Future Farming in India 2025

Enabling datasets Several datasets are needed for macro crop planning, as depicted below. Crop-suitability data based on agroclimatic zones Creation of national database of crops suitable for different agroclimatic zones, if possible with granularity to district level.Global and local pricing data zones Includes data (historic and current) on the market prices of different crops in different geographical areas and export markets to identify crops that may be most lucrative to grow.Market glut and shortage data Includes data on past market gluts and shortages to correlate with other datasets in order to forecast such imbalances and reduce pricing risk. Strategic crop data Includes data on strategic crops for different state governments and national governments. For instance, could include crops that are prioritized for import substitution.Land records and farm demographic data Includes farm-level data on land records, farm income (projections or estimates) and cropping patterns to understand the suitability of a farmer planting recommended crops.Climate and weather data Includes climate forecast for cropping season, such as details about rainfall, precipitation, humidity, sunlight, temperature and wind at the district level.Enabling datasets required for macro crop planning FIGURE 4 Indicative roadmap for operationalizing macro crop-planning models AI-enabled macro crop planning is a novel concept, especially in India, so a staggered approach should be followed while deploying the use case. Crop planning should be based initially on models that draw on crop-suitability data followed by adding additional layers of market intelligence data and climate-risk data. 17 Future Farming in India