A Simple Protocol for AMMI Analysis of Yield Trials

By Hugh G. Jr Gauch. 2013

438 citations recorded by the source corpus.

Subfields: Genetics, Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/f52bcf0c452757d75de7e0c42ecd48296eb6f283

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In Genetics

In Management Science and Operations Research

In Plant Science

Citations in the corpus

Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction (2014, methodology): “This basic AMMI analysis is important due to its common nature characteristic and its basic case must be mastered as a foundation for any of the more complicated applications (Gauch, 2013).”
AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, methodology): “…outcomes; (iii) stability is a meaningful objective only within an individual mega-environment, a requirement frequently ignored by the literature (Gauch, 2013); (iv) it is a requirement to have at least 8 trials in each megaenvironment for reasonably reliable estimates of stability…”
metan: an R package for multi-environment trial analysis (2020, methodology): “In metan , if we want to compute the AMMI stability value (Purchase, Hatting, & Deventer, 2000) for several traits, we can combine the functions performs_ammi() , AMMI_indexes() , and get_model_data() with %>% to get a two-way table with the statistic for each genotype and traits (see an example in ”
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, methodology): “The ratio as the yield for AMMI winners within each environment (identified in the first column of AMMI ranks) was calculated by dividing the yield for the overall winner (Gauch 2013).”