Historical and current approaches to decompose uncertainty in crop model predictions (poster)

Abstract

Cropping systems models have become an essential tool to simulate crop growth, development, and yield at different scales to produce actionable information related to climate change, food security, land use, and market dynamics. Despite their increased usage and importance, various sources of uncertainty exist in the modeling process due to the “impossibility to model the cropping system with complete determinism”. In this article, by uncertainty, we mean “any departure from the unachievable ideal of complete determinism”. Uncertainty is prevalent in every step of crop modeling, starting from the field observations used for model development to the value of inputs and parameters to the structure and design of models. Therefore, uncertainty can be grouped into three categories: observation uncertainty, prediction uncertainty, and model uncertainty. These three categories of uncertainty are closely related but different from one another. It is important to address these uncertainties because not knowing the level of reliability of the output impedes the user’s ability to implement actionable practices with confidence. As such, this article has assessed the historical and current research gaps, trends and challenges in estimating uncertainty in crop modelling.