Exogenously applied gibberellic acid and benzylamine modulate growth and chemical constituents of dwarf schefflera: a stepwise regression analysis

Abstract

Ornamental foliage plants that have a dense appearance are highly valued. One way to achieve this is by using plant growth regulators as a tool for plant growth management. In a greenhouse with a mist irrigation system, a study was conducted on dwarf schefflera, an ornamental foliage plant, which was exposed to foliar application of gibberellic acid and benzyladenine hormones. The hormones were sprayed on dwarf schefflera leaves at 0, 100, and 200 mg/l concentrations, at 15-day intervals in three stages. The experiment was conducted as a factorial based on a completely randomized design, with four replicates. The combination of gibberellic acid and benzyladenine at 200 mg/l concentration had a significant effect on leaf number, leaf area, and plant height. The treatment also resulted in the highest content of photosynthetic pigments. Furthermore, the highest soluble carbohydrate to reducing sugars ratio was observed in treatments of 100 and 200 mg/l benzyladenine, and 200 mg/l gibberellic acid + benzyladenine. Stepwise regression analysis showed that root volume was the first variable to enter the model, explaining 44% of variations. The next variable was root fresh weight, and the two-variable model explained 63% of variations in leaf number. The greatest positive effect on leaf number was related to root fresh weight (0.43), which had a positive correlation with leaf number (0.47). The results showed that 200 mg/l concentration of gibberellic acid and benzyladenine significantly improved morphological growth, chlorophyll and carotenoid synthesis, and reducing sugar and soluble carbohydrate contents in dwarf schefflera.

Fire protection priorities in the oak forests of Iran with an emphasis on vertebrate habitat preservation

“…These variables were selected based on their relevance to species distribution modeling. It's noteworthy that variables with a correlation coefficient |r|< 0.8 were included to prevent multicollinearity issues and ensure the independence of predictor variables in the modeling framework 68 . This meticulous selection of variables ensures robustness and accuracy in modeling the geographic distributions of vertebrate species within the study area.…”