ArticleProceedings of the National Academy of Sciences of the United States of America2026
Rainfall extremes drive cocoa yield losses across the tropics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Rainfall extremes drive cocoa yield losses across the tropics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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3 authors.
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Abstract
There is a growing need for human-centered weather and climate forecasts that provide actionable information to farmers and other stakeholders. Cocoa, which sustains the livelihoods of nearly six million smallholder farmers, illustrates this need: Recent production shortfalls have threatened incomes, yet their underlying causes remain unclear, with explanations invoking both climatic and nonclimatic factors. Here we show that heavy rainfall, rather than temperature or mean water availability, is the dominant climatic constraint on cocoa yields across major producing regions. Using newly available district-level production records from Ghana combined with daily rainfall and temperature data, we find that excess wet-season rainfall and dry-season drought together explain 68% of interannual yield variability. National yield data from Ecuador and Indonesia also give a consistent negative response to heavy rainfall, indicating a common mechanism across the tropics, likely linked to damage and disease risks associated with intense precipitation during key phenological stages. Because rainfall extremes covary with El Niño and other sea surface temperature patterns, and there is a robust physical expectation of increases in rainfall extremes with warming, these results provide a physical basis for risk forecasts. By shifting attention from average climate conditions to extreme precipitation, we highlight an underappreciated constraint on a perennial tropical crop and identify opportunities for adaptation and forecast-informed decision-making.
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