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

被引:1
|
作者
Hogan, Dylan [1 ]
Schlenker, Wolfram [2 ]
机构
[1] Columbia Univ, Sch Int & Publ Affairs, New York, NY 10027 USA
[2] Columbia Univ, Sch Int & Publ Affairs, NBER & CEPR, New York, NY USA
基金
美国食品与农业研究所;
关键词
CLIMATE-CHANGE; ADAPTATION;
D O I
10.1038/s41467-024-48388-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region. Estimating weather-induced shocks on food production requires reliable global weather datasets. Here, the authors compare global (GMFD and ERA5-Land) and regional (PRISM) datasets, showing that global datasets can uncover non-linear temperature relationships despite their lower predictive skill.
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收藏
页数:10
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