Impacts of ENSO on Spatiotemporal Variability of Rainfall and Crop Yield in the Agricultural Frontier of the Pampas, Argentina

BOCA, TERESA; CIPRIOTTI, PABLO A.

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

AMER METEOROLOGICAL SOC

Año: 2025 vol. 64 p. 687 - 702

1558-8424

Transitional climate zones between humid and semiarid regions are particularly vulnerable to climate change. Agricultural expansion in these zones over recent decades poses high risks for environmental degradation. The Pampean region in South America, a leading global agricultural area, faces productivity impacts from climate change and the El Niño–Southern Oscillation (ENSO) phenomenon, especially in the semiarid frontier. Our study aimed to (i) explore the associations between precipitation and crop yields over recent decades at a fine spatial scale and (ii) develop a spatiotemporal model of precipitation and its relationship with a quantitative multivariate ENSO index (MEI). We used monthly precipitation data from 74 rain gauges and crop yield data at the departmental level from 1965 to 2014. The relationship between MEI and precipitation was estimated using a Bayesian technique for mixed models [integrated nested Laplace approximation (INLA)]. The average correlations between yield and precipitation were moderate (0.41–0.47) with spatial variations among crops. Wheat showed the highest number of significant correlations (94%) with annual precipitation, reaching coefficients of 0.65. Sunflower showed correlations in fewer southern departments (60%), while maize and sorghum correlated better with growing season precipitation (September–March) and a higher number of departments (73%–87%). The MEI-estimated coefficient obtained from the spatiotemporal INLA model was 38 mm (24–58 mm) for growing season precipitation. Interpolated precipitation maps revealed a regional pattern following a southwest-to-northeast gradient. However, this pattern exhibited variability and inconsistency across different years and ENSO phases, underscoring the influence of random effects.