Biplots of Linear-Bilinear Models for Studying Crossover Genotype × Environment Interaction

By Jose Crossa, P. Cornelius, Weikai Yan. 2002

179 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/03b5e46b3164ca6e360315aac4f15002f8b9b73d

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Citations in the corpus

Associations of environments in south asia based on spot blotch disease of wheat caused by Cochliobolus sativus (2007, methodology): “ Crossa et al. (2002) pointed out that if the primary eff ects of sites were all of the same sign, the fi rst component in biplots of SREG would be related to noncrossover genotype plus G × E interaction variability, whereas the second component accounted for crossover genotype plus G × E interacti”
Interpretation of hybrid × sowing date interaction for oil content and oil yield in sunflower (2012, background): “, 2000) has capacity of identification of subsets of treatments with negligible crossover rank change interactions (Crossa et al., 2002).”
Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses (2005, background): “At Bay City, Cocodrie was the 2002; Crossa et al., 2002).”
Combining superior agronomic performance and terminal heat tolerance with resistance to spot blotch (Bipolaris sorokiniana) of wheat in the warm humid Gangetic Plains of South Asia (2007, methodology): “To identify superior lines across locations, we performed stability analysis using the SREG model (Crossa et al., 2002) for the response of the 109 EGPYT genotypes (105 lines and 4 common checks) on the combination of the 5 sites and 5 years for AUDPC percent days, grain yield, days to maturity and ”
Studying crossover genotype × environment interaction using linear-bilinear models and mixed models (2004, background): “Crossa, Cornelius, and Yan (2002) showed that, for SHMM2 and SREG2 models, the biplot of the first two multiplicative components graphs the interaction variation due to non-COI (first bilinear term) (or, more precisely, due to proportionality of genotype response in sites) versus the interaction va”
Model Selection and Cross Validation in Additive Main Effect and Multiplicative Interaction Models (2003, methodology): “This paper first summarizes the AMMI model and outlines ditions. the available methodology for selecting the number of multiplicative Crossa et al. (2002) give a comprehensive review ofcomponents to include in it.”
Earliness in wheat: A key to adaptation under terminal and continual high temperature stress in South Asia (2013, methodology): “…The SREG was performed to better understand the genotype ain effect and the genotype by environment interaction effect Crossa and Cornelius, 1997, Crossa et al., 2002). earch 151 (2013) 19–26 21 Briefly, the SREG model is represented by; ȳij = + ıj + t∑ k=1 k˛ik jk + ε̄ij Where ȳij is the…”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, background): “More recently, Crossa et al. (2002) pointed out that if the PC1 scores of environments from SREG2 or SHMM2 models are of the same sign, then the interaction variation of the PC1 is due largely to the presence of few or no COIs and the variation accounted for by the PC2 is due to COI; if, on the…”
AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “The biplot from the SREG model is used for assessment of ideal genotype and test location in multi-environment data provided that a given data set has a high correlation between PC1 and G main effects (Crossa et al., 2002; Yan et al., 2000).”
Breeding crops for reduced-tillage management in the intensive, rice–wheat systems of South Asia (2006, background): “Stability analysis (Crossa et al. 2002) confirmed the superior performance of two variants (HUW 234-5-44 and HUW 234-5-346) (Fig.”
Performance of yield and stability of advanced wheat genotypes under heat stress environments of the Indo-Gangetic plains (2007, methodology): “Multiplicative models for multienvironment trials have been used for studying G×E interaction, examining genotypic yield stability and adaptation and for developing methods for clustering sites or cultivars into groups with statistically negligible crossover G×E interaction (Crossa and Cornelius, 19”