Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Repayment Performance of Smallholder Farmer Revolving State-credit in Luwero District, Uganda

Steven Yiga

Department of Agribusiness and Natural Resource Economics, Makerere University, Uganda

Abstract: Many state-sponsored agricultural credit schemes in developing countries such as Uganda collapse due to high default incidences. Using Tobit regression and descriptive statistic analysis, this study inquired into repayment performance of one state-provided credit – the Integrated Support to Farmer Groups (ISFG). Structured questionnaires were developed, pre-tested and then used to collect data from 153 beneficiaries. Respondents were sampled by purposive and then multi-stage random technique. The respondents were; male dominated, aged 45 years and with formal education level of seven years. Repayment of the ISFG significantly depended on; the distance of the beneficiary from the sub-county, the period taken by the farmed enterprises to mature, beneficiary education level and credit use experience, amount to be repaid and returns to the credit. The paper recommends promotion of short-period maturing enterprises under the ISFG, integration of adult education in the scheme and selection of beneficiaries farther away from the sub-county if the likelihood of repayment of the ISFG is to be enhanced.

Keywords: ISFG credit, repayment, tobit model

Received November 01, 2018; Revised December 10, 2018; Accepted December 22, 2018

Citation: Steven Yiga, Repayment Performance of Smallholder Farmer Revolving State-credit in Luwero District, Uganda. Journal of Applied Agricultural Economics and Policy Analysis, 1 (1). 15-21. January 2018. doi:10.12691/jaaepa-1-1-3

1. Introduction

Uganda through the ISFG scheme adopted credit provision to small scale farmers under the Rural Development Strategy (RDS) because agricultural credit has a significant role in reducing poverty and enhancing food security [1]. Most of Uganda’s policy documents such as the National Development Plan – NDP and Agricultural Sector Investment Plan – ASIP (2010/11- 2014/15) fully recognize credit provision as an Agricultural development strategy. Since adoption of national agricultural advisory services in the 1950s, credit provision has always been undertaken as a development instrument by many governments. Credit assists farmers to break the bondage of poverty and elevate them to an advantaged position in the global market. Its provision also augments input use, accelerates technological change and is an anti-poverty strategy at least in the short term.

Even then supply of rural credit in Uganda remains poor. It is concentrated in urban areas and dominated by a few. Formal lending excludes many. Okurut et al., [2] reported that close to 43% of Uganda’s farmers have difficulties in accessing credit. While 80% of Uganda’s population derive livelihood from Agriculture, its share of total credit is low. Exacerbated by policy inconsistencies and ineffective institutional frame works, default rates remain high in agricultural credit schemes such as the ISFG. Subsequently, several credit schemes in Uganda are characterized by dismal performance and eventual total collapse.

The ISFG credit scheme was undertaken to facilitate agrarian productivity and reduce poverty by 28% by 2014. Through the ISFG, farmers in a group receive state credit mostly in-kind. Since public institutions have limited funds to meet demand for credit [3], the budget per farmer under ISFG is small and therefore implementation of ISFG adopts a principle of revolving it within farmers. The credit is given to a section of farmers selected once every year in a Village Farmers’ Forum (VFF) and the beneficiaries must pay it back to their groups such that the fund revolves to other farmers and gets recapitalized.

The Ministry of Agriculture Animal Industry and Fisheries (MAAIF) targeted that 80% of the ISFG beneficiaries would repay and replicate the fund at household level, but default still persists negating the objectives of the ISFG. Lukwago [4] also noted that farmers are not aware of pay backs. Finscope reported that 70% of people in Uganda who need credit do not access it due to default vice by the first beneficiaries. Consequently, the proportion that is revolved remains low and as such credit does not reach or benefit subsequent or all potential beneficiaries [1]. Where default plagues credit schemes like in developing countries such as Uganda [5], the critical role credit plays in agricultural development is constrained.

It is imperative to inquire into credit repayment such that issues responsible for its default are understood. Yet Mpuga [6] analyzed demand for credit in Uganda not its repayment and Muhumuza [7] compared the role of government and private sector in credit provision not its repayment. This study asked what are the attitudes of the beneficiaries to the credit, what issues drive default and what is the likelihood that the credit will be repaid. We therefore employ a censored regression and descriptive statistical analysis to characterize the ISFG beneficiaries and assess their current perceptions towards the ISFG. We also determine and compare the level of ISFG repayment in the two categories of beneficiaries and then assess the determinants of its repayment.

2. The Beneficiary Sample

Table 1 and Table 2 illustrates the socio-demographic characteristics of the beneficiaries.

Over all, participation in the ISFG was dominated by males at 52.2% (Table 2) implying that revenues from ISFG accrue more to men than females. The beneficiaries were 45 years on average (Table 1) an indication that they are still innovative and economically active. Their level of formal education was seven years in school therefore the ISFG beneficiaries are lowly educated and may not form a better caucus for inventive productivity and increased innovation to enhance repayment. Averagely, respondents farmed 3 acres which is considered low and hence the output and the value thereof. Standard deviations associated with farmed land are high, an indication of a great variation in the acreage farmed by the respondents. Farming is the major income earner as reported by 70.5% of the sample (Table 2). Of the whole sample, 84.3% own land for production implying that the majority can access or have land over which to manage the enterprises given under the ISFG scheme. Since 69.3% of the households of the ISFG beneficiaries are headed by men (Table 2), most decisions on the ISFG including the decision to repay or default are mainly taken by men. More than half of the respondents have never borrowed credit; they have no experience in credit management. This negates repayment of the ISFG credit. The chi-square statistics in Table 2 portray no dependency between females and males as concerns gender of the ISFG beneficiary and that of the household head.

Table 1. Continuous variables

Table 2. Categorical variables associated with the beneficiary sample

3. Theoretical Considerations

ISFG beneficiaries are selected on the basis that all households in a village should participate in the scheme. The beneficiary selection guidelines uphold that all households participate in this credit scheme and wealth is equitably distributed within a village.

However, repayment of credit is influenced by characteristics of borrower and lender and the loan design itself IFPRI [8]. To enhance its repayment therefore, ISFG should not just be about all to get or a fair wealth distribution in a village, but rather beneficiaries should be selected basing on their characteristics, the loan design itself and the wishes of the borrower. Previous research on credit repayment by Otunaiya et al., [9] indicates that farmer characteristics such as their income and degree of diversification influence repayment. Therefore to condition repayment of the ISFG, beneficiary selection should consider such factors as those argued by Otunaiya et al., [9]. In addition, Briggeman et al., [10] explained that household characteristics such as family size, economic activeness and political frameworks also shape a farmer’s decision to repay state credit. Summed up together, character, capacity and capital of the beneficiary should be considered because they influence repayment of credit.

4. Review of Econometrics and the Econometric Models Used in Repayment Analysis

Credit repayment could be analyzed by binary response models, in which the repayment likelihood Yi measures probability of total default (0 % repayment) or full repayment and is explained by a vector of socio-economic, demographic and institutional factors in the equation below;

The choice Y made by the beneficiary to repay or default ISFG is observable; it measures the likelihood Yi of repayment in equation 1 above. Y is measured by the researcher in a binary response; where Y= 1 implies total repayment, otherwise Y = 0 total default. X’ is a vector of covariates that explain repayment. β is the vector of parameters to be estimated, εij is the stochastic component arising out measurement errors as according to Maddala [11].

However, the use of binary choice models in analyzing credit repayment although used by Wadonda [12] is inappropriate. Even discriminant analysis applied by Afolabi [13] and Walekhwa [14] requires binary responses (default or repayment) in which a beneficiary characteristic is used to categorize them as a defaulter or otherwise.

Analysis of repayment performance by binary responses does not show whether a given covariate significantly explains credit repayment and if it does; how. Indeed, the choice to repay credit is not strictly binary [15] but continuous. If binary choice models are used to measure how factors explain repayment, a lot of information is foregone in such discrete responses [16].

The Tobit model is appropriate since it has the ability to put restrictions on the values taken by the regressand Maddala [11] such as the proportions of ISFG paid by the beneficiaries. Such an analysis has been adopted before by Feronze et al., [17], Abebe [18] and IFPRI [8] to study credit repayment.

( is the point of censoring)

if otherwise

Where is the proportion of ISFG repaid by the beneficiary, 1- is that defaulted, and is the likelihood of repayment of by the beneficiary.

5. The ISFG Repayment Models

The specified ISFG repayment models are guided by the theoretical influence of specific factors on credit repayment. The model is;

ISFG Repayment model for the food security farmers

ISFG Repayment model for market oriented farmers

ISFG Repayment model for a pooled sample farmers

DIST=Distance in Kilometers of the beneficiary’s home from the administrative unit for ISFG implementation.

FG_BELONG =Dummy for belonging to a farmer group or not. (D=1 if yes, 0 if otherwise).

MAJ_SOURCE=Dummy for Major income source of the ISFG beneficiary (D =1 for agricultural farming and D=0 otherwise)

HH_SIZE=The size of a household measured by the number of economically active members there.

ENT_YIELD =Gestation period in months of the ISFG enterprises farmed.

EDUC=Level of formal education of the ISFG beneficiary measured in years of completed schooling.

GRPCPCTY=Dummy variable representing beneficiary perception on capacity of the group to recover the loan from member (1 =Group had capacity, 0= otherwise).

AGE=Age of the ISFG beneficiary in years

ISFG_COSTS=The transaction cost the ISFG beneficiary incurs in receiving the credit in Uganda Shs

RETURNS=Amount of money received from sale of ISFG enterprises.

LANDSIZE=The size of land under farming given in acres.

HH_GENDER =The gender of the household head (Where D=1 if the head is female, otherwise D= 0)

INNOVCOSTS=Costs incurred by the farmer as a result of taking up ISFG given enterprise.

DELAYTIME=The number of days for which the ISFG given enterprises delays behind the season.

EXP=Experience of the ISFG beneficiary in credit use.

TRAININGNO=The number of trainings attended by the beneficiary.

Beneficiary distance from the credit administration explains repayment because beneficiaries nearer to the credit administrative unit can easily be monitored and supervised. They also more easily and often benefit from extension services and are thus expected to pay higher proportions of the loan. Chauke et al., [19] also found distance to have a significant influence on repayment of loans whereby farmers farther away would pay less of the credit.

When enterprises take long to mature, production costs and risks become high and consequently compromise credit repayment. Therefore, farmers who have enterprises of shorter gestation period should pay higher proportions of ISFG. Hunt [20] indicated that extending credit to enterprises with long-time to maturing period (like coffee and cotton) may lead to net loss to farmers.

Credit beneficiaries who belong to groups can be managed and penalized by internal rules of conduct in their groups. Groups also confer support, monitoring, joint liability and pressure to fellow members thereby removing moral hazards. Therefore; belonging to a group or not; and group capacity to influence members determines credit repayment. In Haryana, Feronze et al., [17], proved that belonging to a group or not significantly affect credit repayment. Oke et al., [21] also indicated that groups’ capacity to exert pressure on member beneficiaries had a positive effect on credit repayment.

The variable ISFG_COSTS is included because farmers usually incur some level of transactions costs in order to receive loans. Costs are a burden to any business and reduce net profit. Farmers who incur higher costs are more likely to pay less or even default the credit. Zia [22] in his work on effective costs of rural loans in Bangladesh proved that borrowers incurring higher transaction costs of borrowing will actually bear high effective rates of interest on their loan which decreases their repayment likelihood.

Education is the driving force behind any strong economy and is a prerequisite for social and economic growth. Education provides society with a better knowledge and skills necessary to stimulate development. Therefore more educated beneficiaries should have better managed enterprises and managerial skills and thus pay higher ISFG proportion. Oni et al., [23] noted that an increase in the educational level of the loan beneficiary decreases the probability of the farmers’ defaulting on loan repayment. We hypothesized education to have a positive effect on ISFG repayment.

Engaging in other non-farm activities to receive off-farm income augments farm income and farm credit repayment because farmers commonly use off farm income to offset any shortages in funds for agricultural activities. What farmers consider as a main income source should therefore influence how much of agricultural credit they can repay. We hence consider this dummy in the credit repayment model, D =1 if it is farming, otherwise D = 0. Also among Yam farmers in Ghana, Wongnaa and Awunyo, [24] noted that the farmer major income source determines the volume and value of production and consequently what proportion of credit repaid.

Land size worked is one measure of the scale of operation and farm size. Land size was proved to explain credit repayment in Oladeebo and Oladeebo [25]. Repayment of credit by smallholder farmers in Osun State in Nigeria depended on land area as a worked. Increasing farm size is an incentive to sustain productivity and expand production capacity of the farm. Therefore farmers who work larger land sizes should pay higher proportions of the ISFG credit. The variable land size is therefore included and expected to bear a positive sign.

Training assists farmers to learn, retain and upgrade their skills for better enterprise productivity (Uganda Debt Network UDN – 2010). Therefore farmers who had more training were expected to pay higher proportions of the ISFG. Accordingly, the variable TRAININGNO is expected to have a positive sign.

How better credit is managed (and repaid) depends on the gender of the beneficiary because gender distributes roles between men and women defining their daily activity calendars. In Wongnaa and Awunyo [24], females were found to be more disciplined borrowers than males and would make sure production resources given to them are used for their intended purposes. Grameen [15] also noted to be effective users of credit. Since in a household the head is pivotal to all decisions including the decision to repay ISFG or not, we include the dummy for household gender in ISFG repayment model.

6. Empirical Findings/Results

Tobit regression analysis was used to establish factors influencing the proportion of ISFG credit paid by its beneficiaries. Three separate models were run; one for food security another for commercializing farmers and then for a pooled sample. The dependent variable was the proportion of the credit paid back by the farmer computed from the amount the beneficiary had paid back compared to what they were supposed to pay. The proportion ranged between 0≤Y≤1, where Y = 0 implies total default and Y=1, the beneficiary has paid fully.

Explanatory variables were chosen following Oni et al [23]; Wongnaa and Awunyo [24] basing on the presumed theoretical importance of the variable on credit repayment. Table 3, Table 4 and Table 6 present the results of the regression.

The four factors that significantly influence credit repayment among market oriented farmers as indicated in Table 3 were; education level of the beneficiary, the amount the beneficiary has to pay back, returns to the enterprises and the innovations costs introduced by ISFG beneficiaries.

The coefficient associated with innovation costs is negative and significant at 5%. This indicates a negative relationship of costs and credit repayment earlier argued by Nogbu and Walter [26]. New enterprises require farmers to innovate thereby increasing costs. Given the marginal analysis, a unit shs increase in natural log of these costs would reduce credit repayment likelihood by 12.8% conditional that the beneficiary pays.

In Table 3, the coefficient associated with the amount to pay back is negative as expected. Further analysis gave marginal effect of -0.1454 conditional that the beneficiary pays a certain amount and -0.2005 unconditional on the amount repaid. Therefore; for a one shs increase in the logarithm of the amount charged to the beneficiaries to be repaid, the likelihood of repayment would decrease by 14.5 % if the beneficiary chooses to pay. This finding is plausible because telling farmers to pay back a higher amount implies an effective increase in the interest rate which discourages repayment.

Table 3 shows a positive and significant relationship between the returns to enterprises and the likelihood of repayment. If the natural log of returns to the enterprises increased by a unit, there would be an increase of 10.4% in the probability of repaying ISFG among the market oriented farmers (Table 3). Indeed Wadonda [12] argued that enterprise performance influences both credit access and repayment.

Table 3. Tobit regression results showing determinants of ISFG repayment among market oriented farmers

Table 4. Tobit regression results showing determinants of ISFG repayment among food security farmers

Table 5. Tobit regression results showing determinants of ISFG repayment among food security farmers

In Table 4, ISFG repayment among the food security farmers was significantly influenced by; beneficiary distance from credit administration center, beneficiary education level, enterprise maturity period, experience in credit use, belonging to a farmer group or not; and group capacity to recover credit (Table 4).

Given the coefficient corresponding to the variable distance, there is a positive relationship between beneficiary isolation from the sub-county and credit repayment. There would be a 16% increase in the likelihood of repayment if the distance of the beneficiary increased by one kilometer for the beneficiaries who choose to pay. Credit beneficiaries farther away are more likely to pay back ISFG perhaps because they are more monitored and cared for compared to those nearer the sub-county.

Belonging to a farmer group significantly bears on credit repayment because the coefficient associated with the variable is positive and significant (Table 4). Credit beneficiaries who belong to functional farmer groups are 10.3% more likely to pay than those who don’t.

The education level of the beneficiary also bears positively and significantly on repayment of the credit among the ISFG beneficiaries. The marginal effects indicate that the likelihood of credit repayment would increase by 80.7% if the beneficiary attended an additional year in formal education. The finding is plausible since education increases capacity of farmers to utilize technologies.

Perception of farmers on capacity of their group to recover credit from them also influences ISFG repayment significantly. Based on marginal analysis in Table 4, farmers who perceive their groups to have such capacity are 112% more likely to pay back the credit than those who think their groups do not have the capacity.

The period of maturity of enterprises given to the beneficiaries under the ISFG scheme also bears significantly but negatively on credit repayment given a negative coefficient associated with this variable (Table 4). As enterprises take one additional month longer to mature, the likelihood of repayment decreases by 11.4% conditional on fact that the beneficiary pays any amount. This could be case because longer maturing enterprises present higher production costs and risks that negate repayment.

The credit use experience explains ISFG repayment significantly among the ISFG beneficiaries.

7. Recommendations

Since enterprise yield period significantly and influenced repayment of the ISFG, it is recommended that enterprises which yield within one farming season such as beans, maize and ground nuts be promoted under the ISFG credit scheme rather than those which take long to yield such as coffee and pineapples.

The distance the farmer is isolated from the sub-county affected ISFG repayment significantly and positively whereby repayment likelihood increased with distance the beneficiary is isolated from the sub-county. The sub-county should therefore strengthen extension and monitoring farmers that are close to it.

Farmer educational and adult literacy programs should be integrated in the ISFG credit scheme to augment its repayment because education level of beneficiaries positively and significantly influenced ISFG repayment. Additional years in formal education of the farmer increased the repayment likelihood. Educational opportunities also enhance group capacities to recover credit from fellow group members yet group capacity also significantly explained ISFG repayment.

