The determinants and extent of crop diversification among smallholder farmers

Transcription

1 WORKING PAPER 05 June 2014 The determnants and extent of crop dversfcaton among smallholder farmers A case study of Southern Provnce, Zamba Kru Schoongwe, Lawrence Mapemba, Daves Ng ong ola, and Gelson Tembo

2 TABLE OF CONTENTS Abstract Introducton Methodology Results and Dscussons Concluson and Polcy Recommendatons... 9 References LIST OF TABLES Table 1 Descrpton of varables used n the study... 7 Table 2 Summary of demographc and soco-economc characterstcs of farmers... 7 Table 3 Determnants and extent of crop dversfcaton by smallholder farmers... 8

3 ABSTRACT Agrculture s vtal to Zamba s economc development and s a manstay for the lvelhoods of a large proporton of the populaton. Agrcultural producton s manly dependent on ran-fed hoe cultvaton wth maze as the prncpal staple food crop. About 18 percent of natonal maze producton comes from Zamba s Southern provnce. In order SUMMARY to mprove APRIL food 2010 securty and mnmze rsks assocated wth heavy dependence on maze, the government of Zamba has been promotng crop dversfcaton. Ths study analyzed the determnants of crop dversfcaton as well as the factors nfluencng the extent of crop dversfcaton by smallholder farmers n Southern provnce. The study used secondary data from the Central Statstcal Offce of Zamba. Results from a double-hurdle model analyss ndcates that landholdng sze, fertlzer quantty, dstance to market, and the type of tllage mechansm adopted have a strong nfluence on whether a farmer practces crop dversfcaton. Our fndngs have mportant mplcatons for polces that are desgned to enhance crop dversfcaton. In partcular, our results suggest the need for government to consder undertakng polces that wll enhance farmers access to and control over land, that wll provde farmers wth mproved access to agrcultural mplements lke ploughs, and that wll brng tradng markets closer to farmers. Keywords: crop dversfcaton, double-hurdle model, Zamba. 1. INTRODUCTION Agrculture s a manstay for the lvelhoods of a large proporton of the populaton, an mportant sector of Zamba s economy, and generates approxmately 10 percent of the country s foregn exchange earnngs (Chwele 2010). Collectvely, crop agrculture, lvestock, fsheres, and forestry account for about 20 percent of Zamba s Gross Domestc Product (GDP) and some 35 percent of earnngs from non-tradtonal exports (ZDA 2011). There s a consensus throughout the research lterature that Zamba s large potental n agrculture has not yet been fully exploted. If well managed, the sector could contrbute to substantal mprovements n GDP, employment, and tax revenue (FAO Zamba 2005). It s n ths regard that the Zamban government seeks to poston the agrcultural sector as one of the drvng forces for the economc growth that s requred to reduce poverty n the country (Gbson 2005). The agrcultural sector n Zamba can be dsaggregated nto three categores; commercal, medum, and small scale. Commercal farmers cultvate areas of 20 hectares and above and are characterzed by extensve mechanzaton, use of modern technology and management, and the rearng of exotc breeds of lvestock. They also rely extensvely on hred labor. However, nearly two-thrds of agrcultural land and a large share of the natonal herd are held by smallholder farmers. The smallholder farmers are classfed as ether small-scale or medum-scale. The former cultvate land areas of less than 5 hectares, whle medum-scale farmers are those who cultvate areas between 5 and 20 hectares. The majorty of smallholder farmers rely on ran-fed hoe cultvaton and the use of unpad famly labor and focus much of ther crop producton on maze. Ther producton also s characterzed by the low use of modern nputs (Chomba, 2004). Followng several drought cycles, n 2004 the Government of Zamba, through the Mnstry of Agrculture and Cooperatves, ntroduced a programme to promote crop dversfcaton. Crop dversfcaton s defned as the growng of two or more crops on a pece of land by a farmer. The crops consdered n the dversfcaton programme ncluded cassava, sweet potato, groundnut, sunflower, soya bean, Bambara nut, velvet bean, cashew nut, kenaf, paprka, Irsh potato and cowpea. The programme was mplemented wth the objectve of ncreasng the food securty and nutrton status of farm households. It was antcpated that ths, n turn, would enhance the lvng standards of farm households, whle offerng varous croppng alternatves to farmers, as opposed to ther relyng on a sngle crop, maze. Among the addtonal advantages to the farm household of growng more than one crop s an opportunty to mtgate rsks assocated wth cropspecfc falure due to adverse weather condtons, pests, and dseases (MACO 2004). 1.1 Concepts of crop dversfcaton Crop dversfcaton s regarded as one sub-set of a large matrx of producton optons n the croppng sector. From an economc pont of vew, dversfcaton can be examned from two analytcal vewponts: frst, as a problem of determnng, gven a set of prces, the optmal crop mx on a producton possblty fronter; and second, as a mechansm for ncorporatng rsk averson nto a farmer's decson makng process n whch crop specalsaton may lead to hghly unstable ncome due to varance n output, producton, or prce for the partcular crop (Hazell 1987). Dversfcaton s seen as havng two man propertes. Frst, t expands the producton possblty set or area allocaton fronter for a farmer, thereby ncreasng opportuntes for ncome generaton and employment creaton. Second, t reduces the rsk of havng all of one's eggs n a basket wth one crop only or a few crops wth potentally hgh covarance rsk (Samuelson 1967). In the context of agrculture, dversfcaton s regarded as the re-allocaton of some of a farm's productve resources, such as land, captal, labour, and farm equpment, nto new farm actvtes. Crop dversfcaton s usually vewed as a shft from 1

4 tradtonally grown, less proftable crops to newer, more proftable crops. It s also a strategy that s used to maxmze the use of land, water, and other resources for the overall agrcultural development n a country. It provdes farmers wth feasble optons to grow dfferent crops on ther land. Therefore, a farmer s decson to dversfy s consdered a major economc decson that has a strong bearng on the farmer s ncome level and food securty (Pope and Prescott 1980). SUMMARY APRIL 2010 There are many factors that may lead a farm household to dversfy ts croppng enterprses. These nclude the need to reduce rsk, respondng to changng consumer demands or changes n government polcy, respondng to external shocks, and, more recently, as a copng strategy to the challenges arsng from clmate change. Crop dversfcaton provdes a broader choce n the producton of a varety of crops n a gven area and also lessens the rsk of crop falure. It also can offer comparatvely hgher net returns from crops, hgher net returns per unt of labour, optmzaton of resource use, and hgher land utlzaton effcency (Ashfaq et al. 2008). 1.2 Objectves of the study The objectves of the study were to: a) Compare the demographc and soco-economc characterstcs of farmers who produce a dversfed crop mx wth those who do not. b) Identfy the major determnants that nfluence farmer s decsons to dversfy n crop producton. c) Determne the factors nfluencng the extent of crop dversfcaton by smallholder farmers. 1.3 Lterature on crop dversfcaton Numerous studes have been done on the determnants of crop dversfcaton. A study on the nature and extent of crop dversfcaton n Karnataka state n Inda done by Saraswat et al. (2011) revealed that crop dversfcaton was determned by a number of nfrastructural and technologcal factors. Ther fndngs suggested that the creaton of basc nfrastructural facltes, such as sustaned supply of rrgaton water, markets, fertlzer avalablty, proper roads, and transportaton, was an essental pre-requste for creatng enablng condtons for crop dversfcaton. The study found that crop dversfcaton had an mportant postve effect on producton. Ths study employed secondary data for the perod 1982 to The data was analyzed usng the Composte Entropy Index (CEI) and multple lnear regresson analyss. The CEI for dfferent crop groups showed that almost all the crop groups had a hgher crop dversfcaton ndex durng the post-world Trade Organzaton (WTO) perod (1996 to 2008) than durng pre-wto perod (1982 to 1995) perod, except for olseeds and vegetable crops, suggestng that broader polcy ssues have a bearng on the degree to whch farmers dversfy ther crop producton (Saraswat et al., 2011). Another study on crop dversfcaton carred out n Pakstan by Ashfaq et al. (2008) found that crop dversfcaton levels were determned by the sze of landholdng, the age, educaton level, farmng experence, and off-farm ncome of the farmer, the dstance of the farm from the man road and from the man market, and farm machnery ownershp. In ther study an entropy ndex was used to measure dversfcaton and, thereafter, a multple regresson model was used to determne factors affectng crop dversfcaton. A study by Bhattacharyya (2008) on crop dversfcaton as a search for alternatve ncome for farmers n the state of West Bengal n Inda showed that the agrcultural sector there was gradually dversfyng towards hgh value commodtes, such as fruts, vegetables, and flowers. The research revealed that most crop dversfcaton came through the ndvdual efforts of small farmers, wth lttle support from government, as government polces manly emphaszed cerealbased producton for household food securty. The study used the Smpson Dversty Index as a dependent varable n a regresson equaton so as to determne the separate effects of each ndvdual explanatory varable on crop dversfcaton. The major determnant of dversfcaton was a demand-sde factor that had nduced farmers to shft towards producton of hgh value crops. In addton, road development and ncreased technology adopton were key determnants n ths respect. Crop dversfcaton was more promnent n ran-fed areas than n rrgated zones. The ran-fed areas were seen as becomng the hub of non-cereals due to the low water requrements of these crops and the abundant labour supply n ran-fed areas. As the cost of cultvaton of fruts, vegetables, and flowers was relatvely low, the hgh value crops were becomng popular among small farmers who could not afford hgh agrcultural-related nvestment costs. However, the decsons to dversfy the crops farmers produced n West Bengal were affected by a lack of proper nsttutonal support. Farmers requred from government proper fnancal resources, gudance, encouragement, and tranng n new producton technques (partcularly for creatng crop nurseres) to attract farmers towards hgh value crop cultvaton. A study by Ibrahm et al. (2009) on crop and ncome dversfcaton among farmng households n a rural area of north-central Ngera reported that crop and ncome dversfcaton were strateges that were essental for reducng rural poverty and rasng ncome. The study used the Smpson Index of Dversfcaton and Ordnary Least Squares regresson 2

5 approaches for data analyss. The study dentfed the key determnants of ncome dversfcaton as the number of adults above 60 years old n a household, number of chldren less than 12 years old, dstance from local market, and avalablty of electrcty n the household, whle the determnants of crop dversfcaton were age of household head, level of educaton of the household head, number of extenson vsts the farmer receved, avalablty of tractor hrng servces, and returns from crop producton. SUMMARY APRIL 2010 A study by Smwambana (2007) n the Southern provnce of Zamba revealed that most farmers dd not dversfy ther crop producton. Ths study used common rapd apprasal methods and had ts own weaknesses because t focussed only on cassava and sweet potato, gnorng crops lke groundnut and sunflower, whch are also mportant crops n the dversfcaton programme. Furthermore, the study lmted tself to three dstrcts out of the eleven n the provnce. Despte the Zamban government havng a polcy programme wth regard to crop dversfcaton, ths study showed that crop dversfcaton s low, wth the agrculture sector beng hghly undversfed, and maze beng the man staple crop (JCTR 2008). Kankwamba, et al. (2012) conducted a study on the determnants of crop dversfcaton n Malaw whch used the Herfndahl Index. The agrcultural sector n Malaw s hghly undversfed, wth maze and tobacco beng the domnant staple and export crops, respectvely. Despte ths, the government had snce the 2005/06 croppng season mplemented the Farm Input Subsdy Program amed prmarly at ncreasng maze productvty and output. They found that, although crop dversfcaton had deterorated natonally and regonally, benefcares of the subsdy program had become more dversfed n ther croppng practces. Ther study concluded that, whle varous polces n Malaw all encourage agrcultural dversfcaton n broad terms, there was a lack of strategc thnkng around how exactly t was to be acheved, and more mportantly, how crop dversfcaton could be promoted among dfferent types of farmers wth the am of contrbutng to economc growth, rsk mtgaton, and nutrton securty. The economc lterature dentfes a number of analytcal approaches that can be used to determne the nature and extent of crop dversfcaton. Saraswat et al. (2011) employed a multple lnear regresson analyss, whereas Ibrahm et al. (2009) adopted a lnear regresson approach. The problem wth lnear regresson s that one cannot be sure about the underlyng causal mechansm between the dependent varable and the explanatory varables, but can only ascertan that a relatonshp exsts. In other words, lnear regresson analyss does not prove causalty (Wan 2012). The doublehurdle model s approprate, as t allows for separate estmaton of the probablty of partcpaton n crop dversfcaton and, f a farmer s dversfyng producton, the extent of dversfcaton (Cragg 1971). In addton, the Herfndahl Index and the Crop Dversfcaton Index can be used to understand the determnants of dversfcaton for those farmers who dversfed ther crops. As far as the research lterature s concerned and to our knowledge, no study has been conducted n Zamba wth regard to the determnants and extent of crop dversfcaton among farmers n Southern Provnce. Therefore, the purpose of ths study was to examne these ssues n the Zamban context usng a double-hurdle model to analyze the determnants and the factors nfluencng the extent to whch farmers dversfed ther crop producton. 2. METHODOLOGY 2.1 Theoretcal framework The analytcal model used for ths study draws upon the theory of crop dversfcaton among smallholder farmers. The fundamental assumpton s that a farmer s decson on whether to dversfy or not s based upon utlty maxmzaton (Rahm and Huffman 1984). The expresson U (W j, L j) s a non-observable underlyng utlty functon, whch ranks the preference of the th farmer for the j th dversfcaton process (j = 0, 1; where 0 = no dversfcaton and 1 = dversfcaton). Thus, the utlty derved from crop dversfcaton depends on W, whch s a vector of farm- and farmer-specfc attrbutes of the dversfer and L, whch s a vector of the attrbutes assocated wth crop dversfcaton. Although the utlty functon s unobserved, the relaton between the utlty dervable from the j th dversfcaton process s postulated to be a functon of the vector of observed farm, farmer, and crop dversfcaton specfc characterstcs and a dsturbance term havng a zero mean: U j F ( W L ) e j = 0, 1; =0, 1, n (1) j j Snce the utltes U j are random, the th farmer wll select the alternatve j = 1 f U l > U 0 or f the non-observable (latent) random varable y* = U l U 0 > 0. The probablty that Y equals one (.e., that the farmer practces crop dversfcaton) s a functon of the explanatory varables: 3

6 P P ( Y P [ F ( W, L ) e r P [ e r 1 1 P ( F ( W, L ) ) r F ( X ) r 1) P ( U e F ( W, L ) e F ( W, L )( )] r U 0 ) (2) SUMMARY APRIL 2010 Where X s the n x k matrx of the explanatory varables and β s a k x 1 vector of parameters to be estmated, Pr(.) s the probablty functon, μ s the random error term, and F (X β) s the cumulatve dstrbuton functon for μ evaluated at X β. The probablty that a farmer wll dversfy n crop producton s a functon of the vector of explanatory varables and of the unknown parameters and error term. Equaton (2) cannot be estmated drectly wthout knowng the form of F. It s the dstrbuton of μ that determnes the dstrbuton of F. The functonal form of F s specfed wth a Cragg s Tobt alternatve or a double-hurdle model. A double-hurdle model s used to assess the determnants of crop dversfcaton as well as the factors nfluencng the extent of crop dversfcaton by smallholder farmers. Snce some farmers dd not dversfy, the dependent varable has a lot of zeroes, resultng n a corner-soluton outcome. In such a stuaton, ordnary least square regresson cannot be used snce ts outcome generates nconsstent and based parameter estmates (Wan, 2012). Instead, the Tobt model can be used, but s very restrctve gven that t smultaneously estmate the determnants of the probablty of partcpaton n crop dversfcaton and the extent of dversfcaton (Keelan, et al. 2006). The Tobt model also assumes that the coeffcents on the probablty and extent are equal, and ths assumpton may not always be reasonable (Ln and Schmdt 1984). Consequently, we use a double-hurdle model, whch s a twostage estmaton approach, to overcome the Tobt restrcton. The double-hurdle allows for separate estmaton of the probablty of partcpaton and the extent of dversfcaton (Cragg 1971). The frst stage of the model s a probt whch allows for a separate estmaton of the probablty of partcpaton n crop dversfcaton, whle the second stage examnes the decson by the farmer wth regard to the extent of crop dversfcaton. y * 1 y y ' w v * 2 x ' x Partcpaton decson (3) Extent decson (4) ', f y 1* > 0 and y 2* > 0 (5) y 0, otherwse (6) Where y* 1 s a latent varable descrbng the farmers decson to partcpate n crop dversfcaton, y* 2 s a latent varable descrbng the extent of crop dversfcaton, y s the observed dependent varable (farmer s extent to dversfy), w s a vector of varables explanng the partcpaton decson, x s a vector of varables explanng the extent decson, and v and μ are the respectve error terms assumed to be ndependent and dstrbuted as v ~ N(0,1) and μ ~ N(0,σ 2 ). 2.2 Specfcaton of the emprcal model We employ the Crop Dversfcaton (CDI) ndex n determnng crop dversfcaton for the partcular crops of nterest. The CDI s obtaned by subtractng the Herfndahl ndex (HI) from one. The CDI s an ndex of concentraton and has a drect relatonshp wth dversfcaton such that a zero value ndcates specalzaton and a value greater than zero sgnfes crop dversfcaton. Thus, t becomes easy to dentfy those farmers that are practcng crop dversfcaton. The CDI ndex s calculated as follows: where, P n A A 1 P proporton of A area under n 1 A th th crop crop Total cropped area (7) 1,2,3,... n (number of crops) 4

7 HI n 2 P 1 CDI 1 n 1 2 P 1 HI Herfndahl ndex (8) Crop dversfcaton ndex (9) SUMMARY APRIL 2010 After that, the Cragg s Tobt alternatve model s appled n estmatng the determnants of the probablty of a farmer practcng crop dversfcaton and the determnants of the extent of crop dversfcaton. The emprcal model s specfed as follows: Stage 1: P(D = 1 X ) = w α + v Partcpaton decson (10) Stage 2: Y = x β + μ Extent decson (11) where D takes the value of 1 f a farmer practced crop dversfcaton; Y s the crop dversfcaton ndex; w and x are the vectors of explanatory varables assumed to nfluence partcpaton and extent of crop dversfcaton, respectvely, and are the same for both stages; α s the vector of coeffcents assocated wth w n the frst stage; and β s the vector of coeffcents assocated wth x n the second stage. Descrptve statstcs help n dentfyng the sgnfcant dfferentatng soco-economc characterstcs of the two groups (dversfers and the non-dversfers). The statstcal sgnfcance of the descrptve varables s tested usng Chsquare and t-tests for dummy and contnuous varables, respectvely. By applyng descrptve statstcs, one can descrbe, compare, and contrast dfferent categores of sample unts (dversfers and non-dversfers) wth respect to the desred characterstcs. 2.3 Data Ths study uses secondary data from the Central Statstcal Offce of Zamba (CSO). The CSO keeps nformaton for most of the government departments of the country and conducts varous research projects and surveys. The data for ths study s based on the Crop Forecast Survey (CFS) that CSO conducts annually. The survey s representatve at the natonal level. Ths study uses cross sectonal data from ths survey for the year The purpose of the Crop Forecast Survey s to obtan data for the current agrcultural season. In general, the data obtaned every year usually relate to area planted to crops, expected or realzed producton, quantty and varety of seed, type of fertlzer used, quantty of crop harvested, crop sales, carryover stocks, crop marketng, and labor costs, among other varables. 2.4 Sample desgn and sample sze A three-stage samplng procedure s used to select enumeraton areas and households for data collecton purposes (CSO 2010). At the frst stage, Census Supervsory Areas (CSAs) are selected usng Probablty Proportonal to Sze (PPS) wth the number of agrcultural households n the CSA as the measure of sze. The CSAs are stratfed by dstrct wthn each provnce and ordered geographcally wthn each dstrct. At the second stage, Standard Enumeraton Areas (SEAs) are selected usng the same procedure descrbed above on the selecton of CSAs. The SEA s defned as the segment covered by one enumerator durng enumeraton. Only one SEA s selected wthn each sample CSA wth PPS for the survey. Once a SEA s selected, an enumerator vsts all the households wthn the SEA and collects a complete lstng of basc demographc and agrcultural nformaton from all the households n the sampled SEA s. The nformaton collected then forms the bass for stratfyng a household as beng agrcultural or non-agrcultural, wth agrcultural households beng pcked for possble selecton to be part of the local survey sample. At the thrd stage, a count of agrcultural households n selected work areas s conducted by lstng all agrcultural households resdent n these areas before selecton of sample households for data collecton exercse. After a process of stratfcaton, 20 households are then sampled from each SEA usng systematc samplng out of a total of between 100 and 150 households per SEA. Ths represents approxmately 20 percent of the total number of agrcultural households n a SEA. In the case of Southern provnce, 94 SEAs were selected for the CFS sample n However, due to nonresponse and other challenges wth household ntervewng, usable data was obtaned from 1,555 farmers. 2.5 Study area Zamba s Southern provnce has 11 dstrcts, wth Choma as ts provncal captal. The Southern Plateau s the center of the provnce. Southern provnce has the largest area of farmland of any Zamban provnce. Ths study focuses on Southern provnce as t s an mportant croppng regon for Zamba wth maze as the domnant crop grown for commer- 5

8 cal and subsstence purposes. About 18 percent of natonal maze producton comes from Southern provnce (Ngoma 2008). Furthermore, ths provnce s a drought-prone area and receves less than 1000 mm of ranfall annually on average. Most of the farmers n the regon depend largely on ran-fed hoe cultvaton wth lmted usage of modern nputs for crop producton. The government of Zamba has been promotng crop dversfcaton n the Southern provnce to offer farmers alternatve ways of generatng ncome and to mprove food securty by encouragng farmers SUMMARY to grow other APRIL crops 2010 n addton to maze. However, few Southern provnce farmers practce crop dversfcaton (Smwambana 2007). 2.6 Explanatory varables for ths study The followng were expected to be the explanatory varables that determne crop dversfcaton. The choce of these varables was based on a revew of the lterature on the topc and avalable data. Table 1 presents a summary of these varables. Gender of the Household Head: Ths s a dummy varable that takes a value of 1 f the household head s male and 0 f female. Male as well as female headed households can choose to dversfy or not based on ther choce, preferences, and access to resources. Access to resources such as land s an mportant ndcator of welfare among rural farm households and s especally crtcal for women wth no use rghts over a parcel of land. In Zamba and elsewhere n the regon, women rarely own or have control over land and other assets (Shezongo 2005). The nequalty that exsts n accessng and havng resources between males and females determnes how each household wll respond to dversfcaton. Thus the nature of the relatonshp of ths varable s expected to vary. Age of Household Head s a contnuous varable and s one of the factors that affect producton decsons on the part of the farmer. Elderly farmers look at farmng as just a way of lfe, whereas young farmers may be more nclned to look at farmng as a busness opportunty for famly sustenance (FAO 2012). In ths study, t s expected that elderly farmers wll not dversfy, whle younger farmers wll seek to dversfy. Therefore, t s expected that the varable wll be negatvely assocate wth crop dversfcaton. Household Sze: The sze of the household s expected to be postvely related wth crop dversfcaton. The larger the household sze, the more lkely that t wll be able to dversfy so as to ncrease ts food producton levels. Prevous studes also support ths hypothess (Wess and Brglauer 2000; Benn et al. 2004). Level of Educaton of Household Head: It s argued that educated people can understand agrcultural nstructons easly and are better able to apply sklls mparted to them, unlke the uneducated. It s therefore expected that ths varable wll postvely nfluence crop dversfcaton. Prevous fndngs by Ibrahm et al. (2009) ndcate a postve relatonshp between educaton level and crop dversfcaton. Sze of Landholdng: Ths s a contnuous varable referrng to the total area of arable farmland that a farmer owns. The amount of land a farmer has avalable plays a crucal role n determnng how many crops a farmer can produce. Prevous fndngs shows that crop dversfcaton s assocated wth larger farms (Wess and Brglauer 2000; Benn et al. 2004). Therefore, t s expected that the varable wll be postvely assocated wth crop dversfcaton. Number of Felds or Farm Plots: Ths refers to the total number of felds or farm plots that a farmer has. Ths varable s contnuous and t s expected to nfluence crop dversfcaton n a postve way. Accordng to Benn et al. (2004), the more farm plots a farmer has, the more he or she s able to dversfy. Hred Labour: In nstances where farmng households do not have enough domestc labour, hred labour s used as a supplement. In most cases, hred labour s sourced wthn the communty, wth wages beng pad ether n knd or n cash. Culas (n.d.) found that a greater use of both famly and hred labour s assocated wth ncreased crop dversfcaton. As a result, ths varable s expected to postvely nfluence crop dversfcaton. Tllage Tme: refers to the relatve tme perod durng whch tllage s done, ether durng or before the rany season. Tllage done durng the rany season gves farmers a surety that the rans wll be there for ther crops. A study n Malaw (Kankwamba, et al. 2012) found that ranfall determnes crop dversfcaton. As a result, ths varable s expected to postvely nfluence crop dversfcaton. Plough Tllage: Ths refers to land tllng usng a plough. The farmers who use a plough for tllng ther land are more lkely to dversfy because ploughng nvolves a larger area to be brought nto crop producton. Studes ndcates that there s a postve relatonshp between possesson of farm mplements and machnery by a farmer and dversfcaton (Mesfn, et al. 2011). As a result, t s expected that ths varable wll be postvely assocated wth crop dversfcaton. Fertlzer Quantty: Fertlzer s an mportant nput because wthout t, most crops n the Southern provnce do not produce well, expect for legumnous crops. As a result, fertlzer usage by farmers on ther crops has contnued beng an 6

