Role of epicuticular wax in the regulation of plant growth and health as measured by spectral indices

ABSTRACT

Using remote sensing technology for exploring the trait of interest can provide better results without damaging the plants and are comparatively economic. Traditional methods are available for quantification of different pigments and chemicals present in plants, yet these methods do not allow repeated measurements on the same plant throughout development. The reflected values from plant surfaces are a direct representation of plant physiology including plant morphological factors. The hyperspectral imaging indices for vegetation and water-stressed canopies provided a better indication of each genotype proficiency, thus improving their selection efficiency. The presence of epicuticular wax (EW) influences the reflectance from leaf surface which depends on the presence of leaf pigments including carotenoids, photosynthetic light use efficiency biochemical structures, and water content as they absorb the incident light necessary for photosynthesis. The results obtained suggested decrease carotenoid reflectance index (CRI) and photochemical reflectance index (PRI) values for high wax lines indicating the low concentration of stress-related pigments thus improving plant health and extended maturation. The high waxy lines decreased for plant senescence reflectance index (PSRI) and reduce canopy stress at grain filling and maturation growth stages. A positive correlation between high epicuticular wax (EW) and yield was found confirming previous study. A positive correlation between high epicuticular wax (EW) lines and yield indicated its important role in preventing yield losses under drought conditions.

KEYWORDS:

• Remote sensing
• Drought
• Epicuticular wax load
• Spectral indices

Nomenclature

ASD	=	Analytical spectral devices
CRI	=	Carotenoids reflectance index
CS	=	College Station
EMS	=	Ethyl methanesulfonate
EW	=	Epicuticular wax
GNDVI	=	Green normalized difference vegetation index
HW	=	High wax
LW	=	Low wax
NDVI	=	Normalized difference vegetation index
NIR	=	Near infrared
PAR	=	Photo synthetically active region
PRI	=	Photo reflectance index
PSRI	=	Plant senescence reflectance index
RCBD	=	Randomized complete block design
RILs	=	Recombinant inbred lines
SRI	=	Spectral reflectance indices
WL	=	Wax load
WBI	=	Water band index

Acknowledgements

First and foremost, I would like to express my sincere gratitude to my advisor Prof. Dr. Dirk B. Hays for the continuous support of my Master study and research for his patience, motivation, and immense knowledge. My sincere thanks also go to Dr. Chenghai Yang for collecting hyperspectral imagery for this project and offering his timely help and advice whenever needed. I thank my lab colleagues especially Mr. Henry Awika for the stimulating discussions.

Availability of supporting data

The data sets used for calculation and final statistics are available in this article and more details can be shared with the reviewers on demand.

Consent for publication

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Disclosure statement

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Contributors and Funding Sources

I would like to thank my lab colleagues for their generous support and help throughout my research and contributing to finishing up this project. I would also extend my sincere and special thanks to my committee members for always being there to help and guide. Dr. Chenghai Yang from USDA-ARS College Station provided the hyperspectral imagery obtained from College Station. All the other research was conducted at the Department of Soil & Crop Sciences, Texas A&M University. The funding for this project was supported by the Agricultural innovation program (AIP) for Pakistan funded by the USAID, CIMMYT international Maize and Wheat Improvement Center and the University of California Davis. The Pakistan Agriculture Research Centre (PARC) was involved as both hosting partner as well as leading this project.

Ethical Approval and Consent to participate

Not applicable