Abstract
Commentary: Evaluating Risk and Possible Adaptations to Climate Change Under a Socio-Ecological System Approach
Highlights
Maize yields are highly dependent on meteorological conditions (Ray et al, 2015), and climate change could lead to a significant reduction in yields, especially at tropical latitudes (Rosenzweig et al, 2014)
They cross-validated their model by randomly splitting the dataset into training (70% of the data) and a testing datasets. Their model achieved R2 ≈ 0.65, which was interpreted as a robust criterion for validation. This R2 value is insufficient to validate the model for projections under climate change because most of the variability in yields occurs geographically, a dimension that is highly dependent on socioeconomic factors
Observations presented make clear that temporal analyses are crucial for validating a maize yield model
Summary
Maize yields are highly dependent on meteorological conditions (Ray et al, 2015), and climate change could lead to a significant reduction in yields, especially at tropical latitudes (Rosenzweig et al, 2014). The study by Haro et al (2021; hereafter Haro21) provides a much-needed assessment of the socioecological risks facing rainfed maize cultivation in Mexico due to climate change. Given the spatiaotemporal structure of maize yields in Mexico and their machine learning modeling framework, additional justification and more robust validations are needed to substantiate Haro21’s findings.
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