Conversely, in under cropping, where there is excessive vigor or reduced crop level, this is not necessarily deleterious for speed of ripening . However, it may be a wasteful management of resources if there is not a trade-off with farm-gate prices. Given the later fruit development of grapevine and the grape chemistry requirements for red wine making , the length of the growing season is often a limitation for achieving the desired ripening and vintage quality in cool climates . Thus, yield is often sacrificed to balance source-to-sink ratio in favor of accelerated fruit ripening or to mitigate the effects of early fall frosts . Although the initial control of crop level comes during pruning , the number of dormant buds retained at pruning time is maintained constant through the years in warm climate regions. Cluster thinning is a management practice fine-tuned each year to achieve vine balance . Excess vine vigor was linked to deleterious effects on berry flavonoids . This effect could be exacerbated with high nitrogen amounts inhibiting anthocyanin biosynthesis , the absence of water stress, or changes of cluster microclimate due to mutual shading , and thus, not by the under cropping itself. Therefore, growing blueberries in containers grapevine canopy development is managed through the control of inputs, vine spacing, irrigation, rootstocks, pruning, leaf removal, hedging, or cover crops, among others.
A great part of the carbon assimilated through the growing season is incorporated into cellulose or lignin in roots, trunks, and shoots . However, resumption of a new season’s growth depends on the carbon stored as nonstructural carbohydrates, majorly in the form of starch, but also soluble carbohydrates such as sucrose, glucose, and fructose . Roots are the greatest sink of non-structural carbohydrates and root-derived carbohydrates constitute the principal reserve source for annual resumption of growth in the spring. The grapevine’s capacity for replenishment of these carbohydrate reserves increases at mid-ripening, when canopies are at their maximum and fruit demand slows down sugar accumulation in perennial parts . Therefore, the loss of photosynthetically active leaf area or excessive number of clusters may impair the reconstitution of reserves . In addition, high crop levels may delay fruit maturation and shorten the post-harvest period and subsequently reduce the time needed to accumulate reserve carbohydrates. Grape growing systems based on high yields are typically in warm to hot regions, relying on early harvest to replenish these reserves. However, it is common that excessive yields lead to a reduction in yields the following season . Loss of photosynthetically active leaf area or excessive number of clusters may deplete these reserves. High crop levels may reduce the reserve carbohydrate accumulation and delayed fruit maturation and may shorten the post harvest period. Therefore, the grapevine may not have sufficient time to accumulate carbohydrates for the following season in cool climates. Conversely, there is not consensus in literature regarding the effect of high cropping levels on storage reserves .
This was explained by sink limitation as the grapevine was able to maintain equilibrium by adjusting physiological processes . In addition to the modulation of berry ripening and storage reserves, other compensatory mechanisms have been described in response to over and under cropping. Components of yield, which include clusters per vine, berries per cluster, and berry mass, are susceptible to change together with berry ripening in compensation of each other . Although grapevine pruning, canopy, and crop load management are the most frequently reported case of study for source-to-sink ratios, most studies may not offer direct observations , enough combinations, duration of the study, or range of source-to-sink ratios to respond to some fundamental questions. The aim of this study was to determine the in-season and carryover effects of carbon source and sink imbalances in grapevine. Specifically, we investigated the combined effects of defoliation and fruit removal on components of yield, canopy area, and seasonal integrals of leaf gas exchange, shifts in phenology, carbohydrate, and soluble sugar concentration in the roots.The experiment was conducted at the University of California Davis, Oakville Experimental Vineyard from 2017 to 2019 over three growing seasons. Eight-year-old Vitis vinifera “Cabernet Sauvignon” Clone FPS08 grafted on 110 Richter rootstock were used. Plants were trained to a bilateral cordon, manually pruned to 24 buds. The shoots were vertically shoot positioned. Row and vine spacing was 2.4 × 2.0 m, respectively, and rows were oriented Northwest to Southeast. The plants were drip-irrigated with two pressure compensating emitters per plant delivering 2.0 L/h each. The plants were irrigated from fruit-set to end of harvest at 0.5 of crop evapotranspiration replacement as previously reported .The experimental design was a factorial arrangement of treatments. There were three levels of manual defoliation by three levels of manual fruit removal applied . The treatments were applied as follows. Leaves were removed on every shoot in an alternating pattern.
