The proposed project will build off the work previously funded by KSC support. To date, for Obj. 1, we have extensively sampled counties across much of northeast Kansas for the presence of soybean gall midge (SGM). Sixty sites were sampled in 2022 and no records have been found then. However, recently SGM was found in two counties (Nemaha and Marshall). This new pest continues to expand its range across southern Nebraska and southwestern Iowa (15 new counties were added in 2022). It is imperative that sampling continue and education of various stakeholders (farmers, agents, industry, etc.) continue so that we can have an effective communication strategy in place to respond to infestations in a timely manner; see website for upcoming webinars (https://soybeangallmidge.org/). Consequently, the new alert and notification module within myFields.info (Obj. 3) allows us to send alerts to specific counties and users can sign up for a free account to receive notifications via email. For Obj. 1, we also tested the efficacy of new pest monitoring strategies and management practices. For improved monitoring strategies, we tested the efficacy of various stink bug pheromones across 6 fields in central Kansas. Preliminary results show that the lures are effective in attracting several species of economically important stink bugs to the traps. More information will be provided as sticky cards and sweep samples were processed this past winter and are currently being analyzed. For improved management, we started an insecticide efficacy trial this summer examining the effectiveness of two new insecticides in comparison to five older general use synthetic organic products. These two new insecticides are more specific to pests (mostly lepidopterans) and less harmful to non-target organisms such as beneficials. We are in the process of collecting data and we will share results and pertinent information to Kansas stakeholders through as many venues as possible (Obj. 3). We are also working on a project examining the use of nanoparticles for delivering minute quantities of insecticides throughout the soybean plants. We are currently testing dyes to determine how nanoparticles are being translocated throughout the plant. Furthermore, the PhD student currently funded on this grant and several undergraduates are carrying out Objectives 1 and 2. The PhD student and undergraduates sampled 30 fields across eastern KS. They will use collected data along with previously data collected in soybean either from prior years, publicly available data, and data from neighboring states, to understand how landscape features of the environment and land management impact the densities of occasional pests within KS landscapes. They are currently focusing on Japanese beetles since this invasive species is expanding in their ranges and becoming more persistent in soybean fields. They plan to expand modelling eff orts to other important pest insects (e.g., Dectes, soybean podworm, and stinkbugs) and will incorporate to results to myFields (Obj. 3).

Objective 1. Document the distribution of established and/or new pests in Kansas and adapt existing monitoring technologies to manage stink bug pests in soybean.
Objective 2. Create landscape model to predict pest densities and damage to soybean plants using existing and new pest distribution data.
Objective 3: Expand web pages and other educational materials associated with soybean insects.

For Objective 1, our sub-objectives are to: 1) document the expansion of the soybean gall midge distribution adjacent to previously infested counties bordering Kansas; and 2) adapt sticky cards baited with stink bug pheromones as a sampling tool for stink bugs in soybeans. The first sub-objective will increase farmer awareness of soybean gall midge and will provide resources to identify initial infestations in Kansas. In addition, support by the Kansas Soybean Commission will allow us to mobilize resources and direct our education efforts in counties with soybean gall midge presence or pressure. Over the past 3 years, we have scouted over 60 fields in northeast Kansas with limited support from the North Central Soybean Research Program (NCSRP) and partnerships with 13 other states. We will expand our sampling efforts to understand the potential distribution of this pest in Kansas, especially considering the continued expansion of this pest in nearby states. Since its discovery in 2018, soybean gall midge has been found in 162 counties across seven midwestern states. Each year new counties have been identified, so it is important that Kansas is formally surveyed for the presence of this new and damaging species. In the second sub-objective, we propose to determine a new economic threshold for stink bugs based on sticky trap captures using baited stick cards on posts at varying distances from the field edge. This is part of a multi-state project through the NCSRP, but additional funds are needed to ensure fields with varied levels of stink bugs are identified for inclusion in the study. This study will be replicated at six field sites in central to eastern Kansas.

