Search

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]