Economic efficiency of mixed crop-livestock production system in the north eastern highlands of Ethiopia: the Stochastic frontier approach

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1 Journal of Agrcultural Economcs and Development Vol. 1(1), pp , Aprl 2012 Avalable onlne at ISSN Academe Research Journals Full Length Research Paper Economc effcency of mxed crop-lvestock producton system n the north eastern hghlands of Ethopa: the Stochastc fronter approach Hassen Beshr 1, Bezabh Emana 2, Belay Kassa 3 and Jema Haj 3 1 Wollo Unversty, Department of Agrcultural Economcs, P.O. Box 1145, Ethopa, 2 HEDBES Consult, Adds Ababa, Ethopa 3 Haramaya Unversty, School of Agrcultural Economcs and Agrbusness, Ethopa Accepted 29 March, 2012 Ths study analyzed the effcency of crop-lvestock producton and assessng ther potental for mprovement n North-East Ethopan hghlands. Cross-sectonal data were used to analyze the economc effcency of mxed crop and lvestock producton system and dentfy ts determnants factors from 252 farmers who were selected usng probablty proportonal to sample sze samplng technque. The parametrc method stochastc fronter approach was employed to measure economc effcency. The parametrc methods of effcency measurement ndcated that most farmers n the study area were not effcent suggestng that effcency mprovement s one of the possble avenues for ncreasng agrcultural producton wth avalable resource and technology. The mean Techncal Effcency (TE), Allocatve Effcency (AE) and Economc Effcency (EE) of the household calculated from parametrc approach of stochastc fronter analyss were 62%, 51% and 29%, respectvely. The producton effcency of mxed crop-lvestock farmng system was determned by farm sze, lvestock ownershp, labour avalablty, off/non-farm ncome partcpaton, total household asset, total household consumpton expendture and mproved technology adopton. Ths study found that mproved agrcultural technology adopton sgnfcantly mproved producton effcency of households. Such actons may, n turn, allevate the current problem of food nsecurty and lead n the long run to economc development n the country. Key words: Economc effcency, parametrc fronter, techncal effcency, technology. INTRODUCTION The natonal economy of Ethopa s hghly dependent on tradtonal agrculture and agrcultural based actvtes. The overall economc growth of the country s hghly correlated to the success of the agrcultural sector. Agrculture accounts for 43% of the country s Gross Domestc Product (GDP) and 85% of all exports (coffee, lvestock and lvestock products and ol seeds). The ndustral sector s small n sze contrbutng, on average, to less than 13% of the GDP (FAO, 2007; MoFED, 2010). Agrculture provded employment for 85% of the populaton n 2008/09 and raw materals for 70% of the ndustres n the country (MOFED, 2006; MoFED, 2010). The bulk of the agrcultural GDP for the perod had come from the producton of crops and lvestock (FAO, 2007; MoFED, 2010). Especally, the role of agrculture n securng the food needs of the fast growng populaton s consderable. However, the productvty of the agrcultural sector has been very low for several decades. The reasons for the low growth rate of agrculture and GDP were manly severe weather fluctuatons, napproprate economc polces and low adopton of mproved agrcultural technologes and producton effcency and prolonged cvl unrest (Halu, 2008). In the north eastern hghland part of Ethopa, where ths study was conducted, crop and lvestock productons were the means of lvelhood of the people to meet the *Correspondng author. E-mal: hassenhussen@gmal.com. Tel

2 J. Agrc. Econ. Dev. 11 household consumptons and to generate ncome. The major crops grown by sample households were mproved and local wheat, barley, teff (Eragrosts tef), local and mproved horse bean, feld pea, maze, local and mproved potato, oat, fenugreek, garlc, lentl, chckpea, grass pea, sorghum, harcot bean and lnseed. The major lvestock reared by sample households were dary cow, poultry, beehves, sheep and goat products. The outputs of crops and lvestock were used manly for home consumpton and for markets to obtan cash ncome. The straws of crops were used for anmal feed. Anmals lke oxen were also used for draft power n plowng and plantng. Moreover, the wastes of anmal n the form of manure were used for mprovement of sol fertlty. The ntegraton of crop and lvestock producton also serves as a means to cope up wth the market and the envronmental rsks. Ths would also help as mprove the food securty of the producers. However, the productvty of agrcultural system n the study area s very low. Due to ths, more than 700 thousand farmers are n chronc food nsecurty n the study area. Improvng the productvty of crops and lvestock would nfluence the food securty of the prvate peasant households n the study area. Therefore, assessng the factors responsble for low producton and productvty of smallholder mxed crop-lvestock farmers n Ethopa n general and n north eastern hghlands of Ethopa n partcular