Nonlinear models to describe the maize seed quality during the maturation stage: a Bayesian approach

Abstract

The adjustment of linear and non-linear models to describe the longevity of seed was studied here. The Bayesian analysis is a robust statistical procedure with many possible applications. In this study, the Bayesian method was used to fit the seed germination data of two maize hybrids (OC705 and CD5501) as a function of the number of days after female flowering on two sowing dates (E1 and E2) to the following non-linear model:. The accumulated dry biomass was also fit to the following nonlinear model:. Ten consecutive assessments corresponding to intervals of four days were performed with four replicates. The sampling started 23 days after female flowering and the last sample was collected 59 days after this period. The Bayesian method enabled the study of germination curves. The results suggested the first sowing date (E1) for both hybrids as the most viable for planting. The results indicated 57 days after the female flowering as the ideal time for harvesting, when the maximum germination percentage (96 %) occurred. The procedure used in the present study also allowed an accurate comparison through the credibility intervals of the difference of the fit parameters, the hybrid types and the sowing dates. Therefore, this method was successfully applied to the seed industry to study germination percentage.

Multiple-trait model by Bayesian inference applied to environment efficient Coffea arabica with low-nitrogen nutrient

“…The multi-trait model used in the present work showed great performance in estimating genetic and residual variances since the estimates are within the posterior density range (HPD 95%). This model presents credibility intervals that are more accurate when compared to the confidence intervals obtained in frequentist inference (Gazola et al 2016). The selection gains obtained by the FAI-BLUP index considering three different selection intensities: 35, 50, and 60%, which refers to the selection of seven, 10, and 12 genotypes, for 11 traits of arabica coffee cultivars in an efficient low-nitrogen environment, is represented in Table 4.…”

Bayesian analysis of growth, stem straightness and branching quality in full-sib families of Eucalyptus globulus

“…In fact, lower precision is directly proportional to a greater deviation of the real genetic value of a genotype (Faria et al 2007). In this context, the Bayesian method is a useful alternative for scientific inference of the genetic merit of trees because it considers levels of uncertainty in the estimated parameters and, generally, the credibility regions are more accurate than the confidence intervals obtained with frequentist inference (Gazola et al 2016). Bayesian inference has been increasingly used in plant breeding and genetic studies in general (Fresnedo-Ramírez et al 2017;Torres et al 2018) by Markov Chain Monte Carlo (MCMC) methods, which use Markov sequences to effectively simulate complex (or not mathematically addressable) distributions.…”