Zahra Nowghani; Leila Koohi; Parisa Panahi; Yoosef Torabian; Mehdi Poorhashemi; Armin Hashemi
Abstract
Leaf area, as one of the most important characters of plants, has direct relation with physiological and ecological activities such as photosynthisis, evapotranspiration, light absorption, leaf area index and growth. This research was carried out in hyrcanian collection of National Botanical Garden of ...
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Leaf area, as one of the most important characters of plants, has direct relation with physiological and ecological activities such as photosynthisis, evapotranspiration, light absorption, leaf area index and growth. This research was carried out in hyrcanian collection of National Botanical Garden of Iran on seven tree species; Quercus castaneifolia, Carpinus betulus, Alnus subcordata, Zelkova carpinifolia, Tilia platyphyllos, Acer velutinum and A. cappadocicum. The aim of this research was to calculate the best regression models (non-destructive sampling method) for estimation of leaf area of studied taxa by quantitative variables of leaf. At first, 10 sample trees were selected from each species and 200 leaves were gathered randomly from different sections of sample tree crowns. Leaf length (LL) and leaf width (LW) were measured by rule and leaf area (LA) was measured by leaf area meter (LAM) in Laboratory. Different regression models were tested and the best model was proposed for each species. The results showed that the mean of quantitative variables of leaf had significant differences among studied species. Totally, the best regression models were calculated based on compound variables. The Log LL+ Log LW variable was the most effective variable to predict leaf area in Q. castaneifolia, Z. carpinifolia and A. cappadocicum with correlation coefficient of 0.86, 0.91 and 0.9, respectively. Besides, the Log LL× Log LW variable was the best variable to predict leaf area in other species with good correlation coefficients. Finally, Power and Exponential regression models had the best results.
