Sources of Technical Inefficiency of Smallholder Farmers in Sorghum Production in Konso District, Southern Ethiopia

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1 Internatonal Journal of Agrcultural Educaton and Extenson Vol. 5(1), pp , January, ISSN: Research Artcle Sources of Techncal Ineffcency of Smallholder Farmers n Sorghum Producton n Konso Dstrct, Southern Ethopa 1 Kusse Hale Gemeyda*, Jema Haj, 3 Bosena Tegegne 1 Lecturer, Department of Agrcultural Economcs, Mzan-Tep Unversty, P.O. Box 60, Mzan Aman, Ethopa Professor, School of Agrcultural Economcs and Agrbusness, Haramaya Unversty, P.O. Box 138 Dredawa, Ethopa 3 Assstant Professor, School of Agrcultural Economcs and Agrbusness, Haramaya Unversty, P.O. box 138 Dredawa, Ethopa Ths study ams to estmate the techncal effcency and dentfy sources of techncal neffcency n sorghum producton by smallholder farmers n Konso dstrct, southern Ethopa usng data collected from a sample of 14 households. Indvdual levels of techncal effcency scores were estmated usng the Cobb-Douglas functonal form, whch was specfed to estmate the stochastc producton fronter. The estmated stochastc producton fronter model ndcated that nput varables such as land, Urea, DAP, labour, oxen and chemcals found to be mportant factors n ncreasng the level of sorghum output n the study area. The mean techncal effcency of the sample households was about 69%, whch shows exstence of a possblty to ncrease the level of sorghum output by about 31% by effcent use of the exstng resources. The estmated stochastc producton fronter model together wth the neffcency parameters showed that, age, educaton, famly sze, off-farm occupaton, extenson servce, lvestock holdng, plots dstance and sol fertlty were found to be sgnfcant n determnng the level of techncal neffcency of sorghum producton n the study area. Negatve coeffcents of educaton, famly sze, off-farm occupaton, extenson servce and sol fertlty ndcates that mprovement n these factors results n a sgnfcant decrease n the level of techncal neffcency. Akn, postve coeffcents of age, lvestock holdng and plots dstance were found to ncrease households techncal neffcency. Hence, emphass should be gven to mprove the effcency level of those less effcent households by adoptng the practces of relatvely effcent households n the study area. Besde ths, polces and strateges of the government should be drected towards the above mentoned determnants. Keywords: Techncal effcency, Stochastc fronter producton, Smallholders, Konso, Sorghum Producton. INTRODUCTION Background of the Study Ethopa, the country wth an area of about 1,10,000 square klometres, s one of the most populous countres n Afrca wth the populaton of 84.3 mllon n 01 wth annual growth rate of.6% (CSA, 013). In 015 ths populaton sze has reached mllon as projected by the Central Statstcal Agency (CSA, 015) based on the 013 Inter-Censal Populaton Survey (ICPS). Ths growng populaton requres better economc performance than ever before at least to ensure food securty. However, the agrcultural sector n the country s largely small-scale, subsstence orented and heavly dependent on ranfall, whch s hghly varable spatally and temporally. It s a domnant sector n Ethopa and s accountng for about 45% of GDP and 80% for export commodty. About 85% of Ethopans lvelhood depends on agrculture (MoFEC, 01). *Correspondng Author: Kusse Hale Gemeyda, Lecturer, Department of Agrcultural Economcs, Mzan- Tep Unversty, P.O. Box 60, Mzan Aman, Ethopa. Emal: kussehale@mtu.edu.et, TEL:

2 Gemeyda et al. 181 Accordng to WFP (010), Ethopa agrculture s explaned by low productvty, caused by a combnaton of agro clmatc, demographc, economc and nsttutonal constrants and shocks. The same report ndcate that, about 5.3 mllon people n need of relef food assstance and the natonal relef ppelne has a shortfall of 90,000 tonnes of food gran. By any measure, Ethopa s achevements over the past decade have been mpressve: average annual economc growth rates of over 10% over the past decade. Also, mprovements n reducton n the poverty headcount from 38.7 to 6% between 004/05 and 01/13. Despte these postve outcomes, Ethopa stll faces many dauntng human development ssues. About 5 mllon Ethopans reman n poverty, and these and many Ethopans just above the poverty lne are vulnerable to shocks and food nsecurty. Ths s because of hgh populaton growth, the absolute number of the poor has remaned largely unchanged over the past ffteen years (UNDP, 015). Currently, the Ethopan government has desgned a second fve years (015/16-019/0) growth and transformaton plan (GTP II) whch ams at boostng the natonal Gross Domestc Product (GDP). Based on the achevements and lessons drawn from mplementatons of Agrcultural Development Led Industralzaton (ADLI) strategy, buldng on Plan for Accelerated and Sustanable Development to End Poverty (PASDEP) achevements and the country s fve-year ( ) frst Growth Transformaton Plan (GTP I), smallholder agrcultural wll contnue to be the bass for agrculture sector development. Also, ncreasng agrcultural producton and productvty focusng on smallholder agrculture, s contnued to be a prorty durng the Second Growth and Transformaton Plan (GTP II) as source of growth and poverty reducton through ensurng household and natonal food securty (MoFEC, 015). The overall performance of Ethopa s economy s hghly nfluenced by the performance of the agrcultural sector whch tself s subjected to vagares of weather and related natural and man-made factors. Rsng populaton pressure coupled wth declnng land holdng szes and natural resource degradatons were leadng to low level of producton to meet the consumpton requrement of the households n ran dependent agrcultural subsystem. In Ethopa, cereals are the major food crops both n terms of area coverage and volume of producton. Accordng to CSA (014/15) report, of the total area and producton under crops, cereals accounted for about 80.78% and about 87.31% of the total producton n quntals, respectvely. The same source also ndcated that, there was an ncrease n total food gran producton from 51,536,6.39 metrc tons n 013/14 to 70, metrc ton n 014/015. However, ths ncrement n output could not be attrbuted to mprovement n productvty alone as there was smultaneous ncrease n the sze of cultvated land from 1,407, hectares to 1,558, hectares n the same perod. Smallholders account for 96% of the total area cultvated and generate the key share of total producton for the man crops. Cereals are predomnantly produced by smallholders and are consumed as food, and the byproducts are fed to lvestock (Abu, 013). In Ethopa, sorghum accounts for the thrd largest share of total cereal producton. Area under sorghum cultvaton expanded from 1.83 mllon hectare n 014/15 to.01 mllon n 015/16 (CSA, 016). Sorghum s the sngle most mportant staple n drought prone areas; The trade status of the country from shows that, mport