Literature DB >> 25750429

Identifying traits for genotypic adaptation using crop models.

Julian Ramirez-Villegas1, James Watson2, Andrew J Challinor3.   

Abstract

Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  Climate change; crop models; genotypic adaptation; ideotypes; impacts.

Mesh:

Year:  2015        PMID: 25750429     DOI: 10.1093/jxb/erv014

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


  6 in total

Review 1.  Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability.

Authors:  Anil Kumar; Rajesh Kumar Pathak; Sanjay Mohan Gupta; Vikram Singh Gaur; Dinesh Pandey
Journal:  OMICS       Date:  2015-10

2.  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

3.  Improving the use of crop models for risk assessment and climate change adaptation.

Authors:  Andrew J Challinor; Christoph Müller; Senthold Asseng; Chetan Deva; Kathryn Jane Nicklin; Daniel Wallach; Eline Vanuytrecht; Stephen Whitfield; Julian Ramirez-Villegas; Ann-Kristin Koehler
Journal:  Agric Syst       Date:  2018-01       Impact factor: 5.370

4.  Tailoring parameter distributions to specific germplasm: impact on crop model-based ideotyping.

Authors:  Livia Paleari; Ermes Movedi; Fosco Mattia Vesely; Roberto Confalonieri
Journal:  Sci Rep       Date:  2019-12-04       Impact factor: 4.379

5.  To what extent is climate change adaptation a novel challenge for agricultural modellers?

Authors:  R P Kipling; C F E Topp; A Bannink; D J Bartley; I Blanco-Penedo; R Cortignani; A Del Prado; G Dono; P Faverdin; A-I Graux; N J Hutchings; L Lauwers; Ş Özkan Gülzari; P Reidsma; S Rolinski; M Ruiz-Ramos; D L Sandars; R Sándor; M Schönhart; G Seddaiu; J van Middelkoop; S Shrestha; I Weindl; V Eory
Journal:  Environ Model Softw       Date:  2019-10       Impact factor: 5.288

6.  Large genetic yield potential and genetic yield gap estimated for wheat in Europe.

Authors:  Nimai Senapati; Mikhail A Semenov
Journal:  Glob Food Sec       Date:  2020-03
  6 in total

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