For Objective 2, we will use data on the distribution of established soybean pests have been collected in previous years for stem-borers (e.g., Dectes stem borer) and defoliators (e.g., bean leaf beetles, green cloverworm, Japanese beetles) and seed feeders (e.g., soybean podworms). We will use these distribution data along with newly collected data in Objective 1 to assess how pest densities vary along a landscape composition gradient focusing on 4 perennial and occasional pests (Dectes stem borer, stink bugs, Japanese beetles, and soybean podworm). Prior research has found that the composition of the surrounding landscape can influence movement of pests and their natural enemies into agricultural fields, including soybean. We will look at how landscape level features such as field size and shape, proximity to natural areas which house alternative host plants, and amount of soybean in the surrounding area affect the likelihood of pest infestation using ArcGIS and USDA National Cropland Data Layer (https://nassgeodata.gmu.edu/CropScape/). We will also use management data (e.g., spray application rates, irrigation frequency, tillage, crop rotation, double crop) and weather to assess how these factors might interact with landscape features to affect pest densities. Finally, we will supplement data collected from myFields, an extension-based management tool (https://www.myfields.info/). With these data, we will create a predictive model for pest densities to be used as a management tool by soybean farmers and crop consultants for determining pest treatment.

For Objective 3, we will continue our efforts to develop text based discussions, maps, tables, and graphs posted to the KSRE Soybean Insect Management Guide (http://www.ksre. ksu.edu/bookstore/pubs/Mf743.pdf) and update the insect pest management information section for the Kansas Soybean Management publication.
https://www.bookstore.ksre.ksu.edu/pubs/MF3154.pdf. New information will also continue to be inserted in our soybean pest management web-based decision chemical selection tool in myFields.info (https://www.myfields.info/chemical/selector/search) and discussed during field days, radio programs, newsletters, and via other educational opportunities as appropriate. For a list of all soybean pests and associated management guides on myFields.info, visit http:// myfields.info/pests. The support of the Kansas Soybean Commission will continue to be highlighted in all of these endeavors.

Updated January 9, 2025:
This progress report is an update from July 15, 2024 to Jan 15, 2025. Objective 1. Document the distribution of established and/or new pests in Kansas and adapt existing monitoring technologies to manage stink bug pests in soybean. Objective 2. Create landscape model to predict pest densities and damage to soybean plants using existing and new pest distribution data. Objective 3. Expand web pages and other educational materials associated with soybean insects.

For Objective 1, we completed sampling 35 fields across six counties in northeast Kansas (Republic, Washington, Marshall, Nemaha, Brown, Doniphan counties), for the presence of soybean gall midge (SGM). In 2024, SGM was observed in two counties (Washington and Nemaha) with a total of three counties since SGM was first detected in 2023 (Marshall, Nemaha, Washington). Infestation levels for all SGM occurrences were low without much yield loss. We continue to work on ways to improve the new alert and notification modules within myFields.info (Obj. 3) which allows us to send alerts to specific counties and users can sign up for a free account to receive notifications via email.

For Objective 1, we also tested various trapping methods to increase efficacy and surveillance efforts. First we tested automated soybean podworm traps which were composed of Hartstack traps with an infrared sensor that tracks the movement of male moths into the collection trap. We are currently working on models that estimate moth numbers with actual trap numbers and preliminary results show that this automated trapping device is a reliable trapping method. We also continue to use various stink bug pheromones to improve stink bug monitoring. Results from the last two years show that lures can be effective at attracting several economically important stink bug species to the traps, including Brown Marmorated Stink Bug (BMSB) but some discrepancies may be due to time of day, weather conditions, and location of sampling. We collected that information this past summer and are currently incorporating it into our models. We are working with KDA to use the traps for their state-wide BMSB surveillance efforts.

For Objective 2, we are combining field data from traps, previously data collected in soybean, publicly available data, and data from neighboring states, to understand how landscape features of the environment and land management impact the densities of occasional pests within KS landscapes. Our goal is to develop predictive models for the occurrences of key pest insects. To date, our results show that pest pressures is linked with both landscape features surrounding soybean fields, as well as climate variables (precipitation and temperature), however insect responses vary by species. For example, Japanese beetle densities were negatively associated with grasslands surrounding soybean fields but positively associated with the amount of corn fields surrounding soybean fields. For Dectes stem borer, the amount for forest cover and corn in the surrounding fields increased their numbers. For bean leaf beetles, their numbers were lower in soybean fields that were surrounded by corn and soybean. Because pest complexes respond differently to climate and landscape features, we are currently surveying farmers to determine which insect pests are of most concern to them so narrow down modelling efforts and integrating management history into our models. Farmer surveys have been distributed in fall 2024, and so far, we only received 14 completed surveys. We will try to find other ways to reach growers (e.g., Soybean Expo, Corn and Soybean Schools) to increase feedback. We will share results and pertinent information to Kansas stakeholders through as many venues as possible, including incorporating results to myFields (Obj. 3).

