Effects of yield regulation practices on grapevine productivity, health, and economic sustainability

Final report for GNE16-137

Project Type: Graduate Student
Funds awarded in 2016: $14,864.00
Projected End Date: 05/31/2019
Grant Recipient: Penn State University
Region: Northeast
State: Pennsylvania
Graduate Student:
Faculty Advisor:
Dr. Michela Centinari
Penn State University
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Project Information


Yield regulation in grapes is required in several high-yielding varieties to produce optimal fruit maturity while maintaining vine health each season. Traditionally, growers regulate yield using a technique called cluster thinning (CT). This technique selectively removes grape clusters to accelerate the ripening and maturity of the remaining fruit. A newer technique, early leaf removal (ELR), may achieve similar results through imposing a carbohydrate-limiting stress by removing leaves in the fruiting-zone when performed between the onset of bloom through fruit-set. Additionally, ELR may improve the canopy microclimate by reducing fruit shading and improving air flow and pesticide spray penetration through the canopy, while reducing the time required to perform the practice relative to CT.   


In Pennsylvania, V. vinifera Grüner Veltliner is a white grape variety that has been recently introduced to the state and is growing in economic importance. This variety is high-yielding and tends to produce very large clusters with several clusters per shoot. Currently, very little is known about best practices for its cultural management.


To understand the impacts of yield regulating practices on Grüner Veltliner, we implemented a traditional timing of CT at bunch closure and two timings of ELR (trace-bloom; TBLR and fruit-set; FSLR) in 2015 and 2016 at a commercial vineyard. ELR was performed by removing five basal leaves from the fruit-zone. Key production parameters, Botrytis bunch rot incidence and severity, dormant bud freeze tolerance, and production costs were evaluated in response to the three treatments and an untreated control. Additionally, we evaluated the effects of CT and ELR intensity on starch and soluble sugar concentrations, important indicators of energy availability and freeze tolerance, in perennial storage tissues from a related study in high-yielding Vitis hybrid Chancellor.


Compared to CT, we hypothesized that ELR would improve fruit composition, reduce Botrytis bunch rot, and decrease grower costs. Yield decrease from CT was greater (39.3%) than that of TBLR (12.4%) or FSLR (13.5%). Despite yield reductions from CT and ELR, no treatment had a consistent effect on fruit chemistry. Furthermore, no reduction in fruit set, bud fruitfulness, or indications of vine recovery mechanisms (i.e., higher leaf assimilation rate, greater canopy or shoot efficiency) in ELR treatments suggested that more than five leaves would need to be removed in order to impose a carbohydrate-liming response. Although the overall level of bunch rot severity was low (less than 5%), ELR consistently decreased bunch rot intensity (TBLR, FSLR) and severity (FSLR). TBLR improved bud freezing tolerance during vine acclimation in both years. Economically, CT was the most expensive treatment, and the lack of a consistent improvement in fruit chemical composition or tolerance to winter temperatures indicated that Grüner Veltliner is able to properly ripe more than one cluster per shoot. In Chancellor, yield regulation from CT and ELR indicated higher starch concentrations among perennial storage organs but few consistent effects on soluble sugar concentrations.

Project Objectives:

Our project had 4 main objectives outlined in the original proposal:

  1. To conduct a field trial to assess and compare the effects of cluster thinning (CT) and early leaf removal (ELR) on vegetative growth, yield components, Botrytis bunch rot infections, cluster compactness, bud fruitfulness, and fruit composition.
  2. To assess the impact of CT and ELR on parameters associated with vine susceptibility to winter injury, including single-leaf net carbon assimilation, bud and cane cold hardiness, and carbohydrate reserve storage.
  3. To perform an economic analysis that estimates the additional price per tonne of fruit and cost per 750 mL bottle of wine needed to maintain grower economic welfare, if CT and ELR practices were adopted compared to if they were not.
  4. To provide recommendations to stakeholders for managing high-yielding wine grape varieties for optimal fruit composition and vine health.

Towards our objectives, we properly implemented each canopy management tactic at the appropriate time during the 2016 growing season, and collected much of the necessary data to fulfill our first three objectives.  We are continuing to collect data on dormant tissue soluble sugar concentration in 2018 and early 2019. 

For our fourth objective, I presented at the Mid-Atlantic Fruit and Vegetable Convention in February 2017 on crop load management practices, as well as present results from 2015-2016 on this project at the Pennsylvania Wine Marketing and Research Board Symposium in March 2017, and the American Society of Enology and Viticulture in July 2017. 


