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  3. Modelling the prediction of farmers' loan repayment in primary agricutural co-operative credit societies

Modelling the prediction of farmers' loan repayment in primary agricutural co-operative credit societies

Money, the vital element of economy, is indispensable to Agriculturists too. In India the farming community is subject to various vagaries to continue to be farmers and boost GDP of our Nation. INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) ISSN 0976-6502 (Print) ISSN 0976-6510 (Online) IJM Volume 7, Issue 2, February (2016), pp. 326-340 http://www.iaeme.com/ijm/index.asp ©IAEME Journal Impact Factor (2016): 8.1920 (Calculated by GISI) www.jifactor.com MODELLING THE PREDICTION OF FARMERS' LOAN REPAYM

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  1. INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) ISSN 0976-6502 (Print) ISSN 0976-6510 (Online) IJM Volume 7, Issue 2, February (2016), pp. 326-340 http://www.iaeme.com/ijm/index.asp ©IAEME Journal Impact Factor (2016): 8.1920 (Calculated by GISI) www.jifactor.com MODELLING THE PREDICTION OF FARMERS' LOAN REPAYMENT IN PRIMARY AGRICUTURAL CO-OPERATIVE CREDIT SOCIETIES Dr.G.S.David Sam Jayakumar Assistant professor, Jamal Institute of management, Jamal Mohamed College, Trichy-20 J.Sathyamurti Research scholar, Jamal Institute of management, Jamal Mohamed College, Trichy-20 ABSTRACT Money, the vital element of economy, is indispensable to Agriculturists too. In India the farming community is subject to various vagaries to continue to be farmers and boost GDP of our Nation. In this Co-operative banks also play an important role despite the low percentage of repayment by farmers promptly coupled with high level of pressure for farmers for loan for continuing agricultural activities while the resource is requiring its cost to make it readily available at the time of all farming activities. There are many socio psychological factors. Affecting recovery of lending institutions resulting in a hard situation for credit societies and banks to continue lending. Here the study is on factors that could predict ways and means of recovery from farmers. Key words: Credit Societies, Repayment Factors. Cite this Article: Dr. G.S. David Sam Jayakumar and J.Sathyamurti. Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies. International Journal of Management, 7(2), 2016, pp. 326-340. http://www.iaeme.com/ijm/index.asp 1. INTRODUCTION Institutional Credits play a vital role in economic transformation in rural development. Agricultural credit is a crucial input required by the smallholder farmers to establish and expand their farms with the aim of increasing agricultural production, enhancing food sufficiency, promoting household and national income. It enables the poor farmers to take advantage of the potentially profitable investment opportunities. The farm credit is an indispensable tool for achieving socioeconomic transformation of the rural communities. If well applied, it would stimulate capital formation and diversified agriculture, increase productivity, size of farm operations, and promote innovations in farming. Lending by identifying the profile of farmers who is prompt will enable to collect the loan amount easily. This study attempts to bring out the factors which influence the repayment capacity of co-operative farmers of Primary Agricultural Cooperative Credit Society branches of Singalandapuram, Eragudi, and 326 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Venkatesapuram which will serve as guide to bankers to take decisions regarding sanction of farm credit. The study focused on finding out the profile of the farmer who can make prompt payment. Several studies have been carried out to analyze the loan repayment performance of farmers. Ifeanyi A. Ojiako and Blessing C. Ogbukwa (2012) researched that farm credits played vital roles in the socio-economic transformation of the rural economies. However, their acquisition and repayment were characterized by numerous challenges major being default among beneficiaries. The implication is that to enhance loan repayment capacity of smallholder cooperative farmers, policies and programmes capable of increasing sizes of loan and farm holdings, or reducing household size should be promoted. However, higher proportional increases were required for each variable to attain a desired level of increase in loan repayment capacity. Henry De-Graft Acquah and Joyce Addo(2012) researched that farm credit can stimulate the transfer of technology into agriculture and hence lead to increased crop yield. The adequacy of loan amount was also studied. Empirical results from the regression analysis found that the farm size, income and years of farming experience were positive and significant predictors of farm loan size was also seen. Yasir Mehmood, Mukhtar Ahmad, and Muhammad BahzadAnjum (2012) states that the productive use of agricultural credit in Pakistan is quite limited that affects the repayments be havior of the farmers and ultimately categorization of loans into default stages and finally auction of their lands. Data revealed that sloppy supervision by the bank employees, miss-utilization of loans, high interest rate and change in business/residential place of the borrowers etc. caused delay in repayments of agricultural credit. S.U.O. Onyeagocha (2012) analyzed the loan demand requirements of rural staple and poultry farmers and the factors affecting loan size. Results showed that the potent factors affecting loan size were farm size, level of education, enterprise type, farmers experience and dependency ratio. The financial institutions were admonished to consider providing start-up capital for the youths and fresh graduates. Further government was urged to provide fiscal and monetary incentives to financial institutions supporting agriculture because of delicate nature of farm business. Durguner, Sena; Katchova, Ani L(2011)identified the most pertinent factors that explain farmer repayment capacity. He found that the one year lagged working capital ratio, the debt-to-asset ratio, and operator's age are significant variables in explaining the coverage ratio. This finding is important because it can enhance agricultural lenders' ability to assess creditworthiness, screen borrowers, manage loan loss reserves, and price loans, thereby decreasing lenders' costs associated with defaulted loans and ultimately reducing the costs borne by the government and taxpayers. Amjad Saleem, DrFarzand Ali Jan, and Rasheed Muhammad Khattak& Muhammad Imran Quraishi (2011) researched the impact of farm and farmers’ characteristics on repayment of farm credit user for agricultural growth and found it is significant for impact of age, education, marital status, farm type, farm size, farm status and numbers of times credit obtained. But regression result showed significant influence of marital status, farm type and numbers of times credit attained on repayment of farm credit. Victor UgbemOboh and Ineye Douglas Ekpebu (2011) conducted research to determine the effects of socio-economic and demographic factors on the rate of credit allocation to the farm sector and found that only about 56% of the loans were invested directly in farm activities implying that the balance of 43% of the loan was diverted and spent on non-farm activities. Based on these results, the paper recommends increased flow of capital to the bank for on-lending to farmers. In addition, loans should be disbursed on time and banks officials should be encouraged to pay regular supervisory visits to farmers. Finally, benefiting farmers should be given basic training on efficient management of loans in order to curtail the high rate of loan diversion. AbebeMijena (2011) researched the factors influencing timely credit repayment and input use (especially fertilizer) by smallholder farmers and found that there is significant mean difference regarding Age, family size, cultivated land size, number of livestock owned, on-farm income, amount of fertilizer used and saving habits. The result of the model showed that family size, livestock ownership, on-farm income, non-farm income and saving habit were the statistically significant factors influencing timely loan repayment performance positively. The study suggests that improving the livestock sector, educating households and their family member, giving attention in promoting non-farm activities in rural areas and promoting saving habit are some of the important priority areas for the success of future intervention strategies for sustainable credit facilities. J. A. Afolabi (2010) analysed loan repayment among small scale farmers and identified socio- economic characteristics of the respondents and quantitatively determined some socio-economic characteristics of these farmers that influence their level of loan repayments. MalimbaMusafiriPapias; Ganesan.P (2009) state that both formal and informal financial systems 327 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication operate side by side. While the later has been playing a predominant role, cooperative societies have emerged as an apt method of increasing the delivery of formal rural credit and savings facilities on sustainable and non-exploitative terms albeit of financial imprudence stemming from poor credit repayment records. Mohammad Reza Kohansal and HoomanMansoori (2009) investigated the factors influencing on repayment behavior of farmers that received loan from agricultural bank and found that loan interest rate is the most important factor affecting on repayment of agricultural loans. Farming experience and total application costs are the next factors, respectively. Limsombunchai, V. C. Gan and M. Lee (2005) analyzed thatLoan contracts performance determines the profitability and stability of the financial institutions, and screening the loan applications is a key process in minimizing credit risk. A good credit risk assessment assists financial institutions on loan pricing, determining the amount of credit, credit risk management, reduction of default risk and increase in debt repayment.MC Mashatola& MAG Darroch (2003) researched the factors affecting sugarcane farmers using a graduated mortgage loan repayment and found that the estimated probability of a farmer in the scheme being current on loan repayments was higher for clients with higher levels of average annual farm gross turnover relative to loan size, and for clients with access to substantive off-farm income. Access to off-farm income helps to provide additional liquidity to fund future operations and debt repayments.C J Arene and G C Aneke researched (1999) and madeattempts to assess the credit system and showed that high repayment rate farmers had a larger loan size, larger farm size, higher gross income, shorter distance between home and source of loan, higher level of formal education, larger household size, and higher level of adoption of innovations than low repayment rate farmers. Loan programmes for female farmers are of great importance for the development of agriculture. 