Brazil is the world’s leading soybean producer, so early, credible yield signals derived from climate variability are strategically important for growers, traders, and policymakers. Here we provide a systematic, multi-scale applied climatology assessment of whether the El Niño–Southern Oscillation (ENSO), used on its own, offers operationally useful early indications of Brazilian soybean yields. We analyse 2000–2020 crop-years at municipal, state, and national scales, remove long-run technological trends to isolate interannual variability, and quantify ENSO–yield relationships using linear correlation, phase-based contrasts, and multiple-testing control via the Benjamini–Hochberg false discovery rate. At the state level, associations are consistently weak and not robust to multiple-testing adjustment: across ten major producing states, the median absolute correlation is ≈ 0.17 (interquartile range 0.07–0.27), slopes are small (median − 53 kg ha⁻¹ per °C), and no state remains significant after false-discovery-rate control at q = 0.10. Phase contrasts likewise show strong overlap between neutral, El Niño, and La Niña years (only Bahia exhibits a borderline three-phase difference), and El Niño versus La Niña pairwise tests are uniformly non-significant, with Cliff’s δ typically small-to-medium and negative. Municipal correlation maps reveal a fine-grained patchwork of mixed signs that cancels under aggregation, explaining the absence of state- and national-scale signals. We conclude that ENSO-scale proxies alone are insufficient for operational yield forecasting or climate services for soybean in Brazil; investment should prioritise spatiotemporally varying, high-resolution weather-based models, with ENSO signals—at most—serving as weak background context.
- Weak and heterogeneous ENSO teleconnections to Brazilian soybean yields: a municipal-to-national assessment
- Fernando Dupin Da Cunha Mello - Universidade SENAI CIMATECPrashant Kumar - University of Surrey, Mechanical Engineering SciencesErick Giovani Sperandio Nascimento - University of Surrey, School of Computer Science & Electronic Engineering
- Theoretical and Applied Climatology, Vol.157, 322
- 25/04/2026
- 14/04/2026
- Reclaiming Forgotten Cities - Turning cities from vulnerable spaces to healthy places for people [RECLAIM], EP/W034034/1, Engineering and Physical Sciences Research Council (United Kingdom, Swindon) - EPSRC
- The Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614). This study was financed in part by the Coordenacão de Aperfeicoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 - (Proc. 88881.846220/2023-01), Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB) (Proc. 493/2022), and the National Council for Scientific and Technological Development (CNPq, Brazil), through Finance Code 001. Erick G. Sperandio Nascimento is a CNPq technological development fellow (Proc. 308963/2022-9). Prashant Kumar acknowledges the support received through the UKRI (NERC, EPSRC, AHRC) funded RECLAIM Network Plus (EP/W034034/1). The authors also thank the Manufacturing and Technology Integrated Campus—SENAI CIMATEC, the Surrey Institute for People-Centred AI, and the Global Centre for Clean Air Research (GCARE) at the University of Surrey for their valuable support.
- 991119194402346
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- Mechanical Engineering Sciences; School of Computer Science & Electronic Engineering
- Journal article