Literature DB >> 35451783

Integration of Crop Growth Models and Genomic Prediction.

Akio Onogi1.   

Abstract

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
© 2022. The Author(s).

Entities:  

Keywords:  Crop modeling; Genomic selection; Genotype by environment interactions

Mesh:

Year:  2022        PMID: 35451783     DOI: 10.1007/978-1-0716-2205-6_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  45 in total

1.  The Gompertz Curve as a Growth Curve.

Authors:  C P Winsor
Journal:  Proc Natl Acad Sci U S A       Date:  1932-01       Impact factor: 11.205

2.  QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley.

Authors:  Xinyou Yin; Paul C Struik; Fred A van Eeuwijk; Piet Stam; Jianjun Tang
Journal:  J Exp Bot       Date:  2005-02-14       Impact factor: 6.992

Review 3.  Functional-structural plant modelling: a new versatile tool in crop science.

Authors:  J Vos; J B Evers; G H Buck-Sorlin; B Andrieu; M Chelle; P H B de Visser
Journal:  J Exp Bot       Date:  2009-12-08       Impact factor: 6.992

4.  Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics.

Authors:  Bertrand Muller; Pierre Martre
Journal:  J Exp Bot       Date:  2019-04-29       Impact factor: 6.992

5.  Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 2. Thermal stress and spikelet sterility.

Authors:  Michael Dingkuhn; Richard Pasco; Julie Mae Pasuquin; Jean Damo; Jean-Christophe Soulié; Louis-Marie Raboin; Julie Dusserre; Abdoulaye Sow; Baboucarr Manneh; Suchit Shrestha; Tobias Kretzschmar
Journal:  J Exp Bot       Date:  2017-07-10       Impact factor: 6.992

6.  Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

Authors:  Akio Onogi; Maya Watanabe; Toshihiro Mochizuki; Takeshi Hayashi; Hiroshi Nakagawa; Toshihiro Hasegawa; Hiroyoshi Iwata
Journal:  Theor Appl Genet       Date:  2016-01-20       Impact factor: 5.699

7.  Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

Authors:  Frank Technow; Carlos D Messina; L Radu Totir; Mark Cooper
Journal:  PLoS One       Date:  2015-06-29       Impact factor: 3.240

8.  Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture.

Authors:  Vincent Migault; Benoît Pallas; Evelyne Costes
Journal:  Front Plant Sci       Date:  2017-01-12       Impact factor: 5.753

9.  Brief history of agricultural systems modeling.

Authors:  James W Jones; John M Antle; Bruno Basso; Kenneth J Boote; Richard T Conant; Ian Foster; H Charles J Godfray; Mario Herrero; Richard E Howitt; Sander Janssen; Brian A Keating; Rafael Munoz-Carpena; Cheryl H Porter; Cynthia Rosenzweig; Tim R Wheeler
Journal:  Agric Syst       Date:  2017-07       Impact factor: 5.370

10.  Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice.

Authors:  Malachy T Campbell; Alexandre Grondin; Harkamal Walia; Gota Morota
Journal:  J Exp Bot       Date:  2020-09-19       Impact factor: 6.992

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