Identifying Discriminating Locations for Cultivar Selection in Louisiana

By S. Blanche, G. Myers. 2006

96 citations recorded by the source corpus.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/a0b08fb96d19e408e297e72c68fd65be032bf822

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. R. Mohammadi
2. Weikai Yan
3. P. Cornelius
4. Jim Holland
5. Baoluo Ma
6. Manjit S. Kang
7. J. Frégeau-Reid
8. S. Woods

Citations in the corpus

• Multivariate analyses of genotype x environment interaction of popcorn (2009, methodology): “Blanche & Myers (2006) used the GGE Biplot method to identify test locations that optimize genotype selection on the basis of their discriminating ability and representativeness.”
• Identifying essential test locations for oat breeding in eastern Canada. (2010, background): “, 2002), a withinenvironment standard error–scaled GGE biplot (Blanche and Myers, 2006), and a heritability-adjusted GGE biplot (Yan and Holland, 2010).”
• Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials (2010, background): “It also helps evaluate test environments (Yan and Rajcan 2002; Blanche and Myers 2006; Thomason and Phillips 2006) that effectively identify superior genotypes within a megaenvironment. A mega-environment is defined as a group of locations that consistently share the same best genotype(s) (Yan et al”
• GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, background): “Blanche and Myers (2006) used this idea creatively in their study of cotton test locations.”
• A heritability-adjusted GGE biplot for test environment evaluation (2010, methodology): “The SE-scaled GGE biplot has been identified as most appropriate for genotype and test environment evaluation because it accounts for heterogeneity among environments in experimental errors (Yan et al. 2000; Yan and Kang 2003; Blanche and Myers 2006).”
• Yield Stability of Maize Hybrids Evaluated in Multi‐Environment Trials in Yunnan, China (2007, methodology): “The GGE biplot methodology has been used to evaluate test environments in soybean (Yan and Rajcan, 2002), cotton ( Blanche and Myers, 2006 ), and common bean (Kang et al., 2006); to characterize end-use quality in wheat (Morris et al., 2004); and to target cultivars to specific environments in rice ”
• GGE biplot and AMMI analysis of barley yield performance in Iran (2017, background): “Plant breeders and other agronomists have found GGE biplots useful in test-environment evaluation (Blanche and Myers 2006), genotype evaluation (Kang et al. 2006), mega-environment analysis (Yan and Tinker 2005), trait association and trait profile analyses (Yan and Rajcan 2002), and heterotic…”