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Wheat (Triticum aestivum L.) is one of the most important crops in the world. Brazil is one of the largest importers of this cereal, and wheat breeding programs are attempting to increase productivity and reduce external dependence. The objectives of this study were to quantify the spring wheat genetic gain in Brazil between 1984 and 2014 using data from multienvironment trials, and to verify if the genetic gain stagnated after the 2000s. This study used a highly unbalanced dataset containing grain yield (GY) data from 187 spring wheat trials that were performed at 25 locations between 2002 and 2015. In total, 126 cultivars, released between 1985 and 2014, were evaluated over 14 crop seasons. The cultivar evaluation indicated that there was a genetic gain in GY of 33.9 kg ha⁻¹ yr⁻¹ (r² = 0.53, P < 0.01), which represents an increase of 1.28% yr⁻¹. The dataset showed a linear regression for the genetic gain and indicates that wheat genetic gain has not stagnated in Brazil after the 2000s.

The objective of this work was to evaluate the productive performance, and the adaptability and stability parameters of modern soybean (Glycine max) cultivars in multi-environment trials, as well as to identify the ideal genotypes for eight growing environments in Brazil. A randomized complete block experimental design was carried out, with three replicates, for the evaluation of 46 soybean cultivars in eight environments, in the microregions of adaptation 102, 201, and 202, in the 2014/2015 crop season. A complex genotype x environment interaction occurred, with changes in the ranking of genotypes among locations. The NA 5909 RG, M6410IPRO, NS 5959 IPRO, NS6823RR, M5917IPRO, NS 6767 RR, and 6563RSF IPRO cultivars showed the highest mean yields. The NA 5909 RG, NS6823RR, M6410IPRO, and NS 5959 IPRO cultivars showed high adaptability and stability and high grain yield, in the evaluated environments, and were ranked next to the ideal genotype for the analyzed environments. There are modern soybean cultivars, which are adapted, stable, and highly productive, for cultivation in the microregions 102, 201, and 202 for soybean crop adaptation in Brazil.

An optimal number of test locations and replications in yield trials allows improvements in the efficiency of a breeding program and selection reliability. The objectives of this study were to verify the optimal number of test locations and replications per test location in edaphoclimatic regions (ECR) and macroregions (MRs) of soybean adaptation in Brazil. Grain yield data from 184 soybean trials in 57 locations was evaluated in crop seasons 2012–2013 to 2015–2016. The number of replications currently used (3) is sufficient to achieve selection reliability and reduce costs in all MRs. However, the optimal number of replications needs to be determined for each location. Locations that require a high number of replicates to achieve H = 0.75 must be removed and/or replaced to improve selective accuracy. The optimal mean number of test locations in macroregions MR1 and MR2 is seven locations per ECR, and in MR3 and MR4, it is five locations per ECR. Thus, analyses of the optimal number of locations and replications may be applied to other regions to improve the yield trial efficiency of soybean cultivars.

Experimental design without replication, such as Federer's augmented block design, allows us to determine productivity, adaptability and stability in multi-environment trials. This work aimed to select productive wheat lines with high adaptability and stability in preliminary trials. The grain yield of 140 homozygous wheat lines was measured in 2015 at three locations. The cultivar TBIO Mestre was used as a check. Genetic parameters were evaluated by mixed models, and selection was based on the harmonic mean of the relative performance of the genetic values (HMRPGV) using models 74 (individual analysis) and 75 (joint analysis) of Selegen software. In the joint analysis, 33 wheat lines stood out in terms of productivity, adaptability and stability. These lines have the potential to be evaluated in Value for Cultivation and Use (VCU) trials for future release of new wheat cultivars.

Breeding companies release new high-yielding soybean genotypes every year. The main trait is grain yield (GY), and the performance need to be evaluated by statistical methods to identify superior genotypes. Biplot analysis are good tools for selecting superior genotypes and to increase efficiency in selection. This study aimed to: (a) identify genotypes with high GY and stability in edaphoclimatic regions (ECR) in southern of Brazil; (b) compare the year-by-year and across-years analyses to identify ideal genotype (IG) for each ECR; and (c) identify the association between genotypes and locations using the across-year approach. GY data from yield trials performed in the crop seasons of 2013, 2014, and 2015 were used. The analysis of IG was performed for each year independently and across the years. Moreover, environment-genotype relationship analysis was used to identify the association between genotypes and ECR. Genotypes with high GY and widely adapted for each ECR were identify. The across-year analysis was superior to the year-by-year analysis. However, only genotypes evaluated in more than 1 year and in sufficient locations provide accurate information about GY and stability; otherwise, the results should be adopted with caution.

