PROFIT EFFICIENCY OF SMALL SCALE YAM PRODUCTION IN NORTHERN GHANA

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1 PROFIT EFFICIENCY OF SMALL SCALE YAM PRODUCTION IN NORTHERN GHANA Mohammed Tanko 1* and Abdul Fatah Aldu 2 1 Department of Agrcultural and Resource Economcs, Faculty of Agrbusness and Communcaton Scences, Unversty for Development Studes Tamale, Ghana. 2 Department of Clmate Change and Food Securty, Faculty of Agrbusness and Communcaton Scences, Unversty for Development Studes Tamale, Ghana. ABSTRACT: Contrbuton made by small scale yam producers n Northern Ghana cannot be over emphass, however very lttle attenton s gven to ther proft effcency and ts determnants. Ths research used the stochastc effcency fronter model to dentfy the level of yam farmers proft effcency. Multstage random samplng method was adopted to obtan 225 small scale yam farmers across northern Ghana. On average, the proft effcency of yam farmers was 56.75% n the study area wth a mnmum and maxmum effcency of 20% and 100% respectvely. Ths mples there s an opportunty to ncrease proft by 43.25%. The neffcency model showed that sex, household sze, educatonal level, extenson access and land ownershp have negatve coeffcents, meanng that as these varables ncreases the proft effcency of the farmer ncreases. The varable sex nverse relaton suggests that; male farmers are more effcent than ther female counterparts whch needs to close. KEYWORDS: Yam Producton, Stochastc Proft fronter, Proft effcency, Northern Ghana INTRODUCTION The flowerng plants of Yams (Doscorea) genus are Angosperms are monocotyledons (Norman et al., 1984). Yam has about 600 speces whch have an annual lfe cycle characterzed by fve stages. The frst stage s sproutng of the dormant tuber. Tubers functon s to store food reserves manly carbohydrates to ensure contnuty n the lfe cycle of the plant. Rhzome s the outcome of the growth of the new plant or tuber from whch stems and roots emerge. Development of the new tuber s accumulaton of starch reserves. The stem clmbs by twnng, carry petolate leaves and doeceos flowers. The growth of flowers leads to frut whch form trlocular capsules. The tuber maturty s characterzed by the death of the plant shortly after whch the tuber goes nto a dormant lfe. In the whole of West Afrca, yam s one of the most popular staple food. Both small and large scale cultvars of the Doscorea Cayenenss - Doscorea rotuncla complere are among the comparatvely few truly West Afrcan domestcated plants. Yams serve as source of food n Asa, Oceana, and other parts of Afrca, Amerca and beyond these contnent. The most nourshng plant n the det of many nhabtants of nter-tropcal regons s yam. Over 18 mllon are provded wth tons of food a year to the people of West Afrca. 69

2 Yams consttute a crtcal source of food and ncome and play a major role n the soco-cultural lfe of a vared range of smallholder households. As a result of ts mportance n the West Afrcan sub-regon, t s wthn the capacty of yam to allevate poverty and ensure food securty among rural producers, traders, processors and consumers (Chukwu and Ikwelle, 2000; FAO, 1996). Agrculture s the growth foundaton of the Ghanaan economy, and has been the man drver of growth for the last twenty years (World Bank, 2008). Roughly, agrculture contrbutes 34% to Ghana s GDP. About one-half of agrcultural GDP comes from Ghana s roots and tubers (MoFA, 2010). A ffty-fve (55%) rse of farmers n Ghana, root and tuber crop mprovement s a major poverty reducton strategy for the country (RTIMP, 2004). It s crtcal f 90% of marketers and processors of root and tubers are women (IFAD, 2009; RTIMP, 2006). Root and tuber crops especally yam them are consdered to be food securty crops n Ghana and the range of root and tuber consumpton forms between 16% and 31% of per capta daly calore consumpton n the country (GLSS 5; GSS, 2005). Scott et al., (2000) observed that, there s greatest potental n agrcultural crops such as yam and cassava1. Yams are crops that are adaptable to unfavourable envronments, drought resstant and grow well n poor sols. In addton, yams contrbute to the reducton of food nsecurty of household, snce they are cultvated by the poorest sector of the populaton n Ghana (RTIMP, 2004). In mxed croppng systems, yams are also flexble whch beneft the nput and mprovement of sol nutrents. Also, yam s an annual grown crop n Ghana. The uses and advantages of roots and tubers make them an mportant focus for targetng and mprovng the welfare of the poor and smallholder farmers have access to markets, and ensurng ncluson of smallholder farmers n global value addton chans. Developng economy such as Ghana, the features of weak nsttutonal and nfrastructural framework, the