Fruitlet Thinning Improves Juice Quality in Seven High-tannin Cider Cultivars

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David L. Zakalik Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Michael G. Brown Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Gregory M. Peck Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Abstract

Over 3 years (2016–18), tree productivity, biennial bearing, return bloom, and fruit quality were evaluated for seven high-tannin cider apple (Malus ×domestica Borkh.) cultivars. Five treatments were evaluated on each of the seven cultivars: hand-thinned of all fruit (a zero crop load treatment); hand-thinned to crop densities of three, six, or nine fruit/cm2 trunk cross-sectional area (TCSA); or left unthinned. In this paper, we report on the fruit maturity and juice quality properties that were analyzed for the three nonzero crop load treatments and the unthinned control. The effects of crop load on fruit maturity, as measured by starch pattern index and preharvest drop, were cultivar dependent. Crop density (fruit/cm2 TCSA) had a significant effect on all fruit maturity and juice quality variables, although effects were weakest in the “off” year (2017) for the whole planting when initial fruit set was low. As crop density increased, total poly phenols, titratable acidity, soluble solids, and primary amino nitrogen decreased in the juice of all seven cultivars. A partial budget analysis indicated that the reduced costs of nitrogen supplements due to increased primary amino nitrogen concentration alone would not justify cost of chemical or hand-thinning. By extrapolating the spring flowering density in the fourth year to potential fruit yields at harvest, we found that reducing crop load was projected to increase cumulative total polyphenol yields per tree over the long term. For the cultivars in this experiment, a target crop density of nine fruit/cm2 was found to adequately decrease biennial bearing while also not diminishing juice quality for hard cider production. High-tannin cider apple growers should consider juice quality, particularly tannin production, when making crop load management decisions.

In the United States, sales of hard (alcoholic) cider, and the number of commercial cideries, have grown 10-fold from 2008 to 2019 (Brager 2020; Miles et al. 2020). The number of orchards planted with high-tannin apple (Malus spp.) trees—most of which are European Malus ×domestica cultivars—is also increasing, although not enough to meet demand (Merwin et al. 2008; Miles et al. 2015; Pashow 2018; Weinstock 2016; Zakalik 2021). Many of these cultivars were bred and/or selected specifically for use in hard cider and have much greater phenolic (specifically, “tannin”), acid, and/or sugar concentration in their flesh and juice than fresh-market or multipurpose cultivars. Apple phenolics confer positive organoleptic attributes to a cider, such as mouthfeel and aroma (Karl et al. 2022; Lea and Drilleau 2003).

There has been minimal recent experimental research on cider apple cultivars and almost none has specifically examined the relationship between crop load and juice quality measures, such as, phenolic, acid, sugar, or nitrogen concentration (Merwin et al. 2008; Miles et al. 2020). The need for crop load management strategies to mitigate “biennial” or “alternate” bearing tendencies of many cider cultivars makes understanding the effect of crop load on juice quality particularly important (Bradshaw et al. 2020; Green 1987; Wood 1979; Zakalik 2021). Recent work has focused on the effect of crop load on sugar and acid concentration of fresh-market and multipurpose, low-tannin cultivars (Kelner et al. 1999; Peck et al. 2016; Robinson and Watkins 2003; Stopar et al. 2002). Winemakers often pay a premium for greater wine grape (Vitis spp.) fruit quality, which could also happen for cider apples, especially if crop load management practices increase fruit quality, but also production costs.

Apple fruit growth between anthesis and maturity can be divided into two phases: cell division and cell expansion. Cell division, a major sink for carbon, nitrogen, and other nutrients, occurs in developing fruitlets in the first 30 to 50 d after full bloom (DAFB) (Smith 1950), although this period varies among cultivars and particularly among species within the genus Malus (Denne 1960). Some cell expansion (driven largely by water) takes place shortly after pollination, but most occurs after cell division has plateaued or nearly ceased (Denne 1960). The nutrient-intensive nature of early apple fruit development during the cell division phase, means that manipulation of crop load is most likely to improve juice quality when performed during this timeframe.

Tannins, a subset of phenolics, lend bitterness and astringency (sometimes referred to as “mouthfeel” or “body”) to a cider (Lea and Arnold 1978). In the American, English, and French cidermaking traditions, tannins have for centuries been considered essential for a balanced, full-bodied cider (Alwood 1903; Chambray 1764; Ellis 1754; Knight 1797; Latham 1873; Macmahon 1819; Thacher 1825). Of studies examining the effect of crop load on apple phenolic concentration, most focus on peel- or flesh-derived “antioxidants” in low-tannin fresh-market cultivars, of interest for their purported health benefits (Awad et al. 2001; Khanizadeh et al. 2008; Kondo et al. 2002; Stopar et al. 2002; Unuk et al. 2006). Peel-derived phenolics, such as flavonols and anthocyanins, are of limited relevance in cider production because of their low extractability from pomace by traditional pressing practices (Devic et al. 2010; Guyot et al. 2003; Khanizadeh et al. 2008).

Recent research has suggested that that crop load has a negative effect on phenolic concentration of juice, but multiyear studies examining crop load effects on phenolics, or the intersection between juice quality for cider production and total fruit yield, are lacking (Guillermin et al. 2015; Karl et al. 2020a). The overall phenolic yield per tree, rather than concentration per volume of juice, may be the more meaningful metric from an apple production perspective, yet research has not assessed phenolics in this way. The ultimate goal of growing these cider cultivars is to produce as much tannin (a sensorily important subset of phenolics) as possible. Thus, the mass of fruit yielded and the concentration of tannins in juice will be taken together in this paper as “cumulative phenolic yield,” a stoichiometric proxy.