n all years and most of the sorghum mport takes the form of food ad (Demeke and D Marcantono, 013). In southern regon, from the total land sze of 1,110,931.9 hectares planted to all gran crops, cereals covered 883,90.78 hectares wth a total producton of 0,455,69.44 metrc tons. The producton of sorghum s 47,740.9 metrc ton and total area coverage under sorghum s 116, hectare and average yeld per hectare s 1.19 quntals (CSA, 014/15). From the total land sze of 63,733.5 hectares planted wth all gran crops n Segen people s zone, cereals covered 51,46.38 hectares wth a total producton of 888,919.4 quntals. And the producton of sorghum s 169,497.4 quntals whch s the thrd n terms of producton preceded by maze and teff and total area coverage under sorghum s 11, hectare and average yeld per hectare s quntals (CSA, 014/15). Among the dstrcts of Segen Area People s zone, Konso dstrct s known for cereal producton especally sorghum followed by maze and teff. Out of the total 7,600.3 hectares of land allocated for cereals, sorghum occupes 1, hectares or 45.67% of the total land allocated for cereal crops n the dstrct. In 014/15 producton year, the total producton and productvty of sorghum was 165,373.7 quntal and 13.1 quntal per hectare, respectvely, whch s less than the regonal productvty of 1.19 quntal per hectare (KDARDO, 015). In ths area dependng on the ranfall, several hectares are grown annually. However, productvty s as such not much. Therefore, knowledge about the level of techncal neffcency of smallholder sorghum producers n the producton and the underlyng soco-economc and nsttutonal factors causng neffcency may help to assess the opportuntes for ncreasng agrcultural producton. Ths study thus ams to contrbute towards a better understandng of potental producton capacty of ths crop usng extended effcences measurement procedures. Statement of the Problem The major challenges facng most of developng countres such as Ethopa s mprovng food securty and to stmulate underlyng food system development. There s an ever-ncreasng concern that t s becomng more and

3 Int. J. Agrc. Educ. Ext. 18 more dffcult to acheve and sustan the needed ncrease n agrcultural producton based on extensfcaton, because there are lmted opportuntes for area expanson. Hence, the soluton to food problem would depend on measures, whch help to ncrease yeld through ntensfcaton. Though agrculture remans to be the most mportant sector of the Ethopan economy, ts performance has been dsappontng and food producton has been laggng behnd populaton growth (Demeke, 008), whch s unable to fulfll the requrement of the ever-ncreasng number of mouths. Ths s manly attrbuted to the poor use of modern nputs such as fertlzers, mproved seeds and extenson servces whch partly explan the less productvty of the sector and apart from ths, the nternal neffcency of the farmers n usng the avalable agrcultural resources such as land and labor (Knde, 005). Agrcultural producton and productvty can be enhanced through ncreased use of nput or mprovement n technology gven some level of nput. Therefore, f farmers are producng to supply the surplus to the market after feedng themselves wth reducng land per capta due to populaton growth, they need to adopt new farmng practces and ncrease ther effcency (Jema, 008). The other way of mprovng productvty s to enhance the effcency of producers. In countres lke Ethopa (havng captal constrant) t s worthwhle to beneft from ncreasng productvty through mprovng effcency n use of resources avalable. Cereal crops are grown prmarly for the harvestng of mature grans whch are used or processed nto staple food and anmal feed. They are also processed nto varous products such as starch, malt, bofuel (alcohol) and sweetener (FAO, 014). In Ethopa, sorghum s a staple food crop wdely cultvated n dfferent agro-ecologcal zones, predomnantly n dry areas where other crops can survve least and food nsecurty s wdespread. In 011/1, Ethopan man rany season (Meher), 39,51,94.36 quntals of sorghum gran s produced on 1,93, ha of land (CSA, 01). Ths shows that the productvty of the crop s stll low, estmated to be 054 kg ha -1 (CSA, 01), whch s consderably lower than expermental yeld that reaches up to 3500 kg ha -1 on farmers felds n major sorghum growng regons of the country (Geremew et al., 004). Therefore, possble ways should be sought to allevate food shortage and food nsecurty. One way of mtgatng the problem s ncreasng the use of mproved technologes and mprovng the effcency of farmers n usng nputs. Alemayehu et al. (011), however, argued that n the future cereal producton growth need to come ncreasngly from yeld mprovements as there s lttle sutable land avalable for the expanson of crop cultvaton n the country, especally n the hghlands. Despte double-dgt economc growth and substantal decreases n the percentage of the populaton below the natonal poverty lne, the absolute number of the poor s roughly the same as 15 years ago and a sgnfcant proporton of the populaton hovers just above the poverty lne and s vulnerable to shocks. Moreover, the severty of poverty ncreased from.7% n 1999/000 to 3.1% n 010/11 (MoFEC, 013). Also agrculture remans the key sector of the economy, on whch the great majorty of the populaton stll depends for ts lvelhood. GTP II targets focuses on enhancng the productvty and producton of smallholder farmers who are provdng the major share of the agrcultural output n the country, commonly employ tradtonal producton technology and lmted modern nputs and reducng the number of chroncally food nsecure households (ATA, 015). The hghlands of Konso are densely populated and populaton s growng rapdly. The pressure on land s ncreasng, resultng n margnal lands to be taken nto producton and problems resultng from overstressng the farmng system. Lately, declnng sol fertlty and growng populaton ncrease the pressure on lmted land resources and people are unable to hold large stocks of food (Awrars, 01). Even though, cereals especally, sorghum are the most predomnate n the croppng pattern n Konso dstrct n whch ths study was carred out, how much farmers are effcent n the study area was not known. Albet there s lack of pertnent studes on techncal effcency of the smallholder farmers n sorghum producton and the determnants of the varablty of the neffcency levels among farmers n the study area. Hence, ths study tres to analyze the techncal effcency and ts determnants of sorghum producton n Konso dstrct, whch s known for the producton of sorghum crop and ams to brdge the prevalng nformaton gap by provdng emprcal evdence on smallholder resource use effcency. RESEARCH METHODOLOGY Descrpton of the Study Area The study area s located n the Southern Natons, Natonaltes, and Peoples Regon of Ethopa. Konso Dstrct s one of the currently organzed fve Dstrcts under Segen zone n the SNNPR. The dstrct s located about 600 km south of Adds Ababa at 5 o 10 5 o 40 N lattude and 37 o o 40 E longtude. The total land area of the Dstrct s,74 sq.km. (KDAO, 01). Topographcally: the alttude of Konso dstrct vares from 500 m.a.s.l to 000 m.a.s.l. Based on nformaton obtaned from the Konso Dstrct agrcultural offce, the man agroecologcal dvsons of Konso accounts, about 70% ard and 30% tropcal sub-humd. The mean annual temperature of the Dstrct ranges between ºC. Moreover, the clmate of Konso s characterzed as semard wth rregular and seasonally vared ranfall. The

