New modelling methodology for improving crop model performance under stress conditions
Abstract
Crop models exhibit large uncertainty in the quantification of risks imposed to food production by climate change (Asseng et al., 2013). A significant step towards reducing this uncertainty is to improve model structure (Tao et al., 2018). Here, we present a new modelling methodology for improving crop model structure based on simultaneous solution of model equations. The new technique is called SEMAC (Simultaneous Equation Modelling for Annual Crops) and is implemented into the GLAM crop model, resulting in a new model version GLAM-Parti (i.e. GLAM Partitioning). The new model has improved structure, it gives a better connection between the model processes and leads to higher internal consistency. The model skill is significantly increased when tested under different stress environments (i.e. water and ozone stress).
Domains
Modeling and Simulation
Origin : Files produced by the author(s)
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