Resource use efficiency of smallholder crop production in the central highlands of Ethiopia

Transcription

1 Resource use effcency of smallholder crop producton n the central hghlands of Ethopa Essa C. Mussa a 1, Gdeon A. Obare a, Ayalneh Bogale b, Frankln P. Smtowe c a Egerton Unversty, Department of Agrcultural Economcs and Busness Management, Kenya, b Afrcan Centre for Food Securty, Unversty of Kwazulu Natal, Petermartzburg, South Afrca c Internatonal Crops Research Insttute for the Sem-ard Tropcs (ICRISAT), GT - Insttutons, Markets, Polcy & Impacts, Kenya Abstract Resource use effcency n agrcultural producton has been a major concern n Ethopa. In ths artcle data from 700 households n the central hghland dstrcts were used to assess farm-level resource use effcency and to determne factors that nfluence neffcences n the producton of teff (Eragrosts tef), wheat and chckpea, the major mportant crops n the country. The study establshed that smallholder farmers are resource use neffcent. Moreover, a two-lmt Tobt regresson model results reveal that neffcency n resource use s postvely and sgnfcantly affected by famly sze, farmng experence and membershp to assocatons. It s also found that those households whose decson makers have roles n ther communty actvtes show mproved resource use effcency. Moreover, the fndngs show that elmnatng resource use neffcency could contrbute about 31.28% of the mnmum annual ncome requred for the sustenance of an average farm household. The study establshed that resource use effcency and productvty gans are lkely to be sgnfcantly mproved through expanson of nonfarm sectors, reform of farmer related assocatons and ntegratng communty leadershp n varous communty actvtes and programs. Moreover, market nfrastructure development would lkely ncrease effcency and agrcultural productvty. JEL classfcaton: C21, C61, Q12 Keywords: DEA; Tobt; Resource use effcency; Smallholder producton; Crops; productvty; Ethopa 1. Introducton Agrculture s the bass of Ethopan economy whch accounts for almost half of the country s GDP, 60% of ts exports and 80% of total employment (CIA, 2007). Ths makes the sector to play a crtcal role n the country s mportant macroeconomc performance such as 1 Correspondng author: Tel: (Kenya) or (Ethopa). E-mal: essachane@gmal.com (E.C. Mussa). Ths conference paper has not been peer revewed. Any opnons stated heren are those of the author(s) and are not necessarly endorsed by or representatve of IFPRI or of the cosponsorng or supportng organzatons.

2 economc growth, employment, unversal food securty and per capta ncome. However, Ethopan agrculture remans largely domnated by subsstence and smallholder-orented system (Bshaw, 2009). Moreover, Ethopan hghland agrculture s characterzed by hgh dependency on ranfall, tradtonal technology, hgh populaton pressure, and severe land degradaton combned by the low level of productvty (Medhn and Köhln, 2008). The ssue of ncreasng agrcultural productvty has become the man concern to governments followng consderable ncrease n food prce over the last two years that follows decades of low food prce (Conrade et al., 2009). Despte of Ethopan government s polcy to expand crop producton for exports, domestc consumpton and unversal food securty (MoFED, 2006), the productvty of teff s the lowest among cereal crops (Hale et al., 2004). In addton, despte ts huge potental n wheat producton, the country remans the net mporter of the commodty (Rashd, 2010). Moreover, the compettveness of smallholder chckpea producers n Ethopa s restrcted by low productvty and poor qualty of tradtonal varetes (Shferaw and Teklewold, 2007). In addton, n Ethopa, the adopton of modern and ntensve agrcultural practces such as the use of chemcal fertlzer and mproved seeds s qute low. Moreover, napproprate land use plannng combned wth overgrazng and populaton pressure has led Ethopa to experence one of the hghest rates of sol nutrent depleton n sub-saharan Afrca (Chanyalew et al., 2010). Agrcultural productvty depends on how factors are effcently used n the producton process. Therefore, ntensfcaton of agrcultural land and expanson of technology use must be accompaned by resource use effcency that enhances productvty of factors. Improvements n resource use effcency hence ncrease n productvty wll reduce encroachment of populaton to margnal agrcultural lands. In turn, ths wll protect the resource base of the poor aganst degradaton. More mportantly, effcent resource use s the bass for achevng unversal food securty and poverty reducton strateges partcularly n the rural areas. It s also crucal for polcy makers to have adequate and evdence based polcy optons to ncrease effcency and productvty to mprove the lvelhoods of the poor. However, most of the prevous emprcal effcency studes n Ethopa (see Kharo and Battese (2005) and Gebreegzabher et al. (2005)) have been domnated by the use of smple effcency measures to provde requred nformaton on resource use effcency n agrculture. The studes focus on a partcular agro-ecology, or concentrated on techncal effcency otherwse they analyzed specfc crop producton effcency. Such approaches may gve nconsstent results and conclusons that may mslead. Ths study estmates techncal, allocatve, and economc effcences of smallholder major crop producers n the central hghland dstrcts. The study covers a relatvely larger populaton and consders mportant major crops to ncrease farm household ncome and acheve nutrtonal and food securty. In addton, the study focuses on the areas where hgh populaton pressure and land degradaton threatens sustanable agrcultural producton and food securty. Moreover, the sample dstrcts are some of the areas where cereal crops and legumes are largely produced whereby resource use effcency and productvty mprovement can have a substantal mpact on the lvelhoods of many farm households. 2

