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

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. Assessing the effect of climate change on global food systems requires a global analysis of how weather and climate affect agricultural productivity. Recent studies using fine-scaled weather data for individual countries or regions have shown that temperature extremes, especially extreme heat, are a main driver of agricultural yields and whether and how agriculture can adapt to extreme heat has major implications for the impacts of climate change. Incorporating the full temperature distribution between the daily minimum and maximum provides much better predictions of heat-related yield losses. Averaging over time (monthly rather than daily data) or space (larger grids) can mask this nonlinear relationship. However, until recently, most global data sets have only provided monthly data that can mask daily extremes (e.g., CRU, University of Delaware) and global studies were forced to rely on this more aggregated monthly data. Recently, two new daily data sets have become available and are used extensively. They are the Global Meteorological Forcing Dataset (GMFD), which includes daily minimum and maximum temperature measurements on a 0.25° grid, and ERA5-Land, which provides some of the most detailed temperature data both temporally (hourly) as well as spatially (0.1°) for the entire world. Given the global coverage over all agricultural areas, these two data sets have the advantage of offering a standardized weather product, which is crucial for a unified global analysis that drives prices, comparative advantages, production, and trade. However, these global daily weather data sets have not been systematically assessed in how well they explain outcomes of interest compared to more detailed fine-scaled weather data sets that are available for individual countries.

Dylan Hogan, Wolfram Schlenker