambrdge Unversty Press. Hoang, V. and T. Coell Measurement of agrcultural total factor productvty growth n corporatng envronmental factors: a nutrents balance approach. Workng Paper, Centre for Effcency and Productvty Analyss, The Unversty of Queensland, Australa. Hole, D. G., Perkns, A. J., Wlson, J. D., Alexander, I. H., P.V. Grce and A. D. Evans Does organc farmng beneft bodversty? Bologcal Conservaton, 122(1): Howard-Borjas, P Some mplcatons of gender relatons for plant genetc resources management. Botechnology and Development Montor, 37(3): 2-5. Idong, I. C Estmaton of farm level techncal effcency n small-scale swamp rce producton n cross rver state of Ngera: a stochastc fronter approach. World Journal of Agrcultural Scences, 3(5): Isakson, S. R Uprootng dversty? Peasant farmers market engagements and the on-farm conservaton of crop genetc resources n the Guatemalan hghlands. Workng Paper 122, Poltcal Economy Research Insttute, Unversty of Massachusetts Amherst. IUCN The 2007 red lst of threatened fauna and flora of Sr Lanka, IUCN, Colombo, Sr Lanka. 271

287 Jeremy Carew-Red, Bodversty Plannng n Asa. IUCN, Gland, Swtzerland and Cambrdge, UK. Jondrow, J., Lovell, C. A. K., I. S. Materov and P. Schmdt On the estmaton of techncal neffcency n the stochastc fronter producton functon model. Journal of Econometrcs, 19(2-3): Johnson, F. R., K. E. Mathews and M. F. Bngham Evaluatng welfaretheoretc consstency n multple-response, stated-preference surveys. Trangle Economc Research Techncal Workng Paper, No T-0003.Trangle Economc Research, Durham. Johnson, K. H., Vogt, K. A., Clark, H, J., O. J. Schmtz and D. J. Vogt Bodversty and the productvty and stablty of ecosystems. Trends n Ecology and Evoluton, 11(9): Karaganns, G., P. Mdmore and V. Tzouvelekas Separatng techncal change from tme varyng techncal neffcency n the absence of dstrbutonal assumptons. Journal of Productvty Analyss, 18(1): Kasse, G. T., A. Abdula and C. Wollny Valung trats ndgenous cows n central Ethopa. Journal of Agrcultural Economcs, 60(2): Keller, G. B., H. Mndga and B. L. Maass Dversty and genetc eroson of tradtonal vegetables n Tanzana from the farmer s pont of vew. Plant Genet Resource, 3(3):

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289 Kumbhakar, S. C. and C. A. K. Lovell Stochastc Fronter Analyss. Cambrdge: Cambrdge Unversty Press. Kurosak, T. and M. Fafchamps Insurance market effcency and crop choces n Pakstan. Journal of Development Economcs,67(2): Lancaster, K A new approach to consumer theory. Journal of Poltcal Economy, 74(2): Latruffe, L., Balcombe, K., S. Davdova and K. Zawalnska Determnants of techncal effcency of crop and lvestock farms n Poland. Appled Economcs, 36(12): Latruffe, L., Balcombe, K., S. Davdova and K. Zawalnska Techncal and scale effcency of crop and lvestock farms n Poland: does specalzaton matter? Agrcultural Economcs, 32(3): Layton, D. F. and G. Brown Hetergeneous preferences regardng global clmate change. The Revew of Economcs and Statstcs, 82(4): Layton, D. and G. Brown Applcaton of stated preference methods to a publc good: Issues for dscusson. Paper presented at the NOAA Workshop on the Applcaton of Stated Preference Methods to Resource Compensaton, Washngton, DC. L-zh, G The conservaton of Chnese rce bodversty: genetc eroson, ethnobotany and prospects. Genetc Resources and Crop Evoluton,50(1):

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299 Swanson, T The Underlyng Causes of Bodversty Declne: An Economc Analyss, IUCN, Gland, Swtzerland. Stevenson, R. E Lkelhood functons for generalzed stochastc fronter estmaton. Journal of Econometrcs, 13(1): Taylor, E. and I. Adelman Agrcultural household models: geness, evoluton and extensons. Revew of the Economcs of the Household, 1(1): Thrupp, L.A The central role of agrcultural bodversty: trends and challenges. In Conservaton and sustanable use of agrcultural bodversty. Manla, CIP-UPWARD n partnershp wth GTZ, IDRC, IPGRI and SEARICE. Thurstone, L. L A law of comparatve judgment. Psychologcal Revew, 34 (4): Tlman, D., Matson, G.K., P.A. Naylor and S. Polasky Agrcultural sustanablty and ntensve producton practces. Nature, 418: Tsegaye, B The sgnfcance of bodversty for sustanng agrcultural producton and role of women n the tradtonal sector: the Ethopan experence. Agrculture, Ecosystems and Envronment, 62 (2-3): UNEP, Global Bodversty Assessment. Cambrdge, Cambrdge Unversty Press 284

