Use of multispectral airborne images to improve in-season nitrogen management, predict grain yield and estimate economic return of maize in irrigated high yielding environments
Open accessAbstract
Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12–VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0–30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.
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Better cropping systems, fertilization, irrigation practices, and advanced digital agriculture technologies can reduce N fertilizer input and N2O emissions.
“Better cropping systems, fertilization, and irrigation practices, and the use of advanced digital agriculture technologies, such as multi-sensor drone technology to allow farmers to manage crops, soil, fertilization, and irrigation more effectively and precisely, can reduce N fertilizer input and N2O emissions.117,118”
From Technologies and perspectives for achieving carbon neutrality (2021) · cites this paper as [117]
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Technologies and perspectives for achieving carbon neutrality
Wang, F., Harindintwali, J. D., Yuan, Z., Wang, M., Wang, F., Li, S., Yin, Z., Huang, L., Fu, Y., Li, L., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100180 · Cited by 1,330 · see citing works
- Authors
- Maresma, Á., Lloveras, J., & Martínez-Casasnovas, J.
- Affiliations
- Universitat de Lleida (ES)
- Corresponding author
- Maresma, Ángel
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-04-02
- Volume
- 10
- Issue
- 4
- Pages
- 543
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2794728239
- Cited by
- 41
- References
- 77
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 8 · 2023: 5 · 2022: 2 · 2021: 3 · 2020: 6 · 2019: 5 · 2018: 2
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, cc-by)
- UdL Open Repository (University of Lleida) (publishedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by)
- Repositori ObertUDL (University of Lleida) (submittedVersion, cc-by)
- Funders
- Ministerio de Ciencia e Innovación (AGL2012-35122)
- Universitat de Lleida
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Crop Yield and Soil Fertility
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- maize, nitrogen management, vegetation indices, grain yield prediction, economic returns, green chlorophyll index, SPAD chlorophyll meter, plant height, optimal nitrogen rate, nitrogen availability, nitrogen deficiency detection, linear-plateau model
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