Towards an integrated agroecosystem modeling approach for climate-smart agriculture management

Climate-smart agriculture (CSA) management aims to provide solutions to ensuring food security and enhancing soil carbon sequestration capacity for climate mitigation and adaptation, and meanwhile minimizing adverse environmental effects (e.g., greenhouse gas emissions from agriculture). In the context of global change, identifying CSA management practices requires integrated information and full assessment on food production systems as well as associated environmental consequences (e.g., greenhouse gas emissions and changes in hydrological and biogeochemical cycles). Recent decades have witnessed unprecedented development in field observations and measurements, high-resolution remote sensing, and multiscale process-based modeling, which facilitates the comprehensive investigation of agroecosystem responses to and the environmental impacts of land management using an integrated agroecosystem modeling approach. This approach aims to systemically incorporate multi-scale observations and measurements and extensive knowledge of agroecosystems into a process-based modeling-centered framework for fully understanding, assessing, and predicting the agroecosystem structure, function and process changes as well as the underlying mechanism under the dual pressures of climate change and anthropogenic activities at a range of spatial scales and over time. This chapter briefly summarizes the CSA-related studies and proposes a conceptual framework of an integrated agroecosystem modeling approach for future CSA management. Two case studies are provided to illustrate the applications of an integrated agroecosystem model to synthesize multisource datasets and quantify crop productivity and soil biogeochemical cycles in agroecosystems as influenced by multiple global changes in China and the United States.