Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops
In one sentence. Machine learning links cacao yields in Sulawesi to ENSO patterns 8–24 months ahead, explaining 75% of yield variation and timing fertilizer use.
Abstract
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.
Labels
- Topic
- Applications & impacts, Sub-seasonal to seasonal
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1038/s41598-021-87520-4 OpenAlex:W3156136628
- Added
- 2026-10-22
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