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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)
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dc.contributor.author | Lapaz Olveira, Adrián | |
dc.contributor.author | Castro Franco, Mauricio | |
dc.contributor.author | Sainz Rozas, Hernan Rene | |
dc.contributor.author | Carciochi, Walter | |
dc.contributor.author | Balzarini, Mónica | |
dc.contributor.author | Avila, Oscar | |
dc.contributor.author | Ciampitti, Ignacio | |
dc.contributor.author | Reussi Calvo, Nahuel Ignacio | |
dc.date.accessioned | 2023-09-19T16:39:04Z | |
dc.date.available | 2023-09-19T16:39:04Z | |
dc.date.issued | 2023-08 | |
dc.identifier.issn | 1573-1618 (online) | |
dc.identifier.issn | 1385-2256 (print) | |
dc.identifier.other | https://doi.org/10.1007/s11119-023-10054-4 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/15248 | |
dc.identifier.uri | https://link.springer.com/article/10.1007/s11119-023-10054-4 | |
dc.description.abstract | 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) 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.format | application/pdf | es_AR |
dc.language.iso | eng | es_AR |
dc.publisher | Springer | es_AR |
dc.relation | info:eu-repograntAgreement/INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agricultura | es_AR |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_AR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | es_AR |
dc.source | Precision Agriculture : 1-15 (Published: 04 August 2023) | es_AR |
dc.subject | Maíz | es_AR |
dc.subject | Maize | eng |
dc.subject | Nitrógeno | es_AR |
dc.subject | Nitrogen | eng |
dc.subject | Índices de Vegetación | es_AR |
dc.subject | Vegetation Index | eng |
dc.subject | Nutrición | es_AR |
dc.subject | Nutrition | eng |
dc.subject | Vigilancia | es_AR |
dc.subject | Monitoring | eng |
dc.subject | Satélites | es_AR |
dc.subject | Satellites | eng |
dc.subject | Suelo | es_AR |
dc.subject | Soil | eng |
dc.title | Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.rights.license | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | es_AR |
dc.description.origen | EEA Balcarce | es_AR |
dc.description.fil | Fil: Lapaz Olveira, Adrián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Lapaz Olveira, Adrián. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. | es_AR |
dc.description.fil | Fil: Lapaz Olveira, Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Castro Franco, Mauricio. Universidad de los Llanos. Facultad de Ciencias Agropecuarias y Recursos Naturales; Colombia. | es_AR |
dc.description.fil | Fil: Saínz Rozas, Hernán. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. | es_AR |
dc.description.fil | Fil: 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.fil | Fil: Carciochi, Walter. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Carciochi, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. | es_AR |
dc.description.fil | Fil: Balzarini, Mónica. Unidad de Fitopatología Y Modelización Agrícola; Argentina. | es_AR |
dc.description.fil | Fil: Avila, Oscar. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Avila, Oscar. Agencia Nacional de Promoción Científica y Tecnológica; Argentina. | es_AR |
dc.description.fil | Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. | es_AR |
dc.description.fil | Fil: Reussi Calvo, Nahuel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Reussi Calvo, Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. | es_AR |
dc.subtype | cientifico |
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