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Article

Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments

by
Jeffrey M. Hegstad
1,*,
Hua Mo
1,
Adam P. Gaspar
1 and
Dwain Rule
2
1
Corteva Agriscience, 7250 NW 62nd Ave., Johnston, IA 50131, USA
2
Corteva Agriscience, 9330 Zionsville Rd., Indianapolis, IN 46268, USA
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(2), 340; https://doi.org/10.3390/agronomy14020340
Submission received: 11 January 2024 / Revised: 26 January 2024 / Accepted: 3 February 2024 / Published: 7 February 2024
(This article belongs to the Special Issue Crop Management and Productivity by Remote Sensing for Sustainable Agricultural Systems)
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Abstract

:
Soybean (Glycine max) is one of the most important oilseed crops grown in North America and a key contributor to the global protein supply. Insect feeding by a major soybean pest, the bean leaf beetle (BLB; Cerotoma trifurcata), can result in economic yield loss if not controlled. The objective of this research was to use unmanned aerial vehicle (UAV) image analysis to compare the agronomic and efficacy traits of two soybean insecticide seed treatments (IST) in locations with BLB feeding. Across the 2018–2023 field trial locations, 29 had low BLB feeding pressure (less than 25% feeding damage to no IST plots) and 31 had high BLB feeding pressure (greater than 25% feeding damage to no IST plots). In low BLB pressure locations, cyantraniliprole and imidacloprid seed treatments had significantly higher BLB efficacy, significantly higher UAV greenness, and significantly higher final yield as compared to no IST. In high BLB pressure locations, cyantraniliprole and imidacloprid seed treatments were significantly better compared to no IST for BLB efficacy, UAV emergence, UAV vigor, UAV greenness, and final yield. In high BLB pressure locations, cyantraniliprole had significantly higher BLB efficacy, significantly better UAV emergence, and significantly higher yield compared to imidacloprid. The cyantraniliprole treatment had a +254.5 kg/ha increase compared to no IST in low BLB pressure locations and a +213.7 kg/ha increase in high BLB pressure locations. The imidacloprid treatment had a +163.4 kg/ha yield increase compared to no IST in low BLB pressure locations and a +121.4 kg/ha yield increase in high BLB pressure locations. The use of UAV image analysis enabled quantification of the effect of BLB feeding on early-season agronomic traits and, when combined with efficacy and final yield data, successfully differentiated the performance of two soybean ISTs in environments with low or high insect pressure.

