The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXC JANc and P JANc , respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXC FEVc . The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance ( p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence ( r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December.

中文翻译：

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模拟农业气象变量对南马托格罗索州大豆产量的影响：2000-2019

对受气候影响的大豆产量变异性的研究有助于制定减轻其负面影响的策略。因此，我们的目标是校准用于大豆产量预测的农业气象模型，并确定对大豆产量影响最大的天气变量。本研究使用了巴西南马托格罗索州大豆产地的历史气候系列和大豆产量数据。历史气候系列为 20 年（2000-2019 年）。各地大豆产量、产量、种植面积数据为2009-2018年期间。多元线性回归分析是用于数据建模的统计工具。应用该模型所需的最终数据后，北部和中部地区预测 2 个月的模型为 EXC JANc 和 P JANc ，分别。为南部地区校准的模型报告了一个月的预期，因为应用该模型所需的最终数据是 EXC FEVc。用于预测作为气候条件函数的大豆产量的校准模型具有高度显着性（p < 0.05）、高准确度和较低误差。北部和中部地区的模型显示了大豆收获前 2 个月的预测，这一时期对于生产者能够进行收获前和收获后计划至关重要。对南马托格罗索州大豆产量产生最大负面影响 (r = − 0.54) 的气候变量是 12 月的水分胁迫。用于预测作为气候条件函数的大豆产量的校准模型具有高度显着性（p < 0.05）、高准确度和较低误差。北部和中部地区的模型显示了大豆收获前 2 个月的预测，这一时期对于生产者能够进行收获前和收获后计划至关重要。对南马托格罗索州大豆产量产生最大负面影响 (r = − 0.54) 的气候变量是 12 月的水分胁迫。用于预测作为气候条件函数的大豆产量的校准模型具有高度显着性（p < 0.05）、高准确度和较低误差。北部和中部地区的模型显示了大豆收获前 2 个月的预测，这一时期对于生产者能够进行收获前和收获后计划至关重要。对南马托格罗索州大豆产量产生最大负面影响 (r = − 0.54) 的气候变量是 12 月的水分胁迫。生产者能够进行收获前和收获后计划的关键时期。对南马托格罗索州大豆产量产生最大负面影响 (r = − 0.54) 的气候变量是 12 月的水分胁迫。生产者能够进行收获前和收获后计划的关键时期。对南马托格罗索州大豆产量产生最大负面影响 (r = − 0.54) 的气候变量是 12 月的水分胁迫。