Framework to guide modeling single and multiple abiotic stresses in arable crops

被引:16
|
作者
Webber, Heidi [1 ,2 ]
Rezaei, Ehsan Eyshi [1 ]
Ryo, Masahiro [1 ,2 ]
Ewert, Frank [1 ,3 ]
机构
[1] Leibniz Ctr Agr Landscape Res ZALF, Muncheberg, Germany
[2] Brandenburg Tech Univ Cottbus, Inst Environm Sci, Cottbus, Germany
[3] Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Crop Sci Grp, Bonn, Germany
关键词
Climate risk; Crop models; Extreme events; Model improvement; Multiple stressors; Compounded events; Compounded perturbations; Synergy and antagonism; CLIMATE-CHANGE IMPACTS; HEAT-STRESS; HIGH-TEMPERATURE; ELEVATED OZONE; CANOPY TEMPERATURE; SIMULATION-MODELS; PLANT-RESPONSES; LEAF SENESCENCE; USE EFFICIENCY; SHORT-TERM;
D O I
10.1016/j.agee.2022.108179
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
With the occurrence of extreme events projected to increase under climate change, it is critical to assess the risk they pose to food security and identify suitable adaptation options. While mechanisms and impacts of climatic stressors (e.g. frost, drought, heat or flooding) have been studied individually, little is known their combined impacts on crops to be expected under actual production conditions. This lack of process knowledge is reflected in the few instances of crop models considering multiple stressors. Here we provide an overview of the repre-sentation of single stressors in process based crop models. From this basis, a framework to consider multiple stressors in current models is presented, defining four stressor combination types: 1. Single exposure; 2. No direct interaction; 3. Known interaction; and 4. Unknown interaction. An analytical framework from ecological sciences is then presented as an approach to consider when formulating algorithms for the 4th type of unknown in-teractions. In a final section, we discuss new data driven and model based exploration options to support un-derstanding multiple stressor interactions in recognition of the challenges of experimentation around multiple stressors. We assert that process based modeling has a large and largely untapped potential to support scientific investigations of the underlying mechanisms driving crop response to multiple stressors.
引用
收藏
页数:13
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