References

[1]   | Ministry of Agriculture Animal Industries and Fisheries (2001). The ISFG implementation manual.
[2]   | Okurut Nathan, Schoombee Andrie and van der Berg (2004). Credit Demand and Credit Rationing in Uganda: African Development and Poverty Reduction. The Macro-Micro linkage. A forum paper in South Africa.
[3]   | Ministry of Agriculture Animal Industries and Fisheries (2005). Implementation Plan Of the Integrated Support To Farmers’ Groups Component Of The Rural Development Strategy.
[4]   | Lukwago Daniel (2010) Increasing Agricultural Sector Financing. Why it Matters for Uganda’s Socio-Economic Transformation. ACODE Policy Research Series, No.40, 2010. Kampala.
[5]   | Munyambonera Ezra, Dorothy Nampewo, Annet Adong and Musa Mayanja (2012). Access and Use of Credit in Uganda: Unlocking the Dilemma of Financing Small Holder Farmers. Policy brief, Issue No. 25, Economic Policy Research Centre, November 2012.
[6]   | Mpuga Paul (2004). Demand for Credit in Rural Uganda: Who Cares for the Peasants? A paper presented at the Conference on Growth, Poverty Reduction and Human Development in Africa Centre for the Study of African Economies.
[7]   | Muhumuza William (2013). Revisiting State intervention. State sponsored micro-credit and poverty reduction in Uganda. African Journal of Political Sciences and International relations. 7(3);121-132.
[8]   | IFPRI (1997). The Washington consensus on poor quality agriculture. Analysis, Prescription and Institutional gaps.
[9]   | Otunaiya A.O., O.A.C Ologbon and E.O Akerele (2014). Analysis of Agricultural Loan Use among Poultry Farmers in Oyo State, Nigeria. International Journal of Poultry Science 13(2). Published by Asia Network for Scientific Information.
[10]   | Briggeman Brian C., Charles A. Towe and Mitchell J. Morehart (2008). Credit constraints, their existence, determinants and implications for the US farm and non-farm sole proprietors. American Journal of Agricultural Economics, 91(1) 2009.
[11]   | Maddala G.S (2008). Econometrics, 3rd Edition. Chichester publishers, England.
[12]   | Wadonda Ephraim Chirwa (1997). An econometric analysis of determinants of agricultural credit repayment. African review of finance money and banking Vol 1. Giordano Dell Amore foundation publishers – Malawi.
[13]   | Afolabi J. A (2008). Analysis of Loan Repayment among Small Scale Farmers in South Western Nigeria-A Discriminant Approach. Journal of Social Sciences. 17(1). 83-88.
[14]   | Walekhwa (2003). The effect of microfinance programmes on agribusiness small scale enterprises in Uganda. An unpublished Master’s thesis at Makerere University.
[15]   | Grameen (1980). Accelerating poverty reduction. A social business model, Bangladesh.
[16]   | Green William (2003). Modeling the binary choice model. Econometric Analysis, 6th ed. Prentice Hall.
[17]   | Feronze S. M., Chauhan A. K., Malhotra R., and K. S. Kadian (2011). Factors Influencing Group Repayment Performance in Haryana:Application of Tobit Model. Agricultural Economics Research Review Vol. 24.
[18]   | Abebe Mijena (2011). Determinants of Credit Repayment and Fertilizer Use By Cooperative Members in Oromia Region. A Master’s Thesis at Haramaya University.
[19]   | Chauke P. K., M. L. Motlhatlhana, T. K. Pfumayaramba and F. D. K. Anim (2013). Factors influencing access to credit: A case study of smallholder farmers in the Capricorn district of South Africa. African Journal of Agricultural Research. 8(7); 582-585.
[20]   | Hunt. M. Diana (1967). Agricultural Credit in Uganda. A Doctoral thesis at the University of Nairobi.
[21]   | Oke J.T.O., R. Adeyemo and M.U. Agbonlahor (2007). An Empirical Analysis of Microcredit Repayment in Southwestern Nigeria. Journal of Humanity & Social Sciences 2 (1). IDOSI Publications, Nigeria
[22]   | Zia U. Ahmed (1989). Effective Costs of Rural Loans in Bangladesh. World Development, Vol. 17. No. 3, Printed in Great Britain. 0 Pergamon Press.
[23]   | Oni O.A, Oladele, O.I and Oyewole, I. K (2005). Analysis of factors influencing loan default among poultry farmers in Ogun state Nigeria. Journal of Central European Agriculture, 6(4); 619-624
[24]   | Wongnaa C. A. and Awunyo. D.Vitor (2013). Factors Affecting Loan Repayment Performance Among Yam Farmers in the Sene District, Ghana. Agris on-line Papers in Economics and Informatics, Volume (V).
[25]   | Oladeebo J.O and Oladeebo O.E (2008). Determinants of Loan Repayment among Smallholder Farmers in Ogbomoso Agricultural Zone of Oyo State, Nigeria. Journal of Social Sciences, 17(1): 59-62 (2008). Kamla-Raj publishers.
[26]   | Nogbu Cotesu and Walter Milligann (1994), 11 reasons for possible failure of a revolving fund. Reflections on rural development. Okloahoma.
[27]   | Ministry of Agriculture Animal Industry and Fisheries (2010). Agriculture for food and Income security. The Agriculture sector Development Strategy and Investment Plan 2010/11-2014/15.