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<p>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&#xa0;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.</p>

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Weak and heterogeneous ENSO teleconnections to Brazilian soybean yields: a municipal-to-national assessment

  • 期刊论文

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  • 发表日期：

    2026年4月25日

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  • Fernando Dupin da Cunha Mello,
  • Prashant Kumar,
  • Erick G. Sperandio Nascimento

摘要

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.