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301 Analyses of Dversty n Wheat, Maze, and Rce, Smale, M. (Eds.), Kluwer, Dordrecht and CIMMYT, Mexco, pp Wjesnghe, L., Gunatlleke, I., Jayawardana, S. D. G., S. W. Kotagama and C.V.S Gunatlleke Bologcal Conservaton n Sr Lanka: A Natonal Status Report.IUCN Sr Lanka Country Offce. Colombo. Wlson, P., D. Hadley and C. Asby The nfluence of management characterstcs on the techncal effcency of wheat farmers n eastern England. Agrcultural Economcs, 24(3): Wlson, C. and C. Tsdell Globalzaton, concentraton of genetc materal and ther mplcaton for sustanable development. In: Leadng Economc and Manageral Issues Involvng Globalsaton, Aurfelle, J., S. Svzzero and C. Tsdell. (Eds.), Nova Scence Publshers, Inc., Unted States of Amerca, New York, pp Wnkelmann, R Econometrc Analyss of Count Data. (5 th Eds.): Sprnger. Wnters, P., L. H. Hntze and O. Ortz Rural development and the dversty of potatoes on farms n Cajamarca, Peru. In: Valung Crop Bodversty: On-Farm Genetc Resources and Economc Change. Smale, M. (Eds.), CABI Publshng, Wallngford, UK. Wooldrdge, J. M Econometrc Analyss of Cross Secton and Panel Data. Cambrdge, MA, USA: Massachusetts Insttute of Technology. 286

302 World Conservaton Montorng Centre Global Bodversty: Status of the Earth's Lvng Resources. London: Chapman and Hall. Wunsch, D Survey research: determnng sample sze and representatve response. Busness Educaton Forum, 40(5): Xu, Y Contextual tonal varatons n Mandarn. Journal of Phonetcs, 25(1): Xu, X. and S. R. Jeffrey Effcency and techncal progress n tradtonal and modern agrculture: evdence from rce producton n Chna. Agrcultural Economcs, 18(2): Zander, K. K. and A. G. Drucker Conservng what's mportant: usng choce model scenaros to value local cattle breeds n East Afrca. Ecologcal Economcs, 68(1-2):

303 Appendx A (1): Defnng agrcultural bodversty Bodversty Agrcultural bodversty Mxed agro-ecosystems Crop speces/varetes Lvestock and fsh speces Plant/anmal germplasm Sol organsms n cultvated areas Bocontrol agents for crop/lvestock pests Wld speces as landraces or wth breadng Cultural and local knowledge of dversty Source: FAO, 1999a 288

304 Appendx A (2): TEV of agrcultural bodversty on small-scale farm 289

305 Appendx A (3): Defnng TEV of agrcultural bodversty on small-scale farms Bodversty components Drect use values Use values Indrect use values Opton value Bequest value Altrustc value Non-use values Exstence value Cultural value Crop dversty Agrodversty Landrace cultvaton Output, qualty and quantty of food, cash ncome, productvty gans Improvement of functon such as ecosystem productvty, sol and water cycle qualty, habtat protecton Developng resstance, Improvement of functon such as eco- system Productvty, sol and water cycle qualty, habtat protecton Its potental to provde economc benefts to human socety n the future, such as beng nputs to mprovement of many varetes and breeds Opton values of exploraton and nsurance value, Some ndvduals may value the fact that the future generatons wll have the opportunty to enjoy an envronmental asset, such a pcturesque landscape Others may be concerned that the good s avalable for others n ths generaton, whether or not they use t themselves Indvduals may value the smple fact that an envronmental asset exsts, whether or not t s used by these ndvduals The tradtonal or ndgenous knowledge assocated wth certan crop varetes, seed or breed management or farmng technques Cultural values embedded n tradtonal varetes,.e. landraces, wth whch tradtonal Sr Lankan dshes are cooked Lvestock dversty Organc producton Output, qualty and quantty of food, cash ncome Productvty gans Increase sol qualty Increase sol and water qualty Its potental to provde economc benefts to human socety n the future, such as beng nputs to mprovement of many varetes and breeds 290

306 Appendx B: Number of descrbed speces n the World Group Number of descrbed speces Bactera and blue-green algae 4,760 Fung 46,983 Algae 70,900 Bryophytes (mosses and lverworts) 17,000 Gymnosperms (confers) 750 Angosperms (flowerng plants) 250,000 Protozoans 30,800 Sponges 5,000 Corals and Jellyfsh 9,000 Roundworms and earthworms 24,000 Crustaceans 38,000 Insects 751,000 Other Arthropods and mnor nvertebrates 132,461 Mollusks 50,000 Starfsh 6,100 Fshes (teleosts) 19,056 Amphbans 4,184 Reptles 6,300 Brds 9,198 Mammals 4,170 Total 1,435,662 Source: World Conservaton Montorng Centre (1992) 291

307 Appendx C: Bodversty wlderness areas n the world Polynesa - Mcronesa New Zealand Calforna Florstc Provnce Mesoamerca Madrean Pne-Oak Woodlands Tumbes- Chocó- Magdalena Tropcal Andes Chlean Wnter Ranfall- Valdvan Forests Carbbean Islands Medterranean Basn Cerrado Gunean Forests of West Afrca Atlantc Forest Succulent Karoo Cape Florstc Regon Caucasus Irano- Anatolan Horn of Afrca Mountans of Central Asa Hmalaya Western Ghats and Sr Lanka Madagascar and the Indan Ocean Islands Mountans of Southwest Chna IInnddoo- I - Burma Coastal Forests of Eastern Afrca Maputaland- Pondoland-Albany Wlderness areas Sundaland Japan Phlppnes Wallacea Southwest Australa New Zealand Polynesa- Mcronesa East Melanesan Islands Source: World Conservaton Montorng Centre (1992) 292

308 Appendx D (1): Topography n Sr Lanka Source: Adopted as Dela (2007) 293

309 Appendx D (2): Major clmatc zones n Sr Lanka Source: Mnstry of Envronment and Natural Resources n Sr Lanka (2007) 294

310 Appendx E: Protected areas under department of wldlfe n Sr Lanka Source: Mnstry of Envronment and Natural Resources n Sr Lanka (2007) 295

311 Appendx F: Lst of protected areas of Sr Lanka Sanctuares Area (ha) Date of declaraton Protected area Area (ha) Date of declaraton Chundkulam 11, /02/1938 Parapuduwa Nuns' Island /08/1988 Wlpattu North /02/1938 Kahalla-Pallekele 21,690 1/07/1989 Telwatta 1, /02/1938 Sgrya 5,099 26/01/1990 Weerawla-Tssa 4, /05/1938 Bellanwla-Attdya /07/1990 Katagamuwa 1, /05/1938 Bar Reef 30, /04/1992 Polonnaruwa 1, /05/1938 Nmalawa 1, /02/1993 Tangamale /05/1938 Madunagala /06/1993 Mhntale /05/1938 Muthurajawela block I 1, /10/1996 Kataragama /05/1938 Muthurajawela block II /10/1996 Anuradhapura 3, /05/1938 Anawlundawa 1,397 11/06/1997 Udawatta Kele /07/1938 Elahera-Grthale 14, /01/2000 Sanctuary Rocky Islets /10/1940 Dahayagala 2, /06/2002 Peak Wlderness 22, /10/1940 Tabbowa 2, /07/2002 Sanctuary Kurulu Kele (Kegalle) /03/1941 Rumassala /01/2003 Pallemalala /10/1942 Kralakele 310 8/09/2003 Welhlla Kateglla /02/1949 Eluwlyaya /09/2009 Kokkla 1,995 18/05/1951 Kaudulla-Mnnerya 8, /06/2004 Senanayake Samudra 9,324 12/02/1954 Krama /10/2004 Gal Oya North-East 12,432 12/02/1954 Kudumbgala 6, /02/2006 Gal Oya South-East 15,281 12/02/1954 Rekawa - 25/05/2006 Gant's Tank 4, /09/1954 Godawaya - 25/05/2006 Vavunkulam 4, /06/1963 Bundala - Wlmanna 3, /06/2006 Sakamam /06/1963 Maduganga 2,300 17/07/2006 Padawya Tank 6,475 21/06/1963 Nature reserves Naval Headworks 18,130 21/06/1963 Trconamadu 25, /10/1986 Sanctuary Great Sober Island /06/1963 Rverne /07/1991 Lttle Sober Island /06/1963 Mnnerya-Grthale Kmbulwana Oya /06/1963 II block 1, /06/1993 Mahakanadarawa Wewa /12/1966 III block 4, /07/1995 Madhu Road 26,677 28/06/1968 IV block 8, /09/1997 Seruwla-Alla 15,540 9/10/1970 Wetahrakanda 3,229 7/06/2002 Parettvu Island /05/1973 Strct nature reserves Honduwa Island /11/1973 Hakgala 1, /02/1938 Buddhangala 1, /11/1974 Yala 28, /03/1939 Ravana Falls 1,932 18/05/1979 Rtgala 1, /11/1941 Mednduwa 0.8 6/06/1980 Kalametya lagoon 2, /11/1984 Sr Jayawardenapura /01/1985 brds Vctora-Randengala- sanctuary 42, /01/1987 Rantambe Mambulkanda /06/1988 Nttambuwa Source: Department of Survey n Sr Lanka (2007). 296

