Abstract
Crop models are essential tools for analysing the effects of climate variability, change on crop growth and development and the potential impact of adaptation strategies. Despite their increasing usage, crop model estimations have implicit uncertainties which are difficult to classify and quantify. Failure to address these uncertainties may result in poor advice to policymakers and stakeholders for the development of adaptation strategies. Since the 1990s, the number of crop model uncertainty assessments that consider different sources of model uncertainty (model structure, model parameters and model inputs such as climate, soil, and crop management practices) has increased significantly. We present the outcomes of a systematic review focused on uncertainty assessments of crop model outputs (mainly grain yield) and crop model uncertainty decomposition. We reviewed 277 articles from 1991 to 2019 which included studies conducted in 82 countries (460 locations) across all continents. 57% of the articles have been published between 2015 and 2019. 52% of the studies focus on input uncertainty assessments with climate change projections as the most frequently considered source of input uncertainty. Only 28% and 20% of the studies, respectively, dealt with uncertainties related to model parameters and model structure. The latter was mainly quantified using multi-model ensembles. Over half the studies were carried out in European and Asian countries, 34% and 23%, respectively. Most articles estimated model uncertainty focusing on the grain yield of major cereal crops (wheat > maize > rice) using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Sensitivity analysis was the most used technique to quantify the contribution of different sources of uncertainty although the range of approaches for uncertainty quantification was wide. There is a need for standard procedures to estimate crop model uncertainty and evaluate estimates. We discuss the challenges of quantifying the components of uncertainty within crop models and identify research needs to better understand sources of uncertainty and thus improve the accuracy of crop models.