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A novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization

Author: Vergara Diaz, O.
Author: Zaman-Allah, M.
Author: Masuka, B.
Author: Hornero, A.
Author: Zarco‑Tejada, P.J.
Author: Prasanna, B.M.
Author: Cairns, J.E.
Author: Araus, J.L.
Year: 2016
URI: http://hdl.handle.net/10883/18835
Abstract: Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.
Format: PDF
Language: English
Publisher: Frontiers
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Switzerland
Pages: pages 1-13
Issue: 666
Volume: v. 7
DOI: 10.3389/fpls.2016.00666
Keywords: Field Phenotyping
Keywords: Nitrogen Fertilization
Keywords: RGB Indices
Country of Focus: HARARE
Country of Focus: ZIMBABWE
Agrovoc: PLANT BREEDING
Agrovoc: CROP MANAGEMENT
Agrovoc: PHENOTYPES
Agrovoc: FIELD EXPERIMENTATION
Agrovoc: REMOTE SENSING
Agrovoc: MAIZE
Agrovoc: NITROGEN FERTILIZERS
Agrovoc: NORMALIZED DIFFERENCE VEGETATION INDEX
Related Datasets: https://www.frontiersin.org/articles/10.3389/fpls.2016.00666/full#h9
Journal: Frontiers in Plant Science


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  • Maize
    Maize breeding, phytopathology, entomology, physiology, quality, and biotech

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