Nitrogen, in the form of amino acids and ammonium (known as yeast assimilable nitrogen or YAN), is an essential nutrient source for yeast, and in raw apple juice is usually deficient to the minimum recommendations of ∼140 mg·L−1 YAN for a finished cider free of off-odors and other sensory faults (Alberti et al. 2011; Bell and Henschke 2005; Bely et al. 1990; Boudreau et al. 2018; Burroughs 1957). The ammonium concentration in apple juice is negligible, therefore primary amino nitrogen (PAN) is often measured instead of total YAN (Karl et al. 2020a, 2020b). Nitrogen deficiency can cause evolution of hydrogen sulfide (H2S) resulting in a “rotten-egg” odor (Jiranek et al. 1995; Mestres et al. 2000; Song et al. 2020), as well as of undesirable higher alcohols such as isobutyl and isoamyl (Ough and Bell 1980). Greater amino acid concentration in juice has been found to correlate with increased metabolic evolution of esters (dos Santos et al. 2015, 2016), which confer desirable aromas to cider (Xu et al. 2007).

Research investigating the effect of crop load on the nitrogen concentration of apple juice is minimal, but the effect appears to be negative (Guillermin et al. 2015; Peck et al. 2016). The cost of nitrogen supplements to ensure healthy fermentations free of microbial spoilage and “off” odors (Cairns et al. 2022; Karl et al. 2020b; Song et al. 2020; Zhang et al. 2008) can thus be mitigated by orchard practices such as fruitlet thinning that increase juice nitrogen concentration (Guillermin et al. 2015; Peck et al. 2016).

Organic acids—predominantly malic acid in apples—lend sourness or tartness to a cider (Jolicoeur 2011) and lower the pH, promoting microbial stability during and after alcoholic fermentation. Organic acid concentration is often reported as titratable acidity. Acidity can also balance out the bitter and drying sensations conferred by tannins (Lea 1992). The relatively small range of crop densities observed in previous studies (generally 12 fruit/cm2 TCSA or less) limits the applicability of this research to often biennial cider cultivars (Alegre et al. 2012; Awad et al. 2001; Unuk et al. 2006), which can bear crop densities of 40 fruit/cm2 TCSA or more in a greater crop “on” year, far exceeding crop densities recommended or reported for fresh-market apple production (Wood 1979; Zakalik 2021). Crop load has been shown to correlate negatively with titratable acidity (Alegre et al. 2012; Henriod et al. 2011; Peck et al. 2016). Thus, the effect of a wide range of crop densities on the acid concentration of some high-tannin cider apples—namely, bittersharps and subacid bittersweets—is an important research topic for cidermakers working with these specialty cultivars.

Sugars are needed for yeast metabolism, and are the precursors of ethanol, the predominant alcohol in cider (Lea and Drilleau 2003). Greater total sugars result in greater alcohol concentration in a finished cider. Residual sugar after fermentation, conferring sweetness, can also moderate the perception of bitterness, astringency, and acidity, making for a more balanced cider (Symoneaux et al. 2014, 2015). The negative relationship between crop load and soluble solids concentration (SSC), a proxy for sugar concentration, has been reported in previous research (Alegre et al. 2012; Henriod et al. 2011; Link 1973; Stopar et al. 2002).

The effect, if any, of crop load on juice extraction, measured as a percentage of pomace weight, is not a commonly measured fruit quality trait. However, greater juice extraction can maximize juice yield for cidermaking. Juice extraction data for high-tannin cider apples is generally only reported by cultivar (Boré and Fleckinger 1997; Peck et al. 2021; Plotkowski and Cline 2021).

The interaction between crop load and fruit maturity is much more widely researched and better understood than fruit quality, yet sources conflict about the effect of crop load on starch pattern index (SPI), as well as on preharvest drop (Awad et al. 2001; Stopar et al. 2002; Ward et al. 2001; Wood 1979). As a qualitative measure of starch degradation, SPI is more indicative of fruit ripeness than any other measurement (Blanpied and Silsby 1992).

The aims of this study were to quantify and analyze the effects of crop density on fruit maturity, as measured by SPI and preharvest drop, and the effects of crop density on juice quality, as measured by SSC, titratable acidity (TA), Folin-Ciocalteu total polyphenol concentration, YAN, and extraction volume. The following hypotheses were tested: 1) SPI would correlate negatively with crop density; 2) preharvest drop would correlate positively with crop density; 3) polyphenol concentration, YAN, extraction volume, and SSC would correlate negatively with crop density; and 4) TA would correlate positively with crop density.

Materials and Methods

Experimental design.

This study was conducted from 2016 to 2018 on seven European cider apple cultivars at a commercial orchard in Lyndonville, NY (lat. 43.324°N, long. 78.373°W), near Lake Ontario on a Galen very fine sandy loam. The cultivars used were Binet Rouge, Brown Snout, Chisel Jersey, Dabinett, Harry Masters Jersey, Michelin, and Geneva Tremlett’s Bitter, all grafted onto Budagovsky 9 (B.9) rootstock. The trees were planted in 2014 at 1.2 × 3.7-m spacing and trained in the tall-spindle system using a trellis with one wire and a metal conduit pole for each tree. There were three rows for each cultivar with ∼120 trees in each row.