The purpose of this project is to investigate the impact of traditional and novel crop regulation practices on yield components, grape composition, vine health, and production economics. Wine grapes are economically valuable, adding $980 million to the Pennsylvania economy in 2011 (1). Wines produced in the northeastern U.S. are gaining regional and national recognition, but continued improvements to wine quality are needed to expand appreciation and sales. Fine-tuning of grapevine crop loads (i.e., fruit vs vegetative biomass) are required to achieve consistent wine quality year after year. Maintaining a balance between vegetative and reproductive growth to avoid over and undercropping situations is paramount for healthy vine canopy growth, adequate fruit production, and desired fruit quality (sugar levels, acid balance, and flavor compounds). Excessively high crop levels lead to delayed fruit and wood maturation, decreased carbohydrate storage reserves, and potentially increased vine susceptibility to winter injury (2, 3). Winter injury is the greatest challenge for economic sustainability identified in 2015 by Pennsylvania wine grape growers (4).

Fruit thinning (hereafter referred to as cluster thinning, CT) has traditionally been used to reduce crop levels and enhance grape ripening in wine grape cultivars that tend to overcrop. However, CT is time consuming, requires skilled labor, and does not always result in fruit and wine quality improvements (5, 6).  Therefore, the additional costs of labor and yield loss associated with implementing CT discourage some growers from adopting it.  Alternatively, removing leaves in the fruit-zone at the beginning of bloom through fruit-set (early leaf removal, ELR) has shown promise for decreasing yield in overcropping wine grape varieties in Italy and Spain (7, 8, 9). Depending on timing, ELR reduces yield by imposing a carbohydrate source limitation during bloom and fruit development, which acts to decrease fruit-set when applied at trace-bloom or berry growth when applied at fruit-set.  ELR has been effective in improving grape and wine composition (7, 8, 9). ELR presents advantages compared to CT, including reduction of cluster compactness that can decrease fruit susceptibility to Botrytis bunch rot, a major fungal disease in cool, humid regions (10), and mechanization, which can improve economic efficiency relative to CT (10). A recent study on ELR conducted on Chancellor (Vitis hybrid) by our research team showed that ELR was effective in reducing yield and cluster compactness and improving mid-winter bud cold hardiness (unpublished data).  However, unknown impacts of CT and ELR on accumulation of carbohydrate reserves important for winter survival and the development of shoots and inflorescences in the following spring warrant in-depth study (3, 9).

Implementing crop regulation practices is expensive and time consuming.  An economic assessment incorporating costs for CT and ELR implementation and resulting yield loss (5) will be used to test whether ELR can be a more cost-effective practice for yield reduction compared with CT (objective 3).

Increased consumer demand for premium wines along with the need for growers to decrease costs while enhancing wine quality requires research into improving current production practices. The Susquehanna area of Pennsylvania presents a cool-climate region to explore ELR as an alternative approach to traditional yield regulation, where results may differ drastically from the Mediterranean climates for which ELR was initially developed.  With grower concerns for winter vine injury and needs for improved Botrytis bunch rot control, results presented from this study will help growers tailor decision-making for implementing best sustainable crop regulation practices (objective 4). 

Literature cited:

  1. MKF Research, LLC. 2011. The economic impact of Pennsylvania wine and grapes.
  2. Howell, G.S. 2000. Grapevine cold hardiness: mechanism of cold acclimation, mid-winter hardiness maintenance, and spring deacclimation, 35– In: J.M. Rantz (Ed.): Proc. Am. Soc. Enol. Vitic., Seattle, WA.
  3. Dami, I., et al. A five-year study on the effect of cluster thinning on yield and fruit composition of ‘Chambourcin’ grapevines. HortSci. 41(3):586–588.
  4. Centinari M., et al. (in press) Assessing Pennsylvania wine grape growers’ challenges and needs. Journal of Extension.
  5. Preszler, T., et al. 2012. Cluster thinning reduces the economic sustainability of Riesling production. J. Enol. Vitic. 64:333–341.
  6. Sun, Q., et al. Impacts of shoot and cluster thinning on yield, fruit composition, and wine quality of Corot Noir. Am. J. Enol. Vitic. 63:49–55.
  7. Poni, S., et al. 2006. Effects of early defoliation on shoot photosynthesis, yield components, and grape composition. J. Enol. Vitic. 57: 397–407.
  8. Risco, D., et al. 2014. Early defoliation in a temperate warm and semi-arid Tempranillo vineyard:  vine performance and grape composition.  J. of Grape Wine Res.   20:111–122.
  9. Sabbattini P. and G.S. Howell. Effects of early defoliation on yield, fruit composition, and harvest season cluster rot complex of grapevines.  HortScience. 45:1804–1808.
  10. Hed, B., et al.   Short- and long-term effects of leaf removal and gibberellin on Chardonnay grapes in the Lake Erie region of Pennsylvania. Am. J. Enol. Vitic.66: 22–29.
  11. Intrieri, C., et al. Early defoliation (hand vs mechanical) for improved crop control and grape composition in Sangiovese (Vitis viniferaL.). Aus. J. Grape Wine Res. 14:25–32.


Click linked name(s) to expand
  • Michela Centinari


Materials and methods:

Vineyard site and experimental designA field experiment was conducted in 2015 and 2016 seasons on a high-yielding white grape cultivar Grüner Veltliner (Vitis vinifera L.) grafted on 101-14 Mgt rootstock and planted in 2010 at a commercial vineyard in Lewisburg, PA.  Vines were spaced 1.5 m between vines and 2.4 m between rows for a density of 2778 vines per hectare with rows oriented north-south.  Vines were pruned to two-bud spurs and trained to a vertically shoot positioned bilateral cordon trellis.  Shoot thinning was performed when shoots were at growth stage 14 according to the modified E-L system (1; 25 May 2015 and 26 May 2016).  The number of shoots was standardized to approximately 15 shoots per linear meter of row.  Canopy disease and insect management were applied according to standard grower practices.   

Four adjacent rows were selected for the study. The experimental design was a complete randomized block design with four blocks. Each block consisted of three panels (sections between three post spaces) comprised of four vines each (12 vines per experimental unit).  Each of the following treatments was randomized within each block: i) an unthinned, non-defoliated control (C), ii) early leaf removal at trace-bloom (TBLR; E-L 19), iii) early leaf removal at fruit-set (FSLR; E-L 27), and iv) cluster thinning (CT).  The first and last four-vine panel of each row were excluded from the experimental area as a buffer.  Early leaf removal treatments were implemented by hand-removal of five primary leaves and laterals that developed from nodes 2 to 6 on each shoot.  Cluster thinning was performed by removing the distal clusters at bunch-closure (E-L 32) leaving one cluster per shoot.  Dates for TBLR performance were 3 June 2015 and 9 June 2016, 18 June 2015 and 21 June 2016 for FSLR, and 1 July 2015 and 13 July 2016 for CT. Treatments were performed on the same vines for both years.

Gas exchange, EPQA, leaf area:  Twelve shoots from the four central vines of each experimental unit (48 shoots per treatment) were flagged prior to application of TBLR for data collection.  Single leaf gas exchange (assimilation rate (A), transpiration rate (E), and stomatal conductance (gs) was recorded on eight fully expanded primary and eight lateral leaves located at nodes 7 and 12 for each experimental unit at one week post-treatment and repeated 2 to 3 times until harvest. Measurements were taken between 10:00 and 14:00 hours under light-saturated (> 1200 mmol m-2 s-1) conditions using an open-system portable infrared gas analyzer (CIRAS-3, PP Systems, Amesbury, MA, USA).  The system utilized a broad leaf cuvette with a 4.5 cm2 window with 400 parts per million (ppm) fixed-rate of CO2 and an adjusted air flow of 400 mL/min.

Enhanced point quadrat analysis (EPQA, 2, 3) was assessed twice, mid-season and pre-harvest, in 2015 (16 July and 25 August) and 2016 (28 July and 7 September) for treatment impacts on canopy density and light availability in the cluster zone.  EPQA was conducted as described by Meyers and Vanden Heuvel (2).

All flagged shoots were collected one day prior to the whole plot harvest to assess whole shoot leaf area at the end of the season.  Primary and lateral leaves were analyzed separately using a scanning leaf area meter (LI-3100c, LI-COR).

Harvest yield, yield components, and grape composition:  All clusters of flagged shoots were collected two days prior to the whole plot harvest to assess impacts of treatments on cluster compactness, cluster weight, number of berries per cluster, fruit set, and basic juice chemistries (total soluble solids, pH, and titratable acidity).

