Chemical and sensory characteristics in the selection of bourbon genotypes

The evaluation of coffee quality in Brazil for commercialization is conducted mainly through sensory analysis, also known as the "cup test", in which professional tasters evaluate and score various attributes. The adoption of chemical methods could complement the sensory classification of beverages, if correlations between these chemical and sensory analyses exist, making classification less subjective. This work aimed to identify the relationships between the chemical and sensorial traits of coffee-beverage quality and to evaluate the use of these traits as criteria for the selection of Bourbon cultivars. Twenty coffee genotypes from the first three harvests across five municipalities of the state of Minas Gerais, Brazil were evaluated. The genotypic values, predicted for each genotype, were used to determine the index based on the sum of ranks from Mulamba and Mock. The genetic correlations among the evaluated traits were also estimated. The presented evaluations were not able to efficiently detect genetic and phenotypic relationships between the chemical and sensorial characteristics of drink quality, but as selection criteria for generation advancement in the beverage quality, it is possible to use these characteristics. Bourbon Amarelo LCJ 9-IAC, Bourbon Amarelo-Procafé, Bourbon Amarelo-Boa Vista, Bourbon Vermelho-São João Batista, and Bourbon Amarelo-Samambaia were the genotypes with the most promising cup quality in the studied regions. Through the selection of these five genotypes, the selection gain was 1.65% for sensory score for beverage quality, when the interaction among the studied environments was removed. The heritability was 92% for improving this trait.

Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits

Key message A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Abstract Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha−1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

Models for optimizing selection based on adaptability and stability of cotton genotypes

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

SELECTION OF COFFEE PROGÊNIES FOR RESISTANCE TO LEAF RUST AND FAVORABLE AGRONOMIC TRAITS

The objective of this study was to select coffee progenies with better assessment that can result in coffee rust resistant cultivars and better agronomic characteristics than the traditional ones. The essay was performed at the EPAMIG experimental field in Patrocínio, state of Minas Gerais, Brazil. Twenty-five progenies in the F3 generation were studied. The experiment was set in a randomized complete block design with three replicates and ten plants per plot, arranged in rows at 3.5x0.7m. Productivity assessment, fruit production, in liters of “farm coffee” per plot, bean rating in a sieve (16 or above), and plant vigor were accessed in three different harvest seasons (2011/2012 harvest to 2014/2015 harvest), and coffee rust incidence and severity were then evaluated for 2016. The production profit estimation through the selection was also assessed, by the gain of direct selection for each characteristic, when compared to the rank addition. Progenies 13 (Icatu V. IAC 4040 x IAC 5002) and 3 (Icatu A. IAC 2944 x IAC 5002) were promising in generation advance, for being among the five most productive progenies. The selection gain reached by direct selection was superior than the gain of the total rank additions.

Estimativas de parâmetros genéticos para ferro, zinco, magnésio e fósforo em grãos de feijão

Information about genetic parameters is required for the development of cultivars with higher nutritional value. In this sense, this study aimed to estimate the following genetic parameters: genetic variances, heritability coefficients in narrow and broad sense and predict the selection gain for the Fe, Zn, Mg and P contents in common bean grains. The crosses were made between cultivars: FT Nobre x IPR Gralha and Diamante Negro x IPR Chopim. The genitors (P1 and P2) were crossed resulting in F1 and F2 generations and backcrosses BC1 (P1 x F1) and BC2 (P2 x RC2). The mineral contents of Fe, Zn, Mg and P in grains were measured by nitric-perchloric digestion using atomic emission spectrometry coupled with plasma (ICP-OES). The predicted gains for the first cycle of selection were positive, indicating the viability of the increaseof the minerals Fe, Zn, Mg and P levels on common bean grains using classical breeding methods. The prediction gains obtained in the first cycle of selection resulted in values ranging from 3.24% to 15.48% for Mg and Fe, respectively. Heritability estimates in narrow sense ranged from 29.48% to 62.04% and the broad range sense heritability ranged from 45.14% to 76.36%.