Volume 36, Issue 4. Does farm size matter? Investigating scale efficiency of peasant rice farmers in northern Ghana

Transcription

1 Volume 36, Issue 4 Does farm sze matter? Investgatng scale effcency of peasant rce farmers n northern Ghana Benjamn Tetteh Anang Unversty of Helsnk Stefan Bäckman Unversty of Helsnk Antonos Rezts Unversty of Helsnk Abstract The study assessed the techncal and scale effcency of small-scale rce producers n northern Ghana as well as the effect of farm sze on effcency. Usng survey data from 300 farm households, the study employed data envelopment analyss (DEA) to measure effcency and a bootstrapped truncated regresson n the second stage to assess the determnants of effcency. The results ndcated that respondents had overall techncal effcency of 46.6 percent, pure techncal effcency of 65.1 percent and scale effcency of 69.5 percent. Farm sze had a sgnfcantly postve effect on scale effcency wth majorty of the farms operatng at ncreasng returns to scale. The determnants of effcency ncluded farm sze, gender of the household head, access to credt and rrgaton, number of extenson vsts, the degree of specalzaton n rce producton and locaton of the farm. Most of the neffcences are ether techncal or scale n nature hence there s justfcaton to ncrease the scale of producton of smaller farms n order to take advantage of unexplored economes of scale. The techncally neffcent farmers also need to reduce waste n resource utlzaton by mprovng ther effcency of resource use. The authors prescrbe other polcy measures needed to mprove rce producton n northern Ghana. The authors acknowledge the Nordc Afrca Insttute (NAI) n Uppsala, Sweden for fundng the data collecton. The authors also acknowledge the Kyöst Haataja Foundaton n Fnland for provdng the frst author wth a study grant to conduct the research. Ctaton: Benjamn Tetteh Anang and Stefan Bäckman and Antonos Rezts, (2016) ''Does farm sze matter? Investgatng scale effcency of peasant rce farmers n northern Ghana'', Economcs Bulletn, Volume 36, Issue 4, pages Contact: Benjamn Tetteh Anang - benjamn.anang@helsnk.f, Stefan Bäckman - stefan.backman@helsnk.f, Antonos Rezts - antonos.rezts@helsnk.f. Submtted: July 15, Publshed: November 28, 2016.

2 1. Introducton The agrcultural sector plays a crucal role n economc development n most developng countres ncludng Ghana. Nevertheless, the agrcultural sectors n most of these countres partcularly n Sub-Saharan Afrca contnue to operate below ther full potental n terms of productvty and effcency levels thus falng to attan the goals of food securty and poverty reducton. The potental of the agrcultural sector n these countres can however be unleashed f the current levels of productvty and effcency can be mproved. One notable feature of agrculture n most developng countres, whch has mplcaton for farm performance, s the predomnance of smallholders who cultvate relatvely small land areas. Sngh et al. (2002) defned smallholder farmers as agrcultural producers cultvatng an average of 2.0 hectares. Accordng to Namara et al. (2011), Ghanaan smallholders have an average landholdng of about 1.2 hectares yet account for about 80 percent of the naton s total food producton. Chamberln (2007) also observed that more than 70 percent of Ghanaan farmers cultvate less than 3 hectares of land. Accordng to the Mnstry of Food and Agrculture (MoFA), an estmated 10 percent of Ghanaan smallholder farm households engage n domestc rce producton wth an average farm holdng of 0.4 hectare (MoFA, 2009). Rce producton n Ghana s therefore a small-scale economc actvty. From a neo-classcal economcs perspectve, farmers can take advantage of the scale of producton to enhance ther productve and scale effcences. In prncple, a farm may be techncally effcent but not scale effcent. Ths means that a farm may be usng the best practce but not takng advantage of economes of scale. The concept of scale effcency s therefore mportant n smallholder agrculture where farms are generally small, and the lkelhood of dervng gans from economes of scale may exst. Even though smallholder farmers cultvate relatvely small landholdngs, there s often consderable heterogenety n farm szes due to unequal land resource endowment and access to agrcultural land n most rural communtes. The ssue of heterogenety n farm szes adds to the debate on whether agrculture n developng countres should focus on large or smallscale farmng. As noted by Akudugu (2016), a current debate s whether Afrcan agrculture should focus on large or small-scale agrculture. Larson et al. (2012) argue that an nverse relaton exsts between farm sze and agrcultural productvty hence the need to promote small-scale farmng. Ths proposton receves further support from arguments lnkng economc transformaton n developng countres to productvty growth n smallholder agrculture (see for example, McErlean and Wu 2003). In addton, Chrstaensen et al. (2011) estmate that growth n agrculture engenders hgher poverty reducton effects n Afrca n comparson to other economc sectors lke ndustry and the servce sector. Snce most of the poor n developng countres lve n rural areas where agrculture s the man economc actvty and source of lvelhood, t seems logcal to focus on the promoton of small-scale farmng as a poverty allevaton strategy. For ths reason, most governments and donor agences tend to focus on the small farm sector n developng countres. Not only dd largescale agrcultural projects n most developng countres n the past prove unsuccessful (Dennger et al. 2011) but smallholder farm unts contnue to supply the bulk of the food

