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Mohamed Benmoussa and Laurent Gauthier

24 ORAL SESSION 9 (Abstr. 078–085) Modeling & Statistics/Cross-commodity

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Isabelle Grechi, Nadine Hilgert, Michel Génard, and Françoise Lescourret

efforts directed toward improved control of fruit quality and its variability are needed. Crop models are powerful tools for such research efforts ( Boote et al., 1996 ; Lentz, 1998 ). However, despite a few exceptions, quality is seldom addressed in

Open access

James A. Schrader, Paul A. Domoto, Gail R. Nonnecke, and Diana R. Cochran

on select phenological stages and prescribing a threshold at which each stage will likely be accomplished. Reports of true predictive models that use real-time environmental data to estimate the arrival of a future phenological stage within the same

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Mark V. Yelanich and John A. Biernbaum

24 ORAL SESSION 9 (Abstr. 078–085) Modeling & Statistics/Cross-commodity

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Paul R. Fisher, Royal D. Heins, Niels Ehler, Poul Karlsen, Michael Brogaard, and J. Heinrich Lieth

29 ORAL SESSION 11 (Abstr. 074-080) Cross-commodity: Modeling/Statistics

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Richard P. Marini, Tara Auxt Baugher, Megan Muehlbauer, Sherif Sherif, Robert Crassweller, and James R. Schupp

identify blocks of trees with high potential for bitter pit so they can sell the fruit immediately. We recently reported a bitter pit prediction model for ‘Honeycrisp’ based on the average shoot length (SL) and ratio of N to Ca in the peel of apples sampled

Open access

Lu Zhang, Emilio Laca, Cara J. Allan, Narges M. Mahvelati, and Louise Ferguson

dormancy chill accumulation requirements, and spring-through-harvest heat accumulation requirements for bloom, nut growth, and ripening ( Benmoussa et al., 2017 ; Costa et al., 2021 ; Egea et al., 2003 ). A recent carbohydrate–temperature model of

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Karen K. Tanino and Ruojing Wang

temperature difference, and daylength were selected as major parameters to construct models for predicting yield potential in this study. Previous studies indicate modeling approaches that are based on physiological responses of plants to their environment

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Gerardo Lopez, Romeo R. Favreau, Colin Smith, and Theodore M. DeJong

individually ( DeJong and Grossman, 1995 ; Grossman and DeJong, 1995a , 1995b ; Pavel and DeJong, 1993 ). However, the integration of all these concepts at the whole-tree level and over multiple years is difficult and requires a modeling approach. Several

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Kenneth J. Boote, Maria R. Rybak, Johan M.S. Scholberg, and James W. Jones

Crop growth simulation models are emerging technological tools with potential uses for interpreting research ( Boote et al., 1996 , 2010 ) and for production management to monitor irrigation and nitrogen (N) uses ( Paz et al., 2007 ; Rinaldi et al