9 essental practce to enhance ther crop producton. Kumar and Chattopadhyay (2010) show that the quanttes of fertlzer obtaned by farmers s postvely assocated wth crop dversfcaton. Thus, ths varable s expected to postvely nfluence crop dversfcaton. Dstance to the Market: Dstance to the market s an ndcator of physcal access to markets and SUMMARY organzed trade, APRIL as 2010 well as proxmty to economc resources. The nearer to the market the farmer s, the easer t becomes for hm or her to dversfy and to take produce to market. Studes on dversfcaton hghlght the mportance of proxmty to man roads and markets for development of other farm enterprses (Benn et al. 2004). However, n some nstances, farmers located farther away from markets or man roads, are found to dversfy n order to meet ther broad subsstence and nutrtonal needs (Kankwamba, et al. 2012). Hence, the nature of the assocaton of ths varable wth crop dversfcaton s ndetermnate and could be negatvely or postvely assocated. Table 1 Descrpton of varables used n the study Varable Type Descrpton Expected relatonshp to crop dversfcaton Gender Dummy Gender of head of household (male = 1) +/- Age Contnuous Age of household head (years) - Household sze Contnuous Number of people n household (proxy for labor supply) + Educaton Dummy Whether household head attended school (prmary and above=1) + Sze of land Contnuous All land operated for agrcultural purposes and owned by farmer (hectares) + Number of felds Contnuous Total number of felds or farm plots that a farmer has (number) + Hred labor Tllage tme Contnuous Dummy Number of people employed for wages durng croppng season (person-days) Whether tllage was done durng or before the rany season (durng = 1) Plough tllage Dummy Land preparaton usng a plough (used a plough=1) + Fertlzer quantty Contnuous Amount of fertlzer obtaned for crop producton (kg) + Dstance Contnuous Dstance from homestead to nearest market (km) +/- 3. RESULTS AND DISCUSSIONS Ths secton presents and dscusses the study fndngs. It starts wth a dscusson on the descrptve analyss to gve a pcture of the characterstcs of dversfer and non-dversfer farmers, soco-economc, demographc, and nsttutonal characterstcs are among them. Thereafter, the econometrc analyss s descrbed through the double-hurdle model whch produced estmates on the determnants and the factors nfluencng the extent of crop dversfcaton, thereby achevng the study objectves. Table 2 Summary of demographc and soco-economc characterstcs of farmers Dversfers, % Non-dversfers, % p-value Male head of household Attended school, head of household Used plough for tllage *** Prepared land durng (rather than before) rany season *** Age of household head, yr Household sze Dstance to market, km *** Landholdng sze, ha *** Felds, number ** Fertlzer quantty used, kg *** Hred laborers, number n 1,

10 From Table 2, we fnd that the dfference n the proporton of male-headed households between dversfers and non-dversfers was not statstcally sgnfcant. Smlarly, regardng educaton, there was no sgnfcant dfference n the proporton of heads of farmng households that had ever attended school between dversfers and non-dversfers. However, a postve assocaton wth crop dversfcaton was found for whether a plough was used for tllage and whether tllage was done durng, rather than before, the rany season. A sgnfcantly larger proporton of dversfers SUMMARY than APRIL nondversfers used a plough as ther mode of tllage and dd ther tllage durng the rany season (although most farmers, 2010 both dversfers than non-dversfers prepared ther land before the rany season). There s a statstcally sgnfcant dfference between dversfers and non-dversfers n the mean dstance to the market, sze of land holdng, number of felds, and fertlzer quantty used dversfers are located farther from the nearest market, have larger landholdngs and more felds, and use more fertlzer. Results of the double-hurdle model (Table 3) shows that whether a farmer dversfes hs or her crop producton s dependent on the sze of the landholdng, the quantty of fertlzer used, dstance from the farm to the market, and whether a plough s used for tllage. As for the extent of crop dversfcaton, t s sgnfcantly nfluenced by fertlzer quantty used and dstance to the market. For all sgnfcant determnants n both model, only drect postve relatonshps are observed. Sgnfcant negatve relatonshps are only seen n the coeffcents for some of the dstrct dummy varables ncluded n the model. Table 3 Determnants and extent of crop dversfcaton by smallholder farmers Varable Probablty of engagng n crop dversfcaton Total crops grown, f engage n crop dversfcaton Gender (male = 1) Age of household head (n years) Household sze (labor supply proxy) Number of felds (ha) Plough tllage (used a plough=1) *** Tllage tme (durng rany season=1) Sze of land (ha) *** Dstance to the man market (km) *** *** Educaton (prmary and above=1) Hred labor (person-days) Fertlzer quantty (kg) *** ** Dstrct dummy varables (base = Snazongwe) Choma (1=yes) *** Gwembe (1=yes) Itezh Tezh (1=yes) *** Kalomo (1=yes) *** *** Kazungula (1=yes) * *** Lvngstone (1=yes) *** *** Mazabuka (1=yes) * Monze (1=yes) * ** Namwala (1=yes) *** * Savonga (1=yes) Number of observatons 1,555 1,073 Wald (χ 2 ) Prob > χ Log Lkelhood We found no multcollnearty between any two or more explanatory varables n our study. When multcollnearty between explanatory varables s present, t s qute dffcult to separate the ndependent effect of each parameter estmate on the dependent varable. If multcollnearty were found, we would have only lmted confdence n any polcy prescrptons based on these estmates. Furthermore, all the varables were tested for heteroskedastcty usng the Breuch-Pagan test, and heteroskedastcty was not found. Heteroskedastcty s a phenomenon where the varance of the dependent varable s not the same for all ndependent observatons or explanatory varables. If t s detected and s not 8

11 taken care of, t leads to hgh standard errors and nconsstent sample estmates, whch may lead to wrong hypothess testng. In addton, a normalty test was done usng the kernel densty plot of resduals. The kernel densty plot provded a farly smooth curve that closely matched the normal curve, ndcatng that the normalty assumpton was not volated. Also, the model specfcaton was carred out usng the Ramsey-reset test, and the results revealed that there were no omtted varables n the model. SUMMARY APRIL 2010 Sze of Landholdng The sze of landholdng ncreases the probablty that a farmer wll engage n crop dversfcaton there s a drect correlaton between sze of landholdng and crop dversfcaton. Ths means that an ncrease n the sze of landholdng wll better enable a farmer to dversfy. Wth the extra landholdng, the farmer can decde how many crops to grow based on hs or her producton decsons. Increasng landholdng s possble n the case of Zamba snce, of the 58 percent of the total land area sutable for arable farmng, only 14 percent s beng cultvated currently (MoFNP 2006). Furthermore, study results are n agreement wth fndngs by Ashfaq et al., (2008) n whch they reported that the more access to addtonal land that a farmer has, the more he or she wll be able to engage n crop dversfcaton. Our results show that a one percent ncrease n the sze of landholdng of a farmer wll ncrease the probablty of a farmer producng more than one crop type by 1.1 percent. Fertlzer quantty The results show that the quantty of fertlzer used s assocated wth both a hgher probablty of a farmer to partcpate n crop dversfcaton and a greater extent n the dversfcaton n crops grown. A one percent ncrease n fertlzer quantty used by a farmer wll ncrease the probablty of a farmer engagng n crop dversfcaton by 9.9 percent. Furthermore, on average, a one percent ncrease n fertlzer quantty used wll ncrease the number of crops a farmer wll grow by 12 percent. The explanaton for ths s that ncreased quanttes of fertlzer wll provde addtonal ncentve to farmers to dversfy snce most farmers lack fertlzer, resultng n ncreased crop falure and poor yelds. Furthermore, fertlzer avalablty enables a farmer to enrch hs or her land, whch oftentmes s exhausted, thus makng t sutable for expandng nto the producton of a greater varety of crops. Our results concur wth fndngs from Inda (Kumar and Chattopadhyay 2010; Sngh, et al. 2006) and from Malaw (Ndhlovu 2010), n whch t was found that the quantty of fertlzer a farmer uses s a sgnfcant determnant of crop dversfcaton. Plough tllage Plough tllage sgnfcantly determnes the probablty of a farmer to partcpate n crop dversfcaton. The probablty of a farmer who uses a plough engagng n crop dversfcaton s 3.2 percent hgher than for a farmer who does not use a plough. Thus a farmer who uses a plough wll more lkely dversfy hs or her crops because tllage usng a plough reduces the drudgery of land preparaton, reduces the requrement for manual labor, and enables the explotaton of a larger land area compared to usng a hand hoe. These study results are n agreement wth fndngs by Mesfn, et al. (2011) n whch they reported a postve relatonshp between possesson of farm mplements and machnery by a farmer and crop dversfcaton. Dstance to market Dstance to the market sgnfcantly determnes both the probablty of a farmer to partcpate n crop dversfcaton and the extent of partcpaton. The results entals that a one percent ncrease n dstance to the market sgnfcantly ncreases the probablty of a farmer s partcpaton n crop dversfcaton by 2.7 percent that s, the further a farmer s from markets, the more lkely he or she wll dversfy crop producton. Wth regards to the extent of crop dversfcaton, on average, a one percent ncrease n dstance to the market wll ncrease the number of crops a farmer wll grow by 1.8 percent. The results mply that farmng households located farther from the nearest market wll dversfy for food securty due to hgher transport costs n accessng market ncentves to dversfy for commercal purposes. A study by Ibrahm et al. (2009) shows that farmng households that are farther away from the man markets face hgh costs of transportaton to get ther produce to the market and n such nstances, they opt to grow crops only for subsstence purposes. 4. CONCLUSION AND POLICY RECOMMENDATIONS Ths study was conducted wth the specfc objectves of comparng the demographc and soco-economc characterstcs of farmers n Southern provnce of Zamba who produce a dversfed crop mx wth those who do not, to dentfy the major determnants that nfluence farmer s decsons to dversfy n crop producton, and to determne the factors nfluencng the extent of crop dversfcaton by smallholder farmers. 9

12 Descrptve statstcs were used to assess any dfferences n the soco-economc characterstcs of farmers who have dversfed ther crop producton from non-dversfers. On average, dversfers have larger landholdngs and use more fertlzer than non-dversfers. Addtonally, a larger proporton of dversfers used a plough as ther mode of tllage than dd non-dversfers and were more lkely to do ther tllage durng the rany season. Results from the double-hurdle model ndcates that the sze of landholdng, fertlzer quantty, dstance to market, and tllage usng a SUMMARY plough sgnfcantly APRIL 2010 nfluence farmers probabltes to practce crop dversfcaton. Furthermore, the extent of crop dversfcaton s sgnfcantly nfluenced by the fertlzer quantty and dstance to the market. Our study found that ncreasng the sze of landholdng of a farmer by one percent wll ncrease the probablty of a farmer to engage n crop dversfcaton by 1.1 percent. The results also ndcate that ncreasng the quantty of fertlzer used by a farmer by one percent wll ncrease the probablty of a farmer engagng n crop dversfcaton by 9.9 percent. Moreover, a one percent ncrease n dstance to the market and fertlzer quantty wll ncrease the number of crops a farmer wll grow by 1.8 percent and 12 percent, respectvely. Furthermore, the results show that farmng households further away from the man market are more lkely to dversfy the crops that they grow, and we suggest that they do not dversfy for commercal purposes, but for food securty reasons. The study suggests a number of recommendatons for promotng crop dversfcaton among smallholder farmers. Frst; there s need for the government to consder undertakng polces that wll mprove farmers access to and control over land. Snce smallholder farmers are the ones who produce the bulk of the food, mproved access to more land wll enable farmers to grow more crops, thereby enhancng food and nutrton securty status and helpng n reducng poverty. Bhattacharyya (2008) shows that land s necessary f Indan farmers are to dversfy. Most farmers n Inda are constraned by nsuffcent land, such that some of them could not dversfy. Also, a study n Ethopa by Goshu, et al. (2012) ndcated that food securty status among farmng households was manly ncreased due to the sze of landholdng that farmers had, thereby helpng n reducng poverty. Furthermore, fndngs by Ashfaq et al., (2008) reveals that the more access to land a farmer has, the more he or she wll be able to engage n crop dversfcaton. Secondly, as the majorty of small-scale farmers are resource poor, have low levels of agrculture producton, and are usually food nsecure, the government should expand mplementaton of ts polcy on mechanzng agrculture producton through provson of farm equpment and mplements. In order to acheve ths, there s a strategy n place of encouragng the nvolvement of the prvate sector n the provson of such servces (MoFNP 2006). However, the polcy has not been mplemented (Xu 2009). As ths study shows that farmers who tll ther land usng a plough are able to dversfy, t s mportant that the government mplements the polcy of provdng smallholder farmers wth agrcultural mplements lke ploughs n order for them to dversfy ther crop producton. One of the benefts of tllng land usng a plough s that t sgnfcantly decreases the tme requred for farmers to accomplsh farm tasks. For example, usng a plough, a farmer can plough n a few hours a feld that would take the household an entre day to tll usng a hoe. The government should also consder brngng tradng markets closer to the farmers gven the fact that dstance to the market s an ndcator of market access, organzed trade and proxmty to economc resources. Much as the farmers farther away from markets are able to dversfy for food securty purposes, food securty s not everythng. Farmers need ncreased fnancal resources (cash) to send ther chldren to school, to buy nputs and so on. Therefore, f markets are brought closer, then farmers wll dversfy for commercal purposes as well. A study by Kumar and Chattopadhyay (2010) revealed that polces drected towards the expanson of nfrastructure lke road networks, marketng and storage facltes are mportant precondtons for the dversfcaton of crops for commercal purposes and are crucal n ensurng sustanable ncome and employment among farmers. 10