2. BANK PROFILE PRIMARY AGRICULTURAL COOPERATIVE CREDIT SOCIETIES (PACCS) PACCS came into being after the enactment of the Cooperative Credit Societies Act in 1904. This Act was subsequently revised in 1912 to promote multi-purpose cooperatives and to organize non-credit cooperatives. The first primary agricultural cooperative credit society was promoted in early 1900s and "by early 1980s, there were about 92,000 PACCS. To align urban banking sector with the other segments of banking sector in the context of application or prudential norms in to and removing the irritants of dual control regime of RBI (banking operations ) as well as State Government . Banking related functions (viz. licensing, area of operations, interest rates etc.) were to be governed by RBI and registration, management, audit and liquidation, etc. governed by State Governments. The different types of Co-operative Banks are Primary Co-operative Credit Society, Central co- operative banks, State co-operative banks, Land development banks, Urban Co-operative Banks . 3. METHODOLOGY AND DATA ANALYSIS The present study is a census survey made to evaluate the repayment of loan amount by farmers of three areas namely Singalandapuram, Eragudi and Venkatesapuram of Primary Agricultural Cooperative Credit Society for the due period of April 2009 to March 2011. Data of 682 farmers were collected from Singalandapuram branch, 243 farmers' data were collected from Eragudi branch, and 125 farmers' data were collected from Venkatesapuram branch. The purpose of study is to know the profile of farmers those who got crop loan from Singalandapuram, Eragudi, and Venkatesapuram branches of Primary Agricultural Cooperative Credit Society1 )to find out the personal and demographical factors that affects the repayment of loan 2) to bring out the factors that makes impact on farmers loan repayment 3) to give the suitable suggestions to the bankers to avoid delayed repayment A structured questionnaire was finalized and it comprised of five personal and demographic factors, and13 conceptual questions . Secondary information were collected from respective branches of Primary Agricultural Cooperative Credit Society and used. After the final data collection was over, the collected data was analyzed with the help of statistical package namely IBM SPSS-20. The analysis was done for three branches and a pooled analysis. The analysis has two phases, in Phase-I, descriptive statistics was prepared and was shown for all the three branches. In Phase- II, neural network analysis was applied to identify the variables which influence the repayment of loan. When there are factors found more related and influential on the repayment of farmers' loan like 1. Gender 2. Age 3. 328 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Annual Income - Non-farmn income / On-farm income 4. Number of Family Members 5. Educational Qualification 6. Land Owned 7. Crop – Paddy, Banana , Onion , Turmeric, Other Crops like cassava, sugar cane, ground nut, Black gram. 8. Loan Amount Sanctioned 9. Loan Amount Sanctioned as Cash 10. Seed 11. Fertilizer 12. Insurance 13. Duration of Loan Period 14.Earlier Payment Period 15. Period of Delay 16. Interest Paid 17. Location of Bank, the above 18 factors were taken into consideration. This is to nullify the effect of factors that encourage default like 1. Waiver of loans by government; 2. Delay in getting money from informal sources;3. Crop failure; 4. Rain defect and unusual rainfall; 5. Price; 6. Interest;7. Duration of loan;8. Low productivity;9. Delay and inadequate maintenance;10. Inefficient work;11. Unexpected expenses like medical bills; 12. Family commitments;13. Investing loan amount in assets; 14. Casual attitude about interest, principal; 15. Waiting for good crop price; 16. Amount given to friends and relatives. 4. RESULTS AND DISCUSSION Analysis is the process of placing the data in an ordered form and extracting the meaning from them. In other words analysis is the answer to the question, “what message is conveyed by each group of data”. The raw data become information, only when they are analyzed and when put in a meaningful form. Tabular representations are used for better projections. After the final data collection was over, the collected data was analyzed with the help of statistical package namely IBM SPSS-20. The analysis was done for three branches and a pooled analysis. The analysis has two phases, in Phase-I, descriptive statistics was prepared and was shown for all the three branches. In Phase- II, neural network analysis was applied to identify the variables which influence the repayment of farmers' loan. Table 1 Location wise Descriptive Statistics of Farmers' Personal and Demographic Variables Location of Bank Constructs Singalandapura Venkatesapuram Eragudi n=243 Total n=1050 m n=682 n=125 Gender Male 586(85.92%) 207(85.19%) 103(82.40%) 896(85.33%) Female 96(14.08%) 36(14.81%) 22(17.60%) 154(14.67%) 20 – 30 1(.15%) 2(.82%) 2(1.60%) 5(.48%) 30 – 40 29(4.25%) 22(9.05%) 19(15.20%) 70(6.67%) Age 40 – 50 224(32.84%) 56(23.05%) 34(27.20%) 314(29.90%) 50 and above 428(62.76%) 163(67.08%) 70(56.00%) 661(62.95%) Below Rs.50, 119(17.45%) 66(27.16%) 41(32.80%) 226(21.52%) 000/- Rs.50, 000/- - 185(27.13%) 63(25.93%) 73(58.40%) 321(30.57%) Annual Rs.75,000/- Income Rs.75,000/- 226(33.14%) 70(28.81%) 8(6.40%) 304(28.95%) Rs.1,00,000/- Rs.1,00,000/- 152(22.29%) 44(18.11%) 3(2.40%) 199(18.95%) and above Number of 2–4 483(70.82%) 217(89.30%) 122(97.60%) 822(78.29%) Family 5–7 184(26.98%) 25(10.29%) 3(2.40%) 212(20.19%) Members 7 and above 15(2.20%) 1(.41%) 0(.00%) 16(1.52%) No formal 142(20.82%) 35(14.40%) 20(16.00%) 197(18.76%) education Primary 151(22.14%) 63(25.93%) 42(33.60%) 256(24.38%) education Educational Secondary Qualification 318(46.63%) 124(51.03%) 56(44.80%) 498(47.43%) education Degree or technical 71(10.41%) 21(8.64%) 7(5.60%) 99(9.43%) education 329 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table No. 2 Location wise Descriptive Statistics of Conceptual Variables Location of Bank Constructs Singalandapura Eragudi Venkatesapuramn=12 Total n=1050 m n=682 n=243 5 Small(below 139 422(61.88%) 53(42.40%) 614(58.48%) 2.5) (57.20%) Nature of Medium(2.5 - farmer (Farm 208(30.50%) 64(26.34%) 43(34.40%) 315(30.00%) 5) in acre) Big(5 and 40 52(7.62%) 29(23.20%) 121(11.52%) above) (16.46%) Below 1 112(16.42%) 26(10.70%) 6(4.80%) 144(13.71%) 146 Land Owned 1-3 413(60.56%) 60(48.00%) 619(58.95%) (60.08%) (Farm in 31 acre) 3-5 105(15.40%) 31(24.80%) 167(15.90%) (12.76%) 5 and above 52(7.62%) 40(16.46%) 28(22.40%) 120(11.43%) 208 Paddy 668(97.95%) 107(85.60%) 983(93.62%) (85.60%) Crop Name Banana 0(.00%) 32(13.17%) 1(.80%) 33(3.14%) Others 14(2.05%) 3(1.23%) 17(13.60%) 34(3.24%) Below Rs.25, 134 396(58.06%) 53(42.40%) 583(55.52%) 000/- (55.14%) Rs.25, 000/- - 216 80 Loan 60(48.00%) 356(33.90%) Rs.50,000/- (31.67%) (32.92%) Amount Rs.50,000/- - Sanctioned 44(6.45%) 15(6.17%) 6(4.80%) 65(6.19%) Rs.75,000/- Rs.75,000/- - 26(3.81%) 14(5.76%) 6(4.80%) 46(4.38%) Rs.1,00,000/- Below 199 60 18(14.40%) 277(26.38%) Rs.10,000/- (29.18%) (24.69%) Loan Rs.10,000/- - 439 165 97(77.60%) 701(66.76%) Amount Rs.40,000/- (64.37%) (67.90%) Received as Rs.40,000/- - 43(6.30%) 18(7.41%) 9(7.20%) 70(6.67%) Cash Rs.70,000/- Rs.70,000/- 1(.15%) 0(.00%) 1(.80%) 2(.19%) and above Below 246 76 27(21.60%) 349(33.24%) Rs.1,000/- (36.07%) (31.28%) Rs.1,000/- - 377 102 Loan 77(61.60%) 556(52.95%) Rs.3,000/- (55.28%) (41.98%) Amount Rs.3,000/- - Received as 48(7.04%) 12(4.94%) 13(10.40%) 73(6.95%) Rs.5,000/- Seed Rs.5,000/- and 11(1.61%) 21(8.64%) 8(6.40%) 40(3.81%) above None 0(.00%) 32(13.17%) 0(.00%) 32(3.05%) Below 154 43 19(15.20%) 216(20.57%) Rs.3,000 (22.58%) (17.70%) Loan Rs.3,000 - 300 104 44(35.20%) 448(42.67%) Amount Rs.7,000/- (43.99%) (42.80%) Received as Rs.7,000 - 125 55 36(28.80% 216(20.57%) Fertilizer Rs.11,000/- (18.33%) (22.63%) Rs.11,000 and 41 103(15.10%) 26(20.80%) 170(16.19%) above (16.87%) 330 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Location of Bank Constructs Singalandapura Eragudi Venkatesapuramn=12 Total n=1050 m n=682 n=243 5 Below 575 190 83(66.40%) 848(80.76%) Rs.500/- (84.31%) (78.19%) Rs.500/- - 67(9.82%) 22(9.05%) 9(7.20%) 98(9.33%) crop Rs.1,000/- Insurance Rs.1,000/- - 3(.44%) 12(4.94%) 0(.00%) 15(1.43%) Rs.1,500/- Rs.1,500/- and 2(.29%) 18(7.41%) 0(.00%) 20(1.90%) above Not applicable 35(5.13%) 1(.41%) 33(26.40%) 69(6.57%) 6 Months 0(.00%) 1(.41%) 2(1.60%) 3(.29%) 669 208 6 - 10 Months 117(93.60%) 994(94.67%) (98.09%) (85.60%) Duration of 10 - 12 Loan Period 10(1.47%) 30(12.35%) 5(4.00%) 45(4.29%) Months 12 - 15 3(.44%) 4(1.65%) 1(.80%) 8(.76%) Months 312 81 Below 30 days 35(28.00%) 428(40.76%) (45.75%) (33.33%) 30 - 60 days 12(1.76%) 2(.82%) 1(.80%) 15(1.43%) Earlier 60 - 90 days 8(1.17%) 2(.82%) 0(.00%) 10(.95%) Payment 251 120 Days Not applicable 73(58.40%) 444(42.29%) (36.80%) (49.38%) Paid on due 99(14.52%) 38(15.64%) 16(12.80%) 153(14.57%) date Below 1 24(3.52%) 2(.82%) 6(4.80%) 32(3.05%) Month 1 - 6 Months 94(13.78%) 29(11.93%) 19(15.20%) 142(13.52%) Period of 6 - 12 Months 49(7.18%) 22(9.05%) 22(17.60%) 93(8.86%) Delay 1 year and 85(12.46%) 66(27.16%) 25(20.00%) 176(16.76%) above 430 124 Not applicable 53(42.40%) 607(57.81%) (63.05%) (51.03%) Below 41(6.01%) 11(4.53%) 5(4.00%) 57(5.43%) Rs.1,000/- Rs.1,000/- - 151 56 31(24.80%) 238(22.67%) Rs.4,000/- (22.14%) (23.05%) Rs.4,000/- - 24(3.52%) 12(4.94%) 16(12.80%) 52(4.95%) Interest Paid Rs.7,000/- Rs.7,000/- and 7(1.03%) 11(4.53%) 1(.80%) 19(1.81%) above 430 124 Not applicable 53(42.40%) 607(57.81%) (63.05%) (51.03%) None 29(4.25%) 29(11.93%) 19(15.20%) 77(7.33%) Status of 431 123 Prompt payer 53(42.40%) 607(57.81%) farmer in (63.20%) (50.62%) loan 251 120 Defaulter 72(57.60%) 443(42.19%) repayment (36.80%) (49.38%) 331 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 3Independent Variable Importance of PACCS Branch of Singalandapuram Normalized Factors Importance Importance Gender .136 54.4% Age .080 32.0% Annual Income .083 33.1% Number of Family Members .159 63.7% Educational Qualification .250 100.0% Nature of farmer (Farm in acre) .117 46.6% Land Owned (Farm in acre) .176 70.3% Table 4 Impact of Hidden Layers on Farmers' Loan Repayment in Singalandapuram branch Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 -.295 -.035 H2 .344 -.012 H3 .362 -.029 H4 -.236 .242 H5 .097 .239 H6 -.704 -.342 H7 -.159 .543 Bias .170 .311 Percent Incorrect Predictions - Training (36.8%), Testing (33.5%) Table 5 Estimation of Parameters for PACCS branch of Singalandapuram Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 3 Layer 7 Gender(Male) .094 7 .259 4 Age(40 - 50 ) .313 4 - - Age(20 - 30) - - -.421 7 Annual income (Rs.1,00,000/- and above) .388 3 .397 3 Family Members(7 and above) .221 6 - - Family Members(2 - 4) - - .097 6 Educational Qualification (Primary Education) .281 5 .467 1 Nature of Farmer (Big - 5 and above acres) .389 2 .411 2 Land Owned (3 - 5 acres) .672 1 - - Land Owned(5 acres and above) - - .190 5 Table 6 Independent Variable Importance of PACCS Branch of Eragudi Normalized Factors Importance Importance Gender .088 37.2% Age .205 87.0% Annual Income .158 66.9% Number of Family Members .159 67.3% Educational Qualification .236 