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Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen

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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.

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  • 28 August 2023

    Three authors' last name has been corrected for citation

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Acknowledgements

The research projects FonCyT [Project PICT 0605, 2022]; and INTA [Project PE-E9-I177-001, 2019] funded this work. We acknowledge the farms of Las Balas (LIAG Argentina), El Cisne (FUMISEM SRL), El Palomar (Mr. Pereyra), and La Masia (Mr. Pernia) for providing the locations for the installation of experimental sites. Also, agronomic engineers Francisco Melcón, Nicolás Spurio, Diego Aguilera, Jorge Ramírez, and Rafael de Velazco collaborate for all their support in the execution of the trials. This work is part of a thesis by Adrián Lapaz Olveira in partial fulfillment of the requirements for the Doctor's degree (Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Argentina). We also gratefully acknowledge contribution No. 23-195-J from the Kansas Agricultural Experiment Station. Their support was crucial to completing this work and has yielded valuable findings in agricultural research.

Funding

The research projects FonCyT [Project PICT 0605, 2022]; and INTA [Project PE-E9-I177-001, 2019] funded this work.

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Authors and Affiliations

  1. Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Ruta 226 km 73.5, Balcarce, Buenos Aires, Argentina

    Adrián M. Lapaz Olveira, Hernán R. Saínz Rozas, Walter D. Carciochi, Oscar Avila & Nahuel I. Reussi Calvo

  2. Agencia Nacional de Promoción Científica y Tecnológica, Balcarce, Buenos Aires, Argentina

    Adrián M. Lapaz Olveira & Oscar Avila

  3. Consejo Nacional de Investigaciones Científicas y Técnicas, Mar del Plata, Buenos Aires, Argentina

    Adrián M. Lapaz Olveira, Hernán R. Saínz Rozas, Walter D. Carciochi, Mónica Balzarini & Nahuel I. Reussi Calvo

  4. Precision Agriculture Research Group, FCAyRN, Universidad de los Llanos, Villavicencio, Colombia

    Mauricio Castro-Franco

  5. Instituto Nacional de Tecnología Agropecuaria Balcarce, Ruta, 226 Km 73.5 (C 7620), Balcarce, Buenos Aires, Argentina

    Hernán R. Saínz Rozas

  6. Cátedra de Estadística y Biometría, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba UFYMA (INTA-CONICET, Ing. Agr. Félix Marrone 746, Ciudad Universitaria, Córdoba, Argentina

    Mónica Balzarini

  7. Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA

    Ignacio Ciampitti

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  1. Adrián M. Lapaz Olveira
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  2. Mauricio Castro-Franco
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  5. Mónica Balzarini
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  6. Oscar Avila
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  8. Nahuel I. Reussi Calvo
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Contributions

AMLO: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, visualization, writing-original draft, writing-review & editing. MC-F: conceptualization, data curation, formal analysis, investigation, writing-original draft, writing-review & editing. HRSR: conceptualization, data curation, formal analysis, investigation, funding acquisition, writing-review & editing, project administration, resources, writing-original draft. WDC: data curation, investigation, writing-original draft, writing-review & editing. MB: conceptualization, formal analysis, investigation, writing-original draft, writing-review & editing. OA: data curation, writing-review & editing. ICC: investigation, writing-original draft, writing-review & editing. NIRC: conceptualization, formal analysis, methodology, supervision, investigation, writing-original draft, writing-review & editing, funding acquisition, project administration, and resources.

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Correspondence to Adrián M. Lapaz Olveira or Nahuel I. Reussi Calvo.

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Lapaz Olveira, A.M., Castro-Franco, M., Saínz Rozas, H.R. et al. Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen. Precision Agric 24, 2592–2606 (2023). https://doi.org/10.1007/s11119-023-10054-4

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