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, …
The objective of this study was to estimate the coefficient of repeatability and the number of measurements required for production and quality variables in a strawberry crop. An experiment was conducted with two strawberry cultivars from two origins …
The aims of this study were to characterize black oat populations by estimating between- and within-populations variance components and genetic parameters, as well as to distinguish the populations using multivariable statistics. The experiment was …
This study aimed to evaluate direct and indirect effects of agronomic traits importance on grain yield with focus in pre-harvest sprouting. Experiment was conducted in 2017 crop season, and conducted in a randomized block design, with three …
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 evaluate the phenotypic interrelation among agronomic characters associated with wheat grain yield of the main Brazilian cultivated genotypes through path analysis in two environments. The tests were conducted in Tenente …
Maize (Zea mays L.) has been the subject of several studies involving correlation coefficient estimates and path analysis. This critical review discusses some systematic errors that have been observed in estimating of correlation coefficients and its …