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Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics
Water ( IF 3.4 ) Pub Date : 2020-09-16 , DOI: 10.3390/w12092592
María del Pilar Jiménez-Donaire , Juan Vicente Giráldez , Tom Vanwalleghem

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Cordoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).

中文翻译:

通过土壤水分动力学的概率建模评估谷物中的干旱胁迫

及早准确地发现干旱事件对于管理农业产量损失和规划适当的政策响应至关重要。本研究旨在评估静态和动态植物水分胁迫这两个新指标在干旱检测和产量预测方面的潜力。该研究在西班牙西南部(科尔多瓦省)进行,为期 13 年(2001-2014 年)。静态和动态干旱指数的计算源自先前的生态水文工作,但使用土壤水分含量的概率模拟,基于桶型土壤水平衡和测量的气候数据。结果表明,这两个指数都令人满意地检测了发生在 2005 年、2006 年和 2012 年的干旱期。它们的频率和长度都与年降水量相关,呈指数下降和线性增加,分别。静态和动态干旱胁迫对土壤深度和年降水量高度敏感,具有复杂的响应,因为胁迫可以根据年降水量作为土壤深度的函数增加或减少。最后,结果表明,静态和动态干旱胁迫均优于传统指标,如标准化降水指数 (SPI)-3 作为作物产量的预测指标,R2 值约为 0.70,而后者为 0.40。这项研究的结果突出了这些新指标在农业干旱监测和管理方面的潜力(例如,作为预警系统、保险计划或水资源管理工具)。具有复杂的响应,因为压力可以作为土壤深度的函数增加或减少,具体取决于年降水量。最后,结果表明,静态和动态干旱胁迫均优于传统指标,如标准化降水指数 (SPI)-3 作为作物产量的预测指标,R2 值约为 0.70,而后者为 0.40。这项研究的结果突出了这些新指标在农业干旱监测和管理方面的潜力(例如,作为预警系统、保险计划或水资源管理工具)。具有复杂的响应,因为压力可以作为土壤深度的函数增加或减少,具体取决于年降水量。最后,结果表明,静态和动态干旱胁迫均优于传统指标,如标准化降水指数 (SPI)-3 作为作物产量的预测指标,R2 值约为 0.70,而后者为 0.40。这项研究的结果突出了这些新指标在农业干旱监测和管理方面的潜力(例如,作为预警系统、保险计划或水资源管理工具)。结果表明,静态和动态干旱胁迫均优于传统指标,如标准化降水指数 (SPI)-3 作为作物产量的预测指标,R2 值约为 0.70,而后者为 0.40。这项研究的结果突出了这些新指标在农业干旱监测和管理方面的潜力(例如,作为预警系统、保险计划或水资源管理工具)。结果表明,静态和动态干旱胁迫均优于传统指标,如标准化降水指数 (SPI)-3 作为作物产量的预测指标,R2 值约为 0.70,而后者为 0.40。这项研究的结果突出了这些新指标在农业干旱监测和管理方面的潜力(例如,作为预警系统、保险计划或水资源管理工具)。
更新日期:2020-09-16
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