Agricultural value chain is complex. Digitisation of this chain, with the help of agritech and agrifintech startups, is the one way to increase farm productivity, enable better resource utilisation, and extend knowledge-based services
There has been a lot of talk about ways in which we can double farmers’ income. One step towards this is to work on the agricultural value chain (AVC). AVC is a complex structure of inputs that includes farm inputs such as seeds, pesticides, fertilisers, and production efficiencies. It also looks into issues of geographic location, soil health, water, climatic conditions, output per hectare, and markets such as prices and market access. The Government of India has three critical agendas aligned for optimal return—increasing farm produce, enabling better resource utilisation, and extending knowledge-based services. Digitisation of the AVC is the one way to achieve the government’s objective of doubling farmers’ income and making a meaningful impact on farmers’ life. It can be achieved by e-governance initiatives (e.g., eNAM) by the government and through the active participation of agritech and agrifintech startups.
Digitisation efforts can be divided into three principal focus areas—Digital Agriculture, Digital Market Access, and Digital Finance. This article explores these areas in today’s context and looks at the challenges and opportunities within them for Indian farmers.
The adoption of improvements in agrarian practices has been a critical challenge for farmers. Collecting data across the AVC and its practical use in agricultural practice to show a visible impact on farm outputs has remained a key challenge.
Emerging technologies such as Internet-of-Things (IoT), Artificial Intelligence (AI), Machine Learning (ML) and Advanced Analytics can help digitise farm value chains. IoT and spatial data analytics of imagery (by satellite/drone) can help determine soil, temperature, precipitation, and climatic factors and even help predict crop choice for profit maximisation. Microsoft and International Crops Research Institute for Semi-Arid Tropics (ICRISAT) have worked on an AI model to identify the best time to sow crops. This model was tested in Andhra Pradesh, resulting in a 30 percent increase in crop yields without any increase in capital expenditure.
The AI model can be further extended to use high-resolution satellite images and utilise data points like leaf area index, the height of plants, canopy, and crop vigour. This can be used to predict farm yield. Instead of utilising high-resolution satellite images, the AI model can also use high-resolution images from drone surveys. The new drone (amendment) rules in 2022 will be a big push for these services in the country.
[This article has been reproduced with permission from the Indian School of Business, India]