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Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Agriculture

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

D. Hogan and W. Schlenker

This research by Dylan Hogan and Wolfram Schlenker explores how daily temperature extremes influence agricultural yields in the US, revealing that models based on these extremes surpass those that use average temperature. They also compare different datasets, highlighting the effectiveness of GMFD and ERA5-Land in capturing critical climate-yield dynamics.

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