Climate risk of Asian soybean rust occurrence in the state of Rio Grande do Sul, Brazil

Radons, Sidinei Zwick; Heldwein, Arno Bernardo; Puhl, Andressa Janaína; Nied, Astor Henrique; Silva, Jocélia Rosa da

doi:10.1007/s40858-021-00431-7

Cited by 7 publications

(9 citation statements)

References 21 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.…”

Section: Discussionmentioning

confidence: 96%

“…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: 96%

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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“…Speculations based on expert knowledge usually consider the complete disease cycle (Boland et al, 2004). Speculations based on expert knowledge and computer‐based simulation studies can be informed, for example, by ongoing and historical climate change effects on plant pathogens and respective crop disease patterns (Bebber, 2019; Bebber et al, 2016; Lewis et al, 2018; Radons et al, 2021; Van der Heyden et al, 2020; Wang et al, 2022). For example, Wang et al (2022) reported that increasing night‐time temperatures and decreasing number of frost days were mainly responsible for the increasing pest and disease occurrence from 1970 to 2016 in China, in addition to trade, transport and other human‐assisted processes.…”

Section: Introductionmentioning

confidence: 99%

Summary of the worldwide available crop disease risk simulation studies that were driven by climate change scenarios and published during the past 20 years

Juroszek¹,

Bartsch²,

Fontaine³

et al. 2022

Plant Pathology

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Here, published crop disease risk simulations are summarized, which were driven by climate change scenarios on a timescale terminating between 2001 and 2100. Thirty different agricultural and horticultural crops were specifically considered. Wheat diseases (mainly leaf rust) were most often simulated, followed by rice diseases (mainly leaf blast), grapevine diseases (mainly downy mildew) and potato diseases (mainly late blight). Most simulations suggest that within the projection period simulated, crop disease risk will more often increase (86 simulations) than decrease (45 simulations) or remain similar (12 simulations). The majority of crop disease risk simulations focus on Europe and Brazil. For example, there is agreement across crop disease risk simulations that in Europe the risk of leaf rust of wheat will increase. In other cases, there is disagreement across crop disease risk simulations, for example, due to contemporary climatic differences of locations. Therefore, it is risky to extrapolate results across locations. Mitigation and adaptation methods should be more often integrated in the crop disease risk simulations in order to inform about potential methods to reduce disease risk in future.

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“…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 risk of Asian soybean rust occurrence in the state of Rio Grande do Sul, Brazil

Cited by 7 publications

References 21 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

Summary of the worldwide available crop disease risk simulation studies that were driven by climate change scenarios and published during the past 20 years

Disentangling environmental effects on plant disease epidemics at the regional scale

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