Amercan-Eurasan J. Agrc. & Envron. Sc., 13 (8): 1080-1087, 2013 ISSN 1818-6769 IDOSI Publcatons, 2013 DOI: 10.5829/dos.aejaes.2013.13.08.11013 Estmaton of Farm Level Techncal Effcency of Small-Scale Cowpea Producton n Ghana Dadson Awunyo-Vtor, J. Bakang and Smth Cofe Department of Agrcultural Economcs, Agrbusness and Extenson, Kwame Nkrumah Unversty of Scence and Technology, Kumas, Ghana Abstract: Ths study nvestgates the determnants of small-scale cowpea producton n Ejura/Sekyedumase Muncpalty n the Ashant Regon usng a stochastc fronter producton functon that ncorporates neffcency factors. Data for the study was collected from 200 randomly selected cowpea farmers wthn the dstrct. A maxmum lkelhood technque was used to analyze the data. The results ndcate that small-scale cowpea farmers were not fully techncally effcent as the mean effcency was 66%. Farm sze, seed, pestcdes and labour were the major nput factors that nfluenced changes n cowpea output. The result also shows that a farmer s educatonal level, membershp of farmer based organzaton and access to extenson servces sgnfcantly nfluenced ther effcency postvely. The mplcatons are that polces that would encourage cowpea farmers to jon farmer based organzatons and provde them wth easy access to extenson servces are optons that would mprove the effcency of the farmers. Key words: Techncal effcency Cowpea producton Ghana INTRODUCTION Regon s also a major commercal cowpea growng area. Lkewse parts of Brong Ahafo, Eastern, Volta and A key feature of Ghanaan agrculture s the Ashant regons savannah areas are consdered cowpea domnance of smallholder farms, whch consttute an growng areas [4]. The Ghanaan government polcy mportant and nvaluable component of the Ghanaan objectve for the cowpea subsector s to encourage economy. In Ghana, over about 12 mllon farmers, ncreased producton so that self-suffcency and food scattered n dfferent ecologcal zones, engage n the securty can be acheved. However, the producton of the producton of a wde varety of arable crops through crop has fluctuated over the years partly due to clmatc tradtonal smallholder agrculture Bresnger et al [1]. condtons and polcy constrants. Indvdually, whle not exertng much nfluence, they Accordng to the food balance sheet 2009 and 2010, collectvely form an mportant foundaton on whch the the gross bologcal producton of cowpea s 205,000 Mt naton s economy rests. It has been establshed that [5]. The avalable domestc producton for human 90% of Ghana s total food producton comes from small consumpton s 174,250 Mt from whch the per capta farms and at least 60% of the country s populaton earn consumpton s 5.0kg (SRID, 2010). The achevable yeld ther lvng from the small farms [2]. Therefore an effectve of cowpea under ran-fed condtons n Ghana s 1.96mt/ha economc development strategy wll depend crtcally on wth average yeld currently beng produced beng promotng productvty and output growth n the 1.3mt/ha [4]. The average yeld currently beng produced agrculture sector, partcularly among small-scale n the study area s 1.02mt/ha [6]. Some of the man producers snce they make up the bulk of the naton s reasons for low productvty of cowpea among agrculture. small-scale producers n Ghana are assocated wth In Ghana, cowpea s manly produced n the Northern the followng challenges; poor access to credt, and Upper West regons [3] however the Upper East nadequate use of recommended technologes, hgh Correspondng Author: Dadson Awunyo-Vtor, Department of Agrcultural Economcs, Agrbusness and Extenson, Kwame Nkrumah Unversty of Scence and Technology, Kumas, Ghana. 1080
cost of nputs, lack of agrcultural extenson and that the systematc and random neffcency servce, poor flow of nformaton from research effect s zero whch means there s no stochastc statons to farmers, low prces from the cowpea market effect. resultng n lower nput use, hgh techncal and allocatve The analytcal framework used to test the above neffcency [7]. hypothess s based on effcency measures. One way of brdgng the gap between the achevable Accordng to Ajbefun and Daramola, [9] the or potental yeld of cowpea under ran-fed condtons fundamental dea underlyng all effcency measures and the current average yeld level recorded so far s to s that the output of goods and servces per unt ncrease farm output by ncreasng techncal effcency of nput must be attaned wthout waste. There are two under the