Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India
Type:
Title:
Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India
Creator:
Hari S. Nayak;
Silva, J.V.;
Parihar, C.M.;
Krupnik, T.J.;
Sena, D.R.;
Kakraliya Suresh Kumar;
Jat, H.S.;
Sidhu, H.S.;
Sharma, P.C.;
Jat, M.L.;
Sapkota, T.B.
Silva, J.V.;
Parihar, C.M.;
Krupnik, T.J.;
Sena, D.R.;
Kakraliya Suresh Kumar;
Jat, H.S.;
Sidhu, H.S.;
Sharma, P.C.;
Jat, M.L.;
Sapkota, T.B.
Year:
2022
Copyright:
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Journal:
Field Crops Research
Journal volume:
287
Article number:
108640
Place of Publication:
Amsterdam (Netherlands)
Publisher:
Elsevier
Related Datasets
CGIAR Initiatives
Initiative:
Transforming Agrifood Systems in South Asia
Impact Area:
Nutrition, health & food security
Action Area:
Resilient Agrifood Systems
Donor or Funder:
CGIAR Research Program on Climate Change, Agriculture and Food Security
United States Agency for International Development
Bill & Melinda Gates Foundation
United States Agency for International Development
Bill & Melinda Gates Foundation
CGSpace URL:
