|ZHU, PENG - Purdue University|
|ZHUANG, QIANLAI - Purdue University|
|ARCHONTOULIS, SOTIRIOS - Iowa State University|
|MUELLER, CHRISTOPH - Potsdam Institute|
Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/25/2019
Publication Date: 3/31/2019
Citation: Zhu, P., Zhuang, Q., Archontoulis, S., Bernacchi, C.J., Mueller, C. 2019. Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration. Global Change Biology. https://doi.org/10.1111/gcb.14632.
Interpretive Summary: A rapidly changing climate is leading to losses in corn yield, particularly caused by extreme temperature events, i.e. heatwaves. From the historical record, there is uncertainty whether losses are driven by plant growth, changes in the growing season length, or grain formation. This research uses ecosystem models, satellite observations, ground-based field observations, and field experimental data to better understand the drivers for changes in corn production. The results show that a loss of yield is driven by a sensitivity of the plant during the period when corn grain is being filled by the plant. The results also show that heat has a bigger impact on the loss in yield than a lack of water. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in management systems need to be better accounted for to achieve progress in heat stress estimates.
Technical Abstract: Evidence suggests that global maize yield declines with a warming climate, particularly with extreme heat events. However, the degree to which important maize processes such as biomass growth rate (BGR), growing season length (GSL) and grain formation, are impacted by an increase in temperature is uncertain. Such knowledge is necessary to understand yield responses and develop crop adaptation strategies under warmer climate. We integrated crop models, satellite observations, survey, and field experiment data to investigate how high temperature stress influences maize yield in the US Midwest. Observational evidence suggests there is a nonlinear increase in temperature sensitivity of maize yield with higher temperature, which is primarily driven by the intensified sensitivity of harvest index (HI). Although crop model ensemble mean (MEM) reproduced the intensified temperature sensitivity in yield, the warming effects through HI are significantly underestimated. Further analysis shows the intensified temperature sensitivity in HI mainly results from a greater sensitivity of yield to high temperature stress during grain filling period (GFP), which explained more than half of the yield reduction. When warming effects were decomposed into direct heat stress and indirect water stress, observational data suggests yield is more reduced by direct heat stress (-4.6±0.34%/°C) than water stress (1.7±0.16%/°C), while MEM gives opposite results. This discrepancy implies yield reduction by heat stress is underestimated while the yield benefit of increasing atmospheric CO2 might be overestimated in crop models, since elevated CO2 brings yield benefit through water conservation effect but produces limited benefit over heat stress. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in management systems need to be better accounted for to achieve progress in heat stress estimates.