| Journal: | Acta scientiarum. Agronomy |
| Database: | PERIÓDICA |
| System number: | 000460099 |
| ISSN: | 1679-9275 |
| Authors: | Cargnin, Adeliano1 |
| Institutions: | 1Empresa Brasileira de Pesquisa Agropecuaria, Uva e Vinho, Bento Goncalves, Rio Grande do Sul. Brasil |
| Year: | 2019 |
| Volumen: | 41 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English abstract | Canonical correlation analysis allows conclusions to be drawn about the occurrence and magnitude of associations between two groups of characteristics. This study estimated the magnitude of association and interdependence between two trait groups of clones of two varieties of Vitis vinifera grapes. The study was based on the mean data of eight characteristics from two experiments to test the performance of these clones: the first experiment provided data from seven clones of Cabernet Sauvignon, which evaluated in the northern San Joaquin Valley, California, United States, and the second data set was from six clones of the Chardonnay variety, which were evaluated in the Napa Valley, California, United States. The canonical correlations among the two varieties were significant, indicating that the two groups of characteristics were related or not independent. The quality and quantity of the raw material required for fine-wine production were inversely proportional and specific to each variety. For Cabernet Sauvignon, the high number of berries per bunch and high number of bunches per plant resulted in fruits with a high °Brix level and increased pH. For Chardonnay, the increase in the bunch weight and number of berries decreased the fruit pH |
| Disciplines: | Agrociencias |
| Keyword: | Plantas para uso industrial, Fitotecnia, Vitis vinifera, Vid, Análisis multivariado, Clones, Variedades |
| Keyword: | Plants for industrial use, Crop husbandry, Vitis vinifera, Grapevine, Multivariate analysis, Varieties, Clones |
| Full text: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
