We aimed to evaluate the currently used allometric models, as well as to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W), for estimating the area of leaves of eight field-grown coffee cultivars. For model construction, a total of 1563 leaves were randomly selected from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes (0.3-263 cm 2 ) for each genotype. Power models better fit coffee leaf area (LA) than linear models. To validate the model, an independent data set of 388 leaves was used. We demonstrated that the currently used allometric models are biased, underestimating the area of a coffee leaf. We developed a single power modelbased on two leaf dimensions [LA = 0.6626 (LW) 1.0116 ; standard errors: b 0 = 0.0064, b 1 = 0.0019; R 2 = 0.996] with high precision and accuracy, random dispersion pattern of residuals and also unbiased, irrespective of cultivar and leaf size and shape. Even when the L (but not width) alone was used as the single leaf dimension, the power model developed still predicted with good accuracy the LA but at the expense of some loss of precision, as particularly found for 8% of the leaves sampled with length-to-width ratios below 2.0 or above 3.0.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.