Can a Non-Destructive Method Predict the Leaf Area of Species in the Caatinga Biome?

Abstract

Understanding the leaf area is essential in plant physiology and ecological studies, as it directly influences photosynthesis, transpiration and plant productivity. This study aimed to develop non-destructive allometric models to estimate the leaf area of three species from the Caatinga biome: Cynophalla flexuosa, Libidibia ferrea and Tabebuia aurea. A total of 1293 leaves were collected from these species, scanned, and analysed using ImageJ software to obtain their length, width, and actual leaf area. In addition, the product of length and width was calculated. Linear, power and exponential regression models were used. The best equations were chosen based on the coefficient of determination, Pearson’s linear correlation coefficient, Willmott’s agreement index, mean squared error, root mean squared error, mean absolute error and mean absolute percentage error. The best equations for all species were constructed using linear and power models, which were indicated for accurate prediction of leaf area. These findings confirm the efficiency of allometric equations as a non-destructive method for predicting leaf area, providing an accessible and economical alternative for ecological studies in semi-arid environments.