3 The paper s organzed as follows. In the next secton we dscuss the emprcal modelng strateges of the paper ncludng data envelopment analyss (DEA) technque and two-lmt Tobt model. In secton 3 we descrbe the study areas and the data used by the study. Results of effcency scores and dentfyng sources of resource use neffcency are presented n secton 4. In the last secton we present our conclusons and key polcy mplcatons. 2. Modelng strategy 2.1 Data Envelopment Analyss (DEA) Based on Farrell s (1957) nfluental work, Charnes et al. (1978) were the frst to ntroduce DEA approach to estmate effcency. Snce ts ntroducton, the approach has served as the corner stone for all subsequent developments n the nonparametrc approach to the measurement of techncal effcency (Had-Vencheh and Matn, 2011). As ponted out by several authors (see Ray, 2004; Coell et al., 2005; Headey et al., 2010 for detals), DEA strategy has several advantages. It s a nonparametrc technque that does not requre a pror specfc functonal form for the producton fronter. In addton, multple outputs and multple nputs wthout necessarly beng aggregated can be handled n DEA technque. Furthermore, t s possble to dentfy the best practce for every decson-makng unt under consderaton and estmate the output or cost gap of neffcent frms to be fully effcent. Regardng ts potental weaknesses, however, apart from ts senstvty to extreme observatons, a hypothess testng at the frst stage of DEA s not possble. Moreover, the technque attrbutes all devatons from the fronter (best practce) to resource use neffcency. However, ths study adopts the two-stage DEA method to estmate effcency of multple nputs and outputs producton process. Supposng a group of n homogenous Decson-Makng Unts (DMUs), n order to produce r number of outputs ( r=1,2,3, k) s number of nputs are utlzed (s=1,2,3, m,) by each DMU (=1,2,3, n). In order to maxmze the level of weghted outputs subject to weghted nputs the followng lnearly expressed equaton developed by Charnes, Cooper and Rhodes (CCR) approach (Charnes et al., 1978) s estmated: : Max µν θ = µ Y + Y + µ Y (1) 1 1 µ r r Subject to: ν X ν X ν X 1 (2) s s = µ 1 Y + µ 2Y µ Y ν 1X + ν 2 X ν X (3) 1 j 2 j r rj 1 j µ, 0, and ( and j = 1, 2, 3,..., k) ν j s sj where θ s the techncal effcency and represents th DMU. Whle Y r s the amount of output r produced by th DMU, X s represents the amount of nput s used by DMU. In the expresson μ r s weght gven to output r, and ν s s weght gven to nput s. 3

