Abstract: In order to define the phytotoxic potential of Salvia species a database was developed for fast and efficient data collection in screening studies of the inhibitory activity of Salvia exudates on the germination of Papaver rhoeas L. and Avena sativa L.. The structure of the database is associated with the use of algorithms for calculating the usual germination indices reported in the literature, plus the newly defined indices (Weighted Average Damage, Differential Weighted Average Damage, Germination Weighted Average Velocity) and other variables usually recorded in experiments of phytotoxicity (LC50, LC90). Furthermore, other algorithms were designed to calculate the one-way ANOVA followed by Duncan's multiple range test to highlight automatically significant differences between the species. The database model was designed in order to be suitable also for the development of further analysis based on the artificial neural network approach, using Self-Organising Maps (SOM).

Data collection and advanced statistical analysis in phytotoxic activity of aerial parts exudates of Salvia spp.

GIACOMINI, MAURO;BISIO, ANGELA;GIACOMELLI, EMANUELA;PIVETTI, SUSANNA;BERTOLINI, SIMONA;ROMUSSI, GIOVANNI;
2011

Abstract

Abstract: In order to define the phytotoxic potential of Salvia species a database was developed for fast and efficient data collection in screening studies of the inhibitory activity of Salvia exudates on the germination of Papaver rhoeas L. and Avena sativa L.. The structure of the database is associated with the use of algorithms for calculating the usual germination indices reported in the literature, plus the newly defined indices (Weighted Average Damage, Differential Weighted Average Damage, Germination Weighted Average Velocity) and other variables usually recorded in experiments of phytotoxicity (LC50, LC90). Furthermore, other algorithms were designed to calculate the one-way ANOVA followed by Duncan's multiple range test to highlight automatically significant differences between the species. The database model was designed in order to be suitable also for the development of further analysis based on the artificial neural network approach, using Self-Organising Maps (SOM).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/276238
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