Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/29767
metadata.artigo.dc.title: A probabilistic model for tropical tree seed desiccation tolerance and storage classification
metadata.artigo.dc.creator: Pelissari, Fabieli
José, Anderson Cleiton
Fontes, Marco Aurélio Leite
Matos, Antônio César Batista
Pereira, Wilson Vicente Souza
Faria, José Marcio Rocha
metadata.artigo.dc.subject: Seed storage behavior
Forest seeds
Orthodox seeds
Recalcitrant seeds
metadata.artigo.dc.date.issued: Jan-2018
metadata.artigo.dc.identifier.citation: PELISSARI, F. et al. A probabilistic model for tropical tree seed desiccation tolerance and storage classification. New Forests, [S.l.], v. 49, n. 1, p. 143–158, Jan. 2018.
metadata.artigo.dc.description.abstract: Knowledge of seed desiccation tolerance is fundamental for conservation and use of forest species. The protocol used for classification of seed desiccation tolerance and storage is time consuming and many times limited by the lack of information about optimum conditions for seed germination and treatments to overcome seed dormancy. This study evaluated 66 Brazilian tree species aiming to correlate seed characteristics with desiccation tolerance. For this purpose, a model was established to explain the relationship of tegument/seed mass ratio (SCR), seed mass, and water content of embryo + endosperm with desiccation tolerance. The principal component analysis showed the establishment of two groups, indicating the interaction between desiccation tolerance and seed characteristics. Recalcitrant seeds are more often associated with the water content of embryo + endosperm and water content of tegument + endocarp, while orthodox seeds are more associated with SCR and number of seeds per kilogram. The classification found using the model proposed was significantly correlated with desiccation tolerance and storage, with 92% confidence for the analyzed species. Seeds morphological characteristics can be used for prediction of desiccation tolerance and storage behavior; however, the use of a model that combines more variables increases the chance of accurate classification.
metadata.artigo.dc.identifier.uri: https://link.springer.com/article/10.1007/s11056-017-9610-8
http://repositorio.ufla.br/jspui/handle/1/29767
metadata.artigo.dc.language: en_US
Appears in Collections:DCF - Artigos publicados em periódicos

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