We appled a genetic and phenotypic multi-character predicted approach to the use of the multivariate methods Additive Main effects and Multiplicative Interaction (AMMI) and Genotype Main Effects and Genotype Environment Interaction (GGE). The experiment was carried out in the agricultural crop year of 2016 in the state of Rio Grande do Sul, Brazil. The experimental design was a randomized block design, with 14 growing environments x five wheat genotypes arranged in three replications. The characters were falling number, gluten strengthand protein content, which were used to make multi-character the technological index of the industrial quality of the wheat grains and multi-character the technological index of the industrial quality of the wheat grains. Multi-character selection can be a useful tool for identifying genotypes and growing environments that maximize the industrial quality of wheat grain. The GGE method provides greater explicability of the effects of genotype x environment interaction based on multi-character selection. The multicharacter genetic approach predicted for the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (3): gmr18223 V.J. Szareski et al. 2 selection of the industrial quality of wheat grain results in reliable inferences in the indication of adaptability and stability for the AMMI method and for GGE.
The aimed to characterize common beans genotypes utilizing multivariate models and artificial neural network thru the agronomic attributes and seeds dimensions. The experiment was conducted in the 2017/2018 crop season at the city of Tenente Portela - RS. The experimental design was expanded blocs, were 53 segregating F2 populations and ten cultivars considered checks, disposed in four repetitions. The accurate characterization of bean genotypes can be based in the reproductive period, cycle and seeds length. Genotypes with longer cycle increase the potential of ramifications, legume and seeds magnitude per plant and increase the seeds yield independent of the commercial group. The use of biometric approach allows revealing patterns to the genotype grouping, being the patterns magnitude dependent of the intrinsic premises to the Standardized Average Euclidian Distance, Tocher optimized grouping and Artificial Neural Network with non-supervised learning. It is defined that the Artificial Neural Network are determinant to define associative patterns, being these inferences indispensable to the common beans genotype selection that answer the agronomic attributes and seeds production.
Genetic variability is essential for maize breeding, being source of determining alleles and genes that maximize traits of agronomic interest, minimize abiotic and biotic stresses, as germplasm sources for breeding, one can use landraces, adapted populations, exotic populations and commercial hybrids, which are readily available to lineages extraction and improved open pollinated varieties (OPVs). Thus, the aim of this review is to highlight the main dynamics involved in the genetic improvement of maize, the use of biometric models to select genotypes superior to grain yield and nutritional components. In this study it was possible to contextualize on: Botanical description, morphological and physiological characteristics, the genetic breeding, development of inbred lines, development of hybrids, variance components and genetic parameters, heterosis, diallel analysis, genotype x environment interaction, associations between traits and Restricted Maximum Likelihood and Best Linear Unbiased Predictor (REML / BLUP).
The study had the purpose to evidence the agronomic performance, inter-relations of characters and the multivariate differentiation of soybean genotypes cultivated in the preferential season, in the state of Rio Grande do Sul, Brazil. In the crops season of 2017/2018, The experimental design was the completely randomized blocks, being 25 genotypes with three replicates. The data obtained was submitted to presuppositions based on normality and homogeneity of residual variances, variance analysis, Tocher method, Euclidian algorithm, linear correlations, relative contribution of characters by Singh and artificial neural networks. The agronomic performance of the genotypes presents superior seeds yield per plant through the elevated magnitude of reproductive nodes, legumes and seeds per plant. The plant height of the soybean is positively associated with the number of total nodes and reproductive nodes in the main stem and branches, where they are directly linked with the soybean productive potential. The most polymorphic characters correspond to the number and mass of thousand seeds, being possible to differentiate in a multivariate way the soybean genotypes though the similarity profiles.
The growing demand for maize creates a challenge for breeders; they need to constantly develop higher yielding and higher quality genotypes. We estimated the most relevant genetic parameters, along with heterosis and variance components. A multivariate approach was used in order to define profiles of narrow sense heritability for yield and nutritional components in half-sibling maize progenies. The applied experimental design was random blocks with a male parent (hybrid tester), five inbred lines (S 5) as maternal parents and the progenies (hybrid Top Cross), totaling 11 maize genotypes arranged in six replicates. Agronomic and nutritional characters were evaluated. Half-sibling progenies revealed greater additive genetic contribution to phenotypic expression with grain width and thickness, iron content, total flavonoids and carotenoids, soluble solids, and methionine. Narrow sense heritability values between and within progenies were higher for manganese content, glycine, proline and tryptophan. Regardless of the S 5 inbreeding line used, heterosis gains were obtained for ear insertion height, number of grain rows per ear, stalk diameter, zinc content, total carotenoids, soluble solids and pH. Specific heterosis was found ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 17 (4): gmr18024 I.R. Carvalho et al 2 for grain yield, glycine, serine, threonine and phenylalanine. The multivariate analysis defined eight profiles of traits according to their genetic tendencies, and indicated narrow sense heritability of the progeny mean as the main reason for this classification.
The climate unpredictability causes long periods of drought, becoming the main risk factor in soybeans production fields and consequent losses to farmers in Brazil and worldwide. As sessile organisms, plants are constantly challenged by a wide range of environmental stresses, including drought. Growth constraints and stress due to these environmental changes result in reduced yield and significant harvesting losses. The response to abiotic stresses is a very complex phenomenon, since several stages of plant development can be affected by a particular stress and often several stresses affect the plant simultaneously. In order to mitigate the damages caused by the climate, new soybean cultivars adapted to the drought and the diversified climate are necessary, as well as technological advances in the production of soybeans that must advance with the increase of cultivated area. Therefore, the mechanisms underlying tolerance and adaptation to stress have been the focus of intensive research. In this sense, the objective of this review is to provide an overview of the evolution of genetic improvement regarding the search for more drought-tolerant cultivars, as well as to verify which strategies are used in the genetic improvement of soybean in the search of these genotypes.
Maize (Zea mays L.) is the cereal most produced in the world, due to its wide scope and utilization in human and animal diet. This study aims to evaluate the agronomic performance of intervarietal maize hybrids, as well as the linear associations, interrelations of cause and effect, and the genotypes dispersion through canonical variates. The experiment was conducted in the agricultural year of 2014/2015. The crosses that originated the hybrids were carried out on the growing season of 2013/2014 and hybrids evaluated on 2014/2015. The hybrids were arranged in randomized blocks, being 13 treatments with five repetitions. The measured characters were: plant height, spike insertion height, stem diameter, spike diameter, spike length, spike mass, number of rows of grains per spike, number of grains per row, cob diameter, cob mass, spike grains mass, mass of a thousand grains, grain length and grain yield. The data were submitted to analysis of variance and mean values compared by the Tukey test at 5% of probability. The Pearson’s linear correlation analysis and path analysis were performed using grain yield as a dependent character. Furthermore, the analysis of canonical variables was carried out. The hybrid H5: G3 X G4 revealed higher grain yield, spike grains mass, number of grains per row and spike diameter. Grain yield of intervarietal hybrids presented positive correlations with the traits such as stem diameter, spike diameter, spike length, number of grains per row, mass of a thousand grains, grain length and spike grains mass. Spike diameter and spike length presented higher direct effects on grain yield of intervarietal hybrids. The canonical variates revealed the formation of five phenotypically distinct groups of intervarietal hybrids.
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