3 consumed n Afrca and Asa (Nwanze 2011) as well as the producton of most cash crops for export. On the other hand, Coller and Dercon (2013) are of the vew that promotng large-scale farmng wll lead to potental gans by way of economes of scale that can promote commercal agrcultural producton as pertans n many developed countres. Further arguments supportng ths vewpont are that large-scale farms can better manage rsks and ensure faster spread of nnovatons at less cost as opposed to the slow spread of nnovatons among smallholder farmers (Alston et al. 2008). An argument used to oppose the dea that small farms are more productve than large farms s that nnovatons are scale neutral. Ths means that, n prncple, a producton technology should not perform better on small farms than on large farms. However, t s worth notng that farmers manageral ablty may dffer across dfferent scales of operaton. Hence, due to dfferences n resource endowments and manageral abltes, the sze of landholdng avalable to the farm household may nfluence ts farm performance, ncludng scale effcency. Furthermore, a farmer may be techncally effcent but not scale effcent, whch may reflect dfferences n manageral ablty. Controllng for dfferences n farm sze may therefore hghlght some scale effects n smallholder producton n order to shed more lght on the topc. The foregong arguments justfy the need for further nvestgaton nto the scale effcency of smallholder producers n order to understand how the scale of operaton relates to effcency. Accordng to Dercon (2009) and Golln et al. (2011), the queston of whether transformaton n agrculture rests solely on nvestng n small-scale farmng remans unanswered. The current study therefore employs a non-parametrc effcency analyss to nvestgate whether farm sze matters to scale effcency of peasant rce producers usng northern Ghana as a case study. A farm s scale effcent when t operates at a sze that s optmal such that changng ts sze makes the farm less effcent. Hence, the study seeks to fnd out whether at the current level of land allocaton to rce producton, smallholders are scale effcent n ther producton actvtes. The fndngs of the study wll justfy whether smallholder rce farmers need to adjust ther landholdngs to acheve hgher effcency of producton. The study uses a two-stage data envelopment analyss (DEA) to measure techncal and scale effcency and the determnants among peasant farmers n northern Ghana. The applcaton of DEA to agrculture s very common n the lterature on effcency analyss. Authors such as Coell et al. (2002), Hambrusch et al. (2006), Rahman and Awerje (2015) and Watkns et al. (2014) used DEA to estmate measures of farm performance notably techncal, allocatve, economc and scale effcency n agrculture. In addton, authors such as Bjurek et al. (1990), Førsund (1992) and Wanke (2012) have appled parametrc and non-parametrc approaches to study scale effcency n varous felds. DEA models rely on nput and output data and the basc models nclude the CCR (Charnes, Cooper and Rhodes) and the BCC (Banker, Charnes and Cooper) models. The CCR model proposed by Charnes et al. (1978) reles on the assumpton of constant returns to scale (CRS)

4 whle the BCC model proposed by Banker et al. (1984) assumes varable returns to scale (VRS) of actvtes. The effcency measures n DEA are defned n three ways namely overall techncal effcency, pure techncal effcency and scale effcency. Overall techncal effcency refers to techncal effcency usng the CCR model (that s, techncal effcency under constant returns to scale) whle pure techncal effcency relates to techncal effcency usng the BCC model (that s, techncal effcency under varable returns to scale). The pure techncal effcency measure accounts for the nfluence of manageral ablty on farmers productve effcency, whch s relevant to the dscusson of smallholder agrcultural producton. The overall techncal effcency measure ndcates how farmers allocate resources judcously n the producton process. It deals wth how effectvely producers turn nputs nto outputs n relaton to the data-drven fronter of the best-practce farms n the sample. Scale effcency s the rato of overall techncal effcency to the pure techncal effcency. Ths effcency measure assesses whether the producers are operatng at an optmal scale and therefore provdes nsght nto whether or not the scale of producton can be adjusted to enhance effcency. The focus of the current paper s determnaton of scale effcency. However, snce ths effcency measure derves from pure techncal effcency and overall techncal effcency measures, we nclude the dscusson of these effcency measures n the artcle. The authors also observe that n most of the publshed artcles on scale effcency, a concurrent dscusson s usually made of these three effcency measures. 2. Materals and Methods 2.1 Model specfcaton The current study assesses the techncal and scale effcency of small-scale rce producton n northern Ghana and the effect of farm sze on effcency. The study uses a non-parametrc approach namely data envelopment analyss (DEA) under constant and varable returns to scale assumptons. The DEA approach generates a data envelopment surface by lnkng ponts n the nput-output space n a way that no longer permts the producton of more output usng the same nput level or producton of the same output usng less nput. The data envelopment surface serves as a benchmark for measurng the relatve effcency of the rest of the frms outsde the envelopment surface (Marwa and Azakpono 2016). All effcent farms n the sample are lnked by a contnuous locus to form an effcent fronter to whch the effcency score for every DMU s measured by how far ts devates from the effcent fronter. The fronter n the case of the constant returns to scale s lnear whle that for the varable returns to scale s convex hull (Favero and Pap 1995). We decompose the effcency analyss nto three dmensons n order to understand the possble sources of neffcency. The frst dmenson relates to overall techncal effcency, the second relates to pure techncal effcency whle the thrd relates to scale effcency. The estmaton of overall techncal effcency nvolves measurng the rato of the dstance between neffcent ponts to the constant returns to scale (CRS) effcent fronter whle estmaton of pure techncal effcency nvolves measurng the rato of the dstance between

