  Almonds are one of the most lucrative products of California, but are also
among the most sensitive to climate change. In order to better understand the
relationship between climatic factors and almond yield, an automated machine
learning framework is used to build a collection of machine learning models.
The prediction skill is assessed using historical records. Future projections
are derived using 17 downscaled climate outputs. The ensemble mean projection
displays almond yield changes under two different climate scenarios, along with
two technology development scenarios, where the role of technology development
is highlighted. The mean projections and distributions provide insightful
results to stakeholders and can be utilized by policymakers for climate
adaptation.
