A Synoptic Analysis of the Temporal and Spatial Aspects of Grapevine Leafroll Disease in a Historic Napa Vineyard and Experimental Vine Blocks

Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Temporal Progress and Spatial Patterns of Northern Corn Leaf Blight in Corn Fields in China

Northern corn leaf blight (NCLB), caused by Exserohilum turcicum, is a devastating disease of corn in China. To enhance our understanding of NCLB epidemiology, the temporal progress and spatial patterns of NCLB were investigated. A susceptible corn cultivar, Xianyu 335, was planted in a field in Beijing in 2016 and 2017. Leaf lesions of NCLB on each plant were counted twice a week during the growing seasons. In addition, temporal disease progress was monitored for 8 weeks in three commercial corn fields in each of Yanqing, Miyun, Daxing, and Haidian Districts of Beijing in 2017, and the spatial patterns of diseased plants and NCLB lesion counts per plant were assessed in three commercial corn fields with moderate to high NCLB incidence in Yanqing District. The results demonstrated that a logistic model was the most appropriate to describe the temporal progress of NCLB incidence. The initial disease incidence was the key factor affecting disease epidemics under various conditions in the four districts of Beijing during the study. The higher the initial incidence of NCLB, the higher the final incidence. Thus, the earlier in the season NCLB incidence attained 1%, the higher was the final disease incidence. Greater than 1.0 variance-to-mean ratios suggested that the leaf lesions of NCLB tended to be aggregated on a plant. According to results from join-counts, variance of moving window averages, and semivariogram analysis, diseased corn plants and lesion numbers on each plant were aggregated in the field. The clustered pattern of NCLB lesions and infected plants suggested that conidia produced locally on diseased plants were important for disease spread within the field. The aggregated pattern of diseased plants suggested that plants should be sampled from more sites in a field to accurately estimate incidence of NCLB.

Exploring Grower-sourced Data to Understand Spatiotemporal Trends in the Occurrence of a Vector, Pseudococcus maritimus (Hemiptera: Pseudococcidae) and Improve Grapevine Leafroll Disease Management

Grapevine leafroll disease is a significant concern in the wine grape industry, as it spreads rapidly and contributes to economically significant reductions in yield and grape quality. Our objective was to utilize 5 yr of grower-sourced data from Napa (California, USA) to improve local and regional disease management efforts. Specifically, we applied a spatially integrated multivariate clustering technique to improve understanding of spatiotemporal trends in Pseudococcus maritimus (Ehrhorn) male populations-the primary vector in the region. We also implemented generalized linear mixed models to evaluate the effects of two key practices, insecticide sprays and roguing, on disease incidence. Results show P. maritimus has a biannual flight pattern in the study area, with the first flight peaking in early May and the second between early August and early September. Clusters of P. maritimus flight data fall largely within the vineyard footprints of individual growers, but also showed clear neighborhood effects. We found that when disease incidence within a block is <1%, consistent monitoring and removal of diseased vines is required to contain within-block spread. As within-block disease incidence grows to 1-20%, both insecticide applications and roguing are effective practices to reduce spread. At incidence levels >20%, roguing is a critical practice. Our results emphasize the importance of individual management efforts, but also the value of programs that engage the wider neighboring community and highlight the power of community data collection to guide decision-making.

Spatial Distribution Patterns of Parthenolecanium corni (Hemiptera, Coccidae) and of the Ampelovirus GLRaV-1 and the Vitivirus GVA in a Commercial Vineyard

Distribution patterns of the European fruit lecanium Parthenolecanium corni (Bouché) and of grapevine leafroll-associated virus-1 (GLRaV-1) and grapevine virus A (GVA) were monitored from 2003 to 2015 in a Riesling vine plot in the northeast of France. Virus spread was compared between two periods: 2003-2008 and 2009-2014. The percentage of infected vines increased from 54 to 78% for GLRaV-1 and from 14 to 26% for GVA. The spatial distribution of viruses and of P. corni was analysed using permutation tests and revealed an aggregative pattern. Virus distribution was not associated with the density of P. corni population on grapevines. However, GLRaV-1 and GVA spread mainly from initially infected vines. New GLRaV-1 and GVA infections were more frequent on vines near primarily infected vines, first anisotropically along the row, then between neighbouring rows. Virus spread was similar to those described in literature with grapevine mealybug species. This slow vine-to-vine progression suggests that P. corni was responsible for the virus spread, in accordance with the low mobility and low transmission capacities of its local population.

Twenty-Five Years of the Binary Power Law for Characterizing Heterogeneity of Disease Incidence

Spatial pattern, an important epidemiological property of plant diseases, can be quantified at different scales using a range of methods. The spatial heterogeneity (or overdispersion) of disease incidence among sampling units is an especially important measure of small-scale pattern. As an alternative to Taylor's power law for the heterogeneity of counts with no upper bound, the binary power law (BPL) was proposed in 1992 as a model to represent the heterogeneity of disease incidence (number of plant units diseased out of n observed in each sampling unit, or the proportion diseased in each sampling unit). With the BPL, the log of the observed variance is a linear function of the log of the variance for a binomial (i.e., random) distribution. Over the last quarter century, the BPL has contributed to both theory and multiple applications in the study of heterogeneity of disease incidence. In this article, we discuss properties of the BPL and use it to develop a general conceptualization of the dynamics of spatial heterogeneity in epidemics; review the use of the BPL in empirical and theoretical studies; present a synthesis of parameter estimates from over 200 published BPL analyses from a wide range of diseases and crops; discuss model fitting methods, and applications in sampling, data analysis, and prediction; and make recommendations on reporting results to improve interpretation. In a review of the literature, the BPL provided a very good fit to heterogeneity data in most publications. Eighty percent of estimated slope (b) values from field studies were between 1.06 and 1.51, with b positively correlated with the BPL intercept parameter. Stochastic simulations show that the BPL is generally consistent with spatiotemporal epidemiological processes and holds whenever there is a positive correlation of disease status of individuals composing sampling units.