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Articles by Gessi Ceccon
Total Records ( 2 ) for Gessi Ceccon
  Agenor Martinho Correa , Allan Robson de Souza Lima , Douglas Candido Braga , Gessi Ceccon , Paulo Eduardo Teodoro , Carlos Antonio da Silva Junior and Flavia Alves da Silva
  In order to evaluate the agronomic performance and estimate the genetic variability of 24 common bean genotypes grown in the Savanna-Pantanal ecotone and see, which characters can be used for selection of superior genotypes. Treatments consisted of 24 common bean genotypes (CNFC 10429, CNFC 10408, CNFC 10467, CNFC 10470, CNFC 10762, CNFP 10104, CNFP 10793, CNFP 10794, BJ4, CNFRJ 10556, VR3 VC3 e VC6, IAPAR 81, BRS Campeiro, BRS 7762 Supremo, BRS Esplendor, BRS Valente, BRS Pitanga, BRS Radiante, BRS Requinte, BRS Pontal, BRS 9435 Cometa e BRS Estilo). The following traits were evaluated: Early flowering, early maturity, height of the first pod, number of pods per plant, number of grains per pod, weight of 100 grains and grain yield. The following parameters were estimated: environmental, phenotypic and genotypic variances, experimental and genotypic coefficient of variation, genotypic coefficient of determination, b quotient, environmental, phenotypic and genetic correlations. The CNFP 10794 genotype had the best agronomic performance in the Savanna-Pantanal ecotone region. The population presents genetic variability and potential for selection of all traits. Based on genetic parameters estimates, the characters number of grains per pod and weight of hundred grains can be used in direct selection for more productive genotypes.
  Gessi Ceccon , Adriano dos Santos , Paulo Eduardo Teodoro and Carlos Antonio da Silva Junior
  This research aimed to identify the relationships between the primary and secondary components of the maize yield using the techniques of canonical correlation and factors analysis. The base population was composed of nine randomized crossing hybrids in an isolated field, in the years 2006-2012. Canonical correlations were estimated between the variable group consisting of primary (GI) and secondary (GII) yield components. To Factor Analysis (FA), we chose a number of common factors equal to the number of eigen values higher than the existing unit in the phenotypic correlations matrix of variables and the orthogonal factor model was opted. Primary and secondary yield components of maize grains are not independent. Inter-group associations are established by plants with higher height, stem diameter, dry weight and lower ear height, which positively influence primary yield components (dry ear weight, ear length and hundred-grain weight). Factor analysis allowed to reduce a large number of original variables observed to a small number of abstract variables and can be used to complement the canonical variables technique.
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