Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Credit Diversion and Default Risk in Dairy Farming; Understanding the Determinants and Implications for Financial Sustainability

K
K. Nithin Raj1
A
Ajay Verma2,*
A
Ajmer Singh1
A
A.K. Dixit1
G
G. Bhandari1
B
B. Sen1
1Division of Dairy Economics, Statistics and Management, ICAR- National Dairy Research Institute, Karnal-132001, Haryana, India.
2ICAR-Indian Institute of Wheat and Barley Research, Karnal-132 001, Haryana, India.

ABSTRACT

Background: Diversion of institutional agricultural credit for non-productive purposes poses greater challenges to the intended goals of credit programs in developing countries. Understanding the causes of farm credit diversion and credit default is crucial for implementing effective preventive measures.

Methods: This study aimed to evaluate the extent of credit diversion among the dairy farmers in Tamil Nadu. Multivariate regression analysis and logit models were employed to analyze the determinants of credit diversion and default risk. Data were collected in 2023 through formal surveys conducted with dairy farmers.

Result: Study conducted on 200 dairy farmers from two districts, Coimbatore and Perambalur of Tamil Nadu, India revealed that, credit diversion emerged as a concerning issue, with 43.00 per cent of dairy farmers observed diverting their dairy loans for purposes unrelated to dairying. It was observed that about 93.18 per cent of total investments in dairy were accounted by institutional dairy loans. Key factors influencing credit diversion in Tamil Nadu included the age and education level of the farmer, total landholding (ha), farming experience, dependency ratio, herd size and membership in dairy cooperatives.

INTRODUCTION

Institutional credit serves as a vital component of financial systems worldwide, facilitating economic growth, fostering entrepreneurship and alleviating poverty (Zhuang et al., 2009; Yadav and Sharma, 2015). In developing countries, access to institutional credit is crucial for smallholder farmers, micro-entrepreneur and marginalized communities bringing about positive changes in their social status, economic well-being and overall quality of life. Access to appropriate credit reduces poverty by enhancing the productivity and yield, thus increases the income of agricultural households (Awotide et al. 2015; Luan and Bauer 2016; Kumar et al., 2017). Improved access to adequate credit also increases farmers’ investment choices and provides them with more effective tools to manage the risks and increases the farm efficiency (Pandey, 2017; Memmel et al., 2019). Institutional sources were preferred by agricultural households as 61.00 per cent of farmer’s availed credit from such sources (NABARD, 2019). Majority of Indian farmers face shortage of funds from their personal savings to invest in their agricultural activities. A substantial 86.00 per cent of the farming community in India falls within the categories of marginal, small and landless laborers, representing the majority of the population living below the poverty line (Kumar and Moharaj, 2023). Given that approximately 75.00 to 80.00 per cent of dairy farmers belong to the landless and marginal categories, the provision of credit becomes a crucial factor in stimulating and sustaining the growth of dairy farming as well (Vedamurthy, 2015). The diversion or redirection of agricultural loans for non-agricultural purposes, poses greater challenges to the intended goals of credit programs in developing countries. Loan diversion refers to the practice of utilizing an entire loan or a portion of it permanently for purposes other than those stated at the time of taking the loan. Such diversion can either be intentional or non-intentional. The loan amounts fixed by financial agencies for farmers are often insufficient to cover various aspects of agricultural operations. Thereby, results farmers redirecting their funds for non-farm purposes, as they perceive the sanctioned amount as inadequate and insignificant. This in turn undermines the entire purpose of financial assistance. In cases where borrowers redirect funds from the intended purposes to alternative uses, the risk of default increases. 50.00 per cent of defaults among borrower farmer households categorized as economically disadvantaged are attributed to the diversion of funds from their intended agricultural use (Hanley and Girma, 2006). Additionally, among the broader group of defaulters, 10.00 per cent experience challenges in loan repayment directly linked to the diversion of funds for purposes other than originally specified (Wakuloba, 2008). The diversion of farm credit can be attributed to various factors. Understanding these causes is crucial for implementing effective measures to prevent diversion.
 

MATERIALS AND METHODS

Tamil Nadu accounts for 5.39 per cent of the total livestock population in India. The growth rate in milk production in Tamil Nadu has exhibited a consistently positive trend over the past decade, from 2010 to 2020. During this period, the average annual growth rate of milk production in Tamil Nadu has been recorded at 2.83 per cent. Two districts were selected from Tamil Nadu based on the level of institutional credit disbursed: one from the high disbursal category and one from the low disbursal category. Coimbatore and Perambalur were chosen randomly from the high and low disbursal categories, respectively. Two blocks were then randomly selected from each chosen district. Pollachi and Thondamuthur blocks were selected from Coimbatore district, while Perambalur and Veppanthattai blocks were chosen from Perambalur district. Following consultations with local bank staff in the study area, 25 borrowers and 25 non-borrowers of institutional credit were selected from each designated block. Thus, the total sample size for the study was 200.  The data was collected through formal interviews during 2023.
 
Determinants of rate of diversion of institutional credit by dairy farmers
 
To identify the factors influencing the rate of diversion of institutional credit by dairy farmers, multivariate regression analysis was employed. Various functional forms were tested using the selected variables and the most suitable form was determined based on criteria such as higher R2 values and the economic significance of the explanatory variables.
       
The functional equation showing factors affecting rate of diversion of institutional credit:
 
Y = f (X1, X2, X3‚ X„ ‚ X… , X6, X7,… X10)
 
Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+……+ vi
 
Where,
Y = Diversion of loan (%)
β = Vector of the parameter estimates.
 
Logistic regression
 
Determinants of loan default among dairy farmers
 
To enhance the efficiency of dairy financing, it is essential to comprehend and pinpoint the factors contributing to loan default (Vedamurthy, 2015). This understanding can aid in formulating pragmatic lending policies at regional, state and national levels, thereby mitigating the extent of overdue payments to a manageable extent. To identify factors affecting the loan default of dairy farmers in the study area, Logistic regression was employed.
       
The general form of the function showing determinants of loan default is;
 
Yi = SβiXi + μ
 
The explicit form of the model is stated as follows:
 
Y= ln (Pi/1-Pi) = β0 + β1 X1 + β2 X2+ β3 X3 + β4 X4+ β5 X5 + β6 X6 +…+ β13 X13 + μ i
 
Where,
Yi = Binary variable that equals 1 for Defaulter and 0 if otherwise.
βi = Vector of the parameter estimates.
Xi = Vector of explanatory variables.
μi = Error term.
 
Determinants of wilful loan default among dairy farmers
 
To identify factors discriminating wilful defaulters from non-wilful defaulters of among dairy farmers logistic regression analysis was used.
       
The general form of the function showing determinants of wilful loan default is:
 
Yi = ΣβiXi + μ
 
The explicit form of the model with variables was stated as follows in Table 1:
 
Y= Ln (Pi/1-Pi) = β0 + β1 X1 + β2 X2+ β3 X3 + β4 X4+ β5 X5+ β6 X6 +…+ β11 X11 + μi
 
Yi = Binary variable that equals 1 for Wilful defaulter and 0 if otherwise.
βi = Vector of the parameter estimates.
Xi = Vector of explanatory variables.
μi = Error term.

Table 1: Description of explanatory variables.

RESULTS AND DISCUSSION

Addressing credit diversion is crucial to ensure the efficient utilization of financial resources in dairy farming. Table 2 reveals the purposes of credit diversion among dairy farmers in Tamil Nadu. The primary purposes of credit diversion among dairy farmers in Tamil Nadu included diverting credit for other agricultural activities, repaying old debts and undertaking house construction/renovation. In most instances credit was diverted for multiple purposes. The data revealed that credit diversion among dairy farmers in Tamil Nadu was primarily directed towards spending on other agricultural activities (25.58%) and repaying old debts (16.27%), with a notable proportion involving more than one activity (37.20%). House construction/renovation also accounted for a considerable portion of credit diversion (11.62%). However, no instances were reported for education of children, other businesses, or medical and consumption expenditure. It was observed that the farmers from Perambalur district were found to have diverted more instances of credit compared to Coimbatore.

Table 2: Distribution of respondents based on the purpose of credit diversion in Tamil Nadu.


       
The average share of loan in investments and percentage of diversion of dairy loans in Tamil Nadu was presented in Table 3. The average share of dairy loans in dairy investments in the state was found to be 93.18 per cent. This higher percentage indicates a greater dependence of farmers on institutional credit for investments in dairy farming. It was observed that the average share of loan in total investment was comparatively lesser in Coimbatore (92.50%) than that of Perambalur (93.86%). The high dependence of dairy farmers on institutional credit for investments can be attributed to the capital-intensive nature of dairy farming and also due to limited financial resources of farmers. Institutional credit was observed to be diverted for purposes other than dairying, including repaying old debts, house construction, business ventures and other agricultural activities (Raj et al., 2023; Kumar et al., 2021). The percentage of credit diversion was estimated as the ratio of the amount of loan diverted for purposes other than dairying to the total amount of loan borrowed. The average credit diversion in the state was estimated to be 22.95 per cent. Borrower farmers from Perambalur district had a higher credit diversion rate at 24.63 per cent compared to borrowers from Coimbatore district, which had a credit diversion rate of 21.26 per cent.

Table 3: Share of loan in investments and percentage of diversion of dairy loans in Tamil Nadu.


       
The diversion of farm credit can be attributed to various factors. Understanding these causes is crucial for implementing effective measures to prevent diversion (Achoja, 2020). Factors affecting the rate of diversion of loans obtained by farmers were estimated separately for the dairy farmers from Coimbatore and Perambalur districts of Tamil Nadu. A multivariate regression analysis was also done for the pooled data from Coimbatore and Perambalur in order to get the complete picture of factors affecting credit diversion in the overall state. The results were furnished in Table 4. The R2 value obtained from regression analysis of the dairy farmers in Coimbatore district was 0.86, which defined that, 86 per cent of the changes in rate of diversion of credit were explained by independent variables such as gender, age of the respondent, education level of farmer, annual income, total landholding (ha), farming experience, dependency ratio, herd size, membership in dairy cooperatives and monthly per capita consumption expenditure of the farmers. The educational level of the farmer had a negative impact on credit diversion, with more educated farmers showing a lower tendency to divert institutional credits. Farmers with larger land holdings were observed to redirect their dairy loans for different purposes, such as engaging in various agricultural activities. This behavior could be attributed to their need for additional funds to support their diverse farming operations or to take advantage of investment opportunities beyond dairy farming alone (Papias and Ganesan, 2009; Ray and Das, 2023). The coefficients associated with farming experience and membership in dairy co-operatives was found significant with a negative impact on the rate of credit diversion. This suggests that as farming experience increases and farmers become members of dairy cooperatives, they are less likely to divert credit for non-dairy purposes. One possible explanation for this could be that experienced farmers and cooperative members may have better financial management skills and access to alternative sources of funding (Hananu et al., 2015; Saqib et al., 2018).   

Table 4: Factors affecting the rate of diversion of loans obtained by dairy farmers in Tamil Nadu.

                

The R2 value obtained from regression analysis of the dairy farmers in Perambalur district was 0.89, which defines that, 89.00 per cent of the changes in the model were explained by independent variables. Among the independent variables total landholding of the farmer significantly influenced the rate of diversion of credit at 1 per cent level of significance. The age of the dairy farmers in Perambalur district was found to have a positive impact on credit diversion, signaling older farmers are more likely to divert loans for purposes other than dairy. The coefficient of dependency ratio was found significant at 5% level of significance. The dependency ratio in the household was observed to have a proportional effect on the rate of credit diversion. This suggests that households with more members relying on the primary income earner are more likely to divert credit for non-dairy purposes, potentially due to increased financial obligations or pressures (Mejeha ​ et al., 2018; Banerjee et al., 2015). This trend may be attributed to additional household expenditures on consumption. Conversely, households with higher income contributions from members were less prone to diverting the credit.
       
The R2 value obtained from regression analysis of the pooled data of dairy farmers from both Coimbatore and Perambalur district was 0.84. Among the independent variables age of farmer, education level, total landholding (ha) farming experience dependency ratio, herd size and membership in dairy cooperatives were found significantly influencing the rate of diversion of credit at varying level of significances. The educational level of the farmer had a positive impact on reducing the credit diversion, with more educated farmers showing a lower tendency to divert institutional credits. Farmers with larger land holdings and higher dependency ratio were observed to divert more institutional credit for purposes other than the intended ones. Farmers possessing larger land holdings were observed redirecting their loans primarily for agricultural purposes rather than the intended use. This can lead to hinder the growth and sustainability of dairy operations and also impact the overall income stability of the farms in the long run. Farmers with less farming experience were found to be diverting their loans to a greater extent. This can be attributed to multiple factors such as, farmer’s less developed financial management skills and lack of financial stability (Sivakumar et al., 2013).
       
A logit regression analysis was done in order to examine the factors affecting loan default of borrower farmers in Tamil Nadu and the results were presented in Table 5.  Out of the 100 borrower dairy farmers in the study area, 53 farmers were found to divert their loan wilfully or non-wilfully for non- productive purposes. The pseudo-R2 value of 0.63 indicates a good fit of the model, meaning that the variables included in the analysis explains 63.00 per cent of the variation between defaulters and non- defaulters of dairy loans. According to the results of the logistic model, it was observed that the defaulting behavior of borrowers are significantly influenced by factors such as the farmer’s age, education level, disease occurrence to the cattle, herd size, annual income and dependency ratio in the household. Farmers of higher age were noticed to either delay or not fulfill their regular loan repayments. This behavior may be attributed to utilizing dairy loans for alternative income-generating purposes and also the expectation of potential loan waivers. This result was in accordance to the findings of Uddin et al., (2019) that the probability of loan default increases when the age of a borrower increases. Farmers with higher levels of education, larger herd sizes and higher annual incomes were noted to have lower likelihood of loan default compared to their counterparts. Farmers who experienced disease outbreaks among their cattle in recent times were also noted to default on their loan repayments. This observation may be attributed to the financial strain incurred by the costs of managing and treating livestock illnesses, which could impact farmers’ ability to meet their loan obligations. An examination of the district-wise data revealed that dairy farmers in Perambalur district were more likely to default on their dairy loans when compared to those in Coimbatore. The dependency ratio was observed to have a positive effect on loan default, suggesting a higher likelihood of default. This finding could be explained by the increased financial burden on households with a higher dependency ratio, as they may struggle to meet loan repayment obligations amidst greater financial responsibilities and limited resources.

Table 5: Factors affecting the loan default of borrower farmers in Tamil Nadu.


       
Table 6 provides information on the determinants of wilful default of dairy loans by the sample respondents. Among the 100 borrower dairy farmers in the selected districts, 53 farmers had defaulted on their loan repayment, with 28 farmers defaulting wilfully, either in the anticipation of future loan waivers or due to financial strain. Explanatory variables viz., age of the dairy farmer, education level and disease occurrence to cattle, total landholding (ha), annual income and district (Perambalur) were found significantly affecting the wilful default of dairy farmers. Elderly and less educated farmers were found to have a higher likelihood for willful default on their dairy loans. This may be attributed to factors such as limited financial literacy or resources among older and less educated individuals, leading them to resort to willful default due to lack of awareness on the potential consequences or with the expectation of loan waivers (Grohmann et al., 2018). More over with an increase in education level of dairy farmer; their tendency to default the loan wilfully also gets decreases (Bamisha and Nidheesh, 2022). Farmers with substantial annual income and extensive areas under farming were observed to refrain from willfully defaulting on their loans. This behavior may stem from their ability to implement risk mitigation measures, such as diversification of income sources, insurance coverage and efficient resource allocation, which can contribute to financial stability and avoid intentional loan default. This finding contradicted the results of Gandhimathi and Vanitha, 2009, who found that farm income positively influenced willful default on loans in their research. The analysis of district-wise data indicated that dairy farmers in Coimbatore district were less likely to engage in willful default on their dairy loans compared to those in Perambalur. This difference could be attributed to the relatively higher education levels of farmers in Coimbatore.

Table 6: Factors affecting wilful default of dairy loans by the sample respondents in Tamil Nadu.

CONCLUSION

This paper explores the factors influencing dairy farmers’ diversion of credit for unproductive purposes, such as repayment of old debts, house construction, education expenses for family members, family consumption and other agricultural activities. Additionally, a logistic regression analysis was employed to investigate the factors influencing default on dairy loans and to discriminate between willful and non-willful defaulters. It was observed that the, farmers from Perambalur district had a higher credit diversion rate compared to borrowers from Coimbatore district, the average credit diversion was estimated 22.95 per cent. The age and education level of the farmer along with the total landholding (ha), farming experience, dependency ratio, herd size and membership in dairy cooperatives were found significantly influencing the rate of diversion of credit. Farmers who faced disease outbreaks among their cattle in recent times were observed to be more likely to default on their loan repayments. The empirical analysis of the district effect on default showed that dairy farmers in Perambalur district had a higher likelihood of defaulting, including wilful defaulting on their dairy loans compared to those in Coimbatore. Supporting and providing resources to farmers to explore income diversification opportunities beyond solely relying on dairy production can potentially reduce their dependence on diverting loans for non-farm needs. Additionally, developing mechanisms for debt restructuring or rescheduling in cases of genuine hardship can help farmers overcome temporary financial hurdles and avoid wilful defaults.

ACKNOWLEDGEMENT

The sincere guidance of the Head of the Department and kind support of other faculties along with research_scholars has been sincerely acknowledged by the first author.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

REFERENCES


  1. Achoja, F.O. (2020). Loan diversion and effect on the growth of small scale poultry farms in Nigeria, Scientific Papers Series Management. Economic Engineering in Agriculture and Rural Development. 20(3): 25-32.

  2. Awotide, B.A., Abdoulaye, T., Alene, A. and Manyong, V.M. (2015). Impact of Access to Credit on Agricultural Productivity: Evidence from Smallholder Cassava Farmers in Nigeria. Paper Presented at the International Conference of Agricultural Economists (ICAE), Milan, Available online: http://purl. umn.edu/210969.

  3. Bamisha, K.P. and Nidheesh, K.B. (2022). Relationship between financial capability-financial well-being of organic farmers in Kerala, India. The Journal of Contemporary Issues in Business and Government. 28(3): 373-385.

  4. Banerjee, T., Roy, M., Raychaudhuri, A. and Ghosh, C. (2015). What drives households to divert loans? A village level study. Asia-Pacific Social Science Review. 15(2): 33-55.

  5. Gandhimathi, S. and S. Vanitha. (2009). Repayment and over dues determinants of agricultural credit: Some results for commercial and cooperative banks. The IUP Journal of Bank Management. VIII ( 3 and 4):  54-72.

  6. Grohmann, A., Klühs, T. and Menkhoff, L. (2018). Does financial literacy improve financial inclusion? Cross country evidence. World Development. 111: 84-96.

  7. Hananu, B., Abdul-Hanan, A. and Zakaria, H. (2015). Factors influencing agricultural credit demand in Northern Ghana. African Journal of Agricultural Research. 10(7): 645-652.

  8. Hanley, A. and Girma, S. (2006). New ventures and their credit terms. Small Business Economics. 26(4): 351-364.

  9. Kumar, N., Toor, J.S. and Singh, G. (2021). Level and pattern of consumption expenditure of rural households among different regions of Punjab. Indian Journal of Economics and Development. 17: 468-73.

  10. Kumar, A., Mishra, A.K., Saroj, S. and Joshi, P.K. (2017). Institutional versus Non-institutional Credit to Agricultural Households in India: Evidence on Impact from a National Farmers’ Survey (March 2, 2017). IFPRI Discussion Paper 1614, Available at SSRN: https://ssrn.com/abstract=2930177.

  11. Kumar, K.K. and Moharaj, P. (2023). Farm size and productivity relationship among the farming communities in India. Outlook on Agriculture. 52(2): 212-227.

  12. Luan, D.X. and Bauer, S. (2016). Does credit access affect household income homogeneously across different groups of credit recipients? evidence from rural vietnam. Journal of Rural Studies. 47(A): 186-203.

  13. Mejeha, R.O., Bassey, A.E. and Obasi, I.O. (2018). Determinants of loan repayment by beneficiary farmers under the integrated farmers scheme in Akwa Ibom State of Nigeria. Journal of Agriculture and Food Sciences. 16(2): 75-87.

  14. Memmel, C. Raupach, P. and Bundesbank, D. (2019). Banks’ Credit Losses and Lending Dynamics, available at: https:// www.bde.es/f/webbde/INF/MenuHorizontal/Sobre ElBanco/Conferencias/2019/Raupach.pd

  15. NABARD. (2019). National Bank for Agricultural and Rural Develop- ment. Annual Report (2018-19). Retrieved from: https:// www.nabard.org/auth/writereaddata/tender/1008203730 Nabard%20English%20Annual% 20Report%20for% 20Website.pdf.

  16. Pandey, U.K. (2017). Unit-14 Structure of Farming Sector: Dynamics and Implications. IGNOU. Retrieved from: https://www. egyankosh.ac.in/handle/123456789/39957.

  17. Papias, M.M. and Ganesan, P. (2009). Repayment behaviour in credit and savings cooperative societies: Empirical and theoretical evidence from rural Rwanda. International Journal of Social Economics. 36(5): 608-625.

  18. Raj, N.K., Dixit, A.K., Singh, A., Bhandari, G., Ponnusamy, K., Sen, B. and Verma, A. (2023). Study on flow of institutional credit and its diversion by dairy farmers in Kerala. Journal of Agricultural Development and Policy. 33(2): 132-137.

  19. Ray, P. and Das, B. (2023). Agricultural Credit Utilization and Repayment by Farm Households in Tripura. Indian Journal of Extension Education. 59(2): 30-35.

  20. Saqib, S.E., Kuwornu, J.K., Panezia, S. and Ali, U. (2018). Factors determining subsistence farmers’ access to agricultural credit in flood-prone areas of Pakistan. Kasetsart Journal of Social Sciences. 39(2): 262-268.

  21. Sivakumar, S.D., Jawaharlal, M., Palanichamy, N.V. and Sureshkumar, D. (2013). Assessment of farm financial literacy among jasmine growers in Tamilnadu, India. Assessment. 3(13): 67-76.

  22. Uddin, M.S., Chi, G., Habib, T. and  Zhou, Y. (2019). An alternative statistical framework for credit default prediction. Journal of Risk Model Validation. 14(2): 65-101. doi: 10. 21314/ JRMV.2020.220

  23. Vedamurthy, K.B. (2015). Analysis of institutional credit for dairy farming in Karnataka: A study of Shimoga Milk Zone. Indian Journal of Dairy Science. 68. doi: 10.5146/ijds. v68i3.44450.

  24. Wakuloba Bwonya, R.A. (2008). Causes of default in government microcredit programs: A case study of the Uasin Gishu district trade development joint loan board scheme, Kenya. Awarded Theses, 1.

  25. Yadav, P. and Sharma, A.K. (2015). Agriculture credit in developing economies: A review of relevant literature. International Journal of Economics and Finance. 7(12): 219-244.

  26. Zhuang, J., Gunatilake, H.M., Niimi, Y., Khan, M.E., Jiang, Y., Hasan, R. and Huang, B. (2009). Financial sector development, economic growth and poverty reduction: A literature review. Asian Development Bank Economics Working Paper Series. (173).
Published In
Asian Journal of Dairy and Food Research
Article Metrics
Metrics Icon
0
Views
0
Citations
Reviewed By
Share Article
Download Article
In this Article
    Contact us
    Follow us

    Full Research Article

    Credit Diversion and Default Risk in Dairy Farming; Understanding the Determinants and Implications for Financial Sustainability

    K
    K. Nithin Raj1
    A
    Ajay Verma2,*
    A
    Ajmer Singh1
    A
    A.K. Dixit1
    G
    G. Bhandari1
    B
    B. Sen1
    1Division of Dairy Economics, Statistics and Management, ICAR- National Dairy Research Institute, Karnal-132001, Haryana, India.
    2ICAR-Indian Institute of Wheat and Barley Research, Karnal-132 001, Haryana, India.

    ABSTRACT

    Background: Diversion of institutional agricultural credit for non-productive purposes poses greater challenges to the intended goals of credit programs in developing countries. Understanding the causes of farm credit diversion and credit default is crucial for implementing effective preventive measures.

    Methods: This study aimed to evaluate the extent of credit diversion among the dairy farmers in Tamil Nadu. Multivariate regression analysis and logit models were employed to analyze the determinants of credit diversion and default risk. Data were collected in 2023 through formal surveys conducted with dairy farmers.

    Result: Study conducted on 200 dairy farmers from two districts, Coimbatore and Perambalur of Tamil Nadu, India revealed that, credit diversion emerged as a concerning issue, with 43.00 per cent of dairy farmers observed diverting their dairy loans for purposes unrelated to dairying. It was observed that about 93.18 per cent of total investments in dairy were accounted by institutional dairy loans. Key factors influencing credit diversion in Tamil Nadu included the age and education level of the farmer, total landholding (ha), farming experience, dependency ratio, herd size and membership in dairy cooperatives.

    INTRODUCTION

    Institutional credit serves as a vital component of financial systems worldwide, facilitating economic growth, fostering entrepreneurship and alleviating poverty (Zhuang et al., 2009; Yadav and Sharma, 2015). In developing countries, access to institutional credit is crucial for smallholder farmers, micro-entrepreneur and marginalized communities bringing about positive changes in their social status, economic well-being and overall quality of life. Access to appropriate credit reduces poverty by enhancing the productivity and yield, thus increases the income of agricultural households (Awotide et al. 2015; Luan and Bauer 2016; Kumar et al., 2017). Improved access to adequate credit also increases farmers’ investment choices and provides them with more effective tools to manage the risks and increases the farm efficiency (Pandey, 2017; Memmel et al., 2019). Institutional sources were preferred by agricultural households as 61.00 per cent of farmer’s availed credit from such sources (NABARD, 2019). Majority of Indian farmers face shortage of funds from their personal savings to invest in their agricultural activities. A substantial 86.00 per cent of the farming community in India falls within the categories of marginal, small and landless laborers, representing the majority of the population living below the poverty line (Kumar and Moharaj, 2023). Given that approximately 75.00 to 80.00 per cent of dairy farmers belong to the landless and marginal categories, the provision of credit becomes a crucial factor in stimulating and sustaining the growth of dairy farming as well (Vedamurthy, 2015). The diversion or redirection of agricultural loans for non-agricultural purposes, poses greater challenges to the intended goals of credit programs in developing countries. Loan diversion refers to the practice of utilizing an entire loan or a portion of it permanently for purposes other than those stated at the time of taking the loan. Such diversion can either be intentional or non-intentional. The loan amounts fixed by financial agencies for farmers are often insufficient to cover various aspects of agricultural operations. Thereby, results farmers redirecting their funds for non-farm purposes, as they perceive the sanctioned amount as inadequate and insignificant. This in turn undermines the entire purpose of financial assistance. In cases where borrowers redirect funds from the intended purposes to alternative uses, the risk of default increases. 50.00 per cent of defaults among borrower farmer households categorized as economically disadvantaged are attributed to the diversion of funds from their intended agricultural use (Hanley and Girma, 2006). Additionally, among the broader group of defaulters, 10.00 per cent experience challenges in loan repayment directly linked to the diversion of funds for purposes other than originally specified (Wakuloba, 2008). The diversion of farm credit can be attributed to various factors. Understanding these causes is crucial for implementing effective measures to prevent diversion.
     

    MATERIALS AND METHODS

    Tamil Nadu accounts for 5.39 per cent of the total livestock population in India. The growth rate in milk production in Tamil Nadu has exhibited a consistently positive trend over the past decade, from 2010 to 2020. During this period, the average annual growth rate of milk production in Tamil Nadu has been recorded at 2.83 per cent. Two districts were selected from Tamil Nadu based on the level of institutional credit disbursed: one from the high disbursal category and one from the low disbursal category. Coimbatore and Perambalur were chosen randomly from the high and low disbursal categories, respectively. Two blocks were then randomly selected from each chosen district. Pollachi and Thondamuthur blocks were selected from Coimbatore district, while Perambalur and Veppanthattai blocks were chosen from Perambalur district. Following consultations with local bank staff in the study area, 25 borrowers and 25 non-borrowers of institutional credit were selected from each designated block. Thus, the total sample size for the study was 200.  The data was collected through formal interviews during 2023.
     
    Determinants of rate of diversion of institutional credit by dairy farmers
     
    To identify the factors influencing the rate of diversion of institutional credit by dairy farmers, multivariate regression analysis was employed. Various functional forms were tested using the selected variables and the most suitable form was determined based on criteria such as higher R2 values and the economic significance of the explanatory variables.
           
    The functional equation showing factors affecting rate of diversion of institutional credit:
     
    Y = f (X1, X2, X3‚ X„ ‚ X… , X6, X7,… X10)
     
    Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+……+ vi
     
    Where,
    Y = Diversion of loan (%)
    β = Vector of the parameter estimates.
     
    Logistic regression
     
    Determinants of loan default among dairy farmers
     
    To enhance the efficiency of dairy financing, it is essential to comprehend and pinpoint the factors contributing to loan default (Vedamurthy, 2015). This understanding can aid in formulating pragmatic lending policies at regional, state and national levels, thereby mitigating the extent of overdue payments to a manageable extent. To identify factors affecting the loan default of dairy farmers in the study area, Logistic regression was employed.
           
    The general form of the function showing determinants of loan default is;
     
    Yi = SβiXi + μ
     
    The explicit form of the model is stated as follows:
     
    Y= ln (Pi/1-Pi) = β0 + β1 X1 + β2 X2+ β3 X3 + β4 X4+ β5 X5 + β6 X6 +…+ β13 X13 + μ i
     
    Where,
    Yi = Binary variable that equals 1 for Defaulter and 0 if otherwise.
    βi = Vector of the parameter estimates.
    Xi = Vector of explanatory variables.
    μi = Error term.
     
    Determinants of wilful loan default among dairy farmers
     
    To identify factors discriminating wilful defaulters from non-wilful defaulters of among dairy farmers logistic regression analysis was used.
           
    The general form of the function showing determinants of wilful loan default is:
     
    Yi = ΣβiXi + μ
     
    The explicit form of the model with variables was stated as follows in Table 1:
     
    Y= Ln (Pi/1-Pi) = β0 + β1 X1 + β2 X2+ β3 X3 + β4 X4+ β5 X5+ β6 X6 +…+ β11 X11 + μi
     
    Yi = Binary variable that equals 1 for Wilful defaulter and 0 if otherwise.
    βi = Vector of the parameter estimates.
    Xi = Vector of explanatory variables.
    μi = Error term.

    Table 1: Description of explanatory variables.

    RESULTS AND DISCUSSION

    Addressing credit diversion is crucial to ensure the efficient utilization of financial resources in dairy farming. Table 2 reveals the purposes of credit diversion among dairy farmers in Tamil Nadu. The primary purposes of credit diversion among dairy farmers in Tamil Nadu included diverting credit for other agricultural activities, repaying old debts and undertaking house construction/renovation. In most instances credit was diverted for multiple purposes. The data revealed that credit diversion among dairy farmers in Tamil Nadu was primarily directed towards spending on other agricultural activities (25.58%) and repaying old debts (16.27%), with a notable proportion involving more than one activity (37.20%). House construction/renovation also accounted for a considerable portion of credit diversion (11.62%). However, no instances were reported for education of children, other businesses, or medical and consumption expenditure. It was observed that the farmers from Perambalur district were found to have diverted more instances of credit compared to Coimbatore.

    Table 2: Distribution of respondents based on the purpose of credit diversion in Tamil Nadu.


           
    The average share of loan in investments and percentage of diversion of dairy loans in Tamil Nadu was presented in Table 3. The average share of dairy loans in dairy investments in the state was found to be 93.18 per cent. This higher percentage indicates a greater dependence of farmers on institutional credit for investments in dairy farming. It was observed that the average share of loan in total investment was comparatively lesser in Coimbatore (92.50%) than that of Perambalur (93.86%). The high dependence of dairy farmers on institutional credit for investments can be attributed to the capital-intensive nature of dairy farming and also due to limited financial resources of farmers. Institutional credit was observed to be diverted for purposes other than dairying, including repaying old debts, house construction, business ventures and other agricultural activities (Raj et al., 2023; Kumar et al., 2021). The percentage of credit diversion was estimated as the ratio of the amount of loan diverted for purposes other than dairying to the total amount of loan borrowed. The average credit diversion in the state was estimated to be 22.95 per cent. Borrower farmers from Perambalur district had a higher credit diversion rate at 24.63 per cent compared to borrowers from Coimbatore district, which had a credit diversion rate of 21.26 per cent.

    Table 3: Share of loan in investments and percentage of diversion of dairy loans in Tamil Nadu.


           
    The diversion of farm credit can be attributed to various factors. Understanding these causes is crucial for implementing effective measures to prevent diversion (Achoja, 2020). Factors affecting the rate of diversion of loans obtained by farmers were estimated separately for the dairy farmers from Coimbatore and Perambalur districts of Tamil Nadu. A multivariate regression analysis was also done for the pooled data from Coimbatore and Perambalur in order to get the complete picture of factors affecting credit diversion in the overall state. The results were furnished in Table 4. The R2 value obtained from regression analysis of the dairy farmers in Coimbatore district was 0.86, which defined that, 86 per cent of the changes in rate of diversion of credit were explained by independent variables such as gender, age of the respondent, education level of farmer, annual income, total landholding (ha), farming experience, dependency ratio, herd size, membership in dairy cooperatives and monthly per capita consumption expenditure of the farmers. The educational level of the farmer had a negative impact on credit diversion, with more educated farmers showing a lower tendency to divert institutional credits. Farmers with larger land holdings were observed to redirect their dairy loans for different purposes, such as engaging in various agricultural activities. This behavior could be attributed to their need for additional funds to support their diverse farming operations or to take advantage of investment opportunities beyond dairy farming alone (Papias and Ganesan, 2009; Ray and Das, 2023). The coefficients associated with farming experience and membership in dairy co-operatives was found significant with a negative impact on the rate of credit diversion. This suggests that as farming experience increases and farmers become members of dairy cooperatives, they are less likely to divert credit for non-dairy purposes. One possible explanation for this could be that experienced farmers and cooperative members may have better financial management skills and access to alternative sources of funding (Hananu et al., 2015; Saqib et al., 2018).   

    Table 4: Factors affecting the rate of diversion of loans obtained by dairy farmers in Tamil Nadu.

                    

    The R2 value obtained from regression analysis of the dairy farmers in Perambalur district was 0.89, which defines that, 89.00 per cent of the changes in the model were explained by independent variables. Among the independent variables total landholding of the farmer significantly influenced the rate of diversion of credit at 1 per cent level of significance. The age of the dairy farmers in Perambalur district was found to have a positive impact on credit diversion, signaling older farmers are more likely to divert loans for purposes other than dairy. The coefficient of dependency ratio was found significant at 5% level of significance. The dependency ratio in the household was observed to have a proportional effect on the rate of credit diversion. This suggests that households with more members relying on the primary income earner are more likely to divert credit for non-dairy purposes, potentially due to increased financial obligations or pressures (Mejeha ​ et al., 2018; Banerjee et al., 2015). This trend may be attributed to additional household expenditures on consumption. Conversely, households with higher income contributions from members were less prone to diverting the credit.
           