current exstng technologes. In ths regard, t basc methods of measurng techncal effcency: the s necessary to quantfy current levels of techncal classcal and the fronter approach. There are effcency so as to estmate losses n producton that controverses and dssatsfacton as well as some could be attrbuted to neffcency due to dfference n shortcomngs assocated wth the classcal approach. socoeconomc characterstcs and management practces. Ths has led to the development of the advanced The man objectve of the study therefore s to estmate econometrc, statstcal and lnear programmng the farm level techncal effcency of smallholder cowpea technques by economst for the analyss of techncal producton n Ejura-Sekyedumase Muncpalty whch s effcency related ssues. Both technques have n a major producton area n Ghana. common the concept of a fronter whch s regarded as the measure of effcency. Thus, effcent farms are MATERIALS AND METHODS those operatng on the producton fronter, whle neffcent farms are those operatng below the Study Area: The study was carred out n the Ejura producton fronter. Sekyedumase Muncpalty n the Ashant Regon The amount by whch a farm les below ts producton because t s one of the major cowpea producton belts n fronter s regarded as the measure of neffcency. Ghana. The dstrct s located n the northern part of Agner et al. [10] defne stochastc fronter producton the Ashant Regon (7 22 30 N1 22 1.2 W) and covers a functon as: total area of 1,782 square klometres. Ths s about 7.3% of the total land area of the Ashant Regon, makng t the Y = f ( X; ) + e (1) ffth largest of the twenty-one dstrcts n the regon [8]. In 2004, the populaton was estmated at 81,119. It s where, = 1,2,...N estmated that there are about 19,000 farmers n the dstrct, of whch 80% are cowpea farmers [4]. e = v = u (2) Method of Data Collecton: A multstage samplng Where Y represents the output level of the th technque (purposve and random samplng technque sample farm; f( X ; ) s a sutable functon such as was adopted n selectng 200 small-scale cowpea farmers Cobb-Douglas or translog producton functons of from four Operatonal Zones (Nkyense, Bayere Nkwanta, vector, X of nput for the th farm and a vector, of Ejura and Dromankuma) n the muncpalty. The farmers unknown parameters [7]. e s an error term made up of were vsted on a fortnghtly bass to collect nformaton two components: v s a random error havng zero mean, on nputs used n producton as well as output of N(0; v) and t s assumed to be symmetrc ndependently harvested cowpea. Informaton on soco-economc dstrbuted as N(0; v) random varables and characterstcs and producton factors of the farmers ndependent of uon the other hand, u s a non-negatve was obtaned on the vsts wth the use of truncated normal, N(0; v) random varable assocated questonnares. Data collecton was carred out between wth farm-specfc factors, whch leads to the th farmer August and December 2012. Analytcal Framework and Technques: It s hypothessed that the small-scale cowpea farmers were techncally effcent n ther producton not attanng maxmum effcency of producton; u s assocated wth techncal neffcency of the farmer and ranges between zero and one. N represents the number of farmers nvolved n the cross-sectonal survey of the farms [7]: 1081
Y TE = Where * Y ( ) *, Y = f X = the hghest predcted out of the farm ( ) TE = Exp U Techncal effcency = 1 TE Emprcal Model Specfcatons: The emprcal stochastc producton fronter model used the Cobb-Douglas specfcaton for the analyss of techncal effcency of sole cowpea farmers. The model s specfed as follows 4 0 ln Y = + ßlnX + e =1 e = v u Where Y s the output of cowpea measured n klograms and x are the nputs (Farm sze, Seed, Pestcdes, Labour) and the X s are the parameters to be estmated. The effcency model s specfed as follows: u 7 0 1 1Z = + = The u s the neffcency model and the varable Z (that s, educatonal level, household sze, number of years of farmng, access to extenson servces, access to credt, access to off farm ncome and a membershp of farmer based organzaton) are the farm/farmer characterstcs that have drect nfluence on the farmers effcency [11]. The emprcal model estmated s specfed as In yeld = 0+ 1In Farmsze + 2Seed + 3In Pestcdes + 4 labor + 1Educa + 2HHS+ 3Farmexp + 4Extcon + 5 Cred + 6 Offnc + 7 Fbo + v (8) Where: Yeld (3) (4) (5) (6) (7) = Output of cowpea measured n Metrc tonnes Farm sze = land area measured n