absent of organzaton and coordnaton, usual put small-scale producers (and others) n extremely dsadvantage status. The small-scale producers often lack fnance to nvest, rely on famly and relatves for man-power and employed olden or tradtonal technques to process, and are at rsk of beng excluded from world markets (Trenekens, 2011: De Janvry and Sadoulet 2005; Davron and Gbbon 2002; Reardon and Barret 2000). Small-scale producers have lmted access to work n a secure envronment means that they have to rely on other parameters n order to reduce rsk and transacton costs n generatng ncome and marketng. However, studes on yam has concentrated more on pre-producton ssues to the neglect of postharvest ssues lke marketng, consumer demand and storage. The connecton between the producer and the consumer s marketng. But the challenges assocated wth transportaton, wholesalng and retalng actvtes, popularly called dstrbuton actvtes n the yam sector, have less attenton pad by researchers, partcularly n Ghana (Bancroft, 2000). Small scale farmng faces several constrants ncludng an effectve lack of access to producton nputs and effcent produce markets. Advance technologes such as mproved seed varetes and agro-chemcals have been found to be consdered unaffordable by the average small scale farmer who usually has very lmted access to credt from the formal sector. Ths means that adopton of technologes s lmted among small scale. Therefore, resultng n low annual yelds and ncomes. Yam farmers who engaged n small scale producton contnue to use tradtonally unproductve 70

3 methods that leads to low productvty and hgh post-harvest losses (Bdzakn, 2014). The persstent use of the same plots of land season after season, wthout applyng fertlzer leads the sol to becomes less fertle whch consequently contrbutes to low yelds. On the contrary, some farmers are able to make ther best out of ther lmted resources and sklls to rase themselves out of ths stuaton. Such successful farmers have shown that they can be asssted to help other framers out of poverty through better agro-busness management so that they can become more effcent and compettve. Accordng to Bravo-Ureta and Pnhero, (1993), most less developed economy studes that examne effcency concentrate on techncal effcency. Wth or wthout understatng the mportance of techncal effcency, understandng and mprovement n economc effcency wll lead to greater producton effcency. Xu and Jeffrey, (1998) and Peran and Rzz, (2003) observed that, few studes have assessed the effects of techncal change of effcency. Wth the avalablty of varety of emprcal tools, selecton of the most approprate method s ambguous (Xu and Jeffrey, 1998). Though, there has been many schemes and research work done n Ghana (such as Adoo et al, 2012, and n 2009/2010 by the Internatonal Insttute of Tropcal Agrculture (IITA)), a wde gap of doubt and lmted knowledge on the proft effcency of small scale yam producers n Northern Ghana stll exst and t needs redress. Ths research ams at fndng soluton to the margn of proft effcency and the determnng factors that affects the proft effcency of yam farmers n Northern Ghana who engaged n small unt producton. LITERATURES REVIEW A frm performance s determnng through the concept of economc effcency, whch s made up of two components - techncal effcency and allocatve effcency (Kalarjan and Shand, 1999). Vensher (2001) assert that every proft orented organsaton s sad to be techncally effcent when t produces greater output wth a gven amount of nputs or produces a specfed output wth lttle amount of a gven resources. Relatvely, Ells (1988) sees techncal effcency as the optmum possble level of outputs ganed from a gven set of nputs through the employment of a range of alternatve technologes avalable. Comparatvely, techncal effcency s old lke neoclasscal economcs n terms of ts measurement. Probably ths s explaned by the fact that neoclasscal economcs assumes full techncal effcency. Kalarjan and Shad, (1999) assert that two man reasons justfy the measurement of techncal effcency; Frstly, the gap that exsts between realzed effcency and theoretcal assumpton of full techncal effcency whch has been observed by Bauer (1990) and Kalarjan and Shad (1999) that, where techncal neffcency functon, t wll call for an nverse nfluence on allocatve effcency wth a drect mpact on economc effcency. The thematc area of technologcal effcency has also caught the attenton of researchers. Changes n technology occurs stage by stage, frstly, t can yeld more output for the same or less unts of nput than older processes. Some researchers argue that, ntroducng such a new technology to producton process can render all prevous processes techncally neffcent (Ells, 1988). Accordng to Meer (1995), n the context of effcent research, technology entals the seres of all known technques for producng a partcular output. Though, s crtcal to menton that, the nventon of a new technology does not guarantee ts avalablty to all producers, especally, small scale farmers. Two types of neffcency can be realzed, that s, neffcency that occurs due to 71