was paramount mportance. Ths study amed at fllng ths gap. The specfc objectves of the study were to: (1) estmate the farm level effcency of the mxed crop-lvestock producton system; and (2) dentfy the sources of effcency dfferental among the farmers. METHODOLOGY OF THE STUDY Descrpton of the study area Ths study was carred out n South Wollo. It s located n the North East hghland part of Ethopa. It s one of the eleven admnstratve zones of the Amhara Natonal Regonal State. It s stuated between the Eastern hghland plateaus of the regon and the North Eastern hghland plateaus of Ethopa. It s dvded nto 20 admnstratve dstrcts (weredas) and has two major towns (Kombolcha and Desse) and 18 rural dstrcts. Among the eghteen rural dstrcts, Desse Zura and Kutaber are selected for ths study. South Wollo s located between lattudes N and N and longtudes and E. Accordng to the Central Statstcal Agency s populaton census data, n 2007 the total populaton of South Wollo was 2,519,450 of whch 50.5% were females and 88% were rural resdents (CSA, 2008). The total land area n South Wollo, Desse Zura and Kutaber s 1,773,681 hectares, 180,100 hectares and 72,344 hectares, respectvely. The cultvated land area accounts for 39%, 20% and 35.3% of the total area of Desse Zura, Kutaber and South Wollo, respectvely. Sample sze and samplng procedure Desse Zura and Kutaber dstrcts were selected purposvely based on ther accessblty and relevance of the study. The relevance of the study was concerned wth the mportance of mproved dary producton n the study dstrcts. A multstage random samplng method was used for the selecton of the sample respondents. In the frst stage of samplng, 6 Peasant Assocatons (PAs) were selected randomly from a total of 54 FAs (3 from Desse Zura and 3 from Kutaber). Snce there are equal numbers of Peasant Assocaton n the two dstrcts, three Peasant Assocatons were selected from each dstrct usng smple random samplng procedure. In the second stage, a total of 252 farmers were selected usng probablty proportonal to sample sze samplng technque. Data collecton and sources A structured questonnare was desgned, pre-tested and refned to collect prmary data. Experenced numerators were recruted and traned to facltate the task of data collecton. Farm vst, drect observaton and nformal ntervew were undertaken both by the researcher and the enumerators. The secondary data were extracted from studes conducted and nformaton documented at varous levels of Central Statstcal Agency, Mnstry of Agrculture and Rural Development and Fnance and Economc Development Offces n the study area. Stochastc fronter approach to measure effcency The theory and concept of measurement of effcency has been lnked to the use of producton functons. Some authors measure performance of frms by computng productvty usng output over nputs. However, ths s not the approprate measurement technques n effcency. Dfferent technques have been employed to ether calculate (non-parametrc) or estmate (parametrc) the effcent fronters. These technques are classfed as parametrc and non-parametrc methods. Farrell (1957) was the frst to formulate a non-parametrc fronter method to measure producton (economc) effcency of a frm. Accordng to hm, effcency ratos are calculated from sample observatons. He defned techncal, allocatve and economc effcences. Techncal effcency (TE) reflects the ablty of a frm to obtan maxmum output from a gven resources. Allocatve effcency (AE) reflects the ablty of a frm to use nputs n optmal proporton gven the nput prces and producton technology. Economc effcency (EE) s the overall effcency of a decson makng unts (frms or farmers). It s the multplcatve effect of techncal and allocatve effcences. Ths study estmates the overall effcency of farm households. Hence, the reader could understand for economc effcency and producton effcency as the

3 Beshr et al. 12 same to mean estmatng techncal, allocatve and economc effcency. The parametrc fronter method can be classfed nto determnstc and stochastc fronter technques. The determnstc parametrc fronter approach s formulated wth the producton behavor of frms. It can be expressed as Y f X ; )exp( U ) (1) =1,2,,N ( Where f (X ;) s a sutable functonal form, s vector of unknown parameters, U assesses the socoeconomc, nsttutonal and technologcal factors that are responsble for low producton and productvty of the frm. U s a nonnegatve random varable assocated wth techncal neffcency of the th frm whch mples that exp (-U ) s bounded between 0 and 1. Y s the vector of output. The stochastc fronter approach splts the devaton (error term) nto two parts to accommodate factors whch are purely random and are out of the control of the frm. One component s the techncal neffcency of a frm and the other component s random shocks (whte nose) such as bad weather, measurement error, bad luck, omsson of varables and so on. The model can be expressed as: ln Y e 0 ln Xj exp (2) Where ln denotes the natural logarthm; represents the th farmer n the sample, Y represents value of output of crop and lvestock of the th farmer, X j refers to the farm nputs of the th farmer, e = v -u whch s the resdual random term composed of two elements v and u. The v s a symmetrc component and permts a random varaton n output due to factors such as weather, omtted varables and other exogenous shocks. The v s are assumed to be ndependently and dentcally dstrbuted N(0,σ 2 v), ndependent of u. The other component, u s, s non-negatve random varable and reflects the techncal neffcency relatve to the stochastc fronter. The us are assumed to be ndependently and dentcally dstrbuted as half-normal, u~ N(0, σ 2 v). The parameters β, σ 2 = σ 2 v +σ 2 u and γ= σ 2 u / σ 2 of the above stochastc producton functon can be estmated usng maxmum-lkelhood method, whch s consstent and asymptotcally effcent (Agner et al., 1977). The dual cost fronter of the producton functon s gven by: ln C 0 j l ln W lny * (3) Where refers to the th sample farmer, C s the mnmum cost of producton, W j are nput prces, Y * represents the value of output adjusted for nose v ; and α are parameters. Followng Bravo-Ureta and Reger (1991) for a gven level of value of output (Y *), the techncally effcent nput vector of the th farmer, X t, s derved by solvng (2) and X1 m X the observed nput rato ( 1) smultaneously. Assumng that the producton functon n equaton (2) s self dual (e.g., Cobb-Douglas), the dual cost fronter s derved algebracally and wrtten n the followng form: C C( W, Y *, ) (4) Where C s the mnmum cost of the th farm assocated wth the adjusted value of output Y * and α s a vector of parameters to be estmated. The economcally effcent nput vector of the th farm, X e s derved by applyng Shepard s Lemma and substtutng the frm s nput prces and adjusted value of output level nto the resultng system of nput demand equatons C / W X ( W, Y *, ). (5) k ke Where k represents the total number of nputs used. The observed, techncally and economcally effcent costs of producton of the th farm are then equal to W X, W X t and W X e, respectvely. Accordng to Sharma et al. (1999) these cost measures are used to compute techncal effcency (TE), TE W ' X / W ' X (6) Economc effcency (EE), EE t W ' X e / W ' X Followng Farrel (1957) allocatve effcency (AE) can be derved from equaton (6) and (7) as, AE (7) W ' Xe / W ' Xt (8) ndces of the th farm. The producton fronter was estmated usng fronter model whereas the cost fronter s derved analytcally from producton assumng self dual. Emprcal studes on measurng effcency n mxed crop and lvestock producton system Several researchers have estmated producton effcency of mxed farmng system n developng countres and a few of them are revewed below. Taylor et al. (1986) used the Cobb-Douglas producton functon to evaluate the effectveness of an agrcultural credt program. They used a determnstc fronter producton functon to ft data collected from Brazlan farmers that were partcpant or

4 J. Agrc. Econ. Dev. 13 non-partcpant n the program. Ther techncal and allocatve effcences were determned by farm sze postvely and sgnfcantly. Lu and Zhuang (2000) used a stochastc fronter producton functon model to analyze the determnants of techncal effcency n Post-Collectve Chnese agrculture from farm-level data of households. They found out that effcency levels were affected by farm sze, access to credt, nutrton ntake, educaton attanment, and farmng experence. Yohannes et al. (1993) used stochastc fronter producton functon method to examne crop and mlk producton effcency among peasants n Ada and Selale dstrcts of the central hghlands of Ethopa. They found out that the producton effcency of a farm household was determned by experence, educaton, worker to consumer rato, and mproved technologes used postvely and sgnfcantly. Jema (2007) studed the determnants of effcency of vegetable-domnated mxed farmng system n two dstrcts of eastern Ethopa. He employed the nonparametrc DEA to calculate techncal, allocatve, and economc effcences of vegetable-domnated mxed crop farmers and he used Tobt regressons to dentfy factors that explaned effcency dfferentals among farmers. He found out that asset, off/non-farm ncome, farm sze, extenson vsts, and famly sze were the sgnfcant determnants of techncal effcency, whereas asset, crop dversfcaton, consumpton expendture and farm sze had sgnfcant mpact on allocatve and economc effcency. The results of all these authors depcts that producton effcency s low n developng country and determned by varous socoeconomc and nsttutonal settng. They have estmated the effcency by convertng the quantty of output nto value of output. Ths research has followed the same procedures but n dfferent agroecologcal and socoeconomc and nsttutonal settng. Moreover, t ncorporates agrcultural technology adopton as a covarate n dentfyng effcency determnant. Estmaton of the determnants of producton effcency In effcency analyss, factors that nfluence effcency are of paramount mportance. Followng the quantfcaton of the producton effcency measures, a second stage analyss nvolved a regresson of these measures on several hypotheszed socoeconomc, nsttutonal and technologcal factors that affect the effcency of the farmers. The most common procedure s to examne the determnants of effcency, n that the neffcency or effcency ndex s taken as a dependent varable and s then regressed aganst a number of other explanatory varables that are hypotheszed to affect effcency levels (Bravo-Ureta and Reger, 1991; Sharma et al., 1999; Arega, 2003; Jema and Andersson, 2006). However, few authors (e.g., Kumbhakar et al., 1991; Battese and Coell, 1995) used a specfc model that allowed researchers to estmate the effcency scores and smultaneously to test the effects of explanatory varables. For the former approach, techncal, allocatve and economc effcency estmates were regressed, usng Tobt model (Sharma et al., 1999; Jema and Andersson, 2006) or lnear regresson model (Sharma et al., 1999; Arega, 2003) on the farm specfc explanatory varables that mght explan varatons n effcency across farms. Techncal, allocatve and economc effcency estmates derved from Stochastc Producton Fronter (SPF) were regressed, usng a censored Tobt model on the followng farmspecfc explanatory varables that mght explan varatons n producton effcences across farms. The ratonale behnd usng the Tobt model was that there were a number of farms for whch effcency was one and the bounded nature of effcency between zero and one (Jackson and Feth, 2000). That s, due to large number of fully effcent SPF estmates, the dstrbuton of effcency measures was censored above from unty. Estmaton wth Ordnary Least Square (OLS) regresson of the effcency scores would lead to based parameter estmates snce OLS assumes normal and homoscedastc dstrbuton of the dsturbance and the dependent varable (Greene, 2003). As the dstrbuton of the estmated effcences are censored from above at the value one, Tobt regresson (Tobn, 1958) s specfed as E E E * 1... f... E E X V... f... E 1 1 Where E s an effcency score, and V~N(0,σ2) and β j are the parameters of nterest. Descrpton of varables for effcency measurement Producton functon varables The varables that were used n the stochastc fronter model were defned as follows.. Outputs: physcal yeld of crops and lvestock and ther respectve prces were used to compute the value of output of the farm. The value of crop and lvestock output was derved from output of mproved and local wheat, barley, teff, local and mproved horse bean, feld pea, maze, local and mproved potato, oat, fenugreek, garlc, lentl, chckpea, grass pea, sorghum, harcot bean, lnseed, mlk of mproved and local dary cow mlk, mproved and local poultry, local and mproved beehves, number of sheep and goat products. These outputs were multpled by ther respectve market prce to obtan the value of crop and lvestock output. The respectve monthly market prces were collected from South Wollo department of agrculture and rural development offce. The averages of these prces were used for computatonal analyss. (9)

5 Beshr et al. 14 Table 1. Descrptve results of nput-output varables Varable Mean SD Mn Max Value of output n Brr 1 10,144 6, ,201 Land area n hectare Hum labour n man days (MD) Oxen labour n oxen days (OD) Materal nput cost n Brr , ,079 Source: Own survey, Inputs: these were defned as the major nputs used n the producton of crop and lvestock. They were: Land: Ths represented the physcal unt of cultvated land and grazng land n hectares; Human labour: Ths was man days worked by famly, exchange and hred labour for land preparaton, plantng, weedng, or cultvaton, rrgaton, harvestng and rearng lvestock; Oxen labour: Ths was oxen days worked by the household usng oxen labour for land preparaton, plantng and threshng; Materal nputs: Ths ncluded the cost of veternary, feed, organc and chemcal fertlzers, mproved and local seeds and pestcdes used by the farm household. Cost functon varables:. Input prces: the nput prces of land, human labour and oxen labour needed for dervng the dual cost fronter n the parametrc method were collected. Moreover, the value of the output of crop and lvestock was used as computed above and adjusted for statstcal nose. Varables ncluded n the determnants of effcency model The dependent varable was the producton effcency scores, whch were computed from parametrc methods of effcency measurement. Producton effcency n ths context denotes techncal, allocatve and economc effcences. v. Effcency factors: these denoted varous factors hypotheszed to explan dfferences n producton effcency among farmers. These were: Age: ths was the age of the household head n years. Farm sze: t was defned as the total area of cultvated and grazng land n hectare. Educaton: t was a contnuous varable defned as years of formal schoolng; Labour avalable: t was defned as the total actve labour avalable n the famly n man equvalent. Lvestock ownershp: t was defned as the total lvestock avalable n TLU. Off/non-farm ncome: ths ncluded ncome from off-farm and non-farm actvtes. It was a dummy