hectares Seed = Seed nput measured n Metrc tonnes Pestcdes = measured n ltters Labour = measured n man-days Educa = number of years n schoolng (Educaton) HHS Farmexp Extcon Cred Offnc Fbo V = Household sze of farmers (Numbers) = Farmng experence n number of years = Extenson contact (1=Contact and 0= No contact) = credt access (1=Access and 0= No access = Off-farm ncome n Ghana Ced (GH ) = Farmer Based Organsaton (1=Member and 0=Non-member) = Error term. In ths study, parameters of the stochastc fronter producton functon ( 0-4 and 0-7) are estmated usng maxmum lkelhood estmaton method, usng the computer programme STATA (Verson 10) RESULTS AND DISCUSSIONS Summary statstcs of the socoeconomc characterstcs of the respondents are presented n Table1 and Table 2. The age of the farmers studed ranged between 22 to 60 years wth an average of 45 years. The results mply that farmers n the area are relatvely old, a condton that may affect ther overall effcency. The average household sze of the farmers s fve (5) whch means, the farmers have some source of labour, snce cowpea producton s labour ntensve. Furthermore, the average farmng experence of the farmers s 16 years, mplyng that the farmers have an apprecable number of years of experence n cowpea producton. Ths should enhance the farmer s productvty/ effcency snce experenced farmers have the ablty to predct clmatc, sol condtons and pest and dsease occurrences on the feld. The average number of years of schoolng (educaton) among the farmers s 5 years. It mples that most of the farmers had ether no formal educaton or spent very few years schoolng. Ths attrbute of the farmers n the study area decreases producton effcency. The respondents mean off farm ncome s GH 120 per annum. Ths may have double-edged mplcaton for cowpea producton. Frst there s the lkelhood that farmers wll spend ther tme elsewhere to gan ths off-farm ncome, thus spendng less tme on ther feld and therefore not beng able to perform all the necessary operatons that would enhance producton effcency. Ths could then reduce techncal effcency. Secondly, other sources of ncome could help farmers fnance ther farm operatons leadng to enhanced producton effcency and for that matter techncal effcency. 1082
Table 1: Summary Statstcs of Age, Household Sze, Farmng Experence and Educaton of the Respondents Varable Mean St. dev Mnmum Maxmum Age 45.12 7.15 22 60 Household sze 5.35 2.07 2 10 Farmng experence 15.99 6.41 2 22 Educaton(years of Schoolng) 5.38 4.22 0 12 Off-farm ncome 125 12.6 0 243 Table 2: Socoeconomc Characterstcs of the Respondents Varable Frequency Percentage Sex Male(1) 148 74 Female(0) 52 26 Extenson Contact Contact(1) 76 38 No contact(0) 124 62 Access to Credt Access(1) 68 34 No access(0) 132 66 Source: feld survey 2012 Table 3: Summary Statstcs of Varable used n the Stochastc Fronter Model Varable Unt Mean Standard devaton Mnmum Maxmum Output Mt/ha 0.98 65.54 0.272 1.09 Pestcdes Ltres/ha 4.76 0.49 1 3 Seed Ma/ha 0.07 1.10 0.025 0.0375 Farm sze Hectares 1.506 0.88 1 5 Labour Man-days 68.42 6.74 45 75 The gender dstrbuton n the study area saw males farm sze among the farmers was 1.5 Hectares whch beng domnant 74% of the respondents are male whle mples that the farmers are ndeed operatng on a only 26 are female. Most of the farmers had no extenson small-scale level. Seed and pestcde usage was contact pertanng to advce or nformaton acquston adequately used by the farmers, snce the level of output on cowpea producton technologes and management to be obtaned depends on the seed rate and pest control practces. Ths s a hndrance to the farmers n gettng closer to the fronter. Furthermore, the majorty of Estmates of the Parameters of the Producton Factors: the farmers had no access to credt. Credt accessblty Estmates of the parameters of the stochastc fronter helps farmers to acqure nputs lke labour, seed and producton model revealed that all the estmated pestcdes whch would enhance the farmer s techncal coeffcent of the varables of the producton functon effcency. were postve and sgnfcant (Table 4). Ths mples that these nputs are very much needed Summary Statstcs of the Output and Input Varables: n ncreasng the output of cowpea. Farm sze s The summary of the varable used n the producton sgnfcant at 1 percent mplyng that, among the factors, functon estmaton s presented n Table 3. The results output of cowpea s affected greatly (99 percent n terms ndcate that the mean output per farmer n small-scale of confdence nterval) by farm sze. Seed and pestcdes cowpea producton s about 0.98mt/ha. Ths output per are sgnfcant at 5 percent ndcatng that output of hectare was relatvely lower than the natonal mean yeld cowpea s affected (95 percent n terms of confdence of approxmately 1.03mt/ha [4]. The mean labour usage nterval) by seed and pestcdes. Labour whch s was 68.42 man-days. Ths s expected gven the tedous sgnfcant at 10 percent affects output (by 90 percent n operatons n small-scale cowpea producton. The mean terms of confdence nterval). 1083