Trees were either hand-thinned to four target crop densities: zero, three, six, and nine fruit/cm2 TCSA or left unthinned (control). Five single-tree replications per cultivar were assigned to each of the five treatment groups using a randomized complete block design, for a total of 25 experimental units (trees) per cultivar.

Harvest.

Preharvest fruit maturity was assessed by SPI for each cultivar using fruit from nonexperimental trees to determine appropriate harvest dates (Blanpied and Silsby 1992). For cultivars with strong tendencies to preharvest drop, such as Harry Masters Jersey, fruit was harvested considerably before fruit on the tree was ripe (SPI <6). Fruits that dropped before harvest were counted and removed before the fruits remaining on trees were picked. Harvest timing (calendar date and DAFB) was kept as similar as possible from year to year, with some differences due to bloom date variability, weather conditions, and time constraints.

External fruit analysis.

Subsets of 10 tree-harvested fruit per tree were randomly sampled and brought back to Cornell University for analyses. Where fewer than 10 fruit remained on a tree, drops were included in the subsets. For trees with fewer than 10 total fruit, all fruits were used. Subsets were refrigerated at 4 °C for up to 3 d until it was possible to analyze fruit maturity.

Internal fruit analysis.

Subset fruits were assessed for flesh firmness using a GÜSS penetrometer fitted with an 11.1-mm probe (Jennings, Strand, South Africa). Peel was removed at two opposite locations at the equator of each apple (the sun-exposed and shaded sides) and then probed once at each location. The SPI was determined by removing equatorial wedges 5 to 10-mm thick and saturating the surface with a 2.2 g·L−1 iodine, 8.8 g·L−1 potassium iodide (EMD Millipore Corp., Billerica, MA) solution (Blanpied and Silsby 1992). The number of seeds per apple was counted in 2018.

Juice extraction.

The remaining fruit was diced and then milled in a Norwalk 290 (Bentonville, AR, USA) hydraulic tabletop juicer into Good Nature (Buffalo, NY, USA) filter bags, which were then pressed on the Norwalk 290 until the stream of juice discontinued. This method closely mimics a typical “rack and cloth” cider press. Juice samples were then aliquoted into sample tubes and frozen at –20 °C or –80 °C. In 2018, pulp was weighed before pressing, and total juice yield was weighed after pressing. The extraction percentage was calculated by dividing final juice weight by initial pulp weight.

Juice chemical analysis.

SSC was measured on a PAL-1 BLT digital refractometer (Omaeda, Saitama, Japan). TA was measured on a Metrohm 809 Titrando autotitrator (Herisau, Switzerland) by titrating 5 mL juice aliquot in 40 mL ultrapure Milli-Q water (Darmstadt, Germany) against a standardized 0.1 M NaOH solution to an endpoint of pH 8.1. Acidity was reported as g·L−1 malic acid equivalent (MAE) and initial pH. Samples for these analyses, stored at –20 °C, were thawed to room temperature and homogenized via VWR Analog Vortex Mixer (Radnor, PA, USA).

Total polyphenol concentration was measured using the Folin-Ciocalteu method (Singleton et al. 1999) on a Spectramax 384 Plus microplate spectrophotometer and SoftMax Pro 7 Microplate Data Acquisition and Analysis Software (Molecular Devices, San Jose, CA, USA). Frozen (–80 °C) samples were thawed, vortexed, and then centrifuged at 500 gn for 8 minutes. Reaction mixtures consisted of 1.5 µL of sample or standard, 34.9 µL of water and 90.9 µL of 0.2 N Folin-Ciocalteu reagent (Sigma Aldrich, Darmstadt, Germany); 72.7 µL of 70 g·L−1 sodium carbonate buffer was added 6 minutes after the Folin-Ciocalteu reagent. Reaction mixtures were then incubated at room temperature in the dark. Reactions were carried out in Cellistar 96-well microplates (Greiner Bio-One, Monroe, NC, USA). Standards were generated using an eight-point standard curve with gallic acid from 0 to 3 g·L−1. Samples were measured at 765 nm and total polyphenol concentration was determined by using the linear equations from the standard curve plot.

Cumulative tannin yield per tree was calculated by multiplying the total fruit yield per tree (Supplemental Table 1) by the average juice extraction percentage for each cultivar and by the total polyphenol concentration for each year, and then summing these values over 3 years (Supplemental Table 2).

PAN was determined using a Megazyme K-PANOPA assay kit (Bray, Ireland). Subsets of juice samples in 1.5-mL microfuge tubes and stored at –80 °C were thawed in a warm water bath, vortexed to homogenize, and then gently spun in a centrifuge at 1100 gn for 5 min. A standard curve was prepared by diluting standard solution (140 mg N·L−1 as isoleucine) to the following concentrations: 0, 20, 40, 60, 80, 100, 120, and 140 mg N·L−1. Standards and samples were analyzed in duplicate (two wells each). An amount of 3.33 µL of sample, Milli-Q deionized water (Darmstadt, Germany), or standards were mixed into 200 µL N-acetyl-L-cysteine (NAC) buffer solution (Megazyme, Bray, Ireland). About 2 min after the last cell was pipetted, the absorbance at 340 nm was measured on a Spectramax 384 Plus microplate spectrophotometer. Subsequently, 6.67 µL ortho-phthaldialdehyde (OPA) was added to each sample or standard well on the plate, with gentle mixing with the pipettor. About 15 min after adding OPA to the last cell, absorbance was again recorded at 340 nm. Both reagents (NAC and OPA) were supplied with the Megazyme kit.

Economic analysis of nitrogen supplement cost.

Exogenous nitrogen supplement costs (Table 1) were estimated by extrapolating 2016–18 yield data from the same experiment (Zakalik 2021), assuming a planting density of 2220 trees/ha and multiplying yield by juice extraction volume data to estimate total volume of juice per hectare under different crop densities. Nitrogen deficiency (relative to 140 mg·L−1) was calculated based on measured PAN concentrations and then extrapolated to grams of supplement needed per hectare. Costs for common nitrogen supplements were calculated as follows: diammonium phosphate (DAP) contains 180 mg N·g−1 and costs $2.60/kg; Fermaid O™ (inactivated yeast hulls, Lallemand Oenology, Blagnac Cedex, France) 40 mg N·g−1, $33.80/kg; and Fermaid K™ (DAP + hulls + micronutrients, Lallemand Oenology, Blagnac Cedex, FR) 100 mg N·g−1, $15.70/kg (Karl et al. 2020b). If PAN was not deficient, there was no cost attributed to that treatment.

Table 1.

Partial budget based on 2016–18 yield and juice primary amino nitrogen (PAN) data from a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY. Estimated costs of diammonium phosphate (DAP), Fermaid K™, and Fermaid O™ supplements for a model hectare (ha). Trunk cross-sectional area (TCSA) measured 40 cm above the graft union.

Table 1.

Excluded experimental units, original experiment.

Of a total 525 experimental units (175 trees × 3 years), 37 were excluded from the final dataset. The close spacing of trees made misattribution of drops likely in some cases. Trees were excluded from the dataset based on location, percent drop, and where possible, by comparison of final total fruit count to initial fruit set. Confidence in drop counts was lowest in 2018 because of universally greater fruit set in the orchard. There was greater confidence in drop counts in 2017, because nonexperimental trees had little or no fruit set in that “off” year.

Projection of 2019 yield and juice quality.

Although treatments were only imposed for 3 years, Spring 2019 bloom data were recorded and then yields were extrapolated for a fourth year based on potential crop densities. Yields were projected using regression formulae for the relationship between crop density and fruit weight, to estimate total yield mass (kg) per tree, assuming conservatively that each bloom cluster set at least one fruit (Zakalik 2021). Using formula estimates for the correlation of crop density to total polyphenol concentration calculated for each cultivar using 2016–18 data (Fig. 1), the total polyphenol concentration was projected based on target crop densities. Projected phenolic concentration values and projected total yield mass per tree were used to project per-tree phenolic yield for a fourth year (2019). Projected phenolic yield was then added to the 3 years of recorded data to project 4-year cumulative phenolic yield.

Fig. 1.
Fig. 1.

The relationship between crop density and Folin-Ciocalteu total polyphenol concentration measured as gallic acid equivalents (GAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Statistical analysis.

All statistical analysis was conducted in R (R Core Team 2014). All regressions were analyzed as mixed models with a random block term, using the lmer function from the lme4 package (Bates et al. 2021). Lines of best fit were determined from intercept and slope estimates generated by the lmer function in R. All fruit maturity and juice quality variables were regressed against at-harvest crop density. Mean separation for a family of estimates (estimated marginal means, emmeans package) (Lenth 2021) was performed with the cld function (multcomp package) using Tukey’s honestly significant difference post hoc analyses (Hothorn et al. 2021).

Results

Juice quality. Crop density (fruit/cm2 TCSA) had a significant impact on fruit maturity and juice quality, but the effects were not always consistent among cultivars. In addition, there was a strong year effect for many juice quality measures, with 2017 (the “off” year, meaning there was minimal crop that year) having the greatest overall juice quality. However, manipulating crop density in 2017 had little or no effect on most juice quality variables. Aggregating the data over the 3 years of this study and regressing the variable of interest against crop densities recorded at harvest was an effective means of identifying trends.

Greater crop density resulted in a logarithmic reduction in total polyphenol concentration for six of seven cultivars (Fig. 1). ‘Dabinett’ was the exception with a quadratic correlation—there was a reduction in total polyphenols until ∼20 fruit/cm2 TCSA and then an increase in total polyphenols in fruit from the greatest crop densities, which were recorded in 2018. At the lowest crop densities (<10 fruit/cm2), ‘Binet Rouge’ and ‘Chisel Jersey’ had greatest overall phenolic concentration among the seven cultivars in this study, but they also had the greatest reduction in total polyphenols due to crop density. Conversely, at the lowest crop densities, ‘Michelin’ and ‘Brown Snout’ had the lowest phenolic concentration and the least reduction as crop density increased. In 2017, the “off” year, manipulating crop density affected the phenolic concentration for ‘Harry Masters Jersey’, but not the other six cultivars (Supplemental Table 3).

Crop density significantly reduced TA for four of the seven cultivars (Fig. 2). However, Geneva Tremlett’s Bitter, the sole bittersharp cultivar in the study, was the only cultivar that had a meaningful reduction in TA from a cider production perspective. For ‘Geneva Tremlett’s Bitter’, TA from trees with the lowest crop density was about double (∼20 g·L−1 MAE) that of the most heavily cropped trees (∼10 g·L−1 MAE). The other cultivars, all bittersweets, showed either a negligible crop density effect on TA (Binet Rouge, Brown Snout, and Chisel Jersey) or none at all (Dabinett and Harry Masters Jersey). Furthermore, TA for ‘Geneva Tremlett’s Bitter’ was greatest overall in 2017 (Supplemental Table 4). There was no clear trend for pH among crop densities, cultivars, or years (Supplemental Table 5).

Fig. 2.
Fig. 2.

The relationship between crop density and titratable acidity measured as malic acid equivalents (MAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Crop density correlated negatively with SSC for all seven cultivars (Fig. 3). ‘Brown Snout’ and ‘Binet Rouge’ had greatest overall SSC (∼17°Brix), followed by ‘Chisel Jersey’ and ‘Dabinett’ (∼14.6°Brix), ‘Harry Masters Jersey’, and ‘Michelin’ (∼14.3°Brix), and finally ‘Geneva Tremlett’s Bitter’ (∼13°Brix). Similar to the response for TA, the cultivars that had the greatest SSC at the lowest crop densities had the greatest decrease in soluble solids as crop density increased. Manipulating crop density resulted in significant, but less pronounced effects on SSC in 2017 than in “on” years 2016 and 2018 (Supplemental Table 6).

Fig. 3.
Fig. 3.

The relationship between crop density and soluble solids concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Crop density correlated negatively with PAN for all seven cultivars (Fig. 4). ‘Chisel Jersey’, ‘Dabinett’, and ‘Harry Masters Jersey’ had relatively low PAN overall, whereas ‘Michelin’ and ‘Geneva Tremlett’s Bitter’ had relatively greater PAN. Again, the greatest losses in PAN were for the cultivars with the greatest concentrations at low crop density. Overall, PAN was greatest in 2017 when crop densities were lowest (Supplemental Table 7).

Fig. 4.
Fig. 4.

The relationship between crop density and primary amino nitrogen concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Fruit maturity.

Crop density had a significant effect on SPI (Fig. 5, Supplemental Table 8) and preharvest drop (Fig. 6, Supplemental Table 9) for all cultivars, although the effect differed by cultivar. ‘Binet Rouge’, ‘Brown Snout’, ‘Chisel Jersey’, ‘Dabinett’, and ‘Michelin’ exhibited a quadratic correlation between crop density and SPI, with advanced ripeness (high SPI) at lowest and greatest crop densities, and delayed ripeness (lower SPI) ∼20 to 25 fruit/cm2. A similar correlation was observed between preharvest drop and crop density for ‘Binet Rouge’ and ‘Dabinett’ (Fig. 6). SPI and preharvest drop correlated negatively with crop density for ‘Harry Masters Jersey’, and positively for ‘Geneva Tremlett’s Bitter’, although SPI was higher overall and drop was lower overall for ‘Geneva Tremlett’s Bitter’. Unlike SPI, preharvest drop for ‘Chisel Jersey’ and ‘Michelin’ correlated negatively with crop density. ‘Brown Snout’ showed no significant relationship between crop density and preharvest drop.

Fig. 5.
Fig. 5.

The relationship between crop density and starch pattern index (SPI) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Fig. 6.
Fig. 6.

The relationship between crop density and preharvest drop (percent of total fruit yield per tree) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Flesh firmness correlated negatively with crop density for ‘Binet Rouge’ and ‘Geneva Tremlett’s Bitter’; positively for ‘Brown Snout’, ‘Harry Masters Jersey’, and ‘Michelin’; and quadratically for ‘Dabinett’, for which firmness was lowest at crop densities of 15 to 20 fruit/cm2 (Fig. 7). ‘Dabinett’ and ‘Geneva Tremlett’s Bitter’ were the firmest (up to 100 N) and ‘Binet Rouge’, ‘Brown Snout’, ‘Harry Masters Jersey’, and ‘Michelin’ were moderately firm (50 to 85 N) (Supplemental Table 10). Flesh firmness did not correlate with crop density for ‘Chisel Jersey’. Flesh firmness correlated negatively with SPI for ‘Binet Rouge’, ‘Chisel Jersey’, ‘Harry Masters Jersey’, ‘Michelin’, and ‘Geneva Tremlett’s Bitter’; there was no significant correlation between SPI and flesh firmness in ‘Brown Snout’ or ‘Dabinett’ (data not shown).

Fig. 7.
Fig. 7.

The relationship between crop density and flesh firmness (Newtons) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

Citation: HortScience 58, 10; 10.21273/HORTSCI17096-23

Juice yield had statistically significant but negligible positive correlations with crop density for ‘Brown Snout’, ‘Chisel Jersey’, ‘Michelin’, and ‘Geneva Tremlett’s Bitter’ (data not shown). Differences were not meaningful from a cider production perspective. Extraction volume differed more by cultivar than by crop load treatment, with ‘Chisel Jersey’ and ‘Dabinett’ having the greatest juice yield overall and ‘Michelin’ having the least (Supplemental Table 11).

Nitrogen supplement costs.

For all seven cultivars, the costs for supplementing juice with exogenous nitrogen were greatest when there was no chemical or hand-thinning (Table 1; Supplemental Table 12). Crop density treatments were not significantly different for ‘Chisel Jersey’ or ‘Dabinett’. The reduction of 3-year cumulative nitrogen supplement costs alone did not compensate for the increased cost of thinning spray applications for any cultivar or treatment, except for ‘Chisel Jersey’ thinned to three fruit/cm2. In addition, hand-thinning did not increase profitability (data not shown).

Discussion

Juice quality.

Overall, reducing crop density improved fruit and juice quality attributes important for cider production. For the studied cultivars in a tall-spindle training system, a balance between yields and fruit quality appeared to be achieved at a crop density of nine fruit/cm2 (Zakalik 2021).

A large “on” year crop resulted in lower total juice polyphenol concentration, which would then be followed by little to no fruit in the “off” year. Thus, cumulatively over a 2-year cycle there would be less total polyphenol production per tree than there would be for 2 years with a moderate crop density achieved through crop load management with hand or chemical thinning. Although per-tree tannin yield may be greater in the “on” year for trees without crop load management, the resultant loss of a crop in the “off” year, due to return bloom suppression (i.e., biennial bearing), reduces cumulative production of tannin. In addition, juice quality is less consistent when unmanaged trees have highly variable yields from year to year. Given that the primary purpose of growing these cultivars is to produce tannins for cider, it is important to consider tannin production cumulatively. Crop load management can result in more consistent yields and more consistent juice quality, and greater production of tannins when measured over successive years.

In North America, high-tannin cider apple cultivars command a price premium over fresh-market cultivars. However, buyers of high-tannin cider apples do not incentivize growers to undertake specific management practices to increase fruit quality (Zakalik 2021). Perhaps, as the cider market continues to mature, cider apple buyers and sellers will develop premium schemes based on managing crop load similar to the wine industry. Such a practice could benefit the cider producer, as consumers’ willingness to pay for hard cider correlates positively with greater tannin concentration (Martin et al. 2017; Tozer et al. 2015).

Over 3 years, there was no clear trend in “cumulative tannin yield” across all cultivars (Supplemental Table 2). However, our extrapolation from the Spring 2019 return bloom data indicated that if treatments had been imposed for a fourth year, all cultivars in this study would likely achieve sufficient fruit yield and sufficient phenolic concentration, for cumulative tannin yield to be greater when trees were thinned to nine fruit/cm2 TCSA than if left unthinned (Supplemental Tables 13 and 14). This was in large part because unthinned trees had little to no bloom, and thus potential crop, in 2019. Trees thinned to nine fruit/cm2 TCSA had sufficient potential yields and were projected to bear sufficiently high-tannin fruit, to compensate for reduced fruit yields and tannin yields in the preceding “on” years due to thinning. Although based on the 3 years of data from the seven cultivars in this experiment, the extrapolated data are speculative; longer-term (at least 4 years) research and studies at more locations and on more rootstock genotypes are needed to verify these assumptions.

The observed relationships between crop density and total polyphenol concentration strongly refute the belief that having more fruit on a tree will result in greater tannin production per tree. Unlike in wine grapes, phenolics in cider apples are mostly extracted from flesh, not peel tissue. A greater peel-to-flesh ratio on more heavily cropped, and thus smaller-fruited trees, coincided with reduced tannin concentration in juice, and a projected reduction in cumulative tannin yield per tree when accounting for the extrapolated fourth year. Our findings, together with previous research on phenolic biosynthesis in apples, suggest that crop density effects on phenolic concentration would be greatest early in the season (Zhang et al. 2010). In fact, most phenolic biosynthesis appears to occur by 40 DAFB, the timeframe during which the crop density treatments were implemented in our experiment (Renard et al. 2007).

Cell expansion is a result of water uptake, but larger apples in our study did not have more dilute juice. Furthermore, lower crop density correlated both with larger average fruit size (data not shown), and with greater phenolic concentration, SSC, TA, and PAN. Reducing competition among fruitlets during cell division possibly resulted in greater allocation of carbon and nitrogen to fruit flesh, and thus increased production of these important juice quality components.

The negative effect of crop density on phenolic concentration agrees with Guillermin et al. (2015) for the French bittersweet cultivars Douce Moën and Douce Coëtligné, as well as Karl et al. (2020a), who reported that lower crop load in one year coincided with greater total polyphenol concentration, compared with the following year, for Medaille d’Or a French bittersweet cultivar. However, our results did not concur with Peck et al. (2016), who examined the low-tannin cultivar York and found no difference in juice total polyphenol concentration at harvest from trees with different crop densities. It is possible that the crop load effect is not as apparent in cultivars with lower total polyphenol concentration.

The negative correlation we observed between crop density and SSC agrees with a broad corpus of previous research (Alegre et al. 2012; Henriod et al. 2011; Link 1973; Stopar et al. 2002), as does the negative correlation we observed between crop density and TA (Alegre et al. 2012; Henriod et al. 2011; Peck et al. 2016). The effect of crop density on TA was statistically significant but perhaps not organoleptically meaningful in bittersweet cultivars Binet Rouge, Brown Snout, Chisel Jersey, and Michelin (Fig. 2). TA was similarly low, and pH was greater, in ‘Dabinett’ and ‘Harry Masters Jersey’, and neither variable correlated with crop density in those cultivars. Variation in acidity among apple genotypes is primarily genetically controlled and did not appear to be strongly affected by crop density in the six low-acid bittersweet cultivars in this experiment (Krishna Kumar et al. 2021; Xu et al. 2012). Because currently available cider apple supply in North America comprises more low-acid bittersweet cultivars than high-acid bittersharps (Wojtyna 2018; Zakalik 2021), crop density effects on acid concentration may not be as important a consideration as phenolic concentration for growers of cider cultivars and commercial cider producers. In this experiment, only ‘Geneva Tremlett’s Bitter’ had enough endogenous malic acid for sensory significance, and low enough pH to be meaningful in terms of microbial stability. ‘Geneva Tremlett’s Bitter’ also exhibited the only meaningful correlation of crop density to TA and pH for cidermaking.

Juice PAN concentration correlated negatively with crop density, which agrees with previous reports (Guillermin et al. 2015; Karl et al. 2020a; Peck et al. 2016). Similar to total polyphenols, the correlation between crop density and PAN is likely a result of early season nutrient partitioning and competition among fruitlets.

In 2017, the same experimental design was replicated on a separate set of trees in the same orchard, due to low overall bloom in the orchard (Table 2). Cultivars Chisel Jersey, Dabinett, Harry Masters Jersey, and Michelin were used (Zakalik 2021). This experiment was conducted to examine the effect of thinning in the “off” year only. Phenolic concentration correlated negatively with crop density in ‘Chisel Jersey’, ‘Harry Masters Jersey’, and ‘Michelin’, and not at all in ‘Dabinett’. Likewise, SSC correlated negatively to crop density in ‘Chisel Jersey’ and ‘Harry Masters Jersey’ but not for ‘Dabinett’ or ‘Michelin’. Similar to the 3-year experiment, TA was only affected by crop density in ‘Chisel Jersey’ (negative correlation). The lack of a crop density effect on juice quality in 2017 (an “off” year for this orchard) indicates that thinning may not be necessary in an “off” year to improve juice quality, because low fruit set would already result in greater total polyphenol, sugar, acid, and PAN concentrations.

Table 2.

Coefficient estimates from regressions of crop density to Folin-Ciocalteu total polyphenol concentration, titratable acidity, soluble solids concentration, starch pattern index, and preharvest drop rate, in a 1-year (2017) crop load experiment at a commercial orchard in Lyndonville, NY, USA.

Table 2.

Ripeness and maturity.

Although we were not able to harvest all cultivars at the same number of DAFB due to weather, time constraints, and preharvest drop, we can nonetheless say that fruit maturity (measured by SPI) was delayed as crop density increased up to 20 to 25 fruit/cm2 TCSA, above which maturity became advanced. The two exceptions to this trend were ‘Geneva Tremlett’s Bitter’, which showed a positive linear correlation, and ‘Harry Masters Jersey’, which showed a negative logarithmic correlation. Our finding in ‘Harry Masters Jersey’ agrees with that of Palmer et al. (1997), who found that SPI was greater (i.e., maturity was advanced) as thinning became more severe in the fresh-market cultivar ‘Braeburn’.

The quadratic relationship between SPI and crop density observed in the other five cultivars (greater SPI at very low or very high crop densities) does not comport with previous research, likely because the range of crop densities in our experiments is much wider than that of most previous studies. Conflicting trends among previous authors may represent “snapshots” of the larger picture. The quadratic relationship may be due to two different crop load effects: 1) very large crop densities may induce a stress response in a tree, resulting in advanced ripening and drop; and 2) at very low crop densities, fruit may reach maturity earlier due to reduced competition for carbon assimilates from the tree. In the middle (20–25 fruit/cm2 TCSA), these two effects may counteract each other.

In the 2017-only experiment (Table 2), SPI and flesh firmness both decreased as crop density increased in ‘Chisel Jersey’, ‘Dabinett’, and ‘Harry Masters Jersey’, but neither correlated significantly with crop density in ‘Michelin’. Conversely, preharvest drop correlated negatively with crop density in ‘Michelin’ only, and not at all in ‘Chisel Jersey’, ‘Dabinett’, or ‘Harry Masters Jersey’.

Although TA, pH, and SSC can serve as measures of fruit maturity, crop density effects on maturity did not explain crop density effects on acid or sugar concentrations. The different correlations of SSC and SPI to crop density indicate that reduced SSC at larger crop densities is not attributable to delayed ripeness, but rather that even if all fruits were pressed and analyzed at peak maturity, SSC would correlate negatively with crop density. In ‘Geneva Tremlett’s Bitter’, the positive linear correlation between SPI and crop density directly opposes the negative logarithmic correlation between SSC and crop density.

Nitrogen supplement costs.

The reduced 3-year costs of nitrogen supplements due to crop load reduction alone would not compensate for the increased costs associated with thinning plant growth regulator applications. Nor would these reduced costs, when considered alongside 3-year revenues and reduced harvest costs (Zakalik 2021), bring about a net positive change in profitability over 3 years. It is likely that PAN would be greater in a low crop density fourth year based on 2017 data, and that greater bloom on thinned trees in 2019 would likely have led to increased revenue from greater yields compared with unthinned trees that had little to no bloom. However, savings on nitrogen supplements due to improved PAN in a thinned model orchard would not compensate for the increased costs associated with thinning, and thus would not result in a net increase in profitability. However, taken into consideration with other economic benefits of reducing crop density, reduced nitrogen supplement costs are one piece of a larger puzzle.

Conclusion

Nutrient competition among developing fruitlets, resulting in diversion of nutrients to other sinks, such as cell walls, rather than to sugar/starch accumulation and polyphenol biosynthesis, is suggested by our findings. In addition, cumulative tannin yield per tree was projected to increase with reduced crop density over 4 years, which could provide cider apple buyers a reason to incentivize growers to manage crop load in their orchards. Partial budget analysis of PAN concentration found that PAN was greater in fruit from thinned trees, which would reduce nitrogen supplement costs, but these reduced costs alone are likely less than the increased costs associated with chemical applications of plant growth regulators used to manage crop load, let alone hand fruit removal. Importantly, our data suggest that nine fruit/cm2 TCSA may provide a balance in which reducing crop density results in improved juice quality without overly reducing yields over the long term.

