Principles of the Atmospheric Pathway for Invasive Species Applied to Soybean Rust

Isard, Scott A.; Gage, Stuart H.; Comtois, Paul; Russo, Joseph M.

doi:10.1641/0006-3568(2005)055[0851:potapf]2.0.co;2

Cited by 194 publications

(155 citation statements)

References 31 publications

(32 reference statements)

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“…Predicted spore depositions are mapped and uploaded onto the restricted website to be used by university extension specialists (30,32). This model was used to further support the notion that SBR inoculum likely arrived in the continental United States in association with Hurricane Ivan (31,33).…”

Section: Predictive Modeling For Sbrmentioning

confidence: 99%

“…These detections followed the inland track of Hurricane Ivan, which made landfall on 16 September 2004 near Gulf Shores, AL. P. pachyrhizi may have been transported to the United States from the Caribbean or South America in this tropical weather system (31,33).…”

Section: Time To Monitor: Detection Of Sbr In North Americamentioning

confidence: 99%

“…Based on model predictions, additional scouting occurred in areas of putative inoculum deposition. Field observations from the disease-monitoring program are the most important data used to develop predictive models for SBR development (2,31,37,72,81). The HYSPLIT model is often used in predictive modeling for future SBR events.…”

Section: Predictive Modeling For Sbrmentioning

confidence: 99%

“…The HYSPLIT model is often used in predictive modeling for future SBR events. In addition to the HYSPLIT model, the Soybean Rust Aerobiology Prediction System (SRAPS) was created as an application of the Integrated Aerobiology Modeling System, or IAMS (31). Initially created to predict introduction potential of important invasive plant pathogens, IAMS uses large, archived meteorological data sets to determine potential P. pachyrhizi deposition events.…”

Section: Predictive Modeling For Sbrmentioning

confidence: 99%

See 3 more Smart Citations

A Coordinated Effort to Manage Soybean Rust in North America: A Success Story in Soybean Disease Monitoring

et al. 2014

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Existing crop monitoring programs determine the incidence and distribution of plant diseases and pathogens and assess the damage caused within a crop production region. These programs have traditionally used observed or predicted disease and pathogen data and environmental information to prescribe management practices that minimize crop loss. Monitoring programs are especially important for crops with broad geographic distribution or for diseases that can cause rapid and great economic losses. Successful monitoring programs have been developed for several plant diseases, including downy mildew of cucurbits, Fusarium head blight of wheat, potato late blight, and rusts of cereal crops. A recent example of a successful disease-monitoring program for an economically important crop is the soybean rust (SBR) monitoring effort within North America. SBR, caused by the fungus Phakopsora pachyrhizi, was first identified in the continental United States in November 2004. SBR causes moderate to severe yield losses globally. The fungus produces foliar lesions on soybean (Glycine max) and other legume hosts. P. pachyrhizi diverts nutrients from the host to its own growth and reproduction. The lesions also reduce photosynthetic area. Uredinia rupture the host epidermis and diminish stomatal regulation of transpiration to cause tissue desiccation and premature defoliation. Severe soybean yield losses can occur if plants defoliate during the mid-reproductive growth stages. The rapid response to the threat of SBR in North America resulted in an unprecedented amount of information dissemination and the development of a real-time, publicly available monitoring and prediction system known as the Soybean Rust-Pest Information Platform for Extension and Education (SBR-PIPE). The objectives of this article are (i) to highlight the successful response effort to SBR in North America, and (ii) to introduce researchers to the quantity and type of data generated by SBR-PIPE. Data from this system may now be used to answer questions about the biology, ecology, and epidemiology of an important pathogen and disease of soybean.

