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A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Overview of attention for article published in Frontiers in Plant Science, May 2016
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Title
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization
Published in
Frontiers in Plant Science, May 2016
DOI 10.3389/fpls.2016.00666
Pubmed ID
Authors

Omar Vergara-Díaz, Mainassara A. Zaman-Allah, Benhildah Masuka, Alberto Hornero, Pablo Zarco-Tejada, Boddupalli M. Prasanna, Jill E. Cairns, José L. Araus

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 R (2)~ 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.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 233 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Mexico 1 <1%
Colombia 1 <1%
United States 1 <1%
Unknown 229 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 20%
Researcher 46 20%
Student > Master 38 16%
Student > Bachelor 19 8%
Student > Doctoral Student 11 5%
Other 27 12%
Unknown 45 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 113 48%
Engineering 16 7%
Environmental Science 15 6%
Biochemistry, Genetics and Molecular Biology 8 3%
Computer Science 5 2%
Other 14 6%
Unknown 62 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 June 2016.
All research outputs
#18,458,033
of 22,870,727 outputs
Outputs from Frontiers in Plant Science
#13,805
of 20,251 outputs
Outputs of similar age
#250,836
of 334,246 outputs
Outputs of similar age from Frontiers in Plant Science
#307
of 529 outputs
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So far Altmetric has tracked 20,251 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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We're also able to compare this research output to 529 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.