5 neffcent ponts to the varable returns to scale (VRS) effcent fronter. Scale effcency (SE) s the rato of overall techncal effcency (OTE) and pure techncal effcency (PTE). The study employs the nput orentaton n the estmaton because producers have more control on nput than output. The formulaton of DEA can follow ether a constraned maxmzaton or mnmzaton objectve functon usng lnear programmng. The study employs the mnmzaton formulaton because of ts mathematcal tractablty (Coell et al. 2005). Consder that the data covers N farms or decson-makng unts (DMUs), K nputs and M outputs. We represent an ndvdual DMU by the vectors x and q respectvely. For all N DMUs, let X represent the KxN nput matrx and Q, the MxN output matrx. The expresson of the mnmzaton formulaton for the constant returns to scale (CRS) assumpton accordng to Coell et al. (2005) s as follows: mn (, ) s.t. q Q 0 = 1, 2, 3,, N. (1) x X 0 0 where θ stands for the effcency score for the th farm, q represents a column vector of outputs, Q s an MxN output matrx, x s a column vector of nputs, X s a KxN nput matrx for all DMUs and λ s an Nx1 vector of constants (weghtng coeffcents). The value of θ ranges from 0 to 1 where 1 ndcates a techncally effcent DMU operatng on the effcent fronter. Values of θ less than 1 operate below the effcent fronter. We formulate the varable returns to scale (VRS) lnear programmng problem by addng the convexty constrant to the VRS specfcaton as follows: mn (, ) s.t. q Q 0 = 1, 2, 3,, N. (2) x X 0 N1' 1 0 where N1 represents an Nx1 vector of ones and the rest of the varables mantan ther prevous defntons. Furthermore, we derve scale effcency (SE) as follows: CRS SE VRS DEA DEA (3) where CRSDEA represents the effcency score obtaned from the CRS assumpton and VRSDEA s the score from the VRS model.

6 To determne whether a DMU s operatng n an area of ncreasng, decreasng or constant returns to scale, we mpose the non-ncreasng returns to scale (NIRS) assumpton as follows: mn (, ) s.t. q Q 0 = 1, 2, 3,, N. (4) x X 0 N1' 1 0 where the varables have ther usual defntons. The authors observe that many of the prevous studes used a two-stage DEA analyss where the effcency scores from the frst stage were regressed on soco-economc varables that had nfluence on the effcency estmates usng ether Tobt or truncated regresson analyss. The argument for usng these approaches s that the effcency scores are censored or truncated. Hence, Hoff (2007) asserts the suffcency of the Tobt procedure n representng the second stage DEA models. However, authors lke Smar and Wlson (2007) have questoned these procedures on the bass that the effcency estmates from DEA are prone to complex correlatons whle the procedure lacks a well-defned data generaton mechansm. McDonald (2009) s also of the vew that the process of generatng the DEA scores does not follow a censorng process hence napproprate to use Tobt analyss n the second stage. Smar and Wlson (2007) therefore proposed a bootstrappng technque for generatng relable standard errors and confdence ntervals for the second stage analyss. The Smar and Wlson (2007) approach attempts to construct and smulate a data generatng process by generatng artfcal bootstrap samples from whch standard errors and confdence ntervals are constructed. Ths procedure s an mplementaton of the truncated regresson model wth bootstrappng of the effcency scores usng smulaton. The current study followed ths approach and mplemented the procedure n Stata verson 14 usng the smarwlson user-wrtten command (algorthm #1). Detaled explanaton of the procedure s presented n Appendx A. 2.2 Data and descrptve statstcs of the respondents Data for the study was collected by the authors n 2014 for the farmng season and covered 300 rce-producng farm households. A multstage stratfed random samplng was used to select the farmers. Admnstratvely, northern Ghana comprses of the Upper East, Upper West and Northern Regons. The Upper East and Northern Regons were purposvely selected because of ther mportance n rce producton n Ghana. Smallholder farmers were selected from three major rrgaton schemes n the study area: Vea and Tono Irrgaton Schemes n the Upper East Regon, and Botanga Irrgaton Scheme n the Northern Regon. Equal numbers of rrgaton-users and ran-fed producers were ncluded n the sample. We present the descrptve statstcs of the contnuous varables used n the study n Table 1a and the dscrete varables n Table 1b. The respondents are small-scale producers judgng

7 from the small farm szes averagng less than a hectare 1. The respondents also used relatvely low amounts of nputs n producton suggestng that they are resource-poor farmers. The level of educaton and number of contacts wth extenson agents were also very low, whch are lkely to affect the manageral ablty of the farmers. The farmers are n ther productve ages and have cultvated rce for more than 15 years. The farmers also allocate about 45 percent of ther total land to rce producton. Ths fgure s hgh consderng that smallholder farmers typcally produce crops consumed by the household and trade the extra output for cash. Hence, one may classfy rce producton as an mportant cash croppng system among the respondents. Majorty of the respondents were males wth 40 percent usng credt n producton. Furthermore, two-thrd of the respondents planted mproved rce varetes. Accordng to the samplng desgn, equal number of rrgators and non-rrgators were ncluded n the study wth two-thrd of the respondents comng from the Upper East Regon. The dstrbuton of major rrgaton schemes n the study area meant that we selected two rrgaton schemes n the Upper East Regon and one n the Northern Regon for the study. Table 1a: Descrptve statstcs of the varables n the study 2 Varable Mean Std. Dev. Mnmum Maxmum Output (kg) Farm sze (ha) Labor (man-days) Seed (kg) Fertlzer (kg) Expendture (Ghana Ced) Captal (Ghana Ced) Age (Years) Years of rce farmng experence Years of formal educaton Share of land under rce (%) Number of adult workng members Number of extenson contacts Table 1b: Descrptve statstcs of dscrete varables n the study Varable Frequency Percent Sex (1=Male, 0 otherwse) Access to credt (1 = access, 0 otherwse) Rce varety (1=Improved, 0 otherwse) Access to rrgaton (1= access, 0 otherwse) Regonal dummy (1=Northern Regon, 0 otherwse) Tradtonally, smallholders are defned as those farmers operatng less than 2 hectares. In sparsely populated sem-ard areas, the land sze could be up to 10 hectares for smallholders (Dxon et al., 2003). 2 The data reveals dscrepancy n the mnmum and maxmum of the nput and output varables, partly reflectng the varablty n farm sze. However, dagnostc tests dd not reveal the presence of outlers. Furthermore, we estmated the model wthout farms exceedng 2.5 hectares but the result dd not change.

8 3. Results and Dscusson 3.1 Analyss of scale effcency of smallholder rce farmers We present the effcency scores obtaned from the DEA analyss n Table 2. The techncal effcency estmates under constant as well as varable returns to scale are reported alongsde the scale effcency scores. Mean techncal effcency under constant returns to scale (CRS- TE) was 47 percent as aganst 65 percent under the varable returns to scale assumpton (VRS-TE). The mean scale effcency (SE) was 70 percent. The results show that 10 percent of the DMUs were scale effcent, meanng that these farms were located on the effcent fronter. About 9.3 percent of the DMUs were techncally effcency under constant returns to scale. For the techncal effcency under varable returns to scale, the result showed 20 percent DMUs operatng on the effcent fronter. Hence most of the neffcences are ether techncal or scale n nature meanng that smaller farms may need to ncrease ther scale of producton n order to enhance ther effcency. The results also ndcate very hgh varablty n the effcency scores. Ths suggests that some of the respondents are very neffcent n ther resource utlzaton or operatng at sub-optmal scale of producton. The techncally neffcent farmers therefore need to mprove ther effcency of resource use n order to reduce wastng resources n ther producton actvtes. Table 2: Frequency dstrbuton of the effcency scores Effcency range OTE (CRS-TE) PTE (VRS-TE) SE Mean Mnmum Maxmum Number of effcent DMUs Note: OTE, overall techncal effcency; PTE, pure techncal effcency; SE, scale effcency Table 3 presents the dstrbuton of farms across dfferent categores of returns to scale n the study area. The results ndcate that 81.3 percent of all farms operate at ncreasng returns to scale wth 8.7 and 10.1 percent operatng at decreasng and constant returns to scale, respectvely. Thus, majorty of the farms are operatng at the neffcent part of the producton functon.

9 Table 3: Returns to scale summary statstcs Scale Classfcaton a Number Percent CRS 30 10% DRS % IRS % Total % a CRS, constant returns to scale; DRS, decreasng (dmnshng) returns to scale; IRS, ncreasng returns to scale We llustrate the relatonshp between scale effcency and farm sze n Fgure 1. A postve lnear relatonshp s shown ndcatng hgher scale effcency wth ncrease n landholdng. The result ndcates that ncreasng landholdngs s benefcal to smallholder farmers n the study area, by takng advantage of scale effects n producton. Encouragng farmers to ncrease ther share of land under rce can therefore boost rce producton n Ghana. Hence, efforts to facltate access to land for rce cultvaton can brng the desred results of ncreasng domestc rce producton n Ghana as enshrned n the Natonal Rce Development Strategy (NRDS). Fgure 1: Graph of scale effcency and farm sze 3.2 Factors explanng effcency We present the results of the bootstrapped DEA analyss of the determnants of effcency usng the Smar and Wlson (2007) approach n Table 4. The varable of nterest n the study, that s, farm sze had a postvely sgnfcant effect on scale effcency at the 1 percent level, mplyng that larger farms acheve hgher scale effcency. The result agrees wth Karaganns and Sarrs (2005) who found a postve and statstcally sgnfcant assocaton between scale effcency and farm sze among Greek tobacco farmers. The result ndcates that farmers wth

10 smaller farm holdngs can mprove ther scale effcency by ncreasng ther land area. As ndcated by Coell and Battese (1996), farmers wth smaller farms may have alternatve sources of ncome, whch they may consder more mportant hence devotng less attenton to farmng. However, the quadratc term had a negatvely sgnfcant effect on scale effcency at the 10 percent level. The result ndcates that scale effcency ncreases at a decreasng rate wth an ncrease n farm sze. On the other hand, we found the farm sze varable to have a sgnfcant and negatve assocaton wth pure techncal effcency ndcatng that an ncrease n farm sze decreases the pure techncal effcency of the farmers. Snce pure techncal effcency relates to manageral effcency of the producers, we conclude that smallholder farmers become less managerally effcent when farm sze ncreases. The quadratc term had a postve and sgnfcant coeffcent ndcatng that smaller farms become relatvely less effcent when farm sze ncreases. However, the effect of farm sze was nsgnfcant n the case of overall techncal effcency. The result of our study agrees wth Watkns et al. (2014) n ther analyss of rce producton n Arkansas n the Unted States. The authors found a postvely sgnfcant effect of land sze on scale effcency but a negatvely sgnfcant effect of land sze on pure techncal effcency. They also found the effect of land sze on overall effcency to be negatve and nsgnfcant. Taraka et al. (2010) also support our fndngs accordng to ther study that found techncal effcency to be lower on larger rce farms n Thaland. In the vew of Ross et al. (2009), msallocaton of resources on larger farms s a factor that accounts for the declne n techncal effcency on these farms. Matchaya (2007) also reported an nverse relaton between farm sze and farm productvty n Malaw. What the result of ths study seems to suggest s that scale and techncal effcency may dffer n ther relatonshp wth farm sze. Thus, n relaton to farm sze, techncal and scale effcency may seem to be separate goals to the farmer, the attanment of whch may depend on dfferent combnaton of factors. The second-stage DEA analyss usng the Smar and Wlson approach showed that the regonal dummy varable, access to rrgaton and the degree of specalzaton n rce producton had sgnfcant effect on all three effcency measures. The result ndcates that access to rrgaton, the degree of specalzaton n rce farmng, and beng located n the Northern Regon lead to hgher effcency of smallholder rce farmers n northern Ghana. Hence, expandng access to rrgaton, promotng the specalzaton n rce producton and addressng the factors mpedng the productvty of producers n the Upper East Regon, wll contrbute to hgher rce producton effcency n northern Ghana leadng to hgher ncomes from farmng. The result of our study agrees wth Karmov (2013) who found access to rrgaton water to enhance the techncal effcency of Ngeran farmng households. Anang et al. (2016) also found the degree of specalzaton to enhance the producton effcency of smallholder farmers n northern Ghana.

11 Table 4: Second-stage analyss of factors explanng effcency Varable Coeffcent Std. Error 95% Confdence Int. Lower Upper Scale effcency Farm sze 0.404*** Farm sze squared * Regonal dummy 0.227*** Access to rrgaton 0.351*** Sex of household head 0.087* Age of household head Age of household head squared Access to credt 0.116** Extenson vsts 0.008* Degree of specalzaton 0.002** Constant Sgma 0.246*** Pure techncal effcency Farm sze 0.269*** Farm sze squared 0.052*** Regonal dummy 0.118*** Access to rrgaton 0.066** Sex of household head Age of household head Age of household head squared Access to credt Extenson vsts Degree of specalzaton 0.002*** Constant 0.689*** Sgma 0.195*** Overall techncal effcency Farm sze Farm sze squared Regonal dummy 0.144*** Access to rrgaton 0.169*** Sex of household head Age of household head Age of household head squared Access to credt 0.043* Extenson vsts 0.006** Degree of specalzaton 0.002*** Constant Sgma 0.208*** * sgnfes statstcal sgnfcance at the 10% level. ** sgnfes statstcal sgnfcance at the 5% level. *** sgnfes statstcal sgnfcance at the 1% level. We also observed that access to mcrocredt mproved scale effcency as well as overall effcency but the effect was not sgnfcant n the case of pure techncal effcency. The result of our study s contrary to Ly et al. (2016) who found access to credt to decrease both scale effcency and pure effcency of pg producers n Vetnam but supported by Mugera and

