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Data-driven machine learning model to assess on-farm yield variability

Data-driven machine learning model to assess on-farm yield variability

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The relationship between biophysical (environmental conditions) and crop management variables can explain 25% yield variability. This was revealed by a study using interpretable machine learning methods using large-scale farmers’ field data.

Researchers from the Indian Agricultural Research Institute (ICAR), International Maize and Wheat Improvement Center (CIMMYT) and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) undertook a study that outlines the different machine learning methods available to explain on-farm yield variability and to demonstrate the practicality of these interpretable techniques for agronomic studies. The study used extensive data for irrigated wheat in the north-western Indo-Gangetic Plains (IGP) of India.

Research revealed the most important management variables explaining yield variability were nitrogen application rate and crop residue management. ​ The average monthly cumulative solar radiation during February and March, during the reproductive phase of the crop, was found to be the most crucial biophysical variable. The data also highlighted the correlation between crop residue management and disease management and nitrogen application rates and seeding rates. ​

"The study also compared machine learning model outputs against empirical data to contextualize results and provide a blueprint for future analysis in other production systems, " said Dr ML Jat, Global Research Program Director, Resilient Farm and Food Systems, ICRISAT.

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About The International Crops Research Institute for the Semi-Arid Tropics

The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is a pioneering, international non-profit scientific research for development organization, specializing in improving dryland farming and agri-food systems.  The Institute was established as an international organization in 1972, by a Memorandum of Agreement between the Consultative Group on International Agricultural Research and the Government of India.  ICRISAT works with global partners to develop innovative science-backed solutions to overcoming hunger, malnutrition, poverty, and environmental degradation on behalf of the 2.1 billion people who reside in the drylands of Asia, Sub-Saharan Africa, and beyond. 

Our offices:

Asia: India (Headquarters - Hyderabad)

East and Southern Africa:  Ethiopia, Kenya, Mozambique, Malawi, Zimbabwe

West and Central Africa: Mali, Niger, Nigeria

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The International Crops Research Institute for the Semi-Arid Tropics
Hyderabad, Telangana, India