Literature DB >> 17768603

Global adaptation patterns of Australian and CIMMYT spring bread wheat.

Ky L Mathews1, Scott C Chapman, Richard Trethowan, Wolfgang Pfeiffer, Maarten van Ginkel, Jose Crossa, Thomas Payne, Ian Delacy, Paul N Fox, Mark Cooper.   

Abstract

The International Adaptation Trial (IAT) is a special purpose nursery designed to investigate the genotype-by-environment interactions and worldwide adaptation for grain yield of Australian and CIMMYT spring bread wheat (Triticum aestivum L.) and durum wheat (T. turgidum L. var. durum). The IAT contains lines representing Australian and CIMMYT wheat breeding programs and was distributed to 91 countries between 2000 and 2004. Yield data of 41 reference lines from 106 trials were analysed. A multiplicative mixed model accounted for trial variance heterogeneity and inter-trial correlations characteristic of multi-environment trials. A factor analytic model explained 48% of the genetic variance for the reference lines. Pedigree information was then incorporated to partition the genetic line effects into additive and non-additive components. This model explained 67 and 56% of the additive by environment and non-additive by environment genetic variances, respectively. Australian and CIMMYT germplasm showed good adaptation to their respective target production environments. In general, Australian lines performed well in south and west Australia, South America, southern Africa, Iran and high latitude European and Canadian locations. CIMMYT lines performed well at CIMMYT's key yield testing location in Mexico (CIANO), north-eastern Australia, the Indo-Gangetic plains, West Asia North Africa and locations in Europe and Canada. Maturity explained some of the global adaptation patterns. In general, southern Australian germplasm were later maturing than CIMMYT material. While CIANO continues to provide adapted lines to northern Australia, selecting for yield among later maturing CIMMYT material in CIANO may identify lines adapted to southern and western Australian environments.

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Year:  2007        PMID: 17768603     DOI: 10.1007/s00122-007-0611-4

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.574


  6 in total

1.  Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend.

Authors:  A Smith; B Cullis; R Thompson
Journal:  Biometrics       Date:  2001-12       Impact factor: 2.571

2.  Joint modeling of additive and non-additive genetic line effects in single field trials.

Authors:  Helena Oakey; Arūnas Verbyla; Wayne Pitchford; Brian Cullis; Haydn Kuchel
Journal:  Theor Appl Genet       Date:  2006-08-02       Impact factor: 5.699

3.  Joint modeling of additive and non-additive (genetic line) effects in multi-environment trials.

Authors:  Helena Oakey; Arūnas P Verbyla; Brian R Cullis; Xianming Wei; Wayne S Pitchford
Journal:  Theor Appl Genet       Date:  2007-04-11       Impact factor: 5.699

4.  The analysis of the NSW wheat variety database. II. Variance component estimation.

Authors:  B R Cullis; F M Thomson; J A Fisher; A R Gilmour; R Thompson
Journal:  Theor Appl Genet       Date:  1996-01       Impact factor: 5.699

5.  Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments.

Authors:  M Cooper; I H Delacy
Journal:  Theor Appl Genet       Date:  1994-07       Impact factor: 5.699

6.  Biplot Analysis of Diallel Data.

Authors:  Weikai Yan; L. A. Hunt
Journal:  Crop Sci       Date:  2002-01       Impact factor: 2.319

  6 in total
  6 in total

1.  Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments.

Authors:  Dion Bennett; Matthew Reynolds; Daniel Mullan; Ali Izanloo; Haydn Kuchel; Peter Langridge; Thorsten Schnurbusch
Journal:  Theor Appl Genet       Date:  2012-07-08       Impact factor: 5.699

2.  Multi-environment QTL mixed models for drought stress adaptation in wheat.

Authors:  Ky L Mathews; Marcos Malosetti; Scott Chapman; Lynne McIntyre; Matthew Reynolds; Ray Shorter; Fred van Eeuwijk
Journal:  Theor Appl Genet       Date:  2008-08-12       Impact factor: 5.699

3.  Genetics of flowering time in bread wheat Triticum aestivum: complementary interaction between vernalization-insensitive and photoperiod-insensitive mutations imparts very early flowering habit to spring wheat.

Authors:  Sushil Kumar; Vishakha Sharma; Swati Chaudhary; Anshika Tyagi; Poonam Mishra; Anupama Priyadarshini; Anupam Singh
Journal:  J Genet       Date:  2012       Impact factor: 1.166

4.  Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions.

Authors:  C Lynne McIntyre; Ky L Mathews; Allan Rattey; Scott C Chapman; Janneke Drenth; Mohammadghader Ghaderi; Matthew Reynolds; Ray Shorter
Journal:  Theor Appl Genet       Date:  2009-10-29       Impact factor: 5.699

5.  Factor analytic mixed models for the provision of grower information from national crop variety testing programs.

Authors:  Alison B Smith; Aanandini Ganesalingam; Haydn Kuchel; Brian R Cullis
Journal:  Theor Appl Genet       Date:  2014-10-19       Impact factor: 5.699

6.  Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

Authors:  Pierre Casadebaig; Bangyou Zheng; Scott Chapman; Neil Huth; Robert Faivre; Karine Chenu
Journal:  PLoS One       Date:  2016-01-22       Impact factor: 3.240

  6 in total

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