Data-driven agriculture can help India manage droughts better and improve long-term profitability of produce
News about Fani and the trail of devastation in large parts of coastal Odisha put the spotlight on cyclones and with it extreme weather-related events that impact agriculture came into focus. Arguably, one of the most frequently experienced weather events in India is drought, which has, unfortunately, become commonplace. But with new trends in this sector like data-driven agriculture and precision farming, drought situations can be combated and negative impact on yields and farmer incomes can be minimised.
Farming and data seem to occupy opposite sides of the spectrum but the link between the two is at its strongest ever, giving rise to the concept and practice of ‘data-driven agriculture’, which is a result of the need to participate in a globally competitive and market-oriented system of agriculture. What it entails is the thoughtful use of big data to supplement on-farm precision agriculture. It means having the right farm data at the right time to make better decisions to improve long-term profitability. Precision agriculture is another buzzword and it specifically refers to the use of data that has been generated on the farm. Data-driven agriculture combines this on-farm data with other statistics that is compiled by a third party, catalysing it into useful information that helps make correct decisions. Data here is both an input and a product and putting them to use can resolve many issues. When it comes to different types of data that are generated as a result of the entry of digital technologies into agriculture, there are many. And combating drought is supported not directly by a single set of data but by applying a combination of them — some indirect parameters and others more straightforward. The types of data generated range from analysing weather patterns to advice on when to plant, to analyse prices and to advice on when to harvest. These decisions are subject to a variety of influences. The best combination of data needs to be applied to arrive at the right decision. Looking at the types of data in greater detail, in the normal case, there are two streams of data used by farmers. The first relates to data that is generated and collated on the farm, to be used on the farm itself. This is ‘localised’ and includes soil data — form, depth and composition, seed and fertiliser use, date of sowing, production practices, water use and the like. This kind of data can both be generated and managed by the farmer or by an agent. The farmer ‘owns’ this data. The second stream is data generated and collated off the farm for use on the farm. Examples are climatic data and market prices that have been interpreted and customised for on-farm use. This is called ‘imported’ data.
When it comes to combating drought, direct data available will be of weather forecast which is available through weather advisory services that farmers may subscribe to. But this must be combined with other data such as soil moisture, soil type and type of crops to grow, when to sow and when to harvest. It may also help to know the pricing patterns as well. Even the farmer’s access to financial services like banking, credit and insurance will have a bearing, helped on by data-driven services that ease access to finance. Fortunately, remote sensing satellite data combined with unmanned aerial vehicles and mobile weather stations are providing a huge amount of data that models can integrate. Hyper-localised analysis and recommendations are becoming available more easily, with GPS-enabled phones that provide exact latitude, longitude and elevation — all of which can sharpen decision-making.
Taking a deeper look into how such weather data can be collected, the good news is that infrastructure to collect weather and rainfall data has drastically improved. There is a system in which meteorological stations send out field staff to collect localised weather data and transmit it into a central repository. Adding to this are automated weather data collection systems using sensors, which are also evolving. Other features like ground water monitoring and monitoring of water flow in canals are also carried out and captured. All this data put together helps in ‘water budgeting’, which in turn can reduce the damage due to drought. Andhra Pradesh, Karnataka and Maharashtra are some States investing in this area.
An app developed by ICRISAT in collaboration with Microsoft does just that. The app advises farmers on the best time to sow crops depending on weather conditions, soil and other indicators. Using business intelligence tools with a dashboard provides important insights around soil health, fertiliser recommendations and seven days’ weather forecast. There are more than 500 million farmers worldwide who play a vital role in food production. Unlike earlier times, it has become possible to bring them into the fold of data-driven farming because of the spread of mobile technology and ubiquitous connectivity, irrespective of whether they are literate or not. While this diversity enhances resilience, it can also become a challenge to data-driven service providers whose business models need to match delivery of precise and personalised services with the need to reach many farmers at low costs.
(The writer is CEO of a farm software solution company)