4 Gemeyda et al. 183 Source: Own Draw, 015 Fgure 1: Locaton of Konso dstrct average total annual ranfall s 550 mm; the temperature of the area s mostly hot and warm. The annual ranfall varaton s between 400 and 1000 mm. The ran follows a bmodal pattern of ran that s Belg hgh ranfall n the perod startng from md-february and lasts untl Aprl and the short ran season whch s Meher from October and November (Cheung, 008), as cted n (Awrars, 01). Based on CSA (015), the Dstrct has a populaton of 76,985 of whom 133,715 (48.8%) are men and 143,70 (51.7%) are women. Konso dstrct has a populaton densty of per Sq.km. The arable land holdng sze per household s estmated to be less than one hectare. Farmng sustans the lvelhoods of more than 90% of the Konso farmers that depends on ran fed agrculture (Awrars, 01). There are two croppng seasons n Konso: Belg (February-May) accountng for 65-75% of the annual crop producton and Meher (Hagaya) croppng season. The most common crops are cereals manly sorghum followed by maze (Besha, 003). Sorghum s one of the major staple crops grown n the poorest and most food nsecure regons of Ethopa lke Konso dstrct. The crop s typcally produced under adverse condtons such as low nput use and margnal lands. It s well adapted to a wde range of precptaton and temperature levels and s produced from sea level to above 000 m.a.s.l. Its drought tolerance and adaptaton attrbutes have made t the favorte crop n drer and margnal areas. Ethopa s often regarded as the center of domestcaton of sorghum because of the greatest genetc dversty n the country ncludng Konso dstrct (Fetene, 011 as cted n Demeke and D Marcantono, 013). The clmate of the Konso dstrct s most favorable for the cultvaton of a wde varety of crops lke sorghum, maze, teff, wheat, barley, bean, pea, ol seeds (sesame), vegetable and frut. The dstrct has fertle sol although under pressure due to human actvtes often-unplanned actvtes. However, productvty n the peasant sector s of subsstence nature. Due to the ncrement of populaton 1 man shares 0.53 ha n Konso where one can easly understand how dense the populaton settlement of Konso s, n relaton to land sze (Tamru, 014). Types of Data, Sources and Data Collecton Methods Ths study used relevant data collected from both prmary and secondary data sources. Prmary data were collected from sample households usng questonnares, focus group dscussons and key nformants ntervew. Secondary data were collected from both publshed and unpublshed sources whch nclude data on agrcultural

5 Int. J. Agrc. Educ. Ext. 184 producton, farmng systems and other baselne nformaton. The nformaton was collected from regonal, zonal and dstrct-level offces of agrculture and rural development, and concerned government and nongovernmental nsttutons. Samplng Technque A combnaton of both purposve and random samplng technques were employed to draw an approprate sample. Konso dstrct was purposvely selected for the study because of the presence of large number of sorghum producng households and ts extent of producton n the area. Konso dstrct actually comprses of 41 rural kebeles. Snce the research focus bascally on sorghum producton, sorghum producer kebeles were the major target areas for sample selecton. In the frst stage, fve kebeles were selected randomly. In the second stage, based on the lst of households of the kebeles who produce sorghum durng 014/15 producton year, 14 sample farm households were selected from the total households of fve kebeles by usng smple random samplng (SRS) technque based on probablty proportonal to sze (PPS). There are several approaches to determne the sample sze, out of them the one by Yamane (1967) was used. The sample sze for the study was determned based on the followng formula. N n 14Households 1 N( e) (0.09) (7) Where: n s the sample sze needed, N s the total populaton and e s the desred level of precson. Fnally, a total of 14 sample households was selected for ntervew as presented n Table 1 below. Table 1: Kebele, number of households, and sample sze selected from sample Kebeles Kebele Sorghum producng Sample Percent households sze Debana Mecheke Borqara Gamole Arfade Total Source: Own samplng desgn, 015 Methods of Data Analyss Descrptve statstcs In ths study, descrptve tools lke mean, standard devaton, frequency, percentage and nferental statstcal tests lke ch-square test for potental dscrete (dummy) explanatory varables and t-test was used to test the sgnfcance of the mean dfference of contnuous varables for the sample households. Econometrc models Specfcaton of the functonal form The study used the stochastc fronter functonal approach, whch requres the pror specfcaton of the producton functon to estmate the level of techncal effcency. Among the possble algebrac forms, Cobb-Douglas and the translog functons have been the most popularly used models n most emprcal studes of agrcultural producton analyss. As a result, takng the approprate functonal form that better ft the data was selected. Estmaton of the emprcal model The model parameters n stochastc producton fronter were analyzed by employng a sngle stage estmaton procedure. In usng the two-stage estmaton procedure of effcency level and factors determnng, the effcency ndex s estmated by the stochastc producton functon n the frst stage and then regressed aganst a number of other farm specfc and socoeconomc varables n the second stage. The one-stage estmaton procedure of the neffcency effects model together wth the producton fronter functon would be used n the study. The two-stage procedure produces nconsstency n the assumpton (Coell et al., 1998). Moreover one-stage procedure s the most commonly used method n the analyss of techncal effcency. Thus one-stage procedure s selected for ths study. Usng Ordnary Least Squares (OLS) estmaton procedure, t s possble to get consstent estmators for all the parameters n the model except the ntercept term. But f we use the OLS method to estmate the ntercept term t wll be based. Ths problem occurs due to the fact that the mean of (v - u) s not zero by assumpton. However, a consstent estmator of the ntercept can be formed by usng Corrected Ordnary Least Squares (COLS), whch nvolves adjustng the OLS ntercept by the mean of u. In addton to the COLS method, one can also adopt the more effcent maxmum lkelhood (ML) approach (Sharf and Dar, 1996), whch requres numercal maxmzaton of the lkelhood functon. The ML estmaton s asymptotcally more effcent than the COLS estmator and emprcal nvestgatons suggested that the ML estmaton s sgnfcantly better than the COLS estmator, when the contrbuton of the techncal neffcency effects to the total varance term s large (Coell et al., 1998). In ths study, ML estmaton procedure s used to estmate the stochastc producton functon model descrbed n equaton (8). To use ML estmaton procedure, the assumptons made about the dstrbutons of the error components u and v should be respected (Coell, 1998). It s assumed that us are ndependently and dentcally dstrbuted half-normal random varables wth mean zero

