Geospatial and Temporal Analyses of Bean pod mottle virus Epidemics in Soybean at Three Spatial Scales

Abstract

A statewide survey was carried out from 2005 through 2007 to quantify, map, and analyze the spatial dynamics and seasonal patterns of Bean pod mottle virus (BPMV) prevalence and incidence within Iowa. In all, 8 to 16 soybean fields were arbitrarily sampled from 96 counties in 2005 and all 99 counties in 2006 and 2007. Field- and county-scale BPMV prevalence and incidence data were mapped using geographic information systems software. BPMV prevalence was highest in the 2006 soybean growing season, when BPMV was detected in 38.7% of all soybean fields, 91.9% of all counties, and 100% of the agricultural climate districts. BPMV incidence at the field scale was highest in 2006, when mean statewide end-of-season incidence was 24.4%. Spatial analyses indicated that BPMV incidence was spatially clustered at the county scale in all three growing seasons. Prevalence at the county scale was clustered in 2005 and 2007 but not in 2006. Semivariogram analyses at the field scale indicated the presence of significant (P ≤ 0.05) spatial dependence (clustering) at distances ≤23.4 km in 2005, 297.7 km in 2006, and 45.2 km in 2007. Data for county-scale incidence displayed a north (low incidence) to south (high incidence) BPMV gradient in each year of the survey. High county-scale BPMV prevalence and incidence levels in 2006 were significantly associated with BPMV prevalence and incidence in 2007 (P ≤ 0.05). Soybean fields with narrow row spacings (≤38 cm) were associated with higher levels of BPMV incidence. Soybean fields infected with BPMV had a higher probability of infection by Phomopsis pod and stem blight than did non-BPMV-infected fields. This study provides new quantitative tools and information to better understand the seasonal, temporal, and geographical distribution of BPMV disease risk at several spatial scales.

Effects of single and mixed infections of Bean pod mottle virus and Soybean mosaic virus on host‐plant chemistry and host–vector interactions

“…BPMV incidence has also been found to be positively correlated with SMV infection in some years but not in others (Byamukama et al . ). Previous work on within‐plant interactions between these two viruses suggests that BPMV may benefit from co‐infection with SMV, as it exhibits higher viral titres in dual compared to single infections (Anjos, Järlfors & Ghabrial ), as well as enhanced levels of seed transmission, which occurs only at low levels in singly infected plants (Nam et al .…”

“…Widespread co-occurrence of these viruses in US soyabean crops appears to be a relatively recent phenomenon, having arisen following the introduction of the soyabean aphid [Aphis glycines Matsumura (Hemiptera: Aphididae)] in the early 2000s (Hill et al 2001); however, rates of co-infection between 10% and 40% have subsequently been reported for soyabean fields in Nebraska where SMV is present (Giesler & Ziems 2006). BPMV incidence has also been found to be positively correlated with SMV infection in some years but not in others (Byamukama et al 2014). Previous work on withinplant interactions between these two viruses suggests that BPMV may benefit from co-infection with SMV, as it exhibits higher viral titres in dual compared to single infections (Anjos, J€ arlfors & Ghabrial 1992), as well as enhanced levels of seed transmission, which occurs only at low levels in singly infected plants (Nam et al 2013).…”

Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka

“…Many novel approaches have been utilized to describe the spatial pattern analysis of plant diseases 12 – 14 . Among them, geostatistical techniques are widely used to characterize the spatial patterns of plant diseases and to identify the potential risk factors involved in epidemics 15 – 18 . In the recent past, geographical information system (GIS) offers a platform to integrate geographical information, plant disease status, and meteorological data into one system, thereby enabling the relationship between plant disease progress and the environment 19 .…”

“…Using GIS, the spatial positions of the pathogens and the disease-affected fields can be characterized 8 . With the GIS, geostatistical and hot spot analysis, interpolation, interpretation of semivariograms, and another modeling can be made to understand the progress of plant diseases over time and space 15 , 20 .…”

Landscape-Scale Disease Risk Quantification and Prediction

“…Geographic information systems (GISs) persist as one of the most extensively used methods of describing and displaying these static snapshots. Most GIS software allows the creation of data surfaces through interpolation of point data, using statistical methods such as variograms and kriging (7,48,50,68). Spatially linked data surfaces then can be analyzed using statistical approaches, such as multiple regression, that account for spatial dependency and autocorrelation (31,48,68).…”