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We assessed the vegetative growth and fruit production behavior of different sweet cherry cultivars grown using multiple new ultra-high-density planting (HDP) and training systems. An experimental orchard established in 2007 in the Ferrara province of Italy was used for this trial. The sweet cherry cultivars under evaluation were ‘Giorgia’ and ‘Grace Star^®’ grafted on Gisela^® 6; and ‘Black Star^®’, ‘Early Bigi^®’, ‘Early Star^®’, ‘Ferrovia’, ‘Grace Star^®’, ‘Kordia’, ‘Regina’, ‘Summit’, ‘Sweet Early^®’, and ‘Sylvia’ grafted on Gisela^® 5 rootstock. Each cultivar–rootstock combination was trained to spindle, V-system, or Super Spindle Axis (SSA). Planting densities ranged from 1905 trees/ha for spindle with Gisela^® 6 to 5714 trees/ha for V-system and SSA with Gisela^® 5. Vegetative growth, yield productivity, and fruit quality were evaluated. Among the three systems grafted on Gisela^® 5, trees trained to the spindle system had the highest trunk cross-sectional area (26.2 cm²), followed by V-system (21.8 cm²) and SSA (20.2 cm²). Seven years after planting, ‘Ferrovia’ had the highest cumulative yield per hectare among cultivars on Gisela^® 5, especially with V-system (50.5 t·ha^–1) and SSA (52.2 t·ha^–1) training systems. For cultivars on Gisela^® 6, ‘Giorgia’ on had the highest cumulative yield per hectare after 7 years, but ‘Grace Star^®’ on had higher production (≈14.0 t·ha^–1 with V-system and SSA and 12.8 t·ha^–1 with spindle) than ‘Giorgia’ in 2013.

Dry matter (DM) has recently been proposed as a new quality index for apple, inspiring similar investigations in other tree fruit crops. Near-infrared spectroscopy (NIR) enables the nondestructive estimation of DM and other quality attributes, although the accuracy and reliability of this technology on North American pear varieties remain untested. In this study, predictive NIR regression models were developed for nondestructive determination of postharvest DM and soluble solids content (SSC) in d’Anjou and Bartlett pears (Pyrus communis L.) using a commercially available NIR spectrometer. At calibration, models performed reliably with coefficients of determination (R ²) of 0.940 (DM) and 0.908 (SSC) for model trained on d’Anjou pears and 0.860 (DM) and 0.839 (SSC) for model trained on Bartlett pears. Application of the models to independent validation datasets demonstrated acceptable performance with R ² values ranging from 0.722–0.901 and 0.651–0.844 between measured and predicted DM and SSC values, respectively. Differences in performance can be attributed to the different DM and SSC values and maturity levels between the fruit used for model calibration and those in the validation datasets. Although not all models developed in this study were accurate enough for quantitative determinations, NIR devices may be useful for orchard management decisions and fruit sorting purposes.

Leaf area is evaluated as leaf area index (LAI), the ratio of leaf to ground area, and is known to be crucial to understanding forests and high-quality fruit production in orchards. Nondestructive tools have been available for decades that pair hemispherical photography with gap fraction theories to understand LAI. Those tools do not allow for rapid assessment in the field, and there is no standardized protocol to acquire accurate estimates yet. This experiment has developed an optimized method with the CID Plant Canopy Imager (CI-110) in a high-density apple orchard. This novel tool for LAI estimation allows image acquisition and processing in real time in the field. LAI assessments of hemispherical images were taken under five light environments, at three imaging heights, processed with two thresholding methods, and were compared with destructive LAI values for accuracy. The difference between estimated and destructive LAI (∆LAI) was determined for trees on an individual or grouped by a three tree basis. Estimations for triplet groupings were more accurate, and the significantly lower ∆LAI in each treatment occurred for the no-net environment, 10 cm from the ground and processed with the Otsu threshold. When combined as triplet groupings, this methodology sequence yielded an LAI estimation with a 13% prediction error (∆LAI = 0.19). The use of the CI-110 with this methodology can give useful, real-time information regarding orchard canopies to address pruning and training decisions for high-quality fruit production.

The apple variety, ‘Honeycrisp’ has been extensively planted in North America during the last two decades. However, it suffers from several agronomic problems that limit productivity and postharvest quality. To reduce losses, new information is needed to better describe the impact of crop load on productivity and postharvest fruit quality in a desert environment and the major region where ‘Honeycrisp’ expansion is occurring. Here, 7-year-old ‘Honeycrisp’ trees on the M9-Nic29 rootstock (2.5 × 0.9 m) were hand thinned to five different crop loads [from 4.7 to 16.0 fruit/cm² of trunk cross-sectional area (TCSA)] to compare fruit quality, maturity, fruit size, elemental concentration, and return bloom. Fruit size distribution was affected by crop load. Trees with the highest crop load (16 fruit/cm²) produced smaller fruit. Index of absorbance difference (I _AD) measurements (absorption difference between 670 and 720 nm), a proxy indicator of the chlorophyll content below the skin of fruit measured by a DA-meter, were made shortly after harvest (T0) and after 6 months of storage (T1). Fruit from the trees with the lowest crop load had lower I _AD values indicating advanced fruit ripeness. The comparison between the I _AD classes at T0 and T1 showed that fruit belonging to the lowest I _AD class had significantly higher red-blushed overcolor percentage, firmness, dry matter, and soluble solid content than those in the “most unripe” class (highest I _AD readings) regardless of crop load. The percentage of blushed color, firmness, titratable acidity (TA), soluble solids content, and dry matter were all higher in the lowest crop loads at both T0 and T1. Fruit calcium (Ca) concentration was lowest at the lowest crop load. The (K + Mg + N):Ca ratio decreased as crop load increased until a crop load of 11.3 fruit/cm², which was not significantly different from higher crop loads. For return bloom, the highest number of flower clusters per tree was reported for 4.7 fruit/cm² crop load, and generally it decreased as crop load increased. Here, we highlight the corresponding changes in fruit quality, storability, and elemental balance with tree crop load. To maintain high fruit quality and consistency in yield, careful crop load management is required to minimize bienniality and improve fruit quality and storability.

Globally, apple production often occurs in semiarid climates characterized by high summer temperatures and solar radiation. Heat stress events occur regularly during the growing season in these regions. For example, in the semiarid eastern half of Washington State, historic weather data show that, on average, 33% of the days during the growing season exceed 30 °C. To mediate some of the effects of heat stress, protective netting (PN) can be used to reduce the occurrence of fruit sunburn. However, the impacts of reduced solar radiation in a high light environment on light-use efficiency and photosynthesis are poorly understood. We sought to understand the ecophysiological response of apple (Malus domestica Borkh. cv. Honeycrisp) under blue photoselective PN during days with low (26.6 °C), moderate (33.7 °C), or high (38.1 °C) ambient temperatures. Two treatments were evaluated; an uncovered control and blue photoselective PN. Maximum photochemical efficiency of PSII, or photosystem II (F_v/F_m) was significantly greater at all measurement times under blue photoselective PN compared with the control on days with high ambient temperatures. F_v/F_m dropped below 0.79, which is considered the threshold for stress, at 1000 hr in the control and at 1200 hr under blue photoselective PN on a day with high ambient temperature. On days with low or moderate ambient temperatures, F_v/F_m was significantly greater under blue photoselective PN at 1400 hr, which coincided with the peak in solar radiation. ‘Honeycrisp’ apple exhibited dynamic photoinhibition as shown by the diurnal decline in F_v/F_m. Quantum photosynthetic yield of PSII (ΦPSII) was also generally greater under blue photoselective PN compared with the control for days with moderate or high ambient temperatures. Photochemical reflectance index (ΔPRI), the difference in reflectance between a stress-responsive and nonstress-responsive wavelength, was greater under PN compared with the control on the day with high ambient temperatures, with no differences observed under low or moderate ambient temperatures. Leaf gas exchange did not show noticeable improvement under blue photoselective netting when compared with the control despite the improvement in leaf-level photosynthetic light use efficiency. In conclusion, PN reduced incoming solar radiation, improved leaf-level photosynthetic light use efficiency, and reduced the symptoms of photoinhibition in a high-light, arid environment.

Annual accumulation of starch is affected by carbon reserves stored in the organs during the growing season and is controlled mainly by sink strength gradients within the tree. However, unfavorable environmental conditions (e.g., hail events) or application of management practices (e.g., defoliation to enhance overcolor in bicolor apple) could influence the allocation of storage carbohydrates. This preliminary research was conducted to determine the effects of early defoliation on the dry matter, starch, and soluble carbohydrate dynamics in woody organs, roots, and mixed buds classified by age and two levels of crop-load for one growing season in ‘Abbé Fétel’ pear trees (Oct. 2012 to mid-Jan. 2013 in the northern hemisphere). Regardless of the organs evaluated (woody organs, roots, and mixed buds), an increase of soluble carbohydrate concentration was observed in these organs in the period between after harvest (October) and January (dormancy period). Among all organs, woody short-old spurs showed the highest increase (+93.5%) in soluble sugars. With respect to starch, woody organs showed a clear trend of decreasing in concentration between October and January. In this case, short-old spurs showed the smallest decline in starch concentrations, only 6.5%, whereas in other tree organs starch decreased by 34.5%. After harvest (October), leaves showed substantially higher starch and soluble sugar concentrations in trees with lower crop-loads. These results confirm that in the period between October and January, dynamic interconversions between starch and soluble carbohydrates occur at varying magnitudes among organs in pear trees.

Physiological variability within a large canopy ‘d’Anjou’ tree results from agronomic and environmental factors. Fruit diversity within the canopy was surveyed using metabolic profiling to identify metabolism associated variability within the canopy. Different portions of the same fruit were evaluated to determine future precise sampling protocols for metabolic profiling of pear. We expected that the metabolic profile of the peel and cortex would be diverse and these differences would highlight specific metabolic pathways as influenced by these conditions. Another focus of this work was developing an untargeted metabolic profiling protocol tailored for pear using a combination of extractions coupled with GC-MS and LC-MS analysis. ‘d’Anjou’ pear fruit harvested from two different zones of trees trained to an open vase canopy were maintained at room temperature for 24 days to observe any changes in external phenotype and metabolic profile. Fruit harvested from the internal canopy were greener as also indicated by high Index of Absorbance Difference (I_AD) and hue angle values. Metabolic profile differences between tree positions were widespread and included metabolites from many pathways beyond those associated with peel color. In addition, peel metabolic profile was different depending upon the tissue position (top vs. bottom) sampled from the pears. Specific pathways altered by tree position included those potentially linked to fruit quality and ripeness, including malic acid and aroma volatile (V) levels, as well as light environment, such as flavonol glycoside levels. Present results warrant further future work targeting these changes over time during storage and alongside fruit quality analyses to validate the impacts on ripening and tree factors. In addition, outcomes indicate tissue sampling strategies require consistency with respect to the region of the pear fruit sampled for metabolomics.