12 Featherstone (2008) who found credt to ncrease scale effcency, overall effcency and pure effcency of hog producers n the Phlppnes. Karmov et al. (2013) also found access to credt to enhance the techncal effcency of farmng households n Ngera. Snce credt enables farmers to hre n labor and acqure mportant producton nputs, we antcpated effcency gans from ts use n producton. Concernng the weak relatonshp between mcrocredt and pure techncal effcency, we argue that ths mght reflect farmers low manageral ablty snce the pure techncal effcency measure accounts for the manageral ablty of farmers. The study also found access to extenson servces to enhance the scale effcency and overall techncal effcency of farmers but the effect was not sgnfcant n the case of pure techncal effcency. The result s consstent wth Karmov et al. (2013) who found access to extenson to enhance the techncal effcency of farmng households n Ngera. Taraka et al. (2010) also found extenson contact to ncrease the techncal effcency of rce farmers n Central Thaland. Extenson servces equp farmers wth the techncal knowhow to enhance ther level of effcency. The low level of extenson contacts, averagng three vsts per producton season mght play a role n ts weak effect on pure techncal effcency. Fnally, gender of the farmer had a sgnfcant effect on scale effcency but not on the techncal effcency measures. Scale effcency s therefore hgher for men than women n the study area. The result agrees wth other studes n developng countres where men tend to domnate decson-makng and have greater access to producton resources (Anang et al., 2016). The result alludes to the fact that male farmers are usually the custodans of producton resources n many rural communtes and the control of these resources tend to enhance ther effcency of producton. Mugera and Featherstone (2008) also found male hog producers n the Phlppnes to be 21 percent more scale effcent than ther female counterparts. The result of our study s however at varance wth Dhungana et al. (2004) n ther study of economc effcency of rce farmers n Nepal. 4. Concluson and Recommendatons The study employed data envelopment analyss to estmate the techncal, pure techncal and scale effcency of rce farms n northern Ghana, usng data from a cross-secton of 300 farmers. The results ndcated techncal effcency of 46.6 percent, pure techncal effcency of 65.1 percent and scale effcency of 69.5 percent among the respondent smallholder farmers. Majorty of the farms operated at ncreasng returns to scale whle a postve lnear relatonshp exsted between scale effcency and farm sze. The second-stage DEA analyss showed the determnants of scale effcency to nclude gender, farm sze, contact wth extenson agents, access to credt and rrgaton, degree of specalzaton n rce producton and locaton of the farm. Scale effcency was hgher for the followng: farmers wth larger farms, producers wth more extenson vsts, rrgaton users, farmers who specalze more n rce producton, farmers wth access to credt, and farmers located n the Northern Regon. For the determnants of pure techncal effcency, we found farm sze, locaton of the farm, access to rrgaton, and the degree of specalzaton n rce

13 producton to be the nfluental factors. Pure techncal effcency ncreased wth all these varables wth the excepton of farm sze. Concernng overall techncal effcency, we found the locaton of the farm, access to rrgaton and credt, number of extenson vsts and the degree of specalzaton n rce producton to be the nfluental factors. The overall techncal effcency ncreased wth all these factors wth farmers n the Northern Regon havng hgher techncal effcency. The farm sze varable was however not sgnfcant n ts effect on overall techncal effcency of the farmers. The result shows that most of the neffcences are ether techncal or scale n nature. However, the effect of farm sze vares for the two effcency measures. Hence farm expanson wll enable farmers to explot economes of scale but (due to lack of manageral abltes) techncal effcency wll declne. Therefore at the current level of technology, farmers cannot explot economes of scale and techncal effcency gans at the same tme. However, f the farmers use mproved technques of producton and gan manageral sklls, they could expand farm sze and derve both scale and techncal effcency gans. From a polcy perspectve, we recommend the expanson of rrgaton access to smallholder farmers to enhance ther effcency of producton. Furthermore, promotng specalzaton n rce producton wll rase the effcency levels of producers, thereby ncreasng farm ncomes. In addton, the study recommends further nvestgaton nto the causes and possble remedes to the factors mpedng farmers productve and scale effcences n the Upper East Regon. Furthermore, snce most of the neffcences are ether techncal or scale n nature, the study calls for an ncrease n the scale of producton for smaller farms n order to take advantage of unexplored economes of scale. Ths may however conflct wth the goal of techncal effcency as smaller farms n the sample are more techncally effcent. Hence, a more realstc approach could be for farmers to specalze more by ncreasng the area under rce cultvaton and foregong the producton of other crops. The techncally neffcent farmers on the other hand need to reduce waste n resource utlzaton by mprovng ther effcency of resource use. A lack of manageral and techncal know-how may also hnder the explotaton of economes of scale by smallholder farmers. Snce most smallholders depend on publc extenson servces for producton advce and nformaton, the provson of extenson servces can go a long way to mprove farmers manageral ablty thus nfluencng ther effcency of producton. There s therefore the need to mprove extenson servce delvery to farmers as well as talor tranng needs to those farmers who lack the requste manageral sklls. The avalablty of credt to farmers s also mportant f producers are to take advantage of unexplored economes of scale. From the study, access to credt postvely nfluenced scale effcency. Hence, polcy measures that enhance smallholder farmers access to credt for agrcultural producton are needed. References Akudugu, M. A. (2016) Agrcultural productvty, credt and farm sze nexus n Afrca: a case study of Ghana Agrcultural Fnance Revew 76,

