Can extreme climatic and bioclimatic indices reproduce soy and maize yields in Latin America? Part 1: an observational and modeling perspective

Abstract

According to the IPCC, most regions worldwide will be gradually exposed to the amplification of the duration, frequency, and intensity of extreme climatic events, and the effects that extreme events can cause on human well-being and the economy.This study aims to develop linear regression models to estimate the soy and maize yields from extreme climatic and bioclimatic indices in three geographical subregions of Latin America (Mexico, Brazil, and Argentina) between 1979 and 2005. We used daily datasets from observations (CPC), reanalysis (ERA5), and regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to investigate the impact of extreme eventsof temperature and precipitation on maize and soy yields over the CORDEX Central America and South America domains.We first assessed the RCMs’ performance in reproducing extreme indices by comparing them against observations. The validation process evidenced the need for applying bias correction techniques to simulate daily precipitation and temperature for a better performance of the indices. The results show a higher correlation between the daily temperature range (DTR), cold nights and warm nights for soy production in Argentina (R2: − 0.74, − 0.80 and 0.75, respectively) and Mexico (R2: − 0.80, − 0.81, 0.70) for maize. Regionally, the linear model (simulated with observed data) using these indices presented an agreement with observed yield data in Mexico and Brazil, with explained variances exceeding 70% for maize in these subregions, while Argentina presented a better performance for soy yield. An intriguing finding was the superior performance of linear models when used with CPC-corrected RCM data compared to ERA5. Taken together, our results highlight the capabilities and constraints of linear models as valuable tools for developing adaptation and mitigation strategies, enabling precise yield forecasting, and informing policy decisions.