Table 4: Estmates of Parameters of the Stochastc Producton Fronter and the Ineffcency Model Varable ------------------------------------------------------------------------------------------------ Stochastc Fronter coef Z-values P> Z Intercept 4.6792 4.29 0.000 ln Farm sze 0.3812 *** 4.31 0.000 ln Seed 0.2174 ** 2.21 0.015 ln Pestcdes 0.5234 ** 1.91 0.010 ln Labor 0.0716* 1.71 0.056 Ineffcency Model Educaton - 0.1125** -2.16 0.032 Household sze 0.0093 0.76 0.451 Farm experence -0.0097-0.15 0.892 Extenson contact -0.2428** -1.32 0.022 Credt access -0.8682-1.69 0.122 Off-farm ncome 0.3943*** 3.89 0.000 Fbo member -0.6341*** -2.07 0.007 Varance parameters Sgma-Squared 0.7243 Gamma 0.7076 Lamda 1.5540 Log-lkelhood functon 62.697 Mean Techncal Effcency 0.6621 ***, **, * are 1%, 5% and 10% sgnfcant level respectvely Table 5: Input Elastcty Input Varables Elastcty Farm sze 0.082 Seed 0.278 Pestcdes 0.681 Labour 0.223 Return-To-Scale (RTS) 1.264 Dagnostc Statstcs and Gamma Parameters: 2 The estmated lamda ( ) and sgma-squared ( ) values of 1.55 and 0.72 are sgnfcant at 1%, ndcatng a good ft and correctness of the specfed dstrbuton assumpton. The lamda s the rato of the U and V error term and t ndcates that the one sded error term U domnates the symmetrc error term V, so varaton n actual cowpea yeld comes from the dfference n farmers specfc factors rather than the random varablty. Gamma ( ) s also a measure of the level of neffcency n the varance parameters and ranges between 1 and 0. For the Cobb-Douglas model used n the study area, t s estmated to be 0.7076 or approxmately 71%. Ths ndcates that 71% of the total varatons n cowpea output are due to techncal neffcency n the study area. The result of the dagnostc statstc therefore confrms the relevance of the stochastc parametrc producton functon and the maxmum lkelhood estmaton model employed. Input Elastcty: Determnaton of elastcty s necessary for the estmaton of the responsveness of yeld to nputs. All of the nputs on the stochastc fronter are statstcally sgnfcant and have the expected sgns. Table 5 shows results of the nput elastctes for each nput n the Cobb-Douglas stochastc fronter producton functon. A one percent ncrease n the acreage wll ncrease cowpea yeld by 0.28 percent (p=0.000), ceters parbus. In addton, a one percent ncrease n seed rate wll ncrease output by 0.22 percent (p=0.015) and an ncrease n the quantty of pestcdes appled ncreases output by 0.68 percent (p=0.070). Furthermore, a one percent ncrease n the man-days of labour ncreases output by 0.08 percent (p=0.056) The study also showed that yeld of cowpea was most responsve to pestcdes, followed by seed, labour and farm sze. The pror expectaton was that yeld of cowpea s more responsve to pestcdes. Ths result supports the fndngs put forward by Internatonal Insttute of Tropcal Agrculture (IITA) ndcatng that output of cowpea depends mostly on how pest and dseases are controlled snce the crop s very susceptble to pest and dseases n the Afrca contnent. A total of 95 percent of the output could be lost f pre-harvest pests and dseases are not controlled well. As observed n the above results, all the nputs elastctes are nelastc; a one percent ncrease n each nput results n a less than one percent ncrease n yeld [12]. Scale Effcency: Summaton of the partal elastcty of producton wth respect to every nput for a homogenous functon (all resources vared n the same proporton) from Table 5 above s 1.26. Ths represents the Returns-To-Scale (RTS) coeffcent, also called the functon coeffcent or total output elastcty. If all factors are vared by the same proporton, the functon coeffcent ndcates the percentage by whch output wll be ncreased. In ths case, the producton functon can be used to estmate the magntude of the return-to-scale. Constant return-to-scale only holds f the sum of all partal elastcty s equal to one. If the sum s less than one, the functon has a decreasng return-to-scale: If more than one, as n the case of the study, an ncreasng return-toscale exsts. Therefore, an ncrease n all nputs by one percent ncreases cowpea yeld by 1.26 percent. 1084
