Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat

Quemada Saenz-Badillos, Miguel and Pancorbo de Oñate, José Luís and Alonso Ayuso, María and Gabriel Pérez, José Luis and López Herrera, Juan and Pérez Martín, Enrique (2019). Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat. In: "12th European Conference on Precision Agriculture", 08/07/2019-11/07/2019, Montpellier, Francia. ISBN 978-90-8686-337-2. p. 7. https://doi.org/10.3920/978-90-8686-888-9_40.

Description

Title: Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat
Author/s:
  • Quemada Saenz-Badillos, Miguel
  • Pancorbo de Oñate, José Luís
  • Alonso Ayuso, María
  • Gabriel Pérez, José Luis
  • López Herrera, Juan
  • Pérez Martín, Enrique
Item Type: Presentation at Congress or Conference (Article)
Event Title: 12th European Conference on Precision Agriculture
Event Dates: 08/07/2019-11/07/2019
Event Location: Montpellier, Francia
Title of Book: Precision Agriculture'19
Date: July 2019
ISBN: 978-90-8686-337-2
Subjects:
Freetext Keywords: nitrogen fertilization; crop sensors; unmanned aerial vehicle
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Producción Agraria
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The use of vegetation indices (VIs) might help to adjust fertilizer and irrigation, increase yield and reduce costs and nitrogen (N) losses. The objectives were to evaluate the use of VIs extracted from remote sensing imagery to estimate crop N status, yield and grain N content in wheat (Triticum aestivum, L.). A field experiment conducted with four N fertilizer levels randomly distributed in 32 plots (25×25 m2), half irrigated and half rain-fed. At two sampling times, biomass and N uptake were determined, ground sensors measurements were taken, and multi-spectral imagery was acquired by an unmanned aerial vehicle. At harvest, yield was recorded with a combine and grain N determined. VIs obtained from ground measurements were highly correlated with those from the aerial platform. Using planar domain VIs, which relate N concentration and biomass, correlated well to crop N status and showed high potential for fertilizer recommendation, as well as yield and grain N prediction in wheat.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainAGL2017-83283-C2-1-RUnspecifiedUnspecifiedRotaciones en regadío para un uso eficiente de agua y nitrógeno
Madrid Regional GovernmentP2018/BAA-4330AGRISOST-IIIAntonio Vallejo GarcíaSistemas agrarios sostenibles. Manejo de carbono, nitrógeno y agua para optimizar producción y calidad

More information

Item ID: 64671
DC Identifier: http://oa.upm.es/64671/
OAI Identifier: oai:oa.upm.es:64671
DOI: 10.3920/978-90-8686-888-9_40
Official URL: https://ecpa2019.agrotic.org/
Deposited by: Memoria Investigacion
Deposited on: 15 Oct 2020 11:51
Last Modified: 15 Oct 2020 11:51
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