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The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039–0.80, and 0.02–0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice. [ABSTRACT FROM AUTHOR]

Rice is one of the most consumed cereals in the world and of extreme social and economic importance in Brazil. Minas Gerais is the leading State in irrigated rice production in the Southeast Region. This study aimed to estimate the genetic progress of 26 years of the irrigated rice breeding programme of Epamig in Southeast Brazil by time meta‐analysis. Genetic progress was estimated for grain yield, plant height and days to flowering. The dataset understood 162 genotypes in 91 trials of value for cultivation and use of 26 seasons. The rate of annual genetic gain for grain yield was estimated.99% (49.85 kg year−1). It was also verified that was a reduction in plant height and an increase in days to flowering, even if very low. Due to distinct historical phases of the rice breeding programme, there were changes in research focus and, consequently, affected the genetic gains. With this study, it was possible to evaluate the selection strategies carried out in the past and identify important points to maintain. [ABSTRACT FROM AUTHOR]

The aim of this study was to use fuzzy logic as an auxiliary tool in the assessment of adaptability and stability, using grain‐yield data from flood‐irrigated rice, evaluated in different agricultural years. Eighteen rice genotypes belonging to flood‐irrigated rice breeding programme were evaluated over four agricultural years, 2012/2013 to 2015/2016, totalling 12 environments (3 sites × 4 years). The methodologies of Eberhart and Russell (1966), Lin and Binns (1988) modified by Carneiro (1998) and Centroid's were used. Fuzzy logic was applied to the results of these methodologies as a tool for interpretation and decision‐making regarding the recommendation of genotypes. Performances of the different flood‐irrigated rice genotypes were influenced by environmental conditions, thereby justifying the use of multiple adaptability and stability methodologies. The use of fuzzy logic in the selection of flood‐irrigated rice genotypes is a useful and promising tool in breeding programmes, allowing information from different parameters to be used to understand the influence of environmental variations on the performance of crop genotypes. [ABSTRACT FROM AUTHOR]

The importance of rice (Oryza sativa) is indisputable this cereal is the staple food for half of the global population. Genetic progress estimation allows evaluation of the effectiveness of genetic improvement of crops and helps in the planning of breeding programs. This paper aims to estimate the genetic yield progress made by the program in the state of Minas Gerais, Brazil, which is run by the Epamig/UFV/Embrapa consortium. A total of 210 lines were evaluated by value for cultivation and use testing that was conducted in the municipalities of Janaúba, Leopoldina, and Lambari, from 1993 to 2016. Based on Vencovsky's (1986) method, the genetic gains in Lambari, Janaúba, and Leopoldina were 1.46, 0.14, and 0.11%, respectively. The gain in Leopoldina was considered very low during the evaluated period. According to Breseghello's (1998) method, the annual average genetic gain was 0.23 and 0.04% in Lambari and Janaúba, respectively. In Leopoldina, a yield gain of - 2.37% per year was observed for the evaluation period from 1994 to 1999, and a yield gain of 0.1% per year was observed from 2000 to 2016. These results can be explained by the focus on quality as a strategy for the breeding program in Minas Gerais State in the 1990s. Vencovsky's (1986) and Breseghello's (1998) methods were able to quantify yield gains and demonstrate the dynamics of the irrigated rice genetic improvement program in Minas Gerais State. [ABSTRACT FROM AUTHOR]

This study presents self-organizing maps (SOM) as an alternative method to evaluate genetic diversity in plant breeding programs. Twenty-five genotypes were evaluated in two environments for 11 phenotypic traits. The genotypes were clustered according to the SOM technique, with variable topology and numbers of neurons. In addition to the SOM analysis, unweighted pair group method with arithmetic mean clustering (UPGMA) was performed to observe the behavior of the clustering when submitted to these techniques and to evaluate their complementarities. Genotype ordering according to SOM was consistent with UPGMA results, evidenced by the basic structure of UPGMA groups being preserved in each group of the maps. Regarding genotype arrangement and the group neighbors, maps involving five neurons presented inferior organization efficiency compared to the six-map arrangements in both environments. It was observed that the organization pattern among the rice genotypes evaluated by the maps was complementary to the UPGMA approach, as observed in all scenarios. It can be concluded that self-organizing maps have the potential to be useful for genetic diversity studies in breeding programs. [ABSTRACT FROM AUTHOR]

The lowland rice breeding program, developed in the state of Minas Gerais, has as basic objective to release new commercial cultivars. In this way, this program is making available a new cultivar named BRSMG Rubelita, which has as main characteristics: medium cycle, high tillering, lodging tolerance, diseases tolerance, high grain quality, and high grain yield. [ABSTRACT FROM AUTHOR]

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Data from 11 years of a breeding program for upland rice of the partner institutions Embrapa arroz e feijão, Epamig and UFLA conducted in 11 regions of Minas Gerais, were analyzed by the mixed model methodology. The genotypic correlations between environments ranged from medium to high, suggesting the predominance of simple interaction, but with differentiated ranking of genotypes at the different locations. Lines with specific performance, as well as lines of broad adaptation were identified. The performance of cultivar BRSMG Caravera, released in 2007, was superior to the others in terms of grain yield at all sites assessed, in other words, broad adaptation, in addition to a high genotypic performance. [ABSTRACT FROM AUTHOR]