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- Author or Editor: Tara Auxt Baugher x
Photosynthesis, light (PAR) and transpiration were measured with an ADC portable infrared gas analyzer on apples and grapes. Measurements were taken on north and south sides of the rows, in the morning and afternoon, on sun and shade leaves, and with the leaf chamber in a horizontal position and in a natural leaf orientation position. Measurements were made on three cloudless days in August 1990 and 1991. Subsequently, fruit adjacent to sampled leaves were harvested and soluble solids determined. Sampled leaves were then harvested and leaf areas and dry weights measured. Correlation coefficients of variables were then subjected to analysis of variance to determine which techniques gave the best correlations. Grapes and apples responded differently. For grapes, soluble solids were most closely correlated to light and photosynthesis measurements when measured on south side shade leaves, while with apples, blush side soluble solids were best correlated with measurements on south side sun leaves in the afternoon. Specific leaf weight was best correlated to photosynthesis and light with grapes when measured on north side sun leaves and with apples when measured on the south side in the morning.
A 2-year study was designed to test the effect of four growth-suppressing treatments on the incidence of nectarine [Prunus persica (L.) Batsch] pox, nectarine fruit quality, and the growth and nutritional status of nectarine trees. Root pruning was the only treatment that significantly reduced the incidence of nectarine pox. The percentage of red surface was increased by root pruning, foliar-applied paclobutrazol, and girdling. Root pruning and paclobutrazol suppressed extension shoot growth. Root pruning decreased fruit N, P, K, Mg, Mu, Fe, B, and Zn levels and increased fruit Ca. Results of the study support earlier observations that nectarine pox is associated with excessive shoot growth, excessive levels of fruit N and K, and low levels of fruit Ca. Chemical name used: Beta-[(4-chlorophenyl) methyl] -alpha-(1,1-dimethylethyl)-l-H-1,2,4 -triazole-l-ethanol (paclobutrazol).
Advances in horticultural production technology are often hindered by slow grower adoption. Low adoption rates are largely the product of skepticism, which can lead to weaknesses in the commercialization process and affect future research and product development. To better understand industry concerns and design effective outreach methods, an information technology survey was designed as part of the U.S. Department of Agriculture Specialty Crop Research Initiative project titled Comprehensive Automation for Specialty Crops (CASC). This study outlines the survey results from 111 participants at tree fruit meetings in the Pacific northwestern and eastern United States in 2009. Many of the misgivings about new automated technologies, such as equipment cost and reliability of harvest assist, sensor systems, and fully automated harvest machinery, were consistent across the country. Subtle differences appeared between the eastern U.S. and Pacific northwestern U.S. responses, including justifiable equipment price points and irrigation and pest concerns; these are likely attributable to regional differences in climate, operation size and scale, and marketing strategies. These survey data will help the project team better address grower concerns and uncertainty on a regional and national level, thereby improving adoption speed and rates after CASC-developed technologies are rolled out.
Three growth suppression treatments were compared during 1991 to 1993 on `Stayman' apple (Malus domestica Borkh.) trees grown in the T-trellis and the MIA trellis systems. All treatments—root pruning, K-31 fescue (Festuca arundinacea Schreb.), and K-31 fescue plus root pruning—suppressed tree growth compared to the nontreated control, but results were inconsistent between years and systems. Sod or sod plus root pruning reduced terminal shoot length in both systems in 2 out of 3 years. Root pruning decreased shoot length in the T-trellis in 1992. Sod decreased trunk cross-sectional area in the T-trellis in 1993. Treatments did not affect 3-year average yield efficiency but did appear to increase biennial bearing. Sod, with or without root pruning, decreased fruit cracking in the T-trellis 69% and 42%, respectively, in 1992, and sod plus root pruning decreased cracking in the MIA trellis 50%. Sod reduced fruit diameter in the T-trellis in 1992. Secondary effects of growth suppression treatments included increased light penetration and improved fruit color. Sod decreased leaf N and Mg and increased leaf P, K, and Cu. The Oct. 1993 stem water potential gradient from root to canopy was more negative in the sod plus root pruning treatment, and the osmotic potential of rootsucker leaves in the combination treatment was greater than in the control, indicating that sod plus root pruning alters the distribution of water within a fruit tree.
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.
‘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.
During a 3-year study of bitter pit in commercial ‘Honeycrisp’ apple (Malus ×domestica) orchards, incidence was associated with low calcium (Ca) levels in fruit peel; high ratios of nitrogen (N), potassium (K), and/or magnesium (Mg) to Ca in fruit peel; excessive terminal shoot length; and low crop load. Peel N and Mg concentrations were negatively correlated and peel Ca concentration positively correlated with crop density (CD). Shoot length (SL) was not consistently correlated with peel N, Mg, or phosphorus (P) and was negatively correlated with only Ca. A two-variable model that included SL and the ratio of N to Ca explained more than 65% of bitter pit incidence. The model has implications for best management of the cultivar in the field and during storage.
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.
‘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.