Soybean yield prediction in argentina using climate data

Agriculture, and especially soybean production, has a critical role in Argentina's economy, as a major contributor to GDP and export revenue. This paper studies the impact of climate variability on soybean yields in Argentina using a novel department-level dataset spanning 1980-2023. We estimate a fixed effects spatial error model (SEM) to quantify the long-run effects of weather shocks-measured by extreme heat, precipitation, and ENSO phases-while controlling for economic and technological factors such as seed technology and relative prices. Our results show that extreme heat significantly reduces yields, while moderate rainfall boosts them up to a nonlinear threshold. El Niño phases increase yields, whereas La Niña events are detrimental. Technological adoption and favorable price signals also enhance productivity. These findings highlight the importance of accounting for both climatic and spatial dynamics when analyzing agricultural outcomes. The model provides a strong empirical basis for forecasting soybean yields and informing policy decisions under increasing climate uncertainty. These models can be employed as effective tools for anticipating yield outcomes under different climate scenarios and utilized in climate-related stress exercises. This work provides valuable insights for policymaking decisions, contributing to prepare for potential economic impacts stemming from climate risks on Argentina's agricultural sector.