Agronomic Performance and Genetic Divergence in Corn (Zea mays) in the Cerrado-Amazon Ecotone
Rafael Marcelino da Silva
Rural Brasil Company, Redenção, Pará, Brazil.
Weder Ferreira dos Santos
Department of Bioprocess Engineering and Biotechnology, Federal University of Tocantins, Brazil.
Matheus Rodrigues de Andrade
Department of Forestry Engineering, Federal University of Tocantins, Brazil.
Zildiney Dantas da Silva *
Department of Agricultural Engineering, Federal University of Tocantins, Brazil.
Layanni Ferreira Sodré Santos
Department of Agricultural Engineering, Federal University of Tocantins, Brazil.
Joênes Micci Peluzio
Department of Plant Production, Agroenergy, Biodiversity and Biotechnology, Federal University of Tocantins, Brazil.
Lara Rythelle Souza Bequiman
Department of Agricultural Engineering, Federal University of Tocantins, Brazil.
Claúdia Nolêlo Maciel Luz
Administration Course, ITOP College, Brazil.
Vanderlan Carneiro Dias
Department of Agricultural Engineering, Federal University of Tocantins, Brazil.
Thaís Alves da Silveira Lourenço Borges
Department of Agricultural Engineering, Federal University of Tocantins, Brazil.
Albert Lennon Lima Martins
Department of Plant Production, Federal University of Tocantins, Brazil.
Magno De Oliveira
Department of Biological Sciences and Exact Sciences, Federal University of Tocantins, Brazil.
*Author to whom correspondence should be addressed.
Abstract
The objective of this work is to study the agronomic performance and genetic divergence in corn in the Cerrado-Amazon ecotone. The trials were conducted in the 2017/18 harvest at a property in the state of Pará. The experimental design was a randomized block with nine treatments and three replications, where the treatments are represented by nine cultivars of corn. The characteristics to evaluate agronomic performance and genetic divergence were: ear height (cm), plant height (cm), ear length (cm), ear diameter (mm), number of rows, number of grains per row and grain yield (kg ha−1). The cultivars were separated into a multivariate model in five groups using the Tocher optimization method. The cultivar AG 1051 showed the best agronomic performance. The results of genetic divergence were according to the generalized distance of Mahalanobis (D2), with the commences AG 8088 x CATIVERDE and AG 1051 x AL BANDEIRANTE, the most promising for future crosses.
Keywords: Mahalanobis, nitrogen, productivity, randomized block, variability.