Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
Estimation of leaf nitrogen content from nondestructive methods in Eucalyptus tereticornis and Eucalyptus saligna plantations
Estimación del contenido de nitrógeno foliar por métodos no destructivos en plantaciones de Eucalyptus tereticornis y Eucalyptus saligna
DOI:
https://doi.org/10.15446/rfnam.v74n3.93619Keywords:
CIEL*a*b*, RGB, SPAD, Digital color photography, Leaf color (en)CIEL*a*b*, RGB, SPAD, Fotografía digital en color, Color de hoja (es)
The determination of leaf nitrogen content (LNC) by indirect methods is essential for silvicultural management of forest crops. The application of photography or rapid measurement equipment, such as chlorophyll index (soil-plant analysis development-SPAD), is increasingly used due to its low-cost, ease of estimation and accuracy. Therefore, the aim of this study was to estimate foliar nitrogen content from nondestructive methods in plantations of Eucalyptus tereticornis and Eucalyptus saligna using three urea treatments (120 kg N ha-1, 240 kg N ha-1 and a control treatment without urea). For each treatment, 10 trees were selected, including four for the validation of the equations. The LNC was directly evaluated for color with the CIEL*a*b* model, photographic measurement with the RGB model, SPAD measurement and destructive estimation of nitrogen in leaves. The results showed negative relationships with the L* (luminosity) and b* (trend from yellow to green) indices, while the a* (red to green trend) index was discarded, with SPAD positive relationships were found with LNC and RGB space. In the R and B indices, the greatest negative relationships were found. It was determined that the multivariate equation Y=a+b1x1+b2x2+…+bnxn can be used for this type of study. It was also determined that the LNC=0.389+0.026SPAD model was the optimum for E. tereticornis and the LNC=3.826-0.001R-0.10B equation was the optimum for E. saligna.
La determinación del contenido de nitrógeno foliar (LNC) por métodos indirectos es esencial para el manejo silvícola de cultivos forestales. La aplicación de fotografía o equipos de medición rápida, como el índice de clorofila (SPAD), se utiliza cada vez más debido a su bajo costo, facilidad de estimación y precisión. Por tanto, el objetivo de este estudio consistió en estimar el LNC a partir de métodos no destructivos en plantaciones de Eucalyptus tereticornis y Eucalyptus saligna utilizando tres tratamientos de urea (120 kg N ha-1, 240 kg N ha-1 y un tratamiento testigo sin urea). Para cada tratamiento, se seleccionaron 10 árboles, incluidos cuatro utilizados para la validación de las ecuaciones. El LNC se evaluó directamente en cuanto a color con el modelo CIEL*a*b*, medición fotográfica con el modelo RGB, medición SPAD y estimación destructiva de nitrógeno en hojas. Los resultados mostraron relaciones negativas con los índices L* (luminosidad) y b* (tendencia de amarillo a verde), mientras que se descartó el índice a* (tendencia de rojo a verde), encontrándose SPAD relaciones positivas con el espacio LNC y RGB. En los índices R y B, mostraron las mayores relaciones negativas. Se determinó que la ecuación multivariada Y=a+b1x1+b2x2+…+bnxn se puede utilizar para este tipo de estudio. También se determinó que el modelo LNC=0,389+0,026SPAD fue el óptimo para E. tereticornis y la ecuación LNC=3,826-0,001R 0,10B fue la óptima para E. saligna.
References
Alchanatis V, Schmilovitch Z and Meron M. 2005. In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precision Agriculture 6: 25-39. https://doi.org/10.1007/s11119-005-0682-7
Aldea M, Frank TD and Delucia EH. 2006. A method for quantitative analysis for spatially variable physiological processes across leaf surfaces. Photosynthesis Research 90: 161-172. https://doi.org/10.1007/s11120-006-9119-z
Ali M, Al-ani M, Eamus A, Tan D. 2013. Al algorithm based on the RGB colour model to estimate plant chlorophyll and nitrogen contents. International Conference on Sustainable Environment and Agriculture 57: 52-60.