The vines were harvested by hand on 24 September 2015 and 28 September 2016. Immediately prior to harvest, 30 randomly selected clusters per experimental unit were visually assessed for Botrytis rot incidence and severity (4).  Ten vines per experimental unit, excluding the first and last vine, were used to measure yield per vine and the number of clusters per vine.  A hanging scale accurate to 0.1 kg (Pelouze 7710, Rubbermaid, Inc., Huntersville, NC) was used to determine yield, and the average cluster weight calculated by the yield divided by the number of clusters.      

The individual clusters harvested from flagged shoots were frozen at -20 °C until deconstruction for individual berry weight and number of berries measurements. Berries were divided in the following categories: normally developed, healthy berries, f berries infected with rot, undeveloped (shot) berries, green ovaries.  Berries of each category were counted separately.

Berries were then thawed in a 60 °C water bath, crushed, and strained to remove skin and seeds for juice chemistry.  Total soluble solids (TSS) was measured using a hand-held refractometer (Master, Atago USA, Inc., Bellevue, WA), pH was conducted using a pH-meter (Orion Star A111, Thermo Fisher Scientific, Waltham, MA), and titratable acidity (TA, expressed as g/L tartaric acid equivalents) was measured using an autotriator (G20, Mettler Toledo, Columbus, OH).  Titrations were made using a 10 mL juice sample size titrated with 0.1 N NaOH to an endpoint pH of 8.2.

Pruning weight were taken during the dormant season using a 0.1 kg accuracy hanging scale (Pelouze 7710).  The Ravaz index was calculated as the ratio of yield to pruning weight and used to estimate the crop load of each treatment.  Bud fruitfulness was evaluated prior to shoot thinning during the growing season following treatments implementation when inflorescences were clearly visible.  The number of shoots and number of inflorescences were recorded to calculate bud fruitfulness (number of inflorescences per shoot). 

Dormant tissue freeze tolerance:  Freeze tolerance of dormant cane and primary bud tissue was estimated monthly between November and March, using differential thermal analysis (DTA) according to Mills et al. (6).  Eight canes were collected across the ten central vines of each experimental unit, wrapped in moist paper and stored in plastic bags for transport.  Canes were cut at internode two in order to leave two basal buds on the vines for the following year vegetative growth.  DTA was performed across two days, using four canes per experimental unit each day. Canes were stored at 4 °C until analysis.  Five buds were excised from nodes two through six with approximately 2 mm of tissue intact surrounding the bud and placed with bud contact on a thermoelectric module (Melcor Corporation, Trenton, NJ).  Two internode sections of approximately 35 mm length were randomly selected from between the first five nodes for estimating cold hardiness of xylem and phloem tissues.  Trays were placed in a programmable freezer (Tenney, Thermal Products Solutions, New Columbia, PA).  The freezer was programmed to begin at 4 °C and decrease at a rate of 4 °C/hr until -40 °C before recovering to 4 °C.  A total of 20 buds and 4 cane segments for each experimental unit were used to estimate the average median low temperature exotherm (LT50) for buds or the temperature at which primary buds experience 50% mortality occurs (7).

Non-structural carbohydrate concentration:  Non-structural soluble carbohydrates (CHO) and starch were analyzed in Chancellor (Vitis hybrid) in a parallel study focused on comparing traditional CT and intensity of hand early defoliation (3-leaves vs. 5-leaves) in a high-yielding interspecific hybrid variety. This study was conducted in 2014 and 2015 at The Pennsylvania State University Lake Erie Grape Research and Extension Center in Northeast, PA.  The Chancellor experimental design was similar to that of the Grüner Veltliner, in which 16 experimental units of 12 vines was established in a complete randomized block design. 

Three tissue-types (root, trunk, and cane) at three time points during the dormant season (November, January, and March) of 2014 and 2015 were collected on four central data vines for each experimental unit.  Root sampling was only performed in November and March due to frozen soil in mid-winter.  Root samples encompassed multiple branches of roots collected near the trunk to ensure roots emerged from the correct treatment vines.  Trunk tissue was sampled using a 1.8 mm diameter drill bit to core half-way through three sections of the trunk height (base, center, and head). Concurrently, 4 cm long cane tissues were collected from nodes three to five.  Tissue samples were immediately frozen on dry ice for transport back to storage and stored at -80 °C until processing.  Tissues were then dehydrated with a freeze drier (Labconco Corporation, Kansas City, MO) for seven to ten days and ground finely using a Cyclone Mill (UDY Corporation, Fort Collins, CO) with 1.0 mm mesh size. 

