Is Crop Estimation More Challenging in a Poor Fruit Set Year?

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.

Figure 1. Pinot noir berry mass increase based on day count post mid-bloom through harvest in 2020. Data points represent berry mass from 60 clusters, and error bars are standard deviations of the mean. The error bars are difficult to see given the small berry weight in the first six sample dates.
Figure 2. Pinot noir cluster mass increase based on day count post mid-bloom through harvest in 2020. Data points represent means from 60 clusters, and error bars are standard deviations of the mean.

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.

Figure 3. Increase factors for cluster weights at day count post 50% bloom. The 2020 data points represent the increase factor mean of 60 clusters sampled at each date compared to the mean weight at harvest on 13 Sept 2020 (n=54 clusters). The calculated values are based on an increase factor model developed by Skinkis and McLaughlin (in progress).

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

Additional Information

Skinkis, P. 2017. Crop Estimation: It’s all about timing and good data. Oregon Wine Research Institute Vine to Wine Newsletter, July 2017.

Skinkis, P. 2019. Improved Crop Estimation Methods for Oregon Pinot Noir. Oregon Wine Symposium, Portland, OR (seminar video).

Can Altering Canopy Shape Increase Productivity of Pinot noir: a new experiment at OSU’s Research Vineyard

Dr. R. Paul Schreiner, Research Plant Physiologist, USDA-ARS, Corvallis, OR

The newest block at Woodhall Research Vineyard is now six years old, and we will begin work in earnest next growing season to ask some fundamental production questions for Pinot noir. The key question is whether opening the top of a standard VSP training system (resulting in a Y-shaped canopy) will increase Pinot noir productivity without sacrificing quality (Figure 1). A second question is whether planting vines at a higher density impacts vine productivity or fruit quality. These questions are being addressed using a factorial experiment where two trellis treatments (traditional VSP & wide VSP) and two vine density treatments (3-foot and 6-foot in-row spacing) are applied in a randomized block design with five blocks. Each experimental plot has five continuous rows of vines about 100 feet long. Data will be collected from the middle three rows, allowing a border row of identical treatment on each side. Different crop levels will be applied to each of the trellis × density treatments by randomly assigning the north or south half of each plot to either low or high crop levels. The trellis and vine density treatments have been in place since 2015, and crop load will be manipulated for the first time next year. The vines were established using industry-standard practices (irrigation, fertilization, no crop in first two years, slowly increasing crop levels thereafter). In the last two years, vines were irrigated only twice each summer, when leaf water potential values reached about -1.4 MPa.

Why this design? Pinot noir producers in western Oregon use a VSP trellis system nearly exclusively where the shoots exist in a tight vertical plane that exposes only a small fraction of leaves to sunlight at midday when solar radiation is maximal. Opening the top of the trellis using a wide VSP system should increase net vine photosynthesis and the vine’s overall carbon budget, allowing more fruit to be produced per acre compared to a traditional VSP. This change can be implemented without removing the existing trellis, keeping costs low for this modification. A similar trellis design was shown to increase yield without compromising quality in Riesling vineyards (Reynolds et al. 1996). Pinot noir producers still thin crop to low levels, leaving 25-40% of their fruit on the vineyard floor. If opening up the canopy can allow Pinot noir producers to ripen more fruit per acre without negatively affecting quality, this approach can increase profits and sustainable production. Vine density per acre may also impact vine productivity or quality directly or by interacting with the altered trellis system. Still, such impacts cannot be predicted based on current knowledge. Since grafted grapevines cost about $5 each, reducing the number of plants needed per acre will significantly reduce establishment costs.

We have collected baseline data from the past five years. The block produced 2.2 US tons per acre in 2019 when the fruit was thinned to one cluster per shoot. Yield in 2020 was 2.5 tons per acre when no fruit thinning was applied due to low set in 2020. Thus far, yield has not been altered by the trellis or vine density treatments. However, vine vegetative growth based on pruning weights was altered for the first time in 2019. The high-density vines produced more shoot biomass in the wide VSP than the traditional VSP, but the low-density vines did not. Thus, the wide VSP appeared to capture more carbon than the traditional VSP in 2019, but only in high-density vines. We do not yet know if a similar response occurred in 2020 since pruning weights have not been obtained yet. Treatments have not altered yield parameters such as cluster weight and berry weight. Fruit composition based on must soluble solids, pH, titratable acids, and mineral nutrient concentrations has not been altered either. The application of different crop levels next year will result in a different yield, and this will begin to provide the true test of this experiment. I am excited to test these ideas on a large scale.

This research addresses improving vineyard production efficiency by altering the most common Pinot noir training system. If our hypothesis is correct, this research will improve Pinot noir wine grape growers’ profitability by increasing yield per acre, thus improving overall land and resource use efficiency.

Figure 1. Pinot noir in the Trellis Experiment at Woodhall Research Vineyard near midday on August 26, 2020. Top panel: Standard VSP. Bottom panel: Wide VSP. Note: larger shadow under Wide VSP vines.

Literature Cited

Reynolds AG, Wardle DA and Naylor AP. 1996. Impact of training system, vine spacing, and basal leaf removal on Riesling. Vine performance, berry composition, canopy microclimate, and vineyard labor requirements. Am J Enol Vitic 47:63-76.

Avoid mixing biologicals with antimicrobials

Dr. Jay W. Pscheidt and Lisa Jones, Dept. of Botany and Plant Pathology, Oregon State University

Actinovate AG (Streptomyces lydicus WYEC 108) and many other biological products are used in the management of organic grapes. Tank mixing more than one product is both economical and time-saving but tank mix compatibilities with biological control products such as Actinovate have not been thoroughly evaluated. In 2016, we examined the tank mix compatibility of Actinovate AG with commonly used organic products.

