As a heatwave spectre hangs again over India’s wheat harvest, its home-grown crop simulation model can help

Subject :Environment

Section: Agriculture

Context: InfoCrop, available for free on IARI’s website, can forecast climate impact on farm yield in real time.

More on the News:

Currently, the country does not have a system to forecast crop loss due to heatwaves or most other extreme weather conditions. The Mahalanobis National Crop Forecast Centre, under the Union Ministry of Agriculture and Farmers Welfare, provides pre-harvest forecasts for eight major crops at the national, state and district levels.
The agency also puts out forecasts accounting for drought events, but not for other extreme weather conditions. Besides, the agency forecasts with static crop models, which cannot factor in real-time changes.
The IARI scientists, in contrast, used InfoCrop version 2.1, India’s only dynamic crop simulation model developed and released by the institute in 2015 to study the long-term impact of climate change and crop management practices on yield.
The model has an 85 per cent accuracy rate, which is on par with widely used dynamic models such as the Decision Support System for Agrotechnology Transfer model, developed by the US, and Agriculture Production Systems sIMulator, developed by Australia.
InfoCrop is more suited for India as it has the life cycle data for almost all the local varieties of 11 crops: paddy, wheat, maize, sorghum, pearl millet, pigeon pea, chickpea, soybean, groundnut, potato and cotton.

InfoCrop

InfoCrop is a process based dynamic simulation model for simulating growth, development and yield of rice, wheat, maize, sorghum, pearl millet, mustard, soybean, chick pea, pigeon pea, potato and cotton.
It simulates the effects of weather, soil and crop management (sowing, seed rate, organic matter nitrogen and irrigation) and pests. It provides daily and summary outputs on various growth and yield parameters, nitrogen uptake, greenhouse gas emissions, soil water and nitrogen balance. It is used for several applications including yield forecast and climate change studies. It is known to perform better for tropical regions.