AutoML-based Almond Yield Prediction and Projection in California

TL;DR

This paper employs AutoML to model and project almond yields in California under climate change, integrating historical data and climate scenarios to inform stakeholders and policymakers.

Contribution

It introduces an AutoML framework for almond yield prediction and future projection using climate data, highlighting the impact of technological development scenarios.

Findings

Ensemble models achieve high prediction accuracy.

Projected almond yields vary significantly under different climate scenarios.

Technological advancements can mitigate some climate impacts.

Abstract

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.