Being able to accurately predict crop pricing two months before harvest, at a district level plays a huge role in ensuring the small & marginal farmer gets fair remuneration and in mitigating farmer stress and suicides.
Picture this real-life decision-making scenario:
A Bihar Government bureaucrat is trying to forecast tomato’s post-harvest prices to avoid the heartbreaking crisis last season when prices crashed due to a sudden glut. Tomatoes dumped on the road and farmer suicides attracted bad press.
He pores over submissions from each district, showing crop-wise acreage and sowing week. From experience, he knows this data could be 2-3 months old, while tomato’s harvest in 3-4 months. He observes that gross crop acreage varies 15-25% across submissions and he suspects some districts fill in data sheets without stepping out of the office.
Based on this, how can he forecast prices or take action?
Should he believe the input suggesting Tomato acreage is 15% lower than the previous cycle? How would the forecasted winter rains and colder weather impact tomato yield?
He wishes he could advise farmers better because staggering sowing and harvesting by 1-2 weeks play a big role in smoothening market price swings.
But for that, he needs automated, real-time data.
To solve this problem, an enabling framework like the IAP will use multi-year Agri Data-sets from multiple sources (government, enterprises, start-ups), seamlessly translating it into information and then into precisely actionable insight.
For instance, digital crop signature, from satellites, combined with AI, can reveal crop-wise acreage under plantation, by district within 4-6 weeks of planting. As the crop matures, it estimates crop yield, and then combined with acreage and processing capacity in proximity – likely post-harvest prices. The IAP also provides an alternate forecast by analysing aggregated seed sales data, district wise, from suppliers to predict acreage under tomatoes. Both estimates are correlated for accuracy
Early price forecasts enable faster tactical actions avoiding price crashes, like helping stagger harvesting, or tying-up additional quantities with processing plants in advance. Powered by Artificial Intelligence (AI) & Data Analytics, IAP helps tactical and strategic decision making, leveraging aggregated insights from the farms to state/national levels.
The Agri Collaboratory 