India Wheat Yield Forecast Model

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Gro’s Yield Forecast Models use a suite of machine-learning models to estimate in-season yields at the district, province, and/or national levels on a daily basis. Gro’s India Wheat Yield Forecast Model follows the process set forward in our successful US corn and Argentina Soybean Yield Forecast Models. The model  is updated daily and provides in-season yields for wheat at the district, province, and national levels throughout the growing season.

Customers Use the Model to

  • Predict future trends such as crop yields and production, agricultural input demand, and significant weather patterns
  • Gauge crop availability and crop prices
  • Monitor variations in temperature and drought indicators, key drivers of wheat yield
  • Inform other models focused on damage caused by pests and diseases
  • Monitor crop production, the most significant part of the balance sheet, by tracking its most variable component, which is in-season yield
  • Track India’s wheat production at the district level and predict nationwide yield

Why It Matters

India is the world’s second-largest producer of wheat, but struggles to meet high domestic demand for the cereal. With a growing population of 1.3 billion, it is projected that by 2024, India will overtake China as the world’s most populous country. This, together with a rising preference for wheat as consumers move into the middle class, will deepen the challenge of food security. As such, being able to accurately predict India’s wheat crop yield is critical to better understanding the dynamics of Indian and global wheat trade.

India winter wheat production concentration by district (left). India final yield versus Gro yield model estimates (right). Gro’s model performs best in India’s major winter wheat-producing districts.


A major challenge in building the India wheat model was a lack of good, objective acreage data of where the wheat grows. This information is needed to construct a crop distribution map, or land cover, in order to use our satellite data to estimate the crop’s health and size. We faced a similar problem in creating our earlier Argentina Soybean Forecast Yield Model and solved it by estimating the crop’s distribution using the timing of its planting and growth. We determined planting and growth dates by the crop calendars of wheat (winter) in India. Instead of a single crop calendar for the entire country, crop calendars specific to individual states were used. Given that knowledge, dated satellite greenness data indicated which crop was planted where.

Related Resources

Hiding in Plain Sight: India's Agricultural Volatility 

Argentina Soybean Yield Forecast Model 

US Corn Yield Forecast Model

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