14 Alston, J. M., Pardey, P. G., and Ruttan, V. W. (2008) Research lags revsted: Concepts and evdence from U.S. agrculture Unversty of Mnnesota staff workng paper number P Anang, B. T., Bäckman, S., and Splänen, T. (2016) Techncal effcency and ts determnants n smallholder rce producton n northern Ghana The Journal of Developng Areas 50, Banker, R. D., Charnes, A., and Cooper, W. W. (1984) Some models for estmatng techncal and scale neffcences n data envelopment analyss Management Scence 30, Bjurek, H., Hjalmarsson, L., and Forsund F. R. (1990) Determnstc parametrc and nonparametrc estmaton of effcency n servce producton. A comparson Journal of Econometrcs 46, Chamberln, J. (2007) Defnng Smallholder Agrculture n Ghana: Who are smallholders, what do they do and how are they lnked wth markets? Ghana Strategy Support Program (GSSP). Background Paper No. GSSP Accessed 1 May Charnes, A., Cooper, W. W., and Rhodes, E. (1978) Measurng the effcency of decson makng unts European Journal of Operatonal Research 2, Chrstaensen, L., Demery, L., and Kuhl, J. (2011) The (evolvng) role of agrculture n poverty reducton an emprcal perspectve Journal of Development Economcs 96, Coell, T. J., and Battese, G. E. (1996) Identfcaton of factors whch nfluence the techncal neffcency of Indan farmers Australan Journal of Agrcultural Economcs 40, Coell, T. J., Pradasa Rao, D. S. P., O Donnell, C. J., and Battese, G. E. (2005) An Introducton to effcency and productvty analyss 2 nd Ed., Sprnger: New York, NY, USA. Coell, T. J., Rahman, S., and Thrtle, C. (2002) Techncal, allocatve, cost and scale effcences n Bangladesh rce cultvaton: A non-parametrc approach Journal of Agrcultural Economcs 53, Coller, P., and Dercon, S. (2013) Afrcan agrculture n 50 years: Smallholders n a rapdly changng world? Stanford Symposum Seres on Global Food Polcy and Food Securty n the 21st Century. Centre on Food Securty and the Envronment: Stanford Unversty. Dennger, K., Byerlee, D., Lndsay, J., Norton, A., Selod, H., and Stckler, M. (2011) Rsng global nterest n farmland. Can t yeld sustanable and equtable benefts? Washngton DC: The World Bank. Dercon, S. (2009) Rural poverty: Old challenges n new contexts The World Bank Research Observer 2009.

15 Dhungana, B. R., Nuthall P. L., and Nartea, G. V. (2004) Measurng the economc effcency of Nepalese rce farms usng data envelopment analyss The Australan Journal of Agrcultural and Resource Economcs 48, Dxon J., Tanyer-Abur, A. and Wattenbach, H. (2003). Context and Framework for Approaches to Assessng the Impact of Globalzaton on Smallholders, In Dxon J., K. Tanguch and H. Wattenbach (ed.), Approaches to Assessng the Impact of Globalzaton on Afrcan Smallholders: Household and Vllage Economy Modelng, Proceedngs of Workng Sesson Globalzaton and the Afrcan Smallholder Study. Rome, Italy: FAO and The World Bank. Favero, C. A., and Pap, L. (1995) Techncal effcency and scale effcency n the Italan bankng sector: a non-parametrc approach Appled Economcs 27, Førsund, F.R. (1992) A Comparson of parametrc and non-parametrc effcency measures: The case of Norwegan ferres Journal of Productvty Analyss 3, Golln, D., Lagakos, D., and Waugh, M. E. (2011) The agrcultural productvty gap n developng countres New York Unversty workng papers number Hambrusch, J., Krner, L., and Ortner, K. M. (2006) Techncal and scale effcency n Austran dary farmng Economc Scence for Rural Development 10, Hoff, A. (2007) Second stage DEA: Comparson of approaches for modellng the DEA score European Journal of Operatons Research 181, Karaganns, G. K., and Sarrs, A. (2005) Measurng and explanng scale effcency wth the parametrc approach: the case of Greek tobacco growers Agrcultural Economcs 33, Karmov, A. (2013) Productve effcency of potato and melon growng farms n Uzbekstan: a two stage double bootstrap data envelopment analyss Agrculture 3, Karmov, A., Awotde, B. A., and Amos, T. T. (2013) Producton and scale effcency of maze farmng households n South-Western Ngera Internatonal Journal of Socal Economcs 41, Larson, D. F., Otsuka, K., Matsumoto, T., and Klc, T. (2012) Should Afrcan rural development strateges depend on smallholder farms? An exploraton of the nverse productvty hypothess Polcy Research workng paper number Ly, N. T., Nansek, T., and Chome, Y. (2016) Techncal effcency and ts determnants n pg producton n Vetnam: A DEA approach The Japanese Journal of Rural Economcs 18, Marwa, N., and Azapkono, M. (2016) Techncal and scale effcency of Tanzanan savng and credt cooperatves The Journal of Developng Areas 50,

