In India, agriculture continues to be the major employment sector as more than 50% of the population do farming. The risks associated with the agriculture industry, specifically in India, are aggravated by various factors, ranging from weather variability, frequent natural disasters, uncertainty in crop production and market prices, lack of effective rural infrastructure, and market information asymmetry that reduces the efficacy of risk mitigation instruments like credit lending, insurance, and option markets. The above-mentioned factors not only possess risk to the livelihood and income of the farmers but also hinder the whole agriculture sector in becoming part of the solution to the problems faced by agricultural labor.

The major problems lie in the complexity of agriculture sector in terms of farms size, mixed crop plantation, and drastic climatic changes among others, which has led to the disengagement of banks and other financial institutions towards rural finance, since there is lack of profitability. The remoteness of the rural clients and poor financial networks increases the cost of doing business in these types of areas. India’s rural transformation and national economy are dependent on the agriculture sector, for which fiscal and monetary interventions are required to ensure security of the farming community, but also to generate constant mode of income, savings, and investment. The grossly under-funded Indian agriculture sector provides huge opportunities for banks, insurance and reinsurance companies to provide sustainable solutions to farmers by providing the necessary capital to come out of poverty trap and get insulated from the income shocks.

In last 10 years, a lot of technological advances happened in satellite data mining, artificial intelligence and Big Data cloud computing, that has disrupted several industries in a positive manner for the real-time large area monitoring. Geospatial domain has the ability to provide a viable solution to governments and financial institutions for efficiently assessing, mitigating and coping with the large basis risk that agriculture sector come with. Due to the availability of the satellite data from late 1990s until now, it helps in better assessment of risks, its consequences, and prioritization due to the rich history. The risk-mitigating and transfer strategies can be dominated by geospatial technology, which can be used to generate real-time info, early warning signals, and large area monitoring in terms of vulnerability and crop suitability mapping, production data prediction, and natural disaster signals, to provide intervention insights to the farmers, government and other financial institutions. Also, it is very useful in implementing risk coping strategies by objectively triggering signals for the impending disaster for a large area scalability of social safety net programs.

The agriculture credit risks can be defined in terms of farmer’s ability to repay loans. Most of the farmers in India do not have a bank account and tend to depend on non-institutional sources like local money lenders. These local money lenders thrive on high risks and charge high-interest rates. One of the important reform that took place in 1969 was bank nationalization to increase the availability of the credit to agriculture and free farmers from the holdings of the private money lenders. Banks were advised to allocate lending resources in the rural areas for the purpose of increasing the credits in the rural agriculture sector which led to an expansion of banking branch network in the rural areas. Even after expansion of the banking network in the rural areas most of the farmers are not able to obtain capital help from the banks or insurance companies. The major reason for the failure of the system is due to lack of credit history of farmers in terms of income generated in the past, and complexity in tracing land ownership records.

Geospatial technology and Big Data analytics will play a vital role by going back in time to assess the agriculture profile of the farmers for giving indications to the bank about the loan repaying ability of the farmer. It will also enhance the agriculture insurance from index based to more localized insurance model for better assisting farmers in the catastrophic situations. It will also help government better design the risk management policies that can be effective on a large scale.

Rashmit Singh Sukhmani

Global Head, Product Management, SatSure

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