4 operatng off the soquant for a gven technology and also neffcency due to falure to move to a dfferent soquant when there s an ntroducton of new technology (Ells, 1988). The former can be exemplfed by a stuaton n whch the same output of a partcular crop say yam can be obtaned by usng a small unts of a gven means. An llustraton of the latter wll be a stuaton n whch a new technology s ntroduced and a farmer s not able to use t for varous reasons. Ells (1988) notes two forms of technologcal change or progress; the frst s process nnovaton, whch deals wth mprovement of the producton of exstng products; the second s product nnovaton, whch deals wth development of sustanable mproved outputs. Technologcal change denotes nnovaton, mprovng techncal effcency under a gven technology. t s essentally about catchng up wth what s technologcally possble (Fare et al., 1997). Prmarly, the concept underlyng the computaton of techncal effcency based on the descrpton of a producton technology. Producton technologes are usually by llustrated by soquants, producton functons, costs functons or proft functons. Many studes have attempted to estmate the effcency of agrcultural producton (Xu and Jeffrey, 1998; Khem et al., 1999; Gavan and Ehu, 1999). Accordng to Xu and Jeffrey (1998), research that are emprcal n nature n the estmaton of producton effcency have employed a varety of modellng methods ncludng parametrc versus nonparametrc; programmng methods versus statstcal methods and determnstc versus stochastc. Broadly speakng, the producton effcency estmaton technques can be grouped nto stochastc fronter producton approaches and nonparametrc mathematcal programmng approaches (Khem et al., 1999). Crtcal analyss of the strengths and weakness of these approaches has been done by Ceoll (1995). Analytcally, stochastc fronter approaches have key strengths of dealng wth factors beyond the researcher s control and measurement errors (stochastc nose). It also allows for statstcal test of hypotheses that pertan to producton structure and the degree of neffcency. The weaknesses of stochastc fronter approaches do not exclude the need to mpose an explct parametrc form for the technology n queston and an explct dstrbutonal assumpton for the neffcency varable. Accordng to Mawul Yevu (2013), the key strengths of the nonparametrc approaches whch s usual called Data Envelopment analyss (DEA) are that: parametrc specfcaton of technology s avoded and there s dstrbutonal assumpton of the neffcency varable. The weaknesses of the DEA approach are that; t s determnstc and attrbutes all devatons and abnormaltes from the fronter to neffcences whch render the model lable to measurement errors or other errors n the data set. In the stochastc fronter approach, the techncal lnkages between nputs and outputs of a producton process s explaned by a producton functon whch establshes the maxmum level of output attanable from a gven vector of nput varable. Ths s usual known as the producton fronter. Producton fronter effcency can be traced back to the semnal work of Farrell (1957). Stochastc Producton Fronter (SPF) was however soley developed by Ager et al., (1977) and Meeusen and van den Broeck (1977). Revew of specfc methodologes used by earler researchers s sgnfcant. The work done by Khem et al. n 1999 and Xu and Jeffrey n 1998 adopts dual stochastc fronter effcency decomposton model though the Khem et al. (1999) move ahead by comparng the stochastc approach to a nonparametrc method usng the same data set. The most popular stochastc fronter functon employed by both studes ndcates that; Y s output, X s nput vector and β the vector of producton functon parameters, V s a random error term wth zero mean, and U, a nonnegatve one-sded error term whch gves a measure of neffcency. The two group of researchers used the 72

