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Structural equation modelling for studying genotype x environment interactions of physiological traits affecting yield in wheat

Published on Apr 1, 2007in The Journal of Agricultural Science 1.33
· DOI :10.1017/S0021859607006806
Mateo Vargas22
Estimated H-index: 22
(CIMMYT: International Maize and Wheat Improvement Center),
José Crossa60
Estimated H-index: 60
(CIMMYT: International Maize and Wheat Improvement Center)
+ 2 AuthorsKent M. Eskridge35
Estimated H-index: 35
Cite
Abstract
SUMMARY In plant physiology and breeding, it is important to understand the causes of genotyperenvironment interactions (GEIs) of complex traits such as grain yield. It is difficult to study the underlying sequential biological processes of such traits, their components and other intermediate traits, as well as the main environmental factors affecting those processes. The structural equation models (SEMs) used in the present study allow the external and internal factors affecting GEI of various traits and their interrelations to be accounted for. The study included 86 wheat genotypes derived from three different crosses and evaluated over 3 years. Several attributes, as well as grain yield and yield components, were measured during five crop development stages. Environmental data for the five development stages were averaged. The SEM approach facilitated comprehensive understanding of GEI effects among the different traits, and decomposed the total effects of grain yield components and cross-product covariates on grain yield GEI into direct and indirect effects. External climatic variables were related mostly to main final yield components, and only more intermediate endogenous variables, such as spikes/m 2 , were affected by minimum temperature and radiation in the early stages of plant development.
  • References (12)
  • Citations (14)
Cite
References12
Newest
Published on Feb 1, 2007in The Journal of Agricultural Science 1.33
Matthew P. Reynolds61
Estimated H-index: 61
(CIMMYT: International Maize and Wheat Improvement Center),
Daniel F. Calderini30
Estimated H-index: 30
(Austral University of Chile)
+ 1 AuthorsMateo Vargas22
Estimated H-index: 22
(CIMMYT: International Maize and Wheat Improvement Center)
SUMMARY For many years yield improvement reported in wheat was associated with increased dry matter partitioning to grain, but more recently increases in above-ground biomass have indicated a different mechanism for achieving yield potential. The most likely way of increasing crop biomass is by improving radiation use efficiency (RUE); however there is evidence that sink strength is still a critical yield limiting factor in wheat, suggesting that improving the balance between source and sink (so...
Published on Apr 1, 2006in Theoretical and Applied Genetics 3.93
Mateo Vargas22
Estimated H-index: 22
(CIMMYT: International Maize and Wheat Improvement Center),
F.A. van Eeuwijk35
Estimated H-index: 35
(WUR: Wageningen University and Research Centre)
+ 1 AuthorsJean-Marcel Ribaut35
Estimated H-index: 35
(CIMMYT: International Maize and Wheat Improvement Center)
The study of QTL × environment interaction (QEI) is important for understanding genotype × environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for ea...
Published on Jan 1, 2003
Scott L. Hershberger2
Estimated H-index: 2
,
George A. Marcoulides34
Estimated H-index: 34
,
Makeba M. Parramore1
Estimated H-index: 1
Published on May 1, 2002in Field Crops Research 3.87
Matthew P. Reynolds61
Estimated H-index: 61
(CIMMYT: International Maize and Wheat Improvement Center),
Richard Trethowan35
Estimated H-index: 35
(CIMMYT: International Maize and Wheat Improvement Center)
+ 2 AuthorsK.D. Sayre29
Estimated H-index: 29
(CIMMYT: International Maize and Wheat Improvement Center)
Abstract Wheat cultivars often show highly significant genotype by environment interaction (G×E) for yield, even when comparing different years within a relatively stable location. This study attempts to explain some of the physiological bases of G×E in two experiments: (i) historic yield potential trials (HYPTs) of bread wheat ( Triticum aestivum L.), durum ( T. durum Desf.) and triticale (X Triticosecale Wittmack) cultivars grown under agronomically optimal conditions; (ii) an elite spring whe...
Published on Jan 1, 2001in Agronomy Journal 1.80
Mateo Vargas22
Estimated H-index: 22
(Chapingo Autonomous University),
José Crossa60
Estimated H-index: 60
(CIMMYT: International Maize and Wheat Improvement Center)
+ 2 AuthorsMatthew P. Reynolds61
Estimated H-index: 61
(CIMMYT: International Maize and Wheat Improvement Center)
Multienvironment trials are important in agronomy because the effects of agronomic treatments can change differentially in relation to environmental changes, producing a treatment × environment interaction (T × E). The aim of this study was to find a parsimonious description of the T × E existing in the 24 agronomic treatments evaluated during 10 consecutive years by (i) investigating the factorial structure of the treatments to reduce the number of treatment terms in the interaction and (ii) us...
Published on Aug 1, 1999in Theoretical and Applied Genetics 3.93
José Crossa60
Estimated H-index: 60
(CIMMYT: International Maize and Wheat Improvement Center),
Mateo Vargas22
Estimated H-index: 22
(Chapingo Autonomous University)
+ 3 AuthorsD. Hoisington38
Estimated H-index: 38
(CIMMYT: International Maize and Wheat Improvement Center)
An understanding of the genetic and environmental basis of genotype×environment interaction (GEI) is of fundamental importance in plant breeding. In mapping quantitative trait loci (QTLs), suitable genetic populations are grown in different environments causing QTLs×environment interaction (QEI). The main objective of the present study is to show how Partial Least Squares (PLS) regression and Factorial Regression (FR) models using genetic markers and environmental covariables can be used for stu...
