We note that different grape varieties were used as recipient test vines in the field and laboratory studies, which limits the direct comparison of the two studies. In addition, plants used in the laboratory study were only tested for GLRaV-3 ; it is possible that interactions among virus species could have influenced vector transmission and pathogen establishment. The physiological status of mature field vines compared to greenhouse cuttings that were several weeks old at the time of inoculation may also influence virus transmission. Despite these relevant caveats, the overall finding is suggestive that laboratory studies may overestimate vector transmission efficiency. For example, Hooks et al. also found a higher transmission efficiency of Banana bunchy top virus by aphids based on laboratory experiments compared to commercial agricultural conditions. Another study that included transmission of Cucumber mosaic virus and Zucchini yellow mosaic virus by multiple aphid species found that the relative transmission rates between field and laboratory conditions depended both on aphid and virus species . We propose that this question should be addressed in more detail in future studies due to its epidemiological relevance. While our findings are informative, similar studies should be performed with other mealybug species, genetically distinct variants of GLRaV-3, grape cultivars, and in different climatic conditions to evaluate the general applicability of the results . For example, black plant pots plastic berry quality of white wine varieties is affected by GLRaV-3 infection, yet resulting disease symptoms are not readily identifiable .
Another open question with respect to disease progression is the amount of time that passes after graft-mediated infections before symptoms can be identified or diagnostic tools can detect new GLRaV-3 infections in the recipient vine. Even though spread of grapevine leafroll disease was documented 25 years ago , many parameters that contribute to spread and progression remain poorly understood. In Napa Valley, Ps. maritimus produces two synchronous generations per year that do not overlap, and our inoculation study coincided with the emergence of the second generation in July . Differing climatic conditions during the first generation, which usually emerges in March, could potentially lead to different transmission efficiency and different timing of disease development. Varied climatic conditions could lead to variation in host traits and resulting host susceptibility, variation in the virus life cycle in response to varying environmental conditions within host and vector, and variation in mealybug activity that could lead to higher or lower transmission efficiencies. There is a need for understanding how the interactions between host, vector, and pathogen are affected by climate and seasonality. Other mealybug species in California produce higher numbers of overlapping generations; therefore vines may be vulnerable to new infections throughout the year . For example, Pl. ficus produces four to seven overlapping generations per year, and is not yet present in most areas of Napa Valley, CA, where our field study was conducted . If the invasive Pl. ficus establishes more widespread populations in Napa Valley and other areas of Northern California at the higher densities typically associated with this species, spread of GLRaV-3 could dramatically increase. In summary, the information provided by our study regarding key biological traits of GLRaV-3 can inform sound management practices. For example, when certified virus-free material has been used for planting, newly symptomatic vines can be used to infer that the infection resulted from insect-borne inoculations made during the previous growing season, and that the newly symptomatic vines can be an efficient source for further disease spread.
Decline in crop quality can be expected during the same growing season in which symptoms first appear, which may influence roguing strategies based on economic models .There is natural spatial variability present in vineyards due to the variations in soil characteristics and topography . Soil characteristics are too complex to be thoroughly surveyed effortlessly. With traditional destructive methods, it is difficult to obtain enough comprehensive information from the soil pits at the field scale. These soil characteristics may directly affect the water availability for grapevines, which eventually determine the physiological performance of the plants . However, there is no variable management practices currently available to accommodate the natural spatial variability. Thus, the spatial variability derived from vineyard soils will inevitably be expressed in the whole plant physiology at the cost of homogeneity of vineyard productivity and quality. We previously reported the spatial variation of midday stem water potential affecting grapevine carbon assimilation and stomatal conductance of grapevine . The resultant variations in whole plant physiology were associated to flavonoid composition and concentration at the farm gate. However, there is a lack of information about the effects on the chemical composition in the final wine, which would ultimately determine wine quality as perceived by consumers. Georeferenced proximal sensing tools can capture the spatial and temporal variability in vineyards, making it possible to supervise and manage variations at the field scale . Previous studies showed that soil bulk electrical conductivity may be used to evaluate many soil attributes, including soil moisture content, salinity, and texture . Soil electromagnetic induction sensing has been used in precision agriculture to acquire soil bulk EC at the field scale due to its non-invasive and prompt attributes .
