Historical and current approaches to decompose uncertainty in crop model predictions

Cropping systems models have become an essential tool to simulate crop growth and yield at different scales to produce actionable information related to climate change, food security, land use and market dynamics (e.g. Waha et al., 2015; Wallach et al., 2016; Porwollik et al., 2017). Despite their increased usage and importance, various sources of uncertainty exist in the modelling process due to the "impossibility to model the cropping system with complete determinism" (Ramirez-Villegas et al. 2017). In this article, by uncertainty we mean "any departure from the unachievable ideal of complete determinism" (Walker et al., 2003). Uncertainty is prevalent in every step of crop modelling, starting from the field observations used for model development to value of inputs and parameters to the structure and design of model (Fig. 1).