Selection of Morphoagronomic Descriptors in Physalis angulata L. Using Multivariate Techniques

  • Silva H
  • Passos A
  • Schnadelbach A
  • et al.
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Abstract

This study aimed at selecting determinant morphoagronomic descriptors to characterize and evaluate Physalis angulata L. germplasm. Twelve quantitative and twenty-two qualitative descriptors were analyzed in six accessions of P. angulata coming from the physalis germplasm collection belonging to the State University of Feira de Santana-BA. The selection and discharge of quantitative descriptors was based on the direct selection and on the Singh method, while qualitative descriptors were analyzed through entropy. The statistic analyses were carried out using the GENES and R programs. Ten quantitative descriptors were excluded through direct selection and five through the Singh method. However, only four descriptors were considered redundant by both methods: east-west fruit, weight of five ripe fruits, width of leaf blade and total soluble solids. Although the total soluble solids descriptor was appointed for discharge, it was included in the group of descriptors selected due to its importance in the characterization of physalis fruit. The list of minimum descriptors to describe physalis accessions comprised 15 descriptors: plant height, stem diameter, north-south fruits, number of fruits per plant, leaf blade length, internode length, fruit longitudinal length, fruit transversal length, total soluble solids, growth habit, stem color, leaf margin shape, unripe calyx color, unripe fruit shape and color. These were nine quantitive and six qualitative descriptors, respectively. The discharge of 55.88% of the descriptors did not cause significant loss of information and might allow the reduction of time and resources spent to characterize and evaluate physalis germplasm.

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APA

Silva, H. K. da, Passos, A. R., Schnadelbach, A. S., Moreira, R. F. C., Conceição, A. L. da S., & Lima, A. P. (2018). Selection of Morphoagronomic Descriptors in Physalis angulata L. Using Multivariate Techniques. Journal of Agricultural Science, 11(1), 289. https://doi.org/10.5539/jas.v11n1p289

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