Probabilistic modeling of crop-yield loss risk under drought: A spatial showcase for sub-Saharan Africa

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Kamali, B.; Jahanbakhshi, F.; Dogaru, D.; Dietrich, J.; Nendel, C. et al.: Probabilistic modeling of crop-yield loss risk under drought: A spatial showcase for sub-Saharan Africa. In: Environmental research letters : ERL 17 (2022), Nr. 2, 024028. DOI: https://doi.org/10.1088/1748-9326/ac4ec1

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/12970

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Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited for such assessment owing to reasonably inferring important properties in terms of exceedance probability and joint dependence of different characterization. However, insufficient attention has been given to quantifying the probability of yield loss and determining the contribution of climatic factors. This study applies the Copula theory to describe the dependence between drought and crop yield anomalies for rainfed maize, millet, and sorghum crops in sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food and Agriculture Organization country-level yield data, was used to simulate yields across SSA (1980-2012). The results showed that the severity of yield loss due to drought had a higher magnitude than the severity of drought itself. Sensitivity analysis to identify factors contributing to drought and high-temperature stresses for all crops showed that the amount of precipitation during vegetation and grain filling was the main driver of crop yield loss, and the effect of temperature was stronger for sorghum than for maize and millet. The results demonstrate the added value of probabilistic methods for drought-impact assessment. For future studies, we recommend looking into factors influencing drought and high-temperature stresses as individual/concurrent climatic extremes. © 2022 The Author(s). Published by IOP Publishing Ltd.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
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1 image of flag of Germany Germany 18 33.33%
2 image of flag of United States United States 16 29.63%
3 image of flag of Russian Federation Russian Federation 4 7.41%
4 image of flag of China China 3 5.56%
5 image of flag of Belgium Belgium 3 5.56%
6 image of flag of Australia Australia 3 5.56%
7 image of flag of Netherlands Netherlands 2 3.70%
8 image of flag of Europe Europe 1 1.85%
9 image of flag of Czech Republic Czech Republic 1 1.85%
10 image of flag of United Arab Emirates United Arab Emirates 1 1.85%
    other countries 2 3.70%

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