Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

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.

El Niño Southern Oscillation and the accumulation of chilling hours for dormancy breaking in temperate fruit in Southern Brazil

“…With the increased understanding and predictability of ENSO, there is also a greater awareness of the impacts and opportunities for the use of this information. According to CHAPMAN et al (2021) ENSO-related climate predictions will benefit the agricultural sector, allowing farmers to reduce the negative impacts of climate variability and/or to capitalize on potentially beneficial effects.…”

Drivers of cocoa yield under current and future climates

“…Under field conditions, it is generally impossible to exclude all yield limiting factors due to for instance, the large year-to-year climate variation in some locations which impact optimal management practices (Aggarwal et al, 2008;van Ittersum et al, 2013). As such, attainable yields from experimental trials are often lower than model-based potential yields (Chapman et al, 2021;Hoffmann et al, 2020) and may not represent what can be theoretically obtained in optimally managed fields. Furthermore, for cocoa, such experimental trials are unavailable in most cocoa-growing areas in West and Central Africa.…”

“…Location-specific yield limiting and reducing factors such as year-to-year climate variation can be large for some locations, which means required optimal management practices can vary substantially from one year to another (Aggarwal et al, 2008;Daymond et al, 2020;. These location-specific yield-reducing factors can lower the experimental yields by up to two-thirds of model-based potential yields (Chapman et al, 2021;Hoffmann et al, 2020). In West Africa, experimental trials are unavailable for most cocoa growing areas.…”