2016
DOI: 10.2135/cropsci2015.06.0375
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What Should Students in Plant Breeding Know About the Statistical Aspects of Genotype × Environment Interactions?

Abstract: A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi‐environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the… Show more

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Cited by 218 publications
(224 citation statements)
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“…For the MET analysis, a stage-wise approach was used as elaborated in Piepho, Möhring, Schulz-Streeck, and Ogutu (2012) and van Eeuwijk, Bustos-Korts, and Malosetti (2016). In the first stage, the same nine models as above are fitted but considering the genotype effect as fixed, to obtain the Best Linear Unbiased Estimators (BLUEs) of the genotypes for each individual environment.…”
Section: Crop Sciencementioning
confidence: 99%
“…For the MET analysis, a stage-wise approach was used as elaborated in Piepho, Möhring, Schulz-Streeck, and Ogutu (2012) and van Eeuwijk, Bustos-Korts, and Malosetti (2016). In the first stage, the same nine models as above are fitted but considering the genotype effect as fixed, to obtain the Best Linear Unbiased Estimators (BLUEs) of the genotypes for each individual environment.…”
Section: Crop Sciencementioning
confidence: 99%
“…In addition to the genotypic correlation between traits, another major challenge for breeding programs is to model the effect of the genotype × environment interaction (G × E) on the desirable phenotypic traits [48]. While the genetic correlation of some N-use traits may be correlated to yield at low or high N conditions, their relationship might differ depending on other environmental conditions influencing yield.…”
Section: Genotype × Environment Interaction Of N-use Traitsmentioning
confidence: 99%
“…This deficiency was also discussed by Patterson and Silvey (1980), who stated that "differences between trials means for newly recommended cultivars are, on the average, about 27% too large." Best linear unbiased prediction is a shrinkage method, since information about the distribution is used, in essence, to "shrink" the effects towards zero (Stroup, 2012;Galwey, 2014). The magnitude of the shrinkage depends on the "shrinkage factor," and, in a simple model, the shrinkage factor is a function of heritability as described in Galwey (2014, p. 169).…”
mentioning
confidence: 99%