Non-Destructive Estimation of Leaf Area in Cinchona micrantha and Cinchona pubescens Using Linear Regression Models

Accurate quantification of leaf area is essential for ecophysiological, agronomic, and conservation studies, especially in threatened species such as Cinchona micrantha and Cinchona pubescens.This study evaluated simple, quadratic, and composite linear regression models to estimate leaf area non-destructively using morphometric measurements (length and width).A sample of n=800 leaves from 32 individuals was systematically collected and analyzed using a standardized photographic protocol with digital processing in ImageJ.The most robust models were those incorporating composite variables such as the product of length and width (L W) and the sum squared of both dimensions ((L + W) ), reaching coefficients of determination higher than 0.97.These models consistently outperformed models based on single variables, providing higher accuracy and lower prediction error.High correlations were observed between leaf dimensions and area, and C. pubescens showed greater morphological variability.These findings establish that simple linear models based on L W are efficient, replicable, and low-cost tools for non-destructive estimation of leaf area, which improves ecological monitoring and supports sustainable forest management, essential for the conservation of these Cinchona species, important from an ecological and medicinal point of view, in tropical ecosystems.