Ghana Journal of Science, Technology and Development Vol. 4, Issue 1. April 2016 available online at ISSN:

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1 Ghana Journal of Scence, Technology and Development Vol. 4, Issue 1. Aprl 016 avalable onlne at ISSN: Eamnng the determnants and effects of Contract Farmng on Farm Income n the Northern Regon of Ghana S.B. Azumah1, S.A. Donkoh*, and D.S. Ehakpor 1 IFDC Ghana Feed the Future USAID Ghana Agrculture Technology Transfer Project. P. O. Bo ER 54. Tamale, Ghana Department of Agrcultural and Resource Economcs,Unversty for Development Studes, P. O Bo TL 188, Tamale, Ghana. *Correspondng author: sdonkoh@uds.edu.gh Abstract The study sought to determne the factors that nfluence farmers decson to partcpate n contract farmng as well as the effect of contract farmng on farm ncome n the Northern Regon of Ghana. It nvolved 30 crop farmers selected through mult-stage samplng procedure. A treatment effect model was estmated to determne the factors that nfluenced farmers partcpaton n contract farmng and ts effect on farm ncome. The factors that postvely nfluenced partcpaton n contract farmng were access to etenson servces and credt. However, farm sze and off-farm ncome negatvely nfluenced partcpaton n contractng. In general, farmers who partcpated n contract farmng had a hgher ncome than ther nonpartcpatng counterparts. Other factors that sgnfcantly nfluenced farm ncome postvely were land, labour and fertlzers. Weedcde however mpacted negatvely on ncome, suggestng that t s beng over-used. We recommend that farmers are supported to access the facltes that enable them to partcpate n contract farmng such as credt and etenson servces. To ncrease ther farm ncomes, farmers also need support n ncreasng the levels of farm nputs such as land, labour and fertlzers. Farmers also need educaton on the accurate use of weedcdes Keywords: Adopton, Contract farmng, Crop output, Clmate Change Copng Strateges, Northern Regon, Ghana, Treatment effect model INTRODUCTION Even though contract farmng has been used for agrcultural producton for some tme now, t has ganed popularty n recent tmes. The Food and Agrculture Organsaton (FAO) (008), defned contract farmng as agrcultural producton carred out accordng to an agreement between a buyer and farmers, whch establshes condtons for the producton and marketng of a farm produce or products. The organsaton has observed that contract farmng has become attractve to many farmers because the arrangement can offer them both an assured market and access to producton support. Contract farmng s also of nterest to buyers, who seek supples of products for sale further along the value chan or for processng. Processors consttute the man users of contracts, as the guaranteed supply enables them to mamze utlzaton

2 of ther processng capacty (Charles and Shepherd, 014). Contracts wth farmers can also reduce rsk from dsease or weather and facltate certfcaton, whch s beng ncreasngly demanded by advanced markets. There are also potental benefts for natonal economes as contract farmng leads to economes of scale, whch, as Coller and Dercon (014) argued, are bound to provde for a more dynamc agrcultural sector. However, contract farmng s not wthout problems. Farmers often complan that contractng companes often buy ther produce at much lower prces than the prevalng market prces. On the other hand, the companes also complan that sometmes farmers dvert nputs away from the ntended purposes and also refuse to sell ther produce to them (companes) contrary to what had been agreed by both partes. Eaton and Shepherd (011) dentfed fve dfferent contract farmng models, namely; centralzed model; nucleus estate model; multpartte model; ntermedary model and nformal model. Under the centralzed model a company provdes support to smallholder producton, purchases the crop, and then processes t, closely controllng ts qualty. Ths model s used for crops such as tobacco, cotton, sugar cane, banana, tea, and rubber. Under the Nucleus Estate model, the company also manages a plantaton n order to supplement smallholder producton and provde mnmum output for the processng plant. Ths approach s manly used for tree crops such as ol palm and rubber. The Multpartte model usually nvolves a partnershp between government bodes, prvate companes and farmers. At a lower level of sophstcaton, the Intermedary model can nvolve subcontractng by companes to ntermedares who have ther own (nformal) arrangements