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Section: Predictive Modeling For Sbrmentioning

confidence: 99%

Section: Time To Monitor: Detection Of Sbr In North Americamentioning

confidence: 99%

Section: Predictive Modeling For Sbrmentioning

confidence: 99%

Section: Predictive Modeling For Sbrmentioning

confidence: 99%

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A Coordinated Effort to Manage Soybean Rust in North America: A Success Story in Soybean Disease Monitoring

et al. 2014

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“…SBR was Wrst identiWed in Japan in 1902 (Hennings 1903), Hawaii in 1994 (Killgore and Heu 1994), and Brazil in 2001 (Yorinori et al 2005). After SBR was Wrst discovered in the continental USA in plots at the Louisiana State University research station in 2004 (Schneider et al 2005), the disease spread to 20 US states, to Ontario in Canada, and to 9 states in Mexico (Isard et al 2005;Hershman et al 2011). Most soybean cultivars grown in the USA are highly susceptible to SBR, which could lead to epidemics if weather conditions are conducive to disease development (Miles et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Molecular mapping of soybean rust resistance in soybean accession PI 561356 and SNP haplotype analysis of the Rpp1 region in diverse germplasm

Kim

Unfried²,

Hyten

et al. 2012

Theor Appl Genet

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Soybean rust (SBR), caused by Phakopsora pachyrhizi Sydow, is one of the most economically important and destructive diseases of soybean [Glycine max (L.) Merr.] and the discovery of novel SBR resistance genes is needed because of virulence diversity in the pathogen. The objectives of this research were to map SBR resistance in plant introduction (PI) 561356 and to identify single nucleotide polymorphism (SNP) haplotypes within the region on soybean chromosome 18 where the SBR resistance gene Rpp1 maps. One-hundred F 2:3 lines derived from a cross between PI 561356 and the susceptible experimental line LD02-4485 were genotyped with genetic markers and phenotyped for resistance to P. pachyrhizi isolate ZM01-1. The segregation ratio of reddish brown versus tan lesion type in the population supported that resistance was controlled by a single dominant gene. The gene was mapped to a 1-cM region on soybean chromosome 18 corresponding to the same interval as Rpp1. A haplotype analysis of diverse germplasm across a 213-kb interval that included Rpp1 revealed 21 distinct haplotypes of which 4 were present among 5 SBR resistance sources that have a resistance gene in the Rpp1 region. Four major North American soybean ancestors belong to the same SNP haplotype as PI 561356 and seven belong to the same haplotype as PI 594538A, the Rpp1-b source. There were no North American soybean ancestors belonging to the SNP haplotypes found in PI 200492, the source of Rpp1, or PI 587886 and PI 587880A, additional sources with SBR resistance mapping to the Rpp1 region.

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Early Detection and Diagnosis of High‐Consequence Plant Pests in the United States

Cardwell

Hoffman

2008

Wiley Handbook of Science and Technology for Homeland Security

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The United States has over a billion acres of food, fiber, feed, and fuel production agriculture. US agriculture could be considered a large, soft target to those who would want to strike at the economy, the social stability, or the sense of security of the US citizenry. It would be technically easy to do. Early detection and diagnosis of pests and diseases of plants is the best way to limit spread and impact, whether they arrive naturally or are introduced deliberately. The US Department of Homeland Security and the US Department of Agriculture, together with its State Department of Agriculture partners, the Land Grant University System, the Cooperative Extension Service, and agricultural industry and commodity association partners have formal and nonformal surveillance systems, as well as networks of plant pest diagnostic facilities to accomplish early detection and diagnosis of newly introduced agricultural and forest pest agents. Due to the large geographic area that must be covered to be effective, physical surveillance has to be supplemented by predictive models and remote sensing technologies. Even when surveillance systems are working optimally, diagnostic laboratory throughput could be a bottle‐neck for the generation of information in near real time. The National Plant Diagnostic Network is a distributed system of laboratories designed to manage sample surge and produce data for syndromic analysis.

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Principles of the Atmospheric Pathway for Invasive Species Applied to Soybean Rust

Cited by 194 publications

References 31 publications

A Coordinated Effort to Manage Soybean Rust in North America: A Success Story in Soybean Disease Monitoring

A Coordinated Effort to Manage Soybean Rust in North America: A Success Story in Soybean Disease Monitoring

Molecular mapping of soybean rust resistance in soybean accession PI 561356 and SNP haplotype analysis of the Rpp1 region in diverse germplasm

Early Detection and Diagnosis of High‐Consequence Plant Pests in the United States

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