Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models.

• Autores: Huaccha-Castillo, A.E., Fernandez-Zarate, F., Pérez-Delgado, L., Tantalean-Osores, K.S., Vaca-Marquina, S., Sánchez-Santillan, T., Morales-Rojas, E., Seminario-Cunya, A., Quiñones Huatangari, L.
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Nacional de Cajamarca
• Repositorio: UNC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.unc.edu.pe:20.500.14074/9517
• Enlace del recurso: http://hdl.handle.net/20.500.14074/9517; https://doi.org/10.1080/21580103.2023.2170473
• Nivel de acceso: acceso abierto
• Materia: Cinchona tree; ImagJ software; leaf dimensions; leaf morphology; mathematical models; https://purl.org/pe-repo/ocde/ford#1.06.10

Non-destructive methods that accurately estimate leaf area (LA) and leaf weight (LW) are simple and inexpensive, and represent powerful tools in the development of physiological and agronomic research. The objective of this research is to generate mathematical models for estimating the LA and LW of Cinchona officinalis leaves. A total of 220 leaves were collected from C. officinalis plants 10 months after transplantation. Each leaf was measured for length, width, weight, and leaf area. Data for 80% of leaves were used to form the training set, and data for the remaining 20% were used as the validation set. The training set was used for model fit and choice, whereas the validation set al.lowed assessment of the of the model’s predictive ability. The LA and LW were modeled using seven linear regression models based on the length (L) and width (Wi) of leaves. In addition, the models were assessed based on calculation of the following statistics: goodness of fit (R 2), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the deviation between the regression line of the observed versus expected values and the reference line, determined by the area between these lines (ABL). For LA estimation, the model LA = 11.521(Wi) − 21.422 (R 2 = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R 2 = 0.93, RMSE = 0.56, AIC = 37.36, and ABL = 0.03) was selected. Finally, the LA and LW of C. officinalis could be estimated through linear regression involving leaf width, proving to be a simple and accurate tool.