Ali M, Al-Ani A, Eamus D and Tan D. 2017. Leaf nitrogen determination using non-destructive techniques–A review. Journal of Plant Nutrition 40: 928-953. https://doi.org/10.1080/01904167.2016.1143954
Barrantes K, Ávila C, Murillo R, Solís L, Porras R and Herrera P. 2018. Relación de la clorofila y el nitrógeno foliar Gmelina arborea Roxb. en vivero y en campo. Revista Mexicana de Ciencias Forestales 9: 209-239. https://cienciasforestales.inifap.gob.mx/index.php/forestales/article/view/124
Berntsen J, Thomsen A, Schelde K, Hansen L, Knudsen N, Broge H and Horfarter R. 2006. Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat. Precision Agriculture 7: 65-83. https://doi.org/10.1007/s11119-006-9000-2
Baresel JP, Rischbeck P, Hu Y, Kipp S, Hu Y, Barmeier G and Mistele B. 2017. Use of a digital camera as alternative method for nondestructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture 140: 25-33. https://doi.org/10.1016/j.compag.2017.05.032
Chang S and Robison D. 2003. Nondestructive and rapid estimation of hardwood foliar nitrogen status using the Spad-502 chlorophyll meter. Forest Ecology and Management 181: 331-338. https://doi.org/10.1016/S0378-1127(03)00004-5
Conesa A, Manera FC, Brotons JM, Fernández JC, Simón I, Simón S, Alfosea M, Martínez JJ, Valverde JM and García F. 2019.
Changes in the content of chlorophylls and caretenoids in the rind of lemons during maturation and their relationship with parameters from the CIELAB color space. Scientia Horticulture 243: 252-260. https://doi.org/10.1016/j.scienta.2018.08.030
Coste S, Baraloto C, Leroy C, Marcon E, Renaud A, Richardson A, Roggy J, Schimann H, Uddling J and Herault G. 2010. Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Annuals of Forest Science 67: 607-611. https://doi.org/10.1051/forest/2010020
Dana W and Ivo W. 2008. Computer image analysis of seed shape and seed color of flax cultivar description. Computers and Electronics in Agriculture 61: 126-135. https://doi.org/10.1016/j.compag.2007.10.001
Djumaeva D, Lamers JPA, Martius C and Vlek PLG. 2012. Chlorophyll meters for monitor in foliar nitrogen in three species from arid Central Asia. Journal of Arid Environments 85: 41-45. https://doi.org/10.1016/j.jaridenv.2012.03.008
Hu H, Lui H, Zhang H, Zhu J, Yao X, Zhang X and Zheng K. 2010. Assessment of chlorophyll content based on image color analysis, comparison with SPAD-502. 2nd Internacional Conference on Information Engineering and computer Science 1-12. https://doi.org/10.1109/ICIECS.2010.5678413
IMN. Instituto Meteorológico Nacional. 2020. Histiorical Data of Weather in Costa Rica. http://www.imn.ac.cr
Jannoura R, Brinkmann K, Uteau D, Bruns C and Joergensen RG. 2015. Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems Engineering 129: 341–350. https://doi.org/10.1016/j.biosystemseng.2014.11.007
Kipp S, Mistele B and Schmidhalter U. 2014. The performance of active spectral reflectance sensors as influenced by measuring distance, device temperature and light intensity. Computers and Electronics in Agriculture 100: 24-33. https://doi.org/10.1016/j.compag.2013.10.007
Köppen W. 1990. Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt. Geografic Zeitschrift 6: 593-611.
León-Sánchez MA, Reyes-Pozo JL, Herrero-Echevarría G and Pérez-León VE. 2016. Efecto de la fertilización sobre el crecimiento en diámetro y altura de Pinus caribaea en plantaciones del occidente de Cuba. Madera y Bosques 22: 87-101. https://doi.org/10.21829/myb.2016.2231115
Limantara L, Dettling M, Indrawati R, Indriatmoko R and Panintingjati TH. 2015. Analysis on the chlorophyll content of commercial green leafy vegetables. Procedia Chemistry 14: 225-231. https://doi.org/10.1016/j.proche.2015.03.032
Malavolta E. 2006. Manual de nutrição mineral de plantas, Agronômica Ceres 8: 1-10.
Netto A, Campostrini E, De Oliveira JG and Yamanishi OK. 2002. Portable chlorophyll meter for the quantification of photosynthetic pigments, nitrogen and the possible use for assessment of the photochemical process in Carica. Brazilian Journal of Plant Physiology 14: 203-210. https://doi.org/10.1590/S1677-04202002000300004
Nieto JC, Catzim OD, Torres RA and Díaz JJ. 2017. Detección de clorosis en las hojas de limón con análisis de color. Revista de Ingeniería Innovativa 1: 10-18.
Pagola M, Ortiz R, Irogoyen I, Bustince H, Barrenechea E, Aparicio-Trejo P and Lamsfus C. 2009. New method to assess barley nitrogen nutrition status based on image colour analysis: Comparison with SPAD-502. Computers and Electronics in Agriculture 65: 213-218. https://doi.org/10.1016/j.compag.2008.10.003
Pinkard E, Patel V and Mohammed C. 2006. Chlorophyll and nitrogen determination for plantation-grown Eucalyptus nitens and E. globulus using a non-destructive meter. Forest Ecology and Management 223: 211-217. https://doi.org/10.1016/j.foreco.2005.11.003
R Core Development Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.r-project.org
Riccardi M, Mele G, Pulvento C, Lavini A, D’andria R and Jacobsen E. 2014. Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynthesis Research 6: 263-272. https://doi.org/10.1007/s11120-014-9970-2
Rotbart N, Schmilovitch Z, Cohen Y, Alchanatis V, Erel R, Ignat T, Shenderey C, Dag A and Yermiyahu U. 2013. Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance. Biosystems Engeneering 114: 426–434. https://doi.org/10.1016/j.biosystemseng.2012.09.005
Silla F, González-Gil A, González-Molina M, Mediavilla S and Escudero A. 2010. Estimation of chlorophyll in Quercus leaves using portable chlorophyll meter: effects of species and leaf age. Annals of Forest Science 67: 108-119. https://doi.org/10.1051/forest/2009093
Sun YD, He ZL, Wu Q, Zheng JL, Li YH and Wang YZ. 2020. Zeolite amendment enhances rice production, nitrogen accumulation and translocation in wetting and drying irrigation paddy field. Agriculture Water Management 235:106126. https://doi.org/10.1016/j.agwat.2020.106126
Tao M, Ma X, Huang X, Lui C, Deng R, Liang K and Qi L. 2014. Smartphone-based detection of leaf color levels in rice plants. Computers and Electronics in Agriculture 173: 1-11. https://doi.org/10.1016/j.compag.2020.105431
Wang Y, Wang, D, Shi P and Omasa K. 2014. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods 10: 2-11. https://doi.org/10.1186/1746-4811-10-36
Valverde JC, Arias-Aguilar D, Montero-Zeledón E and GutierrezFallas D. 2021. Fluorescence, reflectance, and physiological response to water stress in Gmelina arborea Roxb seedlings. Revista Uniciencia 35: 1-12. http://doi.org/10.15359/ru.35-1.20
Valverde JC and Arias D. 2020a. Efectos del estrés hídrico en crecimiento y desarrollo fisiológico de Gliricidia sepium (Jacq.) Kunth ex Walp. Colombia Forestal, 23(1): 29-53. https://doi.org/10.14483/2256201X.14786
Valverde JC, Quesada R, Soto C and Arias D. 2020b. Validación de un protocolo fotográfico para la digitalización de muestras de herbario de especies tropicales. Revista Científica 2 (38): 147-159. https://doi.org/10.14483/23448350.15362
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
CrossRef Cited-by
1. Lai Wei, Liping Lu, Yuxin Shang, Xiaodie Ran, Yunpeng Liu, Yanming Fang. (2024). Can SPAD Values and CIE L*a*b* Scales Predict Chlorophyll and Carotenoid Concentrations in Leaves and Diagnose the Growth Potential of Trees? An Empirical Study of Four Tree Species. Horticulturae, 10(6), p.548. https://doi.org/10.3390/horticulturae10060548.
2. Juan Carlos Valverde, Dagoberto Arias-Aguilar, María Rodríguez-Solís, Nelson Zamora Villalobos. (2022). Differentiation of Tropical Tree Species with Leaf Measurements of Hyperspectral Reflectance. The 3rd International Electronic Conference on Forests—Exploring New Discoveries and New Directions in Forests. , p.47. https://doi.org/10.3390/IECF2022-13084.
Dimensions
PlumX
Article abstract page views
Downloads
License
Copyright (c) 2021 Juan Carlos Valverde
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The journal allows the author(s) to maintain the exploitation rights (copyright) of their articles without restrictions. The author(s) accept the distribution of their articles on the web and in paper support (25 copies per issue) under open access at local, regional, and international levels. The full paper will be included and disseminated through the Portal of Journals and Institutional Repository of the Universidad Nacional de Colombia, and in all the specialized databases that the journal considers pertinent for its indexation, to provide visibility and positioning to the article. All articles must comply with Colombian and international legislation, related to copyright.
Author Commitments
The author(s) undertake to assign the rights of printing and reprinting of the material published to the journal Revista Facultad Nacional de Agronomía Medellín. Any quotation of the articles published in the journal should be made given the respective credits to the journal and its content. In case content duplication of the journal or its partial or total publication in another language, there must be written permission of the Director.
Content Responsibility
The Faculty of Agricultural Sciences and the journal are not necessarily responsible or in solidarity with the concepts issued in the published articles, whose responsibility will be entirely the author or the authors.
