Project Details:

Title:
Development of Genetic, Chemical and Population-Based Tactics to Manage Key Kansas Soybean Insect Pests

Parent Project: Development of Genetic, Chemical and Population-Based Tactics to Manage Key Kansas Soybean Insect Pests
Checkoff Organization:Kansas Soybean Commission
Categories:Insects and pests
Organization Project Code:2026
Project Year:2020
Lead Principal Investigator:Brian McCornack (Kansas State University)
Co-Principal Investigators:
Brian McCornack (Kansas State University)
Jeff Whitworth (Kansas State University)
Keywords:

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

Infestations of the soybean stem borer, Dectes texanus, in soybean have increased in area and intensity in soybean crops in Kansas since 1985. Larval feeding in stems causes severe (50-80%) lodging and related yield reduction. Management strategies remain limited. Commercial insecticides do reduce adult stem borer numbers, but several applications are necessary for effective results, making this option cost prohibitive. To date, no soybean varieties adapted to the Midwestern U.S. contain genetic resistance to stem borer larval damage, and there are few research programs on the soybean stem borer. The goal of this project is to develop techniques to manage stem borer damage to soybean in Kansas, as none currently exist. The hypotheses of the proposed research are that management of larval borer damage can be improved using both insecticidal and plant genetic control techniques. The methods used to improve borer management have been and continue to be the development of soybean plants resistant to borer larvae by inserting borer RNA into soybean plants to interfere (RNAi) with genes necessary for borer survival; to improve insecticide efficacy using soybean development and/or environmental cues to adjust insecticide application timing.

Project Objectives

Project Deliverables

Progress of Work

Updated February 15, 2021:
Principal Investigators and Units: C. M. Smith, Dept. of Entomology, (785)-532-4700, Brian McCornack, Dept. of Entomology (785 532-4729, Jeff Whitworth, Dept. of Entomology, (785) 532-5656.

Project Title: Development of Genetic, Chemical and Population-Based Tactics to Manage Key Kansas Soybean Insect Pests

Amount of Funding: $60,000

Progress Report: March 1, 2020– September 15, 2020
Completion Date: February 28, 2021

Objective 1. Create soybean plants resistant to soybean stem borer by inserting borer RNA into soybean plants to interfere (RNAi) with genes necessary for borer survival.
Postdoctoral fellow Dr. Lina Aguirre established experiments to determine if Dectes stem borer larvae were fed artificial diet covered with double stranded (ds) RNA to reduce expression of Laccase 2 (Lac2). Half of the larvae were collected for RNA extraction and gene expression analysis, and the other half were allowed to reach adult stage. Assessment of dsLac2 effects on cuticle pigmentation and sclerotization in adults, and evaluation of changes in gene expression of sixth instar larvae are in progress.

Objective 2. Improve insecticide efficacy by using host plant developmental stages and other environmental cues or conditions to adjust timing of application.
We determined the extent of insect defoliation in commercial soybean fields compared with established economic thresholds, which included 9 fields from 4 counties in Kansas. Most of estimates were well below established economic thresholds for defoliating insects in soybean. In a couple of locations, insecticide strip trials were set up to evaluate current defoliator thresholds but in double-cropped soybean systems. Ground-level imagery as well as a limited amount of aerial drone imagery were collected from select fields to develop mobile-app sampling aids. These tools have the potential to help farmers and consultants determine the extent of defoliation across commercial fields as well the intensity of the infestation. For this study we combined the use of small unmanned aircraft imaging (DJI Mavic Pro) and machine learning (ML) to detect insect induced defoliation within the soybean canopy. Secondly, we also set out to determine the maximum flying height at which defoliation could be detected. For the machine learning model to effectively detect the defoliation, training data had to first be collected. The training data was collected via sUAS, at a height of 1 meter. The images will be processed using the LabelImg software to draw bounding boxes around areas of defoliation. We used 1 x 1 m PBC pipe quadrats to set detection areas and these were systematically placed in a grid format throughout each study field. This was carried out on 2 separate fields with 40 to 50 samples or waypoints per locations. Ground truth data on leaf defoliation was collected using a common protocol. This data will then be compared to the relative defoliation detected by the machine learning model at each altitude images were captured. Data is currently being analyzed.

