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ARS Home » Southeast Area » Fayetteville, Arkansas » Poultry Production and Product Safety Research » Research » Publications at this Location » Publication #420957
Research Project: Developing Best Management Practices for Poultry Litter to Improve Agronomic Value and Reduce Air, Soil and Water Pollution

Location: Poultry Production and Product Safety Research

Title: Development of a vegetation index to quantify the intensity of glyphosate injury in common lambsquarters

Author
item SOTO, MARIO - University Of Arkansas
item PONCE, AURELIE - University Of Arkansas
item FRANCE, WESLEY - University Of Arkansas
item VELASQUEZ, JUAN - University Of Arkansas
item BURGOS, NILDA - University Of Arkansas
item Ashworth, Amanda
item BRY, KRISTOFOR - University Of Arkansas
item KOPARAN, CENGIZ - University Of Arkansas

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/11/2024
Publication Date: 11/17/2024
Citation: Soto, M., Ponce, A.M., France, W., Velasquez, J., Burgos, N.R., Ashworth, A.J., Bry, K., Koparan, C. 2024. Development of a vegetation index to quantify the intensity of glyphosate injury in common lambsquarters. Abstract. American Society of Agronomy.

Interpretive Summary:

Technical Abstract: Weeds decrease crop yield quantity and quality, making proper weed control a priority for farmers. Herbicides are widely used because of their ease of use, greater efficacy, and cost-effectiveness in comparison to other practices. However, the widespread use of chemical methods has promoted the emergence of herbicide-resistant weed biotypes, making their control more complicated. Spectral sensing could help identify and better manage herbicide-resistant biotypes, but no research was conducted to investigate differences in the spectral response of sensitive, tolerant, and resistant weed-biotypes. The project objective was to develop a vegetation index (VI) that quantifies glyphosate injury level in common lambsquarter (Chenopodium Album). A randomized complete block greenhouse experiment was conducted to evaluate common lambsquarter’s spectral response to twelve glyphosate treatments plus an untreated control. The treatments were selected to create a wide range of observable herbicide injury. Visual ratings of injury and hyperspectral radiometric measurements were collected from 400 nm to 1000 nm (0.33 nm spectral resolution) were collected 7 and 14 days after herbicide treatment application. A small number of wavelengths that best describe spectral differences among treatments were identified using dimensionality reduction and used to develop a suite of VI that were correlated with the visual injury ratings. Relationships between the VI and visual ratings were investigated using regression and machine learning. The models were developed using 75% of the dataset and applied to the remaining 25% for validation. The best VI accurately predicted the visual ratings associated with the validation dataset and minimized prediction error. The results provided proof-of-concept that will support future application of the created VI to increase herbicide injury rating efficiency for research and identification of herbicide-resistant weed biotypes using high-throughput phenotyping.