Evaluating the Spectral Response and Yield of Soybean Following Exposure to Sublethal Rates of 2,4-D and Dicamba at Vegetative and Reproductive Growth Stages

Abstract

The commercialization of synthetic auxin-resistant crops and the commensurate increase in post-emergent auxin-mimic herbicide applications has resulted in millions of hectares of injury to sensitive soybeans in the United States since 2016. Visual yield loss estimations following auxin injury can be difficult. The goal of this research was to determine if spectral variations following auxin injury to soybean allow for more precise yield loss estimations. Identical field experiments were performed in 2018, 2019, and 2020 in Columbia, Missouri to compare the ability of established vegetative indices to differentiate between exposure levels of 2,4-D and dicamba in soybean and predict yield loss. Soybeans were planted at three timings for growth stage separation and were exposed to sublethal rates of 2,4-D and dicamba at the R2, R1, and V3 growth stages. A UAV-mounted multispectral sensor was flown over the trial 14 days after the herbicide treatments. The results of this research found that vegetative indices incorporating the red-edge wavelength were more consistent in estimating yield loss than indices comprised of only visible or NIR wavelengths. Yield loss estimations became difficult when soybean injury occurred during later reproductive stages when soybean biomass was increased. This research also determined that when injury occurs to soybean in vegetative growth stages late in the growing season there is a greater likelihood for yield loss to occur due to decreased time for recovery. The results of this research could provide direction for more objective and accurate evaluations of yield loss following synthetic auxin injury than what is currently available.

Genetic Dissection of Bentazone Tolerance Loci in Cultivated Soybeans: A Genome-Wide Association Study

“…Broadleaf weed management is essential for large-scale soybean production systems. However, phytotoxicity is a major concern in the application of POST herbicides, which might disrupt soybean vegetative growth and yield [48]. Bentazone is a selective-contact herbicide that is commonly employed in the production of soybeans, wheat, and rice.…”

Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies

“…Fields with high soil fertility will have high canopy coverage and, by extension, will have their VI's performing better in yield estimation than fields with low soil fertility. This was clearly illustrated in results obtained by Oseland et al (2021) when VI's obtained in the V3 stage performed better in predicting yield loss as compared to those obtained in the R2 stage when soybean was responding to sublethal rates of dicamba exposure. Similar results showing the moderate performance of VI's in predicting yield were observed by Al-Gaadi et al ( 2016) when [a] Comparison of estimated dose (ED) was done using standard error (S.E).…”

“…Therefore, soybean researchers and producers are interested in a cost-effective and precise method of identifying and quantifying dicamba injury in soybean. Oseland et al (2021) used vegetative indices, which included a red-edge wavelength (717 nm), to determine yield loss in soybean from 2,4-D and dicamba at sublethal rates. Huang et al (2015) assessed soybean injuries from glyphosate drift using vegetative indices from multispectral remote sensing.…”

Cover crop termination options and application of remote sensing for evaluating termination efficiency

“…Along with crop nutrition and health assessment, in recent times, vegetative indices and remote sensing tools have been used to evaluate the herbicide injury on crops [ 33 ]. Zhang et al, [ 34 ] used hyperspectral images to calculate the Herbicide Damage Ratio Index (HDRI) and Herbicide Damage Normalized Index (HDNI) for estimating dicamba injury on dicamba sensitive soybean and found that the extent of dicamba injury on soybean can be estimated with hyperspectral images with over 90% accuracy.…”

“…Zhang et al, [ 34 ] used hyperspectral images to calculate the Herbicide Damage Ratio Index (HDRI) and Herbicide Damage Normalized Index (HDNI) for estimating dicamba injury on dicamba sensitive soybean and found that the extent of dicamba injury on soybean can be estimated with hyperspectral images with over 90% accuracy. In another study, Oseland et al, [ 33 ] used different vegetative indices for evaluating 2,4-D and dicamba injury and yield reduction for soybean and found that NDREI is the more accurate index in estimating yield loss. Huang et al, [ 35 ] found that NDVI can efficiently estimate cotton ( Gossypium hirsutum L.) yield loss following glyphosate exposure.…”