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Brent L. Black and Richard H. Zimmerman

Highbush blueberry plants require low-pH, well-drained sandy soils. To increase the range of sites available for highbush blueberry production, by-products were tested as constituents in soilless media and as soil amendments. By-products, including coal ash, municipal biosolid compost, leaf compost, and acid peat, were combined in different proportions and compared to Berryland sand (alone) and Manor clay loam (alone and compost-amended) for a total of 10 media treatments. The pH of all treatment media was adjusted to 4.5 with sulfur. One-year-old tissue-cultured plants of `Bluecrop' and `Sierra' were planted in 15-L pots containing the pH-adjusted treatment media in 1997, producing their first substantial crop in 1999. For the 1999 crop, ripe fruit was harvested at weekly intervals over 5 weeks. ANOVA for yield indicated a significant cultivar × media interaction. `Bluecrop' appeared more sensitive to media treatment as yields on Manor clay loam were 80% less than on Berryland sand. Yields of `Bluecrop' on coal ash-compost mixes were similar to that of Berryland sand, and 1:1 coal ash:compost mixes produced significantly higher yields than did the 3:1 mixes. Yield of `Sierra' on Manor clay loam was 41% less than on Berryland sand, and plants growing on soilless mixes yielded 17% to 58% more than those on Berryland sand. `Bluecrop' fruit size was greatest for Berryland sand, but did not differ significantly among coal ash-compost mixes. For all media treatments, `Sierra' fruit size was inversely correlated with yield. Fruit from `Bluecrop' plants on coal ash-compost mixes ripened slightly earlier than on Berryland sand, but ripening date of `Sierra' did not vary significantly with soil treatment. The potential for employing these by-product mixes in small-scale commercial blueberry production will be discussed.

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Tina Gray Teague

Four week old watermelon (cv. Royal Sweet) transplants were obtained from long distance (FL) and local (AR) commercial transplant growers and set in plots in a commercial watermelon field near Leachville AR. Transplants (plugs) from AR (Burton's Inc., Tupelo, AR) were grown in inverted pyramid, Todd Flats (model 100A; 5/8″ length X 1/2″ width X 3″ height) (Speedling Inc., Sun City, FL). FL transplants (LaBelle Plant World, LaBelle, FL) were grown in 1.5″ square cells, 2″ deep. All transplants were delivered 15 April and set on 16 April. Transit time for local transplants was < 2 hrs, and plants were delivered in original flats. FL transplants were shipped on 14 April and were in transit ca. 28 hrs. They had been pulled from trays and were shipped in cardboard boxes. Plot size was 6 beds, 53.3m long with treatments arranged in a RCB with 4 replications. Bed spacing was 2.9m with between plant spacing of 1.5m. Data were subjected to ANOVA with mean separation by LSD.

Plots were harvested 3, 8, 15 and 22 July. Total number of fruit produced from plots planted with AR transplants was greater than FL treatment plots in the first 3 of 4 harvests; significantly higher total cumulative yield was observed with AR compared to FL transplants (45,115 and 35,172 kg ha-1, respectively). Increases in-yield and earliness resulted in an increase in gross profit of $1225 ha-1 for local transplants (based on national price data from that time period). No differences in average weights of fruit were observed for any harvests. Results indicate that Mid-South watermelon producers could benefit from utilizing locally grown transplants if plants are of comparable quality to those available from distant suppliers.

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S. Serce and J.F. Hancock

A common complaint with day-neutral strawberries is that they perform poorly in mid-summer heat. Since most modern day-neutral cultivars are derived from the same Fragaria virginiana ssp. glauca clone from Utah, we felt it prudent to search for alternate sources of day-neutrality that were more heat-tolerant. We compared the sexual and vegetative performance of nine F. virginiana clones from a wide range of environments including the Utah site, and four F. × ananassa day-neutral types (`Aromas', `Fort Laramie', `Ogallala', and `Tribute') under constant temperatures of 18, 22, 26, and 30 °C and 12-h days. `Aromas' and `Tribute' carry the Utah source of day-neutrality, while `Fort Laramie' and `Ogallala' are old cultivars that have a different, complex background. After a 4-week period of acclimation, we counted the number of crowns, inflorescences, flowers, stolons, and daughter plants that emerged over a 10-week period, and measured the dry weights of component parts. ANOVA tables revealed that temperature regime (T), genotypes (G), and T*G were significant for flower number (FLN) and total dry matter accumulation, while species and T*G were significant for daughter plant number (DPN). Mean FLNs across the four temperatures were 6.8, 3.7, 3.3, and 1.2, while mean DPNs were 0.7, 0.9, 0.7, and 1.8. F. virginiana clones averaged 3.8 FLNs and 1.8 DPNs, while the F. × ananassa clones averaged 4.1 FLNs and 0.2 DPNs. There was generally more variability among the F. virginiana clones than the F. × ananassa clones, but the F. × ananassa cultivars, `Fort Laramie' and `Ogallala', performed best at 30 °C. The Wasatch clone did not flower in any treatment, suggesting it is not day-neutral.

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Kathryn S. Orvis and Irwin L. Goldman

Heart attack and stroke, a leading cause of death in the United States, have been associated with blood platelet aggregation. Onion extract inhibits blood platelet aggregation both in vitro and in vivo. Current trends toward natural foods and health remedies may point to the importance of onion-induced antiplatelet activity (OIAA). The genetic control of OIAA has yet to be revealed. One-hundred-eighty-three F3 families were derived from a long-day mild inbred line crossed to a long-day pungent inbred line that differ by for OIAA by 67%. Families were grown in a RCB design with two replications in muck soil (Randolph, Wis.) in 1997. Extracts were made from crushing bulb tissue in a mechanical juicer. F3 families were evaluated for OIAA and soluble solids (SS). OIAA was measured by electrical impedance aggregometry using two human blood donors. Endpoint (ohms) and slope of the aggregation curve were recorded. SS were measured by refractometry. F3 families were significantly different for OIAA and SS (P < 0.0001) in the ANOVA. A strong positive correlation of 0.96 was revealed for slope of curve and endpoint across families, replications, and blood donors. This correlation has not been previously reported for onion and suggests that for these families, descriptions of OIAA based on either rate of aggregation or endpoint are functionally equivalent. Both SS and OIAA exhibit transgressive segregation in this group of F3 families. Twenty percent exhibit OIAA stronger than the pungent parent and 5% were less than the mild parent. The family with the highest OIAA was 4-fold higher than the pungent parent of the cross, which could be useful in future onion breeding efforts. In addition, transgressive segregation in these families aids in QTL investigations for OIAA, SS and other economically important traits.

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Shadd Taylor, Derald A. Harp, Kristen McDowell, and Roque Lemus

It is generally accepted that plants closer to structures benefit from the warmth emitted via imperfect insulation and solar energy reemitted as long-wave, thermal radiation. However, while claims of protection are given, little quantifiable information exists on the extent or pattern of this protection. We studied existing plantings of Trachelospermum asiaticum, an evergreen groundcover that is frequently damaged in northeast Texas. The plantings studied were part of a landscape with at least five different identifiable microclimates: 1) near building (NB); 2) mid-bed (MB); 3) bed edge (BE); 4) beneath Quercus virginiana (LO); and 5) beneath Pyrus calleryana`Bradford' (BP). We placed HOBO temperature data loggers recording one temperature per minute in each location. Following our first damaging freeze, we waited 7 days before collecting leaf samples. Leaf samples were collected by using a 25-cm square, 2 cm deep on two sides. The square was placed on the groundcover so that the top of the groundcover was level with the top of the square. All leaves and stems that extruded through the top 2 cm of the square were excised. Four samples were taken from each location, and the number of damaged and nondamaged leaves were counted for each sample. Leaves that were at least 50% discolored were considered damaged. Leaf damage data were analyzed using SAS Proc ANOVA. Leaves in the BE and BP locations showed significantly fewer live leaves than any other locations. NB leaves were virtually undamaged. Average temperatures in the BE and BP locations were 4.5 to 5 °F colder than the “near building” locations, comparable to an a or b zone in the current USDA Plant Hardiness Zone map.

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Greg McCollum and Kim D. Bowman

The objective of this experiment was to compare fruit-quality parameters of ‘Ray Ruby’ grapefruit grown on seven rootstocks. Four recent releases from the United States Department of Agriculture (USDA) rootstock breeding program, ‘US-852’, ‘US-897’, ‘US-942’, and ‘US-812’ (all Citrus reticulata × Poncirus trifoliata hybrids), ‘x639’ (C. reticulata × P. trifoliata), along with industry-standard ‘Sour Orange’ and ‘Swingle’ citrumelo were evaluated in a commercial orchard trial in Indian River County, FL. Fruit-quality data were collected in 2011–12 (eight harvests), 2012–13 (five harvests), and 2014 (single harvest). In each season, rootstock effects on fruit size, total solids, and solids acid ratio were significant. ‘Sour orange’ and ‘Swingle’ produced the largest fruit, whereas ‘US-897’ (a semidwarfing rootstock) produced the smallest fruit. Peel thickness (measured only in the 2011–12 season) was greatest in ‘Sour Orange’ early in the season, but not toward the end of the season. Misshapen (“sheep-nosed”) fruit occurred more frequently on ‘Sour Orange’ than on other rootstocks, although the incidence of sheep-nosing was minor. Analysis of variance (ANOVA) for fruit-quality data collected in January of each of the 3 years confirmed that ‘Sour Orange’ and ‘Swingle’ produced the largest fruit and ‘US-897’ produced the smallest fruit. Total solids were the highest in ‘US-897’ and the lowest in ‘x639’ and ‘US-852’. Taken together, our data indicate that ‘US-942’ and ‘US-897’ rootstocks produced fruit with quality characteristics that equaled or exceeded ‘Sour Orange’ and ‘Swingle’, the two most common rootstocks used in the Indian River district.

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Soon O. Park*, Kevin M. Crosby, Jonathan W. Sinclair, Kilsun Yoo, and Leonard M. Pike

Sucrose, fructose, total sugars and soluble solids are major factors in determining mature melon fruit sweetness. Bulked segregant analysis was utilized to detect RAPD markers associated with QTL for sucrose, total sugars and soluble solids in an F2 population from the ananas melon cross of Deltex (high sugars) × TGR1551 (low sugars). Sucrose, glucose, fructose and total sugar data were obtained from 108 F2 plants by means of HPLC. Clear separations for sucrose, total sugars and soluble solids between Deltex and TGR1551 were observed, whereas slight differences for glucose and fructose were found. Continuous distributions for sucrose, total sugars and soluble solids were observed in the F2 population indicating quantitative inheritance for the sweetness traits. A significant negative correlation was observed between sucrose and glucose (r = -25) or fructose (r = -0.31). A significant positive correlation was noted between sucrose and total sugars (r = 0.80) or soluble solids (r = 0.64). Three low and high DNA bulk pairs for sucrose, total sugars and soluble solids were developed. A total of 360 primers were used to simultaneously screen between the low and high bulks, and between Deltex and TGR1551. Sixty-eight RAPD markers were polymorphic for the low and high bulks. Of the 68 markers, 24 were found to be significantly associated with sucrose, total sugars or soluble solids on the basis of single-factor ANOVA. Marker OM15.550 was consistently associated with QTL affecting sucrose, glucose, fructose, total sugars and soluble solids, and accounted for 7% to 25% of the phenotypic variation for the traits. These markers associated with the sugar synthesis QTL could be useful to transfer these genes into a low sugar cultivar to enhance the fruit sweetness.

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Mathura Thillainathan and George C.J. Fernandez

A user-friendly SAS statistical and graphical application to classify genotypes evaluated under multiple sites is presented. First, the test sites are classified into three environments, LOW [(\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}j}\) \end{document}) < Q1], MEDIUM [Q1< = (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}j}\) \end{document})< = Q3], and HIGH [(\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}j}\) \end{document}) >Q3] yielding environments, using the first (Q1) and third (Q3) quartile of the site mean yield (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}j}\) \end{document}) as the cutoff value. Then, in each environment, the genotypes are classified as low [L: (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{i{\cdot}}\) \end{document}) < (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}{\cdot}}\) \end{document})], medium [M: (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{i{\cdot}}\) \end{document}) = (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}{\cdot}}\) \end{document})], and high [H: (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{i{\cdot}}\) \end{document}) > (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}{\cdot}}\) \end{document})] yielding under each of the three environments, by comparing each genotype mean (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{i{\cdot}}\) \end{document}) with the overall genotypic mean (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\bar{Y}}_{{\cdot}{\cdot}}\) \end{document}) based on lsd 0.01 statistic computed from a separate two-way ANOVA models for LOW, MED, and HIGH yielding environments. Using the user-friendly SAS MACRO, EXPLORGE horticulturists can effectively and quickly perform genotype classification under multi-site evaluation. The steps involved in downloading the necessary MACRO-CALL file from the author's home page [] and the instructions for running the SAS MACRO are presented. The features of this graphical method and the graphics produced by the EXPLORGE MACRO are demonstrated and validated by published data.

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electrolyte leakage (REL), and root dry weight (RDW) were log transformed. Mean separation tests were performed on means of transformed data; however, the results were presented using untransformed means. The corrected Table 1 (ANOVA) is as follows

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Albert N. Kishaba, Steven J. Castle, Donald L. Coudriet, James D. McCreight, and G. Weston Bohn

Abbreviations: ANOVA, analysis of variance; AR HBJ, AR Hale's Best Jumbo; AR TM, AR Topmark; AR 45, aphid-resistant `PMR 45'; CMV, cucumber mosaic virus; HBJ, `Hale's Best Jumbo'; EN 50 , 50% infection; WMV, watermelon mosaic virus; ZYMV, zucchini