Progress report for FNC19-1201

Project Type: Farmer/Rancher
Funds awarded in 2019: $8,790.00
Projected End Date: 12/31/2021
Grant Recipient: Danzinger Vineyards
Region: North Central
State: Wisconsin
Project Coordinator:
David Danzinger
Danzinger Vineyards
Expand All

Project Information

Description of operation:

Danzinger Vineyards (DV) currently has 18 acres of grapes in production. Vineyard product began in 2004 with an initial planting of 2,000 vines and increasing to just over 10,000 vines producing between 65-80 tons of grapes per year. DV is an estate winery meaning all wines produced are comprised solely of fruit produced by our vineyard. On average DV sells about 10% of the grapes produced per year to other wineries as juice. The vineyard and winery was started by two brothers, Melvin and David Danzinger. They started the vineyard in an effort to diversify their dairy farming operation. At DV we engage in several practices that help us operate a more effective and efficient spray program that makes us more sustainable. One such practice is mechanical hedging which removes non-essential growth that would otherwise restrict air movement, limit spray penetration, and increase disease pressure. By engaging in mechanical hedging at the appropriate times in the season we help limit how much we spray and when we do spray as part of our IPM strategy it's more effective. This practice has been used since the vineyard's inception.

Summary:

La Crescent is a cold climate grape variety developed by the University of Minnesota and well adapted to production in Wisconsin. La Crescent is very cold hardy and disease resistant and has many other positive characteristics. One issue with La Crescent is that the grape will shatter when near “peak ripeness”. Shatter is when the berries fall off of the rachis. Shatter can be caused by a number of factors including high winds, disease pressure, and movement of the canopy during harvesting, especially during mechanical harvesting. Suggestions within the industry are that the nutrient, calcium, along with adequate levels of other micro- and macro-nutrients may be used in the prevention of shatter. To test this idea we performed field trials using several forms of delivery and products of the nutrient calcium. We used tissue analysis (petiole and hand-held sap analysis) to determine micro- and macro-nutrient levels within our plants through-out the trial. Yield projections and analysis helped us measure the degree to which shatter was or was not occurring.

Soil Test were conducted thru UW-Marshfield lab. Results showed adequate to high levels of N, P, K, Ca, S, Mn, B, Fe, and Cu. While it showed low levels of Mg, and Zn. Our average soil Ph is 6.9 which is acceptable for grapes. We therefore operated on the hypothesis that it likely wasn’t an elemental deficiency in our soil causing shatter but rather also operating on the principle that high level of calcium, specifically at bloom and berry development can help prevent shatter.

To study the effectiveness of calcium management programs in helping prevent shatter we decided it would be best to use several different modes of applications and products. The modes and products we are testing for our calcium study are; soil applied gypsum, a single application of a foliar calcium fertilizer at bloom, two treatments of foliar applied calcium at bloom and then 2 weeks later, and Agro-K ‘s nutrient management program (which includes several calcium applications). We sampled 24 plants for each treatment including a control group of 24 which received no treatment. We sampled 4 rows, with each treatment type being 6 continuous plants. The order in the rows in which the treatment types were selected in each row was randomized. We also skipped a row between each sampled row to avoid drift of foliar sprays. All treatments were applied at industry recommended levels. Agro-K’s testing, analysis, materials, and program were provided to us at no cost or incentive. 

We conducted our spray treatments at 1200 growing degree days (GDD), which is accepted to be the measure of halfway through the grapes physiological development (it’s about half its final weight). By determining clusters per plant, the average weight of those clusters at 1200 GDD, and then multiplying by a factor of 2, we can estimate the final yield in terms of weight. I refer to this as our expected yield (EY). We can then quantify the effectiveness of these different treatments in reducing shatter by harvesting at peak yield, weighing and recording each treatment sample separately and measuring the difference between our actual yield and our EY. We chose to express this value as “% of EY” (harvested). Values of higher EY.

We also conducted petiole analysis in the lab and petiole analysis through hand-held technology. (results and analysis pending)

We also conducted pH, Brix, titratable acidity and yeast assimilable nitrogen. (completed, will insert results following another year of the project).

Yield analysis is presented as a table:

  Expected Yield (EY) Actual Yield % of EY
Control 120.765 lbs 107.03 lbs 88.62%
1Cal (foliar) 145.5 lbs 111.47 lbs 76.60%
2Cal (foliar) 151.32 lbs 109.77 lbs 72.54%
Agro-k (foliar) 154.23 lbs 128.72 lbs 83.45%
Gypsum (soil) 141.13 lbs 113.6 lbs 80.49%

Our data shows that our control treat has the highest % of EY value making it in this instance according to this metric the most effective treatment. This is addressed in other sections of our analysis but short growing season contingencies, mainly vine decline from a recent polar vortex, may have affected our data set in a meaningful way. Because of this we believe it necessary to conduct this study for another year and gather more data and further analyze all data gather such as juice quality, and petiole results and how foliar sprays may or may nor effect them.

