Quantification and comparison of prediction uncertainty associated with different practices of crop modeling

Abstract

Crop models are widely used as decision support tools in agriculture and natural resource management. However, the current practices in crop modeling and evaluation vary significantly, making it challenging to compare uncertainties across different models and studies. This study aims to quantify and compare the uncertainties associated with four different crop modeling practices using a standardized evaluation framework. The four modeling practices are: 1) using a single model, considering only model bias for uncertainty; 2) using a single model, accounting for both model bias and parameter uncertainty; 3) employing a multi-model ensemble to account for model bias, parameter uncertainty, and structural uncertainties; and 4) using a multi-model ensemble to consider uncertainties induced by model bias, parameters, structures, and inputs. We developed a framework that integrates Markov Chain Monte Carlo (MCMC) and Bayesian Model Averaging (BMA) to consistently quantify prediction uncertainty across these practices. The framework was applied to an Asian rice dataset. The results revealed that common model evaluation approach (Practice 1) tends to underestimate the uncertainty of model predictions. Relying on a single process-based model presents a substantial risk in critical decision-making situations. In contrast, the BMA ensemble predictor (e-BMA) demonstrated higher reliability, making it a preferable choice for future decision support. Our Bayesian framework provides a more robust and adaptable approach for project-specific decision-making, with promising applications in digital agriculture.