100.0% Nature of farmer (Farm in acre) .049 20.6% Land Owned (Farm in acre) .106 45.0% 332 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 7 Impact of Hidden Layers on Farmers' Loan Repayment in Eragudi branch Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 .209 -.430 H2 -.276 .182 Bias .254 -.069 Percent Incorrect Predictions – Training (49.1%), Testing (43.4%) Table 8 Estimation of Parameters for PACCS branch of Eragudi Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 1 Layer 2 Gender (Female) .028 7 .109 7 Age (40 - 50 ) .597 2 .416 3 Annual income (Rs.50,000 - 75,000/-) .562 3 .365 4 Family Members (5 - 7) .069 6 - - Family Members (7 and above) - - .164 6 Educational Qualification (Primary Education) .727 1 - - Educational Qualification (No formal Education) - - .464 2 Nature of Farmer (Big - 5 and above acres) .351 4 .474 1 Land Owned (3 - 5 acres) .327 5 .344 5 Table 9 Independent Variable Importance of PACCS Branch of Venkatesapuram Normalized Factors Importance Importance Gender .085 36.9% Age .089 38.4% Annual Income .162 70.2% Number of Family Members .230 100.0% Educational Qualification .147 63.6% Nature of farmer (Farm in acre) .121 52.3% Land Owned (Farm in acre) .167 72.5% Table 10 Impact of Hidden Layers on Farmers' Loan Repayment in Venkatesapuram branch Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 -.429 .721 Bias .352 .877 Percent Incorrect Predictions -Training (37.6%), Testing (20.0%) 333 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  9. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 11 Estimation of Parameters for PACCS branch of Venkatesapuram branch Predictors Hidden Layer 1 Rank Gender(Male) .013 7 Age(30 - 40) .255 4 Annual income (Rs.1,00,000/- and above) .536 2 Family Members(5 -7) .587 1 Educational Qualification (Primary Education) .187 5 Nature of Farmer (2 1/2 - 5 acres) .139 6 Land Owned (3 - 5 acres) .512 3 Table 12 Pooled Analysis of Independent Variable Importance of PACCS Branches Normalized Factors Importance Importance Gender .043 20.8% Age .183 88.7% Annual Income .169 81.9% Number of Family Members .207 100.0% Educational Qualification .199 96.5% Nature of farmer (Farm in acre) .089 43.2% Land Owned (Farm in acre) .110 53.2% Table 13Impact of Hidden Layers on Farmers' Loan Repayment in PACCS branches Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 -.558 .854 H2 -.287 -.257 H3 .322 -.321 H4 -.922 .399 H5 -.230 .062 Bias .160 -.188 Percent Incorrect Predictions - Training (39.9%), Testing (38.5%) Table 14 Estimation of Parameters for PACCS branches Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 3 Layer 1 Gender (Male) -.257 7 .659 3 Age (50 and above) - - .460 5 Age (20 - 30) .368 4 - - Annual income (Rs.75,000 - Rs.$$$$1,00,000/-) - - .335 7 Annual Income (Rs.50,000 - Rs.75,000/-) .404 2 - - Family Members (2 - 4) .298 5 .661 2 Educational Qualification (Secondary Education) - - .829 1 Educational Qualification (Degree or Technical Education) .403 3 - - Nature of Farmer (Small - below 2.5 acres) - - .588 4 Nature of Farmer (Big - 5 and above acres) .079 6 - - Land Owned (Below 1 acre) - - .345 6 Land Owned (5 acres and above) .410 1 - - 334 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  10. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 15 Impact of Hidden Layers on Farmers' Loan Repayment in Singalandapuram branch Percent Incorrect Predictions - Training (0.2%), Testing (0.5%) Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 .451 -.246 H2 -1.141 .951 H3 -.287 .034 H4 1.180 -1.798 H5 .978 -1.505 H6 1.309 -1.520 H7 .389 -.865 H8 -.751 .488 Bias -.786 .311 Table 16 Estimation of Parameters for PACCS branch of Singalandapuram Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 1 Layer 2 Crop Name (Others) .217 9 - - Crop Name (Paddy) - - .534 4 Loan Amount Sanctioned (Rs.75,000/- Rs.1,00,000/-) - - -.073 10 Loan Amount Sanctioned (Rs.25,000/- - Rs.50,000/-) .610 2 - - Loan amount as cash (Below Rs.10,000/-) - - .114 9 Loan amount as cash (Rs.10,000 - Rs.40,000/-) .556 3 - - Loan amount as seed (Rs.5,000/- and above) - - .178 8 Loan amount as seed (Rs.1,000/- - Rs.3,000/-) .431 4 - - Loan amount as fertilizer (Rs.7,000 - Rs.11,000/-) - - .255 7 Loan amount as fertilizer (Rs.3,000/- - Rs.7,000/-) .422 5 - - Insurance (Rs.1,000 - Rs.1,500/-) .381 7 - - Insurance (Not applicable) - - .415 5 Duration of loan (10 - 12 months) .404 6 - - Duration of loan (6 - 10 months) - - .342 6 Earlier payment days (Not applicable) .073 10 .721 2 Period of delay (One year and above) .619 1 .543 3 Interest (Rs.1,000/- - Rs.4,000/-) - - .940 1 Interest (Rs.7,000 and above) .378 8 - - Table 17 Impact of Hidden Layers on Farmers' Loan Repayment in Eragudi branch Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 -1.807 1.677 H2 -.350 -.212 H3 .900 -.575 H4 .379 .037 Bias -.091 -.194 Percent Incorrect Predictions - Training (0.0%), Testing (1.4%) 335 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  11. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 18 Estimation of Parameters for PACCS branch of Eragudi Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 1 Layer 2 Crop Name (Paddy) .325 5 - - Crop Name (Banana) - - .448 6 Loan Amount Sanctioned (Rs. 50,000/- - Rs.75,000/-) .302 6 - - Loan Amount Sanctioned (Rs.25,000/- - Rs.50,000/-) - - .485 5 Loan amount as cash (Below Rs.10,000/-) .256 7 - - Loan amount as cash (Rs.40,000 - Rs.70,000/-) - - -.093 9 Loan amount as seed (Rs.5,000/- and above) .135 9 - - Loan amount as seed (Rs.3,000 - Rs.5,000/-) - - .376 7 Loan amount as fertilizer (Rs.11,000/- and above) .340 3 - - Loan amount as fertilizer (Rs.3,000/- - Rs.7,000/-) - - -.143 10 Insurance (Rs.1,500/- and above) .141 8 .232 8 Duration of loan (10 - 12 months) .014 10 - - Duration of loan (12 - 15 months) - - .559 2 Earlier payment days (Paid on due date) .337 4 - - Earlier payment days (Not applicable) - - .489 3 Period of delay (Not applicable) .532 2 - - Period of delay (One year and above) - - .489 4 Interest (Not applicable) .601 1 - - Interest (Till unpaid) - - .612 1 Table 19 Impact of Hidden Layers on Farmers' Loan Repayment in Venkatesapuram branch Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 -4.335 4.118 Bias -.118 .183 Percent Incorrect Predictions - Training (0.0%), Testing (0.0%) Table 20 Estimation of Parameters for PACCS branch of Venkatesapuram branch Hidden Predictors Rank Layer 1 Crop Name (Paddy) .414 5 Loan Amount Sanctioned (Rs.50,000/- - Rs.75,000/- .300 7 Loan amount as cash (Rs.10,000 - Rs.40,000/-) .219 8 Loan amount as seed (Below Rs.1,000/-) .146 9 Loan amount as fertilizer (Below Rs.3,000/-) .387 6 Insurance (Not applicable) .101 10 Duration of loan (12 - 15 months) .466 4 Earlier payment days (Not applicable) .937 1 Period of delay (1 - 6 months) .472 3 Interest (Rs.1,000/- - Rs.4,000/-) .751 2 336 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  12. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 21 Pooled Analysis on Impact of Hidden Layers on Farmers' Loan Repayment in PACCS branches Farmers' Loan Repayment Strategy Hidden Layers Prompt Payer Defaulter H1 .236 -1.011 H2 -.410 -.131 H3 -.975 .885 H4 .036 -.409 H5 -.808 .686 H6 .959 -.492 H7 .882 -.292 H8 .648 -.899 H9 .308 .010 Bias -.348 .271 Percent Incorrect Predictions - Training (0.3%), Testing (0.0%) Table 22 Estimation of Parameters for PACCS branches Best Hidden Layer Predictors Hidden Hidden Rank Rank Layer 6 Layer 3 Crop Name(Paddy) .013 10 - - Crop Name(Banana) - - .346 4 Loan Amount Sanctioned(Rs.75,000/- - Rs.1,00,000/- .324 6 - - Loan Amount Sanctioned(Below Rs.25,000/-) - - .038 8 Loan amount as cash(Below Rs.10,000/-) .122 8 .335 5 Loan amount as seed(Rs.5,000/- and above) .283 7 - - Loan amount as seed(None) - - .071 7 Loan amount as fertilizer(Below Rs.3,000/-) .532 1 .015 9 Insurance(Rs.1,500/- and above) .084 9 - - Insurance(Not applicable) - - .266 6 Duration of loan(10 - 12 months) .433 4 - - Duration of loan(12 - 15 months) - - -.025 10 Earlier payment days(Paid on due date) .434 3 - - Earlier payment days(Not applicable) - - .857 1 Period of delay(Not applicable) .469 2 - - Period of delay(One year and above) - - .700 2 Interest(None) .373 5 - - Interest(Rs.4,000/- - Rs.7,000/-) - - .437 3 5. FINDINGS Table 1 reveals that in Eragudi branch, major borrowers are male and the age group of farmers those who obtained loan are of above 50. Most of the farmers has annual income of Rs.75,000/- to Rs.1,00,000/- and they have their family size between 2 to 4 members. Majority of the farmers are educated up to secondary level. In Singalandapuram branch, major borrowers are male and the age group of farmers those who obtained loan are of above 50. Most of the farmers has annual income of Rs.75,000/- to Rs.1,00,000/- and they have their family size between 2 to 4 members. Majority of the farmers are educated up to secondary level. In Venkatesapuram branch, major borrowers are male and the age group of farmers those who obtained loan are of above 50. Most of the farmers has annual income of Rs.50,000/- to Rs.75,000/- and they have their family size between 2 to 4 members. Majority of the farmers are educated up to secondary level. To visualize the overall analysis, it states that major borrowers are male and the age group of farmers those who obtained loan are of above 50. Most of the farmers has annual income of Rs.50,000/- to Rs.75,000/- and they have their family size between 2 to 4 members. Majority of the farmers are educated up to secondary level. 337 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  13. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication Table 2 reveals that in Eragudi branch, majority of the farmers are small farmers and they own land of 1 to 3 acres. The major group of farmers crop paddy and they have been sanctioned loan below Rs.25,000/- and they received loan amount as cash of Rs.10,000/- to Rs.40,000/-, as seed Rs.1,000/- to Rs.3,000/-, as fertilizer Rs.3,000/- to Rs.7,000/- and for insurance below Rs.500/-. Most of the farmers' cultivation period is between 6 to 10 months. In case of earlier payment days, major group of farmers do not repay the loan before due date but they have paid on due date so delay is not applicable and they need to pay interest. Finally, majority of the farmers are prompt payers. In Singalandapuram branch, majority of the farmers are small farmers and they own land of 1 to 3 acres. The major group of farmers crop paddy and they have been sanctioned loan below Rs.25,000/- and they received loan amount as cash of Rs.10,000/- to Rs.40,000/-, as seed Rs.1,000/- to Rs.3,000/-, as fertilizer Rs.3,000/- to Rs.7,000/- and for insurance below Rs.500/-. Most of the farmers' cultivation period is between 6 to 10 months. In case of earlier payment days, major group of farmers paid the loan 30 days before due date but they have paid on due date and delay is not applicable hence they need to pay interest. Finally, majority of the farmers are prompt payers. In Venkatesapuram branch, majority of the farmers are small farmers and they own land of 1 to 3 acres. The major group of farmers crop paddy and they have been sanctioned loan between Rs.25,000/- to Rs.50,000/- and they received