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Improving Representation of Crop Growth and Yield in the Dynamic Land Ecosystem Model and Its Application to China


Zhang, Jingting
Tian, Hanqin
Yang, Jia
Pan, Shufen


To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process-based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM-AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O-3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM-AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980-2012. Results show that the DLEM-AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model-based yield estimation for the three major crops at the national scale for the period 1980-2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM-AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site-specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change.