Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

• Published in: Field crops research Vol. 287; p. 108640
• Main Authors: Nayak, Hari Sankar, Silva, João Vasco, Parihar, Chiter Mal, Krupnik, Timothy J., Sena, Dipaka Ranjan, Kakraliya, Suresh K., Jat, Hanuman Sahay, Sidhu, Harminder Singh, Sharma, Parbodh C., Jat, Mangi Lal, Sapkota, Tek B.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2022
• Subjects: Accumulated local effect plot; Big data; crop residue management; disease control; disease incidence; farmers; fertilizer rates; georeferencing; India; Indo-Gangetic Plain; Interaction strength; Partial dependency plot; Quantile regression; Random forest; solar radiation; Variable importance; wheat; Indo-Gangetic Plain; India; Interaction strength; Variable importance; Random forest; Big data; Partial dependency plot; Quantile regression; Accumulated local effect plot

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fo...