Several studies have shown a significant increase in wheat (Triticum aestivum L.) grain yield (GY) worldwide during the 20th century as a result of genetic and environmental improvement. The objective of this study was to measure the genetic gains achieved in a wheat breeding program maintained by the Central Cooperative of Agricultural Research (COODETEC) in Brazil through the annual evaluation of lines in multienvironment trials. The dataset was composed of 836 advanced trials and was evaluated in 40 representative locations to assess the value for cultivation and use (VCU) regions in the south, southeast, and central‐west areas of the country from 2004 to 2013. Each trial consisted of 25 treatments (lines + checks) in a randomized complete block design with three replicates. A linear mixed model was fitted to predict the genetic values of each genotype per year and a restricted maximum likelihood algorithm to estimate the variance components. These genetic values were used to compute the genetic gain over the years. The overall estimated genetic gain of the breeding program was 61.59 kg ha−1 yr−1 (1.68% yr−1) for GY, although it was partially counterbalanced by the estimated negative environmental effects. The genetic gain per VCU region ranged from 31.38 (VCU 4) to 115.33 kg ha−1 yr−1 (VCU 1). After accounting for the environmental changes over years, the yield gain was 39.40 kg ha−1 yr−1 (1.10% yr−1), ranging from −0.82 (VCU 3) to 3.35% yr−1 (VCU 1). The assessment of Brazilian cultivars released between 1998 and 2014 showed genetic gains in GY of 34.8 kg ha−1 yr−1 (1% yr−1), demonstrating that the efforts of Brazilian research institutions to develop cultivars have produced additive results. The implications of genetic gain estimates on breeding programs are discussed.

RESEARCHT he presence of genotype-by-environment interaction (GEI) is common in multi-environment trials in wheat (Triticum aestivum L.) and other crops; it leads to changes in the performance of cultivars in different environments. A GEI demands that trials be conducted at multiple locations for several years to obtain reliable data for the possible release of a new cultivar.Trials conducted across several years and locations increase the possibility of obtaining datasets with unbalanced or incomplete data. This can occur because of planned or unplanned actions by the breeder. The planned actions might be the removal of genotypes that did not perform well in a given year, introducing new genotypes developed by the breeding program and the introduction of newly released cultivars. On the other hand, unplanned actions, which are unrelated to the breeder, might be attributable to human or environmental causes (Yan, 2013).There are three primary strategies for analyzing unbalanced or incomplete datasets. The first strategy consists in the removal of genotypes or environments with missing values. The second ABSTRACT Multi-environment trials often yield unbalanced datasets, thus necessitating the estimation of missing values. It is unknown whether this estimation affects the graphic characteristics of genotype plus genotype-by-environment interaction (GGE) biplots. Therefore, our objectives were to investigate the effects of different percentages of missing values on the number of significant principal components (pCs) and on mega environments, "winner" (highest-performing) genotypes, and the amount of variation explained by the pCs. Two complete sets of two-way data from wheat (Triticum aestivum L.) were used. The first set consisted of the original data (Data1, from which we created scenarios with 0, 30, and 60% missing data. For the second dataset (Data2), we removed 50% data from the original dataset, estimated missing values to make it a new complete dataset, and created scenarios like those for Data1. Missing values were estimated via expectation-maximization-GGE (EM-GGE) and EM-additive main effects and multiplicative interaction (EM-AMMI) methods. The percentage of variation explained by the pCs was affected by the percentage of missing data; a large percentage of missing values considerably increased the amount of variation explained by pC 1 and pC 2 and reduced the complexity of the genotype-by-environment interaction because two pCs accounted for more than 80% of the variation, instead of the three pCs that were required to explain the variation in the original dataset. The EM-GGE estimation method was able to maintain the original conformation of the 'which-won-where' biplot when £30% of estimated data were used. The EM-GGE was superior to the EM-AMMI method for estimating missing data. The estimation of more than 30% of the data should be avoided because it can lead to significant changes in mega environment conformation and the identification of "winner" genotypes.

Soybean [Glycine max (L.) Merr.] maturity group (MG) is an important concept used to determine the most suitable macroregion and edaphoclimatic region (ECR) in which soybean can best use the available resources. This classification is based on the number of days between sowing and maturation in the soybean life cycle. The MG is related to photoperiod; thus, the longer the photoperiod, the shorter the MG of cultivars must be to have an adequate life cycle. However, there is no consensus on which MGs are the most suitable for each region to improve grain yield. The objective of this study was to identify suitable soybean MGs for cultivation in the macroregions and ECRs in Brazil. During 4 yr of evaluation, grain yield data from 247 yield trials over 83 locations, encompassing four macroregions and 14 ECRs in Brazil, were used. Cultivars were grouped according to their MG for statistical analyses. Using these groups, the ideal genotypes and performance according to local analyses were determined. The best adapted and most productive cultivars were those with an intermediate MG in their predefined adaptation region (both macroregions and ECR). The maturities that performed the best in each macroregion were as follows: M1 (cultivars in MGs 5.3–5.9) M2 (cultivars in MGs 6.0–7.0), M3 (cultivars in MGs 7.1–7.9), and M4 (cultivars in MGs 7.7–8.4). A lower productivity was observed in cultivars in extreme MGs for each macroregion. Breeding program efforts should target the MGs identified as ideal for each ECR to develop cultivars with a greater chance of achieving high yields and with greater adaptability to the specific region.

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