4 However, n the maxmzaton process, DMUs always face fnancal lmtatons or mperfect compettve markets where ncreased amounts of nputs do not proportonally ncrease the amount of outputs obtaned (Coell et al., 2005). In order to account for these effects the DEA model for varable returns to scale (VRS) was developed by Banker, Charnes and Cooper (BCC) (Banker et al., 1984). Mathematcally, the DEA method under VRS assumpton for each DMU can be expressed as: Max Subject to : φy φλφ x Xλ 0 ' N1 λ = 1 λ 0 + Yλ 0 (4) In the restrcton N1'λ=1, N1' s convexty constrant whch s an N 1 vector of ones and λ s an N 1 vector of weghts (constants) whch defnes the lnear combnaton of the peers of the th DMU. 1/ϕ defnes a techncal effcency score whch vares between zero and one. If ϕ =1 then the DMU s on the fronter and s techncally effcent and f ϕ< 1 the DMU les below the fronter and s techncally neffcent. Smlarly, to estmate economc effcency scores (EE), a cost mnmzng DEA s specfed as: Mn Subject to : y X λ, X W + Yλ 0 Xλ 0 ' N1 λ = 1 λ 0 ' X (5) where, W s a transpose vector of nput prces for the th DMU and X s the cost-mnmzng vector of nput quanttes for the th farm gven the nput prces W and total output level y. Economc effcency s the rato of potental mnmum cost of producton (W ' X ) to the actual cost of producton (W ' X ) as: EE= W ' X / W ' X (6) Allocatve effcency can be estmated as the rato of economc to techncal effcency as AE=EE/TE. In ths study, for the DEA technque, the output varables are outputs of chckpea, 4

5 teff and wheat (klogram). The nput data consst of total area of land ploughed (hectare), total labor used (man/days), total amount of fertlzers used (klogram), amount of feld chemcals appled (lter) and the amount of seed used for each crop (klogram). The costs assocated wth each nput are also ncluded n the DEA to estmate economc effcency. Effcency scores are estmated usng the computer program, DEAP Verson 2.1 descrbed n Coell (1996). 2.2 The Two-lmt Tobt Model Smallholder farmers are assumed to operate under the same polcy and nsttutonal envronments and face exogenous varables denoted as Z. In addton, t s also assumed that these condtons determne farmers decson to choose set of nput vector x and produce output vector y. Accordngly, n the producton process a gven farmer s consdered to be relatvely full effcent f t operates along the boundary of the fronter (Y) whch also defnes the level of technology n the system. The boundary of the fronter represents a locus of output ponts constructed by best practce farms. In ths case the output of effcent frms (Y ) to the potental output along the fronter s equal (Y=Y ). Relatve effcency measures, computed as the rato of actual (realzed) to the potental (fronter) output level(y/y ), of these farms wll be unty(y/y =1). On the other hand, frms whch are relatvely neffcent compared to the best practce (fronter) operate at ponts n the nteror of fronter and score less than unty (Y/Y <1). Furthermore, the effcency scores of the most neffcent farms n the system are found closer to zero (Y/Y >0). Therefore, whle the scores are bounded between zero and unty wth the upper lmt set at one, the dstrbuton s censored at both tals (0 <Y/Y 1). Thus, followng Coell et al. (2002) and Bravo-Ureta et al. (2007), the study adopts the two-lmt Tobt model. The model s estmated as follows (Amemya, 1985): k U = β 0 + β j Z j + µ, j= 1 1, f U = U, f 0, f U 1 0 < U U 0 < 1 (7) where: refers to the th DMU, U s neffcency scores of the th DMU. U s the latent neffcency, β j are parameters to be estmated and μ s an error term that s ndependently and normally dstrbuted wth mean zero and common varance of δ 2 (μ ~NI (0, δ 2 )). Z j are host of soco economc, nsttutonal and demographc varables. 5

6 3. Methodology 3.1 Descrpton of the Study areas The study s conducted n three dstrcts, namely Mnjar-Shenkora, Gmbchu and Lume- Ejere whch are found n the central hghlands of Ethopa. They have a total populaton of 345,177 persons. The combned total area s about 379, hectares, of whch 138, hectares (36.46%) s arable land. The dstrcts are characterzed by moderate to sub-humd temperature wth mean of 17ºC, 23ºC and 25ºC for Gmbchu, Lume-Ejere and Mnjar-Shenkora dstrcts, respectvely. The dstrcts have alttudes of , , and meters above sea level for Gmbchu, Lume-Ejere, and Mnjar-Shenkora, respectvely. They also get annual ranfalls of rangng 800mm-1000mm, 800mm-1000mm and 500mm-1200mm for Mnjar-Shenkora, Gmbchu and Lume-Ejere dstrcts, respectvely. The study areas represent some of the major cereals and legumes growng areas n the country. The agrcultural producton system s mxed crop-lvestock agrcultural system whereby a smallholder farmer practces crops and lvestock producton under the same management. Teff, chckpea and wheat are mportant and major crops n terms of quantty and area grown n the study areas. 3.2 Data source The data used for ths study orgnates from a baselne survey conducted by the Internatonal Crops Research Insttute for the Sem-Ard Tropcs (ICRISAT) and Ethopan Insttute of Agrcultural Research (EIAR) n A Mult-stage samplng procedure was used to select dstrcts, kebeles 2 and farm households. In the frst stage, three dstrcts namely Mnjar- Shenkora, Gmbchu and Lume-Ejere were purposvely selected from the major legume producng areas based on the ntensty of chckpea producton, agro-ecology and accessblty. Then, eght kebeles from each of Gmbchu and Lume-Ejere dstrcts and ten kebeles from Mnjar-Shenkora dstrct were randomly selected. Fnally, 700 farm households were randomly selected from the dstrcts for the study. 4. Emprcal results 4.1 Effcency scores The descrptve statstcs of varables used for estmaton of effcency scores usng DEA are presented n Table 1. The results for output varables show that an average household produces 1, kg of chckpea, 1, kg of teff and 1, kg of wheat. In the producton process, on average, kg of DAP and kg of urea are used whle the mean cost of these nputs are estmated to be ETB and for DAP and urea fertlzers, respectvely. The mean sze of land used for the three crops producton s 1.96 hectare and ts average cost s computed as ETB 5, In addton, about 0.62 lters of feld chemcal 2 It s usually named as peasant assocaton and s the lowest admnstratve unt n the country. 6

7 s used durng producton of the three major crops and ts average cost s ETB Moreover, the mean quanttes of seed used are kg, kg and kg for producton of chckpea, teff and wheat, respectvely. The assocated average costs of chckpea, teff and wheat seeds are ETB , and , respectvely. Furthermore, the mean level of labor (both famly and hred) use for producton of the three crops n man/days s about and ts average cost s estmated to be ETB 17, Table 1: Descrptve Statstcs of varables used for DEA (N=700) DEA Varable Descrpton of Varables Unt of Measurement Mean a Output Varables Y 1 Output of Chckpea klogram 1, ( ) Y 2 Output of Teff klogram 1, ( ) Y 3 Output of Wheat klogram 1, ( ) Input varables X 1 Plot Sze Hectare 1.96 (1.31) X 2 Labor ( Famly and Hred) Man/days (535.60) X 3 Quantty of Feld chemcal Lter 0.61 (1.24) X 4 Quantty of Chckpea seed klogram (147.93) X 5 Quantty of Teff seed klogram (112.12) X 6 Quantty of wheat seed klogram (108.88) X 7 Quantty of DAP fertlzer klogram (211.26) X 8 Quantty of urea fertlzer klogram (146.56) Input Costs C 1 Cost of Land used ETB 5, ( ) C 2 Cost of Labor used ETB 17, ( ) C 3 Cost of feld chemcal appled ETB (99.72) C 4 Cost of chckpea seed ETB (666.89) C 5 Cost of teff seed ETB (239.38) C 6 Cost of wheat seed ETB (451.84) C 7 Total Cost of DAP fertlzer ETB (832.71) C 8 Total Cost of urea fertlzer ETB (507.66) a Values n the parentheses are standard devatons. Source: Authors calculaton based on ICRISAT 2008 survey data, 2011 Table 2 presents aggregate effcency dstrbuton of farmers. It s shown that about percent of farmers are techncally effcent whereas 99 percent of farmers are both allocatvelly and economcally neffcent. The mean techncal, allocatve and economc effcency scores are 0.79, 0.43 and 0.31, respectvely. Usng the results from one sample t-test, t s concluded that smallholder farmers are not techncally, allocatvelly and economcally effcent. Ths shows that 7

8 there s a sgnfcant margn of ncrease n output and reducton n cost of producton. Furthermore, ANOVA and Kruskal Walls tests show that there s sgnfcant varaton n resource use effcency across the three dstrcts whch call for further studes for dstrct specfc strateges to mprove effcency. Table 2: Dstrbuton of techncal (TE), allocatve (AE) and economc (EE) effcency scores (N=700) Effcency TE AE EE Categores Freq. Percent Freq. Percent Freq. Percent E < E < E < E < E < E < E < E < E < E Effcent Farmers Ineffcent Farmers Mean Scores 0.79 (0.27) 0.43(0.19) 0.31(0.14) Mnmum Scores Maxmum Scores E stands for Effcency, and fgures n parentheses are standard devatons. Source: Authors calculaton based on ICRISAT 2008 survey data, 2011 The mean score of techncal effcency mples that f resources were effcently utlzed, the average farmer could ncrease current output by 21% usng exstng resources and level of technology. Ths means that the average output of chckpea, teff and wheat could be ncreased by 21% from current level of output. The monetary value of the addtonal output (usng real prces) due to mproved techncal effcency s estmated to be ETB 4,342.77, equvalent to USD per household per annum. The result for mean allocatve effcency score also suggests that smallholder farmers could reduce costs by 57% f producers used the rght nputs and outputs mx relatve to nput costs and output prces. The mean economc effcency ndcates that there s a sgnfcant level of cost neffcency n the producton process. That s an average producer could reduce current average cost of producton by 69% to acheve the potental mnmum cost level wthout reducng output levels. The result suggests that the current mean cost of producton of the sample farmers (ETB 8

9 24,951.40) could be reduced by ETB 17,216.47, equvalent to USD Ths mples that reducton n cost of producton through elmnatng resource use neffcency could add about 31.28% of the mnmum annual ncome requred for the sustenance of an average farm household (USD 4,672 per annum for a household wth famly sze of 6.4 persons and 2$ per day per person). Moreover, reducton n cost of producton and ncrease n gross margn of output wll help farmers to partcpate n hgh value crop producton. The study s also found that about 37 percent of farmers operate under decreasng returns to scale, showng that farmers are usng nputs above the recommended rate. Therefore, t s establshed that the use of nputs above the recommended level seems to be the predomnant source of scale neffcency. 4.2 Sources of resource use neffcency Ths secton devotes to nvestgate why there s effcency varaton across farmers. To understand some of the causes of these varatons, techncal and economc neffcency scores estmated usng DEA were separately regressed on selected demographc, soco economc and nsttutonal varables. The descrptve statstcs of varables used n the analyss of resource use neffcency s presented usng Table 3. Table 3: Descrptve statstcs of varables for two-lmt Tobt model (N=700) Varables Descrpton Mean Std. D Contnuous Varables age Age of the household heads (year) hheadeduc Educaton level household heads (year) famlysze Famly sze (number of persons) plotdst Average Plot dstance from resdence (Klometer) farmexpr Experence of growng chckpea (year) wlkdsmnm Walkng dstance to the nearest man market (Klometer) contextag Contacts wth extenson agents ( days/year) cultrany Cultvated land Sze ( hectare) Dummy Varables Response Freq. Percent gender Sex of the household head Male ( 1) Female (0) rolecommu Role of the household head n the communty Yes (1) No (0) membershp Membershp of household n assocatons Yes (1) No (0) credtacc Access to Credt at market nterest rate Yes (1) No (0) Source: Authors calculaton based on ICRISAT 2008 survey data,

10 Age of household heads s on average 47 years whle the mean level of household heads educaton s 1.74 years. The result shows that about 7.15% of households are female headed. Whle walkng dstance to the man market has a mean of 9.94 km, the average plot dstance from resdence has a mean value of 1.67 km. Famly sze has a mean of 6.4 persons per household. About 25.7% of household heads have a role n ther respectve communty actvtes. On the other hand, 87.43% of farm households are members n farmers assocatons, clubs or cooperatves. In addton, results ndcate that about 66.43% of farmers got access to credt at market nterest rate. It s also shown that the total sze of land allocated to the three crops n the long rany season s on average 2.08 hectares. Moreover, the mean household head s chckpea growng experence s years. In addton, the number of farmers contacts wth extenson agents has a mean value of days per year. Tobt regresson model results for sources of techncal and economc neffcences are presented n Table 4. The results show that age of household head contrbutes sgnfcantly and negatvely to techncal neffcency (P < 0.05). Ths mples that as age of the decson maker ncreases, techncal neffcency wll decrease. Ths may be perhaps due to the fact that farmers learn from ther experence about the allocaton of nputs. The varable famly sze contrbutes postvely and sgnfcantly to resource use neffcency (P < 0.01) suggestng that larger famly sze s lkely to cause farm households to be resource use neffcent. The average famly sze n the study areas s larger than even the natonal average. A farm household wth large famly sze needs more resource to satsfy ts energy and food requrements. Therefore, to meet these needs resources wll be exploted more extensvely that leads to the expanson of margnal lands and envronmental degradaton hence declne n productvty. Ineffcency n resource use n the rural areas could be reduced through absorbng the excess labor force to nonfarm sectors wthout negatvely affectng farm output and productvty (Len et al., 2010). The fndng s consstent wth that of Bnam et al. (2003) and Coell et al. (2002) where larger famles are found more neffcency. In addton, chckpea farmng experence has a sgnfcant and postve effect on resource use neffcency. Ths mples that, perhaps, farmers who are more experenced n chckpea farmng are more conservatve to adopt new technology rather they prefer to reman wth tradtonal producton system. Thus, ther resource use effcency and productvty are negatvely affected. Moreover, ths result also mples that farmers are less lkely to be market orented under current producton system where productvty and effcency are compromsed. Moreover, the effect of walkng dstance to the nearest man market on techncal neffcency was statstcally sgnfcant and affects techncal neffcency postvely (P < 0.05). Ths mples that farmers far from markets are more techncally neffcent compared to ther counterparts resde nearby markets. Ths mght be due to the fact that as farmers locate far from market there s lmted access to nput and output markets and market nformaton. Moreover, dstance to market leads to hgher transacton cost whch reduces the benefts accrue to the farmer. More mportantly, longer dstance from market dscourages farmers from partcpatng n market orented producton. Thus, development of market and road nfrastructure could reduce 10

11 resource use neffcency and ncrease productvty of farmers through facltatng market partcpaton and ntegraton. Table 4: Results of two-lmt Tobt model for sources of resource use neffcency Independent varables Techncal Ineffcency Economc Ineffcency β Std. Err. t-value β Std. Err. t-value Sex of the household head Age of the household head Educaton level of the household head Famly sze Plot dstance from resdence Chckpea farmng experence Walkng dstance to the nearest man market Membershp of household Role of household head n the communty Contacts wth extenson agents Credt Access Total land cultvated Constant Log Lkelhood Sample (N) LR Ch-square(χ 2 )(12) , and denote sgnfcance at 1%, 5% and 10% level, respectvely. Source: Authors calculaton based on ICRISAT 2008 survey data, 2011 Membershp of households n a farmers assocaton contrbutes postvely and sgnfcantly to economc neffcency (P < 0.05). Ths suggests that farmers who belong to assocatons are found to be economcally neffcent. Ths stuaton can happen f membershp n the assocatons and partcpatons, f any, s nomnal and decson makng process do not take n to account the needs of the members. Partcularly, f agrcultural nformaton and technology transfer through assocatons do not address the needs of the poor farmer and the margnal ncludng women, ther effcency and productvty wll not mprove. Smlar results were found by Bnam et al. (2003) n a study of Coffee Farmers n Cote d Ivore but t s n contrary wth the fndngs by Nyagaka et al. (2010) n a study of smallholder Irsh Potato farmers n Kenya. 11

12 It s also found that households whose heads have a role n ther communty actvtes sgnfcantly mproves ther level of economc effcency (P<0.1). Ths mples that when decson makers hold responsblty n ther communty actvtes they are at the forefront of changes. They are consdered as role models for the rest of farmers or change agents and are more wllng to adopt new deas and nnovatons. Ths ndcates that resource use effcency seem to be mproved through ntegratng communty leadershp n varous communty actvtes and programs. Credt access contrbutes postvely and sgnfcantly to techncal neffcency (P < 0.01). Ths mples that farmers wth access to credt n the study areas are more techncally neffcent than ther counterparts who have no access. Ths mght happen due to varous possble reasons. Frst, f credt system s not respondng to the needs of farmers n terms of amount, tme and repayment procedure, the servce mght rather brng neffcency than reducng t. Second, the level of loan dverson problem and napproprate use of funds by farmers may also cause the servce to be neffectve n reducng neffcency. Thrd, absence of compettve credt systems can also tghten the alternatves regardng collateral requrements, tme of repayment and nterest rate determnaton and condtons regardng on falure to repay the loan. Smlar results were also reported by Seyoum et al. (1998). However, a study by Nyagaka et al. (2010) concluded that access to credt ncreases resource use effcency. In addton, total area cultvated durng long rany season has a postve and sgnfcant effect on techncal neffcency (P < 0.01). The results mply that as farm sze ncreases techncal neffcency wll also sgnfcantly ncrease. Perhaps, tmely and approprate agrcultural operaton on larger land sze wth tradtonal technology may not be effectve whch leads to hgher level of neffcency. Moreover, larger plot sze n the study areas mples larger fragmentaton of plots whch are wdely scattered, makng t dffcult for farmers to work on all ther felds at the same tme. Larger plot sze may also mean expanson of agrcultural lands to margnal areas whch makes effcent crop producton dffcult. As a result, effcency and productvty can be negatvely affected when plot sze s large gven the current level of technology. 5. Conclusons The study establshed that there s a sgnfcant amount of resource use neffcency n chckpea, teff and wheat producton n the sample dstrcts of central hghlands. It s also found that producers could ncrease ther output by 21% wth current level of resources and could reduce cost of producton by 69% to acheve the mnmum cost level gven current crop output. The study also reveals that ths mprovement n resource use effcency could add about 31.28% of the mnmum annual ncome requred for the sustenance of an average farm household. In addton, most resource use neffcent farms are operatng under decreasng returns to scale whch mples that farmers use above the recommended level of nputs. There should be polces and strateges toward expanson and promoton of natonwde nonfarm sectors whch provde off farm employment and absorb the excess labor from the 12

13 agrcultural sector. Ths would reduce the pressure on agrcultural land whch n turn mproves labor productvty n the agrcultural sector. Moreover, reducton of populaton pressure on the rural farm wll also help to reduce encroachment of agrcultural practce to the margnal areas. Development of market, road nfrastructure, and decson makng process of farmers nsttutons could reduce resource use neffcency and ncrease productvty. These also reduce transacton cost and mprove productvty hence encourage farmers to partcpate n hgh value and market orented producton actvtes. Moreover, supportng and facltatng of communty based organzatons wth agrcultural producton orentaton and enhance ther ablty to partcpate n communty actvtes would lkely mprove farmers effcency and productvty. Furthermore, access to credt should be combned wth contnued avalablty of complementary agrcultural support servces, ncludng extenson and tranng. Ths facltates transfer and adopton of technologes by farmers that leads to mprovement n resource use effcency and productvty. However, there s a need for further nvestgaton of why farmers use more resources than recommended level. References Amemya, T., Advanced Econometrcs, Harvard Unversty press, Cambrdge, Massachusetts. Banker, R.D., Charnes, A., Cooper, W.W., Some Models of Estmatng Techncal and Scale Ineffcences n Data Envelopment Analyss. Management Scence 30, Battese, G.E., Coell, T.J., A model for techncal neffcency effects n a stochastc producton functon for panel data. Emprcal Economcs 20, Bnam, J.N., Sylla, K., Darra, I., Nyamb, G., Factors Affectng Techncal Effcency among Coffee Farmers n Cote d Ivore: Evdence from the Centre West Regon. Afrcan Development Revew 15, Bshaw, B., Deforestaton and Land Degradaton n the Ethopan Hghlands: A Strategy for Physcal Recovery. Ethopan e-journal for Research and Innovaton Foresght 1(1), Inaugural Issue, Bravo-Ureta, B.E., Solfs, D., Lopez, V.H.M., Marpan, J.F., Tham, A., Rvas, T., Techncal effcency n farmng-a meta-regresson analyss. Journal of Productvty Analyss 27, Chanyalew, D., Adenew, B., Mellor, J., Ethopa s Agrcultural Sector Polcy and Investment Framework (PIF) ( ), Draft Fnal Report. Federal Democratc Republc of Ethopa, Mnstry of Agrculture and Rural Development. Adds Ababa. 13

