A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks

Regional and global impact assessment tools are increasingly used to explore and evaluate the impact of climate change and extreme events on crop yield and environmental externalities. However, the large uncertainties associated with the inputs or the parameters in crop models within these tools, limits their predictive ability, exceeding the spatiotemporal variability of observed yields. The objective of this study is to explore and quantify different sources of uncertainties and assumptions made behind initial conditions (IC), soil input, meteorological forcing, management practices and model cultivar parameters by running regional simulations for the time period between 2009 and 2019. Simulations were performed for maize and soybean using the pSIMS platform across the U.S Midwest by incrementally accounting for five sources of uncertainty with a 7 kmx7 km

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This study aims to explore and quantify different sources of uncertainties by conducting regional simulations for maize and soybean crops in the U.S. Midwest. The research analyzes the impact of uncertainties related to initial conditions, soil input, meteorological forcing, management practices, and model parameters on crop yields.