6 Gemeyda et al. 185 and varance u. Whle v s are assumed to be ndependent and dentcally dstrbuted normal random varables wth mean zero and constant varance v. (v X = s (1 k) row vector wth the frst element equal to 1, of nput quantty used by the th household for the producton of sorghum, (0, v)), ndependent of the us. In most emprcal β = (β 0, β 1, β, β 3, β 4, β k) s (1 k) column vector of applcatons the half normal dstrbuton for the techncal unknown parameters to be estmated, neffcency effects (u) has been more frequently U = s a non-negatve random varable assocated wth assumed. techncal neffcency of the th household for the sorghum producton, Gven a symmetrc normal dstrbuton for v and half V = s random error term of the model whch capture normal dstrbuton for u and by defnng a parameter, random shock of the producton of sorghum n the th household and = 1,, 3,, n s number of samples n whch les between zero and one and s equal to u / s a populaton., (where s = u + v ), Battese and Corra (1977) showed that the log-lkelhood functon n terms of ths parametersaton s equal to: For the fronter model defned by equaton (8), the null hypothess, that there are no techncal neffcency effects N s N 1 n the model s conducted by testng the null and alternatve N LnL( Y ) ln( ) ln 1 s hypothess H0: γ = 0 versus H1: γ > 0. The hypothess 1 s (8) Where, V U ln y ' X and u v (.)= Is the dstrbuton functon of the standard normal random varable. ln LY = logged output level for the th farm X = logarthm of the level of nput for the th farm β = Regresson coeffcent = Dscrepancy parameter as defned above s = varance of standard error of the composed error term and N = number of observatons The ML estmates of β, s, and s obtaned by fndng the maxmum of the loglkelhood functon defned by equaton (8). The ML estmators are consstent and asymptotcally effcent (Coell et al., 005). Once the estmates of the model parameters are found, the results can be used to estmate the techncal effcency levels of each ndvdual farm n the sample observaton as well as the mean level of the techncal effcency of the total sample households. The rato of the observed output for the th farm, relatve to the potental output, defned by the fronter functon; gven the nput vector X s used to defne the techncal effcency of the th frm. TE ' Y exp( X V U ' ' exp( X V ) exp( X V ) ) exp( U (9) Where, Y = exp(x β+v-u) s the stochastc fronter model for th household, Y = denotes output of sorghum produced by the th household, ) nvolvng γ are consdered due to the fact that, the Battese and Corra (1977) parametersaton was adopted for ths study and the test must be performed as a one sded test because γ cannot take negatve values. As a result the One-sded Generalzed Lkelhood rato Test suggested by Coell (1995) should be performed when maxmum lkelhood estmaton s nvolved. Ths test statstc requres the estmaton of the model under both the null and alternatve hypotheses. Under the null hypothess H0: γ = 0, the model s equvalent to the tradtonal average response functon, wthout the techncal neffcency effect, U. The test statstc s calculated as: LR { Ln[ L( H ) / L H )]} 0 0 H1 ( 1 { Ln[ L( H ) L( )]} (10) Where: L(H0) = the log lkelhood value of the null hypothess; L(H1) = the log lkelhood value of the alternatve hypothess; Ln s the natural logarthms In ths case f H0: γ = 0 s true the LR has asymptotc dstrbuton whch s a mxture of Ch- square dstrbutons Coell (1995). Then the crtcal value for the one-sded Generalzed Lkelhood rato Test of H0: γ = 0 versus H1: γ > 0 can smply be calculated. The crtcal value for a test of sze α s equal to the value, 1 ( α), where ths s the value exceeded by the χ 1 random varable wth probablty equal to α. Thus the one-sded generalzed lkelhood rato test of sze α s: reject H0: γ = 0 n favor of H1: γ > 0 f LR exceeds, χ 1 ( α). As far as factors determnng techncal effcency are concerned, farmers have dfferent characterstcs that make them attan dfferent levels of techncal effcency. Gven a partcular technology to transform physcal nputs n to outputs, some farmers are able to acheve maxmum output whle others are not. These, factors need to be dentfed n order to defne the problem of neffcency thereby nvestgated for remedal measures to solve the problem. Most of the tme n the area of effcency analyss the followng varables are commonly used. Gven the

7 Int. J. Agrc. Educ. Ext. 186 specfed explanatory varables below, the functonal relatonshp between nput and output used n the stochastc producton functon can be specfed as follows: Ln( OUTP ) f ( AREA, UREA, DAP, OXD, HLAB, SEED, CHEM ; ) (11) Where: Ln(OUTP) = s the total output of sorghum obtaned from the th farm n quntal. AREA = the total sze of land n hectare allocated for sorghum crop by the th household. UREA = the total amount of urea fertlzer n klogram appled by the th household DAP = the total amount of DAP fertlzer n klogram appled by the th household. OXD = the total number of oxen days used by the th household. HLAB = the total labor force (famly and hred) whch are all measured n terms of man-day. SEED = the total quantty of sorghum seed used by the th household measured n kg. CHEM = Chemcals such as herbcdes or pestcdes used as an nput partcularly n sorghum due to serous weed, pest and dsease attack by the th household. f ( ) = Approprate functonal form (e.g. Cobb-Douglas or Translog functonal form) β = vector of unknown parameters to be estmated, and = composed error term, Where: = V -U U = non-negatve random varable, ndependently and dentcally dstrbuted as N(u, ) whch s ntended to capture the techncal neffcency effects n the producton of sorghum measured as the rato of observed output to maxmum feasble output of the th farm and V = a dsturbance term ndependently and dentcally v dstrbuted as N (0, ) whch s ntended to capture events or factors outsde the control of the farmers. The techncal neffcency effects model by Battese and Coell (1995) n whch both the stochastc fronter and factors affectng neffcency (neffcency effect model) are estmated smultaneously s specfed n Equaton (1) as a jont estmaton of a stochastc fronter producton functon: LnOUTP f { ln AREA lnurea ln DAP lnoxd ln HLAB lnseed lnchem V ( AGEHH EDUCLVL FAMSIZE 6 u DISTRES FERTILITY SLOP SEX