Multivariate analysis for the detection of Passiflora species resistant to collar rot

• Autor(a) principal: Preisigke, Sandra da Costa
• Data de Publicação: 2015
• Outros Autores: Neves, Leonarda Grillo, Araújo, Kelly Lana, Barbosa, Nayaro Renero, Serafim, Milson Evaldo, Krause, Willian
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bioscience journal (Online)
• Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/29300
• topic: Fusarium solani; Genetic resistance; Passion fruit.; Agricultural Sciences

Collar rot is a disease difficult to control that has hindered passion fruit cultivation in many regions of Brazil. Therefore, this study aimed to find genetic resistance to the fungus Fusarium solani in Passiflora species using the multivariate analysis methodology to discriminate the most resistant species. The following fourteen Passiflora species were assessed: P. quadrangularis, P. nitida, P. foetida, P. tenuifila, P. alata, P. setacea, P. cincinnata, P. mucronata, P. micropetala, P. suberosa, P. morifolia, P. eichleriana, P. edulis and P. coccinea. These plants were arranged in a casualized block design with 14 treatments (species), three replications and three plants per plot. The reactions of the inoculated species of Passiflora were evaluated with the use of 12 resistance traits. The generalized Mahalanobis distance was used to form groupings by the UPGMA method. 3D projection with the canonical variables and quantification of the relative contribution of characters were also conducted. The UPGMA method revealed the formation of three distinct groups of species, which composed the susceptible, moderately resistant and resistant groups. The groups formed by three-dimensional dispersion were similar to those formed by the dendrogram. The following traits contributed most to genetic diversity: inoculation time until the lesion reached more than 50% of the circumference of the injured stem and area under the curve of the expansion of the lesion width. The use of the set of traits and their joint assessment through multivariate analysis allowed greater accuracy in the inference of the most resistant species, mainly P. nitida and P. cincinnata.