Objective 3: Expand web pages and other educational materials associated with soybean insects.
Plots were established on 20 July in north-central Kansas to determine the efficacy of new insecticides against the Dectes stem borer. Plots were treated on 22 July when the first adult stem borers were detected within these plots. Plots are 4 rows (10' x 30') with 4 replications. Insecticide efficacy and treatment timing will be determined by calculating percent infestation and plant lodging prior to harvest. Results will be made available and as widely disseminated as possible to all stakeholders.

Final Project Results

Updated June 27, 2021:
Objective 1. Create soybean plants resistant to soybean stem borer by inserting borer RNA into soybean plants to interfere (RNAi) with genes necessary for borer survival.

Postdoctoral fellow Dr. Lina Aguirre established experiments to determine if Dectes stem borer larvae were fed artificial diet covered with double stranded (ds) RNA to reduce expression of Laccase 2 (Lac2). Half of the larvae were collected for RNA extraction and gene expression analysis, and the other half were allowed to reach adult stage. Assessment of dsLac2 effects on cuticle pigmentation and sclerotization in adults, and evaluation of changes in gene expression of sixth instar larvae are in progress. Fifth-instar Dectes stem borer larvae were fed artificial diet covered with double stranded (ds) RNA to reduce the gene expression of Laccase 2 (Lac2), a gene involved in cuticle pigmentation and hardening. Control larvae were fed artificial diet covered with dsGFP. All larvae were fed daily until pupation. Half of the individuals were collected for RNA extraction and gene expression analysis, and the other half were allowed to reach adult stage. Lac2 gene expression and mortality was 30% lower and 35% higher in larvae treated with dsLac2 compared to those treated with dsGFP. However, treatments were not statistically significantly different.

To identify genes putatively involved when feeding soybean, we compared gene expression of D. texanus third-instar larvae fed soybean to those fed sunflower, giant ragweed, or artificial diet. Dectes texanus larvae differentially expressed 514 unigenes when fed on soybean compared to those fed the other diet treatments. Enrichment analyses of gene ontology terms from up-regulated unigenes in soybean-fed larvae compared to those fed both primary hosts highlighted unigenes involved in oxidoreductase and polygalacturonase activities. Cytochrome P450s, carboxylesterases, major facilitator superfamily transporters, lipocalins, apolipoproteins, glycoside hydrolases 1 and 28, and lytic monooxygenases were among the most commonly up-regulated unigenes in soybean-fed larvae compared to those fed their primary hosts. These results suggest that D. texanus larvae differentially expressed unigenes involved in biotransformation of allelochemicals, digestion of plant cell walls and transport of small solutes and lipids when feeding in soybean.

Previous studies funded by the commission were published and evaluated different soybean introductions. Plant Introduction (PI) 165673 exhibits antibiosis resistance to the larval stage. The objectives of this study were: (1) to determine the inheritance of D. texanus resistance in PI165673; (2) evaluate PI165673 antibiosis resistance before 21 d post infestation; (3) evaluate girdling damage in PI16563 at the end of the season. K07-1544/PI165673 F2 and F2:3 populations were tested for resistance to D. texanus in 2011 and 2012, and PI165673 antibiosis resistance and girdling damage were evaluated in 2014. Segregation for resistance to D. texanus and heritability estimates in the F2 and F2:3 populations indicated that resistance was controlled by two genes with dominant and recessive epistasis. Antibiosis evaluations indicated: (1) PI165673 contained lower number of larvae and eggs relative to the number of oviposition punctures at 15 d post infestation; (2) the proportion of first-instar larvae was higher in PI165673 at 15 d post infestation; (3) larvae reach the sixth-instar stage in PI165673. None of the PI165673 plants were girdled at the end of the season. Identification of additional sources of D. texanus resistance is required to impair larval development in the stem.

Objective 2. Improve insecticide efficacy by using host plant developmental stages and other environmental cues or conditions to adjust timing of application.