varable that the varable was 1 f the household earned off/non-farm ncome and 0 otherwse. Credt servce: t ncluded access to credts for farm nputs and other farm producton actvtes from formal and sem-formal sources. It was a dummy varable defned as 1 f the farmers have receved credt and 0 otherwse. Extenson servce: t was defned as whether the farmer had access to the extenson servce durng the survey year or not. It was a dummy varable defned as 1 f the household had access to extenson servce and 0 otherwse Expendtures: t was the total yearly consumpton expendture of the household n goods and servces. Assets: t was defned as the sum of current values of all furnture, farm mplements and other equpments and lvestock owned by the household. Technology adopton: ths was whether or not the household adopted at least one mproved agrcultural technology. It was a dummy varable defned as 1 f the farmer had been adopted at least one mproved technology and 0 otherwse. The mproved agrcultural technologes consdered were mproved wheat seed, chemcal fertlzer, mproved forage and dary. RESULTS AND DISCUSSION The descrptve statstcs of output and nput varables used n stochastc fronter approach are summarzed n Table 1. As t was explaned n the methodology, the total value of crop and lvestock outputs were derved from output of mproved and local crop and lvestock products. These outputs were multpled by ther respectve average market prce to obtan the value of crop and lvestock output. The average value of the output per farm was Brr 10,144 wth mnmum and maxmum values of Brr 205 and Brr 32, 201, respectvely. The average total land area was 0.72 hectare. The mean of the human and oxen labour for the farm households for whch the producton functon was estmated was 180 man-days and 28 oxen-days, respectvely. The mean of the materal nputs appled by the farm households for the sample perod was Brr 1,971 wth a mnmum of Brr 8

6 J. Agrc. Econ. Dev. 15 Table 2. Cobb-Douglas stochastc producton fronter Maxmum lkelhood and OLS estmate Varable Parameter OLS estmate ML estmate Coeffcent Coeffcent (standard error) error) Intercept β0 4.46*** (0.29) 7.125***(0.25) Land n ha β (0.05) *** (0.0298) Human labour n MD β *** (0.057) 0.232*** (0.0443) Oxen labour n OD β *** (0.06) 0.161*** (0.0468) Materal nputs n brr β4 0.28*** (0.039) 0.113*** (0.0266) F(4, 247) *** Adjusted R-squared σ 2 (Sgma) 0.082*** (0.0114) γ (gamma) 0.485*** (0.104) Log-L LR test of the one-sded error 250*** ***, ** and * mples sgnfcant at 1%, 5% and 10% probablty level, respectvely MD=Man Days OD=Oxen Days LR=Lkelhood rato Source: Own computaton, 2009 (standard and a maxmum of Brr 7, brr s the local currency whch s exchanged at 17 brr for a dollar. The maxmum lkelhood estmates of the parameters of the Cobb-Douglas stochastc producton fronter functon were estmated by usng a computer program FRONTIER 4.1c (Coell, 1996). The results are summarzed n Table 2. The sgns for nput elastctes of the stochastc producton fronter were all postve and sgnfcant at 1% probablty level. Ths mples the sgnfcant contrbuton of land, labour and materal nputs n producng crops and lvestock actvtes. The Cobb-Douglas producton functon was ftted due to ts well behaved property n dervng the dual cost fronter and ts convenence n estmaton and nterpretaton of parameter estmates. Because of estmatng techncal and economc effcency usng stochastc producton fronter, Cobb-Douglas producton functon was used n estmatng the parameters of nterest and hence measure effcency ndces. The estmated value of sgma squared (σ 2 ) was sgnfcant at less than 1% probablty level. Ths means the conventonal average producton functon was not an adequate representaton of the data. That s, the stochastc producton fronter model s very dfferent from the determnstc model specfcaton n whch there s a statstcal nose n the producton fronter. The estmaton result presented n Table 3 showed that the returns to scale for ths study were The result ndcated that crop and lvestock farmers n the study areas operated under decreasng returns to scale. Thus, the producton structure, gven these nputs and producton technology, was characterzed by decreasng returns to scale. The dual cost fronter was derved from the producton fronter by ncorporatng Lagrangan multpler and by takng partal dervatve of the nput demand equaton. The dual Cobb-Douglas cost fronter derved usng equaton (4) from the maxmum lkelhood estmate of the Cobb-Douglas producton functon (Table 3) s defned as follows: ln C K lnW lnW lnW lnW4 1.64ln Y * K (10) Where C k s the mnmum cost of the K th farmer n producng crop and lvestock; Y* k s the value of crop and lvestock output of the K th farmer adjusted for statstcal nose; W 1 s the average tax pad for a hectare of land estmated at 50 Brr; W 2 s the wage rate estmated at 20 Brr/day; W 3 s the rental value of one par of ox estmated at 30 Brr/day; and W 4 s the prce ndex of materals nputs (fertlzers, seeds, veternares, feeds and herbcde and pestcde chemcals) assumed at unty.