For instance, 66% of leaf treatments retained leaves in positions 1st, 2nd, 4th, 5th, 7th, 8th etc. while 33% of leaf treatments kept leaves in positions 1st, 4th, 7th, etc. in every shoot . The fruit removal treatments retained a percentage of clusters after standardizing the cluster numbers in each year. Each treatment combination was replicated four times and each treatment-replicate consisted of three experimental units. In 2017, all vines were standardized at fruit set to 20 shoots and 30 clusters per vine, and laterals were removed prior to defoliation and fruit removal treatments. In 2018, all vines were standardized to 24 shoots and 45 clusters and laterals were removed prior to treatment application. Treatments were applied at pepper-corn size . In 2019, after two seasons of growth under the nine combinations of treatments, the carryover effects were studied by leaving all vines untreated . For each experimental unit one vine was shoot thinned to 24 shoots, and others were left unmanaged . All clusters at pepper-corn size in all treatment-replicates were dipped in a 5.5% kaolin solution to provide protection from the afternoon sun due to the row orientation of the vineyard in every year of the experiment.Leaf gas exchange was measured bi-weekly in all years of the experiment with an infra-red gas analyzer . Three sun-exposed leaves were selected from the main shoot axis in each experimental unit, and three readings were taken from each leaf. Gas exchange measurements were taken when the sunlight conditions were close to saturating levels in all instances. The relative humidity was set at 40%, the reference CO2 concentration was set at 400 µmol CO2 mol−1 as the standard environmental condition setting in CIRAS-3. Net carbon assimilation rate and stomatal conductance were obtained. To express the season-long response of AN, and gs , their integrals were calculated by using natural cubic splines for plant water status and gas exchange measurements to assess the cumulative values for these parameters over the whole experiment period during the growing season. Then, these cumulative values were normalized as divided by the number of days elapsed between the first measurement date and the last measurement date to make the data comparable to each individual measurement.After harvest, leaves from one vine per replicate were collected, weighted, square pots and dried in a forced-air oven at 80◦C for 3 days. Dry leaf weights were converted into area by measuring the area of a subsample of 50 random leaves with a leaf area meter as reported previously . On 12 December 2018, after the second season of treatments, one vine per experimental unit of the most extreme treatments were pruned, coppiced, and the root systems were removed with a back-hoe. The sectioned grapevine portions were weighed on a top loading scale, and dried in a forced-air oven at 60◦C until no weight change of tissue was detected. At harvest , clusters were removed, counted, and weighed for each plant in the experiment. Total soluble solids were measured from 55 berries collected randomly at harvest point. A digital refractometer was then used to measure total soluble solids of must.Soon after the harvest of 2017 was completed, root tissues were sampled every 2 months.
The top layer of soil was removed until the roots were visible. Each grapevine root zone was divided into four quadrants and on each date and one single quadrant was sampled, leaving the other 11 quadrants undisturbed. Roots were gently cleaned with water, freeze-dried, and ground to a fine powder with a tissue lyser . Thirty milligrams of the resultant powder were extracted in 80:20 ethanol solution. A 1.5 mL aliquot of the extract was then placed in a 90◦C water bath for 10 min, then centrifuged at 10,000 rpm for 1 min. The supernatant was collected for total soluble sugars determination. The same procedure was repeated for starch determination, in which the pellet was collected for its determination. Total soluble sugars in the roots were determined as reported elsewhere by Torres et al. . Briefly, the 1.5 mL sample was filtered by PTFE membrane filters and transferred into high performance liquid chromatography vials. Equipment consisted of a reversed-phase HPLC system Agilent 1100 coupled to a diode array detector and an Agilent Infinity Refractive Index Detector . The reversed-phase column was Luna Omega Sugar with a guard column of 5 mm. The temperature of the column compartment was maintained at 40◦C and the RID flow cell was kept at 35◦C. The mobile phase system consisted in an isocratic elution with acetonitrile:water at a flow rate of 1.0 mL•min−1 with a run time of 22 min. Standard solutions of 10 mg/L of D-glucose, D-fructose, D-sucrose, and D-raffinose were injected to obtain the retention time for each compound, and detection was conducted by RID. Sugar standards were purchased from VWR . Sugar concentration of each sample was determined by comparison of the peak area and retention time with standard sample curves. Starch content of roots was measured using the Starch Assay Kit SA-20 following the manufacturer’s instructions. Briefly, pellets of root tissues were dissolved in 1 mL DMSO, and incubated for 5 min in a water bath at 100◦C. Starch digestion was started by adding 10 µL α- amylase and incubated in boiling water for another 5 min. then, the ddH2O was added to a total volume of 5 mL. Then, 500 µL of the above sample and 500 µL of starch assay reagent were mixed and incubated for 15 min at 60◦C. Negative controls with the starch assay reagent blank, sample blank, and glucose assay reagent blank and positive controls with starch from wheat and corn were performed. Reaction started with the incubation of 500 µL of each sample and 1 ml of glucose assay reagent at 37◦C and was stopped with the addition of 1 mL of 6 M Sulfuric acid after 30 min. Reaction was followed with analytical measurements with a Cary 100 Series UV-Vis Spectrophotometer and starch content expressed as mg of starch per tissue dried weight.The same grapevines were measured on each date throughout the execution of the experiment. Season-long measurements of root starch and soluble sugars, and phenology were analyzed separately for each year via three-way ANOVA for a date × defoliation × fruit removal design using PROC MIXED procedure of SAS using REPEATED option for measurement dates. Measurements of season-long leaf gas exchange integrals, grapevine vegetative growth, yield and yield components, and total soluble solids variables were analyzed via three-way ANOVA for year × defoliation × fruit removal using the same procedure of SAS. Whenever the year and treatment interactions were significant the analyses was conducted by year. Post-hoc analyses were conducted using Tukey’s HSD at p < 0.05. The trend analysis was carried to the quadratic level and was conducted with planned orthogonal contrasts using PROC GLM procedure of SAS. Certain variables were log transformed based on most-likelihood analysis.In our experiment the results indicated that there was an interaction of year and defoliation on cluster weight, berries per cluster and yield per vine . When we analyzed the data by year, the effect of defoliation was clearer. In both experimental years , there was a strong linear trend of defoliationon all components of yield except for cluster number; which was only affected by the fruit removal treatments. In 2017 defoliating 66% of the leaves resulted in an 8% decrease in berry weight.