Table 6: Margnal Physcal Productvty and Average Physcal Productvty of the Inputs Inputs MPP (Mt/Ha) APP (Mt/Ha) Farm sze 0.844(0.775bags) 0.302(2.767bags) Seed 0.068(0.625bags) 0.310(2.840bags) Pestcdes 0.155(1.425bags) 0.228(2.095bags) Labour 0.043 (0.400bag) 0.545(5.000bags) 1bag of cowpea =0.109mt Ths ndcates that farmers are operatng n the stage one of the producton surface whch s an rratonal zone and that output can stll be ncreased by usng more of the nputs. Margnal Physcal Productvty and Average Physcal Productvty of the Inputs: The Margnal Physcal Product (MPP) and the Average Physcal Product (APP) for each of the nputs were estmated. Table 6 below shows the results of the MPP and APP values of the varous nputs Pestcdes had the hghest MPP: therefore an ncrease n the quantty of pestcdes by one ltre s estmated to ncrease output by 0.16mt/ha, equvalent to 1.4 bags per hectare. An ncrease n quantty of seed by one klogram s estmated to ncrease output by 0.7 mt/ha. Furthermore, an addtonal labour, whch s a person-day, s estmated to ncrease output by about 0.04mt/hat. An addtonal usage of land s estmated to ncrease output by 0.844mt or 0.8bag per hectare. The varous APP of the nputs depcts the average output attaned when the total quantty of each of the nputs s used on per acre bass. Ths mples that, the average productvty of land on per hectare bass s 0.30mt/ha of output. Ths nterpretaton apples to the APP values of the rest of the nputs. Sources of Techncal Ineffcency: The sources of neffcency were examned by usng the estmated -coeffcent assocated wth the neffcency effects. The neffcency effects are specfed as those relatng to farmng experence, educaton, credt access, extenson contact, membershp of a farmer based organzaton, household sze and off-farm ncome. The estmated coeffcent of farmng experence, household sze, credt access and off-farm ncome were statstcally nsgnfcant. However, only farmng experence and credt access have the expected sgn. On the other hand, educaton, extenson contact and membershp of a farmer based organzaton were statstcally sgnfcant and negatvely related to neffcency effect. The estmated coeffcent of educaton s negatve and statstcally sgnfcant at 5 percent. Ths ndcates reducton n techncal neffcency. Ths mples that more years of educaton or years of schoolng brngs about a decrease n techncal neffcency (ncrease n techncal effcency) n small-scale cowpea producton. Thus, farmers wth more years of formal schoolng tend to be more effcent n cowpea producton presumably due to ther enhanced ablty to acqure knowledge, whch makes them closer to the fronter output. Besdes, farmers who had some level of educaton respond readly to the use of mproved technology. Ths results s consstent wth Lockheed et al. [13], Al and Byerlee [14], Al and Flnn [15], Bravo-Ureta and Reger [16], Abdula and Huffman [17] and Wang et al. [18]. On the other hand, the estmated coeffcent of extenson contact s negatve and statstcally sgnfcant at 5 percent. The result shows that access to extenson servces helps to reduce techncal neffcency n cowpea producton. The advce gven by the extenson agents helps the farmers to mprove ther management sklls and also to acqure knowledge on new practces. Ths result s consstent wth the fndng obtaned by other researchers [16, 19, 20]. Ths result therefore serves to emphasze the role of extenson servce n reducng techncal neffcency n cowpea producton. Fnally, the coeffcent of the dummy varable for membershp n a farmer based organzaton s negatve and statstcally sgnfcant at 1 percent. Membershp of a farmer based organzaton s part of socal captal. Bnam et al. [21] also used assocaton membershp to capture the role of socal captal n provdng ncentves for effcent farm producton. Furthermore, membershp of farmer based organzaton affords the farmers the opportunty of sharng nformaton on modern cowpea producton practces by nteractng wth other farmers. Ths result s consstent wth those of Onyenweaku [22], Effong [23] and Okke [24] all n Ngera. Results of Tests of Hypothess: The null hypothess specfes that the small-scale cowpea farmers were techncally effcent n ther producton and that the systematc and random neffcency effect s zero whch means there s no stochastc effect. 1085
Table 7: Lkelhood-Rato Test Null Hypothess Ch2 dff P-value Decson H 0: U=0 32.53 17 0.000 Reject HO Source: Feld survey 2012 The alternatve hypothess states that the small-scale cowpea farmers were not techncally effcent n ther producton and that the systematc and random techncal effcent n the neffcency effect are not zero. The above hypothess was tested usng the log lkelhood rato test (Table 7). The null hypothess s rejected n favour of the alternatve hypothess that s presence of neffcency effects. Summary: In summary, the Ths study set out to provde estmates of techncal effcency of small-scale cowpea producton and to explan varatons n techncal effcency among farms through manageral and soco economc characterstcs. Farm specfc techncal effcences were computed usng cross-sectonal data of small-scale cowpea producton from the Ejura/sekyeodumase Muncpalty. A stochastc fronter approach was used to generate techncal effcency estmated usng STATA 10 software. The coeffcents of the neffcency determnants were obtaned usng the Cobb-Douglas fronter producton functon through maxmum lkelhood estmaton procedure. Concluson and Recommendatons: The mean techncal effcency for the small-scale cowpea farmers n the study area was 0.66. The mportant factor nputs that ncreased farm output are farm sze, seed, pestcdes and labour. The farm specfc techncal dstrbuton revealed that none of the farmers reached the fronter threshold. Thus, wthn the context of the effcent agrcultural producton, cowpea output per hectare producton can stll be ncreased by 44% wth usng avalable nputs and technology. The farmers were found to have a return-toscale of 1.26, mplyng that the farmers are operatng n stage one of the producton surfaces. Among the seven socoeconomc varables estmated n the neffcency model, only three were statstcally sgnfcant. These are educaton, extenson contact and membershp of farmer based organzaton. However, the coeffcent of farmer based organsaton exhbt whch had a negatve relatonshp wth the neffcency effects. It was further shown that all the nput elastctes were nelastc; a one percent ncrease n each nput results n a less than one percent ncrease n yeld. In order to mprove effcency, farmers need to organze themselves nto groups as group membershp postvely nfluences ther effcency. Also more effort should be ntensfed on the part of extenson agents n educatng the farmers on cowpea producton. REFRENCES 1. Bresnger, C. and X. Dao, 2008. Thurlow. J and Al-hassan R (2008) Agrculture for Development n Ghana. Paper presented at the PEGNet Conference. Assessng Development Impact; Learnng from Experence. Accra (Ghana) 11-12 September. 2. SRID, 1998. Food Balance sheet (1997/1998) report, pp: 17. 3. Nmoh, F., S. Asumng-Brempong and D.B. Sarpong, 2012. Consumer Preference for Processed Cowpea Products n Selected Communtes of the Coastal Regons of Ghana. Asan Journal of Agrculture and Rural Development, 2(2): 113-119. 4. SRID, 2010. Food and Balance Sheet (2009/2010) report, pp: 14. 5. FAO, 2013. Food and Agrculture Organzaton Producton Year Book, pp: 138. 6. SRID, 2007. Crop producton of Ejura/Sekyedumase Dstrct report, pp: 10. 7. Kbaara, T., 2005. Techncal effcency n Keyan s maze Producton: An applcaton of Stochastc Fronter Approach. MPhl Thess. Department of Agrcultural and Resource Economcs, Colorado State Unversty. 8. Mnstry of Local Government, Rural Development and Envronment. 2006. Dstrcts n Ghana: Ejura- Sekyedumase Dstrct. Accra: Mnstry of Local Government, Rural Development and Envronment. 9. Ajbefun, I.A and A.G. Daramola, 1999. Measurement and Sources of Techncal Ineffcency n Poultry Egg Producton n Ondo State. J. Rural Econ. Dev., 13(2): 85-94. 10. Agner, D.K., Lovell, C.K and Schmdt, P. 1977. Formulaton and Estmaton of Stochastc: Fronter Producton Functon Models. J. Econom., 6 :21-37. 11. Idong, I.C., 2007. Estmaton of Farm Level Techncal Effcency n Small scale Swamp Rce Producton n Cross Rver State of Ngera: A Stochastc Fronter Approach. World J. Agrc. Sc., 3(5): 653-658. 12. Sngh, D.S., K.G. Narendra and B.K. Gupta, 2009. A study of post-harvest loss of Cowpea and ts management: n Ogun State, Ngera. Indan Journal of Agrcultural Marketng, 16: 53-64. 1086
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