Toona ciliata genotype selection with the use of individual BLUP with repeated measures

Abstract

The increasing demand for raw material for multiple uses of forest products and byproducts has attracted the interest for fast growing species, such as the Australian Cedar (Toona ciliata), which presents high productive and economic potential. This study aimed at estimating genotypic parameters and values for the species through the use of the BLUP procedure, at individual level, with repeated measures, by means of the conventional evaluation procedures and the introduction of innovative digitalization of the measurements by digital camera with the images provided by the Imagej software system. The main objective is to subsidize the beginning of a breeding program for the species. The assays were carried out in private properties, in plantations located in the state of Rio de Janeiro, Brazil. The results generated by three evaluations revealed that the image digital analysis is adequate to quantify characteristics of Toona ciliata. It is also an effective and accurate alternative to minimize the costs of data collection in evaluations with the species. There was high accuracy for the characters plant height, diameter at breast height and cylindrical volume. Out of the 90 genotypes evaluated, 38 expressed genotypic values predicted for the diameter at breast height higher than the general average of this character, 33 for the cylindrical volume and 49 for height, allowing gains of up to 24.9 % in average for cylindrical volume. The method of mixed models (REML/BLUP) applied via the SELE-GEN software system, using the BLUP procedure at individual level and repeated measures in each individual proved to be adequate to estimate the genetic parameters and predict genotypic values in situations of unbalanced data. Therefore, it is very useful and practical for Toona ciliata genetic breeding programs.

Model-assisted phenotyping by digital images in papaya breeding program

“…Digital image analysis has also been used in other crops with the purpose of evaluating traits of interest. For example, Ferreira et al (2012) found no significant differences between the averages of the morphological traits in Australian red cedar measured manually and those measured by digital image analysis. In turn, Sritarapipat et al (2014) and Miller et al (2015) validated algorithms of digital image processing to estimate the plant height of rice and Tilia platypyllos, Acer campestre, Acer rubrum, Juglans regia, respectively, obtaining similar results to those found using the traditional methodology.…”

“…It is expected that the application of these methodologies allows for high performance phenotyping in order to increase the number of assessed genotypes, improving the acquisition and analysis of data and minimizing the experimental error (Li et al, 2014;Roscher et al, 2014). A methodology based on digital image analysis was validated by Ferreira et al (2012) to measure plant height, stem base diameter and diameter at breast height in Australian red cedar (Toona ciliata). The authors obtained similar average results for measurements performed manually.…”

Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

“…Regarding the selection methods in statistical genetics, some methodology categories are significant and have been widely used. They include the selection index (Verardi, Oliveira, Silva, Gouvêa, & Gonçalves, 2014), the combined selection method (Ribeiro, Mambrin, Storck, Prigol, & Nogueira, 2013;Verardi et al, 2014), and the REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Prediction) method (Ferreira, Viana, Barroso, Resende, & Amaral Júnior, 2012). However, a new paradigm can be employed in genetic breeding for selection purposes which does not involve stochastic modeling.…”

Neural networks for predicting breeding values and genetic gains

“…With regard to the statistical genetic methods for selection, some methodology categories are noteworthy, and have been widely used as follows: selection index (Verardi et al, 2014), the combined selection method (Ribeiro et al, 2013;Verardi et al, 2014) and the REML-BLUP method (Ferreira et al, 2012). Nonetheless, a new paradigm can be employed in genetic breeding for selection purposes that does not involve stochastic modeling, but instead the principles of learning in a computational intelligence approach.…”