The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO₂

Abstract

Heat and drought are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and future US rainfed maize and soybean production and for the first time characterizes spatial shifts in the relative importance of individual stress. Crop yields are simulated using the Agricultural Production Systems Simulator (APSIM), driven by high-resolution (12 km) dynamically downscaled climate projections for 1995-2004 and 2085-2094. Results show that maize and soybean yield losses are prominent in the US Midwest by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of loss highly depends on the current vulnerability and changes in climate extremes. Elevated atmospheric CO partially but not completely offsets the yield gaps caused by climate extremes, and the effect is greater in soybean than in maize. Our simulations suggest that drought will continue to be the largest threat to US rainfed maize production under RCP4.5 and soybean production under both RCP scenarios, whereas high temperature and heat stress take over the dominant stress of drought on maize under RCP8.5. We also reveal that shifts in the geographic distributions of dominant stresses are characterized by the increase in concurrent stresses, especially for the US Midwest. These findings imply the importance of considering heat and drought stresses simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management. The modeling framework of partitioning the total effects of climate change into individual stress impacts can be applied to the study of other crops and agriculture systems.

Pivotal roles of environmental sensing and signaling mechanisms in plant responses to climate change

“…The existence of multiple concomitant cues or stressors implies that, under climate change conditions, plant sensing and signaling systems are confronted with complex signal interactions (Côté et al, 2016;Jin et al, 2017). The existence of multiple concomitant cues or stressors implies that, under climate change conditions, plant sensing and signaling systems are confronted with complex signal interactions (Côté et al, 2016;Jin et al, 2017).…”

“…The existence of multiple concomitant cues or stressors implies that, under climate change conditions, plant sensing and signaling systems are confronted with complex signal interactions (Côté et al, 2016;Jin et al, 2017). Conjugation of stresses and environmental cues can lead to neutrality, additivity, synergy, antagonism, idiosyncrasy, or dominance (Côté et al, 2016;Foyer et al, 2016;Jin et al, 2017;Pandey, Ramegowda, & Senthil-Kumar, 2015;Prasch & Sonnewald, 2015;Zhang & Sonnewald, 2017). Conjugation of stresses and environmental cues can lead to neutrality, additivity, synergy, antagonism, idiosyncrasy, or dominance (Côté et al, 2016;Foyer et al, 2016;Jin et al, 2017;Pandey, Ramegowda, & Senthil-Kumar, 2015;Prasch & Sonnewald, 2015;Zhang & Sonnewald, 2017).…”

“…However, the dose-response range to stress intensity (Carmody et al, 2016) and the complexity of stress combinations (Jin et al, 2017) result in important limitations. Moreover, several integrative hubs can coordinate and orchestrate responses to multiple and concomitant cues of climate change.…”

Indiana ’s Agriculture in a Changing Climate: A Report from the Indiana Climate Change Impacts Assessment

“…Sources: Long et al, 2006;Jin et al, 2017;Jin et al, 2018;Myers et al, 2014;Loladze 2014 Do Crops Benefit from Increased CO 2 ?…”

How does inclusion of weather forecasting impact in-season crop model predictions?

“…Over the past years the APSIM model has been successfully applied in this region to simulate production (Hammer et al, 2009;Archontoulis et al, 2014a, b;Dietzel et al, 2016;Puntel et al, 2016;Jin et al, 2017) and environmental aspects of US Midwestern corn and soybean cropping systems (Malone et al, 2007;Archontoulis et al, 2016a, b;Basche et al, 2016;Martinez-Feria et al, 2016).…”