At the end of November 2024, we submitted a scientific paper about Japanese Beetle invasion in the Great Plains, using data collected from this project and surrounding states to inform distribution models. Additionally, we are leveraging information from this study to apply for other grants. In 2024, we submitted a USDA grant examining the socio-economic and environmental trade-offs of double cropping in soybean. Because the USDA has expanded double cropping insurance to 42 counties in KS in 2022, it is important to understand possible environmental trade-offs with double cropping for soil health, weed control and pest pressure with the economic incentives and farmers concerns. While the proposal project was not funded in 2023, we received very positive reviews and was encouraged to submit again in 2024. We are also working on a project examining the use of nanoparticles for delivering minute quantities of insecticides throughout the soybean plants. This past year, we successfully tested dyes to determine how nanoparticles are being translocated throughout the plant. Finally, we are testing out how drone technologies and mobile phones can be used detect and monitor insect pests such as Japanese beetles and stink bugs in the field. This project currently supports a PhD student and several undergraduates that are carrying out Objectives 1, 2, and 3. We are currently processing field collected data, refining models, gathering farmer input and will use future funding to complete project goals.

Infestations of new and established pests in soybean is an on-going concern for growers in Kansas. For example, stem borer larval infestations of 50 to 80 % cause severe lodging problems in north-central and southwestern Kansas. Reports of damage severity continue to increase and expand across KS counties. For example, damage severity ratings for soybean stem borer increased in one-third of Kansas counties from 1985 to 2015. Soybean podworms also continue to threaten soybean yields through direct consumption of seeds where compensatory pathways are ineffective late in the season. Expansion may be due to reduced availability of alternate host plants such as wild sunflower, increased larvae winter survival, increased soybean acreage, increased adoption of non-tillage practices, or continuous planting of soybean. Finally, with the continued expansion of the soybean gall midge (SGM) across the Midwest and Great Plains and the discovery of SGM in two KS counties, we need a landscape approach to understanding those causes is necessary for control and to minimize further spread. Results from coPD McCornack’s group demonstrate that adult colonization patterns vary between fields and through time and need to be better predicted, including when and where lodging is most likely to occur. The creation of predictive models based on location, surrounding landscape, management practices, and climate are needed to generate tools for effective pest management. Although commercial insecticides can be used to treat these insect pests, other practices such as cultural control can reduce costs and minimize insecticide resistance.

Although the basic biology of soybean pests such as Dectes stem borer is well established, few management options have been identified in over 30 years. Rotation and stubble destruction have been recommended but are now impractical because residue is needed to protect soil (with legal requirements) and rotation loses effectiveness when regional acreage increases to the point that borer beetles simply move from field to field. Because the timing of harvest is important to minimize the effects of girdling, understanding which fields are likely to be greatly affected by infestation will be important to improve monitoring efforts (e.g., increased monitoring in fields adjacent to alternative host plants, near other soybean fields). Furthermore, although fipronil seed treatment kills larvae in the plant stem, this insecticide remains commercially unregistered for use on soybean stem borer. Topical insecticides kill adult beetles but existing soybean sampling plans for beetles are unreliable, requiring several applications for effective results. This cost-prohibitive option has left producers with no options and has forced consultants to discontinue recommending this tactic. Very few research programs exist to develop or improve soybean stem borer management tactics. These facts have led this project to conduct the research described above to carefully use KSC support to identify landscape level features that increases the likelihood of stem borer infestation, and to continue to refine ways to monitor stem borer beetle presence and movement to better manage damaging populations. The use of predictive models has been used to detect the likelihood of pest outbreaks in soybean fields in WI by the PD Kim (e.g., soybean aphids); similar models can be generated for soybean pests in KS as ways to integrate data into decision making tools in an easy and cost effective way.