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Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe

Author: Gracia-Romero, A.
Author: Vergara Diaz, O
Author: Thierfelder, C
Author: Cairns, J.E.
Author: Kefauver, S.C.
Author: Araus, J.L.
Year: 2018
ISSN: 2072-4292
URI: https://hdl.handle.net/10883/19468
Abstract: In the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes and management practices for CA conditions has been explored using remote sensing tools. They may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have any negative impact on the performance of the indexes. Most of the calculated indexes (Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affected by tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels with vegetation. The results of this study highlight the applicability of remote sensing approaches based on RGB images to the assessment of crop performance and hybrid choice.
Format: PDF
Language: English
Publisher: MDPI
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
Country focus: Zimbabwe
Place of Publication: Basel, Switzerland
Issue: 2
Volume: 10
DOI: 10.3390/rs10020349
Keywords: UAV
Keywords: RGB
Keywords: Multispectral
Country of Focus: SUB-SAHARAN AFRICA
Agrovoc: MAIZE
Agrovoc: REMOTE SENSING
Agrovoc: MULTISPECTRAL IMAGERY
Agrovoc: CONSERVATION AGRICULTURE
Agrovoc: AERIAL SURVEYING
Related Datasets: https://www.mdpi.com/2072-4292/10/2/349/s1
Journal: Remote Sensing


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

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