14 Charnes, A., Cooper, W.W., Preface to topcs n data envelopment analyss. Annals of Operatons Research 2, Charnes, A., Cooper, W.W., Rhodes, E., Measurng the Effcency of Decson Makng Unts. European Journal of Operatonal Research 2, Coell, T. Rahman, S, Thrtle, C., Techncal, Allocatve, Cost and Scale Effcences n Bangladesh Rce Cultvaton: A Non-parametrc Approach. Journal of Agrcultural Economcs 53(3), Coell, T.J., A Gude to DEAP Verson 2.1: A Data Envelopment Analyss (Computer) Program. CEPA Workng Paper 96/08, Department of Econometrcs, Unversty of New England, Armdale. Coell, T.J., Prasada Rao, D.S., O Donnell, C.J., Battese, G.E., An Introducton to Effcency and Productvty Analyss (2 nd Ed.). Sprnger Scence + Bussness Meda Inc. Conrade, B., Pesse, J., Thrle, C., Dstrct-level total factor productvty n agrculture: Western Cape Provnce, South Afrca, Agrcultural Economcs 40(3), Farrell, M.J., The Measurement of Productve Effcency. Journal of the Royal Statstcal Socety 120 (3), Gebreegzabher, Z., Oskam, A., Woldehanna, T., Techncal Effcency of Peasant Farmers n Northern Ethopa: A Stochastc Fronter Approach. In proceedngs of the Second Internatonal Conference on the Ethopan Economy, 3-5 June 2004, Adds Ababa. Had-Vencheh, A., Matn, R.K., An applcaton of IDEA to wheat farmng effcency. Agrcultural Economcs 42(4), Hale, M., Tesfaye, A., Aregu, L., Mulat, E., Market access versus productvty: The case of Teff. Paper prepared for the Ethopan Economc Assocaton, Conference on Ethopan Economy, Adds Ababa, June 3-5, Headey, D, Alauddnb, M., Prasada Rao, D.S., Explanng agrcultural productvty growth: an nternatonal perspectve. Agrcultural Economcs 41, 1-14 Isaksson,A., Determnants of Total Factor Productvty: A Lterature Revew. Research and Statstcs Branch, Unted Natons Industral Development Organzaton (UNIDO). Accessed August, 2011, avalable at: 14

15 Kharo, S.A., Battese, G.E., A Study of Techncal Ineffcences of Maze Farmers wthn and outsde the New Agrcultural Extenson Program n the Harar Regon of Ethopa. South Afrcan Journal of Agrcultural Extenson 34(1), Len, G., Kumbhakar, S.C., Hardaker, J.B., Determnants of off-farm work and ts effects on farm performance: the case of Norwegan gran farmers. Agrcultural Economcs 41(6), Medhn, H., Köhln, G., Sol conservaton and small-scale food producton n Hghland Ethopa: A stochastc meta-fronter approach. Envronment for Development, Dscusson Paper Seres EfD DP Pp. 34. Mnstry of Fnance and Economc Development (MoFED), Ethopa: Buldng on Progress; a Plan for Accelerated and Sustaned Development to End Poverty (PASDEP). (2005/ /10), Volume I: Man Text. Adds Ababa. Nyagaka, D.O., Obare, G.A., Omt, J.M., Nguyo, W., Techncal Effcency n Resource Use: Evdence from Smallholder Irsh Potato Farmers n Nyandarua North Dstrct, Kenya. Afrcan Journal of Agrcultural Research 5(11), Rashd, S., Staple Food Prces n Ethopa. A paper prepared for the COMESA polcy semnar on Varaton n staple food prces: Causes, consequence, and polcy optons, Maputo, Mozambque, January 2010, under the Afrcan Agrcultural Marketng Project (AAMP). Ray, S.C., Data Envelopment Analyss: Theory and Technques for Economcs and Operatons Research. Cambrdge Unversty Press, Cambrdge. Seyoum, E.T., Battese, G.E., Flemng, E.M., Techncal Effcency and Productvty of Maze Producers n Eastern Ethopa: A Study of Farmers wthn and Outsde the Sasakawa Global 2000 Project. Journal of Agrcultural Economcs 19, Shferaw, B., Teklewold, H., Structure and Functonng of Chckpea Markets n Ethopa: Evdence based on analyses of Value Chans Lnkng Smallholders and Markets. Improvng Productvty and Market Success (IPMS) of Ethopan Farmers Project Workng Paper 6. ILRI (Internatonal Lvestock Research Insttute), Narob, Kenya. pp. 63. Unted States Central Intellgence Agency (CIA) (2007). World FactBook, Ethopa Economy Accessed June 2011, avalable at 15