W } OFFOC FRAGMENT ACCTRAIN EXTSERV ACSCDT LIVSTOCK (1) Where: δ = Parameter vector assocated wth neffcency effect to be estmated; w = Error term. RESULTS AND DISCUSSION Descrptve statstcs of the nput varables and output The producton functon for ths study was estmated usng seven nput varables. To draw some pcture about the dstrbuton and level of nputs, the mean and range of nput varables s presented n Table 13 below. The average sorghum yeld produced by sample households was 1.8 quntal per ha, wth a standard devaton of 7.94, maxmum of 36 and mnmum of 3 quntal per ha whch s dependent varable n the producton functon (Table 13). In the study area, sorghum s produced two tmes per annum. The land allocated for sorghum producton, by sample households durng the survey perod, ranges from 0.15 to 1.75 ha wth average land sze and standard devaton of 0.68 ha and 0.38 respectvely. In the study area, farmers use both Urea and DAP fertlzers for sorghum producton. The average amount of Urea and DAP fertlzers appled n the producton of sorghum by sample households were 4.43 Kg per hectare and Kg per hectare, respectvely durng 014/15 sorghum producton season. There was hgh varaton of fertlzer utlzaton (both urea and DAP) n sorghum producton by sample households. Lke other nputs human labour and oxen power nputs were also decsve n the study area. Sample households, on average, use man days per ha of labour for the producton of sorghum durng 014/15 producton season. In the producton process labor nput s used for major farmng actvtes such as land preparaton, sowng, chemcal applcatons and fertlzer applcatons and weedng and other actvtes. For sorghum land preparaton oxen power was used by the sample households. Feld survey result showed that about 75% sample households use oxen power for ploughng ther sorghum land, and ths oxen power s computed to oxen days. The average oxen power used by sample households was oxen days per ha wth standard devatons of 5.0. The other very mportant varable, out of whch producton s mpossble, s seed. The amount of seed sample households used was Kg, on average wth standard devaton of 8.15 (Table 13). There are dfferent sorghum seed varetes used by households n the study area. Households used broadcastng method of sowng and on average the seed rate was kg/ha whch s greater than the recommended rate of 1 kg/ha for the study area. On average, sample households appled 0.3 lter of chemcals such as herbcdes or pestcdes per hectare n the study area for the protecton of sorghum farms durng producton year. Table : Output and nput varables used to estmate the producton functon Varable descrpton Summary statstcs Mean St. devaton Max Mn Sorghum output (Qt/Ha) Land (Ha) Seed (kg/ha) Human labor (MDs/Ha) Oxen power (ODs/Ha) DAP (Kg/Ha) Urea (Kg/Ha) Chemcals (Lt/Ha) Source: Own computaton result, 015

8 Gemeyda et al. 187 Econometrc Model Outputs Ths secton presents the econometrc model outputs of the study. In ths secton, the producton functon, ndvdual effcency scores and sources of dfferences n techncal neffcency of sorghum producton n the study area are presented and dscussed. Test of hypothess Before dscussng the model output, let us begn wth lkelhood rato (LR) tests to assess varous assumptons related to the model specfcaton. Tests of hypotheses for the parameters of the fronter model were conducted usng the generalzed lkelhood rato statstcs. Accordngly four hypotheses were tested, to select the correct functonal form for the gven data set, for the exstence of neffcency, for varables that explan the dfference n effcency, and fnally to dentfy type of dstrbutons. Table 3: Generalzed lkelhood rato tests of hypothess for the parameters of the SPF Null LH0 LH1 Calculated Crtcal Decson hypothess (LR) value value H0: = βj = Accept H0: = 0 Reject H0 H0: = Reject H0 1=.13 = 0 H0: = μ= Accept Source: Own computaton result, 015 The frst test was the null hypothess that dentfes an approprate functonal form between restrctve Cobb Douglas and the non-restrctve Translog producton functon whch specfes that square and cross terms are equvalent to zero. The Cobb-Douglas and the Translog functonal forms are the most commonly used stochastc fronter functons n the analyss of techncal effcency n producton. The Translog fronter functon turns nto Cobb- Douglas when all the square and nteracton terms n the translog are zero. In order to choose between the two alternatve functonal forms that can better ft to the survey data collected, the null hypothess that all the nteracton and square terms are all equal to zero (H0 : βj = 0),.e. Cobb-Douglas fronter functonal specfcaton, s tested aganst the alternatve hypothess that these coeffcents are dfferent from zero (H1 : βj 0). The test s made based on the value of lkelhood rato (LR) statstcs, whch can be computed from the log lkelhood value obtaned from estmaton of Cobb-Douglas and Translog functonal specfcatons usng Equaton (7). Then, ths computed value s compared wth the upper 5% crtcal value of the at the degree of freedom equals to the dfference between the numbers of explanatory varables used n the two functonal forms (n ths case df = 8). For the sample respondents, the estmated log lkelhood values of the Cobb-Douglas and Translog producton functons were -5.9 and , respectvely. The computed value of lkelhood rato (LR = -( ) = 0.19 s lower than the upper 5% crtcal value of the wth ts respectve degree of freedom (Table 15). Thus, the null hypothess that all coeffcents of the square and nteracton terms n Translog specfcaton are equal to zero was not rejected. Ths mples that the Cobb-Douglas functonal form adequately represents the data. The second null hypothess was H0: γ = 0, whch specfes that the neffcency effects n the SPF were not stochastc.e. sorghum producng farms are effcent and have no room for effcency mprovement. After the approprate producton functon s selected, the next step s a test for adequacy of representng the data usng SPF over the tradtonal mean response functon, OLS. The null hypothess, H0: = 0, whch specfes that the neffcency effects are absent from the model (that s all sorghum producers are fully effcent). Whereas, the alternatve hypothess, H1: > 0, states that there s neffcency n producton of sorghum n the study area. Snce ths study s usng the STATA verson 1.1 computer programs, after fttng the functon wth the requred defned varables the computer output dsplays results whch nclude the test of null hypothess about neffcency component. From ths computer program output t s found that, log lkelhood value = 5.9, (chbar (01)-value = 7.11 and p = 0.004). Hereafter, the decson of null hypotheses H0: = 0, whch specfes that the neffcency effects are absent from the model s rejected at 1% level of sgnfcance for the sampled households. The coeffcent for the dscrepancy rato ( ) could be nterpreted n such a way that about 90 percent of the varablty n