wth farmers. Fnally, the Informal model nvolves small and medum enterprses who make smple contracts wth farmers on a seasonal bass. Although these are usually just seasonal arrangements they are often repeated annually and usually rely for ther success on the promty of the buyer to the seller. In the Northern Regon of Ghana, the centralsed and nformal models of contract farmng are common among crop farmers. Under the centralsed model, companes such as Wenco Ghana, SAVBAN, Presbyteran Agrculture Servces, BUSAKA, and Karaga Agrbusness Centres have provded and contnue to provde supports to smallholder producton, and then purchase the crops for onward sale to end markets that add value to these produce. Ths support comes n the form of nputs such as fertlzer, mproved seed, herbcdes and techncal backstoppng n good agronomc practces to produce what has come to be known as food securty crops such as maze, rce, and soybeans. The farmers are asssted n groups n a form of collateralsaton for these nput credts to ensure accountablty. Contracts are then sgned by the farmers through ther leadershp such that ther produce are sold to the companes that asssted them wth ther producton actvtes. Under ths legal arrangement of contractng, the farmers are assured of a stable marketng channel for ther produce and acceptable economc prce for ther produce. Ths approach adopted by the companes s also sad to be an nformal model because smple contracts are entered nto wth the farmers on a seasonal bass. Several studes, ncludng those of Setboonsarng et al. (008), and Ca et al. (008), have been conducted on contract farmng, many of whch are lsted n FAO (008). Smlarly, the Asan Development Bank Insttute (ADBI) n Tokyo has conducted a seres of case studes n selected Asan countres to assess the condtons for benefts to be acheved by margnal rce Page

3 farmers. Also, n Lao PDR, a study suggested that contracted farmers earned sgnfcantly hgher profts than non-contracted farmers. Ths facltated the transton of subsstence farmers to commercal agrculture, offerng potental to reduce rural poverty (Setboonsarng et al., 008). Furthermore, a study n Camboda on organc rce for eport assessed the effect of contract farmng on farmers performance. Ths suggested that younger and more educated farmers wth larger famles and fewer assets were more lkely to jon the contract. However, farmers wth access to good road communcatons often left the contract, ndcatng that contract farmng had helped them to develop nto ndependent farmers (Ca et al., 008). Lastly, Wang et al. (014) revewed a large number of emprcal studes of contract farmng. They concluded that contract farmng has had a sgnfcant mpact on mprovng farm effcency and productvty, and farmer ncomes and that ths should gve governments the confdence that allocaton of resources to the topc of contract farmng could yeld postve results (Wang et al., 014). Gven the advantages and challenges of contract farmng n the Northern Regon of Ghana, t s mportant that studes such as the ones revewed above are carred out to nvestgate (emprcally) the etent to whch contract farmng mpacts on farm ncome. However, to the best of the authors knowledge there has not been any econometrc study to that effect, hence ths study to dentfy the determnants of farmers decson to go nto contract farmng and the effects on farm ncome. MATERIALS AND METHODS The Study Area The Northern Regon, whch occupes an area of about 70,384 square klometres s the largest regon n Ghana n terms of land mass (GSS, 014). It shares boundares wth the Upper East and the Upper West Regons to the north, the Brong Ahafo and the Volta Regons to the south, Togo to the east, and Côte d Ivore to the west. The land s mostly low lyng ecept n the north-eastern corner wth the Gambaga escarpment and along the western corrdor. The regon s draned by the Black and Whte Volta Rvers and ther trbutares such as the Nasa and Daka rvers. The clmate of the regon s relatvely dry, wth a sngle rany season that begns n May and ends n October. The amount of ranfall recorded annually vares between 750 mllmetres and 1,050 mllmetres. The dry season starts n November and ends n March/Aprl wth mamum temperatures occurrng towards the end of the dry season (March-Aprl) and mnmum temperatures n December and January. The majorty of people n the regon are engaged n agrculture. Lke t s natonally, a larger percentage of the farmng populaton are small-scale farmers. The crops that they produce nclude yam, maze, mllet, gunea corn, rce, groundnuts, beans, soya beans and cowpea. Lvestock producton s also very common n the regon. The northern regon can also boast of the presence of a relatvely large number of prvate sector actors such Wenco Ghana, SAVBAN, Presbyteran Agrculture Servces, BUSAKA and Karaga Agrbusness Centre. Samplng Technque and Data The selecton of the respondents nvolved mult-stage samplng technque. In the frst stage, s dstrcts noted for ther agrcultural actvtes were purposvely selected from the Northern Regon of Ghana. The choce of the Page 3

4 dstrcts was based on the fact that some crop farmers n the dstrcts have had contracts arrangements wth organsatons to produce for them. In the second stage, stratfed samplng method was used to select communtes n each of the selected dstrcts, gvng a total of 1 communtes. Fnally, twenty (0) respondents were selected from each communty, usng smple random samplng method. Ths gave a total sample sze of two hundred and forty (40) respondents. However, 30 questonnares were completed and returned for the purpose of analyss. Prmary data were bascally collected drectly from crop farmers usng sem-structured questonnares. Intervew gude was used to collect data from MoFA, and some NGOs ncludng BUSAKA Agrbusness Centre, RAINS, ACDEP, EPDRA Cherepon, CARE Internatonal, Tree Ad, and Presbyteran Agrcultural Servces, Tamale. Analytcal Framework A treatment effect model (eplaned below) was estmated at two stages frst to eamne the factors nfluencng farmers decson to enter nto contract farmng, and second to determne the effect of contract farmng on the ncome levels of crop farmers n the Northern Regon of Ghana. Theoretcal Model Specfcaton - Treatment Effect Model One form of the Heckman Two Stage Procedure for correctng selectvty bas s the treatment effect model (Maddala, 1983). Ths has been used wdely n programme evaluatons snce the selecton crtera for observatons n such studes are non-random. The man objectve of ths study was to determne the effect of contract farmng on the ncome level of crop farmers. By mplcaton, we were not only nterested n correctng selectvty bas but also, measurng the effect of contract farmng on crop value. Consequently, the treatment effect model s adopted. Lke the Heckman two stage, the treatment effect model estmates the selecton equaton n the frst stage to obtan the predcted values of the selecton varable, whch s then used to generate an Inverse Mlls Rato (IMR) also known as lambda. Both the predcted values of the selecton varable (contractng) and the IMR are then added to the outcome equaton n the second stage as an addtonal varable. Mathematcally, Y = X % $ β + δc $ + u +$ (1) % where Y s ncome, X $ are eogenous varables that are beleved to nfluence ncome, C $ s contractng whch takes the value 1 f a farmer s a contract farmer and (0) f otherwse. u s a two sded error term wth N 0, σ v. β and δ are parameters to be estmated. From Maddala (1983), ths may not provde an adequate result snce C $ s endogenous. Therefore, a selecton equaton of C $ s frst estmated as: C $ = Z % $ γ + u /$ () % Where Z $ s a set of eogenous varables that may nfluence the selecton varable C $, γ s a parameter to be estmated and u / s also a two-sded error term wth N( 0, σ v ). Note that we cannot smply estmate the substantve equaton (wthout frst estmatng the selecton equaton) because the decson to contract may be nfluenced by unobservable varables lke nnovatveness that may also nfluence ncome. Ths mples that the two error terms (n the selecton and substantve equatons) are correlated, leadng to based estmates of β and δ. If we assume that u 1 and u have a jont normal dstrbuton wth the form: u u ( ) N, 0 ρ ~ ρ, σ (3) Page 4

5 Then t follows that the epected output of those who contract s gven as: E[ X C = 1] = Zβ + δ + E[ u C = 1] (4) = Z β + δ + ρσλ ' φ( Zγ ) Where λ = s the IMR ' 1 Φ( Zγ ) (5) Equaton 5 mples that when we estmate equaton wthout the Inverse Mlls Rato (IMR), the coeffcents β and δ wll be based. Accordng to Maddala (1983), when ncome of both contract and non-contract farmers are consdered then equaton 1 takes the form; Y = β '( Φ X ) + δ '( ΦC ) + σφ + e (13) ' where Φ Φ γ z ( ) Emprcal Models Specfcaton Followng the above theoretcal model, the emprcal model to be estmated to determne the factors nfluencng farmers decson to enter nto contractng and the effect on output are as follows: contract farmng = δ ; + δ + Age + δ / Se + Martal status + δ D Educaton + δ G Off farm + δ I Source of land + δ J Etenson servce + δ L Eperence + δ N farm sze + u / In the second stage: The defntons and the a pror epectatons of the varables are ndcated n Table 1. RESULTS Background nformaton of the respondents Majorty of the respondents were male, representng 73.5% of the sampled populaton. Also, the hghest percentage of the respondents (34.8%) was aged between years. The average age recorded was 39.7 years, whch s far below the natonal average age of 55 years for farmers (MoFA, 013a). From Table, t can be seen that 7.8% of the respondents schooled up to prmary level, whle 8.3% and 5% schooled up to the JHS/Mddle school level and SHS levels respectvely. Only 1 respondent (0.4%) had up to the tertary level of educaton. Also, about 79% of the respondents had no formal educaton. Ths s above the regonal fgure of 54.9% (GSS, 01). Ths revelaton s understandable snce most of the lterate populaton of Ghana lve n urban areas (GSS, 01). Also, 9.6% of the respondents had a household sze of between 11 and 15 members. Ths s also above the natonal and regonal averages of 4.4 and 7.7 respectvely (GSS, 01). Agan, majorty of the respondents (35.6%) had over 0 years eperence n crop farmng. About 70% of them were n formal groups and had access to etenson servces. However, only 33% of the respondents had access to research servces. Y = β ; + β β / / + β D D + β G G + β I I + β J J + β L L + β N N + u + Page 5

6 Table 1: Defnton of Varables and a pror epectatons Varable Defnton Epected sgn Age How old the farmer s n years +/- Educaton Dummy (1 for receved formal educaton, 0 otherwse) + Off-farm ncome A measure of ncome from sources other than crop farmng n GH + Source of land Indcates whether the farmers plot s rented or selfowned + Etenson servce The number of tmes a farmer receves etenson + servce n a year Credt Dummy (1 for receved credt, 0 otherwse) +/- Farm sze Total sze n acreages of a farmer s rce, maze and soybean + y Natural log of output (where output s the market value N/A 1 of the total output for the farmng season). Thus ths varable can also be referred to as farm ncome. 1 Natural log of farm sze + Natural log of labour (measured n number of farm + hands) Natural log of norganc fertlzer (measured n total + 3 amount n Ghana Ceds used) Natural log of organc fertlzer (measured n total + 4 amount n Ghana Ceds used) Natural log of seed (measured n quantty of seed used + 5 n kg) 6 Natural log of weedcde + Contract farmng Contract farmng (dummy, 1 for contract farmer, 0 otherwse) + + means the varable has a postve effect on the dependent varables and means t has a negatve effect. The Determnants of contract farmng To determne the effects of contract farmng on crop output, a treatment effect model was estmated at two stages. The dependent varable n the frst stage probt equaton s farmers' contract farmng status (Presented n Table 3). The sgnfcant varables were offfarm actvtes, etenson servce, etra credt and farm sze. The nsgnfcant varables were age, educaton and, land source. The Ch squared value was also sgnfcant at 5%, mplyng that all the varables jontly determned the dependent varable. Page 6

7 Table 3: Mamum lkelhood estmaton of the determnants of contract farmng (probt) Varable Margnal Effect Standard Error Age Educaton Off-Farm -0.55*** Land Source Etenson 0.66*** 0.17 Credt 0.66* Farm Sze *** Constant Wald ch square 531*** LR test of ndependent equatons (rho=0): ch (1) =13.93, P-value =0.000 * and *** ndcate statstcal sgnfcance at 10% and 1% respectvely The effects of contract farmng on farm ncome Table 4 shows the second stage result of the treatment effect model. The table presents the mamum lkelhood estmates of the output equaton. The lkelhood rato (LR) test of ndependence shows a Ch squared value of and s sgnfcant at 1%. Ths means that selectvty bas was present n the model, meanng that there were some unobserved varables that nfluenced both the decson to contract and output so that f we had not corrected for t the eplanatory varables (especally contract farmng varable) would not have measured the pure effects on the dependent varable. Table 4: Mamum lkelhood estmaton results of the ncome model Varable Coeffcent Standard Error 1 (Farm sze) 0.453*** (Labour) 0.099** (Inorgfertlzer) 0.357*** (Org. fertlzer) 0.16** (Seed) (Weedcde) ** Contract farmng 0.440*** Constant ** and *** ndcate statstcal sgnfcance at 5% and 1% respectvely From the table, contract farmng was postve as epected, and was also sgnfcant at 1%. Farm sze was also postve and sgnfcant at 1%, wth an estmated coeffcent of Whle farm sze was sgnfcant at 1%, labour was sgnfcant at 5% level and also mantaned ts epected postve effects on output. The estmated coeffcent was Furthermore, both organc and norganc fertlzers were sgnfcant and mantaned ther postve effects on crop output. Ther coeffcents were 0.57 and 0.16 Page 7

