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Fungal species involved in Esca cause the formation of grapevine wood necroses. It results in the deterioration of vascular network transport capacity and the disturbance of the physiological processes, leading to gradual or sudden grapevine death. Herein, for two consecutive growing seasons, a detailed analysis of the structural (wood necrosis and leaf discoloration) and physiological parameters related to the water use of healthy and esca-symptomatic grapevines was conducted. Measurements were carried out on 17-year-old grapevines that expressed, or not, Esca-leaf symptoms in a vineyard of the Bordeaux region (France). Whole-plant transpiration was recorded continuously from pre-veraison to harvest, using noninvasive sap flow sensors. Whole-plant transpiration was systematically about 40–50% lower in Esca-diseased grapevines compared with controls, and this difference can be observed around 2 weeks before the first Esca-foliar symptoms appeared in the vineyard. Unlike grapevine sap flow disruption, structural (e.g., leaf discolorations), functional (e.g., stomatal conductance, photosynthetic activity, phenolic compounds), and genetic (e.g., expression of leaf-targeted genes) plant responses were only significantly impacted by Esca at the onset and during leaf symptoms development. We conclude that sap flow dynamic, which was related to a high level of a white-rot necrosis, provides a useful tool to predict plant disorders due to Esca-grapevine disease.
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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 .
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- Award ID(s):
- 2226649
- PAR ID:
- 10497001
- Publisher / Repository:
- Phytopathology
- Date Published:
- Journal Name:
- Phytopathology®
- Volume:
- 113
- Issue:
- 8
- ISSN:
- 0031-949X
- Page Range / eLocation ID:
- 1439 to 1446
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1094/PHYTO-01-23-0030-R
