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Improving process-based crop models to better capture genotype×environment×management interactions.

E. Wang,H. Brown,G. Rebetzke,Zhigan Zhao,Bangyou Zheng,S. Chapman

Published 2019 in Journal of Experimental Botany

ABSTRACT

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Journal of Experimental Botany

  • Publication date

    2019-04-29

  • Fields of study

    Agricultural and Food Sciences, Medicine, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1093/jxb/erz092PMID 30921457
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar, PubMed

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