modelling the prediction of farmers' loan repayment in primary agricutural co-operative credit...

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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)

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

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ISSN 0976-6510 (Online)

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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

http://search.proquest.com/business/indexinglinkhandler/sng/author/Durguner,+Sena/$N?accountid=62745

http://search.proquest.com/business/indexinglinkhandler/sng/author/Katchova,+Ani+L/$N?accountid=62745



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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.



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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

Constructs

Location of Bank

Singalandapura

m n=682 Eragudi n=243

Venkatesapuram

n=125 Total n=1050

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%)

Age

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%)

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%)

Annual

Income

Below Rs.50,

000/- 119(17.45%) 66(27.16%) 41(32.80%) 226(21.52%)

Rs.50, 000/- -

Rs.75,000/- 185(27.13%) 63(25.93%) 73(58.40%) 321(30.57%)

Rs.75,000/-

Rs.1,00,000/- 226(33.14%) 70(28.81%) 8(6.40%) 304(28.95%)

Rs.1,00,000/-

and above 152(22.29%) 44(18.11%) 3(2.40%) 199(18.95%)

Number of

Family

Members

2 – 4 483(70.82%) 217(89.30%) 122(97.60%) 822(78.29%)

5 – 7 184(26.98%) 25(10.29%) 3(2.40%) 212(20.19%)

7 and above 15(2.20%) 1(.41%) 0(.00%) 16(1.52%)

Educational

Qualification

No formal

education 142(20.82%) 35(14.40%) 20(16.00%) 197(18.76%)

Primary

education 151(22.14%) 63(25.93%) 42(33.60%) 256(24.38%)

Secondary

education 318(46.63%) 124(51.03%) 56(44.80%) 498(47.43%)

Degree or

technical

education

71(10.41%) 21(8.64%) 7(5.60%) 99(9.43%)



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Table No. 2 Location wise Descriptive Statistics of Conceptual Variables

Constructs

Location of Bank

Singalandapura

m n=682

Eragudi

n=243

Venkatesapuramn=12

5 Total n=1050

Nature of

farmer (Farm

in acre)

Small(below

2.5) 422(61.88%)

139

(57.20%) 53(42.40%) 614(58.48%)

Medium(2.5 -

5) 208(30.50%) 64(26.34%) 43(34.40%) 315(30.00%)

Big(5 and

above) 52(7.62%)

40

(16.46%) 29(23.20%) 121(11.52%)

Land Owned

(Farm in

acre)

Below 1 112(16.42%) 26(10.70%) 6(4.80%) 144(13.71%)

1 - 3 413(60.56%) 146

(60.08%) 60(48.00%) 619(58.95%)

3 - 5 105(15.40%) 31

(12.76%) 31(24.80%) 167(15.90%)

5 and above 52(7.62%) 40(16.46%) 28(22.40%) 120(11.43%)

Crop Name

Paddy 668(97.95%) 208

(85.60%) 107(85.60%) 983(93.62%)

Banana 0(.00%) 32(13.17%) 1(.80%) 33(3.14%)

Others 14(2.05%) 3(1.23%) 17(13.60%) 34(3.24%)

Loan

Amount

Sanctioned

Below Rs.25,

000/- 396(58.06%)

134

(55.14%) 53(42.40%) 583(55.52%)

Rs.25, 000/- -

Rs.50,000/-

216

(31.67%)

80

(32.92%) 60(48.00%) 356(33.90%)

Rs.50,000/- -

Rs.75,000/- 44(6.45%) 15(6.17%) 6(4.80%) 65(6.19%)

Rs.75,000/- -

Rs.1,00,000/- 26(3.81%) 14(5.76%) 6(4.80%) 46(4.38%)

Loan

Amount

Received as

Cash

Below

Rs.10,000/-

199

(29.18%)

60

(24.69%) 18(14.40%) 277(26.38%)

Rs.10,000/- -

Rs.40,000/-

439

(64.37%)