sorghum output n the study area was attrbutable to techncal neffcency effect, whle the remanng 10 percent varaton n output was due to the effect of random nose. Ths mples presence of scope for mprovng output of sorghum by frst dentfyng those nsttutonal, socoeconomc and farm specfc factors causng ths varaton. Therefore ths data can be better represented by the stochastc producton fronter than the average response functon. The null hypothess was rejected (Table 15). Ths mples the tradtonal average producton functon does not adequately represent the data. Therefore, the ncluson of the techncal neffcency term s an mportant ssue to the model. The thrd null hypothess that the explanatory varables assocated wth neffcency effects are all zero (H0: 1= = 13 = 0) was also tested. To test ths hypothess lkewse, LR (the neffcency effect) was calculated usng the value of the Log-Lkelhood functon under the stochastc producton functon model (a model wthout explanatory varables of neffcency effects: H0) and the full fronter model (a model wth explanatory varables that are supposed to determne neffcency of each: H1). For

9 Int. J. Agrc. Educ. Ext. 188 the sample households, the calculated value LR = - ( ) = 71.0 s greater than the crtcal value of.36 at 13 degree of freedom (Table 15) the value of LR mplyng that, the null hypothess (H0) that explanatory varables are smultaneously equal to zero was rejected at 5% sgnfcance level. Hence, these varables smultaneously explan the sources of effcency dfferences among sample farmers n the study area. Thus the observed neffcency among the sorghum farmers n Konso dstrct could be attrbuted to the varables specfed n the model and the varables exercsed a sgnfcant role n explanng the observed neffcency. Therefore, the result confrms as the null hypothess was rejected, mplyng that there s at least one varable that explan the dfference n effcency. The fourth test conducted was, gven such functonal forms for the sample households; t was consdered whether the techncal effcency levels were better estmated usng a half normal or a truncated normal dstrbuton of μ The results ndcated that the half normal dstrbuton was approprate for the sample households n the study area as the calculated LR rato value of 0.06 was less than the crtcal -value of.71 at 5% sgnfcance level. That means the null hypothess (H0: μ=0 dstrbuton assumpton) was accepted at 5% sgnfcance level. Estmaton of parameters of producton functon model Indvdual techncal effcency levels n sorghum producton were estmated usng the stochastc fronter producton functon (Appendx Table 9). The nput varables used n the stochastc fronter producton model were Land allocated for sorghum (ha), Urea(Kg), DAP n kg, oxen power (ODs), Human labour (MDs), Quantty of seed (kg) and chemcals (herbcdes or pestcdes). The coeffcents of the nput varables were estmated under the full fronter producton functon (MLE). (Table 16). The maxmum lkelhood estmaton for the best practce households, the output elastcty wth respect to all the selected varables has the expected sgn. That means all nput varables entered n the producton fronter functon have been found postve sgn of estmated coeffcents that generally confrm to pror expectatons. The result of the Cobb- Douglas stochastc producton fronter showed that land allocated for sorghum, chemcal fertlzer (Urea and DAP), oxen power, human labour and chemcals (herbcde or pestcdes) nputs were found to postvely and sgnfcantly (at 1% sgnfcance level except oxen power whch s at 5% level of sgnfcance) explaned the level of effcency of sorghum producton (Table 16), whch are mportant varables n shftng the fronter output to the rght. Ths ndcated that at each and every unt of these varables there s a possblty to ncrease the level of output. But the mount of seed s nsgnfcant. Table 4: Maxmum lkelhood estmate of stochastc producton fronter model Varables Parameters Coeffcents Std. Err. Z-value Constant β0 1.97*** Ln(LAND) β *** Ln(UREA) β 0.075*** Ln(DAP) β *** Ln(OXD) β ** Ln(HLAB) β5 0.38*** Ln(SEED) β Ln(CHEM) β *** Sgma *** square Gamma 0.899*** Lambda.98*** Log lkelhood -5.9 functon Returns to scale *, **, ***, Sgnfcant at 10%, 5% and 1% level of sgnfcance Source: Model output, 015 One of the appealng features of the Cobb-Douglas functonal form s the drect nterpretaton of ts parametrc coeffcents as a partal elastcty of producton wth respect to the nput used. Ths attrbute allows one to evaluate the potental effects of changes n the amount of each nput on the output. The varables land allocated for sorghum, oxen power, human labour and chemcals are the man nputs n determnng the output level of sorghum for sample households n the study area. Whereas, the elastcty of fertlzer (urea and DAP) are very low mplyng that these have less effect n determnng the output level at the best practce (the maxmum techncal effcency score). The postve coeffcents of nputs ndcate a 1% ncrease n land allocated for sorghum, urea, DAP fertlzer, oxen power, human labour and herbcdes or pestcdes yelds 0.189%, 0.075%, 0.035%, 0.13%, 0.4% and 0.141% ncrease n sorghum output, respectvely. In other words, f all the nputs are ncreased by 1%, sorghum output would ncrease by 0.887% (Table 16). Labor nput used for sorghum producton was found to be statstcally sgnfcant and wth expected sgn for sample households. Hence, there may be shortage of labor durng sorghum producton. That means there s overlappng of actvtes wth other crops usually happened and shared the avalable labors. So, n ths case labor would be the mportant varable n determnng sorghum output level n the study area. Snce the major concern of ths study s to know the level of techncal effcency of sorghum growng households and the major factors determnng the techncal effcency dfferentals n the study area, n-depth dscussons on the structure of producton functon and coeffcents of nput varables s not requred as such. The maxmum-lkelhood parameter estmates of the model show the relatve mportance of ndvdual nputs and ther aggregate

