Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models
AbstractNon-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 an...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Forest Science and Technology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21580103.2023.2170473 |
| _version_ | 1797894819855466496 |
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| author | Annick Estefany Huaccha-Castillo Franklin Hitler Fernandez-Zarate Luis Jhoseph Pérez-Delgado Karla Saith Tantalean-Osores Segundo Primitivo Vaca-Marquina Tito Sanchez-Santillan Eli Morales-Rojas Alejandro Seminario-Cunya Lenin Quiñones-Huatangari |
| author_facet | Annick Estefany Huaccha-Castillo Franklin Hitler Fernandez-Zarate Luis Jhoseph Pérez-Delgado Karla Saith Tantalean-Osores Segundo Primitivo Vaca-Marquina Tito Sanchez-Santillan Eli Morales-Rojas Alejandro Seminario-Cunya Lenin Quiñones-Huatangari |
| author_sort | Annick Estefany Huaccha-Castillo |
| collection | DOAJ |
| description | AbstractNon-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 (R2), 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 (R2 = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R2 = 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. |
| first_indexed | 2024-04-10T07:15:27Z |
| format | Article |
| id | doaj.art-b3baabf60985460a84b48999773d1dfe |
| institution | Directory Open Access Journal |
| issn | 2158-0103 2158-0715 |
| language | English |
| last_indexed | 2024-04-10T07:15:27Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Forest Science and Technology |
| spelling | doaj.art-b3baabf60985460a84b48999773d1dfe2023-02-25T17:05:45ZengTaylor & Francis GroupForest Science and Technology2158-01032158-07152023-01-01191596710.1080/21580103.2023.2170473Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear modelsAnnick Estefany Huaccha-Castillo0Franklin Hitler Fernandez-Zarate1Luis Jhoseph Pérez-Delgado2Karla Saith Tantalean-Osores3Segundo Primitivo Vaca-Marquina4Tito Sanchez-Santillan5Eli Morales-Rojas6Alejandro Seminario-Cunya7Lenin Quiñones-Huatangari8Instituto de Ciencia de Datos, Universidad Nacional de Jaén, Cajamarca, PerúFacultad de Ciencias Agrarias, Universidad Nacional Autónoma de Chota, Cajamarca, PerúEscuela Profesional de Ingeniería Forestal y Ambiental, Universidad Nacional de Jaén, Cajamarca, PerúEscuela Profesional de Ingeniería Forestal y Ambiental, Universidad Nacional de Jaén, Cajamarca, PerúFacultad de Ciencias Agrarias, Universidad Nacional de Cajamarca, Cajamarca, PerúInstituto de investigaciones de la Amazonía Peruana, Iquitos, PerúFacultad de Ciencias Naturales y Aplicadas, Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua, Bagua, PerúFacultad de Ciencias Agrarias, Universidad Nacional Autónoma de Chota, Cajamarca, PerúInstituto de Ciencia de Datos, Universidad Nacional de Jaén, Cajamarca, PerúAbstractNon-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 (R2), 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 (R2 = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R2 = 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.https://www.tandfonline.com/doi/10.1080/21580103.2023.2170473Cinchona treeleaf dimensionsImagJ softwareleaf morphologymathematical models |
| spellingShingle | Annick Estefany Huaccha-Castillo Franklin Hitler Fernandez-Zarate Luis Jhoseph Pérez-Delgado Karla Saith Tantalean-Osores Segundo Primitivo Vaca-Marquina Tito Sanchez-Santillan Eli Morales-Rojas Alejandro Seminario-Cunya Lenin Quiñones-Huatangari Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models Forest Science and Technology Cinchona tree leaf dimensions ImagJ software leaf morphology mathematical models |
| title | Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models |
| title_full | Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models |
| title_fullStr | Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models |
| title_full_unstemmed | Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models |
| title_short | Non-destructive estimation of leaf area and leaf weight of Cinchona officinalis L. (Rubiaceae) based on linear models |
| title_sort | non destructive estimation of leaf area and leaf weight of cinchona officinalis l rubiaceae based on linear models |
| topic | Cinchona tree leaf dimensions ImagJ software leaf morphology mathematical models |
| url | https://www.tandfonline.com/doi/10.1080/21580103.2023.2170473 |
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