8 respectvely. Lastly, whle seed was nsgnfcant, weedcde was sgnfcant at 5% but had a negatve sgn. From the results the coeffcents of the conventonal nputs sum up to Ths means that there s decreasng returns to crop producton n the study area. DISCUSSION Ordnarly, well-to-do farmers are less lkely to partcpate n contract arrangements for crop producton. In the contet of ths study, off-farm ncome had the epected negatve sgn and was hghly sgnfcant at 1%. The margnal effect means that a 1-unt decrease n off-farm ncome would result n 0.53 probablty of a farmer gong nto contract farmng. However, the postve margnal effect of credt means that farmers who had access to credt had a greater probablty of gong nto contract farmng. The fndngs show that farmers who had access to credt had about 0.3 probablty of gong nto contract farmng as opposed to those who dd not have access to credt. Spo (00) opned that agrcultural fnance s a major constrant that lmts market access, partcpaton and commercalzaton of the smallholder farmers. The contrastng sgns of the coeffcent and margnal effect of off-farm actvtes and credt s qute surprsng because our argument s that the farmer who has other sources of ncome may not want to partcpate n contract farmng, gven ts assocated dffcultes such as contractng companes offerng lower prces for farmers output. However, the present results mply that even though off-farm ncome and credt are both etra ncomes ther assocated factors relatve to contract farmng may be dfferent. The sgnfcance of etenson servces n determnng farmer decsons have been dscussed n many studes ncludng those of Doss and Morrs (001), and Ransom et al. (003). By the present fndng, f the number of tmes a farmer had access to etenson servce ncreases by one, the probablty of contract partcpaton would ncrease by about In effect, farmers who had more contacts wth agrcultural etenson offcers had a hgher probablty of engagng n contract farmng than those who had no or less etenson servce contact. In a stuaton where the etenson offcer to farmer rato s hgh (such as 1:1800 n Ghana) (GSS, 01), the etenson offcers preferred contactng farmers that were operatng n groups to contactng ndvdual farmers. In other words, etenson offcers encouraged farmers to form groups whch were a prerequste to engagng n contractng farmng. The coeffcent of farm sze (-0.013) means that f farm sze s ncreased by 1 unt holdng all other nputs constant, the probablty of a farmer partcpatng n contract farmng would reduce by 0.013unt. Ths dd not also meet our a pror epectaton n the sense that one would have thought that relatvely large scalefarmers would have gone nto contractng because they are normally commercally orented. Farm sze negatvely nfluenced contract farmng but mantaned ts epected postve effects on farm ncome. As per the estmaton results, f a farmer was to ncrease hs/her farm holdng by 100%, farm ncome would ncrease by about 65%, other thngs beng equal. Smlarly, a 100% ncrease n the labour supply would result n 10% ncrease n ncome. In the case of organc and norganc fertlzers, a 100% ncrease n ther usage resulted n about 6% and 16% ncrease n farm ncome respectvely. These fndngs are consstent wth that of Abdula et al (013) and Bruce et al (014). Anka et al. s (01) study however, specfcally conveys the mportance of havng an organc norganc fertlzer m for mproved output. Furthermore, the coeffcent of weedcde mples that a 100% ncrease n the amount Page 8

9 spent on weedcde would result n a 16% decrease n revenue. Ths could mean that weedcde s over-used to the etent that output s adversely affected. Lastly, the postve coeffcent of the contract varable means that, n general, farmers who partcpated n contract farmng had a greater ncome level than non-contractng farmers. As ndcated earler, contractors gve credt to farmers n the form of nputs as part of ther contractual arrangements to support them n the producton processes. The farmers would n turn pay back n knd or sell out all ther output to these contractors. These arrangements make avalable scarce nputs or resources such as mproved seeds and fertlzers to farmers, hence resultng n good yelds. Wang et al. (014) revewed a large number of emprcal studes of contract farmng and concluded that contract farmng has had a sgnfcant mpact on mprovng farm effcency and productvty, and farmer ncomes due to the resources that the farmers are asssted wth. farm sze, labour and fertlzers. Weedcdes however had a negatve effect on ncome. RECOMMENDATIONS By way of recommendaton, government must provde the favourable envronment and regulaton for NGOs and the prvate sector n general to go nto contractng wth farmers. At the same tme farmers must be supported n the thngs that facltate partcpaton n contract farmng such as access to credt and etenson servces so that they would partcpate n t. Furthermore, to ncrease yeld the ssue of land tenure that does not encourage prvate ownershp of farmland should be looked nto serously, so that hardworkng farmers can obtan enough farm plots to epand ther farmng actvtes. A rentroducton of the fertlzer subsdsaton programme could also go a long way to ncrease farmers access to fertlzers to enable them ncrease yeld. Lastly there s the need for educaton on the use of weedcde so that they are not overused, snce ths could lead to low yelds. CONCLUSION Ths paper eamned the determnants of contract farmng as well as the effect of contract farmng on the ncome levels of crop farmers n the Northern Regon of Ghana. The probablty of farmers gong nto contract farmng was greater for the followng: farmers who had access to etenson servces; farmers who had access to etra credt facltes; full tme farmers; and small-scale farmers. Partcpaton n contract farmng led to hgher farm ncome than non-partcpaton. Ths means that notwthstandng the anecdotal evdences that contractng farmers are often cheated; contractng farmng s stll relevant as t has the potental of makng farmers rcher. Farm ncome was also sgnfcantly and postvely nfluenced by contract farmng, ACKNOWLEDGEMENT The study was supported fnancally by the GSSP of the Internatonal Food Polcy Research Insttute (IFPRI) through ts Scholarshp program for Master s level research. REFERENCES Abdula, S., Nkegbe P. K., and Donkoh S.A. 013.Techncal effcency of maze producton n Northern Ghana. Afrcan Journal of Agrcultural Research 8(43): Anka, J. N., Amans, E. B., Olontola, C. O., Okutu, P. C., Dodo, E. Y. 01. Effect of Organc and Inorganc Fertlzer on Amaranthus Caudatus. World Journal of Engneerng and Pure and Appled Scence (): 9. Page 9

10 Bruce, A. K. K., Donkoh, S. A. & Ayamga, M Improved rce varety adopton and ts effects on farmers output n Ghana. Journal of Development and Agrcultural Economcs, 6, Ca, J., Ung L., Setboonsarng S., and Leung P. S Rce Contract Farmng n Camboda: Empowerng Farmers to Move beyond the Contract toward Independence. ADBI Dscusson Paper 109. ADBI, Tokyo. Charles, E., and Shepherd A. W. (014). Contract Farmng: Partnershps for growth. FAO Agrcultural Servces Bulletn, 145, Rome. ISBN Coller, P., and Dercon S. (014).Afrcan Agrculture n 50 Years: Smallholders n a Rapdly Changng World. Accessed 7 Aprl 014. Doss, C. R., and Morrs M. L How does gender affect the adopton of agrcultural nnovatons? The case of mproved maze technology n Ghana. Agrcultural Economcs 5:7-39. Eaton, C., and Shepherd A. W Contract Farmng: Partnershps for growth. FAO Agrcultural Servces Bulletn No. 145, Rome. ISBN FAO (008). Contract Farmng Resource Centre, FAO, Rome. Ghana Statstcal Servce (01). Populaton and Housng Census, 010. Ghana. Ghana Statstcal Servce (014). Natonal Accounts Statstcs. Fnal 01 Gross Domestc Product & Revsed 013 Gross Domestc Product. Madalla, G. S. (1983). Lmted Dependent and Qualtatve Varables n Econometrcs, Cambrdge, Cambrdge Unversty Press. Mnstry of Food and Agrculture (MoFA) (013a). Agrculture n Ghana. Facts and Fgures 01. [Onlne]. [Accessed 3 May 014]. Avalable at Ransom, J.K, Pandyal, K. Adhkar, K. (003). Adopton of Improved varetes n the hlls of Nepal. Agrcultural Economcs, 9: Setboonsarng, S., Leung P. S., and Stefan, A. (008). Rce Contract Farmng n Lao PDR: Movng from Subsstence to Commercal Agrculture. Dscusson Paper 90. ADBI, Tokyo. Spo, K. (00). The Impact and Accessblty of Agrcultural Credt. A case Study of Smallholder Farmers n Lmpopo. Provnce of South Afrca. Unversty of South Afrca. Pretora. Wang, H., Wang, Y., and Delgado, M. (014). The Transton to Modern Agrculture: Contract Farmng n Developng Economes. Amercan Journal of Agrcultural Economcs 1 15; do: /ajae/aau036 (Advanced Access). Page 10