TEORIA DE VALORES EXTREMOS E TAMANHO AMOSTRAL PARA O MELHORAMENTO GENÉTICO DO QUANTIL MÁXIMO EM PLANTAS
Main Article Content
Abstract
This study aimed to propose and evaluate a statistical methodology to improve the extreme value of the distributions. Such an approach is based on the upper quantiles of GEV (Generalized Extremes Values Distribution) of individual genotypic values between and within families. From real and simulated data sets from sugarcane families, generalized extreme value distributions (Gumbel, Fréchet and Weibull) were fitted to the maximum of each family. Stochastic simulations and experimental data resampling consistently indicated that the evaluation of 200 families is enough to maximize the efficiency in order to select extreme individuals. Weibull distribution fitted best and indicated an increase in selection efficiency is about 1.10 (gain of 10%) when going from 20 to 100 individuals per family and 1.12 (gain of 2%) when going from 100 to 200 individuals. These numbers are approximately constant regardless of the number of evaluated families. A good practical option would be the evaluation of 200 families with 100 individuals, in a total of 20,000 individuals. The methodology is also suitable to classify the families or progenies ability to generate exceptional individuals and inform the sample sizes to be practiced in every family to capture these individuals.
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).