1. Introduction

Oilseed crops are grown in North America and used for very diverse human, animal, and industrial applications [1,2,3,4]. Soybean (Glycine max. L. Merr.) is one of the largest oilseed crops produced annually in North America, with the United States planting over 87 million acres [5] and Canada planting over 5 million acres [6] in 2023.
Feeding from insects can be a yield-limiting factor for soybean producers. Bean leaf beetles (BLB, Cerotoma trifurcata) are native to North America and are present throughout soybean-producing regions of the United States [7,8,9,10,11,12,13]. Adult beetles are approximately 6 mm in length and can vary in colors of brown, green, gray, orange, tan, or red, with a distinguishing black triangle behind the thorax. They often have quadrangular black markings on the forewings [11,12,13]. Two generations of BLB typically occur in northern and midwestern US states, while there may be three generations in southern US states [12,13,14]. Adult BLB feed on soybean leaves, stems, and pods, potentially causing economic yield loss [10,11,12,13,14,15,16]. BLB may also transmit virus vectors that can further reduce seed quality and final yield [14,15,16,17,18,19,20].
Seed-applied insecticides were found to be an economical method of protecting seedlings by lowering the amount of overall pesticide utilized and reducing the risk of chemical handling by growers [21]. Neonicotinoids have been successfully used as an insecticide seed treatment to protect seedlings of different crops from damage from multiple insect species [22,23,24,25,26,27]. Imidacloprid (IRAC group 4A) was the first neonicotinoid insecticide commercialized in 1991, with activity against aphids, flea beetles, planthoppers, and whiteflies [22,23,24,25,26,27]. Over the past several years, neonicotinoid compounds have become the most widely used insecticide seed treatment across North America [22,23,24,25,26,27].
Cyantraniliprole is an anthranilic diamide insecticide (IRAC group 28) with activity against a broad spectrum of coleopteran, dipteran, hemipteran, lepidopteran, and thysanopteran insects [28,29,30,31,32,33,34]. Cyantraniliprole is water-soluble and was found to be highly mobile within the xylem of plants and into leaf tissue, where insect feeding occurs [35]. Additional efficacy against below-ground soil pests such as wireworms (Agriotes spp.) and white grubs (Melolontha spp.) was observed for cyantraniliprole-treated seed [36]. As a seed treatment, both imidacloprid and cyantraniliprole have a use rate of 0.075 mg ai/seed, which is dramatically lower compared to the amount of active ingredient needed for broadcast spraying of fields.
Seed treatments with insecticides have been found to improve early-season crop emergence and stand establishment [37,38,39,40,41,42]. Plant vigor has typically been evaluated visually, and plant stand has been quantified by counting individual plants and correlating those data to final yield performance [37,38,39,40,41,42]. The use of unmanned aerial vehicles to capture digital images of crop growth throughout the growing season has greatly improved the accuracy of evaluating agronomic performance, weed pressure, water stress, nutrient stress, or plant maturity [43,44,45,46,47]. Seedling emergence, early seedling vigor, and seedling greenness were quantified using UAV image-to-data analysis to evaluate the performance of soybean fungicide seed treatments [48]. These data had a high level of precision and repeatability, which differentiated performance that could not be easily determined by the subjectivity of multiple individuals using visual scoring at different time points.
It was of interest to utilize UAV image analysis to compare different soybean ISTs for emergence, vigor, and greenness agronomic traits at the time when BLB feeding was occurring. The objective of this research was to combine UAV image analysis with BLB efficacy and yield data to compare the performance of ISTs in locations with low BLB feeding pressure (less than 25% feeding damage to no IST plots) or high BLB feeding pressure (greater than 25% feeding damage to no IST plots).

2. Materials and Methods

Soybean field trials were conducted from 2018 to 2023 using Corteva Agriscience commercial soybean varieties. The varieties that were selected represented popular commercial cultivars grown by soybean producers in their respective regions (Table 1). A workflow diagram outlined the processes used each year for field trial evaluations (Figure 1). The seeds were treated with 0.075 mg ai/seed cyantraniliprole + fungicide seed treatment (FST), 0.075 mg ai/seed imidacloprid + FST, or FST no IST (Figure 1). The same commercial base FST was used across all treatments within each year. The seed treatment active ingredients were mixed with a sufficient volume of water to create a slurry. Soybean seed was treated using a Hege 11 laboratory bowl seed treater (Wintersteiger AG, Ried im Innkreis, Austria). Seed treatment slurries were pipetted in volumes of 90–130 mL per 140,000 seeds, and the seed was cycled in the treater bowl for 15–25 s to allow uniform coating. Treated seed was allowed to completely air dry prior to planting.
Field trials were designed as a randomized complete block with three replications of two-row plots 4.57 m in length with 0.76 m row spacing. Plots were planted with a precision plot planter (ALMACO, Nevada, IA, USA) at a rate of 150 seeds per row (300 seeds per two-row plot) (Figure 1). BLB insect feeding damage was scored at the VC to V2 growth stage using a visual 1 to 9 score based upon the estimated amount of feeding on plants in the no IST plots (Figure 1). Scores were 1 = excessive feeding on all leaves with large holes and >75% leaf tissue missing, 3 = feeding on most leaves with 50–75% leaf tissue missing, 5 = feeding on several leaves evident with 25% to 50% leaf tissue missing, 7 = feeding on some leaves with up to 25% leaf tissue missing, or 9 = minimal to no feeding observed across the plot (Figure 2). Previous publications have determined 20% to 30% of the missing tissue due to BLB feeding as an economic threshold for foliar application of insecticide [11,12]. Based upon this rationale, a BLB feeding score of 5 on the no IST plots at the locations evaluated would represent the economic threshold where a chemical treatment would be justified. Locations with an average BLB feeding score of 6 or 7 on the FST no IST plots were considered low pressure, while locations with an average BLB feeding score of 1 to 5 on the FST no IST plots were considered high pressure (Table 1).
Plots were flown with a DJI Matrice series UAV drone fitted with a 24.3-megapixel Sony A6000 camera and 35 mm lens to capture plot images for early season agronomic traits (Figure 1). Plots were flown as a single image/data capture for each location between the V1 and V2 growth stages. UAV image-to-data analysis was utilized to quantify emergence, vigor, and plant health (greenness) traits as described in Hegstad et al. [48]. For emergence, a lower value indicated fewer gaps between plants and better emergence, while a higher value indicated more gaps between plants and worse emergence. A higher value for vigor indicated more green pixels in the image and bigger plants as compared to a lower value with fewer green pixels and smaller plants. For greenness (the intensity of green pixels within the image), a higher value represented better plant health compared to a lower value that had less intense green coloration of the plant tissue.
The harvest weight, grain moisture (adjusted to 13%), and total area of the plot were used to calculate the final yield in kg/ha using the formula ([plot harvest weight × plot moisture]/[total plot length × total plot width]). Analysis of variance (ANOVA) and best linear unbiased estimators (BLUEs) for BLB feeding score, emergence, vigor, greenness, and final yield were calculated using a linear mixed model [49] of the SAS/STAT software, Version 9.4 (SAS Institute Inc., Cary, NC, USA). In the mixed model, seed treatment and BLB location pressure were considered fixed effects, and the year, location (nested within the year), replication (nested within the location), and the interaction between location and seed treatment were considered random effects. Fischer’s least significant difference (LSD) values were calculated, and values followed by a common letter were not significantly different at the α = 0.05 level.

3. Results

Of the 60 soybean locations evaluated from 2018 to 2023, 29 were considered to have low BLB feeding pressure and 31 were high BLB feeding pressure locations (Table 1). The seed treatment had a significant effect on the BLB feeding score in both low BLB pressure and high BLB pressure locations, according to the results of the ANOVA test (Table 2). In low BLB pressure locations, cyantraniliprole and imidacloprid were not statistically different compared to each other and were significantly better for BLB efficacy compared to the no IST treatment (Table 3). Both cyantraniliprole and imidacloprid were significantly higher for BLB efficacy compared to the no IST control in high BLB pressure locations (Table 3). In the high BLB pressure locations, the cyantraniliprole treatment had a significantly better efficacy score compared to the imidacloprid treatment (Table 3).
The ANOVA for UAV emergence in low BLB pressure locations showed the seed treatment effect was not significant, while in high BLB pressure locations, the seed treatment effect was significant (Table 2). In low BLB pressure locations, there was not a statistical difference among the no IST, cyantraniliprole, and imidacloprid seed treatments for UAV emergence (Table 3). The imidacloprid treatment had significantly better UAV emergence compared to the no IST seed treatment in high BLB pressure locations (Table 3). In high BLB pressure locations, the cyantraniliprole treatment had significantly better UAV emergence compared to both the imidacloprid and no IST seed treatments (Table 3).
The seed treatments were not significantly different in low BLB pressure locations but were significantly different in high BLB pressure locations when UAV vigor data were subject to ANOVA (Table 2). In low BLB pressure locations, there was not a statistical difference among the no IST, cyantraniliprole, and imidacloprid seed treatments for UAV vigor (Table 3). Both cyantraniliprole and imidacloprid had significantly better UAV vigor compared to the no IST treatment in locations with high BLB pressure (Table 3). In high BLB pressure locations, the cyantraniliprole treatment had significantly better UAV vigor compared to the imidacloprid treatment (Table 3).
The ANOVA for UAV greenness showed the seed treatments were significantly different in low and high BLB pressure locations (Table 2). In both low BLB pressure locations and high BLB pressure locations, the cyantraniliprole and imidacloprid treatments were not statistically different compared to each other, but both ISTs had significantly higher UAV greenness values compared to the no IST treatment (Table 3).
For yield, the ANOVA indicated the seed treatments were significant in low BLB pressure and high BLB pressure locations (Table 2). The cyantraniliprole and imidacloprid treatments had significantly higher yields compared to the no IST treatment in both low BLB pressure and high BLB pressure locations (Table 3). The cyantraniliprole treatment had a significantly higher yield compared to the imidacloprid treatment in both low BLB pressure and high BLB pressure breakouts (Table 3).

4. Discussion

Seed treatments offer the advantage of targeted application of different active ingredients at a dramatically lower use rate, a smaller area of pesticide contact as compared to broad-acre foliar applications, and a reduced risk of pesticide exposure for crop producers [21]. Neonicotinoid seed treatments were effective in controlling C. trifurcate feeding in snap beans [50]. The presence of BLB and the occurrence of the soybean bean pod mottle virus were greatly reduced by an insecticide seed treatment on soybeans [51]. It was of interest to contrast the efficacy and performance of soybean ISTs in locations with different levels of BLB feeding pressure. In low BLB pressure locations, both imidacloprid and cyantraniliprole had similar BLB efficacy scores that were significantly higher compared to no IST (Table 3). A difference was observed in high BLB pressure locations, where the cyantraniliprole treatment had a significantly higher efficacy score compared to the imidacloprid treatment (Table 3). These data demonstrate that different soybean ISTs can significantly reduce the amount of BLB feeding and early plant damage. There was also an efficacy advantage for the cyantraniliprole treatment when compared to the imidacloprid treatment in locations with high BLB feeding pressure.
Seedling emergence and plant stand establishment were increased by using different fungicides, insecticides, and/or nematicide seed treatment active ingredients [37,38,39,40,41,42]. Using the UAV to measure the total gaps between plants in soybean plots was found to be an effective method for quantifying emergence for different soybean fungicide seed treatments [48]. In low BLB pressure locations, there was not a significant difference detected for UAV emergence between the no IST, cyantraniliprole, and imidacloprid treatments (Table 3). In high BLB pressure locations, the cyantraniliprole and imidacloprid treatments had significantly less UAV emergence gaps (and thus a better overall initial plant stand) compared to the no IST treatment (Table 3). The cyantraniliprole treatment had significantly better UAV emergence compared to the imidacloprid treatment in high BLB pressure locations (Table 3). These data demonstrate that high levels of BLB feeding (and potentially other below-ground insects, which were not measured in this manuscript) on plots with no insecticide can reduce seed emergence and initial stand establishment. In high BLB pressure locations, both ISTs tested had a significant advantage in providing better emergence compared to no IST. Using the UAV image analysis differentiated the performance of soybean ISTs for emergence in locations with high BLB feeding pressure, as the cyantraniliprole treatment had significantly better UAV emergence compared to the imidacloprid treatment.
Seedling vigor is a complex interaction between seed germination, emergence, and early growth factors in the context of the interaction between plant genetics and environmental factors [52,53,54,55]. Neonicotinoid-treated corn did not have a significant advantage for vigor in field testing when compared to a fungicide-only treatment using visual scoring [56]. These results are similar to the results obtained in locations with low BLB feeding pressure, where there was not a significant difference between all treatments for the UAV vigor values (Table 3). However, in high BLB feeding locations, both cyantraniliprole and imidacloprid had significantly better UAV seedling vigor as compared to no IST (Table 3). In addition, the cyantraniliprole treatment had significantly better UAV vigor compared to the imidacloprid treatment in high BLB pressure locations (Table 3). These data demonstrate the advantage soybean ISTs can provide in enabling better emergence and early plant growth at locations with high BLB pressure. The UAV image analysis was also able to quantify performance differences in early vigor for different soybean ISTs in locations with a high level of BLB feeding pressure.
UAV image analysis of the greenness trait (intensity of green pixels) was found to be useful for measuring early chlorosis and as an indicator of early plant health [48]. In both low BLB pressure locations and high BLB pressure locations, the cyantraniliprole and imidacloprid treatments had significantly better UAV greenness compared to no IST (Table 3). There was no difference detected for UAV greenness when cyantraniliprole was compared to imidacloprid in either the low BLB pressure or high BLB pressure locations (Table 3). These data demonstrate that the UAV was useful in detecting an improvement in plant health and greenness for both ISTs as compared to no IST. Cyantraniliprole or imidacloprid-treated plots had plants that were significantly healthier compared to the no IST plots across all the locations tested.
Previous research on the effect of seed treatment on final yield performance has been inconsistent. Esker and Conley [57] found yield was more correlated with cultivar selection than seed treatment. Different neonicotinoid insecticide seed treatments were determined to have minimal impact on final yield, and results were variable depending upon the locations evaluated or other cultural practices [58,59,60,61]. In contrast, North et al. [62] found a significant increase in the yield of neonicotinoid-treated soybean compared to fungicide-only treatments in wide-area testing across the southern USA from 2005 to 2014. Furthermore, Gaspar et al. [63] found significant yield increases from the use of neonicotinoid-treated soybeans. Previous publications have not contrasted the yield performance of soybean ISTs in locations with differing levels of insect pressure. In both low BLB pressure locations and high BLB pressure locations, the cyantraniliprole and imidacloprid treatments were significantly higher in yield compared to no IST (Table 3). The cyantraniliprole treatment had a +254.5 kg/ha increase compared to no IST in low BLB pressure locations and a +213.7 kg/ha increase in high BLB pressure locations (Table 3). The imidacloprid treatment had a +163.4 kg/ha yield increase compared to no IST in low BLB pressure locations and a +121.4 kg/ha yield increase in high BLB pressure locations. The cyantraniliprole treatment had a significantly higher yield compared to the imidacloprid treatment in low BLB pressure locations (+91.1 kg/ha increase) and high BLB pressure locations (+92.3 kg/ha increase) (Table 3). These data demonstrate the value of ISTs to soybean producers in providing a significant increase in final yield as compared to a seed treatment without an insecticide component.

5. Conclusions

The utilization of UAV image-to-data analysis enabled quantification of the performance of two different soybean ISTs in locations with low or high BLB feeding pressure. In low BLB pressure locations, cyantraniliprole and imidacloprid seed treatments had significantly better BLB efficacy, significantly higher seedling greenness, and significantly higher final yield as compared to no IST. When compared to no IST in high BLB pressure locations, the cyantraniliprole and imidacloprid seed treatments had significantly higher BLB efficacy, significantly better emergence, significantly better vigor, significantly better greenness, and significantly higher final yield. In high BLB pressure locations, cyantraniliprole was significantly better compared to imidacloprid for BLB efficacy, emergence, and final yield. The cyantraniliprole treatment had a +254.5 kg/ha increase compared to no IST in low BLB pressure locations and a +213.7 kg/ha increase in high BLB pressure locations. The imidacloprid treatment had a +163.4 kg/ha yield increase compared to no IST in low BLB pressure locations and a +121.4 kg/ha yield increase in high-pressure locations. The fact that differences in the performance of ISTs were detectable in both low and high insect feeding pressure environments was unique, as historical evaluations typically relied on high insect pressure locations to separate treatment differences. Combining the UAV data with yield data were useful for contrasting the performance of two different soybean ISTs and demonstrating the effectiveness of ISTs in providing both insect efficacy and improvement in agronomic traits. The methods described in this manuscript for combining UAV image analysis with efficacy and yield data will be useful for future research in characterizing new IST concepts in other crops with different insect pests.

Author Contributions

Conceptualization, J.M.H., A.P.G. and D.R.; Methodology, J.M.H., H.M., A.P.G. and D.R.; Software, H.M.; Validation, H.M.; Formal analysis, J.M.H. and H.M.; Investigation, J.M.H.; Resources, J.M.H., A.P.G. and D.R.; Data curation, J.M.H.; Writing—original draft, J.M.H.; Writing—review and editing, J.M.H., H.M., A.P.G. and D.R.; Supervision, A.P.G.; Project administration, A.P.G. and D.R.; Funding acquisition, A.P.G. and D.R. All authors have read and agreed to the published version of the manuscript.

Funding

No external funding; all research was conducted using internal Corteva funds.

Data Availability Statement

The data are available within the article.

Conflicts of Interest

The authors Jeffrey M. Hegstad, Hua Mo, Adam P. Gaspar, and Dwain Rule are employed by the company Corteva Agriscience. They declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Abbreviations

BLB: bean leaf beetle; BLUE: best linear unbiased estimator; df: degrees of freedom; FST: fungicide seed treatment; IST: insecticide seed treatment; LSD: least significant difference; UAV: unmanned aerial vehicle

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Agronomy 14 00340 g001
Figure 1. Workflow diagram.
Figure 1. Workflow diagram.
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Figure 2. Comparison of bean leaf beetle feeding at the VC growth stage, Johnston, IA, May 2022. FST no IST (left) with a plot visual score of 3 (feeding on all leaves with 50–75% leaf tissue missing) and cyantraniliprole + FST (right) with a plot visual score of 9 (no feeding evident).
Figure 2. Comparison of bean leaf beetle feeding at the VC growth stage, Johnston, IA, May 2022. FST no IST (left) with a plot visual score of 3 (feeding on all leaves with 50–75% leaf tissue missing) and cyantraniliprole + FST (right) with a plot visual score of 9 (no feeding evident).
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Table 1. 2018–2023 Soybean locations with average bean leaf beetle feeding score on plots with no insecticide seed treatment (IST).
Table 1. 2018–2023 Soybean locations with average bean leaf beetle feeding score on plots with no insecticide seed treatment (IST).
YearCity, StateVarietyPlanting DateAvg. BLB Feeding Score/Group 1Harvest Date 2
2018Mansfield, IllinoisP36T36X30 April 20186 Low11 October 2018
2018Pesotum, IllinoisP36T36X28 April 20186 Low12 October 2018
2018Seymour, IllinoisP36T36X26 April 20187 Low28 October 2018
2018Colfax, IndianaP36T36X1 May 20186 Low24 October 2018
2018Windfall1, IndianaP36T36X15 May 20185 High22 October 2018
2018Windfall2, IndianaP36T36X4 May 20185 High25 October 2018
2018Atlantic, IowaP28T71X10 May 20186 Low22 October 2018
2018Johnston, IowaP28T71X24 April 20185 High17 October 2018
2018Montezuma, IowaP28T71X25 April 20185 HighNot Harvested
2018Prairie City, IowaP31A22X30 April 20186 Low25 October 2018
2018Winterset, IowaP28T71X25 April 20186 Low21 October 2018
2019Morrisonville, IllinoisP38A98X3 June 20196 Low23 October 2019
2019Seymour, IllinoisP38A98X18 May 20196 Low15 October 2019
2019Colfax, IndianaP38A98X2 June 20196 Low11 October 2019
2019Windfall, IndianaP38A98X16 May 20196 Low28 October 2019
2019Atlantic, IowaP31A22X4 May 20196 Low15 October 2019
2019Dallas Center, IowaP31A22X3 June 20197 Low23 October 2019
2019Johnston1, IowaP31A22X15 April 20197 Low18 October 2019
2019Johnston2, IowaP31A22X15 April 20196 Low20 October 2019
2019Johnston3, IowaP31A22X15 April 20196 Low22 October 2019
2019Reasoner, IowaP31A22X3 June 20196 Low18 October 2019
2019Washington, IowaP31A22X16 April 20195 High16 October 2019
2019Winterset, IowaP31A22X3 June 20195 High27 October 2019
2019Silverthorn, MissouriP48A60X10 May 20195 High3 October 2019
2019Obion, TennesseeP48A60X14 May 20196 Low5 October 2019
2020Champaign, IllinoisP36A83X10 April 20207 Low12 October 2020
2020Windfall1, IndianaP36A83X9 May 20206 LowNot Harvested
2020Windfall2, IndianaP36A83X25 April 20207 Low10 October 2020
2020DeSoto, IowaP31A22X2 May 20207 Low30 October 2020
2020Johnston1, IowaP31A22X20 April 20204 High30 October 2020
2020Johnston2, IowaP31A22X20 April 20205 High30 October 2020
2020Montezuma, IowaP31A22X12 May 20207 Low7 October 2020
2021Seymour, IllinoisP36A83X16 April 20212 High24 September 2021
2021Groomsville, IndianaP36A83X27 April 20216 Low4 November 2021
2021Atlantic, IowaP33A53X24 April 20216 Low27 September 2021
2021Johnston1, IowaP33A53X6 May 20214 High5 October 2021
2021Johnston2, IowaP33A53X22 April 20215 High5 October 2021
2021Johnston3, IowaP33A53X7 April 20215 High5 October 2021
2021Johnston4, IowaP33A53X7 April 20214 High5 October 2021
2021Reasoner1, IowaP33A53X6 May 20214 High14 October 2021
2021Reasoner2, IowaP33A53X6 May 20215 High8 October 2021
2021Winterset1, IowaP33A53X5 May 20214 High5 October 2021
2021Winterset2, IowaP33A53X5 May 20214 High4 October 2021
2022Colfax, IndianaP29A25X28 April 20221 High2 October 2022
2022Windfall, IndianaP29A25X22 April 20221 High14 October 2022
2022Desoto, IowaP29A25X19 May 20223 High13 October 2022
2022Johnston1, IowaP29A25X31 May 20224 High17 October 2022
2022Johnston2, IowaP29A25X27 April 20221 High17 October 2022
2022Obion, TennesseeP27A64X30 April 20225 High26 October 2022
2023Homer, IllinoisP35T15E13 April 20233 High3 October 2023
2023Perry, IndianaP35T15E19 April 20233 High23 October 2023
2023Windfall, IndianaP35T15E13 April 20236 Low30 September 2023
2023Baxter1, IowaP29A18E27 April 20236 Low10 October 2023
2023Baxter2, IowaP28A21X27 April 20225 High10 October 2023
2023Johnston1, IowaP28A65E27 April 20235 High22 October 2023
2023Johnston2, IowaP28A65E27 April 20236 Low22 October 2023
2023Johnston3, IowaP28A21X27 April 20235 High22 October 2023
2023Johnston4, IowaP28A21X13 April 20234 High30 September 2023
2023Johnston5, IowaP28A21X27 April 20234 High30 September 2023
2023Redfield, South DakotaP09A62X17 May 20237 Low4 October 2023
1 = Average BLB feeding score on the FST no IST plots (1–9 score where 1 = excessive feeding on all leaves with large holes and >75% leaf tissue missing, 3 = feeding on all leaves with 50–75% leaf tissue missing, 5 = feeding on all leaves with 25% to 50% leaf tissue missing, 7 = feeding evident with up to 25% leaf tissue missing, or 9 = minimal to no feeding evident). Locations were grouped as low pressure (average score 6 to 7) or high pressure (average score 1 to 5). 2 = Not harvested indicated yield data were not utilized in the analysis due to high variability or plot quality issues.
Table 2. 2018–2023 Analysis of variance (ANOVA) of agronomic and efficacy traits for soybean insecticide seed treatment field experiments at locations with low or high BLB pressure.
Table 2. 2018–2023 Analysis of variance (ANOVA) of agronomic and efficacy traits for soybean insecticide seed treatment field experiments at locations with low or high BLB pressure.
TraitBLB Pressure 1EffectNum DFDen DFF ValueProb F
BLB feeding scoreLowSeed Treatment249.7594283.5630.000
HighSeed Treatment259.9497268.6250.000
UAV emergence gaps 2LowSeed Treatment238.13041.347450.272
HighSeed Treatment262.068610.5120.000
UAV vigor 3LowSeed Treatment237.50571.626680.210
HighSeed Treatment270.538917.45710.000
UAV greenness 4LowSeed Treatment230.46356.061290.006
HighSeed Treatment263.117425.07770.000
Yield (kg/ha)LowSeed Treatment2348.6916.67670.000
HighSeed Treatment2386.05116.70910.000
1 BLB pressure based on a BLB feeding 1–9 visual score, where 1 = excessive feeding on all leaves with large holes and >75% leaf tissue missing, 3 = feeding on all leaves with 50–75% leaf tissue missing, 5 = feeding on all leaves with 25% to 50% leaf tissue missing, 7 = feeding evident with up to 25% leaf tissue missing, and 9 = minimal to no feeding evident. Locations were grouped as low pressure (average score 6 to 7 for no IST plots) or high pressure (average score 1 to 5 for no IST plots). 2 UAV emergence gaps = the total amount of gaps between plants using UAV drone remote sensing image analysis. 3 UAV vigor = the number of green pixels/other color pixels calculated using UAV drone remote sensing image analysis. 4 UAV greenness = the intensity of green pixels calculated using UAV drone remote sensing image analysis.
Table 3. 2018–2023 Best linear unbiased estimates (BLUEs) of agronomic traits for soybean insecticide seed treatments at locations with low or high bean leaf beetle feeding pressure.
Table 3. 2018–2023 Best linear unbiased estimates (BLUEs) of agronomic traits for soybean insecticide seed treatments at locations with low or high bean leaf beetle feeding pressure.
BLB Feeding Score 1UAV Emergence Gaps 2UAV Vigor 3UAV Greenness 4Yield (kg/ha)
Seed TreatmentLow 5High 5Low 5High 5Low 5High 5Low 5High 5Low 5High 5
FST No IST6.3 b4.2 c61.5 a78.5 a11.8 a12.0 c60.8 b59.7 b4536.1 c4369.0 c
Cyantraniliprole + FST8.4 a8.0 a52.5 a46.7 c12.2 a13.6 a61.1 a60.5 a4790.6 a4582.7 a
Imidacloprid + FST8.3 a7.4 b60.2 a63.9 b12.1 a12.8 b61.2 a60.2 a4699.5 b4490.4 b
1 Bean leaf beetle feeding score (1–9) visual score, where 1 = excessive feeding on all leaves with large holes and >75% leaf tissue missing, 3 = feeding on all leaves with 50–75% leaf tissue missing, 5 = feeding on all leaves with 25% to 50% leaf tissue missing, 7 = feeding evident with up to 25% leaf tissue missing, and 9 = minimal to no feeding evident. 2 UAV emergence gaps BLUE = the total amount of gaps between plants using UAV drone remote sensing image analysis. A lower value indicates fewer total gaps and better emergence. Values followed by a common letter are not significantly different by Fischer’s least significant difference (LSD) test at the 5% level of significance. 3 UAV vigor BLUE = the number of green pixels/other color pixels calculated using UAV drone remote sensing image analysis. A higher value indicates better vigor. Values followed by a common letter are not significantly different by Fischer’s LSD test at the 5% level of significance. 4 UAV greenness BLUE = the intensity of green pixels calculated using UAV drone remote sensing image analysis. A higher value indicates healthier green plants. Values followed by a common letter are not significantly different by Fischer’s LSD test at the 5% level of significance. 5 Locations were grouped as low BLB pressure (average score 6 to 7) or high BLB pressure (average score 1 to 5) by the average visual rating of the FST no IST plots.
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Hegstad, J.M.; Mo, H.; Gaspar, A.P.; Rule, D. Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments. Agronomy 2024, 14, 340. https://doi.org/10.3390/agronomy14020340

AMA Style

Hegstad JM, Mo H, Gaspar AP, Rule D. Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments. Agronomy. 2024; 14(2):340. https://doi.org/10.3390/agronomy14020340

Chicago/Turabian Style

Hegstad, Jeffrey M., Hua Mo, Adam P. Gaspar, and Dwain Rule. 2024. "Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments" Agronomy 14, no. 2: 340. https://doi.org/10.3390/agronomy14020340

APA Style

Hegstad, J. M., Mo, H., Gaspar, A. P., & Rule, D. (2024). Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments. Agronomy, 14(2), 340. https://doi.org/10.3390/agronomy14020340

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