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.