Analysis of climate signals in the crop yield record of sub‐Saharan Africa. (11th October 2017)

• Record Type: Journal Article
• Authors: Hoffman, Alexis L.; Kemanian, Armen R.; Forest, Chris E.
• Is Part Of: Global change biology. Volume 24:Number 1(2018)
• Page Start: 143
• Page End: 157
• Publication Date: 2017-10-11
• Subjects: African agriculture -- climate change -- crop yields -- food security -- Random Forest -- statistical crop modeling -- sub‐Saharan Africa -- temperature increase; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.13901
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Food security and agriculture productivity assessments in sub‐Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale‐compatible climate data for the 1962–2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates. Abstract : We identify the climate signal in yields of maize, sorghum, and groundnut in sub‐Saharan Africa (SSA) during 1962–2014 using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology is three times more important than climate variables for explaining changes in crop yields and increasing temperatures reduced yields for all three crops, while precipitation increased yields. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively (see figure). We conclude that while we expect increases in future crop yields by technological improvements, the potential yields could be progressively reduced in warmer and drier climates.