The objective of this work was to identify core locations in two soybean macroregions in Brazil for the evaluation and selection of soybean (Glycine max) lineages. Twenty-two cultivars were tested in 23 locations in these two macroregions (MR1 and MR2), during four years, from 2012 to 2015. Trials were conducted in a randomized complete block design with three replicates. All analyses were performed using the GGEbiplot software. The genotype main effects plus genotype × location interaction + genotype main effects plus genotype × environment interaction and genotypic effect vs. genotype x environment effect analyses were used to identify core locations, i.e., locations with high representativeness and consistency of results. Chapada, in the state of Rio Grande do Sul, and Maracaju, in the state of Mato Grosso do Sul, were the core locations in MR1 and MR2, respectively. These locations were the most representative and consistent over the years, and the genotypic effect explained a high proportion of phenotypic variance. Resumo: O objetivo deste trabalho foi identificar locais-chave de teste em duas macrorregiões sojícolas no Brasil, para avaliação e seleção de linhagens de soja (Glycine max). Foram testadas 22 cultivares em 23 locais, nessas duas macrorregiões (MR1 e MR2), durante quatro anos, de 2012 a 2015. Os ensaios foram conduzidos em delineamento de blocos ao acaso, com três repetições. Todas as análises foram realizadas com uso do programa GGEbiplot. As análises efeito principal de genótipo mais interação genótipo × local + efeito principal de genótipo mais interação genótipo × ambiente e efeito genotípico vs. efeito do genótipo x ambiente foram utilizadas para identificar locais de teste, isto é, locais com alta representatividade e consistência de resultados. Chapada, no Rio Grande do Sul, e Maracaju, no Mato Grosso do Sul, foram os principais locais em MR1 e MR2, respectivamente. Estes locais foram os mais representativos e consistentes ao longo dos anos, e o efeito genotípico explicou uma alta proporção da variância fenotípica.

Genomic selection (GS) can potentially accelerate genetic improvement of soybean [Glycine max L. (Merrill)] by reducing the time to complete breeding cycles. The objectives of this study were to (1) explore the accuracy of GS in soybean, (2) evaluate the contribution of intrapopulational structure to the accuracy of GS, and (3) compare the efficiencies of phenotypic selection and GS in soybean. For this, phenotypic and genotypic data were collected from 324 soybean genotypes (243 recombinant inbred lines and 81 cultivars) and GS was performed for five yield related traits. BayesB methodology with a 10-fold cross-validation was used to compute accuracies. The GS accuracies were evaluated for grain yield, plant height, insertion of first pod, days to maturity, and 1000-grain weight at eight locations. We found that GS can reduce the time required to complete a selection cycle in soybean, which can lead to increased production of this commercially important crop. Furthermore, genotypic accuracy was similar regardless of population structure correction.

The search for productive and stable genotypes is the main goal of breeding programs. The Genotype × Environment interaction strongly influences genotype performance, and makes the selection of new cultivars difficult. One way to take advantage of this interaction is to identify genotypes with high grain yield (GY) and stability in different environments. The objective of this study was to evaluate the consistency of correlation between GY and stability evaluation methods in multi-environment trials and identify which methods are more suitable for selecting genotypes. GY data from 11 soybean (Glycine max [L.] Merr.) cultivars conducted in Value for Cultivation and Use trials in 10 locations in Paraná and Mato Grosso do Sul states, Brazil, in the 2013-2014, 2014-2015, and 2015-2016 crop seasons. A randomized complete block design with three replicates was used. Seven methods were applied to evaluate stability, and Spearman's correlation coefficient was used to compare methods. Positive associations were observed between GY and the harmonic mean of genotypic values (HMGV) across environments and genotype main effect + Genotype × Environment interaction effect by ideal genotype (GGE IG) methodologies, and between GY and the Lin and Binns method modified by Carneiro for general and unfavorable environments. The Eberhart and Russell, additive main effects and multiplicative interaction (AMMI1), and GGE for stability (GGE STA) methods presented no positive associations with GY in any year. Positive associations were found between the Wricke, AMMI1, and Eberhart and Russell methods because they were related to the static stability concept. The HMGV and GGE IG methods can be used together because they are consistently associated with GY and based on the dynamic stability concept.

Soybean (Glycine max (L.) Merrill) is an important source of oil for biodiesel production in Brazil. However, the traits related to oil content and fatty acid profile are not targeted by soybean breeding programs. Developing methods to increase oleic and reduce linolenic fatty acid content within soybean genotypes will increase the use of soybeans for food and feed, as well as biodiesel production. Biodiesel produced with high polyunsaturated fatty acids (PUFAs) content has a high tendency to undergo oxidation, making storage difficult and overall quality reduced. On the other hand, oils with high levels of monounsaturated fatty acids (MUFAs) are preferred for biodiesel production because oxidation is not increased, improve cold flow properties significantly and nitrogen oxide emissions are decreased. In addition, soybean oil with high concentration of PUFAs causes low oxidative stability, resulting in rancidity, a rapid decline of optimal flavor and reducing storage time of manufactured food products. 242 recombinant inbred lines (RILs) were evaluated in six locations in Brazil for grain yield, oil content, oil production, and fatty acid composition. A multi-trait index based on factor analysis and ideotype-design (FAI-BLUP index) was applied to identify genotypes best related with the ideotype for biodiesel production. This method is based on mixed models, with genotype and environment effects being considered as random and fixed, respectively. In addition, the best linear unbiased prediction (BLUP) method was used to predict the genetic values. Six RILs outperformed both parents and can be selected as genotypes more suitable for biodiesel production. Genetic selection reduced linolenic fatty acid content by 14%. Results indicated the presence of genetic variability for fatty acid composition. The significant increases in oleic and reductions in linolenic contents may be easily achieved with the introduction of mutant FAD2-1A and FAD2-1B genes through breeding programs or biotechnological strategies.