Literature DB >> 20400531

Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops.

Graeme L Hammer1, Erik van Oosterom, Greg McLean, Scott C Chapman, Ian Broad, Peter Harland, Russell C Muchow.   

Abstract

Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. A generic cereal crop growth and development model is outlined here. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. The model builds on existing approaches within the APSIM software platform. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed.

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Year:  2010        PMID: 20400531     DOI: 10.1093/jxb/erq095

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  39 in total

Review 1.  Root systems biology: integrative modeling across scales, from gene regulatory networks to the rhizosphere.

Authors:  Kristine Hill; Silvana Porco; Guillaume Lobet; Susan Zappala; Sacha Mooney; Xavier Draye; Malcolm J Bennett
Journal:  Plant Physiol       Date:  2013-10-18       Impact factor: 8.340

2.  A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

Authors:  Lifeng Xu; Michael Henke; Jun Zhu; Winfried Kurth; Gerhard Buck-Sorlin
Journal:  Ann Bot       Date:  2011-01-18       Impact factor: 4.357

3.  Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

Authors:  Junfei Gu; Xinyou Yin; Chengwei Zhang; Huaqi Wang; Paul C Struik
Journal:  Ann Bot       Date:  2014-07-01       Impact factor: 4.357

4.  Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping.

Authors:  Florian Larue; Damien Fumey; Lauriane Rouan; Jean-Christophe Soulié; Sandrine Roques; Grégory Beurier; Delphine Luquet
Journal:  Ann Bot       Date:  2019-10-29       Impact factor: 4.357

Review 5.  Genetic and physiological controls of growth under water deficit.

Authors:  François Tardieu; Boris Parent; Cecilio F Caldeira; Claude Welcker
Journal:  Plant Physiol       Date:  2014-02-25       Impact factor: 8.340

Review 6.  Plant water uptake in drying soils.

Authors:  Guillaume Lobet; Valentin Couvreur; Félicien Meunier; Mathieu Javaux; Xavier Draye
Journal:  Plant Physiol       Date:  2014-02-10       Impact factor: 8.340

7.  NEMA, a functional-structural model of nitrogen economy within wheat culms after flowering. I. Model description.

Authors:  Jessica Bertheloot; Paul-Henry Cournède; Bruno Andrieu
Journal:  Ann Bot       Date:  2011-06-17       Impact factor: 4.357

8.  3D Sorghum Reconstructions from Depth Images Identify QTL Regulating Shoot Architecture.

Authors:  Ryan F McCormick; Sandra K Truong; John E Mullet
Journal:  Plant Physiol       Date:  2016-08-15       Impact factor: 8.340

9.  Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time.

Authors:  E S Mace; C H Hunt; D R Jordan
Journal:  Theor Appl Genet       Date:  2013-03-05       Impact factor: 5.699

10.  Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench].

Authors:  R Nagaraja Reddy; R Madhusudhana; S Murali Mohan; D V N Chakravarthi; S P Mehtre; N Seetharama; J V Patil
Journal:  Theor Appl Genet       Date:  2013-05-07       Impact factor: 5.699

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