We determined the extent of insect defoliation in commercial soybean fields compared with established economic thresholds, which included 9 fields from 4 counties in Kansas. Most of estimates were well below established economic thresholds for defoliating insects in soybean. In a couple of locations, insecticide strip trials were set up to evaluate current defoliator thresholds but in double-cropped soybean systems. Ground-level imagery as well as a limited amount of aerial drone imagery were collected from select fields to develop mobile-app sampling aids. These tools have the potential to help farmers and consultants determine the extent of defoliation across commercial fields as well the intensity of the infestation. For this study we combined the use of small unmanned aircraft imaging (DJI Mavic Pro) and machine learning (ML) to detect insect induced defoliation within the soybean canopy. Secondly, we also set out to determine the maximum flying height at which defoliation could be detected. For the machine learning model to effectively detect the defoliation, training data had to first be collected. The training data was collected via sUAS, at a height of 1 meter. The images will be processed using the LabelImg software to draw bounding boxes around areas of defoliation. We used 1 x 1 m PBC pipe quadrats to set detection areas and these were systematically placed in a grid format throughout each study field. This was carried out on 2 separate fields with 40 to 50 samples or waypoints per locations. Ground truth data on leaf defoliation was collected using a common protocol. This experiment used a modified Faster Region-based convolutional neural network (R-CNN) model framework with a Visual Geometry Group 16 (VGG16) feature extraction network to explore two similar but different applications. The first study aimed to evaluate the practicality and accuracy of detecting and labeling soybean leaflets based on their specific defoliation level captured via smartphone. This study was conducted by training and testing the model with images of individual soybean leaflets with varying defoliation levels. Using a defoliation analysis application (Bioleaf), the leaflets were categorized as either exceeding 30% defoliation or below 30% defoliation. One hundred fifty images from each category (300 images total) were used for training data, and 30 images from each category were used for test data (60 images total). The results produced an average precision (AP) of 88.96% and an average recall (AR) of 90.55%. Overall, the model identified and labeled 49 of the 60 test images correctly. The second study aimed to evaluate the practicality and accuracy of detecting and labeling soybean defoliation from canopy level RGB images via an unmanned aircraft vehicle (UAV). This study was conducted through training and testing the model with images of the soybean canopy collected with a flying height of approximately 1 meter. Two hundred were used to train the model, and 40 images were used as a test dataset. Two hundred images were used for training, and 40 images were used for the test data. The results produced an average precision (AP) of 36.49% and an average recall (AR) of 66.67%.

Objective 3: Expand web pages and other educational materials associated with soybean insects.

Plots were established on 20 July in north-central Kansas to determine the efficacy of new insecticides against the Dectes stem borer. Plots were treated on 22 July when the first adult stem borers were detected within these plots. Plots are 4 rows (10' x 30') with 4 replications. Insecticide efficacy and treatment timing will be determined by calculating percent infestation and plant lodging prior to harvest. Results will be made available and as widely disseminated as possible to all stakeholders.

These results suggest that Dectes stem borer larvae differentially expressed genes involved in the metabolism of plant defenses, digestion of plant tissue and transport of small sugars and lipids when feeding in soybean. Understanding these basic biological interactions allows us find ways to target genes that could disrupt the feeding habits of this pest. Management of Dectes stem borer using contact insecticides is costly and difficult to time due to the wide application window. Plan incorporated control measures have more environmental benefits but require future testing and registration. Other outcomes from this project include the development of sophisticated sampling protocols to estimate leaf loss due to chewing pests. Sampling and scouting large acres is not always cost effective or efficient. There needs to be more automated ways, which are standardized, to provide growers with nearly instantaneous data for making well-informed decisions. Using machines and algorithms to calculate percent leaf loss is a step in the right direction for quick and reliable estimates of damage. In addition, may thresholds were established decades ago. Soybean varieties, growing conditions, and cultural practices have changed. These results also provide evidence for justifying various chemical control measures by evaluating current thresholds using modern soybean varieties.

Benefit to Soybean Farmers

Performance Metrics

Project Years