Soluble carbohydrates (CHOs) including sucrose, fructose, glucose, raffinose, and stachyose were extracted with a procedure modified from Reed et al. (8).  Fifty milligrams of dry, ground tissue was washed three times at 80 °C for one hour with 3 mL of 80% analytic-grade ethanol adjusted to pH 6.75 using formic acid; 3500 ug/g of lactose was added as an internal standard.  Samples were dried under nitrogen evaporation and residue was re-suspended in analytic-grade water and filtered through a 0.2 mm nylon disk filter into a 1.5 mL HPLC vial in order to be quantified using Capillary Zone Electrophoresis (CZE, 9).  

Following soluble CHO extraction, samples were freeze-dried for 24 hrs.  Non-structural starch remaining from soluble CHO extraction was hydrolyzed with 5 units of amylogucosidase and 2.5 units of a-amylase (Millipore Sigma, Merck KGaA, Germany) for glucose quantification using colorimetic analysis (10, 11).

Economic costs of canopy management: An economic analysis was conducted to estimate the additional price per tonne of Grüner Veltilner grapes and retail price per 750 mL bottle of wine if CT, TBLR, and FSLR were adopted compared to no additional crop load management practices except shoot thinning (12). The time necessary to apply each treatment was recorded for each experimental unit in 2015 and 2016 and averaged across the two years. The additional labor costs per hectare were estimated according to Yeh et al. (13). Expected revenue per hectare was calculated by multiplying yield by the average industry price per tonne for Grüner Veltliner for 2015 and 2016 (https://flgp.cce.cornell.edu). The additional production cost per tonne, the additional price per tonne (i.e., grower preferred price), and retailed price per 750 mL bottle of wine required to provide the same grower revenue were estimated according to Sun et al. (12).

Statistical analysis:  Data analysis was performed using JMP statistical software (v. 12.1, SAS Institute, Cary, NC).  All data were analyzed across years using a mixed-effects analysis of variance (ANOVA) model, with year and treatment as fixed effects, blocks as random effects, and a treatment by year interaction effect.  In the instance of a treatment by year interaction effect, parameters were analyzed separately within each year. A linear mixed model for starch and soluble sugars was performed with PROC GLIMMIX using SAS statistical software (v. 9.4, SAS Institute, Cary, NC) with main effects of treatment, tissue type, and year and block as a random-effect. Due to a three-way interaction between fixed effects, treatment and tissue effects were analyzed within each month and year. Interaction effects between tissue type and treatment were analyzed using the SLICE option. The critical value for statistical difference was considered at P = 0.10. 

Literature cited

  1. Coombe, B.G. 1995. Adoption of a system for identifying grapevine growth stages. J. Grape Wine Res. 1:100-110. 
  2. Meyers, J.M. and J.E. Vanden Heuvel.   Enhancing the precision and spatial acuity of point quadrat analyses via calibrated exposure mapping.  Am. J. Enol. Vitic. 59(4):425-431.
  3. Smart, R.E. and M. Robinson.    Sunlight into Wine:  A Handbook for Winegrape Canopy Management.  Winetitles, Adelaide. 
  4. Horsfall, J.G. and R.W. Barratt.   An improved grading system for measuring plant disease.  Phytopathology. 35:655.
  5. Ipach, R., B. Huber, H. Hofmann, and O. Baus. 2005. Richtlinie zur Prü- fung von Wachstumsregulatoren zur Auflockerung der Traubenstruktur und zur Vermeidung von Fäulnis an Trauben. Outline for an EPPO-Guideline.
  6. Mills, L.J., J.C. Ferguson, and M. Keller.   Cold-hardiness evaluation of grapevine buds and cane tissues.  Am. J. Enol. Vitic. 57(2):194-200.
  7. Wolf, T.K. and R.M. Pool.   Factors affecting exotherm detection in the differential thermal analysis of grapevine dormant buds.  J. Amer. Soc. Hort. 112:520-525.
  8. Reed, A.B., C.J. O’Connor, L.D. Melton, and B.G. Smith. Determination of sugar composition in grapevine rootstock cuttings used for propagation.  J. Enol. Vitic. 55(2):181-186.
  9. Zhao, L., A.M. Chanon, N. Chattopadhyay, I.E. Dami, and J.J. Blakeslee. 2016. Quantification of carbohydrates in grape tissue using Capillary Zone Electrophoresis. Front. Plant. Sci. doi: 10.3389/fpls.2016.00818. 
  10. Somogyi M. 1952. Notes on sugar determination. Biol. Chem. 195:19-23.
  11. Nelson, N. 1944. A photometric adaptation of the Somogyi method for the determination of glucose. Biol. Chem. 153:375-380.
  12. Sun, Q., G.L. Sacks, S.D. Lerch, and J.E. Vanden Heuvel. Impact of shoot and cluster thinning on yield, fruit composition, and wine quality of Corot Noir.  AJEV.  63(1):49-56.
  13. Yeh, A.D., M.I. Gomez, and G.B. White. 2014. Cost of establishment and production of vinifera grapes in the Finger Lakes region of New York-2013. Department of Applied Economics and Management, Cornell University, Ithaca, NY.
Research results and discussion:

