Abstract
While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.
Original language | English |
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Article number | e2022EF003106 |
Pages (from-to) | 1-14 |
Number of pages | 14 |
Journal | Earth's Future |
Volume | 11 |
Issue number | 4 |
Early online date | 5 Apr 2023 |
DOIs | |
Publication status | Published - Apr 2023 |
Bibliographical note
Funding Information:This research has been supported by the European Union's Horizon 2020 research and innovation programme under Grant 820712 (project RECEIPT, REmote Climate Effects and their Impact on European sustainability, Policy and Trade). The GSWP3‐W5E5 climate data set and growing season data were provided by the Global Gridded Crop Model Intercomparison (GGCMI) initiative and the Intersectoral Impact Model Intercomparison Project (ISIMIP). This work started at the Young Scientist Summer Program (YSSP) at the International Institute for Applied Systems Analysis (IIASA). We thank Raed Hamed, Timothy Tiggeloven, Jens de Bruijn, Aaron Alexander, Anaïs Couasnon, Sem Vijverberg, and Rafaela Flach for the support throughout the work.
Funding Information:
This research has been supported by the European Union's Horizon 2020 research and innovation programme under Grant 820712 (project RECEIPT, REmote Climate Effects and their Impact on European sustainability, Policy and Trade). The GSWP3-W5E5 climate data set and growing season data were provided by the Global Gridded Crop Model Intercomparison (GGCMI) initiative and the Intersectoral Impact Model Intercomparison Project (ISIMIP). This work started at the Young Scientist Summer Program (YSSP) at the International Institute for Applied Systems Analysis (IIASA). We thank Raed Hamed, Timothy Tiggeloven, Jens de Bruijn, Aaron Alexander, Anaïs Couasnon, Sem Vijverberg, and Rafaela Flach for the support throughout the work.
Publisher Copyright:
© 2023 The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.
Funding
This research has been supported by the European Union's Horizon 2020 research and innovation programme under Grant 820712 (project RECEIPT, REmote Climate Effects and their Impact on European sustainability, Policy and Trade). The GSWP3‐W5E5 climate data set and growing season data were provided by the Global Gridded Crop Model Intercomparison (GGCMI) initiative and the Intersectoral Impact Model Intercomparison Project (ISIMIP). This work started at the Young Scientist Summer Program (YSSP) at the International Institute for Applied Systems Analysis (IIASA). We thank Raed Hamed, Timothy Tiggeloven, Jens de Bruijn, Aaron Alexander, Anaïs Couasnon, Sem Vijverberg, and Rafaela Flach for the support throughout the work. This research has been supported by the European Union's Horizon 2020 research and innovation programme under Grant 820712 (project RECEIPT, REmote Climate Effects and their Impact on European sustainability, Policy and Trade). The GSWP3-W5E5 climate data set and growing season data were provided by the Global Gridded Crop Model Intercomparison (GGCMI) initiative and the Intersectoral Impact Model Intercomparison Project (ISIMIP). This work started at the Young Scientist Summer Program (YSSP) at the International Institute for Applied Systems Analysis (IIASA). We thank Raed Hamed, Timothy Tiggeloven, Jens de Bruijn, Aaron Alexander, Anaïs Couasnon, Sem Vijverberg, and Rafaela Flach for the support throughout the work.
Funders | Funder number |
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Horizon 2020 | |
International Institute for Applied Systems Analysis | |
Not added | 820712 |
Keywords
- adaptation
- climate change
- compound events
- crop modeling
- hybrid model
- machine learning