ANALYSIS OF FACTORS AFFECTING TECHNICAL INEFFICIENCY OF SMALLHOLDER FARMERS IN NIGERIA: STOCHASTIC FRONTIER APPROACH

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1 Internatonal Journal of Economcs, Commerce and Research (IJECR) ISSN Vol. 3, Issue 1, Mar 2013, TJPRC Pvt. Ltd. ANALYSIS OF FACTORS AFFECTING TECHNICAL INEFFICIENCY OF SMALLHOLDER FARMERS IN NIGERIA: STOCHASTIC FRONTIER APPROACH J. O. OLADEEBO Department of Agrcultural Economcs, Faculty of Agrcultural Scences, Ladoke Akntola Unversty of Technology, Ogbomso, Ngera ABSTRACT Ths study estmated the techncal effcency of small holder farmers and also dentfed the soco-economc factors whch nfluence the farmers specfc techncal neffcences n Oyo State, Ngera. Stochastc fronter producton functon, usng the maxmum lkelhood estmaton technque was used n the analyss of data obtaned from 150 smallholder food crop farmers. The results of the study showed that the techncal effcency of farmers ranged from 84.4% to 99.4% wth a mean techncal effcency of 94.3%. The mean techncal effcency of 94.3% obtaned mpled that there s scope for ncreasng output by 5.7% techncal effcency through more effcent resource utlzaton. Results also show that contact wth extenson agents, farmng experence and credt avalablty are factors whch reduce the techncal neffcency of farmers. KEYWORDS: Techncal Ineffcency, Small Holder Farmers, Ngera INTRODUCTION Agrcultural growth n Ngera s ncreasngly recognzed to be central to sustaned mprovement n economc development. The sector plays a very crucal role n the food securty, poverty allevaton and human development chan n Ngera (Aye and Oboh, 2006). The agrcultural sector n Ngera s domnated by the small holder farmers who produce the bulk of food requrements n the country (Ajbefun, 2002). They are nvolved n the producton of grans, such as rce, maze, sorghum and tuber crops such as cassava, cocoyam and yam whch supply the bulk of energy requrements to the populace, as well as nvolved n rasng lvestock such as goat and sheep whch are source of proten to the populace. In spte of the unque poston of small holder farmers n agrcultural producton n Ngera, they belong to the poorest segment of the populaton and therefore, cannot nvest much on ther farms (Ajbefun, 2002). Rce food crop whch was the focus of ths study, s one of the food crops grown by small holder farmers wth average farm sze of 1 to 3 hectares, has become a major staple n Ngera (Tjan, 2006). It has a great potental and can contrbute to ncome generaton and poverty allevaton of small holder farmers n Ngera. In order to allevate the poverty confrontng small holder rce farmers, two thngs may be done accordng to Aye and Oboh (2006). Frstly, they suggested ncreasng the land area planted to food crops; and secondly, ncreasng the yeld per unt of producton resource. However, doublng the land area planted to crop may create envronmental damage (Aye and Oboh, 2006). Increasng rce yeld per unt of producton resource under the present technology could be acheved by mprovng the soco-economc characterstcs and producton management of farmers (Srsompun and Isvlanonda, 2012). Techncal effcency s defned as the ablty to produce maxmum output from a gven set of nputs, gven the avalable technology (Yao and Lu, 1998). Ths defnton ndcates that dfferences n techncal effcency exst between farms. Accordng to Mjndad and Norman (1982) the observed dfferences mght be attrbuted to at least four sets of factors such as:

2 22 J. O. Oladeebo () () () (v) dfferences n management ablty; the employment of dfferent levels of technology --- ndcated by the qualtes and of types of nputs used; dfferent envronmental factors-sol qualty, ranfall, solar radaton, and, non-economc and non-techncal factors such as famly structure and motvatonal dfferences whch can prevent some farmers from workng hard enough on ther plots thus falng to acheve the hghest level of farm output. In order to allevate poverty of the small holder farmers and promote economc growth n Ngera, the study estmated the techncal effcency of small holder farmers and also dentfed the soco-economc factors whch nfluence the farmers specfc techncal neffcences. METHODOLOGY Theoretcal and Analytcal Framework Emprcal estmaton of techncal effcency s normally done wth the methodology of stochastc fronter producton functon. The stochastc fronter producton model has the advantage of allowng smultaneous estmaton of ndvdual techncal effcences of the respondent farmers as well as determnants of techncal effcency (Battese and Coell, 1995). The stochastc fronter producton functon ndependently proposed by Agner et al (1977) and Meeusen and Van Den Broeck (1977) assumes that maxmum output may not be obtaned from a gven nput or a set of nputs because of the neffcency effects. It can be wrtten as: Y = f ( ; β ) + ε X a (1) Where: Y = the quantty of agrcultural output, X a = a vector of nput quanttes and, β = a vector of parameters ε s an error term defned as: ε =V U =1,2, n farms (2) V s a symmetrc component that accounts for pure random factors on producton, whch are outsde the farmers control such as weather, dsease, topography, dstrbuton of supples, combned effects of unobserved nputs on producton etc. and U s a one-sded component, whch captures the effects of neffcency and hence measures the shortfall n output Y from ts maxmum value gven by the stochastc fronter f(xa; β )+ V. The model s expressed as: Y = exp ( X β + V U )... (3) The farm specfc techncal effcency (TE) of the th farmer was estmated usng the expectaton of U condtonal on the random varable (ε ) as shown by Battese and Coell (1988). The parameters of stochastc fronter producton were estmated by the method of Maxmum Lkelhood Estmaton (MLE), usng the computer program FRONTIER Verson 4.1 (Coell, 1996).

3 Analyss of Factors Affectng Techncal Ineffcency of Smallholder Farmers n Ngera: Stochastc Fronter Approach 23 The TE of an ndvdual farmer s defned n terms of the rato of the observed output to the correspondng fronter output gven the avalable technology, that s: TE Y = Y exp( X β + V U = exp( X β + V ) ) = exp( U * )... (4) So that O T E 1 (Tadesse and Krshnamoorthy, 1997) The Study Area and Data used The study was conducted n Oyo State of Ngera. The State covers an area of 28,454 square klometers (2,845,400 Ha) (FOS, 1997). Accordng to the Natonal Populaton Commsson (2006), Oyo State has a populaton of 5,580,894 people wth females beng 2,778,462 people and males beng 2,802,432. The State has two dstnct ecologcal zones: The most forest to the south and the ntermedate savannah to the north. The State shares borders wth Peoples Republc of Benn n the west, Kwara State n the north, Ogun State n the south and Osun State n the east. Oyo State s currently made up of thrty three Local Government Areas. Prmary data obtaned through sample survey wth the use of structured questonnare, were essentally used for ths study. The prmary data used were supplemented wth secondary data. The secondary data were obtaned from publcatons of the Federal Offce of Statstcs (FOS), Natonal Populaton Commsson of Ngera (NPC), Oyo State Agrcultural Development Programme (OYSADEP), journals and other relevant publcatons. Purposve and mult-stage random samplng technques were used to obtan the relevant data. The frst stage nvolved purposve selecton of two Local Government Areas (LGAs) noted for rce cultvaton n Oyo State. These are Ogo-Oluwa and Surulere LGAs from Ogbomoso agrcultural zone of the State. The second stage nvolved random selecton of sx towns/vllages from the lst of rce-growng towns/vllages obtaned from the Informaton Unt of each LGA-makng a total of twelve vllages from the state. The last stage nvolved a smple random samplng of thrteen rce farmers from each of the twelve vllages n the States. Thus, a total of 156 farmers sampled were ntervewed to elct the necessary nformaton relevant to the study. However, 150 well-completed copes of the questonnare were used for the analyss. The Emprcal Model The Cobb-Douglas fronter producton functon proposed by Battese and Coell (1995) and used by Yao and Lu (1998) was used n the analyss of data for ths study. The Cobb-Douglas functonal form s easly adaptable for most agrcultural productons. It has been wdely used n many emprcal studes n both developed and developng agrcultural countres (Battese, 1992; Bravo-Ureta and Pnhero, 1993; Xu and Jeffrey, 1998; Onyenweaku et al, 2004; Onyenweaku and Nwaru, 2005 and Raphael, 2008). The specfc log-lnearsed model estmated s specfed as: ln Y 7 = β V U (5) Where subscrpt refers to the observaton of the th farmer, and Y = output of rce gran (kg);

4 24 J. O. Oladeebo X 1 = land area devoted to rce cultvaton (ha); X 2 = famly labour used (man-days); X 3 = hred labour used (man-days); X 4 = quantty of fertlzer used (kg); X 5 = quantty of rce seed planted (kg); X 6 = amount spent on agrochemcals (N); X 7 = amount spent on mplements (N); β 0 = ntercept β 's = (=1, 2, 3,..7) the parameters to be estmated. ln's = natural logarthms. V and U are as defned n equaton (2) above. Determnants of Techncal Effcency To determne the factors that affected techncal effcency, the model specfed n equaton (6) was smultaneously estmated wth the techncal effcency model specfed n equaton (5) U = δ + δ Z + δ Z + δ Z + δ Z + δ Z (6) Where: U = techncal neffcency of the th farmer; Z 1 = age of farmer (years); Z 2 = years of educaton; Z 3 = number of contacts wth extenson agent; Z 4 = years of farmng experence; Z 5 = amount of credt avalable; δ 0 = ntercept ' δ s = (=1, 2, 3,..5) the parameters to be estmated. RESULTS AND DISCUSSIONS Table 1 shows the maxmum lkelhood estmates of the stochastc producton functon. The coeffcents of the varables are very mportant n dscussng the results of the analysed data. The varables wth postve coeffcents mpled that any ncrease n such a varable would lead to an ncrease n rce output, whle an ncrease n the value of the varable wth a negatve coeffcent would lead to a decrease n output of rce. Negatve coeffcent on a varable mght ndcate an excessve utlzaton of such a varable. The results presented n table 1 shows that all the varables carred postve sgns wth the coeffcents of farm sze, famly and hred labour beng sgnfcant at 5.0% level of sgnfcance. The estmated gamma parameter s ndcatng that about 16% of varaton of smallholder farmers output s accounted for by

