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Resumen
Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) [ver mas...]
dc.contributor.authorLapaz Olveira, Adrián
dc.contributor.authorCastro Franco, Mauricio
dc.contributor.authorSainz Rozas, Hernan Rene
dc.contributor.authorCarciochi, Walter
dc.contributor.authorBalzarini, Mónica
dc.contributor.authorAvila, Oscar
dc.contributor.authorCiampitti, Ignacio
dc.contributor.authorReussi Calvo, Nahuel Ignacio
dc.date.accessioned2023-09-19T16:39:04Z
dc.date.available2023-09-19T16:39:04Z
dc.date.issued2023-08
dc.identifier.issn1573-1618 (online)
dc.identifier.issn1385-2256 (print)
dc.identifier.otherhttps://doi.org/10.1007/s11119-023-10054-4
dc.identifier.urihttp://hdl.handle.net/20.500.12123/15248
dc.identifier.urihttps://link.springer.com/article/10.1007/s11119-023-10054-4
dc.description.abstractNitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherSpringeres_AR
dc.relationinfo:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agriculturaes_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourcePrecision Agriculture : 1-15 (Published: 04 August 2023)es_AR
dc.subjectMaízes_AR
dc.subjectMaizeeng
dc.subjectNitrógenoes_AR
dc.subjectNitrogeneng
dc.subjectÍndices de Vegetaciónes_AR
dc.subjectVegetation Indexeng
dc.subjectNutriciónes_AR
dc.subjectNutritioneng
dc.subjectVigilanciaes_AR
dc.subjectMonitoringeng
dc.subjectSatéliteses_AR
dc.subjectSatelliteseng
dc.subjectSueloes_AR
dc.subjectSoileng
dc.titleMonitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogenes_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 Balcarcees_AR
dc.description.filFil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.es_AR
dc.description.filFil: Lapaz Olveira, Adrián. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.es_AR
dc.description.filFil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.es_AR
dc.description.filFil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia.es_AR
dc.description.filFil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.es_AR
dc.description.filFil: Saínz Rozas, Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina.es_AR
dc.description.filFil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.es_AR
dc.description.filFil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.es_AR
dc.description.filFil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.es_AR
dc.description.filFil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina.es_AR
dc.description.filFil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina.es_AR
dc.description.filFil: Avila, Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.es_AR
dc.description.filFil: Avila, Oscar. Agencia Nacional de Promoción Científica y Tecnológica; Argentina.es_AR
dc.description.filFil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.es_AR
dc.description.filFil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.es_AR
dc.description.filFil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.es_AR
dc.subtypecientifico


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