Redundancy analysis for the study of the relationship between pineapple vegetative development and quality
The quality of pineapple fruits has been attributed to the development of a mature leaf, called “leaf D”, as if it were a standard for the species, regardless of cultivar and plant management. The management of pineapple flowering is a consolidated agronomic practice and allows the planning of early...
| Autores: | , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Federal de Itajubá (UNIFEI) |
| Repositorio: | Research, Society and Development |
| Idioma: | portugués |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/26618 |
| Acceso en línea: | https://rsdjournal.org/index.php/rsd/article/view/26618 |
| Access Level: | acceso abierto |
| Palabra clave: | Ananas comosus (L.) Merrill Multivariate Analysis Production Scheduling. Análisis multivariante Programación de la producción. Ananas comosus (L.) Merril Análise Multivariada Escalonamento de produção. |
| Sumario: | The quality of pineapple fruits has been attributed to the development of a mature leaf, called “leaf D”, as if it were a standard for the species, regardless of cultivar and plant management. The management of pineapple flowering is a consolidated agronomic practice and allows the planning of early harvest, however, field studies are still needed. Redundancy analysis can be a useful tool to identify response patterns of vegetative and reproductive development correlated with agronomic management, enabling the definition of efficient strategies. Therefore, the objective of this work was to evaluate, through redundancy analysis, which vegetative characteristics explain the quality of the 'Vitória' pineapple fruits, considering the flowering management. The study was carried out with data obtained from an applied research, conducted in the field, in four planting seasons (January, April, July and October), using two types of seedlings (100 to 200g seedling and 201 to 300g sprout) and three ages of floral induction (8, 10 and 12 months). Six plant characteristics were obtained (length and width of leaf “D”, number of leaves, leaf area and number of seedlings of the shoot and young types) and 21 characteristics of physicochemical quality of fruits (length, diameter and circumference of the fruit). , diameter of the central cylinder, pulp thickness, pulp yield, percentage of translucent area, soluble solids, titratable acidity, pH, vitamin C, maturation stage, skin firmness, pulp color). From these data, the relationships between plant characteristics, considered explanatory characteristics, and fruit characteristics, considered response characteristics, were obtained through redundancy analysis (RDA), using software R version 3.5.1. With the elimination of the explanatory characteristic leaf area, the model containing the other five explanatory characteristics remaining was statistically significant, with a valid relationship between the five plant characteristics and the 21 fruit characteristics in the 24 environments. In this way, the RDA analysis employed five explanatory characteristics (length and width of the “D” leaf, number of leaves and number of seedlings of the shoot and young types) for the model. It is concluded that most of the fruit characteristics had low variability among them, and with the environment characteristics. The number of leaves is the characteristic that is most related to the weight of the 'Vitória' pineapple fruit. |
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