10 Gemeyda et al. 189 sgnfcance n the producton process as well as beng useful n predctng the level of ndvdual household s TE. The dagnostc statstcs of neffcency component reveals that sgma squared (δ ) was statstcally sgnfcant at 1%, whch ndcates goodness of ft, and the correctness of the dstrbutonal form assumed for the composte error term. Accordng to the model result of stochastc producton functon (Table 16) the value of λ s.98. The presence or absence of techncal neffcency was tested n the study usng the mportant parameter of log lkelhood n the half normal model = u/v. f = 0 there were no effects of techncal neffcency, and all devatons from the fronter were due to nose as stated n Agner et al. (1977), the estmated value of =.98 sgnfcantly dfferent from zero. The null hypothess that there s no neffcency effect was rejected at 5% level of sgnfcance, suggestng the exstence of neffcency effects for households n Konso dstrct. Another mportant result n the analyss s the varance rato parameter γ whch found to be sgnfcant at 1% level expressng that about 90% of sorghum output devatons are caused by dfferences n farm level techncal effcency as opposed to the random varablty that are outsde ther control of the producers. Returns to scale s the sum of elastctes of Cobb-Douglas fronter producton functon wth respect to all nputs used, reflects the degree to whch a proportonal ncrease n all the nputs ncrease output. The sum of elastcty as presented n Table 16 s 0.89 whch mples decreasng returns to scale such that when all nputs specfed n the model for the producton of sorghum are ncreased by 1 unt, output wll n turn ncrease by 0.89 unts. Even though nonnegatve and less than one value of the sum of elastcty mply that producers are operatng n the stage two of the producton process, they are not effcent n allocaton of resource ths mples producton s neffcent moreover there s a room to ncrease producton wth a decreasng rate. Techncal effcency scores of sample households The results of the effcency scores ndcated that there were wde ranges of dfferences n techncal effcency among sorghum producer households n the study area. The mean techncal effcency of sampled households durng the survey perod was 68.75%. The techncal effcency among households ranged from 1.9 to 96.8% (Table 17). Ths wde varaton n household specfc techncal effcency levels s consstent wth study result reported by Betty (005), Ike and Inon (006) and Berhan (013). Table 5: Summary statstcs of estmated techncal effcences of sample households Descrpton Techncal effcency estmates Maxmum Mnmum 0.19 Mean Standard devaton 0.8 Skewness Source: Own computaton result, 015. Ths shows that there s a wde dsparty among sorghum producer households n ther level of techncal effcency whch may n turn ndcate that there exsts a room for mprovng the exstng level of sorghum producton through enhancng the level of household s techncal effcency. The mean level of techncal effcency further tells us that the level of sorghum output of the sample households can be ncreased by about 31% f approprate measures are taken to mprove the effcency level of sorghum producng households. In other words, there s a possblty to ncrease yeld of sorghum by about 31% usng the resources at ther dsposal n an effcent manner wthout ntroducng any other mproved (external) nputs and practces. Table 6: Frequency dstrbuton of the range of ndvdual techncal effcency levels Range of TE Frequency Percent (%) < Total Source: Own computaton result, 015. It s observed that 47.58% of the sample households are operatng below the overall mean level of techncal effcency whle,.58% of the households are operatng at the techncal effcency level of more than 90%. However, as presented n Table 18 majorty (5.4%) of the sorghum growng households were able to attan above the overall mean level of techncal effcency. Ths mght mply that n the long run mprovng the exstng level of techncal effcency of households alone may not lead to sgnfcant ncrement n the level of sorghum output.

11 Int. J. Agrc. Educ. Ext. 190 Sources of techncal neffcency Havng the nformaton about the exstence of techncal neffcency and measurng ts magntude, examnng the major factors causng ths neffcency level s the next most mportant step of the study. About 13 soco-economc and nsttutonal varables were hypotheszed to affect level of techncal neffcency of sorghum growng households n the study area, out of whch eght of them were dummy varables and the remanng were contnuous varables. Most of the varables were dscussed n the descrptve result secton above. Hence, here we dscuss only some of the varables n the neffcency model. The drvng force behnd measurng households effcency n sorghum producton s to dentfy mportant varables or determnants to generate nformaton n order to make an nterventon and mprove the exstng level of effcency. The parameters of the varous hypotheszed varables n the techncal neffcency effect model that are expected to determne effcency dfferences among households were estmated through MLE method usng one-stage estmaton procedure. The determnants of techncal neffcency n a gven perod vary consderably dependng on the soco-economc condtons of the study area partcularly pertanng to manageral characterstcs and other related factors. Table 19 shows the result of the techncal neffcency model estmates. Before dscussng the sgnfcant determnants of neffcency n sorghum producton t s mportant to see how effcency and neffcency are nterpreted. The result n table 19 s presented n terms of neffcency and hence the negatve sgn shows the ncrease n the value of the varable attached to the coeffcent means the varable negatvely contrbute to neffcency level or conversely t contrbutes postvely to effcency levels. Thus, any negatve coeffcent happens to reduce neffcency whch mples ts postve effect n ncreasng or mprovng the effcency of the farm and vce versa. The coeffcents of those soco-economc and nsttutonal varables ncluded n the model were estmated smultaneously by the ML procedure usng the estmated level of techncal effcency as dependent varable. One mportant pont to be consdered s that the dependent varable s the neffcency component of the total error term estmated n combnaton wth the producton fronter. Table 7: Maxmum lkelhood estmates of the factors determnng techncal neffcency Varables Coeffcent Std. Err z-value Constant Age 0.035** Educaton -0.71* Famly sze ** Off-farm -0.60* occupaton Land fragmentaton Access to tranng Extenson servce -0.90** Access to credt Lvestock holdng 0.101** Plots dstance 0.364*** Sol fertlty * Slope of farm land Sex of the household head *, **, ***, Sgnfcant at 10%, 5% and 1%, level of sgnfcance. Source: Model output, 015. The results of the neffcency model shows that, among thrteen varables used n the analyss, age, educaton level of household head, famly sze, off/non-farm actvtes, extenson contact, lvestock holdng, plots dstance from household s resdence and sol fertlty status were sgnfcantly contrbutng to techncal neffcency of sorghum growng households. (Table 19). The followng sectons are dedcated for the dscussons on the mplcatons of those varables sgnfcantly contrbutng to techncal neffcency. Age: the techncal neffcency model result ndcated that the coeffcents of age for sample households, was statstcally sgnfcant at 5% sgnfcance level and have postve sgn. Ths mples that an ncrease n age of the household head s assocated wth ncreases n techncal neffcency model. Ths can be explaned by the fact that as a household head becomes more aged n lfe, t becomes practcally dffcult f not mpossble for hm/her to take proper utlzaton of nputs and adopton of new technologes on ther farms and therefore becomng more neffcent. The result s consstent wth fndngs of Knde (005) and Halemarm (015).