loan amount as cash of Rs.10,000/- to Rs.40,000/-, as seed Rs.1,000/- to Rs.3,000/-, as fertilizer Rs.3,000/- to Rs.7,000/- and for insurance below Rs.500/-. Most of the farmers' cultivation period is between 6 to 10 months. In case of earlier payment days, major group of farmers do not repay the loan before due date but they have paid on due date, so delay is not applicable hence they need to pay interest. Finally, majority of the farmers are prompt payers. To view from the overall analysis of famers, it is found that majority of the farmers are small farmers and they own land of 1 to 3 acres. The major group of farmers crop paddy and they have been sanctioned loan below Rs.25,000/- and they received loan amount as cash of Rs.10,000/- to Rs.40,000/-, as seed Rs.1,000/- to Rs.3,000/-, as fertilizer Rs.3,000/- to Rs.7,000/- and for insurance below Rs.500/-. Most of the farmers' cultivation period is between 6 to 10 months. In case of earlier pa days, major group of farmers do not repay the loan before due date but they have paid on due date, so delay is not applicable hence they need to pay interest. Finally, majority of the farmers are prompt payers. Table 3 reveals that in Singalandapuram, the variables like educational qualification, land owned, and number of family members helps in having a best prediction of repayment on farmers' loan. Table 4.5 reveals that in Singalandapuram, the factors like land owned, nature of farmer, and annual income contribute for the prompt payment of the farmers. The reasons for being a default payer is due to factors like educational qualification, nature of farmer and annual income. Table 6 reveals that in Eragudi, the variables like educational qualification, age, and number of family members helps in having a best prediction of repayment on farmers' loan. Table 8 reveals that in Eragudi, the factors like educational qualification, age, and annual income contribute for the prompt payment of the farmers. The reasons for being a default payer is due to factors like nature of farmer, educational qualification, and age. Table 9 reveals that in Venkatesapuram, the variables like number of family members, land owned and annual income helps in having a best prediction of repayment on farmers' loan. Table 11 reveals that in Venkatesapuram, the factors like family members, annual income, and land owned contribute for the prompt and default repayment of the farmers. Table 12 reveals that in Singalandapuram, Eragudi, and Venkatesapuram, the variables like number of family members, educational qualification, and age helps in having a best prediction of repayment on farmers' loan.Table 14 reveals that in Singalandapuram, Eragudi, and Venkatesapuram the factors like land owned, annual income, and educational qualification contribute for the prompt payment of the farmers. The reasons for being a default payer is due to factors like educational qualification, number of family members, and gender. Table 16 reveals that in Singalandapuram, the factors like period of delay, loan amount sanctioned, and loan amount as cash contribute for the prompt payment of the farmers. The reasons for being a default payer is due to factors like interest, earlier payment days and period of delay. Table 18 reveals that in Eragudi, the factors like interest, period of delay, and loan amount as fertilizer contribute for the prompt payment of the farmers. The reasons for being a default payer is due to factors like interest, duration of loan, and earlier payment days. Table 20 reveals that in Venkatesapuram, the factors like earlier payment days, interest, and period of delay contribute for the prompt and default repayment of the farmers. Table 22 reveals that in Singalandapuram, Eragudi, and Venkatesapuram the factors like loan amount as fertilizer, period of delay, and earlier payment days contribute for the prompt payment 338 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  14. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication of the farmers. The reasons for being a default payer is due to factors like earlier payment days, period of delay, and interest. 6. SUGGESTIONS SINGALANDAPURAM BRANCH The banker may have a prompt payer when they lend the loan amount to the farmers those who are coming under the following criteria such as the land cultivated by them is of 5 acres and above and the farmers with an annual income of Rs.1,00,000/- and above, the loan amount sanctioned is between Rs.25,000/- to Rs.50,000/-, as cash when it is provided Rs.10,000/- to Rs.40,000/-, as seed when it is provided Rs.1,000/- to Rs.3,000/- . The banker may get a delayed payment from the farmers when they are educated below secondary level of education, they own land of 5 acres and above with the annual income of Rs.1,00,000/- and above. The farmers who pay interest of Rs.1,000/- to Rs.4,000/-, who do not pay on or before the due date, and the farmers who cropped paddy mostly fail to make the prompt payment. It is suggested to the bankers to consider these factors and sanction the loan to the farmers. ERAGUDI BRANCH The banker may have a prompt payer when they lend the loan amount to the farmers those who are coming under the following criteria such as the farmers who are educated at primary education level, coming under the age group of 40 to 50, with an annual income of Rs.50,000/- to Rs.75,000/-, interest and delay for payment is not applicable, and loan amount sanctioned as fertilizer when it is provided Rs.11,000/- and above . The banker may get a delayed payment from the farmers when they own land of 5 acres and above, they