Project Objectives:
  1. Determine if handheld nitrogen and calcium sap testers are accurate in getting immediate field levels as compared to lab petiole tests.
  2. Evaluate if high levels of soil applied calcium and foliar applied calcium reduce shatter in La Crescent Grapes.
  3. Measure grape juice quality on all treatments.
  4. Share data following year one and year two with grape growing organizations in Wisconsin and Minnesota and on social media.

Research

Materials and methods:

This project will have four treatments and a control and each treatment will be replicated four times using a standard randomized block method.  The treatments include:

  • No additional Calcium fertilizer
  • Calcium fertilizer – soil applied
  • Calcium fertilizer – one foliar application
  • Calcium fertilizer – two foliar applications
  • Agro-K Micronutrient Fertilizer Program

Each treatment will be a block of six vines (the vineyard is laid out with 3 vines between trellis poles) and the middle three vines will be harvested and tested.  All fertilizer treatments, testing and harvesting will be done by hand.  Grape berries will be collected in individual buckets (saved from a previous research project with UW-Madison) and juice will be saved and tested from each replication.  Soil type in the vineyard block is consistent.  The treatments will be treated the same for all other management applications.

Statistics will be completed for the following factors each year:

  • Petiole and sap testing comparison
  • Compare yields of each treatment
  • Evaluate shatter on plants (shake test – still to be determined)
  • Evaluate juice quality: Brix, pH titratable acid, and yeast assimilable nitrogen

La Crescent grapes are exceptional wine grapes with most northern wineries using them in their portfolios.  The yields are very good and the grape is very hardy and disease resistant, just need to solve this one issue of shattering.  Information gathered will be shared with other producers.

Research results and discussion:
Participation Summary
1 Farmer participating in research

Educational & Outreach Activities

1 Consultations

Participation Summary

Education/outreach description:

I’ve discussed our project with several other industry members at conferences and others have expressed interest in this issue due to La Crescent being such a high desirable grape in the winery. Specific outreach pertaining to data has been difficult because I believe the data we generated this year is somewhat problematic. But other industry members have expressed an interest in waiting to hear more as we learn more.

Learning Outcomes

Lessons Learned:

Following the conclusion of our first year of fertilizer trials to address the issue of grape shatter in LaCrescent grapes we ran into many issues but also learned several things and produced a meaningful data set. To accurately talk about our study and our data we need to first provide some context on the growing season our study took place in. During the months directly preceding the 2019 growing season at the end of February much of the Upper-Midwest experienced several days of extremely low sub-zero temps (ranging between -35 – 48 degrees). Known as a “polar- vortex” this extremely cold air occurred at our site for about 2 days. We know our vines can handle some extreme temperatures but temperatures that extreme and their effect on the newer “cold-hardy” hybrids have not been well studied or documented. In addition vine decline and cold damage sometimes result in an immediate “death” of the vine but sometimes also manifest as a slower “trickling-out” of the vine preceding “death”.

Because vine death happens at different rates and is difficult to judge selecting “healthy” or “normal” vines after an extreme weather event can be quite problematic. That being said, when we selected vines for our study we excluded any vines that displayed symptoms of decline or frost damage. In addition, the metric we used to measure relative “success” of each treatment was selected to help mitigate the “imperfect vine” situation or any other contingencies the average growing season may produce. I refer to this metric as “% expected yield”, expected yield is obtained by counting clusters on plants at the half-way point in the growing season (about 1200 GDD), then one determines the average mass of those cluster at the half-way point. After that by knowing the number of clusters per vine, and the average weight of those clusters and knowing that 1200 GDD on average marks the half-way point of the physiological development of the fruit (IE half the total weight) you can multiply these factors by 2 to obtain an accurate estimate of your expected yield. Percentage of expected yield is simply the difference between our E.Y. (expected yield) and our actual yield. Using this metric is important because vine performance is not what we are trying to measure but rather we are trying to quantify the effect of several fertilizer treatments and the ability to help vines reach “peak performance” which could include preventing late season shatter. By measuring fruit loss over the year instead of just final yield we are able to track the vines’ and fruits’ development and progression over the growing season. Essentially we measured each vine’s success in terms of that particular vines potential. Despite our best efforts to account for weather and other contingencies we still faced several problems. Toward the end of the growing season it became very apparent that some of the vines included in our study were dropping fruit and/or dying off likely due to the previously mentioned pressures which undoubtedly affected our data. To mitigate this, my plan moving forward is to increase my sample size so that I could have the option of excluding more of these “outliers” without affecting my data set or its ability to produce statistically significant data. Farming is rarely if ever “perfect” therefore any study that involves farming is likely to be met with contingencies. I believe that our study did a good job at recognizing and mitigating most of these problems but its clear to me that we were not able to avoid all. Our best performing “treatment” was our control, where no fertilizer was applied. Just because the control performs better than the variables does not inherently make the results flawed, but it should certainly raise some flags. After examining our data, the context in which that data was generated, and our hypothesis we’ve concluded that the only way we could draw meaningful conclusions is if were were to generate more data in a growing year with far less contingencies 

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.