312 Appendx G: Map showng survey areas n Sr Lanka 297

313 Appendx H: Questonnare used n the survey Agrcultural bodversty, Poverty and farm level effcency: Survey A Survey by K.M.R. Karunarathna PhD canddate Queensland Unversty of Technology Australa We greatly apprecate your partcpaton n ths survey Good mornng/ afternoon/ evenng. My name s., I am conductng ths survey on behalf of Ms Mudtha Karunarathna who s a PhD Student at the Queensland Unversty of Technology, Australa. Her thess s on agrcultural bodversty, poverty and farm level effcency n Sr Lanka. We have selected a sample of farmers to represent your area and your farm has been chosen as part of the sample. I am vstng you today for ths survey. By partcpatng n ths survey you wll be assstng us to better understand and dentfy the value of agrcultural bodversty n farms n Sr Lanka. Please be assured that ths s purely a research project and we do not represent any busness or product or a government nsttuton. No government acton wll be nvolved as a result of your partcpaton n ths study. We assure you that all the nformaton that you provde us wll reman confdental. Please feel free to gve any answer that you thnk s correct or approprate. We would apprecate t very much f you could spend some tme wth us and answer some questons to the best of your ablty. The survey should not last longer than one and half hours. Would you be wllng to take part n ths survey? Yes.1 No...2 Note: If No, the enumerator wll leave the farm Questonnare No:. Vllage:... Date of Intervew:... Tme Started:... Dstrct:... Enumerator Name:... Tme Fnshed:... Mudtha Karunarathna can be contacted n the next few months at the followng address: Department of Economcs and Statstcs Unversty of Peradenya Sr Lanka TP: (offce)/ (moble) Emal: k.mudtha@student.qut.edu.au, mudthak@pdn.ac.lk Note: Ths questonnare was translated nto Snhalese for the fnal survey 298

314 Part A: General Informaton on Farm Characterstcs Intervewer: The followng questons relate to the general nformaton about your farm. Frstly, we would lke to fnd out about your farm, and the methods you use to cultvate them. Please concentrate only on the last cultvaton season. 1. What s the sze of your farm? Please state n acres For how long have you cultvated your land? Number of years: 3. How far s your house from your farm? Number of klometres: Could you please tell us the number of separate plots that you have used for the followng? Crops (No.):... Lvestock 1 (No.):... Poultry 2 (No.)... Mx-both crops and lvestock and/or poultry (No.) What s the most mportant factor that you would consder when makng nvestment decsons on your farm? 1. Revenue 4. Water avalablty 2. Market prces 5. Household consumpton 3. Captal avalablty 6. Other (specfy) How would you rank your farm wth respect to ts sol fertlty? 1. Excellent 2. Good 3. Average 4. Poor 7. How would you rank your farm wth respect to ts land shape? 1. Very steep 2. Average 3. Flat 8. What s the extent of the rrgated area of your farm....% (state as a percentage)? 1 Lvestock refers to one or more domestcated anmals rased n an agrcultural settng to produce commodtes such as food, fbre and labour (e.g. cattle, cow, pg, goat... etc.). The term "lvestock" does not nclude poultry or farmed fsh. 2 Poultry s the category of domestcated brds that people keep for the purpose of collectng ther eggs, or kllng for ther meat and feathers (chckens, ducks...etc.) 299

315 9. Have you receved adequate water durng the last season from the rrgaton canal? 1. Yes, all the farm needs have been met 2. Yes, part of the farm needs have been met 3. No, dd not receve any rrgated water 4. My farm does not rely on rrgaton 10. Could you please tell us the total land area (acres) that you have used for agrcultural purposes durng the last season under the followng headngs? Owned Rented out Rented n Other 11. Do you thnk that the age of your farm has an nfluence on the productvty of your farm? 1. Yes 2. No 3. Don t know 12. Do you use the farm to do the followng: (Please tck relevant box/boxes) Types of farm Startng year 1 Grow crops only 2 Lvestock and poultry only 3 Mx (both crops and lvestock and/or poultry) If you tck number 1 please go to secton B and answer all questons except 4-5 If you tck number 2 please go to secton B and answer all questons except 1,2 and 3 If you tck number 3 please go to secton B and answer all questons 300

316 Part B : Informaton on Agrcultural Bodversty and Farm Level Effcency Intervewer: In ths secton, we are nterested n gettng some nformaton about the dfferent components of agrcultural bodversty and the level of effcency on your farm. Note: The enumerator wll frst gve a broad ntroducton on dverse farmng systems, practced n dfferent areas n Sr Lanka and wll then narrow down hs attenton to the farmng system n the survey areas. 1. Could you please provde us the followng nformaton wth regards to the crops you have cultvated, nput you have used and the market prces that you have receved on your farm durng the last season? Crop Area Tradtonal Fertlzer Pestcdes Producton Market Market HH (m 2 ) varety or (Code) (Yes/No) (kg.) prce value consumpton not (Yes/No) (Rs) (Rs) (%) Total Fertlzer code: 0- no fertlzer, 1- chemcal, 2- organc 301

317 2. Could you please let me know the amount of labour days used to cultvate the above mentoned crops under followng categores? Items Hred labour (days) Famly labour (days) Preparng the Land Cultvatng the crops Applyng pestcdes and fertlzers Harvestng Others (specfy) 3. Please provde me detals of your expendture on the followng tems used to cultvate the above mentoned crops: Items Quantty Rs. Tractor Seeds Pestcdes Fertlzer Others 4. Could you please provde us the followng nformaton about lvestock and poultry producton on your farm durng the last season? Lvestock No. of Area Tradtonal Producton Market Market HH and poultry head (m 2 ) breed or not (kg/ltter/no) prce value consumpton (Yes/No) (Rs.) (Rs.) Total 302

318 5. Please provde me your expendture on lvestock and poultry under the followng: Items Quantty Rs. Lvestock Labour Feed Veternary Other Poultry Labour Feed Veternary Other 6. What s the most common way of marketng your agrcultural products? Co-op Vllage trader/shop Vllage pola Town Crops Lvestock Poultry What s the dstance to the nearest town?...(n km) What s the dstance to the second nearest town? (n km) 7. Have you been satsfed wth the prces that you have receved durng the last season? Satsfed Not satsfed Don t know 303

319 8. Could you please let us know what prces you were expectng and what prces you obtaned for the three most mportant crops and lvestock/poultry sold n the market durng the last season? Crops Expected Actual Lvestock/ Expected Actual prce prce (Rs.) prce (Rs.) poultry prce (Rs.) (Rs.) Could you please provde us wth the maxmum and mnmum prces you have receved for the three man crops and for lvestock/poultry products you have sold n the last two seasons? Crops Maxmum Mnmum Lvestock/ Maxmum Mnmum prce (Rs.) prce (Rs.) poultry prce (Rs.) prce(rs.) What s the dstance to the nearest market (km)? Do you have any facltes to access alternatve markets? Yes/No If Yes, what s the dstance to the alternatve market(km)? Do you drectly sell your farm product n the market? Yes/ No If No, how do you sell them?

320 13. Do you partcpate n agrcultural extenson servces?yes/no If Yes, how many tmes dd you partcpate n the last season? Have you receved any subsdes for agrcultural producton? Yes/No If Yes, what s the approxmate amount(rs.)? How do you fnance your farm cultvaton? 1. Savngs 2. Money borrowed from prvate ndvduals 3. Money borrowed from traders 4. Money borrowed from the fnancal nsttutons 5. Other...(please specfy) 16. What s the amount of famly expendture covered from farm producton (where Applcable)? 1. Crops... (%) 2. Lvestock... (%) 3. Poultry... (%) 17. How much money wll you be nvestng on your farm next season? Rs..(approxmate amount) 18. Assume that your profts wll ncrease by any of the percentages shown below. Takng ths nto consderaton by how much wll you ncrease your farm nvestment? Profts % More Investment % 305

321 Water sources and use on the farm 19. Where s your farm located wthn the feld canal? (please tck the approprate box) Head Mddle Tal 20. What s your man source of water on your farm used for cultvaton? (tck the approprate box) 1.Agrowell 2. Feld canal 3.Both If you tck number 1 please go to queston 21.1 If you tck number 2 please go to questons 21.2 If you tck number 3 please answer all questons (21.1, 21.2 and 21.3) Agrowell Please provde me detals about pumpng water from the agrowell to your farms a. How do you pump water from the agrowell? 1. Usng my own pump 2. Hred pump b. For how many hours s the pump used per day? Number of hours (approxmately): c. For how many days per month s the pump used? Number of days per season (approxmately): d. How long s the cultvaton season durng the Yala/Maha season? Number of months per season (approxmately):... e. What s the sze of the water pump (h/power):. Note: Enumerator wll check pump sze by examnng the pump 306

322 21.2. Feld canal Please gve me detals about usng water from the feld channel to your farm a. How do you obtan water to your farm from the feld canal? Yala Maha Water flows contnuously throughout the season A rotatonal system (water access s restrcted) Any other system (specfy).. b. For how many hours s water taken per day? Number of hours (approxmately): c. For how many days per month s water taken? Number of days (approxmately): d. How long s the cultvaton season durng the Yala/Maha season? Number of months: What proporton of your total water requrements do you obtan from dfferent sources? Please state the approxmate percentage: Agrowell... Feld canal..... Ran water. 307

323 Part C: Evaluatng Poverty, Income and Expendture Intervewer: Now we are gong to ask you about your ncome and expendture. The man purpose of obtanng ths nformaton s to evaluate the relatonshp between your farmng system and your farm ncome. In addton to that, we are nterested n seeng whether there s a way to mprove your farm ncome by changng exstng agrcultural practces. 1. How do you rank the avalablty of food n your household n a typcal year? 1. We have enough food for consumpton 2. Very rarely we have nsuffcent food 3. Very often we are runnng short of food 2. In your vew do you thnk that your household s healthy? 1. Yes 2. No If Yes, what s the reason?... If Not, what s the reason? Soco-economc status/ncome level of the household. (Note: Ths assessed by observaton of the enumerator. The enumerator wll take photographs that defne soco-ncome status) 1. Luxury/ Upper mddle class 2. Ordnary 3. Small house/cottage 308

324 4. We now ask questons related to facltes avalable n your house. Could you please let us know whether you have the followng facltes n your house? Facltes Yes No 1 Telephone 2 Electrcty 3 Ppe water 4 Vehcle road to the house 5 Water sealed tolet 6 Attached bathroom 5. In ths queston we are askng about the captal assets that you own. Could you please provde us all the captal assets wth ther purchased values Assets Quantty Approxmate Value (Rs) Year of purchase Tractors Threshng machne Water pump Vehcles Motorcycle Other(Specfy) 6. Dd you purchase any land or/and houses over the last 5 years 1. Yes 2. No If Yes value: Monthly Income (famly): From farm: 1. Crop (Rs.) Lvestock (Rs.) Poultry (Rs.)... Other sources (Rs.):

325 8. Household expendture last month Items Rs. 1 Household lvng 2 Educaton 3 Health 4 Other 9. Does any member of the household receve a penson or drect welfare payment? 1. Yes 2. No If Yes, please ndcate the number of person(s) and the nature of such contrbuton No of Persons Amount (Rs) Penson Samurd Other 10. Could you please provde us the detals of your total debt up to last month Amount(Rs) 1 Debt owng to the nformal sector 2 Debt owng to the formal sector 11. How would you classfy the economc status of your household relatve to others n ths vllage? (put the approprate number):... a. Much better than most people (rch) b. Better than most people (relatvely well off) 310

326 c. About average d. Below average e. Much worse than average (very poor) f. Don t know / Not sure 12. Whch one of the statements below s true for your household? Please choose only one. a. We can hardly make ends meet. b. We can only afford the necesstes c. We do not have any fnancal problems, however we do not lve n luxury d. We have enough money to lve a comfortable lfe e. We lve a comfortable lfe, sometmes we can afford luxury goods f. We lve n luxury Part D: Conservaton of Agrcultural Bodversty: Choce Experment Survey Enumerator: Ths part of the questonnare s about farmers preference on agrcultural bodversty on farms. We are nterested n how you, as a farmer as well as a consumer, perceve agrcultural bodversty and ts dfferent characterstcs. In ths part we would lke to fnd out the mportant of dfferent components of agrcultural bodversty and your own farm preferences usng dfferent attrbutes level. Therefore, wth the help of several farm producers and agrcultural scentsts we have dentfed fve components of agrcultural bodversty and generated several 311

327 (magnary) farm profles usng dfferng levels of these characterstcs. Farm characterstcs and ther levels nclude: 1. Crop speces dversty. Ths s measured by the total number of crop varetes that are grown on a small-scale farm settng. For example, a farm wth tomatoes, beans and carrots has n total three dfferent crops. We wll present you wth four levels of crop dversty whch nvolve 3, 7, 10, and 15 dfferent varetes. 2. Mx crop and Lvestock dversty: Ths s desgned to ndcate whether you prefer an ntegrated crop and lvestock/ poultry producton system over a system that s specalsed n crops or lvestock/poultry. 3.Organc producton. Ths ndcates whether or not a small-scale farm employs organc methods of producton. For example, when a farmer sells small-scale farm crops that are produced entrely by employng organc methods, these products are certfed as organc. Consder your magnary farm. Decde whether or not you prefer a farm n whch you produce crops wth entrely organc methods. 4. Landrace cultvaton. Ths shows whether or not you prefer to have a farm n whch a landrace s grown as opposed to none. A landrace s defned as a crop varety that was grown by farmers, such as you or your ancestors, before the agrcultural modernsaton programs commenced durng the 1970s. 5. Economc mportance of small-scale farms. Ths s defned as the expected proporton (n percentage terms) of annual household food expendture reducton 312

328 through food producton n the small-scale farm. It ndcates the mportance of the contrbuton of the small-scale farm producton to your household budget. The percentages that wll be presented to you are 5%, 10% and 15%. 6. Estmated cost n terms of addtonal labour days. Ths s defned as a percentage of addtonal labour requrements under dfferent polcy optons. It ndcates the addtonal costs that you have to bear when you are acceptng a new polcy. The percentages that wll be presented to you are 10%, 20% and 30%. The frst four attrbutes reflect the varous attrbutes of agrcultural bodversty found n the farms n Sr Lanka. The sxth factor represents benefts that farmers can receve n terms of net revenue changes under dfferent polcy optons. The last factor s the monetary attrbute n terms of addtonal labour costs that farmers have to use under dfferent polcy optons. We have placed the generated hypothetcal farms n pars on a seres of cards, and we would lke you to ndcate out of the par, whch type of farm you prefer n each card. Now, please magne you wll cultvate a hypothetcal farm. The followng questons wll each present you wth two dfferent farms: farm (A) and farm (B), n each case the farm s equal to half an acre. Could you please compare each farm n the followng cards I wll be presentng to you and tell me whch one you prefer n each case? 313

329 Queston 1 Assumng that the followng farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm (A) Farm (B) Total number of crop varetes grown on a farm Crops are combned wth lvestock/poultry producton Yes No Farm crops are produced entrely usng organc methods Yes Yes Farm has a landrace cultvaton No No Decrease n food expendture (n percentage) 15% 10% Estmated cost n terms of addtonal labour requrement ( n percentage) 20% 10% I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) Nether Smallscale farm (A) nor Smallscale farm (B): Queston 2 Assumng that the followng small-scale farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm (A) Farm (B) Total number of crop varetes grown on a farm 10 5 Nether Crops are combned wth lvestock/poultry producton Yes No Small-scale Farm crops are produced entrely usng organc methods Yes Yes farm (A) Farm has a landrace cultvaton No Yes nor Smallscale Decrease n food expendture (n percentage) 15% 10% farm Estmated cost n terms of addtonal labour requrement ( n percentage) 10% 30% (B): I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) 314

330 Queston 3 Assumng that the followng small-scale farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm (A) Farm (B) Total number of crop varetes grown on a farm Crops are combned wth lvestock/poultry producton Yes No Farm crops are produced entrely usng organc methods Yes Yes Farm has a landrace cultvaton No No Decrease n food expendture (n percentage) 5% 10% Estmated cost n terms of addtonal labour requrement 10% 30% ( n percentage) Nether Smallscale farm (A) nor Smallscale farm (B): I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) Queston 4 Assumng that the followng small-scale farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm Farm (A) (B) Nether Total number of crop varetes grown on a farm 10 5 Smallscale Crops are combned wth lvestock/poultry producton Yes No farm Farm crops are produced entrely usng organc methods Yes Yes (A) nor Farm has a landrace cultvaton No Yes Smallscale Decrease n food expendture (n percentage) 20% 30% farm Estmated cost n terms of addtonal labour requrement 30% 10% (B): ( n percentage) I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) 315

331 Queston 5 Assumng that the followng small-scale farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm (A) Farm (B) Total number of crop varetes grown on a farm Crops are combned wth lvestock/poultry producton Yes No Farm crops are produced entrely usng organc methods Yes Yes Farm has a landrace cultvaton No No Decrease n food expendture (n percentage) 5% 10% Estmated cost n terms of addtonal labour requrement 10% 20% ( n percentage) I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) Nether Smallscale farm (A) nor Smallscale farm (B): Queston 6 Assumng that the followng small-scale farms were the ONLY choces you have, whch one would you prefer to cultvate? Farm Characterstcs Farm Farm (A) (B) Nether Total number of crop varetes grown on a farm 10 5 Smallscale Crops are combned wth lvestock/poultry producton Yes No farm Farm crops are produced entrely usng organc methods Yes Yes (A) nor Farm has a landrace cultvaton No Yes Smallscale Decrease n food expendture (n percentage) 30% 15% farm Estmated cost n terms of addtonal labour requrement 5% 10% (B): ( n percentage) I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) 316

332 7. When answerng Questons 1 to 6, whch of the fve mplcatons were mostly mportant to you and whch were the least mportant? Please rank the fve mplcatons by placng the numbers 1 to 6 n the followng boxes: (1-most mportant; 5-least mportant) Total number of crop varetes grown on the farm Number of anmal breeds on the farm Farm producton s combned wth lvestock/ poultry producton Farm crops produced entrely usng organc methods Farm has at least one landrace 8. If you always chose nether opton, whch of the followng statements most closely descrbed you reason for dong so? I oppose to ncrease agrcultural bodversty on the farm I don t want to change the exstng system I beleve that change wll ncrease the rsk of farm producton I ddn t know whch opton was best so I stuck wth the current stuaton Other reasons (specfy)

333 9. Thnkng about Questons 1 to 6, and the nformaton about the agrcultural bodversty on farms presented earler, please ndcate how strongly you agree or dsagree wth each of statement a) to g) below. For each statement, please crcle the number that represents your vew: AS AG NA DI DS 1. I understood the nformaton n the questonnare 2. I needed more nformaton than was provded 3. The nformaton was based n favour of the scheme 4. The nformaton was based n opposton to the scheme 5. I found questons 36 to 41 confusng 6. I dd not read the enclosed pamphlet n detal 7. I found questons 1-6 n part D meanngful Strongly Agree (AS), Agree (AG), Nether agree or dsagree (NA), Dsagree (DI) and Strongly Dsagree (DS) Part E: Farmers atttudes towards dfferent components of agrcultural bodversty Enumerator: Now we are gong to ask about your atttudes towards conservaton of agrcultural bodversty. All of the followng statements relate to the agrcultural bodversty on your farm. Please ndcate the extent of whch you agree or dsagree wth each of statement. 1. The number of crop varetes makes the vew of the landscape more beautful Fully agree Agree Normal Dsagree Strongly dsagree 318

334 2. Tradtonal varetes represent our cultural hertage Fully agree Agree Normal Dsagree Strongly dsagree 3. Organc farmng methods are better for the envronment than conventonal methods Fully agree Agree Normal Dsagree Strongly dsagree 4. Organc food s better for me than commercal agrculture (e.g. farmng usng chemcal nputs) because t does not contan any chemcal resdues Fully agree Agree Normal Dsagree Strongly dsagree 5. Envronmentally frendly farmng practces reflects prncples and values that are mportant to me Fully agree Agree Normal Dsagree Strongly dsagree 6. Envronmentally frendly farmng practces help mprove consumers perceptons of farmers Fully agree Agree Normal Dsagree Strongly dsagree 7. The number of crops varetes n the farm ncreases the crop varety dversty Strongly agree Agree Normal Dsagree Strongly dsagree 8. Organc fertlzer ncreases the sol qualty and productvty of the farm Strongly agree Agree Normal Dsagree Strongly dsagree 319

335 9. Chemcal fertlzer ncreases productvty but decreases sol qualty Strongly agree Agree Normal Dsagree Strongly dsagree 10. What s your general atttude towards agrcultural bodversty? Very postve Postve Normal Negatve Strongly negatve 11. What s your atttude towards ncrease agrcultural bodversty? Very postve Postve Normal Negatve Strongly negatve Part E: General Informaton of Households In ths secton, we seek general nformaton about you and your household. 1. Respondent s man occupaton: 1. Farmng 2. Other 2. Age of respondent: 3. Gender of respondent: 4. Educaton: Years of schoolng: Any other educaton: 5. Number of famly members n the household: Number of chldren n the famly (< 15 years): Number of regular ncome recpents n the famly (publc or prvate sector employed). Rs For how long have you worked on your farm?...(number of years) If yes whch year? Do you have a busness vehcle? Yes/No If Yes what s t For how long are you engaged n agrcultural actvtes? Number of years:.. 320

336 11. Could you please state the approxmate percentage of your household ncome spent on food consumpton?...% 12. Are you a member of ths farm organsaton? Yes/ No 13. Do you thnk that you can easly access borrowed credt for agrculture? Yes / No 14. For how long have you been n ths vllage? (No of years)... Ths s the end of the ntervew. Thank you very much for your partcpaton. Do you have any general comments about ths study or anythng to say? Comments: Enumerators Name:..... Sgnature:

337 Appendx I(1): A sample choce set s gven to the respondent BLOCK 01: Queston 01 Farm Characterstcs Total number of crop varetes grown on a farm Farm (A) Farm (B) 10 7 Crops are combned wth lvestock/poultry producton Farm crops produced entrely usng organc methods Yes Yes No Yes Nether Small-scale farm (A) nor Smallscale farm (B): Farm has a landrace cultvaton No No Expendture reducton (%) 15% 10% Estmated cost n terms of addtonal labour requrement ( %) I prefer to cultvate Farm (A)... Farm (B)... Nether Farm.... (please pck one opton) 20% 10% 322