Canopy characteristics and gas exchange:  Mid-season (16 July 2015 and 28 July 2016) TBLR and FSLR had significantly lower canopy density (leaf layer number; LLN) as compared with C (Table 1).  The significant decrease in LLN from the C was maintained through pre-harvest (25 August 2015 and 7 September 2016) in the FSLR treatment.  The percentage of gaps and amount of light interception to the leaves (LEFA) and clusters (CEFA) were similar among all four treatments at both time points (Table 1). 

Early leaf removal and CT had inconsistent effects on leaf gas exchange over the 2015 and 2016 seasons (Figure 1). In 2015, leaf A rate was lower for the FSLR vines as compared to the control, but only when measured 24 days following treatment application. However, there were no differences between TBLR or CT treatments and the control at any time point in 2015 or 2016. Similarly, there were no differences in E, gs, and WUE between the control and the crop load management treatments in either year.

Main, lateral, and total leaf area was similar in 2015 between the treatments. In 2016, the total leaf area was highest in the CT treatment and lowest in the FSLR treatment (Table 2).  

Yield components and cluster morphology:   Yield, crop load, cluster compactness, and percentage of visible surface area infected with rot (rot severity) were significantly affected by ELR and CT treatments as compared with the C (Table 3).  There were no significant differences in yield and yield components between the timing ELR (at TBLR and FSLR).  CT was the most effective treatment for reducing crop level.  Compared to the C vines, yield was 49%, 14%, 13% lower in CT, TBLR and FSLR vines, respectively (Table 3). 

Crop load, determined by the Ravaz index, was only significantly reduced in the CT treatment (Table 3).  As expected, CT decreased yield through a reduction in the number of clusters per vine, which had 30.6% fewer cluster per vine than the C (Table 3). Cluster weight was higher in the CT vines as compared to the other treatments (Table 3).

Levels of cluster area infected by rot were significantly lower in the FSLR compared with the C, although were overall below 5% for all treatments.  Correspondingly, FSLR vines had the loosest clusters, with an assessed visual cluster compactness value of 2.88 out of 5 (Table 3).

Berry composition, berry weight, and number of berries per cluster:  For the distal cluster, there were not significant differences in the number of berries per cluster, berry weight and berry maturity parameters (TSS, pH, TA) amongst treatments (Table 4, 5).  For the basal cluster, berry weight was higher in the CT vines as compared to TBLR, but not in the distal cluster, and there were not significant differences in berry number per cluster and TA amongst treatments.

Juice chemistry was affected differently by treatments in the two vintages (Table 5). When combining juice from basal and distal clusters, the only consistent effect across the two years was that FSLR had lower TSS than CT. When analyzing juice chemistry of the basal cluster alone, CT fruit had higher TSS than control in both years, but differences were not consistent across vintages when analyzing the total fruit produced by the shoot (i.e., combined basal and distal clusters for the control treatment). Concurrently, FSLR had the lowest shoot efficiency, defined as total sugar per shoot or unit of leaf area, and lower leaf area-to-yield ratio compared to CT (Table 6).

