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Tailoring seasonal climate forecasts for climate risk management in rainfed farming systems of southeast Zimbabwe.

机译:为津巴布韦东南部雨养农业系统的气候风险管理量身定制季节性气候预测。

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摘要

Inter-annual climate variability is a major source of production risks in rainfed agriculture in semi-arid areas of Zimbabwe. Despite advances in seasonal forecasting since the late 1990 s, there is hardly any evidence of explicit use of forecasts by vulnerable smallholder farmers to manage climate-related risk in agriculture. Forecasts are presented in the language of probabilities, but are often not perceived as such, partly because of differences in end users' needs and their decision-making behaviour. The project used the Indian Ocean Dipole (IOD) index and El Nino-Southern Oscillation (ENSO) to develop a tailored seasonal forecast model termed the 'binary' or 'droughto drought'. The binary forecast model addresses farmers most important question: what is the probability of drought (SPI<=-1) occurring in the crop growing season The 'binary' forecast system allowed the development of climate risk management strategies specifically tailored to farmers' needs. From this study, it can be concluded that rainfed agriculture production systems are most concerned about the risk of drought. A tailored forecast system that provides information on the probability of drought occurring in a given season can therefore lead to proactive drought risk management among smallholder farmers and policy-makers.
机译:每年的气候变化是津巴布韦半干旱地区雨养农业生产风险的主要来源。尽管自1990年代后期以来季节预报取得了进步,但几乎没有任何证据表明脆弱的小农户明确使用了预报来管理农业中与气候相关的风险。预测是用概率语言表示的,但通常不会这样,部分原因是最终用户的需求及其决策行为存在差异。该项目使用印度洋偶极子(IOD)指数和厄尔尼诺-南方涛动(ENSO)来开发量身定制的季节性预报模型,称为“二元”或“干旱/无干旱”。二进制预测模型解决了农民最重要的问题:在作物生长季节发生干旱的概率(SPI <=-1)是多少?“二进制”预测系统允许制定专门针对农民需求的气候风险管理策略。从这项研究可以得出结论,雨养农业生产系统最担心干旱的风险。因此,提供有关给定季节发生干旱可能性的信息的量身定制的预测系统可以导致小农户和政策制定者主动进行干旱风险管理。

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