13 REFERENCES Ashfaq, M., S. Hassan, Z.M. Naseer, A. Bag, and J. Asma Factors Affectng Farm Dversfcaton In Rce-Wheat; Pakstan Journal of Agrcultural Scences. 45 (3): Benn, S., M. Smale, B. Gebremedhn, J. Pender, and S. Ehu The Determnants of Cereal Crop SUMMARY Dversty on APRIL 2010 Farms n the Ethopan Hghlands. Contrbuted paper for 25th Internatonal Conference of Agrcultural Economsts, Durban, South Afrca. Bhattacharyya, R Crop Dversfcaton: A Search for Alternatve Income of the Farmers n the State of West Bengal n Inda. Accessed June 10, Chwele, D., M. Fowler, E. Humphrey, A. Hurrell, and J. Wlls Evaluaton of Budget Support n Zamba: Agrculture Case Study. Oxford Polcy Management, Zamba. Chomba, G.N Factors affectng Smallholder Farmers Adopton of Sol and Water Conservaton Practces n Zamba. MSc dssertaton, Department of Agrcultural Economcs, Mchgan State Unversty, USA. Cragg, J Some Statstcal Models for Lmted Dependent Varables wth Applcaton to the Demand for Durable Goods. Econometrca 39: CSO (Central Statstcal Offce) Manual for Crop Forecast Survey for the 2009/2010 Agrcultural Season. Zamba. Culas, R.J. n.d. Causes of Farm Dversfcaton over Tme: An Australan Perspectve on an Eastern Norway Model. AFBM Journal 3 (1). School of Agrcultural and Veternary Scences, Charles Stuart Unversty. Australa. FAO (Food and Agrculture Organzaton of the Unted Natons) Improvng Food Securty n Vulnerable SADC Countres: Summary Fnal Report. Rome: FAO. FAO (Food and Agrculture Organzaton of the Unted Natons) Sustanable Crop Producton Intensfcaton. Twenty-thrd Sesson. Rome. Gbson, D Conservaton Farmng: Answer to Drought, Expensve Fertlzer. Lusaka, Zamba. Goshu, D., B. Kassa, and M. Ketema Does Crop Dversfcaton Enhance Household Food Securty? Evdence from Rural Ethopa. Advances n Agrculture, Scences and Engneerng Research. 2 (11): Hazell, P Sustanablty Issues n Agrcultural Development. Proceedngs of Seventh Agrculture Sector Symposum. World Bank, Washngton DC. Ibrahm, H., S.A. Rahman, E.E. Envulus, and S.O. Oyewole Income and crop dversfcaton among farmng households n a rural area of north central Ngera. Journal of Tropcal Agrculture, Food, Envronment and Extenson, JCTR (Jesut Center for Theologcal Reflectons) Dversfy Food Crop away from Heavy Dependence on Maze. Press Release, Lusaka, Zamba. Kankwamba, H., A.T.J.M. Mapla, and K. Pauw Determnants and spatotemporal dmensons of crop dversfcaton n Malaw. Llongwe: Internatonal Food Polcy Research Insttute. Keelan, C.D., M.M. Henchon, and C. Newman A Double Hurdle Model of Irsh Households Foodservce Expendture Patterns. A paper prepared for presentaton at the 98 th EAAE Semnar, Marketng Dynamcs wthn the Global Tradng System; New Perspectves. Chana, Greece. Kumar, U. D., and M. Chattopadhyay Crop Dversfcaton by Poor Peasants and Role of Infrastructure: Evdence from West Bengal. Journal of Development and Agrcultural Economcs. 2 (10): Ln, T., and P. Schmdt A test of the Tobt specfcaton aganst an alternatve suggested by Cragg. Revew of Economcs and Statstcs. 66 (1): MACO (Mnstry of Agrculture and Co-Operatves) Natonal Agrcultural Polcy for Zamba ( ). Lusaka. Mesfn, W., B. Fufa, and H. Jema Pattern, Trend and Determnants of Crop Dversfcaton: Emprcal evdence from smallholders n Eastern Ethopa. Journal of Economcs and Sustanable Development. 2 (8): MoFNP (Mnstry of Fnance and Natonal Plannng) Ffth Natonal Development Plan , Government of the Republc of Zamba, Lusaka. Ndhlovu, D Determnants of Farm Household Cropland Allocaton and Crop Dversfcaton Decsons: The Role of Fertlzer Subsdes n Malaw. Accessed Aprl 08, Ngoma, J Effect of Clmate Change on Maze Producton n Zamba. The Tema Insttute, Norrköpng, Sweden. 11

14 Pope, R.D., and R. Prescott Dversfcaton n Relaton to Farm Sze and other Soco- Economc Characterstcs. Amercan Journal of Agrcultural Economcs. 62 (3): Rahm, M.R., and W.E. Huffman The Adopton of Reduced Tllage: The Role of Human Captal and Other Varables. Amercan Journal of Agrcultural Economcs. 66: SUMMARY APRIL 2010 Samuelson, P A General Proof that Dversfcaton Pays. Journal of Fnancal Quanttatve Analyss. 2 (1): Saraswat, P.A., H. Basavaraja, L.B. Kunnal, S.B. Mahajanashett, and A.R.S. Bhat Crop Dversfcaton n Karnataka: An Economc Analyss. Department of Agrcultural Economcs, Unversty of Agrcultural Scences, Dharwad Karnataka, Inda. Shezongo, M Women s property rghts n Zamba. A paper presented to the Strategc Ltgaton Workshop,14-18 August 2005, Johannesburg, South Afrca. Smwambana, M A Study on Cassava Promoton n Zamba. Task Force on Accelerated Cassava Utlzaton, ACF and ASP. Lusaka: ACF and ASP. Sngh, N.P., R. Kumar, and R.P. Sngh Dversfcaton of Indan Agrculture: Composton, Determnants and Trade Implcatons. Agrcultural Economcs Research Revew. 19: Thurlow, J., S. Benn, X. Dao, H. Kalnda, and T. Kalnda Agrcultural Growth and Investment Optons for Poverty Reducton n Zamba. IFPRI Dscusson Paper Washngton, DC: Internatonal Food Polcy Research Insttute. Wan, W., and W. Hu At Home Seafood Consumpton n Kentucky: A double-hurdle model approach. Southern Agrcultural Economcs Assocaton Annual Meetng. Brmngham, AL. Wess, C. R., and W. Brglauer Determnants and Dynamcs of Farm Dversfcaton. Workng paper EWP Department of Food Economcs and Consumpton Studes, Unversty of Kel, Germany. Xu, Z., Z. Guan, T.S. Jayne, and R. Black Factors Influencng the Proftablty of Fertlzer Use on Maze n Zamba. Polcy Synthess Food Securty Research Project Zamba. ZDA (Zamba Development Agency) Agrculture, Lvestock and Fsheres. Agrculture Sector Profle, Zamba. About the Authors Kru Schoongwe (kruschoongwe@yahoo.com) s an MSc student n the Department of Agrculture and Appled Economcs (DAAE) at the Llongwe Unversty of Agrculture and Natural Resources (LUANAR). The paper draws on the student s MSc thess wrtten under supervson of Lawrence Mapemba and Daves Ng'ong ola, Lecturers at LUANAR, and Gelson Tembo, Lecturer n the Department of Agrcultural Economcs, Unversty of Zamba. Acknowledgments Ths paper was selected for the Malaw Strategy Support Program (MaSSP) Bunda Grant Scheme for 2013/14. Under ths capacty buldng program IFPRI provdes fnancal and supervsory support to students to extract a journal-length artcle from ther MSc theses. The authors would lke to thank John Mazunda and Todd Benson for gudance n ths regard. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 2033 K Street, NW Washngton, DC USA T F Skype: fprhomeoffce fpr@cgar.org IFPRI-LILONGWE P.O. Box Llongwe 3, Malaw T fpr-llongwe@cgar.org Ths Workng Paper has been prepared as an output for the Malaw Strategy Support Program, and has not been peer revewed. Any opnons stated heren are those of the author(s) and do not necessarly reflect the polces or opnons of IFPRI. Copyrght 2014, Internatonal Food Polcy Research Insttute. All rghts reserved. To obtan permsson to republsh, contact fpr-copyrght@cgar.org. 12