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Poster Presentations


14

Modeling soybean rust epidemic risk in southern Brazil as influenced by ENSO teleconnections

Presenter: E. M. Del Ponte

All authors and affiliations: E. M. DEL PONTE (1), A. H. Maia (2), T. V. Santos (3), E. J. Martins (3), W. Baethgen (4). (1) Faculdade de Agronomia - UFRGS, Porto Alegre, RS, Brazil; (2) Embrapa Meio Ambiente; (3) Universidade Federal do Rio Grande do Sul; (4) The International Research Institute for Climate and Society

The risk of severe epidemics developing in Rio Grande do Sul (RS), the southernmost Brazilian state, and the key factors involved are unknown. We selected 24 locations where 30 years of daily rainfall (January to April) was available to estimate SBR epidemic risk and identify potential predictors. A rain-based model was used to predict severity in an “epidemic window” (>80% of the rust detection occurs in February). Thirty daily simulations were examined considering each day after January 31 as a hypothetical detection date (HDD) to estimate a severity index (SI) for each year-location. The mean SI (from 30 HDDs) was defined as a seasonal severity index (SSI). The probability of the SSI exceeding threshold values was estimated by empirical probability of exceedance functions. In addition, an oceanic index derived from Pacific (Niño 3.4 region) sea surface temperatures anomalies (SST) in October-November-December (OND) of the previous year was used to define cold, warm, or neutral phases. The influence of SST phases on SBR risk was quantified using nonparametric methods. Overall, the median SSI across location-years was 34%. The risk of SSI > 60% was low and ranged from 0 to 0.20. During a warm phase (El Niño), the risk of SSI exceeding median for all locations increased around 20%, compared with neutral and cold years. This study shows the potential utility of predictors related to the El Niño/southern oscillation (ENSO) phenomena for improving soybean rust risk assessments in the studied region.

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