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James A. Taylor, John-Paul Praat, and A. Frank Bollen

given property. Second, given no a priori information on variability in a production system, they provide a basis for determining sampling density, particularly for grid-based sampling. An understanding of how crop production varies spatially within a

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Graham H. Barry, William S. Castle, Frederick S. Davies, and Ramon C. Littell

Sources of variation in juice quality of `Valencia'sweet orange [Citrus sinensis(L.) Osb.] were quantified and their relative contributions to variability in juice quality were determined, from which sample sizes were estimated. Commercial orchards of `Valencia' sweet orange trees on Carrizo citrange [C. sinensis × Poncirus trifoliata (L.) Raf.] rootstock were selected at four geographic locations representing the major citrus-producing regions in Florida. Within- and between-tree variation in soluble solids concentration (SSC) and titratable acidity (TA) were estimated in two experiments over two or three seasons, respectively. Variance components for all treatment effects were estimated to partition total variation into all possible component sources of variation. Seasonal variation in SSC and TA was relatively small, but larger for TA than SSC. Variation in SSC among blocks within a location was intermediate to low, and was less than variation among locations. In contrast, tree-to-tree variation in SSC and TA was large, in spite of sampling from trees of similar vigor and crop load, and variation in SSC and TA among fruit was relatively large. Based on results of this study, samples consisting of 35 fruit are required to detect differences (P ≤ 0.05) of 0.3% SSC and 0.06% TA, whereas 20-fruit samples can be used to detect differences of 0.4% SSC and 0.08% TA. Seven replications are required to detect differences of 0.5% SSC and 0.1% TA, with small gains in precision when tree numbers exceed 10.

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Ed Etxeberria, Pedro Gonzalez, Ariel Singerman, and Timothy Ebert

., 2011 ) adds to the inconsistency and inexactitude of the results. To overcome the ambiguities in C Las titer determinations by using a different set of leaves for every test and to lower the numerical variability between samples, we developed a

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Craig A. Ledbetter and Mark S. Sisterson

harvest data (n = 862) with 5000 random samples (case resampling) at each sample size. Fruit, nut, and kernel weight and variability in kernel dimensions. All measured variables were significantly affected by harvest year ( Table 1 ). Over 7 years of data

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Karen R. Harris-Shultz, Susana Milla-Lewis, Aaron J. Patton, Kevin Kenworthy, Ambika Chandra, F. Clint Waltz, George L. Hodnett, and David M. Stelly

. Materials and Methods Sample collection and DNA extraction. Six popular clonal zoysiagrass cultivars were collected from five states and were examined for within-cultivar variability. ‘Diamond’, ‘Emerald’, ‘Empire’, ‘JaMur’, ‘Meyer’, and ‘Zeon’ ( Table 1

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Chase M. Straw, Rebecca A. Grubbs, Kevin A. Tucker, and Gerald M. Henry

at the beginning, middle, and end of the season using a sampling grid of unspecified dimensions (135 or 150 samples depending on the field). Maps were created from the data to evaluate the spatial and/or temporal variability of the measured surface

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Michael V. Mickelbart

tissue for nutrient analysis is the inherent variability in the tissue. This is especially true for growers who are often sampling from few trees or have a low total number of samples to evaluate. Based on the cv for samples collected, the lowest

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Shuresh Ghimire, Arnold M. Saxton, Annette L. Wszelaki, Jenny C. Moore, and Carol A. Miles

soil incorporation in Expt. 3 of a soil sampling study at Mount Vernon, WA, in 2016. Horizontal line at 20,000 cm 2 indicates the amount of mulch tilled in the plot. Highly spread data points with lower sample size indicate higher variability in the

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Jennifer A. Kimball, M. Carolina Zuleta, Matthew C. Martin, Kevin E. Kenworthy, Ambika Chandra, and Susana R. Milla-Lewis

. The objective of this study was to assess the genetic variability of ‘Raleigh’ st. augustinegrass produced across the southern United States using AFLP markers. Materials and Methods Plant materials and DNA extraction. In total, 49 samples of ‘Raleigh

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Mustafa Ozgen, Faith J. Wyzgoski, Artemio Z. Tulio Jr, Aparna Gazula, A. Raymond Miller, Joseph C. Scheerens, R. Neil Reese, and Shawn R. Wright

of some well-known midwest black raspberry cultivars; and 2) to investigate the variability among samples within cultivars associated with growing sites. Our objectives were similar to previous studies reporting black raspberry values (e.g., Moyer et