Livestock Research for Rural Development 28 (11) 2016 | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
For millions of households in rural India, smallholder dairying is one of the important livelihood options. This has been identified as one of the tool to move out from poverty. Thus sustaining smallholder farmer in dairying profession becomes mandate. For this, wide ranges of support ecosystem, including financial services are essential. This study makes an attempt to understand the status of financial services of smallholder dairy farmers in Tamil Nadu, India through direct interview of 410 dairy farmers.
This study found that the reachability of institutional financial services were limited. Even after entry of private banks, micro financial institutions to the existing pool of institutional financial service providers (public sector and co-operative banks) in the post liberation era, non institutional lenders such as milk vendors and agents and money lenders have substantial roles in providing credit to dairying with high interest rates.
Key words: credit, gender, loans
Smallholder dairy farming is one of the important livelihood options in rural India with annual production 132 million metric tonnes of milk. More than 70 million rural households are engaged in dairying. It is an important occupation for landless, small and marginal farmers. This section of rural community owns nearing 60 per cent bovines. This activity has been supplementing the income, acting as collateral to secure loans and acting as liquid asset to meet out emergencies of farming households. Further, the dairy farming activity has been generating employment to the rural work force and has been playing vital role in empowering rural women. Researchers, policy makers and developmental agencies view the smallholder livestock farming which encompasses dairying, as tool to move out from poverty and one other means to meet out the United Nation’s millennium development goals. But its role in addressing poverty issues and its own existence as an livelihood option for rural, depends upon support ecosystem. The support ecosystem comprises of research and development, low cost technologies, animal health care services, extension support, financial services, access to inputs, market etc. Improved financial services in rural areas would stimulate agricultural and allied sectoral growth, leading to economic growth and reduction of poverty in rural areas. Taking account of above facts, this study makes an attempt to understand the one of the crucial component namely access to financial services for smallholder dairying.
The research study was carried out for understanding the financial services to smallholder dairying. This was conducted in three Crop livestock system (CLS) namely CLS 5.3, CLS 1.0 and CLS 15) of Tamil Nadu, India. These CLS varies on socio-economic indicators, crop raising pattern, livestock rearing pattern and agro ecological factors (Typology developed by Rao and Birthal 2008). The CLS 5.3 has hot moist humid to sub-humid with high annual rainfall and low irrigation facilities. Unorganised milk market is the dominant pattern. While the CLS 1.0, has coastal plain topography with sub-humid to semi-arid tropical climate and has high annual rain fall and irrigation facilities. Further for milk, poor marketing opportunities for disposal with a limited role for unorganised market. The CLS 15.0 has semi arid tropical climate with low rainfall and irrigation. But it has strong presence of milk marketing opportunities with dairy co-operatives, private dairies and unorganised sector. From the above each CLS, the taluks (administrative units for provincial government, which is under district administration uint) were stratified as high, medium and low milch animal population using Dalenius-Hodges stratification procedure. From each of this sub-stratum (Taluk) a representative unit for high, medium and low milch animal population were drawn based on simple random sampling. From the selected taluks, the villages (villages referred as grama panchayats - grassroot level administrative unit) were also stratified into low, medium and high milch animal population villages similar to the procedure adopted for taluk. From the stratified villages, a representative village was drawn using simple random sampling. Thus a total of 21 villages (CLS1-3 villages, CLS 5.3-9 villages, CLS 15 – 9 villages) were taken for survey. From the selected villages, dairy farmers list were prepared and a total of 410 respondents was selected using simple random procedure. A semi structured pre tested interview schedule was used for data collection and collected data were categorised, tabulated and statistical tools were employed for analysis.
For a detailed understanding of financial services to dairying, a broader outline of a debt pattern among smallholder dairying households were presented in Table 1. From the table 1, it can be concluded that across the CLS, the majority of farmers were in debt. This is in concurrence with the findings of (Singh et al 2014) who reported 88.00 % of the farming households were under debt. While (NSSO 2003) reported 46 per cent farmers were under debt. Financial constraints remain persistent, and they are pricey and lopsidedly distributed, make smallholders mostly as borrowers. Further, from the chi square analysis, it can be interpreted that there was high significant difference in debt status between crop livestock systems. The CLS 15.0 has highest debt households in Tamil Nadu.
Table 1. Distribution of smallholder dairy farming households based on status of debt |
||||
Households Debt status |
CLS 5.3 |
CLS 1.0 |
CLS 15.0 |
Chi square value |
With debt |
20 (66.7) |
130(73.4) |
174(85.7) |
11.6** |
Without debt |
10 (33.3) |
47(26.6) |
29(14.3) |
|
Total |
30(100.00) |
177(100.00) |
203(100.00) |
|
** Highly significant |
The Table 2 describes the distribution of loans (number of loans) from various sources. Totally 490 loans has been taken by to 410 farmers. Thus on an average a farmer has 1.20 loan per household. Majority of loans (59.05 %) was taken from institutional sources. The remaining loan portion availed from non institutional sources such as money lenders, middleman of milk marketing channels, friends and relatives. Among the institutional sources nationalised banks (Public sector banks) were major contributors to credit. While in case of non institutional sources, money lenders were dominant. Further in the total debt amount, 66 % and 34 % were from institutional and non institutional sources respectively. Thus over all institutional sources were dominant in terms of number of loans issued and amount distributed as credit. This is in concurrence with the observation of (Singh et al 2014) who reported that only minority (20%) of loans was from non institutional sources. But the recent past studies (NSSO 2003) found that non institutional sources of credit were dominant and according to (Planning Commission 2010) large segments of India’s poor households continue to be shut out of formal credit mechanisms. These steep changes may be attributed to government regulations pressuring institutional financial services to extend their services through opening branches and services through intermediaries. Further increased competition at urban markets and untapped rural markets might have forced the intuitional financial services to rural markets.
Table 2. Distribution of loans (number of loans) based on sources |
||||
Credit sources |
CLS 5.3 |
CLS 1.0 |
CLS 15.0 |
Total |
Institutional sources |
||||
Nationalised Banks |
15 (50.0) |
77 (37.93) |
75 (29.07) |
167 (34.01) |
Co-operative banks |
5 (16.67) |
29 (14.29) |
48 (18.6) |
82(16.7) |
MFI /Private Banks |
4 (13.33) |
17 (8.37) |
20 (7.75) |
41(8.35) |
Non institutional sources |
||||
Friends and Relatives |
2 (6.67) |
11 (5.42) |
27 (10.47) |
40 (8.15) |
Milk vendors or agents |
0 (0.0) |
21(10.34) |
21(8.14) |
42 (8.55) |
Money lenders |
4 (13.33) |
45 (22.17) |
65 (25.19) |
114 (23.22) |
Others |
0 (0.0) |
2 (0.99) |
2 (0.99) |
4 (0.81) |
Total |
30 (100.0) |
202 (100.0) |
258(100.0) |
490(100.0) |
From the Table 3, it can be interpreted that out of the total loans issued more than 40 per cent of loans were taken for other purpose (consumption purposes). These loans were mainly used to meet out the expenses towards family functions (such as marriages, festivals, etc), emergencies, educational purposes and other consumption needs. Next to the above, loan was availed for crop and dairy activities. Thus loans availed from Nationalised banks and money lenders were mostly utilised for consumption. While in contrast, the loans from a milk vendor or agents were spent for dairying activity. Further, on analysis of overall debt amount composition, it was found that 51.18 %, 41.19 % and 7.63 % and amount went to consumption, crop and dairying respectively. The finding of this study is in accordance with (CGAP 2002) which revealed that poor people choose to invest in a wide range of assets, including better nutrition, improved health, access to schooling, a better roof on their homes, and expansion of their small businesses. While (Vedamurthy 2015) reported among the formal lending agencies, on an average only 2% of agricultural credit was being planned to be disbursed to the dairy sector. Even this lower target was half achieved (52.24%). From above past studies and findings of this research, it may be inferred that, dairy sector was highly neglected in terms of financial services.
Table 3. Purpose of the loans |
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Source and purpose of loan |
Crop |
Dairy |
Others |
Total |
Nationalised Banks |
83 (16.94) |
19 (3.88) |
65 (13.27) |
167 (34.01) |
Co-operative banks |
54 (11.02) |
13 (2.65) |
15 (3.06) |
82(16.7) |
MFI /Private Banks |
13(2.65) |
10 (2.04) |
18(3.67) |
41(8.35) |
Friends and Relatives |
14(2.86) |
6(1.22) |
20(4.08) |
40 (8.15) |
Milk vendors or agents |
0(0.0) |
42(8.57) |
0 (0.0) |
42 (8.55) |
Money lenders |
21(4.29) |
14(2.86) |
79(16.12) |
114 (23.22) |
Other non institutional sources |
3(0.61) |
0(0.0) |
1(0.2) |
4 (0.81) |
Total |
188 (38.4) |
104(21.2) |
198(40.4) |
490 (100 ) |
From further analysis, it was found that out of the overall total loan amount disbursed from various sources, nearing 66 % accounted from institutional sources. The remaining 34 % were from non institutional sources. From the Table 4, it was found that there was a significant difference in loan amount dispersal between institutional and non-institutional sources to farming households.
Table 4. Mann-Whitney U test on loan amount from different sources |
|||
Source of credit |
Mean Rank |
Sum of ranks |
U Value |
Institutional |
475.70 |
195038. 50 |
57320 ** |
Non Institutional |
345.30 |
141571.50 |
|
** Highly significant |
In addition, it is found that on an average a farming household has a debt of INR 123128. The average household debt for crop, dairy and others (mainly used for consumption purposes) was INR 50712, INR 9400 and INR 63016 respectively.
Further, researchers made an attempt to understand the loan distribution for dairying across three farming systems. From the Table 5, it can be interpreted that there was high significant difference in credit taken for dairying between farming system.
Table 5. Kruskal–Wallis one-way analysis of variance for loan distribution for dairying |
|||
Crop livestock system |
n value |
Mean rank |
Chi square value |
CLS 5.3 |
30 |
170.13 |
10.02 ** |
CLS 1.0 |
177 |
197.08 |
|
CLS 15 |
203 |
218.07 |
|
Total |
410 |
||
** High significant |
In order to understand difference among the farming system the difference over the credit assessed for dairying, Mann Whitney U test was carried out and the results were presented in Table 6. It can be interpreted that CLS 15.0 has significant difference and high significant difference with CLS 5.3 and CLS 1.0 respectively. The average credit taken for dairying in CLS 5.3, CLS 1.0 and CLS 15.0 was INR 6667, INR 5780 and INR 12691 respectively. The difference might be due to presence of strong organised milk marketing opportunities in CLS 15.0 which might be providing financial services a confidence to recover the loan without much risk.
Table 6.
Mann–Whitney U test one-way analysis of |
|
Comparison between CLS |
U Value |
Between CLS 5.3 and CLS 1.0 |
2285.00 |
Between CLS 5.3 and CLS 15 |
2354.00* |
Between CLS 15 and CLS 1.0 |
16104.50* |
* Significant |
From the Table 3 it can be understood that non institutional sources issued 60 % (62 loans) loans for dairying against 40 % (42 loans) loans from institutional sources. Even though the difference exists in number of loans, there was no significant difference in the amount disbursed to dairying as a credit between institutional and non institutional sources (Table 7).
Table 7.
Mann–Whitney U test one-way analysis of variance for loan |
|||
Source of credit |
Mean |
Sum of |
Mann Whitney |
Institutional |
402.20 |
164901 |
80646 NS |
Non Institutional |
418.80 |
171709 |
|
NS - Not significant |
Among the non institutional sources milk vendors and milk agents provides 68 % (42 loans) loans for dairying and play a predominant role in financing dairy related activities with the highest number of loans issued to smallholder dairy farmers. Access to institutional credit by small and marginal farmers is minimal and money lender is regaining his grip in the rural credit market (NSSO 2005).
Their financing pattern varies with the location and similarly their loan products are diversified. It ranges from financing to purchase of animal, feed and for accessing health services, etc. Further collection of loan and interest are in the form of milk and cash. The interest rate was ranging from 0 to 228 % . Zero interest rate was given for petty loans which are likely to be used for accessing animal health emergencies and purchase of inputs. The average interest rates worked out to be more than 66 per cent per annum. This interest rate is similar to the interest rates of local money lenders which are of in the range of 24 to 180 per cent per annum (Kainth 2010). Smallholder dairy farmers have easy access to this kind of credit compared to the institutional financial services. From the fields, it was observed that the underlying motives of non-institutional sources for financing smallholder dairy farmers are to retain them for sustained milk procurement and additional earnings. Further, farmers perceived that accessing credit from milk vendor and the agents were easier comparing to institutional sources because diversified loan product portfolio and limited processing procedures. The attitudes of formal sector lenders are often seen as a root cause of low income workers being driven into the arms of less affordable informal credit providers. In addition, the formalities of the loan process followed by formal lenders and the time taken to issue loan approvals obviously are seen by borrowers as deterrents to using formal loan channels – especially for emergency loans.
Institutional sources provided the insurance services to the dairy farmers. The milch animal insurance coverage given in Table 8 illustrates only 20 % of the dairy farming households’ have insurance coverage to their animals. The reaming 80 % does not have any insurance coverage for their dairy animals. Chi square analysis was carried out to interpret the variance in insurance coverage among three crop livestock system. There was significant difference in insurance coverage between farming systems. There is better coverage of insurance was noticed in CLS 1.0 and 15.0.
Table 8. Milch animal insurance coverage pattern |
|||||
Status of animal insurance |
CLS 5.3 |
CLS 1.0 |
CLS 15 |
Total |
Chi square value |
At least one milch animal insured |
1(3.3) |
34(19.2) |
47(23.2) |
82 (20) |
6.54* |
No milch animals insured |
29 (96.7) |
143(80.8) |
156(76.8) |
328(80) |
|
Total |
30(100) |
177(100) |
203(100) |
410(100) |
|
* Significant |
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Received 18 August 2016; Accepted 11 September 2016; Published 1 November 2016