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Wen-hui Li, Jian-rong Feng, Shi-kui Zhang, and Zhang-hu Tang

, IL) was used for correlation analysis between gene expression and stone cell content in the CP and NP fruits. Results Changes in stone cell content during fruit development. Compared with the stone cell content in NP, although the stone cell content

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Rachel P. Naegele and Mary K. Hausbeck

with root rot resistance, and evaluate the correlation between root rot and fruit rot resistance in Capsicum . Materials and Methods Plant material and isolates. Eighty C. annuum lines and one C. frutescens (PI 593920) representing wild, cultivated

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Ah-Chiou Lee, Fang-Shin Liao, and Hsiao-Feng Lo

’s least significant difference test at the 5% level of probability. Pearson’s correlation analysis was used to identify the relationship between MMY and DMMY. Multiple linear regression analysis was used to analyze the differences among cultivars in the

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Kate Evans, Lisa Brutcher, Bonnie Konishi, and Bruce Barritt

one overall value per genotype. The Spearman rank-order correlation was used to analyze the MDT-1 output with the non-parametric sensory evaluation data using the CORR procedure of SAS (version 9.2; SAS Institute, Cary, NC). The Spearman correlation

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Rachel A. Itle and Eileen A. Kabelka

means, were calculated for each trait according to Fehr (1987) . Spearman's coefficient ( r s ) of rank correlations ( Steel et al., 1997 ) were calculated to test differences in rank order among the cultigens between the two locations. Pearson

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Soon Li Teh, Lisa Brutcher, Bonnie Schonberg, and Kate Evans

a breeding program), or evaluation by trained sensory panelists. While there have been correlation studies ( Cliff and Bejaei, 2018 ; Zdunek et al., 2010 ) on instrumental and sensory analyses, a multiyear large-scale examination of such correlation

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Rachel P. Naegele

time, such as grape. In strawberry, leaf disk evaluations were found to have a high correlation with field susceptibility to Botrytis -induced fruit rot ( Olcott-Reid et al., 1993 ). No studies to date have looked at relationships between Botrytis

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Yiwei Jiang, Huifen Liu, and Van Cline

; Sönmez et al., 2008 ; Trenholm et al., 1999 ; Xiong et al., 2007 ). Once the correlations between canopy reflectance and physiological variables have been identified, relationships among these characteristics can be used to predict turf quality as well

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Yiwei Jiang and Robert N. Carrow

Canopy reflectance has the potential to determine turfgrass shoot status under drought stress conditions. The objective of this study was to describe the relationship of turf quality and leaf firing versus narrow-band canopy spectral reflectance within 400 to 1100 nm for different turfgrass species and cultivars under drought stress. Sods of four bermudagrasses (Cynodon dactylon L. × C. transvaalensis), three seashore paspalums (Paspalum vaginatum Swartz), zoysiagrass (Zoysia japonica), and st. augustinegrass (Stenotaphrum secundatum), and three seeded tall fescues (Festuca arundinacea) were used. Turf quality decreased 12% to 27% and leaf firing increased 12% to 55% in 12 grasses in response to drought stress imposed over three dry-down cycles. The peak correlations occurred at 673 to 693 nm and 667 to 687 nm for turf quality and leaf firing in bermudagrasses, respectively. All three tall fescues had the strongest correlation at 671 nm for both turf quality and leaf firing. The highest correlations in the near-infrared at 750, 775, or 870 nm were found in three seashore paspalums, while at 687 to 693 nm in Zoysiagrass and st. augustinegrass. Although all grasses exhibited some correlations between canopy reflectance and turf quality or leaf firing, significant correlation coefficients (r) were only observed in five grasses. Multiple linear regression models based on selected wavelengths for turf quality and leaf firing were observed for 7 (turf quality) and 9 (leaf firing) grasses. Wavelengths in the photosynthetic region at 658 to 700 nm or/and near-infrared from 700 to 800 nm predominated in models of most grasses. Turf quality and leaf firing could be well predicted in tall fescue by using models, evidenced by a coefficient of determination (R 2) above 0.50. The results indicated that correlations of canopy reflectance versus turf quality and leaf firing varied with turfgrass species and cultivars, and the photosynthetic regions specifically from 664 to 687 nm were relatively important in determining turf quality and leaf firing in selected bermudagrass, tall fescue, zoysiagrass and st. augustinegrass under drought stress.

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Patrick P. Moore

Plots of 19 clones of strawberry (Fragaria ×ananassa Duch.) were planted in 1996. Fruit of 16 clones were harvested in 1997 and fruit of 11 of the same clones plus three additional clones in 1998. Individual plots were harvested on three or four dates in 1997 and from three to seven dates in 1998. Fruit firmness was determined with a penetrometer at harvest, and additional samples were processed and frozen for subsequent determination of percentage of drained weight. Clones differed in firmness in both years and in drained weight in 1998, but not in 1997. Drained weight varied considerably from harvest-to-harvest. Correlations between firmness and drained weight were significant (P ≤ 0.01) in both years, but firmness was not a good predictor of drained weight. The correlation between drained weight of fruits in 1997 and those of fruits from the same plots in 1998 was nonsignificant, but that between firmness in 1997 and firmness in 1998 was significant at P ≤ 0.05.