Bud freeze tolerance:  Differences in bud freeze tolerance (i.e., LT50) amongst treatments were mainly found in November, during the acclimation period (Table 7). In both years, TBLR vines had higher bud freeze tolerance than the control in November; the LT50 of TBLR buds was 0.34 °C (2015) and 0.91 °C (2016) lower than that of the control. However, LT50 was lower in FSLR (0.35 °C) and CT (0.37 °C) compared with the control only in November 2015. There were no differences in LT50 amongst treatments during mid-winter, when buds reached maximum winter freeze tolerance, except for January 2016 when FSLR vines had higher bud freeze tolerance as compared to CT and TBLR vines. Bud freeze tolerance was not affected by CT or ELR in March, as the vines start to de-acclimate. The minimum winter temperatures during the two dormant seasons were –19.5 °C on 14 Feb 2016 and –11.7 °C on 9 Jan 2017, which may have not reached critical values likely to have caused extensive damage on Grüner Veltliner. Although there were no differences in bud fruitfulness between the crop load management treatments and the control in either year, TBLR vines had fewer clusters per shoot (1.44) than CT (2.04) after two years of treatments application. Overall, bud fruitfulness ranged from 1.60 (control) to 1.88 (TBLR) in 2015 and from 1.44 (TBLR) to 2.04 (CT) in 2016.

Economic analysis:  Crop load management practices increased grower costs and reduced expected revenue in both years (Table 8). Cluster thinning was the most expensive treatment; if CT was applied, the expected revenue ($/ha) for Grüner Veltliner growers would have been reduced by 44% in 2015 and 46% in 2016. Applying TBLR would have generated 26% (2015) and 16% (2016) less revenue, while applying FSLR would have resulted in 23% (2015) and 19% (2016) less revenue. As a result, Grüner Veltliner producers would have had to increase the retail price for a 750 mL bottle of wine by approximately $0.33 (FSLR) to $1.18 (CT) in 2015 and $0.17 (TBLR) to $1.34 (CT) in 2016 to maintain revenue similar to that of the control (Table 8).

Carbohydrates analysis:  In the Vitis hybrid Chancellor ELR study using CT and two intensities of leaf removal, starch analysis of different tissue types revealed differences in the concentration of starch among major storage organs. In the fall of the first year of study (November 2014), all tissue types had higher starch concentration (Table 9). In April 2016, after two years of treatments, starch concentration was higher in root tissue of all crop load treatments as compared to the control; starch concentration was highest in the CT followed by ELR-3 and ELR-5. Starch concentration was highest in root tissue compared to trunk and cane, regardless of treatment or date However, the mean starch concentration in root tissue across all treatments decreased by 34% from November 2014 to November 2015. Overall, the starch concentration was higher in the trunk than in the cane in the fall (November), except for ELR-5 vines in November 2015. In January 2015 and April 2016, starch concentration in trunk and cane tissue was similar among treatments, except for CT vines in January 2015 when trunk tissue had higher starch concentration than cane tissue.

The effects of CT and ELR on soluble CHOs varied depending on the form of sugar and year (Tables 10-14). The effects of treatment on soluble sugar concentration were inconsistent across sampling dates and tissue types. Initially, we expected higher soluble sugar levels in ELR and CT tissues that correspond with decreased crop load and increased bud freeze tolerance. However, only CT produced higher soluble sugar concentrations in the trunk tissues across all sugars except fructose compared with ELR-5 vines during acclimation (November) in the first year (Tables 10-14). Conversely, ELR-3 and ELR-5 both had lower raffinose and stachyose in November 2014, two sugars which have previously shown strong relationships with freeze tolerance, compared to the control. By April 2015, there were no differences between treatments and the control except for higher raffinose in the roots of the ELR-5 treatment. Across tissue types, all sugars had the highest concentration in cane tissue during the mid-winter (January) sampling date with the exception of sucrose in January 2016 (Table 10). There was no trend in CHO concentrations among tissue types during acclimation (November) or deacclimation (April). Across sugar forms, concentrations were highest during mid-winter for fructose and glucose. However, mid-winter CHO concentrations were only higher for stachyose and raffinose in cane tissues and were inconsistent among any of the tissues in sucrose (Tables 10, 13, 14). 


Figure 1 Tables_GV 2015-2016 Tables 9 through 14


Research conclusions:

Over two consecutive seasons, ELR applied either at trace bloom or fruit set on vigorous Grüner Veltliner vines did not reduce carbohydrate availability in a manner that decreased fruit set to significantly limit yield or improve fruit quality as reported in previous V. vinifera ELR studies. Our study indicated that Grüner Veltliner growers would need to remove more than five basal leaves if they aim to severely regulate crop level; they would also need to apply multiple passes of leaf removal to avoid vegetative re-growth in the fruit-zone. The most relevant and consistent effects of ELR included higher bud freeze tolerance during fall acclimation (TBLR) and lower bunch rot incidence (TBLR and FSLR) and severity (FSLR) compared to the control. Therefore, in humid climates, costs associated with ELR may be justified by healthier fruit at harvest. The lower yield of CT vines did not translate to a consistent improvement in fruit chemical composition, suggesting that Grüner Veltliner can be cropped to more than one cluster per shoot in order to minimize or avoid loss in revenue.

