Quantification of Within-Field Spread of Soybean Mosaic Virus in Soybean Using Strain-Specific Monoclonal Antibodies

Nutter, Forrest W.; Schultz, Patricia M.; Hill, John H.

doi:10.1094/phyto.1998.88.9.895

Cited by 29 publications

(17 citation statements)

References 35 publications

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“…Linear regression analysis (19,31) was used to quantify the relationships between incidence of BYDV (x) with yield gaps, seed weight, and percent shriveled grain (y). Coefficients of determinations (R 2 ), F tests, and standard errors of the Y estimate (SEEy) were calculated using the Statistical Analysis System (version 6.12, SAS Institute, Cary, NC) and used to determine the goodness-of-fit of each single point regression model (19,31,32). Slopes and intercepts within and among experiments were compared using t tests (19,35).…”

Section: Methodsmentioning

confidence: 99%

Quantification of Yield Losses Caused by Barley yellow dwarf virus in Wheat and Oats

2002

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Grain yield data obtained from five field experiments in Western Australia from 1992 to 1994, in which insecticide applications suppressed the spread of Barley yellow dwarf virus (BYDV) in wheat and oats, were used to quantify the relationships between incidence of BYDV and yield gaps, 500-seed weight, and percent shriveled grain. Yield gaps ranged from 0 to 2,700 kg/ha, and the relationship between yield gap and incidence of BYDV was always linear. Single point yield loss models revealed that BYDV infection explained most of the variation in yield gaps. There was a significant linear relationship between incidence of BYDV and 500-seed weight for wheat, but not for oats. The percent shriveled grain always increased with an increase in incidence of BYDV in wheat but not in oats. Cost-benefit relationships were determined for the return on investment when deploying imidacloprid-treated seed and/or one or two foliar applications of pyrethroid insecticides to reduce incidence of BYDV and to decrease the yield gaps in wheat and oats due to BYDV.

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Section: Methodsmentioning

confidence: 99%

Quantification of Yield Losses Caused by Barley yellow dwarf virus in Wheat and Oats

2002

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“…Moreover, to avoid bias in sampling, the choice of sampling designs should take into consideration the fact that the spatial pattern of BPMV is highly clustered. Therefore, a systematic sampling design that employs multiple sampling arms is recommended over arbitrary or random sampling designs for this pathosystem (18,19). This is the first study to demonstrate that the temporal and spatial BPMV dynamics are greatly influenced by time of virus detection (related to time of infection) within a plot (or treatment).…”

Section: Discussionmentioning

confidence: 99%

“…Quantifying the temporal and spatial dynamics of a pathogen population over the course of an entire growing season can provide critical quantitative information regarding the time of epidemic onset, changes in the spatial pattern over time, and the rate of change of disease intensity with respect to time. This research provides valuable, new, quantitative epidemiological information concerning the temporal dynamics and spatial patterns of BPMV epidemics and the relative importance of BPMV infected seed versus viruliferous bean leaf beetles as potential sources of initial inoculum (18,20,25).…”

Section: Discussionmentioning

confidence: 99%

“…Incidence of BPMV-infected quadrats was then plotted against time of sampling to obtain pathogen progress curves. Population growth models (linear, monomolecular, exponential, logistic, and Gompertz) were evaluated for model fit based on the F-statistic for the each model, the coefficient of determination (R²), the standard error of the estimate for pathogen incidence (SEEy) and a visual evaluation of standardized residual plots (18,19). The resulting BPMV spatial pattern maps were analyzed to determine if BPMV incidence within soybean plots was random or clustered using ordinary runs analysis (15,18).…”

Section: Study Area Data Collection and Data Analysesmentioning

confidence: 99%

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Quantification of Temporal and Spatial Dynamics of Bean pod mottle virus at Different Spatial Scales

