Minimum number of measurements for evaluating Bertholletia excelsa

Genet Mol Res. 2017 Sep 27;16(3). doi: 10.4238/gmr16039783.

Abstract

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r - AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

MeSH terms

  • Analysis of Variance
  • Bertholletia / genetics*
  • Bertholletia / growth & development
  • Dimensional Measurement Accuracy
  • Fruit / anatomy & histology
  • Fruit / genetics*
  • Genetic Variation*
  • Genotype
  • Phenotype
  • Plant Breeding / methods
  • Plant Breeding / standards
  • Plant Breeding / statistics & numerical data*
  • Principal Component Analysis
  • Quantitative Trait, Heritable