Dr. Michael C. Qian, Professor (food chemistry), Oregon State University
Grapevine Red Blotch Virus (GRBV) is a single-stranded circular DNA virus that can cause Grapevine Red Blotch Disease (GRBD). The virus was first identified in Cabernet Sauvignon grapes in 2008 in California, and now the disease is known to be widespread in many wine grape-growing regions in North America. The leaves of infected grapevines turn red, and the fruit does not ripen, typically having reduced Brix and color (anthocyanin). Specifically, GRBV inhibits grape ripening pathways by altering transcription factors and hormone networks, disrupting normal grape berry development.
To better understand the impacts of GRBD on grape and wine quality – and potentially remedy the issue – we examined wine aroma composition of wines produced from GRBV positive vines that had undergone two different leaf removal treatments. This work was done in conjunction with Dr. Patty Skinkis (Professor and Viticulture Extension Specialist) and Dr. James Osborne (Professor and Enology Extension Specialist), both of OSU. The leaf removal trial was implemented in 2018-2020 growing seasons with 100% cluster zone leaf removal applied pre-bloom and compared with east-side cluster zone leaf removal by machine at fruit-set (industry-standard method). The result showed that earlier and more complete leaf removal increased monomeric anthocyanin and phenolic compounds in wines. The early 100% leaf removal led to higher levels of bound form grape-derived aroma compounds in wines compared to the standard practice (E side only leaf removal at fruit set by machine). While leaf removal increased bound grape-derived aroma compounds, it did not impact fermentation-derived volatiles as there were no significant differences in these compounds between treatments. This study suggests early leaf removal may lessen the effect of red blotch disease on grape anthocyanin content and potentially improve aroma composition.
This research was funded by industry donations granted to the Oregon Wine Research Institute.
Every summer, vineyard staff spend days to weeks gathering data from field counts and weights to obtain harvest yield estimates. Getting as close to harvest estimates as possible is a primary goal of many producers. It is critical to make cluster thinning decisions to meet contract stipulations, purchase enough winery supplies, and ensure sufficient space is available at the winery for processing.
Over the past six or seven years, Oregon has had some of the highest and lowest wine grape yields. Vine yield was at a record low in 2020 due to poor climatic conditions during bloom. Crop estimation is challenging in a typical year, but it is especially challenging in poor fruit set years. This is due to the greater berry and cluster weight variability that requires more attention to detail in sample collection.
My lab has been working on ways to improve crop estimation for Pinot noir growers for a decade. This work was done to improve our current methods of estimating crop, and I have shared some of that work with the industry over the years (see Additional Information below). By using day count since bud break and bloom, and heat units (growing degree-days in Fahrenheit, GDD50) accumulated after those phenological stages, we found the berry development curve was tightly related to both day count and thermal time. These relationships allowed us to develop equations for cluster weight increase factors that would help growers estimate crop yields. I had many questions come in last year about how crop estimation methods would change due to the poor fruit set, so we took advantage of the year to understand how well our model works.
In 2020, we began monitoring berry development in a new project to quantify vine physiology and growth amongst different soil types. Within that project, we monitor berry development in the same way we did in our prior work from 2011-2016, starting with cluster sampling from ~20 days post-bloom and continuing until harvest. We collected 60 Pinot noir clusters once or twice weekly. Each cluster was measured for cluster weight, berry count, berry weight, rachis weight, rachis length, berry diameter, and seed hardness.
The findings. Berry size was smaller than normal, reaching an average size of 0.85 g (+0.2 g) at harvest (Figure 1). The typical Pinot noir berry is 1.0 g at harvest. There was also more variability in berry size with many “hens and chicks” throughout the entire season. Many small berries persisted with fewer larger berries. Clusters had substantial weight variation (Figure 2) due to varying berry count and berry size per cluster. By harvest, clusters ranged from 21 berries to as many as 186 berries, with the mean size of 81 berries per cluster. Mean cluster weight was under 80 g per cluster. A few veteran grape growers and winemakers comment that small berries do not double in size, so increase factors during lag phase crop estimation need to be lower than normal. We tested this question with our data in 2020, and we found that berries still double in size from lag to harvest (Figure 1). Cluster weight also increased as usual. Berry weight plateaued at 50-60 days post 50% bloom (lag phase), and this related to a cluster weight increase factor of 1.9 by harvest. This matched our prior study findings. The increase factor refers to the number used to multiply the mean cluster weight at sampling to obtain the final cluster weight for harvest. Berries reached their full size by 90 days post-bloom, about 5-10 days later than for berries in our model from 2011-2016.