Byamukama

Robertson

Nutter

2010

Plant Health Progress

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Bean pod mottle virus (BPMV) is the most prevalent virus infecting soybean (Glycine max) in the United States; however, the temporal and spatial dynamics in BPMV at varying spatial scales has not been elucidated. To quantify the temporal and spatial dynamics of BPMV at a field scale, a quadrat-based method was developed in which six soybean rows, each consisting of 30-cm-long quadrats, were established within soybean cv. NE3001 field plots (i.e., 150 quadrats per plot) in BPMV-inoculated and non-inoculated plots. Quadrats were sampled by selecting the youngest fully expanded leaflet from each of four plants within each quadrat beginning 25 days after planting, and continued at 8- to 11-day intervals until crop senescence. Leaf sap was extracted from each 4-leaflet (bulked) sample (from each quadrat), and tested for presence of the BPMV by ELISA. Quadrat position (plot, row, and quadrat number) and the date of sampling that each quadrat first tested positive for BPMV was recorded and mapped. The rate of BPMV incidence in 2006 ranged from 0.09 to 0.12 logits/day, indicating that BPMV incidence was doubling every 5.3 to 7.7 days in 2006. Doubling times for BPMV incidence in 2007 were slower, ranging from 17.3 to 34.7 days. Analysis of spatial patterns using ordinary runs revealed that BPMV-infected quadrats were predominantly clustered within both BPMV-inoculated and non-inoculated plots throughout both growing seasons. In addition to within field plot studies, a threeyear statewide disease survey (2005-2007) was conducted in Iowa to quantify county and field scale BPMV prevalence and incidence by systematically selecting 30 plants/soybean field (8 to 16 soybean fields per county). Leaf samples were then tested for BPMV by ELISA and county-level BPMV incidence maps were generated using ArcGIS software. End-of-season BPMV prevalence was 39/96 counties in 2005 (40%), 90/99 counties in 2006 (90.1%), and 74/99 counties in 2007 (74.7%). The incidence of BPMV within Iowa counties ranged from 0 to 100% and BPMV incidence significantly increased statewide from north to south. Spatial autocorrelation (dependence) analysis using Moran's I revealed clustering for BPMV incidence among Iowa counties, indicating that BPMV incidence among counties was not random. The elucidation of the within-field temporal and spatial dynamics of BPMV and the statewide geographic distribution of BPMV in Iowa has important implications with regards to sampling, plant disease forensics, BPMV management, and risk prediction of BPMV. Accepted for publication 28 January 2010. Published 26 May 2010.

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“…Leaf sap from each subsample was collected into a 5-ml wax paper portion cup and then immediately dispensed into three 1.5-ml microcentrifuge tubes. Tubes were stored at -20°C until a single tube was thawed, and 100-µl aliquots were added to each well to test for the presence of SMV by enzyme-linked immunosorbent assay (ELISA) (22).…”

Section: Samplingmentioning

confidence: 99%

Prevalence, Incidence, and Spatial Dependence of Soybean mosaic virus in Iowa

Lü¹,

Robertson²,

Byamukama³

et al. 2010

Phytopathology®

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The prevalence of soybean fields with plants infected with Soybean mosaic virus (SMV) in Iowa is assumed to be random, because the primary source of the virus is SMV-infected seed. Data collected from 2,500 soybean fields sampled over a 3-year period as part of the Iowa Soybean Disease Survey (2005 to 2007) were used to evaluate this assumption. SMV was first detected in early June of each year but counties in which it was first detected varied among years. Prevalence at the county scale at end of season was 32.3, 27.3, and 89.9% in 2005, 2006, and 2007, respectively. End-of-season incidence of SMV within SMV-positive counties was 1.5 to 25.0, 1.7 to 24, and 1.8 to 58% in 2005, 2006, and 2007, respectively. The number of fields in which plants infected with SMV were detected increased at the linear rate of approximately one new field every 2 days in 2007, compared with one new field every 22 days (2005) and 21 days (2006), with coefficients of determination (R2) of 93.2 to 96.8% using the linear model. Weak spatial dependence for end-of-season SMV incidence was detected using Moran's Index, indicating that the risk for SMV incidence at the county scale within Iowa at the end of the growing season is not random.

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Quantification of Within-Field Spread of Soybean Mosaic Virus in Soybean Using Strain-Specific Monoclonal Antibodies

Cited by 29 publications

References 35 publications

Quantification of Yield Losses Caused by Barley yellow dwarf virus in Wheat and Oats

Quantification of Yield Losses Caused by Barley yellow dwarf virus in Wheat and Oats

Quantification of Temporal and Spatial Dynamics of Bean pod mottle virus at Different Spatial Scales

Prevalence, Incidence, and Spatial Dependence of Soybean mosaic virus in Iowa

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