Abstract Droughts are one of the most damaging and complex natural disasters in the world, and they frequently affect agricultural production. Drought monitoring is essential for decision-makers seeking to minimize the socio-economic impacts related to drought events. In this study, we propose a methodology to identify the most suitable drought indices and data sources for monitoring the impact of drought on crops. Mozambique is used as a case study, as it represents a challenging example because of its poor hydroclimatic monitoring network and a lack of disaggregated data for agricultural production. A total of seven standardized drought indicators (SPI, SPEI, SSI, SVCI, STCI, SVHI, and STWS) at different scales (1, 3, 6, and 12 months) were obtained from global databases and evaluated as possible predictors of the annual variability of agricultural yields at the national level. A statistical model of crop yields based on time series was used to measure the explanatory capacity of each index. SPEI and SSI were the most effective at detecting the country's historical drought records regardless of whether nationally averaged values or the percentages of area affected by drought (PAA) were used. However, PAA was found to be a more accurate predictor of variability in crop yields. The variability of most cereals (maize, millet and sorghum) was adequately explained by the PAA of SPEI-3, with that of other crops (cashew nuts, cassava, potatoes, tea, tobacco and vegetables) being explained by the PAA of SSI-12. Specific indicators were proposed for monitoring wheat and sugar cane. These results can directly support managers and decision makers in developing drought contingency plans in Mozambique. To further demonstrate the potential of this methodology, it should be tested in other regions with a greater availability of agricultural data, including spatial disaggregation.

中文翻译：

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评估干旱指数在全国范围内解释作物产量变异的表现：方法框架和在莫桑比克的应用

摘要 干旱是世界上最具破坏性和复杂性的自然灾害之一，经常影响农业生产。干旱监测对于寻求尽量减少与干旱事件相关的社会经济影响的决策者来说至关重要。在这项研究中，我们提出了一种方法来确定最合适的干旱指数和数据来源，以监测干旱对作物的影响。莫桑比克被用作案例研究，因为它代表了一个具有挑战性的例子，因为它的水文气候监测网络很差，而且缺乏农业生产的分类数据。共有 7 个不同尺度（1、3、6、6）的标准化干旱指标（SPI、SPEI、SSI、SVCI、STCI、SVHI 和 STWS）。和 12 个月）是从全球数据库中获得的，并被评估为国家层面农业产量年度变化的可能预测因子。采用基于时间序列的作物产量统计模型来衡量各指标的解释能力。无论是使用全国平均值还是受干旱影响面积的百分比 (PAA)，SPEI 和 SSI 在检测该国历史干旱记录方面都是最有效的。然而，人们发现 PAA 是作物产量变异性的更准确预测因子。大多数谷物（玉米、小米和高粱）的变异性由 SPEI-3 的 PAA 充分解释，其他作物（腰果、木薯、马铃薯、茶、烟草和蔬菜）的变异性由 SSI- 12. 提出了监测小麦和甘蔗的具体指标。这些结果可以直接支持莫桑比克管理人员和决策者制定干旱应急计划。为了进一步证明这种方法的潜力，应该在其他具有更多农业数据可用性的地区进行测试，包括空间分解。