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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Playback language: English

Index

Abstract

This paper investigates the impact of daily temperature extremes on US agricultural yields using global gridded meteorological datasets (GMFD and ERA5-Land) and compares their performance with a fine-scaled dataset (PRISM). While GMFD and ERA5-Land show slightly lower predictive skill than PRISM, they accurately capture the non-linear temperature-yield relationship. Models using daily temperature extremes outperform those using average temperature. A subsequent analysis in Sub-Saharan Africa demonstrates the superior predictive power of GMFD and ERA5-Land compared to CRU, a previously used global dataset.

Publisher

Nature Communications

Published On

May 31, 2024

Authors

Dylan Hogan, Wolfram Schlenker

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