12 Gemeyda et al. 191 Educaton: Educaton s beleved to enhance the manageral and techncal sklls of households. Accordng to Battese and Coell (1995), educaton s hypotheszed to ncrease the household s ablty to utlze exstng technologes and attan hgher effcency levels. Several other studes also ndcated that educaton beng assocated wth effcent management of producton systems and hence hgher effcency levels lke studes of (Ahmed et al., 00; Assefa, 01; Zalkuw et al., 014; Wondmu et al., 014; Halemaram, 015). Ths could be argued that access to better educaton enables households to better manage ther resources n order to sustan the envronment and produce at optmum levels. The results of ths analyss show that educaton level negatvely and sgnfcantly affectng neffcency. Ths ndcates that, educaton capactatng n human captal that enhances the productvty of households snce they wll be better able to allocate homemade and purchased nputs, select the approprate quanttes of purchased nputs and choose among avalable technques. Famly sze: It s plausble that the household wth more members can perform farmng actvtes on tme. Crop producton s labor-ntensve actvty n Ethopa. In ths regard, the effect of household sze on techncal neffcency cannot be overemphaszed. In fact, the lterature offers mxed results. On the one hand, people argue that an ncrease n the number of famly members could decrease techncal neffcency f t results n ncreased labor allocated to crop producton. The coeffcent of famly sze n the techncal neffcency model s negatve and sgnfcant at 5% sgnfcance level. The result s smlar to the expectaton that those households havng large famly sze are less neffcent than households havng small famly sze, because; famly labor s the man nput n crop producton. As the households has large famly sze, he/she would manage crop plots on tme and may be able to use approprate nput combnatons. The result s consstent wth the fndngs of Abdula and Eberln (001), Essa (011) and Ahmed et al. (00) but nconsstent wth the fndngs of Fekadu (004), Mekdes (011), Endalkachew (01) and Halemarm (015). Off-farm occupaton: Refers to the opportunty that the farm households had to work outsde ther own farm operatons. A number of studes conducted revealed that off/non-farm actvtes have a systematc effect on the techncal neffcency of the households. Ths s because households may allocate more of ther tme to off/non-farm actvtes and thus may lag n agrcultural actvtes. On the other hand, ncomes from off/ non-farm actvtes may be used as extra cash to buy agrcultural nputs and can also mprove rsk management capacty of households. Off/non-farm actvtes can drectly lnk wth the tmely avalablty of famly labor for on-farm operatons. That means, the tme at whch some porton of the famly labor s dverted towards off/non-farm works can delay farm actvtes. It was hypotheszed that there s effcency dfferental among households who are engaged n off/non-farm and those who are not. The result n table 19 shows that, the coeffcents of the varable entered nto the techncal neffcency effect model ndcated that the varable affects the level of techncal neffcency negatvely and sgnfcantly. In other words, those households engaged n some off/non-farm actvtes are less techncally neffcent relatve to those who were not engaged n actvtes other than ther farm operatons. The possble explanaton s that t would assst the households to supplement other costs assocated wth ther lvng, perhaps. It may have affected techncal neffcency negatvely for the reason that the ncome obtaned from such off/non-farm actvtes could be used for the purchase of agrcultural nputs, and augment fnancng of household expendtures whch would otherwse, put pressure on on-farm ncome. Therefore, there s sgnfcant dfference n techncal neffcency between the sample households who partcpate n off/non-farm actvty and who are not n sorghum producton n the study area. The result obtaned s consstent wth studes by Knde (005), Haleselasse (005) and Halemaram (015). But nconsstent wth studes of Wondmu and Hassen (014). Extenson servce: Consultaton gven by extenson agents mproves productvty of households. The output of neffcency model also revealed that extenson servce utlzaton has negatve sgn and s sgnfcant at 10% sgnfcance level. Ths ndcates that the more the household had extenson vst, the less he/she wll become neffcent. Thus, ths result shows that consultaton of extenson agents ncrease sorghum producton by decreasng level of techncal neffcency. Ths mples that a frequent contact facltates the flow of new deas between the extenson agent and the household thereby gvng a room for mprovement n farm effcency. Advsory servce rendered to the households n general can help households to mprove ther average performance n the overall farmng operaton as the servce wdens the household s knowledge wth regard to the use of mproved agrcultural nputs and agrcultural technologes. Ths result s n lne wth the results of Fekadu (004), Abebe (009), Musa (013) and Halemaram (015). Contrary to ths, Jema (008) found postve relatonshp between level of neffcency and extenson servce. Lvestock holdng: Number of lvestock the households have n terms of tropcal lvestock unt was hypotheszed to have negatve nfluence n the neffcency model. That means households who have better lvestock holdng are less neffcent than others. However, the fndng shows that, the coeffcent s postve and sgnfcant at 5% level of sgnfcance ndcatng that household wth hgher lvestock holdng are more neffcent than those who have less lvestock sze. Lvestock n a mxed crop-lvestock farmng system have two fold mportance n that, t supply oxen power (draught power) for ploughng and threshng, provde manure that wll be used to mantan sol fertlty and t serves as shock absorber to an unexpected hazard