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  • Zhang Y, Li P, Cheng L. 2010. Developmental changes of carbohydrates, organic acids, amino acids, and phenolic compounds in ‘Honeycrisp’ apple flesh. Food Chem. 123(4):10131018. https://doi.org/10.1016/j.foodchem.2010.05.053.

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Supplemental Table 1.

Cumulative 3-year yield (kg/tree) of seven cultivars in a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 1.
Supplemental Table 2.

Estimated cumulative 3-year tannin yield per tree (grams of gallic acid equivalents/tree) of seven cultivars in a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY, USA, 2016–18. Estimates are extrapolated from measured total polyphenol, extraction volume, and yield data. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 2.
Supplemental Table 3.

Folin-Ciocalteu total polyphenols (FC) of juice from seven apple cultivars in a 3-year hand-thinning experiment harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 3.
Supplemental Table 4.

Titratable acidity (TA) of juice from seven apple cultivars in a 3-year hand-thinning experiment harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 4.
Supplemental Table 5.

Average pH of juice from seven apple cultivars in a 3-year hand-thinning experiment harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 5.
Supplemental Table 6.

Soluble solid content (SSC) of juice from seven apple cultivars in a 3-year hand-thinning experiment harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 6.
Supplemental Table 7.

Primary amino nitrogen (PAN) of juice from seven apple cultivars harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 7.
Supplemental Table 8.

Average starch pattern index (SPI) of 10-fruit subsets from seven apple cultivars harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 8.
Supplemental Table 9.

Preharvest drop (% of total apples) of seven apple cultivars from a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 9.
Supplemental Table 10.

Flesh firmness (N) of seven apple cultivars from a 3-year hand-thinning experiment harvested at a commercial orchard in Lyndonville, NY, USA, 2016–18. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 10.
Supplemental Table 11.

Extraction volume (% of total pomace weight) of seven cultivars harvested at a commercial orchard in Lyndonville, NY, USA, in 2018. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 11.
Supplemental Table 12.

Comparison of yeast nutrient supplement costs ($/ha) based on 2016–18 yield and juice primary amino nitrogen data from a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY, USA. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 12.
Supplemental Table 13.

Projected 2019 fruit yield (kg/tree) from extrapolated spring bloom data of seven cultivars in a 3-year hand-thinning experiment at a commercial orchard in Lyndonville, NY, USA. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 13.
Supplemental Table 14.

Projected cumulative 4-year tannin yield (grams of gallic acid equivalents/tree) of seven cultivars at a commercial orchard in Lyndonville, NY, USA, 2016–18. Projections are extrapolated from Spring 2019 bloom data and previous years’ yield and juice quality data. Different letters within each column indicate means separation at P ≤ 0.05 level of significance using Tukey’s honest significantly difference test.

Supplemental Table 14.
  • Fig. 1.

    The relationship between crop density and Folin-Ciocalteu total polyphenol concentration measured as gallic acid equivalents (GAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 2.

    The relationship between crop density and titratable acidity measured as malic acid equivalents (MAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 3.

    The relationship between crop density and soluble solids concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 4.

    The relationship between crop density and primary amino nitrogen concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 5.

    The relationship between crop density and starch pattern index (SPI) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 6.

    The relationship between crop density and preharvest drop (percent of total fruit yield per tree) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 7.

    The relationship between crop density and flesh firmness (Newtons) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

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David L. Zakalik Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Michael G. Brown Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Gregory M. Peck Horticulture Section, School of Integrative Plant Science, CALS, Cornell University, Ithaca, NY 14850, USA

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Contributor Notes

Funding for this work was provided by the New York Apple Research and Development Program, the New York State Department of Agriculture and Markets, and the College of Agriculture and Life Sciences at Cornell University. The Cornell Employee Degree Program enabled David Zakalik to steward this experiment as part of his MS thesis. We are very grateful to Craig Kahlke, Elizabeth Tee, Mario Miranda-Sazo, and Mark Wiltberger at Cornell Cooperative Extension Lake Ontario Fruit Program for all their work counting bloom, assessing fruit maturity, and collecting data. Thanks also to Richie Gaisser, Kate Brown, Nina Comiskey, Lindsay Springer, Adam Karl, Yangbo Song, and Nathan Wojtyna, as well as the Cornell Orchards summer interns for help thinning and harvesting fruit from hundreds of trees. Thanks to the members of the Watkins lab group for sharing equipment and space. A special thank you to Chris and Jonathan Oakes and the crew at LynOaken Farms and Steampunk Cider, for providing us with an excellent field site and delicious cider.

G.M.P. is the corresponding author. E-mail: gmp32@cornell.edu.

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  • Fig. 1.

    The relationship between crop density and Folin-Ciocalteu total polyphenol concentration measured as gallic acid equivalents (GAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 2.

    The relationship between crop density and titratable acidity measured as malic acid equivalents (MAE) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 3.

    The relationship between crop density and soluble solids concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 4.

    The relationship between crop density and primary amino nitrogen concentration for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 5.

    The relationship between crop density and starch pattern index (SPI) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 6.

    The relationship between crop density and preharvest drop (percent of total fruit yield per tree) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

  • Fig. 7.

    The relationship between crop density and flesh firmness (Newtons) for seven cider apple cultivars grown in Lyndonville, NY, USA. Each datapoint represents data from a single tree in a given year. Trunk cross-sectional area (TCSA) was measured 40 cm above the graft union.

 

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