165

(67.90%) 97(77.60%) 701(66.76%)

Rs.40,000/- -

Rs.70,000/- 43(6.30%) 18(7.41%) 9(7.20%) 70(6.67%)

Rs.70,000/-

and above 1(.15%) 0(.00%) 1(.80%) 2(.19%)

Loan

Amount

Received as

Seed

Below

Rs.1,000/-

246

(36.07%)

76

(31.28%) 27(21.60%) 349(33.24%)

Rs.1,000/- -

Rs.3,000/-

377

(55.28%)

102

(41.98%) 77(61.60%) 556(52.95%)

Rs.3,000/- -

Rs.5,000/- 48(7.04%) 12(4.94%) 13(10.40%) 73(6.95%)

Rs.5,000/- and

above 11(1.61%) 21(8.64%) 8(6.40%) 40(3.81%)

None 0(.00%) 32(13.17%) 0(.00%) 32(3.05%)

Loan

Amount

Received as

Fertilizer

Below

Rs.3,000

154

(22.58%)

43

(17.70%) 19(15.20%) 216(20.57%)

Rs.3,000 -

Rs.7,000/-

300

(43.99%)

104

(42.80%) 44(35.20%) 448(42.67%)

Rs.7,000 -

Rs.11,000/-

125

(18.33%)

55

(22.63%) 36(28.80% 216(20.57%)

Rs.11,000 and

above 103(15.10%)

41

(16.87%) 26(20.80%) 170(16.19%)



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Constructs

Location of Bank

Singalandapura

m n=682

Eragudi

n=243

Venkatesapuramn=12

5 Total n=1050

crop

Insurance

Below

Rs.500/-

575

(84.31%)

190

(78.19%) 83(66.40%) 848(80.76%)

Rs.500/- -

Rs.1,000/- 67(9.82%) 22(9.05%) 9(7.20%) 98(9.33%)

Rs.1,000/- -

Rs.1,500/- 3(.44%) 12(4.94%) 0(.00%) 15(1.43%)

Rs.1,500/- and

above 2(.29%) 18(7.41%) 0(.00%) 20(1.90%)

Not applicable 35(5.13%) 1(.41%) 33(26.40%) 69(6.57%)

Duration of

Loan Period

6 Months 0(.00%) 1(.41%) 2(1.60%) 3(.29%)

6 - 10 Months 669

(98.09%)

208

(85.60%) 117(93.60%) 994(94.67%)

10 - 12

Months 10(1.47%) 30(12.35%) 5(4.00%) 45(4.29%)

12 - 15

Months 3(.44%) 4(1.65%) 1(.80%) 8(.76%)

Earlier

Payment

Days

Below 30 days 312

(45.75%)

81

(33.33%) 35(28.00%) 428(40.76%)

30 - 60 days 12(1.76%) 2(.82%) 1(.80%) 15(1.43%)

60 - 90 days 8(1.17%) 2(.82%) 0(.00%) 10(.95%)

Not applicable 251

(36.80%)

120

(49.38%) 73(58.40%) 444(42.29%)

Paid on due

date 99(14.52%) 38(15.64%) 16(12.80%) 153(14.57%)

Period of

Delay

Below 1

Month 24(3.52%) 2(.82%) 6(4.80%) 32(3.05%)

1 - 6 Months 94(13.78%) 29(11.93%) 19(15.20%) 142(13.52%)

6 - 12 Months 49(7.18%) 22(9.05%) 22(17.60%) 93(8.86%)

1 year and

above 85(12.46%) 66(27.16%) 25(20.00%) 176(16.76%)

Not applicable 430

(63.05%)

124

(51.03%) 53(42.40%) 607(57.81%)

Interest Paid

Below

Rs.1,000/- 41(6.01%) 11(4.53%) 5(4.00%) 57(5.43%)

Rs.1,000/- -

Rs.4,000/-

151

(22.14%)

56

(23.05%) 31(24.80%) 238(22.67%)

Rs.4,000/- -

Rs.7,000/- 24(3.52%) 12(4.94%) 16(12.80%) 52(4.95%)

Rs.7,000/- and

above 7(1.03%) 11(4.53%) 1(.80%) 19(1.81%)

Not applicable 430

(63.05%)

124

(51.03%) 53(42.40%) 607(57.81%)

None 29(4.25%) 29(11.93%) 19(15.20%) 77(7.33%)

Status of

farmer in

loan

repayment

Prompt payer 431

(63.20%)

123

(50.62%) 53(42.40%) 607(57.81%)

Defaulter 251

(36.80%)

120

(49.38%) 72(57.60%) 443(42.19%)



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Table 3Independent Variable Importance of PACCS Branch of Singalandapuram

Factors Importance Normalized

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

Percent Incorrect Predictions - Training (36.8%), Testing (33.5%)

Table 5 Estimation of Parameters for PACCS branch of Singalandapuram

Predictors

Best Hidden Layer

Hidden

Layer 3 Rank

Hidden

Layer 7 Rank

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

Hidden Layers Farmers' Loan Repayment Strategy

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


Importance

Gender .088 37.2%

Age .205 87.0%








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Table 7 Impact of Hidden Layers on Farmers' Loan Repayment in Eragudi branch

Percent Incorrect Predictions – Training (49.1%), Testing (43.4%)

Table 8 Estimation of Parameters for PACCS branch of Eragudi

Predictors

Best Hidden Layer

Hidden

Layer 1 Rank

Hidden

Layer 2 Rank

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


Importance

Gender .085 36.9%

Age .089 38.4%






Table 10 Impact of Hidden Layers on Farmers' Loan Repayment in Venkatesapuram branch

Percent Incorrect Predictions -Training (37.6%), Testing (20.0%)



H1 .209 -.430

H2 -.276 .182

Bias .254 -.069



H1 -.429 .721

Bias .352 .877



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Table 11 Estimation of Parameters for PACCS branch of Venkatesapuram branch

Table 12 Pooled Analysis of Independent Variable Importance of PACCS Branches


Importance

Gender .043 20.8%

Age .183 88.7%






Table 13Impact of Hidden Layers on Farmers' Loan Repayment in PACCS branches



H1 -.558 .854

H2 -.287 -.257

H3 .322 -.321

H4 -.922 .399

H5 -.230 .062

Bias .160 -.188


Table 14 Estimation of Parameters for PACCS branches

Predictors

Best Hidden Layer

Hidden

Layer 3 Rank

Hidden

Layer 1 Rank

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 - -

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



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Table 15 Impact of Hidden Layers on Farmers' Loan Repayment in Singalandapuram branch


Table 16 Estimation of Parameters for PACCS branch of Singalandapuram

Predictors

Best Hidden Layer

Hidden

Layer 1 Rank

Hidden

Layer 2 Rank

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



H1 -1.807 1.677

H2 -.350 -.212

H3 .900 -.575

H4 .379 .037

Bias -.091 -.194




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



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Table 18 Estimation of Parameters for PACCS branch of Eragudi

Predictors

Best Hidden Layer

Hidden

Layer 1 Rank

Hidden

Layer 2 Rank

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


Table 20 Estimation of Parameters for PACCS branch of Venkatesapuram branch

Predictors Hidden

Layer 1 Rank

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



H1 -4.335 4.118

Bias -.118 .183



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Table 21 Pooled Analysis on Impact of Hidden Layers on Farmers' Loan Repayment in PACCS

branches


Table 22 Estimation of Parameters for PACCS branches

Predictors

Best Hidden Layer

Hidden

Layer 6 Rank

Hidden

Layer 3 Rank

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.



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



338



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



339



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


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


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



340



considering the suggestions given. The study also gives individual suggestions to all the three PACCS

branches

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modelling the prediction of farmers' loan repayment in primary agricutural co-operative credit...

Engineering