Open AccessStrategies for selecting soybean genotypes using mixed models and multivariate approach
Author(s) -
Andréa Carla Bastos Andrade,
Jalles da Silva Alysson,
S eacute rgio Ferraudo Ant ocirc nio,
Helena Un ecirc da Trevisoli Sandra,
Orlando Di Mauro Ant ocirc nio
Publication year - 2016
Publication title -
african journal of agricultural research
Language(s) - English
Resource type - Journals
ISSN - 1991-637X
DOI - 10.5897/ajar2015.9715
Subject(s) - restricted maximum likelihood , best linear unbiased prediction , principal component analysis , multivariate statistics , selection (genetic algorithm) , statistics , mixed model , mathematics , linear model , microbiology and biotechnology , maximum likelihood , biology , computer science , machine learning
The objective of this study was to select soybean genotypes derived from crosses between conventional and transgenic lines Roundup Ready (RR), using jointly Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) approaches, factors analysis and principal components analysis, processed with favorable agronomic traits, during the 2013/2014 growing season. Three agronomic selection processes were identified to select genotypes that discriminate genotypes containing more specific properties. Process 1 (insertion height of first pod, HFP; number of branches, NB; number of pods, NP; number of nodes, NN; and grain yield, GY) was efficient to select earlier, smaller genotypes with good yield/production components and lodging resistance. The junction between mixed model via REML/BLUP and the applied multivariate statistic using factor analysis helped to select suitable genotypes with high performance to carry on the soybean plant-breeding program.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom