Chapman R., Cock J., Samson M., Janetski N., Janetski K., Gusyana D., Dutta S., Oberthür T.
Data Analysis Consultant, 9 Kia Ora Parade, Ferntree Gully, VIC 3156, Australia; Emeritus, Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia; Scientific Data Management Consultant, Los Banos, Philippines; Senior Technical Advisor to the Cocoa Care Program of Community Solutions International, Makassar, Sulawesi, Indonesia; CEO of Community Solutions International, Bali, Indonesia; Lautan Luas TBK, Jakarta, Indonesia; African Plant Nutrition Institute, Lot 660, Hay Moulay Rachid, Ben Guerir, Morocco; Business and Partnership Development, African Plant Nutrition Institute, Lot 660, Hay Moulay Rachid, Ben Guerir, Morocco
Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it’s possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there’s little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain. © 2021, The Author(s).
Publisher: Nature Research
Volume 11, Issue 1, Art No 8292, Page – , Page Count
Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104484397&doi=10.1038%2fs41598-021-87520-4&partnerID=40&md5=ae6d8e39752b8a98681dc403b99a00dc
Type: All Open Access, Gold, Green
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