7 Beshr et al. 16 Table 3. Frequency dstrbuton of techncal (TE), allocatve (AE) and economc (EE) effcency from parametrc fronter method Stochastc producton fronter Effcency n % TE AE EE < _ _ _ _ _ _ _ _ > Mean (%) S D (%) Mnmum (%) Maxmum (%) N=number of farmers Source: Own computaton, 2009 SD=Standard Devaton Lack of farm level prce related data, coupled wth lttle or no nput prce varaton across farms n Ethopa precludes any econometrc estmaton of cost or proft fronter functons. Therefore, the use of self-dual producton fronter functons allows the cost fronter to be derved and used to estmate economc effcency n stuatons where producers face the same nput prces. The frequency dstrbuton and summary statstcs of techncal (TE), allocatve (AE) and economc (EE) effcency scores from parametrc methods are presented n Table 3. The average TE, AE and EE scores for SPF approach were 62%, 51% and 29%, respectvely. They ndcate that there were consderable neffcences n producton and hence rooms for producton gan through effcency mprovement. Ths suggests that farm households can reduce ther producton costs by 38%, 49% and 71% f they could operate at full techncal, allocatve and economc effcency levels, respectvely. For example, the number of farmers whose techncal, allocatve and economc effcency scores were greater than 90% n SPF was 45, 13 and 0, respectvely. Determnants of producton effcency of mxed croplvestock farm household Here, the determnants of effcences were dentfed by ncorporatng agrcultural technology adopton as a covarate. Agrcultural technology adopton was understood to mean, mproved crop and lvestock technologes used to enhance productvty at a household level. It was hypotheszed that the applcaton of mproved agrcultural technologes, among other socoeconomc factors, affected farm household techncal, allocatve and economc effcences whle the mproved technology adopton dummy tself s an endogenous varable whch can be affected by farmers producton effcences. A smultaneous equaton Tobt model was employed to test these hypotheses and to dentfy the factors that nfluence household producton effcences and mproved technology adopton. Therefore, the model result revealed that the estmated parameter coeffcent for the predcted error term (resdual) was not statstcally dfferent from zero. As a result, the null hypothess that there s no a smultanety relatonshp between household effcences and mproved technology adopton was accepted. Ths mples that the effcences of the farmers can be modeled by usng the normal sngle equaton standard Tobt model and by drectly ncorporatng mproved agrcultural technologes dummy, along wth other explanatory varables. Crop and lvestock producers dfferences n techncal,

8 J. Agrc. Econ. Dev. 17 allocatve and economc effcences levels were hypotheszed to be due to several farm and farmers attrbutes, manly reflectng ther manageral ablty and access to nformaton. Ths procedure s to dentfy the determnants of producton effcency among farmers usng the Tobt model. Ths was done by regressng the effcency levels obtaned from stochastc fronter method. The Tobt model estmates and the respectve margnal effects are provded n Tables 4-6. The Tobt model results ndcated that techncal effcency was postvely and sgnfcantly affected by sze of lvestock, off/non-farm ncome, total household asset, and technology adopton. Techncal effcency was negatvely and sgnfcantly related to the total household consumpton expendture. The allocatve effcency of the farm household was postvely and sgnfcantly nfluenced by the farm sze, total household asset and mproved technology adopton. It was negatvely and sgnfcantly affected by household consumpton expendture. Ths study found out that large farm sze was expected to have a sgnfcant postve effect on allocatve effcency levels because such farms realze ncreasng returns to scale. The negatve effect of farm sze mght be related to small farm sze (Coell et al., 2002; Getachew, 1995; Jema and Andersson, 2006). The postve and sgnfcant nfluence of lvestock ownershp, off/non-farm ncome and household asset on allocatve effcency of the household mght be due to the fact that the ncome was used to mprove the skll and human and physcal captal of the farm household, serve as addtonal fundng to farm actvtes and mprove manageral sklls. Moreover, farmers wth these resources had more nformaton and capacty for optmal allocaton of resources. The economc effcency of the farm household was postvely and sgnfcantly affected by total household asset and mproved technology adopton. The rest of the