Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis

Abstract

Process-based crop models are increasingly being used to investigate the impacts of weather and climate variability (change) on crop growth and production, especially at a large scale. Crop models that account for the key impact mechanisms of climate variability and are accurate over a large area must be developed. Here, we present a new process-based general Model to capture the Crop–Weather relationship over a Large Area (MCWLA). The MCWLA is optimized and tested for spring maize on the Northeast China Plain and summer maize on the North China Plain, respectively. We apply the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique to the MCWLA to analyze uncertainties in parameter estimation and model prediction and to optimize the model. Ensemble hindcasts (by perturbing model parameters) and deterministic hindcasts (using the optimal parameters set) were carried out and compared with the detrended long-term yields series both at the crop model grid (0.5° × 0.5°) and province scale. Agreement between observed and modelled yield was variable, with correlation coefficients ranging from 0.03 to 0.88 ( p < 0.01) at the model grid scale and from 0.45 to 0.82 ( p < 0.01) at the province scale. Ensemble hindcasts captured significantly the interannual variability in crop yield at all the four investigated provinces from 1985 to 2002. MCWLA includes the process-based representation of the coupled CO 2 and H 2O exchanges; its simulations on crop response to elevated CO 2 concentration agree well with the controlled-environment experiments, suggesting its validity also in future climate. We demonstrate that the MCWLA, together with the Bayesian probability inversion and a MCMC technique, is an effective tool to investigate the impacts of climate variability on crop productivity over a large area, as well as the uncertainties.