Published on Jan 1, 1999in Structural Equation Modeling 4.43
Li-tze Hu14
Estimated H-index: 14
(UCSC: University of California, Santa Cruz),
Peter M. Bentler79
Estimated H-index: 79
(UCLA: University of California, Los Angeles)
This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald'...
Published on Jan 1, 1999in Crop Science 1.64
Mateo Vargas22
Estimated H-index: 22
,
José Crossa60
Estimated H-index: 60
+ 2 AuthorsK.D. Sayre29
Estimated H-index: 29
Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEl). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only the phenotypic response variable of interest; however, if information on external environmental (or genotypic) variables is available, this can be regressed on the environmental (or genotypic) scores es...
Cited By14
Newest
Published on Jul 1, 2019in Field Crops Research 3.87
Selvakumari Arunachalam1
Estimated H-index: 1
(McGill University),
Timothy Schwinghamer4
Estimated H-index: 4
(McGill University)
+ 1 AuthorsDonald L. Smith46
Estimated H-index: 46
(McGill University)
Abstract The experimental hard red spring wheat ( Triticum aestivum [L.]) cultivars AC Barrie, Cardale, Superb, and Vesper are adapted to the wheat-growing regions of the Canadian Prairies. They were bred to resist diseases, but their response to a biofertilizer that is a consortium of bacteria ( Bacillus subtilis , Candida utilis , Lactobacillus casei , L. helveticus , L. plantarum , L. rhamnosus , Lactococcus lactis , Rhodopseudomonas palustris -1, and R. palustris -2), filamentous fungi ( Asp...
Published on Nov 20, 2015
David Butruille3
Estimated H-index: 3
(Monsanto),
Fufa H. Birru1
Estimated H-index: 1
(Monsanto)
+ 15 AuthorsBrian W. Gardunia3
Estimated H-index: 3
(Monsanto)
Published on Aug 1, 2013in Field Crops Research 3.87
HeYong3
Estimated H-index: 3
(National Research Council),
Yongsheng Wei1
Estimated H-index: 1
(NWAFU: Northwest A&F University)
+ 6 AuthorsH. Wang19
Estimated H-index: 19
(AAFC: Agriculture and Agri-Food Canada)
Abstract The large year-to-year and site-to-site variation in wheat production on rain-fed semiarid areas of the Canadian prairies is mainly due to the timing and amount of precipitation and soil water holding capacity. Here, we identify the critical periods of growing season precipitation on wheat yield and then utilize this information to analyze which type of soil texture had higher drought resistance and higher grain yield when precipitation was more than sufficient. Thirty years (1982–2011)...
Published on Jul 1, 2011in Canadian Journal of Plant Science 0.99
Eric G. Lamb19
Estimated H-index: 19
(U of S: University of Saskatchewan),
Steven J. Shirtliffe16
Estimated H-index: 16
(U of S: University of Saskatchewan),
W. E. May19
Estimated H-index: 19
(AAFC: Agriculture and Agri-Food Canada)
Lamb, E. G., Shirtliffe, S. J. and May, W. E. 2011. Structural equation modeling in the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603-619. Structural equation modeling (SEM) is a powerful statistical approach for the analysis of complex intercorrelated data with a wide range of potential applications in the plant sciences. In this paper we introduce plant scientists to the principles and practice of SEM using as an example an agronomic field trial. We brief...
Published on Mar 1, 2011in Journal of Experimental Botany 5.36
Karine Chenu24
Estimated H-index: 24
(UQ: University of Queensland),
Mark E. Cooper109
Estimated H-index: 109
(UQ: University of Queensland)
+ 3 AuthorsScott C. Chapman48
Estimated H-index: 48
(CSIRO: Commonwealth Scientific and Industrial Research Organisation)
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. Wat...
Published on Jan 1, 2011in Canadian Journal of Plant Science 0.99
LambEric1
Estimated H-index: 1
,
ShirtliffeSteven1
Estimated H-index: 1
,
MayWilliam1
Estimated H-index: 1
Lamb, E. G., Shirtliffe, S. J. and May, W. E. 2011. Structural equation modeling in the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603–619. Structural equation modeling (SEM) is a powerful statistical approach for the analysis of complex intercorrelated data with a wide range of potential applications in the plant sciences. In this paper we introduce plant scientists to the principles and practice of SEM using as an example an agronomic field trial. We brief...
Published on Jun 1, 2010in The Journal of Agricultural Science 1.33
C. Yan1
Estimated H-index: 1
(NAU: Nanjing Agricultural University),
Y. Ding1
Estimated H-index: 1
(NAU: Nanjing Agricultural University)
+ 4 AuthorsS. Wang1
Estimated H-index: 1
(NAU: Nanjing Agricultural University)
A series of field and plant growth chamber experiments were conducted in 2006 and 2007 to study how relative humidity (RH), genotypes and nitrogen application rates affect organ temperatures and spikelet fertility rates in rice. It was observed that organ temperatures varied with air temperature, RH, genotype and nitrogen application rate. Increases in RH at constant air temperature and increasing air temperature with a constant RH both increased organ temperatures significantly. Cultivars also ...
Published on Jan 1, 2009in European Journal of Agronomy 3.38
Christa M. Hoffmann14
Estimated H-index: 14
,
Toon Huijbregts1
Estimated H-index: 1
+ 1 AuthorsRudolf Jansen1
Estimated H-index: 1
Abstract Sugar beet ( Beta vulgaris L.) yield and quality are determined by genotype and environment. This study aimed at analysing the relative importance of the environment for yield and quality of sugar beet genotypes and at assessing parameters which could give essential improvement for beet quality if included as additional selection criteria. For that purpose, root yield and quality (sugar, K, Na, amino N, total soluble N, betaine, glutamine, invert sugar, raffinose) of 9 sugar beet genoty...