Modeling the Adaptations of Agricultural Production to Climate Change
Xiao, Dengpan (editor)
Shi, Wenjiao (editor)
This reprint focuses on quantitatively assessing the impact of climate change on agricultural production based on multi-source model simulation and reveals the role and mechanism of improved management measures in adapting to climate change. Modeling is a key tool for exploring the impacts of climate change on agriculture and proposing adaptation strategies. The insights derived from this reprint will be helpful for relevant decision makers in the areas of agricultural adaptation and food security.
Keywordstemperature; evaluation; CLDAS; GLDAS; ERA5-Land; adaptation; climate change; summer maize; phenology shift; GCM; CNRM-CM6; PET; IPCC-AR6; SSP scenarios; solar induced chlorophyll fluorescence (SIF); winter wheat; yield forecast; random forest; enhanced vegetation index (EVI); geographic distribution; suitable habitat; Rheum nanum; MaxEnt; ArcGIS; range shifts; climate scenario; agriculture; food security; planting boundary; crop modelling; yield; future climate scenarios; soybean; CROPWAT; reference crop evapotranspiration (ET0); crop water requirement (ETc); irrigation water requirement (Ir); crop yield; cultivated area; water use; wheat–corn; DNDC; net greenhouse effect; anaerobic digestion; biogas production; k-nearest neighbours; support vector machine; middle and lower reaches of Yangtze River; rice; heat stress; nitrous oxide; soil gas flux; silicone diffusion cell; soil gas diffusivity; passive gas sampling; soil gas diffusion coefficient; soil gas flux simulation; carbon sequestration; different scenarios; land use; sustainable development; Xinjiang; ETo; yield response factor; irrigation water requirement; cotton; GCMs; CERES-rice model; rice yield; light and heat resource utilization; potential evapotranspiration (ET0); Penman–Monteith; sensitivity analysis; contribution rate; optimal irrigation; sustainable irrigation; water use efficiency; North China Plain; sugarcane; SPEI; waterlogging; growth stage; climatic yield; yield prediction; machine learning; APSIM model; climate indices; range shift; apple trees; n/a
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Publication date and placeBasel, 2023
Research & information: general
Biology, life sciences
Technology, engineering, agriculture