Abstract
While soybeans are among the most consumed crops in the world, the
majority of its production lies in hotspot regions in the US, Brazil and
Argentina. The concentration of soybean growing regions in the Americas
render the supply chain vulnerable to regional disruptions. In the year
of 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. Climate change has already negatively
impacted agricultural systems, and this trend is expected to continue in
the future. 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 the long-term trends of mean climate increase the
occurrence and magnitude of 2012 analogue crop yield losses. 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.