5 Analyss of Factors Affectng Techncal Ineffcency of Smallholder Farmers n Ngera: Stochastc Fronter Approach 25 dfferences n techncal effcency. The generalzed lkelhood rato test ( χ 2 ) result showed that the test for the absence of techncal neffcency effects( null hypothess, γ = 0) was accepted. χ 2 Computed = 6.98 < 2 χ = , 0.05 Ths mpled that the techncal neffcency effects were not strong n the producton of rce n the State and that varaton n ther producton processes was only due to random effects. Table 1: Maxmum Lkelhood Estmates for the Parameters of the Stochastc Fronter Producton Functon for Rce Food Crop Farmers n Oyo State Varables Parameter Coeffcent T-Rato Intercept β Farm Sze β Famly labour β Hred Labour β Fertlzer β Seed planted β Agrochemcals β expendture Expendture on β mplements Sgma squared δ Gamma γ Log lkelhood functon Λ LR-Statstc χ Notes: ndcates estmated coeffcents whch were sgnfcant at 5.0% level. Source: Obtaned from data analyss. Table 2 shows the determnants of techncal neffcency among the smallholder farmers n the study area. Accordng to Ajbefun and Adernola (2004), varables wth postve coeffcents lead to ncrease n techncal neffcency or decrease n techncal effcency whle varables wth negatve coeffcents lead to decrease n techncal neffcency or ncrease n techncal effcency. The coeffcents of age and years of educaton were postve aganst a pror expectaton (Coell and Battese, 1996) whle the coeffcents of contact wth extenson agent, years of experence, and amount of credt avalable to farmers were negatve, a pror. The postve sgn on age varable ndcated that ncreasng age would lead to ncrease n techncal neffcency, based on the fact that ageng farmers would be less energetc to work on the farm, hence, they would have low techncal effcency. The postve sgn on years of educaton ndcated that more educated farmers n the study area were probably nvolved n other enterprses and occupatons and had less tme for effcent supervson of ther farms. The coeffcents of contact wth extenson agents, years of experence and amount of credt avalable obtaned were negatve and conformed wth a pror expectaton. The negatve coeffcents on the amount of credt avalable conformed to the fndngs of Onu et al (2000) as well as Raphael (2008) and the result mpled that avalablty of more credt enhances techncal effcency of farmers n farm producton because avalablty of credt wll facltate easy procurement of fertlzers, agrochemcals and other yeldmprovng nputs on tmely bass.

6 26 J. O. Oladeebo Table 2: Determnants of Techncal Ineffcency Varables Parameter Coeffcent T-Rato Intercept δ Age of Farmer δ Years of Educaton δ Contact wth Extenson Agents δ Farmng experence δ Amount of Credt δ Source: Obtaned from data analyss Table 3 shows the dstrbuton of techncal effcency level of smallholder farmers. The predcted farm specfc techncal effcency ndces ranged from a mnmum of 84.4% for the least practced farm to a maxmum of 99.4% for the best practced farm, wth a mean of about 94.0% and a standard devaton of 4.3%. Accordng to Idong et al (2006), the hgh level of effcency attaned s an ndcaton that only a small fracton of the output can be attrbuted to wastage. Thus, n the short run, there s a scope for ncreasng rce food crop producton of an average farmer by about 6.0% by adoptng the technology and technque used by the best-practced (most effcent) rce farmer. The results conformed to the fndngs of Bravo-Ureta and Pnhero (1997); Aye and Oboh (2006); as well as Raphael (2008). Ths could be acheved by addressng the ssue of low elastctes obtaned for quantty of fertlzer, amount spent on agrochemcals and expendture on mplements (Table 1). The decle range of the frequency dstrbuton of the techncal effcency ndcates that about 83.0% of the rce farms had techncal effcency of over 90.0% and about 17.0% had techncal effcency rangng between 71.0% and 90.0%. CONCLUSIONS Table 3: Dstrbuton of Techncal Effcency Range of Techncal Effcency (%) Frequency Percentage > Total Number of Farms Mean % 94.3 Mnmum % 84.4 Maxmum % 99.4 Standard 4.3 Devaton Source: Results obtaned from Data Analyss The results of the study showed that the techncal effcency of farmers ranged from 84.4% to 99.4% wth a mean techncal effcency of 94.3%. The mean techncal effcency of 94.3% obtaned mpled that there s scope for ncreasng output by 5.7% techncal effcency through more effcent resource utlzaton. Results also show that contact wth extenson agents, farmng experence and credt avalablty are factors whch reduce the techncal neffcency of farmers.

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