Potential for Early Forecast of Moroccan Wheat Yields Based on Climatic Drivers

GEOPHYSICAL RESEARCH LETTERS(2020)

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摘要
Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, Morocco's wheat production is mostly rainfed and thus strongly tied to fluctuations in rainfall, which in turn depend on slowly evolving climate dynamics. This offers a source of predictability at longer time scales. Using physically guided causal discovery algorithms, we extract climate precursors for wheat yield variability from gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December, with around 50% explained variance in an out-of-sample cross validation. The detected interactions are physically meaningful and consistent with documented ocean-atmosphere feedbacks. Reliable yield forecasts at such long lead times could provide farmers and policy makers with necessary information for early action and strategic adaptation measurements to support food security. Plain Language Summary The per capita consumption of cereals in Morocco is one of the highest in the world, placing a significant role to wheat production in the framework of national food security. Early wheat forecasts are crucial to increase the resilience of the agricultural sector to climate risks. So far, operational forecast systems provide first yield estimates in March-April and hence around 1 month before harvest starts in May. These systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to this very time period. Here, we present a different approach based on causal interactions in the climate system to provide accurate forecasts of year-to-year wheat yield changes already in December. We make use of the fact that wheat production is mostly rainfed and thus strongly coupled to prevailing rain conditions which, in turn, are influenced by slowly evolving circulation patterns and sea surface temperatures in the Atlantic and Pacific Ocean. These links between far-away regions, also known as teleconnections, can last for several months and thus provide predictability at seasonal time scales relevant for strategic adaptation decisions, for example, regarding crop import planning or the choice and intensity of agronomic practices.
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关键词
causal discovery algorithms,teleconnections,seasonal forecast,machine learning,wheat forecast,climate precursors
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