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- Author or Editor: Robert Crassweller x
An apple planting was established in 1996 comprised of two cultivars: `Ginger Gold' (GG) and `Crimson Gala' (CG) on Malling 9 NAKB T337 and Budagovsky 9 at the Horticulture Research Farm at Rock Springs, Pa. The trees were planted at a spacing of 1.5 × 3.7 m in a randomized complete-block design with 10 replications. The trees were trained to a vertical axe system with a single wire set at 2.8 m, to which the conduit was attached. Data collected included trunk cross-sectional area (TCA), tree yield, number of fruit, and number of rootsuckers. Calculated data included annual tree growth, tree efficiency, average fruit weight, and crop load. In most years, there were significant cultivar × rootstock interactions for some variables. At planting and for the first two growing seasons, GG/B.9 were significantly larger than GG/M.9 as measured by TCA. At planting, there were no differences in TCA for CG, but, by the end of 1996, M.9 trees were significantly larger and stayed this way for the rest of the study. The GG/M.9 trees did not have significantly larger TCA than those on B.9 until 2005. Trees on B.9 were 23% and 31% smaller in 2005 for GG and CG, respectively, for B.9 than on M.9. Flowering occurred first and in greater abundance for GG/B.9. At the end of the 10th growing season, there was no difference in number of fruit or total yield per tree within cultivars by rootstock. However, for both cultivars, efficiency was highest for trees on B.9. Rootsuckers were greatest for trees on B.9. Fruit weight, when adjusted with number of fruit/tree as a covariate, was different for GG in some years.
Fruit Yield and quality measurements were taken from 10 year-old `Newhaven' peach trees pruned at prebloom (preb), full-bloom (fb), and 2, 4, 6, and 8 weeks following fb. Trees were hand-pruned with dormant type thinning and heading cuts. Fruit weight and circumference tended to decrease from fb onwards, while fruit were firmer at fb+4 and fb+6 than at preb. Firmer peaches were also measured at fb+6 than at fb. Fruit color was measured on a tristimulus color scale. Value (L) indicated that lighter fruit occurred with pruning at preb, fb, and fb+4 weeks, while fruit were darker at fb+2, fb+6 and fb+8 weeks. Hue (a) indicated that trees being pruned at preb had redder fruit than those pruned at fb+2 or fb+8 weeks. Redder fruit were also measured at fb+6 than at fb+8 weeks. Chroma (b) indicated that peaches from trees pruned at fb+4 had the highest degree of yellow, while fruit from tree pruning at fb were more yellow than at fb+2, fb+6 and fb+8 weeks. Also being investigated are the pruning timing effects on cold hardiness and carbohydrate status of one year-old stems, and on Cytospora canker incidence at the pruning cuts.
One hundred forty-nine consumers participated in a sensory evaluation, conducted on 14 Nov. 2008, at The Pennsylvania State University, University Park, PA, to determine consumer acceptance and perceptions of scab-resistant apples (Malus ×domestica). Consumers were exclusively screened for liking and eating apples. The study provides tree fruit growers and marketers in the mid-Atlantic United States with information on consumer preferences for apples that might substitute for common cultivars that require frequent apple scab pesticide applications. Resistant cultivars are also attractive in organic production systems. During the 10-minute sensory evaluation, panelists rated five scab-resistant apples [‘Crimson Crisp’, ‘GoldRush’, NY 75907–49 (NY 49), ‘Crimson Topaz’, and ‘Sundance’] and a commercially available non-resistant cultivar, Jonagold, on appearance, aroma, texture, flavor, and overall liking using a nine-point hedonic scale (9 = “like extremely” and 1 = “dislike extremely”). Three of the four apples tested with a red peel (‘Crimson Topaz’, NY 49, and ‘Crimson Crisp’) were rated significantly higher than the other apples on the basis of appearance, receiving mean ratings that were between “like moderately” and “like very much,” a rating of 7 and 8, respectively. In regards to texture, ‘Crimson Topaz’ and ‘Crimson Crisp’ were significantly higher than ‘Jonagold’ and NY 49, with mean ratings between “like slightly” and “like moderately.” For overall liking scores, ‘Crimson Crisp’, which was rated between “like slightly” and “like moderately,” was not significantly different from ‘Crimson Topaz’ and ‘GoldRush’; however, ‘Crimson Crisp’ was rated higher than ‘Jonagold’, NY 49, and ‘Sundance’. Panelists also responded to questions regarding their food-purchasing attitudes and behaviors. Sixty-two percent of panelists purchased fresh apples for themselves and/or other household members at least “two or three times a month” during an average year. Only 2.7% responded that they purchased fresh apples “more than once a week.” This study of consumer preferences provides an initial assessment of the feasibility of marketing new apple cultivars and organic apples within the mid-Atlantic U.S. region. Those that performed well in the sensory evaluation should be candidates for additional market research.
In three experiments, diameters of apples representing 7% to 30% of the fruit on a tree were measured at ≈60 days after full bloom. Using previously published regression equations, the early-season fruit diameter values were used to estimate apple fruit weight at harvest (FWH). At harvest, all fruit on sample trees were weighed and the distributions of estimated FWH for fruit measured early in the season were compared with distributions of the actual FWH for whole trees. Actual FWH was normally distributed for only one of the three experiments. Although the estimated mean FWH averaged for the 10 trees was within 9% of the actual mean FWH for all three experiments, the distribution of estimated FWH differed significantly from the actual distribution for all three experiments. All fruit were then assigned to appropriate commercial fruit sizes or box counts (number of fruit/19.05 kg). Fruit size tended to peak on the same four box counts for the estimated and actual populations, but the estimated populations had too few fruits in the small- and large-size box counts. Using early-season estimates of FWH, commercial apple growers and packers can predict fairly accurately the percentage of the crop that will fall into the peak box counts, but a more accurate early-season estimate of the fruit size distribution will likely require measuring 50% of the fruit on a tree.
We present a method for predicting firmness of `York Imperial' apples after air or controlled-atmosphere storage. Firmness and soluble solids content in freshly harvested fruit can be plotted on a graph showing a “decision line.” If the prestorage firmness and soluble solids coordinates for a given sample are above the decision line, then firmness after storage is predicted to be greater than the target value. Prestorage flesh firmness and soluble solids content were the best predictors of poststorage firmness. There was no significant improvement in firmness prediction when ethylene, starch, or other indicators of maturity were included.
‘Honeycrisp’ is a popular apple cultivar, but it is prone to several disorders, especially bitter pit. Previously reported models for predicting bitter pit are biased, indicating that the models are missing one or more important predictor variables. To identify additional variables that may improve bitter pit prediction, a study was undertaken to investigate the influence of canopy position, spur characteristics, and individual fruit characteristics on bitter pit development. ‘Honeycrisp’ trees from two orchards over 2 years provided four combinations of orchards and years. Fruits were sampled from spurs at different canopy positions and with varying bourse shoot lengths and numbers of fruits and leaves. Following cold storage, bitter pit was assessed in three ways: 1) bitter pit severity was recorded as the number of pits per fruit, 2) bitter pit was recorded as a binomial response (yes, no) for each fruit, and 3) the incidence of bitter pit was recorded as the proportion of fruit developing bitter pit. As a result of the high fruit-to-fruit variation, bitter pit severity was associated with canopy position or spur characteristics to a lesser extent than bitter pit incidence. Bitter pit incidence was generally greater for fruits developing on spurs with only one fruit and spurs from the lower canopy. Binomial data were analyzed with a generalized linear mixed model. Fruit harvested from trees with heavy crop loads, and those developing on spurs with multiple fruit and spurs with long bourse shoots had the lowest probability of developing bitter pit. Regardless of how bitter pit was assessed, bitter pit related positively to fruit weight (FW), but the relationship usually depended on other variables such as canopy position, fruit per spur, and leaves per spur. The advantages of fitting binomial data with logistic regression models are discussed.
Canopies of ‘Gala’ and ‘Fuji’ trees, trained to the vertical axis, were divided into eight vertical sections, each representing 12.5% of the tree canopy. The diameter of all ‘Gala’ fruit and fruit weight for all ‘Fuji’ fruit were recorded for each canopy section. Fruit size from most canopy sections was normally distributed and distributions were similar for most sections. Therefore, fruit size distribution for a tree can be estimated by harvesting fruit from two sections of a tree, representing 25% of the canopy. For small trees in intensive plantings, with canopy diameters less than 2.0 m, average fruit diameter or fruit weight estimated from all fruit collected from 25% of the canopy may provide estimates within 7% of the true value.
Early-season fruit diameter measurements for ‘Gala’, ‘Fuji’, and ‘Honeycrisp’ apples in three orchards for 3 years were used to develop regression models to estimate fruit weight at harvest. Fruit weight at harvest was linearly related to fruit diameter 60 days after bloom, but intercepts and slopes were not homogeneous for all nine combinations of orchards and years for any of the cultivars. When the entire data set for a cultivar was used to develop a single predictive model, the model was biased and underpredicted fruit weight for small fruit and overpredicted fruit weight for large fruit. Adding the ratio of (fruit weight/fruit diameter) at 60 days after bloom to the model with fruit diameter at 60 days after bloom produced a less-biased model with improved coefficients of determination, and predicted values were more similar to the observed values. The (fruit weight/fruit diameter) ratio was positively related to cumulative growing degree days for the 60 days before the fruit were measured and tended to be lower in years when fruits were exposed to frosts. These multiple regression models can be used to develop tables with predicted fruit weights at harvest for varying combinations of fruit diameter and (fruit weight/fruit diameter) ratio 60 days after bloom.
Apple orchards are highly diversified and complex ecological and economic systems. Production is affected by a wide range of insect, disease, weed, and mammalian pests, and is subject to the same economic and social constraints as any business enterprise. A computer technology, expert systems (ES), is being used to assist fruit growers, county extension agents, and private consultants in making better decisions about the complex horticultural, entomological, and pathological orchard problems.
‘Honeycrisp’ (Malus ×domestica) apples were harvested from a total of 17 mid-Atlantic orchards during 2018 and 2019 to verify a previously published bitter pit prediction model. As in the previous study, bitter pit incidence was associated with low calcium (Ca) levels and high ratios of nitrogen (N), potassium (K), and/or magnesium (Mg) to Ca in the fruit peel and excessive terminal shoot growth. The best two-variable model for predicting bitter pit developed with the 2018–19 data set contained boron (B) and the ratio of Mg to Ca (R 2 = 0.83), which is different from previous models developed with data from three individual years (2015–17). When used to predict the bitter pit incidence of the 2018–19 data, our previous best model containing the average shoot length (SL) and the ratio of N to Ca underestimated the incidence of bitter pit. The model is probably biased because one or more important variables related to bitter pit have not yet been identified. However, the model is accurate enough to identify orchards with a low incidence of bitter pit.