Details

Autor(en) / Beteiligte

• Malikouski, Renan Garcia
• Ferreira, Filipe Manoel
• Chaves, Saulo Fabrício da Silva
• Couto, Evellyn Giselly de Oliveira
• Dias, Kaio Olimpio das Graças
• Bhering, Leonardo Lopes

Titel

Recommendation of Tahiti acid lime cultivars through Bayesian probability models

Ist Teil von

• PloS one, 2024-03, Vol.19 (3), p.e0299290-e0299290

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2024

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Probabilistic models enhance breeding, especially for the Tahiti acid lime, a fruit essential to fresh markets and industry. These models identify superior and persistent individuals using probability theory, providing a measure of uncertainty that can aid the recommendation. The objective of our study was to evaluate the use of a Bayesian probabilistic model for the recommendation of superior and persistent genotypes of Tahiti acid lime evaluated in 12 harvests. Leveraging the Monte Carlo Hamiltonian sampling algorithm, we calculated the probability of superior performance (superior genotypic value), and the probability of superior stability (reduced variance of the genotype-by-harvests interaction) of each genotype. The probability of superior stability was compared to a measure of persistence estimated from genotypic values predicted using a frequentist model. Our results demonstrated the applicability and advantages of the Bayesian probabilistic model, yielding similar parameters to those of the frequentist model, while providing further information about the probabilities associated with genotype performance and stability. Genotypes G15, G4, G18, and G11 emerged as the most superior in performance, whereas G24, G7, G13, and G3 were identified as the most stable. This study highlights the usefulness of Bayesian probabilistic models in the fruit trees cultivars recommendation.