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.

中文翻译：

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巴西玉米 JULES-crop 的校准和评估

玉米（玉蜀黍L.) 是一种重要的巴西商品，是该国生产的第二大作物和第五大出口产品。由于其对农业部门的重要性，人们担心气候变化对作物的影响。基于过程的模型是评估气候对作物产量影响的宝贵工具。英国联合陆地环境模拟器 (JULES) 是一种地表模型，可以通过集成的作物模型参数化运行。由此产生的模型（JULES-crop）因此将作物生理学原理与大气-生物圈耦合的复杂性相结合。它已被证明是一种有价值的工具，可用于大规模模拟作物产量作为环境和管理变量的函数。在这项研究中，我们使用为巴西夏季和淡季玉米田收集的强大实验数据集校准了 JULES-crop。进行了有针对性的局部灵敏度分析，以检测校准过程中的重要参数。校准后，该模型能够令人满意地模拟季节和淡季品种。叶面积指数（夏季和淡季数据集分别为 EF = .73 和 0.71）、作物高度（EF = .89）和谷物干质量（EF = .61）的建模效率 (EF) 很高夏季和淡季数据集分别为 0.89 和 0.89）。该模型在模拟夏季栽培品种 (EF = .39) 和土壤水分 (EF = .44) 中的叶片干重的精度较低，这表明需要进一步改进，包括对雨养条件进行额外的参数化。