Auxin injury on cotton, I: Aerial reflectance data, crop injury, and yield

Abstract Synthetic auxin herbicide movement onto sensitive cotton ( Gossypium hirsutum L.) cultivars has impacted many US cotton hectares. The spatial scope and severity of auxin damage in‐season is typically estimated by an agronomist. The use of remote sensing technology has the potential to objectively quantify the spatial scope and severity of auxin damage. Experiments were conducted in 2019, 2020, and 2021 in Grand Junction, TN, to determine: (1) the effect of reflectance data collection timing; (2) the effect of auxin exposure timing; (3) the value of near infrared and red‐edge (RE) reflectance versus reflectance within the visible spectrum data; and (4) if/how visual injury relates to aerial reflectance data. Applications of 2,4‐D or dicamba were made to susceptible cotton cultivars at 1X, 1/4X, 1/16X, 1/64X, 1/256X, and 1/1024X rates at either matchhead square (MHS) or 2 weeks after first bloom (FB+2WK). Non‐treated controls were also included for each application timing. Aerial reflectance data were collected 7, 14, 21, and 28 days after application. Unsupervised classification of images into pixels with and without vegetation did not increase correlations between vegetation indices (VIs) and application rate. Although Vis, which generated the strongest correlations with application rate, visual injury, and relative lint yield, were generally RE based, similar correlations were also noted with visible spectrum VIs. Correlations were greater when auxin injury occurred at MHS than FB+2WK. Results suggest reflectance measured within the visible spectrum can quantify the scope and severity of auxin injury if the injury occurs early during the growing season.

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Publication Details

Journal
Agronomy Journal
Published
2024-09-26
DOI
https://doi.org/10.1002/agj2.21698
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Auxin injury on cotton, I: Aerial reflectance data, crop injury, and yield

Savana Denton, Thomas C. Mueller, Tyson B. Raper, Lori Duncan et al.
Agronomy Journal
Remote Sensing in Agriculture
article

Auxin injury on cotton, I: Aerial reflectance data, crop injury, and yield

Savana Denton, Thomas C. Mueller, Tyson B. Raper, Lori Duncan, Darrin M. Dodds, Chris Main
article en

Abstract

Abstract Synthetic auxin herbicide movement onto sensitive cotton ( Gossypium hirsutum L.) cultivars has impacted many US cotton hectares. The spatial scope and severity of auxin damage in‐season is typically estimated by an agronomist. The use of remote sensing technology has the potential to objectively quantify the spatial scope and severity of auxin damage. Experiments were conducted in 2019, 2020, and 2021 in Grand Junction, TN, to determine: (1) the effect of reflectance data collection timing; (2) the effect of auxin exposure timing; (3) the value of near infrared and red‐edge (RE) reflectance versus reflectance within the visible spectrum data; and (4) if/how visual injury relates to aerial reflectance data. Applications of 2,4‐D or dicamba were made to susceptible cotton cultivars at 1X, 1/4X, 1/16X, 1/64X, 1/256X, and 1/1024X rates at either matchhead square (MHS) or 2 weeks after first bloom (FB+2WK). Non‐treated controls were also included for each application timing. Aerial reflectance data were collected 7, 14, 21, and 28 days after application. Unsupervised classification of images into pixels with and without vegetation did not increase correlations between vegetation indices (VIs) and application rate. Although Vis, which generated the strongest correlations with application rate, visual injury, and relative lint yield, were generally RE based, similar correlations were also noted with visible spectrum VIs. Correlations were greater when auxin injury occurred at MHS than FB+2WK. Results suggest reflectance measured within the visible spectrum can quantify the scope and severity of auxin injury if the injury occurs early during the growing season.

Agronomy JournalVol. 116(6)
Simplot (United States) (US), Cotton (United States) (US), University of Tennessee at Knoxville (US), Mississippi State University (US)
Openalex Percentile: Top 84%
Remote Sensing in Agriculture
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