Calibration and evaluation of JULES-crop for maize in Brazil

Maize (Zea mays L.) is a prominent Brazilian commodity, being the second largest crop produced and fifth exported product by the country. Due to its importance for the agricultural sector, there is a concern about the effect of climate change on the crop. Process-based models are valuable tools to evaluate the effects of climate on crop yields. The Joint UK Land Environment Simulator (JULES) is a land-surface model that can be run with an integrated crop model parameterization. The resulting model (JULES-crop) thus integrates crop physiology principles with the complexity of atmosphere-biosphere coupling. It has been shown to be a valuable tool for large-scale simulations of crop yields as a function of environmental and management variables. In this study, we calibrated JULES-crop using a robust experimental dataset collected for summer and off-season maize fields across Brazil. A targeted local sensitivity analysis was performed to detect parameters of major importance during the calibration process. After calibration, the model was able to satisfactorily simulate both season and off-season cultivars. Modeling efficiency (EF) was high for leaf area index (EF = .73 and .71, respectively, for summer season and off-season datasets), crop height (EF = .89), and grain dry mass (EF = .61 and .89, respectively, for summer season and off-season datasets). The model showed a lower accuracy for simulating leaf dry mass in summer season cultivars (EF = .39) and soil moisture (EF = .44), demonstrating the necessity of further improvements including additional parametrizations of the rainfed conditions.