Multivariate best linear unbiased predictor as a tool to improve multi-trait selection in sugarcane

Abstract

Abstract: The objective of this work was to evaluate the use of the multivariate best linear unbiased predictor (BLUP) method for multi-trait selection, to estimate the genetic parameters in sugarcane (Saccharum officinarum) genotypes. The experiment was carried out in a randomized complete block design with 21 sugarcane genotypes, in seven crop years, in a factorial arrangement with three replicates. The measured traits were: total yield of stems per hectare, total volume of juice per hectare, production of total soluble sugars, and stem length. The source variation in the crop years strongly contributed for the obtention of the expected values of the sum of squares, without causing distortions in the variance components and genetic variables. The measured traits showed genetic variability and allowed of efficient univariate and multivariate selections. The highest selection efficiency was obtained by using more than eight measurements, since they favored the estimates of heritability, accuracy, and repeatability. The 'IAC873396', 'Nova Iraí', 'IACSP 93-6006', and 'RB 835089' genotypes were superior as to the traits tested, regardless of the crop year. The BLUP multivariate technique for multi-trait selection is robust and allows of the increasing of the selection gains, accuracy, and reliability of predictions for sugarcane breeding.

Keywords

• Saccharum officinarum
• multi-trait selection
• multivariate models
• repeatability