    The R2 value obtained from regression analysis of the pooled data of dairy farmers from both Coimbatore and Perambalur district was 0.84. Among the independent variables age of farmer, education level, total landholding (ha) farming experience dependency ratio, herd size and membership in dairy cooperatives were found significantly influencing the rate of diversion of credit at varying level of significances. The educational level of the farmer had a positive impact on reducing the credit diversion, with more educated farmers showing a lower tendency to divert institutional credits. Farmers with larger land holdings and higher dependency ratio were observed to divert more institutional credit for purposes other than the intended ones. Farmers possessing larger land holdings were observed redirecting their loans primarily for agricultural purposes rather than the intended use. This can lead to hinder the growth and sustainability of dairy operations and also impact the overall income stability of the farms in the long run. Farmers with less farming experience were found to be diverting their loans to a greater extent. This can be attributed to multiple factors such as, farmer’s less developed financial management skills and lack of financial stability (Sivakumar et al., 2013).
           
    A logit regression analysis was done in order to examine the factors affecting loan default of borrower farmers in Tamil Nadu and the results were presented in Table 5.  Out of the 100 borrower dairy farmers in the study area, 53 farmers were found to divert their loan wilfully or non-wilfully for non- productive purposes. The pseudo-R2 value of 0.63 indicates a good fit of the model, meaning that the variables included in the analysis explains 63.00 per cent of the variation between defaulters and non- defaulters of dairy loans. According to the results of the logistic model, it was observed that the defaulting behavior of borrowers are significantly influenced by factors such as the farmer’s age, education level, disease occurrence to the cattle, herd size, annual income and dependency ratio in the household. Farmers of higher age were noticed to either delay or not fulfill their regular loan repayments. This behavior may be attributed to utilizing dairy loans for alternative income-generating purposes and also the expectation of potential loan waivers. This result was in accordance to the findings of Uddin et al., (2019) that the probability of loan default increases when the age of a borrower increases. Farmers with higher levels of education, larger herd sizes and higher annual incomes were noted to have lower likelihood of loan default compared to their counterparts. Farmers who experienced disease outbreaks among their cattle in recent times were also noted to default on their loan repayments. This observation may be attributed to the financial strain incurred by the costs of managing and treating livestock illnesses, which could impact farmers’ ability to meet their loan obligations. An examination of the district-wise data revealed that dairy farmers in Perambalur district were more likely to default on their dairy loans when compared to those in Coimbatore. The dependency ratio was observed to have a positive effect on loan default, suggesting a higher likelihood of default. This finding could be explained by the increased financial burden on households with a higher dependency ratio, as they may struggle to meet loan repayment obligations amidst greater financial responsibilities and limited resources.

    Table 5: Factors affecting the loan default of borrower farmers in Tamil Nadu.


           
    Table 6 provides information on the determinants of wilful default of dairy loans by the sample respondents. Among the 100 borrower dairy farmers in the selected districts, 53 farmers had defaulted on their loan repayment, with 28 farmers defaulting wilfully, either in the anticipation of future loan waivers or due to financial strain. Explanatory variables viz., age of the dairy farmer, education level and disease occurrence to cattle, total landholding (ha), annual income and district (Perambalur) were found significantly affecting the wilful default of dairy farmers. Elderly and less educated farmers were found to have a higher likelihood for willful default on their dairy loans. This may be attributed to factors such as limited financial literacy or resources among older and less educated individuals, leading them to resort to willful default due to lack of awareness on the potential consequences or with the expectation of loan waivers (Grohmann et al., 2018). More over with an increase in education level of dairy farmer; their tendency to default the loan wilfully also gets decreases (Bamisha and Nidheesh, 2022). Farmers with substantial annual income and extensive areas under farming were observed to refrain from willfully defaulting on their loans. This behavior may stem from their ability to implement risk mitigation measures, such as diversification of income sources, insurance coverage and efficient resource allocation, which can contribute to financial stability and avoid intentional loan default. This finding contradicted the results of Gandhimathi and Vanitha, 2009, who found that farm income positively influenced willful default on loans in their research. The analysis of district-wise data indicated that dairy farmers in Coimbatore district were less likely to engage in willful default on their dairy loans compared to those in Perambalur. This difference could be attributed to the relatively higher education levels of farmers in Coimbatore.

    Table 6: Factors affecting wilful default of dairy loans by the sample respondents in Tamil Nadu.

    CONCLUSION

    This paper explores the factors influencing dairy farmers’ diversion of credit for unproductive purposes, such as repayment of old debts, house construction, education expenses for family members, family consumption and other agricultural activities. Additionally, a logistic regression analysis was employed to investigate the factors influencing default on dairy loans and to discriminate between willful and non-willful defaulters. It was observed that the, farmers from Perambalur district had a higher credit diversion rate compared to borrowers from Coimbatore district, the average credit diversion was estimated 22.95 per cent. The age and education level of the farmer along with the total landholding (ha), farming experience, dependency ratio, herd size and membership in dairy cooperatives were found significantly influencing the rate of diversion of credit. Farmers who faced disease outbreaks among their cattle in recent times were observed to be more likely to default on their loan repayments. The empirical analysis of the district effect on default showed that dairy farmers in Perambalur district had a higher likelihood of defaulting, including wilful defaulting on their dairy loans compared to those in Coimbatore. Supporting and providing resources to farmers to explore income diversification opportunities beyond solely relying on dairy production can potentially reduce their dependence on diverting loans for non-farm needs. Additionally, developing mechanisms for debt restructuring or rescheduling in cases of genuine hardship can help farmers overcome temporary financial hurdles and avoid wilful defaults.

    ACKNOWLEDGEMENT

    The sincere guidance of the Head of the Department and kind support of other faculties along with research_scholars has been sincerely acknowledged by the first author.

    CONFLICT OF INTEREST

    The authors declare no conflict of interest.

    REFERENCES


    1. Achoja, F.O. (2020). Loan diversion and effect on the growth of small scale poultry farms in Nigeria, Scientific Papers Series Management. Economic Engineering in Agriculture and Rural Development. 20(3): 25-32.

    2. Awotide, B.A., Abdoulaye, T., Alene, A. and Manyong, V.M. (2015). Impact of Access to Credit on Agricultural Productivity: Evidence from Smallholder Cassava Farmers in Nigeria. Paper Presented at the International Conference of Agricultural Economists (ICAE), Milan, Available online: http://purl. umn.edu/210969.

    3. Bamisha, K.P. and Nidheesh, K.B. (2022). Relationship between financial capability-financial well-being of organic farmers in Kerala, India. The Journal of Contemporary Issues in Business and Government. 28(3): 373-385.

    4. Banerjee, T., Roy, M., Raychaudhuri, A. and Ghosh, C. (2015). What drives households to divert loans? A village level study. Asia-Pacific Social Science Review. 15(2): 33-55.

    5. Gandhimathi, S. and S. Vanitha. (2009). Repayment and over dues determinants of agricultural credit: Some results for commercial and cooperative banks. The IUP Journal of Bank Management. VIII ( 3 and 4):  54-72.

    6. Grohmann, A., Klühs, T. and Menkhoff, L. (2018). Does financial literacy improve financial inclusion? Cross country evidence. World Development. 111: 84-96.

    7. Hananu, B., Abdul-Hanan, A. and Zakaria, H. (2015). Factors influencing agricultural credit demand in Northern Ghana. African Journal of Agricultural Research. 10(7): 645-652.

    8. Hanley, A. and Girma, S. (2006). New ventures and their credit terms. Small Business Economics. 26(4): 351-364.

    9. Kumar, N., Toor, J.S. and Singh, G. (2021). Level and pattern of consumption expenditure of rural households among different regions of Punjab. Indian Journal of Economics and Development. 17: 468-73.

    10. Kumar, A., Mishra, A.K., Saroj, S. and Joshi, P.K. (2017). Institutional versus Non-institutional Credit to Agricultural Households in India: Evidence on Impact from a National Farmers’ Survey (March 2, 2017). IFPRI Discussion Paper 1614, Available at SSRN: https://ssrn.com/abstract=2930177.

    11. Kumar, K.K. and Moharaj, P. (2023). Farm size and productivity relationship among the farming communities in India. Outlook on Agriculture. 52(2): 212-227.

    12. Luan, D.X. and Bauer, S. (2016). Does credit access affect household income homogeneously across different groups of credit recipients? evidence from rural vietnam. Journal of Rural Studies. 47(A): 186-203.

    13. Mejeha, R.O., Bassey, A.E. and Obasi, I.O. (2018). Determinants of loan repayment by beneficiary farmers under the integrated farmers scheme in Akwa Ibom State of Nigeria. Journal of Agriculture and Food Sciences. 16(2): 75-87.

    14. Memmel, C. Raupach, P. and Bundesbank, D. (2019). Banks’ Credit Losses and Lending Dynamics, available at: https:// www.bde.es/f/webbde/INF/MenuHorizontal/Sobre ElBanco/Conferencias/2019/Raupach.pd

    15. NABARD. (2019). National Bank for Agricultural and Rural Develop- ment. Annual Report (2018-19). Retrieved from: https:// www.nabard.org/auth/writereaddata/tender/1008203730 Nabard%20English%20Annual% 20Report%20for% 20Website.pdf.

    16. Pandey, U.K. (2017). Unit-14 Structure of Farming Sector: Dynamics and Implications. IGNOU. Retrieved from: https://www. egyankosh.ac.in/handle/123456789/39957.

    17. Papias, M.M. and Ganesan, P. (2009). Repayment behaviour in credit and savings cooperative societies: Empirical and theoretical evidence from rural Rwanda. International Journal of Social Economics. 36(5): 608-625.

    18. Raj, N.K., Dixit, A.K., Singh, A., Bhandari, G., Ponnusamy, K., Sen, B. and Verma, A. (2023). Study on flow of institutional credit and its diversion by dairy farmers in Kerala. Journal of Agricultural Development and Policy. 33(2): 132-137.

    19. Ray, P. and Das, B. (2023). Agricultural Credit Utilization and Repayment by Farm Households in Tripura. Indian Journal of Extension Education. 59(2): 30-35.

    20. Saqib, S.E., Kuwornu, J.K., Panezia, S. and Ali, U. (2018). Factors determining subsistence farmers’ access to agricultural credit in flood-prone areas of Pakistan. Kasetsart Journal of Social Sciences. 39(2): 262-268.

    21. Sivakumar, S.D., Jawaharlal, M., Palanichamy, N.V. and Sureshkumar, D. (2013). Assessment of farm financial literacy among jasmine growers in Tamilnadu, India. Assessment. 3(13): 67-76.

    22. Uddin, M.S., Chi, G., Habib, T. and  Zhou, Y. (2019). An alternative statistical framework for credit default prediction. Journal of Risk Model Validation. 14(2): 65-101. doi: 10. 21314/ JRMV.2020.220

    23. Vedamurthy, K.B. (2015). Analysis of institutional credit for dairy farming in Karnataka: A study of Shimoga Milk Zone. Indian Journal of Dairy Science. 68. doi: 10.5146/ijds. v68i3.44450.

    24. Wakuloba Bwonya, R.A. (2008). Causes of default in government microcredit programs: A case study of the Uasin Gishu district trade development joint loan board scheme, Kenya. Awarded Theses, 1.

    25. Yadav, P. and Sharma, A.K. (2015). Agriculture credit in developing economies: A review of relevant literature. International Journal of Economics and Finance. 7(12): 219-244.

    26. Zhuang, J., Gunatilake, H.M., Niimi, Y., Khan, M.E., Jiang, Y., Hasan, R. and Huang, B. (2009). Financial sector development, economic growth and poverty reduction: A literature review. Asian Development Bank Economics Working Paper Series. (173).
    In this Article
      Contact us
      Follow us
      Published In
      Asian Journal of Dairy and Food Research

      Editorial Board

      View all (0)