ABSTRACT
Abstract: Climatic variation throughout the day influences the ecophysiological performance of plants at different growth stages and phases. Therefore, this work aimed to evaluate the effect of climatic variation on ecophysiological aspects of T. roseoalba and H. heptaphyllus at different hours of the day and indicate the ideal time for measuring ecophysiological variables in these species. The research was carried out in a greenhouse at the forest nursery of the Federal University of Paraíba, Campus II, in the municipality of Areia, Paraíba state, Northeastern Brazil. The experimental design was completely randomized, consisting of 10 evaluation times throughout the day (from 8 am to 5 pm), with 1 h hour interval between each evaluation. Temperature and air relative humidity inside and outside the greenhouse were evaluated to understand the effect on gas exchange (net assimilation rate of CO2, stomatal conductance, transpiration rate, internal concentration of CO2, and vapor-pressure deficit) and chlorophyll a fluorescence (initial, maximum, and variable fluorescence, photochemical quenching, and electron transport rate). Data were submitted to canonical correlation analysis and principal component analysis to verify the relationship between climatic and ecophysiological variables. For both species, higher correlation was found between internal and external relative humidity with all the ecophysiological variables analyzed, except for initial fluorescence. Thus, climatic factors influenced the photosynthetic performance of T. roseoalba and H. heptaphyllus plants, and 8 am to 9 am is indicated for carrying out ecophysiological evaluations in both species.
ABSTRACT
Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.