Minimum number of measurements for an accurate evaluation of growth traits in eucalyptus species

ABSTRACT The objective of this research was to identify the most effective method to estimate the repeatability coefficients in species of eucalyptus and to predict the minimum number of measurements necessary for growth traits. The experimental design was randomized blocks, with five species, with four repetitions. Data were collected from five measurements during the period from 2014 to 2016, evaluated according to the diameter, chest height and total height. The repeatability coefficient (r) was estimated using different strategies: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (PCCOR), principal components based on the phenotypic variance and covariance matrix (PCCOV), and structural analysis based on the correlation matrix (SACOR). The PCCOR and PCCOV provide accurate estimates of the repeatability coefficient and the number of measurements required. At least five measurements are necessary to predict the real value, with a minimum accuracy of 80%.