In its budget 2017-18, the government has taken up several measures to revive the country’s agriculture economy. The emphasis on agricultural insurance through higher allocation for the Pradhan Mantri Fasal Bima Yojana (PMFBY), and other major allocations for the sector, are expected to boost credit flow to farmers apart from expanding crop insurance and irrigation coverage.
The commitment shown towards agricultural insurance is an important step by the government, as it will help to provide financial stability for farmers. Agriculture is risky business and is susceptible to volatility in production and commodity prices. Hence, it’s important to encourage farmers to use innovative agriculture services and technology, which in turn will improve farm productivity and income, and help them deal with post-harvest challenges.
Until a decade or so, agricultural insurance was a sector that developed mainly outside Asia. This started to change after 2005, when India and China began expanding their own agriculture insurance plans. Since then, we have seen a dramatic development, so much so that India is one of the largest agriculture markets in the world today, with index-based crop insurance covering a wide variety of crops in major provinces of the country.
Still, there has been low penetration of agriculture insurance in India, with challenges like insufficient risk coverage, delayed and inaccurate claim assessment, and leakage.
The banking channel continues to drive distribution of agriculture insurance, but there is a need for insurance companies to reach rural markets through new marketing mechanisms apart from the traditional bancassurance model. The challenges of infrastructure and distribution can be overcome with careful planning, innovative use of technology and favourable government policies.
The government, through the PMFBY, is trying to bring more farmers (targeting 50% by 2018) under the scheme’s ambit. However, several key challenges need to be addressed to achieve this goal.
Firstly, it is important that forecasts for seasonal crop productions are made with the highest possible accuracy, and field warnings detected early so that an action plan may be implemented for irrigation, agri-credit and agri-inputs.
Secondly, stakeholders such as the government, insurers and agricultural research agencies need to be adequately equipped with the necessary technological know-how to deal with some of the farming issues.
The introduction of new technology services into agriculture can provide a more detailed picture of risk at the farm level without the costs of collecting data manually. In addition to technological intervention, it is necessary to keep time lags in publishing crop yield statistics for the cropping period to a minimal.
Historically, government officials in India have conducted random-sample crop-cutting experiments (CCEs) to arrive at estimations of yield at the sub-district level or at even finer granularity. The process is resource-heavy, and prone to sampling and non-sampling errors and manual subjectivities. It is, therefore, essential to bring in inclusive models that take into account ancillary data sets like weather and soil parameters to predict yield with more accuracy.
The Internet of Things (IoT) here finds increased relevance. The IoT promises increased yields, reduced costs and other efficiencies, with the deployment of sensors, connectivity and analytics.Soil sensors as an IoT technology can also be used to broadcast real-time information on the state of the soil. This can be combined with other data to forecast crop yields.
Another possible solution could be to use satellite images to map the crop types, identify potential yield categories, calculate the area under each category, find locations with the maximum area and then select the number of samples for CCEs. Based on the data received, from remote sensing techniques, climate and other weather parameters, one can even try to conduct a large number of CCEs in the area where the probability of loss is high. This can be complemented with hand-held devices and smartphones to procure multiple images, which capture the heterogeneity of different field conditions in a village.
The use of drones to take images, recreate and analyse individual leaves from close-enough heights, assist in pest control, mid-season crop health monitoring, assess the soil-water-holding capacity and create weed maps or frost damage maps is another option.
In addition, mobile apps can also help provide evidence of canopy coverage or estimate the amount of fertilizer needed. They can also be used to collect information on insured area, insurance coverage and farmer profiles, which can help insurers develop customized products for farmers.
A promising outlook for crop insurance, aided by data and technology
The budget allocation of Rs10,000 crore to the BharatNet Project and the set target of reaching nearly 150,000 gram panchayats with high-speed Internet will also lay the foundation for a digital revolution in agriculture in India.
The core focus of the budget allocation is boosting agriculture credit.
To ensure flow of credit to small farmers, all functional primary agriculture credit societies (Pacs) will be integrated with the core banking system of district cooperative banks. Banks are the core distribution channel for the PMFBY and digitization will ensure penetration increases and that each farmer having access to credit is protected. Easy Internet access will allow farmers to learn and implement the latest technologies available in the field of agriculture. The finance minister has proposed that e-NAM (the National Agriculture Market) would be linked to the commodities market to allow farmers to access better prices for their produce.
For insurers too, the potential clearly exists for using technology to ensure implementation of agriculture insurance schemes in a sustainable manner. Insurers are always seeking ways to provide granular and objective risk profiles of individual farmers without the prohibitive costs of visiting and assessing single farms. Advances in technology and data processing may provide them with the means of doing so.