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Resumen
Remote sensing estimation of aerial net primary production (ANPP) is a key challenge in precision agriculture and environmental monitoring. The Monteith model serves as the main conceptual frameworkin pastures.Sun-induced fluorescence (SIF), closely linked to photosynthesis, is a promising candidate for ANPP estimations when radiation use efficiency (RUE) undergoes physiological changes. Our aim was to analyze SIF´s ability to estimate ANPP and RUE in [ver mas...]
dc.contributor.authorMattera, Juan
dc.contributor.authorRomero, Juan Manuel
dc.contributor.authorIrisarri, Jorge Gonzalo Nicolás
dc.contributor.authorGrimoldi, Agustín Alberto
dc.contributor.authorCordon, Gabriela B.
dc.date.accessioned2025-04-29T12:09:30Z
dc.date.available2025-04-29T12:09:30Z
dc.date.issued2025-06
dc.identifier.issn0168-1923
dc.identifier.issn1873-2240
dc.identifier.otherhttps://doi.org/10.1016/j.agrformet.2025.110569
dc.identifier.urihttp://hdl.handle.net/20.500.12123/22094
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0168192325001893
dc.description.abstractRemote sensing estimation of aerial net primary production (ANPP) is a key challenge in precision agriculture and environmental monitoring. The Monteith model serves as the main conceptual frameworkin pastures.Sun-induced fluorescence (SIF), closely linked to photosynthesis, is a promising candidate for ANPP estimations when radiation use efficiency (RUE) undergoes physiological changes. Our aim was to analyze SIF´s ability to estimate ANPP and RUE in forage covers with different structural and physiological characteristics. Moreover, we aimed to determine the underlying mechanisms driving these correlations. We performed a proximal sensing experiment by generating differential forage covers (alfalfa, tall fescue, and mixtures), measured across different seasons. Alfalfa and mixture plots have shown fairly constant RUE and fluorescence emission efficiency (SIFYield) upon APAR variations. This caused SIF to be an interesting monitor of ANPP (R2 ∼ 0.5), because of their strong link with APAR. On the other hand, tall fescue showed decreasing RUE when increasing APAR, which weakens ANPP-APAR correlation (R2 ∼ 0.38). Also, reduced escape fraction weakens SIF-APAR correlation (R2 ∼ 0.09). Consequently, the ANPP-SIF correlation disappears (R2 ∼ 0.02). This is one of the first remote sensing studies analyzing the biomass available for harvest in a field managed according to forage agronomic criteria. Our findings support the use of SIF as a monitor of ANPP of forage crops when RUE and SIF Yield can be assumed to be constant upon APAR variations. Nevertheless, correlations may not be extrapolated between different crops; careful attention must be paid to RUE variations upon APAR.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.relationinfo:eu-repograntAgreement/INTA/2019-PE-E1-I007-001, Incremento sostenible de la producción y utilización de pasturas y forrajes conservados
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceAgricultural and Forest Meteorology 369 : 110569. (June 2025)es_AR
dc.subjectForrajeses_AR
dc.subjectForageeng
dc.subjectCultivoses_AR
dc.subjectCropseng
dc.subjectProductividad Primariaes_AR
dc.subjectPrimary Productivityeng
dc.subjectFluorescenciaes_AR
dc.subjectFluorescenceeng
dc.subjectTeledetecciónes_AR
dc.subjectRemote Sensingeng
dc.subject.otherSun-induced Fluorescenceeng
dc.subject.otherRadiation Use Efficiencyeng
dc.titleDeciphering the link between SIF, ANPP and RUE dynamics in perennial forage crop mixtureses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Pergaminoes_AR
dc.description.filFil: Mattera, Juan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Bovinos; Argentinaes_AR
dc.description.filFil: Mattera, Juan. Universidad Nacional de San Antonio de Areco; Argentinaes_AR
dc.description.filFil: Romero, J. M. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física; Argentinaes_AR
dc.description.filFil: Romero, J. M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas vinculadas a la Agricultura; Argentinaes_AR
dc.description.filFil: Irisarri, J.G.N. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas vinculadas a la Agricultura; Argentinaes_AR
dc.description.filFil: Irisarri, J.G.N. University of Wyoming. Department of Ecosystem Science and Management; Estados Unidoses_AR
dc.description.filFil: Irisarri, J.G.N. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Forrajicultura; Argentinaes_AR
dc.description.filFil: Grimoldi, A. A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas vinculadas a la Agricultura; Argentinaes_AR
dc.description.filFil: Grimoldi, A. A. Universidad de Buenos Aires. Facultad de Agronomía. Catedra de Forrajicultura; Argentinaes_AR
dc.description.filFil: Cordon, G. B. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas vinculadas a la Agricultura; Argentinaes_AR
dc.description.filFil: Cordon, G. B. Universidad de Buenos Aires. Facultad de Agronomía. Área de Educación Agropecuaria y Ambiental; Argentinaes_AR
dc.subtypecientifico


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