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Samples of Clitoria ternatea L. (Cunhã) were harvested at 35, 50, 70, and 90 d after a uniformity harvest in a field study designed as a completely randomized design with a total of 18 experimental plots. The dry matter yield of the whole plant was separated quantitatively into leaves, stems, and pods at each harvesting age. Chemical analyses and in vitro gas production kinetics were performed to assess the quality of the plant parts. Yields, chemical composition, and estimates of gas production parameters were analyzed by fitting a mixed statistical model with two types of covariance structures as follows: variance components and an unrestricted structure with heterogeneous variances. Fast and slow gas yielding pools were detected for both leaves and stems, but only a single pool was detected for pods. The homoscedasticity assumption was more likely for all variables, except for some parameters of the gas production kinetics of leaves and stems. There was no presence of typical pods at 35 and 50 d. In the leaves, the fibrous fractions were affected, whereas the non-fibrous fractions were unaffected by the harvesting age. The harvesting age affected the majority of the chemical constituents and gas kinetic parameters related to the stems. The leaves of this legume were the least affected part by the aging process.

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perennial breeding species demand substantial investment in various resources, mainly the required time to obtain adult and productive plants. estimating several genetic parameters in these species, in a more confidence way, means saving resources when selecting a new genotype. A model using the Bayesian approach was compared with the frequentist methodology for selecting superior genotypes. A population of 17 families of full-siblings of guava tree was evaluated, and the yield, fruit mass, and pulp mass were measured. The Bayesian methodology suggest more accurate estimates of variance components, as well as better results to fit of model in a cross-validation. Proper priori for Bayesian model is very important to convergency of chains, mainly for small datasets. Even with poor priori, Bayesian was better than frequentist approach.