When the cluster weight increase factors for 2020 were compared with the model, there was strong agreement at and after 30 days (Figure 3). The one sample date at 23 days post 50% bloom was higher than the model. However, the model matched precisely for 30 days post-bloom, and all other dates had agreement at 90% or better except for the two dates closest to harvest that underestimated cluster weight by 20-30%. Often the pre-harvest cluster weights may be variable due to berry desiccation with warm weather or extended hang time. These results show that the standard procedures for increase factor determination would apply for clusters with variable set.
How to estimate yield in poor set years. An essential part of crop estimation is obtaining a representative cluster sample that represents the vineyard spatially. Good vine and cluster counts are also needed. How the cluster sample is obtained is important in any year but particularly critical to do well in a poor set year where there is more variability than normal in berries per cluster and berry weight. To ensure the best crop estimates, employ sound sampling protocols to get cluster counts per vine and cluster weights from representative vines spatially distributed throughout the vineyard block and use appropriate increase factors. If you have inadequate sampling procedures (not enough clusters and not well distributed spatially), you can expect that your estimations will be even more variable and likely less accurate. I do not recommend a certain number of clusters, as it will vary by your vineyard size and level of variability. However, it should be a large enough sample to explain variability across the vineyard block accurately. Keep notes on your methods and be sure to train those who are sampling to follow those methods.
If you wish to use the OSU increase factor equations this year, contact Dr. Patty Skinkis, Professor and Viticulture Extension Specialist, OSU at email@example.com.
Dr. Patty Skinkis, Professor and Viticulture Extension Specialist, OSU Dr. Vaughn Walton, Professor and Horticultural Entomologist, OSU
There have been an increasing number of reports of grape cane borer presence and damage in vineyards throughout the Willamette Valley this winter. Typically these reports during the bud break period in April when adults are active and evidence of shoot dieback occurs. However, we have received numerous reports this January and early February as growers begin pruning. This observation may be due to various factors including more suitable weather conditions (winter and summer), higher levels of populations surviving, more suitable host plant materials, increased awareness and improved monitoring. The borers can have a long life cycle within the vine, living as larvae (grubs) within the shoot or cane for nearly one year. Adults lay eggs during early spring and hatch and develop into larvae that feed on the shoot tissues during the growing season. They remain in the wood as pupae during winter and may be found when pruning commences. Both pupae and adults have been reported in southern and mid-Willamette Valley vineyards this winter. This article covers the most salient points for your awareness this winter; please consult additional resources below for further details.
What to look for in the vineyard: Galleries burrowed by larvae can be observed in cane tissue usually in older or dead wood, canes, spurs, or cordons. These holes are round, drill-like holes of ~0.4 mm diameter, and they are often accompanied with sawdust that was produced by the adult when burrowing into the shoot during late summer or early fall the year prior. Cutting into the wood near these holes during pruning will likely reveal a pupa that is 1-8 mm in length (<0.3 in).
Management: Insecticide application is often difficult to apply during the dormancy period due to the difficulty for the application to reach the pest and the inability to get into the vineyard with equipment. There are biological controls, such as the Steinernema carpocapsae, an entomopathogenic nematode, that may be used, but care needs to be taken to ensure that the product is handled properly and applied to the entry points of the pest to be effective. In some cases, the best method will be to cut out any canes that have the burrow holes evident. Remove pruning wood, as the wood contains the pupae that will emerge in spring. Removing the pest from the vineyard will ensure that a population does not exist to allow new infestations into tissues.
For more information about the cane borer, please see the following resources: