Spatial distribution of Beet necrotic yellow vein virus (BNYVV) and Beet soil borne mosaic virus (BSBMV) in sugar beet fields - back

Knowledge of spatial patterns of soilborne plant pathogens is useful in understanding factors associated with pathogen-disease relationships. It is also useful to identify distribution patterns of the pathogen in the field for designing field, experiments and devising sampling strategies. Once the patterns of pathogen distribution are identified, areas of the filed where the pathogen is aggregated, if any, can be selectively managed. BNYVV is a severe disease of sugar beets that cause a disease known as rhizomania, which is characterized by leaf chlorosis, stunting and extensive root proliferation. BSBMV causes similar but mild form of the disease. For this study, soil samples were collected in various grid sizes (2.9 X 2.9 m, 3.4 X 7 .6 m , and 0.4 ha) from fields in four states (Colorado, Minnesota, North Dakota, and Texas) over a two-year period and tested for incidence of the viruses. Overall, the viruses were detected in greater than 49% of the samples. Geostatistical analysis of the data revealed that both viruses, in large part, exhibited similar distribution patterns, and the patterns of their distribution varied among fields. In all but two fields, there was no spatial dependence among the sampling locations. Presence of spatial dependency is an indication of aggregation. Aggregation at large separation distances means that one may be able to sit-specifically manage part of the field that has aggregation of the pathogen. One of the fields in Minnesota (sampled at 3.4 X 7.6 m grid) exhibited special dependency at separation distance of 29.6 m and 13.8 for BNYVV (Figs. 5 & 6) and BSBMV, respectively. The 2nd field that exhibited spatial dependency showed aggregation at much less distance than the first one.

A field map of distribution of BNYVV in one of the fields in Minnesota exhibiting aggregated pattern. Legend values range from 0 to 1 with values of 1 indicating a high probability of virus detection.

A semivariogram for the field showing an increase in semivariance in relation to an increase in separation distance (fitted with spherical model) which is characteristics aggregated pattern.

The rest of the fields exhibited structural patterns that ranged from slight local spatial dependency to near random distribution. All fields sampled at 0.4 ha grid showed random distribution with slight local aggregation.