do not have formal education, and when they come under the age group of 40 to 50. The farmers who do not pay both the interest and capital, who have the cultivation period of 12 to 15 months, and who do not pay before the due date mostly fail to make the prompt payment. It is suggested to the bankers to consider these factors and sanction the loan to the farmers. VENKATESAPURAM BRANCH The banker may have a prompt payer when they lend the loan amount to the farmers those who are coming under the following criteria such as the farmers whose number of family members is between 5 to 7 people, with an annual income of Rs.1,00,000/- and above, and the farmers who own land of 3 to 5 acres. The banker may get a delayed payment from the farmers when they earlier payments days are not applicable, pay interest of Rs.1,000/- to Rs.4,000/-, and the farmers who delay 1 to 6 months mostly fail to make the prompt payment. It is suggested to the bankers to consider these factors and sanction the loan to the farmers. 7. CONCLUSION Each and every bank face the problem in recovery of loan amount on or before due date that is given to their loan receivers. This research is focused on identifying the factors that influence in making the payment by the loan receiver. The research is made on the repayment of loan amount by the farmers of Primary Agricultural Cooperative Credit Society in the branches Singalandapuram, Eragudi, and Venkatesapuram. The result of neural network analysis of personal and demographic factors such as gender, age, annual income, educational qualification, number of family members, nature of the farmer, and land owned shows that well educated people with an age group of 20 to 30 make a prompt payment of the loan amount where as farmers with secondary level of education and with a family size of 2 to 4 and those whose farm size is small which is below 2.5 acres fail to make repayment on or before due date. The result also shows that there are more male farmers make a delayed payment, this is due to the relaxed attitude that a male has. The neural network analysis of different variables brings off the following information that farmers who receive part of loan amount as seed of Rs.5,000/- and above, the farmers with loan duration of 10 to 12 months make prompt payment without any delay. Lower interest and penalty rates induce the farmer to make delayed payment, and the farmers who cropped banana failed to be a prompt payer. Apart from the suggestions derived from the analysis made some other reasons were found responsible for a delayed payment. The reasons are crop failure, unusual rain, low productivity of crop, unexpected expenses in family and friends, investing loan amount in assets such as land, house, jewels, etc. The banker will get benefitted when they sanction the loan by 339 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
  15. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 7, Issue 2, February (2016), pp. 326-340 © IAEME Publication considering the suggestions given. The study also gives individual suggestions to all the three PACCS branches REFERENCES [1] C J Arene and G C Aneke (1999), The Position of Women in the Repayment of Agriculture Loans in Nigeria: An Analysis, Vikalpa, Vol. 24, No. 4, October-December 1999, PP 29 - 34. [2] MC Mashatola& MAG Darroch (2003), Factors affecting the loan status of sugar-cane farmers using a graduated mortgage loan repayment scheme in kwazulu-natal, Agrekon, Vol 42, No 4 (December 2003), PP 353 - 365. [3] Limsombunchai, V. C. Gan and M. Lee (2005), Lending Decision Model for Agricultural Sector in Thailand, Department of Economics, American University of Sharjah, UAE, PP 573 - 579. [4] AbebeMijena (2011), Determinants of Credit Repayment and Fertilizer Use By Cooperative Members in Ada District, East Shoa Zone, Oromia Region, Haramaya University. [5] Ifeanyi A. Ojiako1 and Blessing C. Ogbukwa (2012), Economic analysis of loan repayment capacity of smallholder cooperative farmers in Yewa North Local Government Area of Ogun State, Nigeria, African Journal of Agricultural Research Vol. 7(13), pp. 2051-2062. [6] J. A. Afolabi (2010), Analysis of Loan Repayment among Small Scale Farmers in Oyo State, Nigeria, Kamla-Raj 2010 J SocSci, 22(2): 115-119. [7] Henry De-Graft Acquah and Joyce Addo (2012), Socio-Economic Determinants of Rice Farmers’ Loan Size in Shama, Ghana, American-Eurasian J. Agric. & Environ. Sci., IDOSI Publications, 12 (4): 516-520. [8] Amjad Saleem, DrFarzand Ali Jan, and Rasheed Muhammad Khattak& Muhammad Imran Quraishi (2011), Impact of Farm and Farmers Characteristics On Repayment of Agriculture Credit, Abasyn Journal of Social Sciences; Vo. 4 No.1, PP 23 - 35. [9] Mohammad Reza Kohansal and HoomanMansoori (2009), Factors Affecting on loan Repayment Performance of Farmers in Khorasan-Razavi Province of Iran, Tropentag 2009, University of Hamburg, October 6-8. [10] Victor UgbemOboh and Ineye Douglas Ekpebu (2011), Determinants of formal agricultural credit allocation to the farm sector by arable crop farmers in Benue State, Nigeria, African Journal of Agricultural Research Vol. 6(1), pp. 181-185. [11] Yasir Mehmood, Mukhtar Ahmad, and Muhammad BahzadAnjum (2012), Factors Affecting Delay in Repayments of Agricultural Credit; A Case Study of District Kasur of Punjab Province, World Applied Sciences Journal 17 (4): 447-451. 340 Dr. G.S. David Sam Jayakumar and J.Sathyamurti, “Modelling The Prediction of Farmers' Loan Repayment In Primary Agricutural Co-Operative Credit Societies” – (ICAM 2016)
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