Literature DB >> 21421705

Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia.

K Chenu1, M Cooper, G L Hammer, K L Mathews, M F Dreccer, S C Chapman.   

Abstract

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.

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Year:  2011        PMID: 21421705     DOI: 10.1093/jxb/erq459

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


  27 in total

1.  Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios.

Authors:  Emilie J Millet; Claude Welcker; Willem Kruijer; Sandra Negro; Aude Coupel-Ledru; Stéphane D Nicolas; Jacques Laborde; Cyril Bauland; Sebastien Praud; Nicolas Ranc; Thomas Presterl; Roberto Tuberosa; Zoltan Bedo; Xavier Draye; Björn Usadel; Alain Charcosset; Fred Van Eeuwijk; François Tardieu
Journal:  Plant Physiol       Date:  2016-07-19       Impact factor: 8.340

2.  Can the development of drought tolerant ideotype sustain Australian chickpea yield?

Authors:  Peter Kaloki; Qunying Luo; Richard Trethowan; Daniel K Y Tan
Journal:  Int J Biometeorol       Date:  2019-01-28       Impact factor: 3.787

Review 3.  The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field Environment.

Authors:  Duke Pauli; Scott C Chapman; Rebecca Bart; Christopher N Topp; Carolyn J Lawrence-Dill; Jesse Poland; Michael A Gore
Journal:  Plant Physiol       Date:  2016-08-01       Impact factor: 8.340

4.  QTL for root angle and number in a population developed from bread wheats (Triticum aestivum) with contrasting adaptation to water-limited environments.

Authors:  Jack Christopher; Mandy Christopher; Raeleen Jennings; Shirley Jones; Susan Fletcher; Andrew Borrell; Ahmad M Manschadi; David Jordan; Emma Mace; Graeme Hammer
Journal:  Theor Appl Genet       Date:  2013-03-24       Impact factor: 5.699

5.  Selection on QTL and complex traits in complex environments.

Authors:  Thomas Mitchell-Olds
Journal:  Mol Ecol       Date:  2013-07       Impact factor: 6.185

6.  Managing the risk of extreme climate events in Australian major wheat production systems.

Authors:  Qunying Luo; Richard Trethowan; Daniel K Y Tan
Journal:  Int J Biometeorol       Date:  2018-06-04       Impact factor: 3.787

7.  Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.).

Authors:  Gaëtan Touzy; Renaud Rincent; Matthieu Bogard; Stephane Lafarge; Pierre Dubreuil; Agathe Mini; Jean-Charles Deswarte; Katia Beauchêne; Jacques Le Gouis; Sébastien Praud
Journal:  Theor Appl Genet       Date:  2019-07-19       Impact factor: 5.699

8.  Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions.

Authors:  R Rincent; M Malosetti; B Ababaei; G Touzy; A Mini; M Bogard; P Martre; J Le Gouis; F van Eeuwijk
Journal:  Theor Appl Genet       Date:  2019-09-27       Impact factor: 5.699

Review 9.  Accelerating crop genetic gains with genomic selection.

Authors:  Kai Peter Voss-Fels; Mark Cooper; Ben John Hayes
Journal:  Theor Appl Genet       Date:  2018-12-19       Impact factor: 5.699

10.  Selection for water-soluble carbohydrate accumulation and investigation of genetic × environment interactions in an elite wheat breeding population.

Authors:  Ben Ovenden; Andrew Milgate; Chris Lisle; Len J Wade; Greg J Rebetzke; James B Holland
Journal:  Theor Appl Genet       Date:  2017-08-29       Impact factor: 5.699

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