Abstract

Wheat production plays an important role in Morocco with the country typically producing more than half of Northwest African grain production. 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 timescales. Using physically-guided causal discovery algorithms we extract climate precursors for wheat yield variabilityfrom gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December. 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.