Actinovate AG was prepared at a concentration of 0.1g/ml. A 300 ml solution of Actinovate was prepared in a 500 ml beaker then mixed with each material and allowed to stand for 30 minutes. The mixture was then plated onto agar and incubated for 7 days at room temperature. The number of colony-forming units (CFU) of S. lydicus exposed in each mix was assessed daily and compared to an Actinovate plus water only control. The percentage of S. lydicus CFU in each tank mix compared to the CFU in the Actinovate control was calculated.

An average of 3.2×105 S. lydicus CFU developed after 7 days incubation on the various media when Actinovate was just mixed with water. Several products inhibited the growth of S. lydicus when prepared in as a mixture in the laboratory. No growth of S. lydicus was observed on plates when Actinovate was mixed with Horticultural Vinegar, a high rate of Regalia, Rex Lime Sulfur, Serenade Optimum, or Solubor DF. Less than 10% of the S. lydicus CFU grew when Actinovate was mixed with Biomin Calcium, Botector, Neptune’s Harvest 2-4-1 fish fertilizer, or Thuricide. Significantly fewer S. lydicus CFU grew when Double Nickel, the low rate of Regalia, Serenade Max, the high rate of Stimplex or Toggle were mix with Actinovate. There was no significant difference in the number of S. lydicus CFU that grew when Zen-O-Spore was mixed with Actinovate. The number of S. lydicus CFU was greater than double (219%) or quadruple (482%) that of the Actinovate control when mixed with Nitrozyme or the low rate of Stimplex, respectively.

Many of the biological products in this study grew quicker than S. lydicus under laboratory conditions. These fungi or bacteria generally outcompeted S. lydicus for space and resources on the agar plates. The fungus found in Zen-O-Spore was slower to grow and did not outcompete S. lydicus during the 7-day incubation.

This data does not imply a lack of or enhanced disease control in the field. For example, blueberry field trials over a 2-year period where Actinovate was mixed with Simplex did not result in disease control that was different than when either product was used alone. The data does indicate incompatibility between various products used in organic production.

For a complete data set please visit:

What’s New with Malolactic Fermentation

Dr. James Osborne, Associate Professor and Enology Extension Specialist, OSU

The malolactic fermentation (MLF) is a vital step in the production of cool climate red wines as well as some white wines. But despite its importance, MLF often gets taken for granted and just considered a step to reduce wine acidity. However, MLF is much more than just a biological de-acidification process and can have a number of other impacts on wine quality. Our lab has been conducting a number of projects over recent years investigating various aspects of MLF. One project is investigating interactions between Oenococcus oeni and the spoilage yeast Brettanomyces bruxellensis. An interesting result from this study was discovering that some O. oeni strains were capable of increasing the concentration of the volatile phenol precursors p-coumaric acid and ferulic acid. These pre-cursor compounds are found in grapes and wine mainly bound to a tartaric acid and in this form are not utilized by Brettanomyces. However, some O. oeni strains can remove the tartaric acid through the action of an enzyme, cinnamic esterase, and release free p-coumaric and ferulic acid that Brettanomyces can then metabolize to 4-ethylphenol and 4-ethyl guaiacol. This finding has led to the labelling of many commercial O. oeni strains as either cinnamic esterase (+) or (-) with the recommendation being to avoid use of cinnamic esterase (+) strains in situations where the wine may be at risk for Brettanomyces spoilage.

An additional area of research has been determining the effect of MLF on red wine color. We know that MLF changes wine pH which can cause a shift in red color, but were there other impacts on color due to MLF? Our lab demonstrated that independent of pH change, MLF results in a loss of color and lower formation of polymeric pigments. Results from a number of studies showed that this color loss was likely due to the metabolism of acetaldehyde by O. oeni. Acetaldehyde plays a key role in the development of polymeric pigments and so metabolism of acetaldehyde during MLF reduced formation of these color compounds. Delaying MLF was shown to help mitigate this color loss but delaying MLF for long periods is risky from a microbial spoilage point of view, as SO2 cannot be added to the wine until MLF is complete. Additional strategies to mitigate color loss due to MLF are currently being explored. One such strategy is the use of ML bacteria that do not metabolize acetaldehyde. To date, all O. oeni strains screened can metabolize acetaldehyde but other lactic acid bacterial species such as Lactobacillus look more promising. There has been renewed interest in using certain Lactobacillus species and strains to conduct MLF. In particular, homofermentative species of Lactobacillus have been studied as potential ML starter cultures. These bacteria do not produce acetic acid from glucose metabolism and so could be used for conducting concurrent alcoholic and malolactic fermentations without the risk of increased acetic acid. Currently, there are commercially produced L. platarum cultures available outside of the USA for use in winemaking. However, at this time these cultures are not available for winemaking use in the USA. The use of concurrent alcoholic and malolactic fermentation is one final area our lab has been studying. While there are obvious time advantages to conducting alcoholic and malolactic fermentation at the same time, there are still some concerns over the impact on wine quality, particularly for red wines. We recently completed a study investigating how the timing of MLF impacts Chardonnay aroma and mouthfeel and will be continuing work in this area focused on concurrent fermentations of red wines. As we continue to study malolactic bacteria, we are gaining a better appreciation for the impact they can have on wine quality and potential new strategies for their use. For additional information on any of the studies we have conducted on MLF please contact me at:

Pest Alert: Grape Cane Borer

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).

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: