AutoML-based Almond Yield Prediction and Projection in California
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 8 Nov 2022.
Builds on
3 of 12 references.
References · 3
- 2019AutoML: A Survey of the State-of-the-Artcited by 390
- 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
- 2022AutoML-Based Drought Forecast with Meteorological Variablescited by 1
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