Although research had been conducted on the relationships between soil electrical properties with plant water status, they were mostly point measurements and the results were rarely interpolated to whole fields. There were only a few studies that investigated the EMI sensing and soil-plant water relationships over a vineyard . Previous research suggested that the connection between soil water content and soil bulk EC could have relied on specific soil profiles, and needed to include soil physical and chemical properties to complete this connection . Nevertheless, there is evidence that soil bulk EC may still be useful not only to identify the variability in soil, but also in the plant response affected by vineyard soils such as yield, plant physiology, and grape berry chemistry . Plant available water is a determinant factor on grapevine physiology, together with nitrogen availability in semi-arid regions . Wine grapes are usually grown under a moderate degree of water deficits as yields were optimized at 80% of crop evapotranspiration demand with sustained deficit irrigation . Water deficits would limit leaf stomatal conductance and carbon assimilation rate that sustain grapevines’ vegetative and reproductive growth and development . When grapevines are under water deficits, carbohydrates repartitioned into the smaller berries would enhance berry soluble solids content . Sucrose and fructose, which are the major components of total soluble solids in grape berry, can act as a signaling factor to stimulate anthocyanin accumulation . The effects on grapevine physiology and berry composition also depend on the phenological stages they occur and how severe and prolonged the water deficits are . Flavonoids are the most critical compounds dictating many qualitative traits in both grape berries and wine . The variations in environmental factors could alter the concentration and biosynthesis of flavonoids and can be extrapolated spatially within the same vineyard, including water deficits , solar radiation , and air temperature . Among flavonoid compounds, anthocyanins are responsible for the color of berry skin as well as wine . Moderate water deficits during growing season can increase anthocyanin concentration in berry skin and wine . However, water deficits can impair plant temperature regulation through evaporative cooling . They may also inhibit berry growth by limiting berry size and altering berry skin weight . Thus, in some cases it may be uncertain if water deficit promotes anthocyanins biosynthesis or reduces berry growth, or contributes to anthocyanin degradation . Applying water deficit on grapevines can contribute to greater proportion in tri-hydroxylated over dihydroxylated anthocyanins due to the up-regulation of F30 5 0 H. Another major class in flavonoids, proanthocyanidins, are polymers of flavan-3-ol monomers and they contributes mainly toward astringency or bitterness in wine . Compared to anthocyanins, water deficits showed mild effects on proanthocyanidins . However, water deficits with great severity can still alter the concentration and composition of proanthocyanidins in both berries and wine . Selective harvest is one of the targeted management strategies to minimize the spatial variation in berry chemistry in vineyards . By differentially harvesting or segregating the fruits into batches prior to vinification, large plastic pots for plants the berry composition can be artificially set at a more uniform stage with minimal variations . In our previous work, we reported the use of plant water status to determine the spatial variation of grape berry flavonoids . The goal of this study was to deduce if the spatial variability of soil bulk EC and differences in soil texture can be related to plant physiology and grape and wine composition.
The specific objective of the study was to determine if the spatial variability of proximally sensed vineyard soil bulk EC would affect plantwater status, and if this relation would affect leaf gas exchange, components of yield, berry composition, and flavonoids in both berries and wine.Soil bulk EC was assessed with EM38 in 2016 when the vineyard soil was at field capacity condition. Both vertical dipole mode and horizontal dipole mode were used to assess EC at two depths, including deep soil and shallow soil . The instrument was calibrated according to manufacturer instructions. The device was placed on a PVC sled and driven through the vineyard with an allterrain vehicle along the inter-rows. A distance of approximately 0.5 m from the vehicle to the device was maintained to avoid interference with the vehicle. A stratified grid was used to collect soil samples corresponding to the two depths at which we measured soil bulk EC. Soil texture was assessed according to the soil analysis method: hydrometer analysis in the North American Proficiency Testing program.Geostatistical analysis was performed in the R language by using package “gstat” 1.1-6 . The bulk EC data were filtered by Tukey’s rule to remove outliers either below the first quartile by 1.5 inter-quartile range or above the third quartile by 1.5 inter-quartile range. To further remove the outliers, the data were filtered by the speed that the vehicle was driving, which was between 3.2 km per hour to 8.0 km per hour. Variograms were assessed by “automap” package 1.0-14 , and fitted to perform kriging. The soil bulk EC values were extracted from the location of each experimental unit, these values were further used to perform regression analysis. Kriging and k-means clustering on plant physiology variables were performed with the R packages “gstat” and “NbClust,” v3.0 . Universal kriging was utilized on plant water status because of the existing trend in longitude and latitude. Variograms were assessed by “automap” package 1.0-14 , and fitted to perform universal kriging. The vineyard was delineated into two clusters by k-means clustering, including Zone 1 with higher water deficit and Zone 2 with lower water deficits. The separation described 78.1% in 2017 of the variability in the plant water status according to the result of between sum of squares/total sum of squares. The resulting maps were organized and displayed by using QGIS software . Cluster comparison was analyzed by “raster” package reported as Pearson’s Correlation between two cluster maps . Data were tested for normality by using Shapiro-Wilk’s test, and subjected to mean separation by using two-way ANOVA with the package “stats” in RStudio . Significant statistical differences were determined when p values acquired from ANOVA were <0.05, and the zones were classified according to Tukey’s honestly significant difference test. Regression analysis was performed by SigmaPlot 13.0 . Correlation coefficient between variables were calculated in by Pearson’s correlation analysis, and p-values were acquired to present the significances of the linear fittings.Berry skin anthocyanins were different between the two zones in 2016 . Total delphinidins, petunidins, malvidins, and the sum of them as trihydroxylated anthocyanins were all higher in Zone 2 than Zone 1 . Total cyanidins, peonidins, and the sum of them as di-hydroxylated anthocyanins were greater in Zone 2 on 23 August, 15 September, and at harvest . Total skin anthocyanins were 2.2 mg per g of berry fresh weight in Zone 2 which was higher than the 1.85 mg measured in Zone 1 . In 2017, there were no differences between the two zones in delphinidin, cyanidin, petunidin, or peonidin at harvest . Zone 1 had higher malvidins from 24 August until harvest, and tri-hydroxylated anthocyanins, total anthocyanins from 7 September until harvest . Conversely, total malvidins, tri-hydroxylated anthocyanins, and total anthocyanins were higher in Zone 1 at harvest .