Climate variability and maize yield throughout Argentina: individual and interactive effects of four large-scale climate indices

Abstract

Maize (Zea mays L.) is Argentina’s second most exported crop, predominantly rainfed and cultivated across a broad range of agro-ecological zones. Given the strong sensitivity of maize yields to climatic variability, this study explores the predictive power of large-scale climate modes on maize productivity at the county level over a 30-year period (1994–2024). We analyzed four climate indices—ONI (Oceanic Niño Index), IOD (Indian Ocean Dipole), AAO (Antarctic Oscillation),and TSA (Tropical South Atlantic Index)—and their monthly correlation with detrended maize yield anomalies across 171 counties (22–39° latitude South). Based on the strength and significance of these correlations, we developed an Empirical Predictive Value (EPV) for each county by weighing the most informative index-month combinations. We then evaluated the performance of the EPV through categorical comparisons with observed yield terciles using confusion matrices and chi-squared tests. Results show that ONI exhibited the strongest and most widespread correlations, particularly for central-eastern Argentina during critical maize growth stages. IOD and AAO showed moderate but regionally relevant signals, while TSA presented a limited predictive capacity. EPV-based classifications significantly aligned with observed yield anomalies in over 40% of counties, especially in the central corn belt production region. The spatial patterns of model accuracy, near-miss, and critical errors revealed a consistent geographic gradient in yield predictability. This studydemonstrates the potential of climate indices as operational tools for anticipating yield variability in rainfed systems. The EPV model offers a scalable, data-driven approach to support agricultural decision-making, especially under increasing climate uncertainty.