Name: JASMYN TOGNERE
Publication date: 31/07/2025
Examining board:
Name |
Role |
|---|---|
| EDILSON ROMAIS SCHMILDT | Presidente |
| EDNEY LEANDRO DA VITORIA | Examinador Interno |
| ENILTON NASCIMENTO DE SANTANA | Examinador Externo |
| SARA DOUSSEAU ARANTES | Coorientador |
Summary: The coefficient of variation is a widely used statistical measure to express experimental precision in agricultural and forestry studies, as it allows for the comparison of relative variability among different traits. However, the traditional classification of the coefficient of variation, proposed by Pimentel-Gomes, presents significant limitations, particularly due to its generalist nature and its failure to consider fundamental aspects such as the type of variable evaluated and the size of the experimental plot. This fixed approach may lead to misinterpretations regarding the quality of experiments, especially when applied to different crops or specific experimental conditions. In light of these limitations, a new classification of the coefficient of variation is proposed, which is better adapted to the context of experiments involving eucalyptus seedlings at the dispatch stage. Eight morphological traits were evaluated in seedlings of six eucalyptus clones (144, 224, BA7346, CO1407, TP361, and GG100), totaling eight thousand and two hundred units. The estimation of the optimal plot size was conducted using the modified maximum curvature method with bootstrap simulation, a method widely recognized for its capacity to identify the point at which the addition of experimental units ceases to provide significant gains in precision. The values of the coefficient of variation obtained were subsequently analyzed according to the data distribution. For variables that presented a normal distribution, a classification based on means and viii standard deviations was used; for variables with a non-normal distribution, a methodology based on the median and pseudo-sigma was adopted. The results showed that the behavior of the coefficient of variation as a function of the number of plants per plot is non-linear. As the number of plants increases, the required values of the coefficient of variation to classify the data within ranges of low variability become more stringent, correctly reflecting the reduction in experimental error. This variation in behavior was also influenced by the nature of the morphological trait evaluated, which reinforces the inadequacy of fixed and generalist classifications. The new proposal presented classification ranges with well-defined transitions between classes and greater adherence to the variability observed in real data. Moreover, the proposed methods proved to be more effective than the traditional model by considering the data distribution, thus providing greater sensitivity in the evaluation of experimental precision. Therefore, the proposed approach constitutes a more appropriate statistical tool for the reality of forest nurseries, contributing to increased reliability of experiments and to improved criteria for the evaluation of eucalyptus seedlings at the dispatch stage.
