Multi-environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, …
Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high …
Additive main effect and multiplicative interaction (AMMI) and best linear unbiased prediction (BLUP) are popular methods for analyzing multi‐environment trials (MET). The AMMI has nice graphical tools for modeling genotype‐vs.‐environment …
Modeling the genotype × environment interaction (GEI) and quantifying genotypic stability are crucial steps for selecting/recommending genotypes in multi‐environment trials (METs). The efficiency in selection/recommendation could be greater if based …
The aim of this study was to assess the behavior of morphological components and yield of simple hybrid corn grains cultivated in different environments. Three field trials were conducted in the state of Rio Grande do Sul under an experimental …
The implementation of a network of maize trials is an onerous task, so breeding programs seek to eliminate redundant environments, remaining only contrasting ones. The objective was to perform the homogeneous environment grouping by studying the …