Saulo Chaves

Theobroma grandiflorum is a perennial fruit tree native to the Amazon region. As a perennial species with continuous production throughout the years, breeders should seek well-conducted trials, accurate phenotyping and adequate statistical models for genetic evaluation and selection that can leverage the information provided by the repeated measures. We evaluated 13 models with different covariance structures for genetic and residual effects for T. grandiflorum evaluation, using an unbalanced dataset with 34 hybrids from the triple-crossing of nine parents, planted in a randomized complete block design. For nine consecutive years, the fruit yield of these hybrids was evaluated. Each model had its goodness-of-fit tested by the Akaike information criterion. The most adequate model for estimating the variance components and the breeding values were modelled with the first-order heterogeneous autoregressive for residual effects and third-order factor analytic for genetic effects. From this model, we used the factor analytic selection tools for selecting the top 10 families, providing a genetic gain of 10.42%. These results are important not only for T. grandiflorum breeding but also to show that in any repeated measures’ data from fruit-bearing perennial species the modelling of genetic and residual effects should not be neglected.

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R, which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package’s basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs.

We propose an “enviromics” prediction model for recommending cultivars based on thematic maps aimed at decision-makers.

Key message: Eucalyptus breeding can benefit from strategies that capture dominance effects, as shown by the improvement in mean annual increment of wood volume across cycles of RRS. Abstract: There is no empirical validation of reciprocal recurrent selection (RRS) in Eucalyptus breeding. Our study helps to fill this gap by quantifying the realized response to selection achieved after two cycles of RRS involving Eucalyptus urophylla and E. grandis. We also investigated the selection effects on the genetic parameters of the breeding populations. We evaluated 25 trials of the first cycle (C1) of RRS and 12 trials of the second cycle (C2) of RRS. These trials were established in two different regions, separated according to altitude. Fitting linear mixed models enabled the estimation of variance components and the prediction of mean components (general and specific hybridizing abilities). The realized response to selection was calculated as the difference between the mean of the predicted genotypic values of the C1 and C2. The RRS effectively improved the mean annual increment of wood volume by 28.5% in the high-altitude region and 12.3% in the low-altitude region from the C1 to C2. The genetic variability also increased as a result of the new genotypes that arose through recombination. These findings provide insights for decision-making and reinforce that Eucalyptus breeding can benefit from strategies that capture dominance effects.

Genetic competition can obscure the true merit of selection candidates, potentially leading to altered genotype rankings and a divergence between expected and actual genetic gains. Despite a wealth of literature on genetic competition in plant and animal breeding, the separation of genetic values into direct genetic effects (DGE, related to a genotype’s merit) and indirect genetic effects (IGE, related to the effects of a genotype’s alleles on its neighbor’s phenotype) in linear mixed models is often overlooked, likely due to the complexity involved. To address this, we introduce gencomp, a new R package designed to simplify the use of (spatial-) genetic competition models in crop and tree breeding routines. gencomp includes functions for constructing the genetic competition matrix, fitting (spatial-) genetic competition models via the variance-component approach, and extracting key results such as variance components, heritabilities, competition classes, and total genetic values. For tree breeding, gencomp also calculates the merit of different clonal mixtures using the estimated DGE and IGE of the selection candidates. In this paper, we first present the theoretical foundation of the methods implemented in the package. We then demonstrate the use of gencomp with two datasets: one simulated from a Eucalyptus spp. trial and a real potato dataset. We used both datasets to demonstrate the influence of genetic competition in variance component estimates, heritabilities and selection. Despite the dependency on ASReml-R, a paid resource, gencomp is a user-friendly tool that can popularize genetic competition models, contributing to more informed decision-making in plant breeding.