Calibration and evaluation of JULES‐crop for maize in Brazil

Abstract

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.

Improving the representation of sugarcane crop in the Joint UK Land Environment Simulator (JULES) model for climate impact assessment

“…Some structural improvements required to refine sugarcane simulations with JULES‐crop are related to the implementation of multiannual growing seasons and in carbon partitioning equations. We found high sensitivity to the carbon partitioning parameters (Figure S19), which was also confirmed for maize (Prudente Junior et al, 2022). Cuadra et al (2012) implemented the carbon partitioning equations of the DSSAT‐CANEGRO model to simulate sugarcane growth in the Agro‐IBIS model.…”

Application of the JULES-crop model and agrometeorological indicators for forecasting off-season maize yield in Brazil

“…The FSMC output is related to hydric stress, and it is directly related to the SWC output, being the second calibrated by Prudente Jr et al [ 27 ] (EF:0.44), the accuracy and feasibility of SWC could be the reason to be less selected. LDM output is related to the LAI output and the second presented more modeling efficiency in the calibration procedure developed by Prudente Jr et al [ 27 ] (EF LAI: 0.73; EF LDM:0.39). High accuracy of JULES-crop to simulate LAI was also observed by Williams et al [ 21 ] when developed a new parameterization of JULES-crop for maize in Nebraska-USA.…”

“…After, a leave-one-out cross-validation was made as a strategy to calibrate JULES-crop in different sites of Brazil, reaching high efficiency of modeling in main outputs as LAI (EF = 0.73), crop height (EF = 0.89) and grain dry mass (0.61). More details about methodology and results are described in Prudente Jr et al [ 27 ]. To examine maize yield in different growing regions of Brazil, simulations were set up using the most common date of sown in each CZ ( Table 1 ), which was collected from Cruz et al [ 28 ] and Duarte and Sentelhas [ 29 ].…”