Assessing the Impact of Climate Variability on Asian Rust Severity and Soybean Yields in Different Brazilian Mega-Regions

Fattori, Izael Martins; Sentelhas, Paulo César; Marin, Fábio Ricardo

doi:10.1007/s42106-021-00169-x

Cited by 9 publications

(12 citation statements)

References 29 publications

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“…per crop season across the locations studied was not clear (Radons et al 2021). By linking a process-based soybean model and a rainfall-based SBR model adjusted to predict daily severity progress that penalized simulated yields, Fattori et al (2021) predicted increased severity levels during El Niño years. Consequently, higher yield losses were reported in Southern Brazil than any other mega production regions of Brazil, agreeing with Del Ponte et al (2011) and our present results on the large-scale temporal and spatial spread.…”

Section: Discussionmentioning

confidence: 98%

“…Previous works on the effects of ENSO-related variables on SBR were based on simulation of disease prediction/risk models using relatively long historical observations of weather data (Radons et al 2021;Del Ponte et al 2011;Fattori et al 2021) of rainfall observation in several locations across Rio Grande do Sul (RS), the southernmost state in the Southern Brazilian region (Fig. 1).…”

Section: Discussionmentioning

confidence: 99%

“…In Brazil, the apparent infection rates of coffee leaf rust epidemics were predicted to be greatest during El Niño seasons in most locations of the subtropical region (Hinnah et al 2020). For soybean rust, three Brazilian studies used weather-based models, which were linked to a long series of historical weather and crop models, to predict SBR risk and investigate its association with the ENSO phases (Del Ponte et al 2011;Radons et al 2021;Fattori et al 2021). The effect of SST in the El Niño 3.4 region on the SBR final seasonal prevalence (cumulative number of reports) was investigated for two states: Paraná and Mato Grosso during 11 growing seasons (2004 to 2014) (Minchio et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the effect of El Niño Southern Oscillation on the onset of soybean rust in Southern Brazil

Alves¹,

Ponte²

2021

Preprint

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Soybean rust (SBR; Phakopsora pachyrhizi) is one of the most damaging fungal diseases of soybean, particularly in Brazil where farmers rely on fungicide sprays. Disease risk is variable among regions, as well as between years; the main drivers include the availability of inoculum early in the season and both pre- and within-season weather conditions. El Niño-southern oscillation (ENSO) is known to affect global climate. In southern Brazil, precipitation levels are above normal during spring and summer in the El Niño phase. Here, we describe the temporal and spatial features of SBR spread across municipalities in Southern Brazil and evaluate the effects of ENSO events and anomalies on the sea surface temperature (SST) in the El Niño 3.4 region in the central Pacific on those features. We used data provided by the 'Consórcio Antiferrugem' on disease reports during 17 crop seasons (2004/05 to 2020/21). The temporal and spatial aspects of SBR epidemics were variable among the years and significantly affected by El Niño events. A spatially structured Cox proportional Bayesian model suggested that the anomalies in the normal SST during May-June-July and October-November-December trimesters increase the risk of early SBR onset in municipalities. In general, epidemics are expected to establish and spread early and faster across the entire region of southern Brazil during the El Niño seasons. Results of this study provide insights into the large-scale spread of soybean rust across southern Brazil, which may be useful for seasonal outlook and decision-making when planning disease management.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the effect of El Niño Southern Oscillation on the onset of soybean rust in Southern Brazil

Alves¹,

Ponte²

2021

Preprint

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“…Previous works on the effects of ENSO-related variables on SBR were based on simulation of disease prediction/risk models using relatively long historical observations of weather data (Radons et al 2021;Del Ponte et al 2011;Fattori et al 2021) SST (El Niño) should influence the regional spread of SBR. Our results are in general agreement with those.…”

Section: The Effect Of Oni On the Disease Onset Timementioning

confidence: 99%

“…In Brazil, the apparent infection rates of coffee leaf rust epidemics were predicted to be greatest during El Niño seasons in most locations of the subtropical region (Hinnah et al 2020). For soybean rust, three studies conducted in Brazil used weather-based models, which were linked to a long series of historical weather and crop models, to predict SBR risk and investigate its association with the ENSO phases (Del Ponte et al 2011;Radons et al 2021;Fattori et al 2021). The effect of SST in the El Niño 3.4 region on the SBR final seasonal prevalence (cumulative number of reports) was investigated for two states: Paraná and Mato Grosso during 11 growing seasons (2004 to 2014) (Minchio et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Disentangling environmental effects on plant disease epidemics at the regional scale

Alves¹

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The environment plays an essential role in driving the occurrence and dynamics of plant disease epidemics. The weather is known to influence pathogen and host biology and should modulate the stages of the disease cycle. On the other hand, climate, the average of weather within long periods of time (i.e. 30 years), should set the predominance of pathogens and host genotypes across regions, and therefore the spatial distribution of plant diseases and their respective intensities. In this thesis, three studies aiming to unravel the effects of environmental factors on plant disease epidemics will be presented. The first study will demonstrate how the climate shapes the spatial distribution of citrus Huanglongbing prevalence in Minas Gerais. In the second study, the time to onset of soybean rust in commercial soybean fields from Southern Brazil was associated with the El Nino Southern Oscillation, a phenomenon that triggers extreme weather events around the globe and also in Brazil; The third study integrates cutting- edge statistical methodology to associate weather time series and soil properties data to white mold prevalence in snap bean fields in New York, United States. The results led to novel insights into pathogen biology and disease risk at the regional and local scales for the three pathosystems under study. Keywords: Epidemiology. Huanglongbing. Soybean rust. White mold. Bayesian. Machine learning.

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Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

de Oliveira Aparecido,

de Lima,

Torsoni

et al. 2024

J Sci Food Agric

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BACKGROUNDClimate significantly influences the interaction between pathogens and their hosts, and this is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown eye spot, can drastically reduce yields. Our study focuses on forecasting coffee brown eye spot using various models that incorporate agrometeorological data, allowing for predictions at least one week prior to disease occurrence. Data was gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais State. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown eye spot across 2,681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's POWER platform, and the Penman‐Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather (1955) water balance calculation. Six ML models — K‐Nearest Neighbors (KNN), Artificial Neural Network Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GradBOOSTING) — were employed, considering a 7‐day disease latency to define input variables.RESULTSThese models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown eye spot varied noticeably between high and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, RMSE = 10.53), while under low‐yielding conditions, the Support Vector Machine (SVM) model performed better (precision 0.76, RMSE = 12.82).CONCLUSIONThe study's application of agrometeorological variables and ML models successfully predicted the incidence of brown eye spot in coffee plantations with a seven‐day lead time, illustrating a valuable tool for managing this significant agricultural challenge.This article is protected by copyright. All rights reserved.

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Assessing the Impact of Climate Variability on Asian Rust Severity and Soybean Yields in Different Brazilian Mega-Regions

Cited by 9 publications

References 29 publications

Modeling the effect of El Niño Southern Oscillation on the onset of soybean rust in Southern Brazil

Modeling the effect of El Niño Southern Oscillation on the onset of soybean rust in Southern Brazil

Disentangling environmental effects on plant disease epidemics at the regional scale

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

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