Growth model of the pineapple guava fruit as a function of thermal time and altitude

The growth of the pineapple guava fruit is primarily stimulated by temperature but is also influenced by other climactic factors, such as altitude. The goal of this study was to develop a growth model for the pineapple guava fruit as a function of thermal time (GDD, growing-degree day) and altitude...

Full description

Autores:
Parra-Coronado, Alfonso
Fischer, G.
Camacho-Tamayo, J. H.
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/67595
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/67595
http://bdigital.unal.edu.co/68624/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Acca sellowiana
fruit weight
growing-degree days
Acca sellowiana
peso del fruto
grados-día de crecimiento
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:The growth of the pineapple guava fruit is primarily stimulated by temperature but is also influenced by other climactic factors, such as altitude. The goal of this study was to develop a growth model for the pineapple guava fruit as a function of thermal time (GDD, growing-degree day) and altitude (H) of the production area. Twenty trees per farm were marked in two sites in the Cundinamarca department (Colombia) during the 2012 and 2014 seasons. The measurements were performed every seven days after day 96 and 99 post-anthesis until harvest in the sites of Tenjo (2,580 m.a.s.l.) and San Francisco de Sales (1,800 m.a.s.l.), respectively. A growth model was produced for weight as a function of fruit length and diameter as well as for the weight of the fruit as a function of GDD and H, with this last measure adjusted to a sigmoidal logistic growth model. The parameters for the regression analysis showed that the models satisfactorily predicted fruit growth for both of the sites, with a high determination coefficient. The cross-validation showed good statistical fit between the predicted and observed models; the intercept was not significantly different than zero, and the slope was statistically equal to one.