A longitudinal study of sweet orange flowering with grouped count data



Título del documento: A longitudinal study of sweet orange flowering with grouped count data
Revista: Acta scientiarum. Agronomy
Base de datos: PERIÓDICA
Número de sistema: 000460003
ISSN: 1679-9275
Autores: 1
2
3
4
1
5
Instituciones: 1Universidade de Sao Paulo, Escola Superior de Agricultura "Luiz de Queiroz", Piracicaba, Sao Paulo. Brasil
2Universidade Federal do Parana, Departamento de Estatistica, Curitiba, Parana. Brasil
3Maynooth University, Department of Mathematics and Statistics, Maynooth, County Kildare. Irlanda
4National University of Ireland, School of Mathematics, Statistics and Applied Mathematics, Galway. Irlanda
5Universidade Federal do Ceara, Departamento de Estatistica e Matematica Aplicada, Fortaleza, Ceara. Brasil
Año:
Volumen: 42
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico
Resumen en inglés The orange variety “x11”, which is a spontaneous mutant of the sweet orange, has a short juvenile period with early flowering. The data used in this paper are from a randomized design experiment that aimed to assess the plants' flowering characteristics when grafted onto two different varieties of lemon rootstock. The plants were pruned in each of the four seasons, and on each pruning occasion, the number of branches on each plant was counted and classified into four mutually exclusive flowering categories. The data presented large variability and many zeros. The statistical analysis included the use of generalized linear mixed models with a Bayesian approach. The results showed that flowering is not equal over the seasons, i.e., there are significant differences in the classification of the branches across the four seasons and the two varieties, with interactions between seasonal and branch effects
Disciplinas: Agrociencias
Palabras clave: Frutales,
Naranja dulce,
Citrus sinensis,
Modelos mixtos,
Análisis bayesiano,
Datos discretos
Keyword: Fruit trees,
Sweet orange,
Citrus sinensis,
Discrete data,
Mixed models,
Bayesian analysis
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