varables, ncludng labour force avalable, sze of farm, sze of lvestock, access to extenson and credt servce and educaton had the expected postve sgns but nsgnfcant effect on economc effcency at less than 1% probablty level. The statstcally sgnfcant negatve effect on the estmated coeffcents on all effcency scores for the household consumpton expendture may reveal a stuaton where household that spent excessvely on consumpton goods were unable to support ther agrcultural actvtes. Therefore, these households became less effcent (Jema and Anderson, 2006). The household asset or wealth sgnfcantly and postvely affected economc effcency of the crop and lvestock farmers. Ths means that relatvely wealther farm households were more economcally effcent than less wealthy ones. That s, the farmers capacty to selffnance may ncrease as they get wealther, reducng demand for credt. However, f wealther farm households expand ther farm operatons and demand addtonal external resources, they wll be more credtworthy and less ratoned n the credt market than the less wealthy farmers. The postve and sgnfcant effect of the total household asset s consstent wth the results obtaned by Jema and Anderson (2006) and Hussen and Öhlmer (2007). The postve and sgnfcant effects of farm households mproved technology adopton on all effcency scores were related to the fact that the households were adaptng mproved practce and technology, acqurng and analyzng nformaton. Ths suggested that better utlzaton of mproved agrcultural technologes such as chemcal fertlzers, mproved dary cows, forage seeds and wheat varetes mproved the techncal, allocatve and economc effcences of mxed crop and lvestock farmers. SUMMARY AND CONCLUSIONS The general objectve of the study was to estmate the producton effcency of the mxed crop-lvestock farmers n two dstrcts of north eastern hghlands of Ethopa. The parametrc methods of effcency measurement ndcated that most farmers n the study area were not effcent suggestng that effcency mprovement s one of the possble avenues for ncreasng agrcultural producton wth avalable resource and technology. The mean TE, AE and EE of the household calculated from parametrc approach of stochastc fronter analyss were 62%, 51% and 29%, respectvely. Ths techncal effcency ndex ndcated that these farmers could, on average, produce as much as 38% of the value of output wthout utlzng addtonal nputs. The economc (producton) effcency of mxed crop-lvestock producton system was determned by farm sze, lvestock ownershp, labour avalablty, off/non-farm ncome partcpaton, total household asset, total household consumpton expendture and mproved technology adopton. The emprcal results showed that emphass should be gven to mprove the effcency of mxed crop-lvestock producton system. In ths regard, scalng up strategy to ncrease the number of best performng farmers should be gven prorty. Best performng farmers were those farmers whose economc effcency scores were 100%. These farmers operated at the fronter. There s a possblty of ncreasng farm ncome and resource use by ntegratng crop and lvestock enterprses for all farm households. Ths could be attaned by mprovng the producton and productvty of dfferent crops and anmals. Therefore, there s a need to desgn approprate polcy for mprovng crop and lvestock producton systems by solvng the shortage of seed, feed and health problem and provdng varous techncal and advsory support servces. Ths mples that mprovng the asset of croplvestock farmers can ultmately brng about mprovement n agrcultural productvty by mprovng producton effcency. Based on the results of ths study,

9 Beshr et al. 18 Table 4. Determnants of techncal effcency of smallholder mxed crop and lvestock farmers Varables Coeffcent Std. Err. Margnal effect Age Educaton Labour avalable Farm sze Lvestock ownershp *** Off/non-farm ncome ** Household asset *** Household expendture *** Extenson servce Credt servce *** Technology adopton *** Constant 1.824*** Test statstcs LR χ 2 *** (11) = 215 Log L*** = 116 *** and ** mples sgnfcant at 1% and 5% probablty level, respectvely Log-L stands for Log Lkelhood functon Source: Own computaton, 2009 Table 5. Determnants of allocatve effcency of smallholder mxed crop and lvestock farmers Varables Coeffcent Std. Err. Margnal effect Age Educaton Labour avalable Farm sze *** Lvestock ownershp Off/non-farm ncome Household asset *** Household expendture *** Extenson servce Credt servce Technology adopton ** Constant *** Test statstcs LR χ 2 *** (11) = 85 Log L*** = 107 *** and ** mples sgnfcant at 1% and 5% probablty level, respectvely Log-L stands for Log Lkelhood functon Source: Own computaton, 2009 t s suggested that the technology adopton and producton effcency of the crop-lvestock farmers should be mproved by rasng ther educaton, farm household asset formaton and by provdng extenson and credt servces. Such actons may, n turn, allevate the current problem of food nsecurty and lead n the long run to economc development. ACKNOWLEDGEMENTS The authors are very grateful to data collectors Ahmed, Alemu, Alfya, Mohammed and Sed. The support of

10 J. Agrc. Econ. Dev. 19 Table 6. Determnants of economc effcency of smallholder mxed crop and lvestock farmers Varables Coeffcent Std. Err. Margnal effect Age Educaton Labour avalable Farm sze Lvestock ownershp Off/non-farm ncome Household asset *** Household expendture *** Extenson servce Credt servce Technology adopton ** Constant Test statstcs LR χ 2 ***(11) = 133 Log L*** = 94 *** and ** mples sgnfcant at 1% and 5% probablty level, respectvely. Log-L stands for Log Lkelhood functon. Source: Own computaton, 2009 mnstry of educaton for data collecton s also apprecable. We are very grateful to two anonymous revewers for very useful comments that helped us mprove upon the paper. REFERENCES Arega D (2003). Improved producton technology and effcency of smallholder farmers n Ethopa: Extended parametrc and nonparametrc approaches to producton effcency analyss. PhD dssertaton submtted to the Department of Agrcultural Economcs, Extenson and Rural Development, Faculty of Natural and Agrcultural Scences, Unversty of Pretora, South Afrca. Battese GE, Coell TJ (1995). A model for techncal neffcency effects n a stochastc fronter producton functon for panel data. Emp. Econ., 20: Bravo-Ureta BE, Reger L (1991). Dary farm effcency measurement usng stochastc fronters and neoclasscal dualty. Am. J. Agrc. Econ., 73(2): Coell T (1996). A Fronter Verson 4.1c. Computer program for stochastc fronter and cost functon estmaton. Center for Effcency and Productvty Analyss, Unversty of Queens land. Coell TJ, Sandura R, Coln T (2002). Techncal, allocatve and cost and scale effcency n Bangladesh rce producton: A non-parametrc approach. J. Agrc. Econ., 53: CSA (Central Statstcal Agency) (2008). The 2007 Populaton and housng census of Ethopa. Statstcal Report on Populaton Sze and Characterstcs, 1(1), Federal Democratc Republc of Ethopa Central Statstcal Agency, Adds Ababa, Ethopa. FAO (Food and Agrculture Organzaton) (2007). Statstcal database for Ethopa, , Food and Agrculture Organzaton, avalable on lne from the web ste at (Accessed on 4 February 2010). Farrell MJ (1957). The measurement of productve effcency. J. Royal Statst. Socety, 120: Getachew A (1995). Producton Effcency Analyss: The case of smallholder farmng n the coffee sector of Ethopa and Kenya. Farmng systems and Resource Economcs n the Tropcs, vol. 23, Germany. Greene WH (2003). Econometrc analyss. 5th edton. Macmllan, New York, p Halu B (2008). Adopton of mproved teff and wheat producton technologes n crop and lvestock mxed systems n Northern and Western Shewa zones of Ethopa, PhD Dssertaton, Unversty of Pretora. Hussen H, Öhlmer B (2007). Influence of credt constrants on producton effcency: The case of farm households n Southeastern Ethopa. PhD Dssertaton, Swedsh Unversty of Agrcultural scence, Sweden. Jema H, Andersson H (2006). Determnants of effcency of vegetable producton n smallholder farms: The Case of Ethopa. Food Econ., 3(3): Jema H (2007). Producton effcency of smallholders vegetabledomnated mxed farmng system n Eastern Ethopa: a Non-parametrc Approach. J. Afrcan Econ., 16(1): Kumbhakar SC, Ghosh S and McGuckn JT (1991). A generalzed producton fronter approach for estmatng determnants of neffcency n US mproved forage, mproved seed and fertlzer farms. J. Bus. and Econ. Stat. 9: Lu Z, Zhuang J (2000). Determnants of techncal effcency n postcollectve Chnese agrculture: Evdence from Farm-Level Data. J. Comp. Econ., 28: MoFED (Mnstry of Fnance and Economc Development) (2006). Ethopa: Buldng on Progress a Plan for Accelerated and Sustaned Development to End Poverty (PASDEP) (2005/ /10).Volume I: Man text, Mnstry of Fnance and Economc Development, September, 2006, Adds Ababa MoFED (Mnstry of Fnance and Economc Development) (2010). Natonal Income accounts, Mnstry of Fnance and Economc Development (MoFED), avalable at (Accessed on 4 February 2010). Sharma AR, Leung P, Zalesk HM (1999). Techncal, allocatve and economc effcences n swne producton n Hawa: A comparson of

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