In Chancellor, limiting crop levels using CT, low (3-leaf), or high (5-leaf) ELR indicated higher carbon availability for starch storage among perennial organs, which may benefit vine health. However, crop control did not generally affect the concentrations of soluble CHOs. The higher concentrations of soluble CHOs during mid-winter suggested that the concentrations are related to the periods of highest freeze tolerance.

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Participation Summary

Education/outreach description:

The topic of early leaf removal (ELR) as an innovative yield management tool and results from the 2015-2016 growing seasons were presented in various programs throughout 2017, including presentations and extension publications. One presentation was provided in 2016 at the Pennsylvania Wine Marketing and Research Board on the 2015 results of ELR in Grüner Veltliner. Three presentations were given in 2017 that targeted a diverse range of stakeholders, including wine grape producers of the Northeastern and Mid-Atlantic regions and scientists. One presentation covered crop load management (including ELR) at the Mid-Atlantic Fruit and Vegetable Convention in Hershey, PA, and the other two were provided specifically on ELR in Grüner Veltliner at the Pennsylvania Wine Marketing and Research Board in State College, PA and the American Society of Enology and Viticulture-Eastern Section conference in Charlottesville, VA. One newsletter was created in 2017 to address timing of ELR application, as well as potential benefits and drawbacks of using the practice. This newsletter is available to the public through the Penn State Wine and Grapes U. (https://psuwineandgrapes.wordpress.com/) blog. The information from the experiment on ELR in Grüner Veltliner was prepared in a manuscript and submitted to the American Journal of Enology and Viticulture in November 2018. Early-season-grapevine-canopy-managemen…-leaf-removal-ELR-Wine-Grapes-U. 2017-Program-ASEV-ES-full

Project Outcomes

3 New working collaborations
Project outcomes:

A major outcome of our work demonstrated that early leaf removal (ELR) may significantly reduce severity and incidence of late season bunch rot (Botrytis), which can be a major factor in crop and, therefore, economic losses. This is an important finding for growers in humid climates of the Eastern US where late season disease pressure is a challenge for those with highly valued but disease-susceptible varieties. However, ELR should still be considered an experimental practice, particularly when performed for crop adjustments, as the outcomes may depend on the variety and environmental conditions.

Knowledge Gained:

One of the most important lessons that we learned through this project is that outcomes of various sustainable production practices need to be tailored for specific cultivars grown in varying geographical regions and climates. This is especially true if the goal is to maximize the benefits achieved for a specific practice. 

To improve our understanding of early leaf removal (ELR) on Gruner Veltliner, we chose to expand our research on ELR in 2017 to include multiple and higher levels of leaf removal to discern its impacts on yield regulation and fruit aroma profiles and the thresholds required to alter them. This work has involved the collaboration of several new students and other faculty. 

From my PhD program, I have moved on to a position in viticulture extension, where this work has greatly impacted the way that I approach new practice adoption with growers. With respects to ELR adoption, growers seem most enthusiastic about its potential for rot reduction, however, caution must be strongly emphasized when adopting it. Furthermore, we are making sure that we are working directly with growers who are adopting ELR to collect data and information that can better inform the directions they need to take with their vine management.


Assessment of Project Approach and Areas of Further Study:

For Grüner Veltliner, five leaves were insufficient to induce a carbohydrate-limiting response. As a result, a threshold study from zero to 12 leaves removed at trace-bloom was initiated in 2017 to determine the response at six intensities of ELR. 

In an assessment of bud tissue freeze tolerance, we found no differences in crop load among ELR treatments. Because of this, we decided to not perform the time consuming and expensive work of extracting and quantifying carbohydrates in Grüner Veltliner that would have helped to determine how differences in crop load and cold hardiness relate to carbohydrate storage.  Instead, we performed carbohydrate extraction and quantification in a closely related project on intensity (instead of timing) of early leaf removal for yield regulation in Vitis hybrid ‘Chancellor’. This quantification incorporated three different storage tissues (roots, trunk, and cane) across three time periods (November, January, and March) for both soluble carbohydrates and non-structural starches associated with cold hardiness.  The results of this work suggested that further investigation is required to elucidate the role of carbohydrates and metabolism for vine winter survival. 

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.