16 Matchaya, G. (2007) Does sze of operated area matter? Evdence from Malaw s agrcultural producton Internatonal Journal of Agrculture and Rural Development 10, McDonald, J. (2009) Usng Least Squares and Tobt n second stage DEA effcency analyses European Journal of Operatons Research 197, McErlean, S., and Wu, Z. (2003) Regonal agrcultural labor productvty convergence n Chna Food Polcy 28, MoFA (2009) Natonal Rce Development Strategy (NRDS) (Draft) Mnstry of Food and Agrculture. Republc of Ghana. Mugera, A. W., and Featherstone, A. M. (2008) Backyard hog producton effcency: Evdence from the Phlppnes Asan Economc Journal 22, Namara, R. E., Horowtz, H., Nyamad, B., and Barry, B. (2011) Irrgaton development n Ghana: Past experences, emergng opportuntes, and future drectons Ghana Strategy Support Program (GSSP), GSSP workng paper number 0027, March Nwanze, K. F. (2011) Smallholders can feed the world. Vewpont Presdent of the Internatonal Fund for Agrcultural Development. Accessed 30 May Rahman, S., and Awerje, B. O. (2015) Techncal and scale effcency of cassava producton system n Delta State, Ngera: an applcaton of Two-Stage DEA approach Journal of Agrculture and Rural Development n the Tropcs and Subtropcs 116, Ross, K., Dalton, T. J., and Featherstone, A. M. (2009) A nonparametrc effcency analyss of bean producers from North and South Kwu Selected paper for the Southern Agrcultural Economcs Assocaton annual meetng, Atlanta, Georga, January 31 February 3, Smar, L., and Wlson, P. W. (2007) Estmaton and nference n two-stage sem-parametrc models of producton processes Journal of Econometrcs 136, Sngh, R. B., Kumar, P. and Woodhead, T. (2002) Smallholder farmers n Inda: Food securty and agrcultural polcy RAP Publcaton number 2002/03. Taraka, K., Latf, I. A., and Shamsudn, M. N. (2010) A nonparametrc approach to evaluate techncal effcency of rce farms n Central Thaland Chulalongkorn Journal of Economcs 22, Tauchmann, H. (2015) Smarwlson: DEA based two-step effcency analyss 2015 German Stata Users Group Meetng, Nuremberg, IAB. Accessed 1 January 2016.

17 Wanke P. F. (2012) Determnants of scale effcency n the Brazlan thrd-party logstcs ndustry from 2001 to 2009 Brazlan Admnstratve Revew 9, Watkns, K. B., Hrstovska, T., Mazzant, R., Wlson Jr., C. E., and Schmdt, L. (2014) Measurement of techncal, allocatve, economc, and scale effcency of rce producton n Arkansas usng data envelopment analyss Journal of Agrcultural and Appled Economcs 46, Appendx A: The Smar and Wlson (2007) Procedure (algorthm #1) 3 Step 1: Estmate the effcency scores θ usng DEA ( = 1, N) Step 2: Ft z usng truncated regresson by maxmum lkelhood estmaton and obtan estmates for and. Exclude effcent DMUs j ( j 1, j 1,... M) wth n (0,1] for nput orentaton (rght truncaton at 1). Step 3: Loop over the next 3 steps B tmes (where b = 1,, B) We draw from N(0, ) wth rght truncaton for the nput orentaton (otherwse b left truncaton for the output orentaton) at (1 z ) for M 1,..., N Compute b b z for M 1,..., N Use the artfcal effcency scores b by truncated regresson. b as the dependent varable to estmate b and Step 4: The fnal step s to construct standard errors for and and ) from smulated dstrbuton of b b and. (confdence ntervals for 3 The Smar and Wlson approach descrbed here s adapted from Tauchmann (2015).