5 Cobb-Douglas functonal form, whch charactersed by less flexblty compared to the translog functonal form whch s self-dual and has been used n many emprcal studes. Approprate estmaton methods exst for the estmaton of effcency and neffcency equatons. The methods nclude: the maxmum lkelhood procedure, the corrected Ordnary Least Square method (COLS) (Jaforullah and Premachendra, 2003) and Zellner s Seemngly Unrelated Regressons (SURE) approach. In stochastc effcency estmaton the use of OLS leads to parameter estmates whch are under cast n terms of ther effcency, partcularly the ntercept compared to maxmum lkelhood estmates (Greene, 1980). The stochastc fronter model s nonlnear, as a result of that, ts estmaton procedure produces consstent and effcent estmates (Greene, 1995). Accordng to Greene (1995), by mert, OLS provdes best lnear unbased estmates and computed standard errors at the slope, however, t provdes estmated ntercept whch s downwardly based. Consequently, Green suggests that, the ntercept that s computed usng OLS should be adjusted by the largest postve OLS error. The two-step procedure suggested by Green s what s called the Corrected Ordnary Least Squares (COLS) method. Estmaton of the factors that nduce neffcency has generated consderable debate n the studes of fronter. Accordng to Khem et al. (1998) estmaton of effcency scores frst s the most popular procedure, after whch, regress the effcency scores aganst a set of dentfed factors. Alternatvely, nonparametrc or analyss of varance (ANOVA) can be use. Kalrajan (1991) and Ray (1988) stood for the two-step procedure, Kumbhakar et al. (1991), Battese and Coell (1995) crtcsed the approach by argung that, the dentfed factors should be ncorporate drectly n the estmaton of the producton fronter because such factors have a drect effect on effcency. Notwthstandng ths crtcsm, Green error correcton two-step procedure s stll qute popular n examnng the relatonshp between effcency and dentfed varables (Khem et al., 1998). Studes that am at ntegratng specfc effects drectly nto the fronter model are lmted or restrcted to the parametrc approach (Kumbhakar et al., 1991; Battese and Coell, 1995). On the same pace, the expresson of the neffcency effects as an explct functon of a varable vector and a random dsturbance, as well as the estmaton of all the parameter estmates n a sngle-stage maxmum-lkelhood procedure s lmted Refschneder and Stevenson (1991). Bonlla et al. (2001) suggest a model for a stochastc producton functon. In Bonlla et al., model, techncal neffcency effects are expressed as a functon to some specfc factors n addton to nput varables of a producton fronter that nteracts wth the dentfed factors. METHODOLOGY Study Area and Samplng Technques The small scale yam farmer lvng wthn northern Ghana was the unt of study. Small scale farmers were sampled through a multstage samplng approach. The area was classfed n to three man regons representng the three ecologcal zones n the northern sector. These ncluded northern, upper east and upper west regons. From each regon three major yam producng dstrcts were purposvely chosen, after whch fve communty each from the dstrcts was randomly selected gvng us a total of nn communtes. Fve small scale yam farmers were randomly selected from each communty gvng us a total sample sze of 225. The man data for the study was prmary data, whch was collected from the farmers usng structured questonnares. Data was analysed usng descrptve statstc and the stochastc proft fronter functon model. 73

6 The Stochastc Proft Fronter (SPF) The SPF method of analysng effcency s chosen for ths study. The justfcaton s that, unlke other methods such as the Data Envelopment Analyss (DEA), the SPF usual gve way for the senstvty of data to random shocks by ncludng a conventonal random dsturbance term n the estmaton of the proft fronter such that only devatons that s nfluenced by controllable decsons are attrbuted to neffcency (Joforullah and Premachandra, 2003). Ineffcency s assumed to be part of the error term consstng of two parts-a random error term whch s normally dstrbuton N (0, σ 2 ) and represents random shocks and statstcal errors, and the neffcency term whch s one sded (non-negatve). The neffcency error term s assumed to have a half normal dstrbuton. The SPF s expressed mathematcally as: V U X e f,.. (1) Expressng SPF to logarthm terms leads to ln ln f X, V U.. (2) Where π s the output vector, X s the nput vector, β s parameter vector whch s not known, V s the random error term assumed to be dentcally and ndependently dstrbuted (N (0, σ 2 )), U s the neffcency term ndependently dstrbuted from V. In ths study the half normal dstrbuton of the error term used by Jaforullah and Premachandra (2003) n a cross sectonal data smlar to ths study s adopted. Model specfcaton Proft effcency n ths study s defned as proft gan from operatng on the proft fronter, takng nto accounts farm-specfc prces and dentfed factors. Consderng a farm that maxmzes proft subject to perfectly compettve nput and output markets. The explct Cobb-Douglas functonal form for the small scale yam farmers n the study area s therefore specfed as follows: ln ln ln FS ln Z 6 o ( V U 1 ln LC ln PF )...(3) 2 ln PYS ln PFT Where: represents normalzed proft computed as total revenue less varable cost dvded by farm specfc yam prce; FS represents Farm sze; LC represents average cost per man day of labour; PF represents average prce per tuber of fertlzer; PYS represents average prce per tuber of yam seed; PFT represents average prce of farm tools and Z represents other operatng cost. The neffcency model (U) s defned by: U Age Sex HHSze Edu Exp Ext Cre Lown...(4) 7 o Where: Age, Sex, HHSze, Edu, Exp, Ext, Cre and Lown represent age of respondents, respondent gender, household sze, educatonal level, farmng experence, Extenson servce, Credt Access, and Land ownershp of respondents respectvely. Sex of respondents s a dummy varable

7 representng 0 = female and 1 = male, Extenson servce and credt access are also dummy varables, where 1 = havng access and 0 s otherwse. The neffcency model dffers slghtly from that of Ogundar Kolawole (2006) by the ntroducton of the sex varable. These soco-economc varables are ncluded n the model to ndcate ther possble nfluence on the proft effcences of the yam farmers (determnant of proft effcency). The varance of the random errors, σ 2 v and that of the proft neffcency effect σ 2 u and overall varance of the model σ 2 are related thus: U V...(5) Ths measures the total varaton of proft from the fronter whch can be attrbuted to proft neffcency (Battese and Corra, 1977). The log lkelhood functon estmates the gamma (λ) as: 2 2 U 2 U...(6) V The parameter λ represents the porton of neffcency n the whole resdual varance wth values rangng between 0 and 1. A value of 1 suggests the exstence of a determnstc fronter, where as a value of 0 can be seen as evdence n the favour of OLS estmaton. All the parameters estmate of the stochastc fronter proft functon and the neffcency model are smultaneously arrved usng the program Lmted Dependent varables (LIMDEP). A three-step estmaton method s used n obtanng the fnal maxmum lkelhood estmaton (MLE) and the MLE procedure uses Davdson Fletcher Powel Quass Newton algorthm. In ths research for relatvty purpose, two vared models were estmated n the fnal MLE. Model 1 s the tradtonal response functon OLS n whch the effcency effects are not present (U = 0). It s a specal form of the stochastc fronter producton functon model n whch the aggregate varaton of output as a result of techncal neffcency s zero that s γ = 0. Model 2 s the MLE model where there s no restrcton and thus γ 0. The two models were compared for the presence of proft neffcency effects usng the gamma (γ) test of sgnfcance. Hypothess and sgnfcance test The followng null hypothess s tested usng the gamma test: There s no neffcency of proft (γ = 0) whch mean each yam farm s operatng on the proft fronter. RESULTS AND DISCUSSION The summary statstcs of varables used n the proft fronter model are presented n Table 1. The results ndcate a wde varaton n proft earned by the yam producers, rangng from a mnmum of GH 0.87 to GH 2.43, and the mean proft earned per tuber s GH 1.32, means that most of the yam producers produce below the maxmum proft per tuber. Ths mght be due to neffcent methods of farmng at the gven technology. The proft gap between the mean and the mnmum proft s GH 0.45 per tuber, and that between the mean and the maxmum proft s GH 1.11 per tuber. Ths also suggests that there s potental for ncreasng the mean proft per tuber of yam producton n the study area. 75

8 Varables Unt of Measurement Mnmum Maxmum Mean Standard Devaton Proft Ghana ced/tuber Age Number of year Educaton level Number of year Farmng Experence Number of year Farm sze Hectares Wage of labour Ghana ced/man day Prce of fertlzer Ghana ced/tuber Prce of yam seed Ghana ced/tuber Cost of captal nput Ghana ced/tuber Other operatng cost Ghana ced/tuber Table 1: Summary Statstcs of Varables Used n the Proft Fronter Model Source: Authors` computaton of feld data, Maxmum lkelhood estmates of the parameters of the stochastc proft fronter The maxmum lkelhood estmates (MLE) of the parameter estmates of the stochastc proft fronter model are presented n Table 2. The Table 2 below show that, cost per man day of labour, cost of farm tools and cost of other operatons estmated coeffcents of the parameters of the normalzed proft functon whch works on the assumpton that, compettve markets are postve. The varables - farm sze, prce of fertlzer and prce of yam seed n the normalzed proft model whch have postve coeffcent, ndcate that as these varables ncrease the normalzed proft of the farmer ncreases, whles the varables that have negatve sgns show that, when such varables ncrease, the normalzed proft of the farmers decreases. OLSE Model MLE Model Varable Estmate Stand. Error t-rato Estmate Stand. t-rato Error Farm sze *** *** Wage of *** labour *** Prce of 0/3892*** ** fertlzer Prce of yam ** * seed Prce of farm *** *** tools Other operatng cost Constant *** *** Gamma 0.996*** Sgma *** Log lkelhood Table 2: Maxmum Lkelhood Estmates of the Stochastc Proft Fronter Model Source: Authors` computaton of feld data, 2015 ***, ** and * denote 1%, 5% and 10% respectvely 76

9 Comparng the t-calculated value of 2.47 to the t-tabulated value of led to the rejecton of the null hypothess (γ = 0) of no neffcency of proft. The decson s that γ 0. Ths means that there was proft neffcency among yam farmers n the study area as confrmed by the sgnfcance of the gamma (γ) estmate. The estmated gamma parameter (γ) of MLE model of n Table 2 was hghly sgnfcant at 99 percent level of confdence. Ths mples one-sded random neffcency component strongly outweghs the measurements error and other random dsturbance. Ths means that about 99.7 percent of the varaton n actual gans from proft fronter whch represents maxmum proft among farmers manly arose from dfferences n farmers practces rather than random varablty. Determnants of proft effcency of yam producers The parameters estmate for determnants of proft effcency usng the stochastc Cobb Douglass proft functon are presented n Table 3. However, crtcal analyss of neffcency models reveals that the sgns and sgnfcance of the estmated coeffcent n the neffcency model have sgnfcant mpact on the proft effcency of the farmer. Based on ths, the varables n the neffcency model whch have negatve coeffcent, meanng that as these varables (sex, household sze, educatonal level, extenson access and land ownershp) ncrease, the proft effcency of the farmer ncreases, hence ncrease n proft. Whles the varables (age, farmng experence and credt access) are postve and hence has negatve mpact on the proft effcency of yam farmers n the study area. The postve coeffcent of age s n agreement wth the work of Abdula and Huffman (1988) whle the nverse coeffcent of educatonal level was n conformty wth Kumbhakar and Bhattacharya (1992b), Al and Fln (1989), Abdula and Huffman (1988) and Huffman (1974). The results from the stochastc proft fronter analyss has ndcated that ther proft effcency was drectly or postvely nfluenced by sex, household sze, educatonal level, extenson access and land ownershp. These fndngs have mportant polcy mplcatons n mprovng producton effcency among farmers n Northern Ghana. Nevertheless, government should make t a prorty to encourage both men and women to go nto yam farmng n an attempt to brdge the gap between them. Ensurng effectve extenson delvery program wth the key am of nvestng n farmers educaton especally, rural sector n the current poltcal and economc envronment n Ghana wll provde farmers wth sklls essental to ncrease effcency. In concluson, the result of ths study has clearly shown that employng the stochastc proft fronter paves way for detaled analyss of the determnant of specfc farm effcency. Averagely, proft effcency of suggest that consderable amount of proft s ganed among maze producers n the sampled area. The neffcency assocated wth controllable decsons s about 99.7% hence government through MOFA should educate farmers on how to reduce controllable neffcency n ther producton. Farmers need to be educated and young men and women should be encouraged to go n to farmng. The study examned the performance of mcro and small agrbusnesses n Northern Ghana. Two objectves were set and these nclude; assess the proft effcency of mcro and small agrbusnesses (yam producers), and determne the factors that nfluence proft effcency. The stochastc proft fronter analyss was used to assess the proft effcency of yam farmers. Determnants of proft effcency among the small-scale maze farmers were dentfed usng stochastc Cobb-Douglas proft fronter model. The parameters estmated usng the Cobb-Douglas 77

10 proft fronter ndcate that majorty of the nputs have postve sgns on the proftablty of yam farmng n Northern Ghana except few. The negatve sgn of cost of farm tools may be due to the hgh cost of fuel leadng to excessve cost of the use of such equpment s by the farmers, thus leadng to extra cost ncurred on the part of the producers. Proft effcency dstrbutons has shown that yam farmers were farly effcent n ther resource allocaton, judged by the fact that more than half of the farmers havng proft effcency of 60% and above wth an average proft effcency of 56.75% suggestng that consderable amount of proft s ganed due to the relatve level of effcency observed n the sample area. Varable Parameter Estmate Stand. Error t-rato Constant Age *** Sex ** Household sze Educaton level Farmng ** experence Extenson Access *** Credt ** Access Land Ownershp Table 3: Maxmum Lkelhood Estmates of the Proft Ineffcency Model Source: Authors` computaton of feld data, 2015 ***, ** and * denote 1%, 5% and 10% respectvely Proft Effcency Analyss The research reveals that proft effcency vares wdely among producers, rangng from a mnmum of 20.12% to a maxmum of 99.97%. The wde varaton n the proft effcency estmates can be assocated wth dfferences n effcent allocaton and use of nputs among the producers. The mean estmated proft effcency of 56.75% means that yam producers n the study areas have the scope of ncreasng ther proft by 43.25% by adoptng the avalable producton technques used by the most effcent farmer. 78

11 Percentage of yam farmers >90 Range of Proft Effcences Mean Proft Effcency = 56.75% Fgure 1: Dstrbuton of proft effcences of yam farmers. Source: Authors` computaton of feld data, 2015 In Fgure 1, t s observed that majorty (20%) of the yam producers have proft effcency scores rangng from 51 to 60, followed by 17% of yam producers havng ther proft effcency score between 61 and 70. Out of the 225 producers 40% have ther proft effcency score rangng from 21 to 50. Forty-fve percent of yam producers have ther proft effcency score rangng from 51 to 80. About 19% yam producers have ther scores rangng from 71-90, If the least effcent yam producer s to acheve the effcency status of the most effcent yam farmer, then that yam farmer must reduce ther cost by 96%. On average, for a yam farmer to acheve the optmal proft effcency, he/she must reduce cost by 43.25%. CONCLUSIONS AND RECOMMENDATIONS Based on the results arrved, the followng conclusons and polcy recommendatons are made. The results from the stochastc proft fronter analyss showed that yam farmers n the study area proft effcency was postvely nfluenced by sex, household sze, educatonal level, extenson access and land ownershp. These fndngs have crtcal polcy mplcatons n mprovng producton effcency among farmers n Northern Ghana. Polcy makers and government of Ghana should make t a prorty to encourage both men and women to go nto yam farmng n an attempt to brdge the gap between gender dscrmnaton. The nvestments n rural educaton on the path of effectve delvery of extenson program n the current poltcal and economc envronment n 79

12 Ghana wll provde farmers wth sklls essental to ncrease effcency. Ths research has clearly shown that employng the stochastc proft fronter does not restrct detaled analyss of the determnant of specfc farm effcency. The proft effcency of suggest that consderable amount of proft s ganed among yam producers n the sampled area. The neffcency assocated wth controllable decsons s about 99.7% hence government through MOFA should educate farmers on how to reduce controllable neffcency n ther producton. Farmers need to be educated and young men and women should be encouraged to go n to the producton of yam. REFERENCES Abdulal, A. and Huffman, W.E. (1988) An Examnaton of Proft Ineffcency of Rce Farmers n Northern Ghana. Workng Paper n Department of Economcs Iowa State Unversty, Ames, USA. Adoo, R. (2009) An analyss of yam consumpton patterns n Ghanaan urban communtes, Ph.D thess submtted to the Department of Agrcultural Economcs, Agrbusness and Extenson, Kwame Nkrumah Unversty of Scence & Technology, Kumas, Ghana. Agner, D. J., Lovell, C. A. K., and Schmdt, P. (1977) Formulaton and estmaton of stochastc fronter producton functon models. Journal of Econometrcs, 6, Al, M. and Fln, J.C. (1989) Proft effcency among basmatc rce producers n Pakstan Punjab. Amercan Journal of Agrcultural Economcs. 71: Bancroft, R. D. (2000) Relevng post-harvest constrants and dentfyng opportuntes for mprovng the marketng of fresh yam n Ghana, Crop Post Harvest Programme, Fnal Techncal Report, No: R6505; ZB No: ZB0016. Battese, G.E and Coell, T.J. (1995) A Model for Techncal Ineffcency Effect n Stochastc Fronter Producton for Panel Data. Emprcal Economcs. 20: Bdzakn, J. K; Falor, S. C and sumng-brempong, D. A (2014) Small Scale Maze Producton n Northern Ghana: Stochastc Proft Fronter Analyss, ARPN Journal of Agrcultural and Bologcal Scence, VOL. 9, NO. 2, FEBRUARY Bonlla et al. (2001). Determnants of Growth and Proftablty n Small Entrepreneural Frms. Internatonal Journal of Behavour and Research. 4(1): Bravo-Ureta, B. E., and Pnhero, A. E. (1993) Effcency analyss of developng country agrculture: A Revew of the Fronter Functon Lterature. Agrculture and Resource Economcs Revew, 22, Bravo-Ureta, B. E., and Pnhero, A. E. (1993) Effcency analyss of developng country agrculture: A Revew of the Fronter Functon Lterature. Agrculture and Resource Economcs Revew, 22, Chukwu, G. O. and Ikwelle, M. C. (2000) Yam: Threats to ts Sustanablty n Ngera, PALAWIJA NEWS, The CGPRT Centre Newsletter, Volume 17, Number 1 March Coell, V. J. (1991) Gude to Fronter Verson 4.1. A Computer programme for stochastc fronter producton and cost functon estmaton. Dept. of Economcs, Unversty of New England, Arm dale, Australa. Davron, B., and Gbbon, P. (2002) Global Commodty Chans and the Afrcan Export Agrculture. Journal De Janvry, A., and Sadoulet, E. (2005) Achevng Success n Rural Development: Toward Implementaton of an Integral Approach. Agrcultural Economcs 32 (1):

13 Ells, H. (1988) Cost and Proft Effcency n European Banks. Journal of Internatonal Fnancal Markets, Insttutons and Money. 12: FAO (1996) Food for consumers: marketng, processng and dstrbuton, Techncal background document presented at the World Food summt, Nov, 1996, Produced by: Economc and Socal Development Department, Food and Agrculture Organzaton of the Unted Natons (FAO), Rome, Italy. Fare C. H, Anyro, C. O., and Chuku, J. A. (1997) The measurement of productve effcency. Journal of the Royal Statstcal Socety. Seres A, 120 (3) Farrell, M. J. (1957) The measurement of productve effcency. Journal of the Royal Statstcal Socety. Seres A, 120 (3) Greene, W. (1995) LIMDEP (Verson 7): User's Manual and Reference Gude, Econometrc Software Inc., New York. Huffman W.E. (1974) Decson Makng: The Role of Educaton. Amercan Journal of Agrcultural Economcs. 56: IFAD (2009) The use of cassava wastes to produce energy: outcomes of a feasblty study mplemented n Ghana. A presentaton made at the Global Consultaton on Cassava as a potental boenergy crop, n Accra from October, Internatonal Insttute of Tropcal Agrculture (IITA) (2014) Accessed (26/12/2014) Jaforullah M. and Premachandra E. (2003) Senstvty of Techncal Effcency Estmates to Estmaton Approaches: An Investgaton Usng New Zealand Dary Industry Data. Unversty of Otago. Economc Dscusson Papers. No Kalrjan K. P. and Shand R. T. (1999) Blackwell Publshers Ltd. Cowley Rd., Oxford OX4 IJF, UK and 350 man st, Malden, MA 02148, USA. Kalrjan K. P. and Shand R. T. (1999) Blackwell Publshers Ltd. Cowley Rd., Oxford OX4 IJF, UK and 350 man st, Malden, MA 02148, USA. Khem N. G; Ike, P. C., and Ugwumba, C. O. A. (2011) Proftablty of Small scale Broler Producton n Ontsha North Local Government Area of Anambra State, Ngera. Internatonal Journal of Poultry Scence, 10(2), Kumbhakar, M. and Bhattacharyya, F. (1992) Prce Dstrbuton and Restrcted Proft Functon Approach. Revew of Economc and Statstcs. 74: Kumbhakar, S. C., Ghosh, S., and McGuckn T. (1991) A generalsed producton fronter approach for estmatng determnants of neffcency n US dary farms. Journal of Busness and Economc Statstcs, 9, Mawul, Y. (2013. Proft Effcency of Layer Producton n Brong Ahafo and Greater Accra Regons of Ghana, Thess Submtted to the Unversty of Ghana, Legon n Partal Fulfllment of the Requrement for the Award of Master of Phlosophy Degree n Agrcultural Economcs Meeusen, W., and Van den Broeck, J. (1977. Effcency estmaton from Cobb-Douglas producton functons wth composed errors. Internatonal Economc Revew, 18, Meer A. (1995) A generalsed producton fronter approach for estmatng determnants of neffcency n US dary farms. Journal of Busness and Economc Statstcs, 9, MoFA (2010) The State of Agrculture n Ghana n 2009; Publshed by Statstcs, Research and Informaton Department (SRID) of the Mnstry of Food and Agrculture (MoFA), Accra, Ghana. 81

14 Norman, J. K; Orkwor, G.C and A.A.Adenj (1984) Producton constrants and avalable technologes for food yam (Doscorea spp.) producton n Ngera. In: J. Berthaud, N. Brcas and J. mardand (eds). Yam, old plant and crop for the future. Actes du Semnare Inter. Crad Intra- Orstom- Coraf. June Montpeller, France. : of Agraran Change 2: Ogundar K. (2006) Determnants of Proft Effcency among Small Scale Rce Farmers n Ngera: A Proft Functon Approach. Internatonal Assocaton of Agrcultural Economst Conference, Australa. Reardon, T., and Barrett, (2000) Agrondustralzaton, Globalzaton, and Internatonal Development. An Overvew of Issues, Patterns, and Determnants. Agrcultural Economcs 23: Refschneder J. and Stevenson W. (1991) Stochastc fronter estmaton: a revew of the software optons. Journal of Appled Econometrcs, 14, RTIMP (2004) Annual Report for 2003; Root and Tuber Improvement and Marketng Programme, Mnstry of Food & Agrculture, Accra, Ghana. Vansh I. (2001) Determnants of Proft Effcency among Small Scale Rce Farmers n Ngera: A Proft Functon Approach. Internatonal Assocaton of Agrcultural Economst Conference, Australa. World Bank. (2008) Agrculture for Development n World Bank Development Report. World Bank, Pp Xu Y. and Jeffrey, S. (1998) Techncal effcency of rce farms n Thaland: a non-parametrc approach. Paper presented to the 2003 Hawa Internatonal Conference on Busness, Honolulu, June,