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Craig A. Ledbetter and Mark S. Sisterson

performing ANOVAs, data were subjected to tests ensuring variance homogeneity. Linear regressions were conducted on specific variable pairs in each harvest year and slopes were compared among harvest years. Results Estimating minimum sample size. The upper

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Adriano dos Santos, Francisco Eduardo Torres, Erina Vitório Rodrigues, Ariane de Andréa Pantaleão, Larissa Pereira Ribeiro Teodoro, Leonardo Lopes Bhering, and Paulo Eduardo Teodoro

end of plant breeding programs. Among them, the Toler method (1990), which uses a nonlinear regression model in the parameters, offers alternatives to overcome the difficulties related to the estimate of the environmental index. Furthermore, it

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Wei-Chin Lin, Dietmar Frey, Gordon D. Nigh, and Cheng C. Ying

yields with light and temperatures. The significant factors identified by time series analysis were further examined by NN modeling and regression analyses to elucidate the strength of their influence on pepper yield in greenhouse production. The

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Arthur Villordon, Christopher Clark, Tara Smith, Don Ferrin, and Don LaBonte

.S. #1 yield outcome using least squares-based linear regression and machine learning approaches. Machine learning generally refers to the class of computational methods for deriving insightful knowledge (including heuristics, strategies, or structure

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Hiroshi Iwanami, Shigeki Moriya, Nobuhiro Kotoda, Sae Takahashi, and Kazuyuki Abe

shelf life conditions at 20 °C and proposed a regression parameter that could be used as an indicator of storage potential in breeding programs. Changes in fruit quality in shelf life conditions were rapid, and cultivar differences regarding the changes

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Ryan J. Hayes, Bo Ming Wu, Barry M. Pryor, Periasamy Chitrampalam, and Krishna V. Subbarao

regression of vine maturity on disease severity evaluated in field experiments was used to identify resistance that was independent of late vine maturity ( Bradshaw et al., 2004 ). Modification of this method for evaluation of resistance to Sclerotinia spp

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Patricia I. Garriz, Hugo L. Alvarez, and Graciela M. Colavita

Nondestructive estimation of pear fruit weight is an important horticultural element for size prediction, particularly when repeated measurements of the same tree must be made without affecting growth. Our objective was to develop a method for determining pear fruit weight (W) using models correlating it with fruit maximum diameter (D), an easily measured dimension. A mature crop of Pyrus communis L. cv. Williams was studied at our Experimental Farm. Five trees were selected at random and fruits were sampled at weekly intervals, starting in September, 21 days after full bloom (DFB) and ending in January, 142 DFB, during three growing seasons (1991–92, 1992–93, and 1993–94). Regression equations were developed using SYSTAT procedure. Data for three years were amalgamated because analysis showed that their curves did not differ. W vs. D was best fitted to the model W = 0,8236 D2.778 R 2 = 0,98. Variability of W and D increased with fruit growth.

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Arthur Villordon, Christopher Clark, Don Ferrin, and Don LaBonte

regression (LR) ( Stenzel et al., 2006 ; Viator et al., 2005 ). The cv method identifies candidate GDD accumulation methods through comparison of cv values from combinations of GDD methods, B, and C. The LR method identifies the candidate GDD method with

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Pinghai Ding, Leslie H. Fuchigami, and Carolyn F. Scagel

sensitivity explains how sensitive the reflectance is at a specific wavelength for measuring Chl, whereas r 2 is a measure of accuracy (goodness of fit) of regression response at specific wavelength to Chl concentration. Theoretically, the OW for a

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Hsuan Chen, Lan Xue, Tong Li, and Ryan N. Contreras

plants would satisfy both thresholds and be selected. In this case, a simultaneous selection threshold using a regression line of correlated traits may overcome this problem. However, using simple regression to build a selection threshold can be