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 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. Therefore, using 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. We propose developing a tool by integrating new and existing pest distribution data with landscape models to predict fields with the highest likelihood of pest damage based on landscape-level features and past management strategies. We will first focus on Japanese beetles, but will develop similar models for other perennial pests such as soybean podworms, stink bugs, Dectes stem borers, and new pests such as soybean gall midge.

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.

We will generate new data through extensive field sampling and synthesize existing data from previous years. These data will be used to generate predictive models for the occurrences of key insect pests in Kansas soybean. These maps will be available online to farmers to assess whether their fields are at risk for infestation.

Update:
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). We sampled 22 sites in 2023 and 4 records were found, 1 in Marshall and 3 in Nemeha Counties. This new pest continues to expand its range across southern Nebraska and central Iowa (8 new counties were added in 2023). 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/). A field day was held in Nebraska and several presentations were recorded and will be available online this winter. Consequently, the new alert and notification module within myFields.info (Obj. 3) allowed us to send alerts to specific counties; users can sign up for a free account to receive notifications via email. In addition, webinars will be offered to train farmers and consultants on how to scout for SGM in 2024. 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 in 2023. Preliminary results show that the lures are effective in attracting several species of economically important stink bugs to the traps, including Brown marmorated stink bug. More information will be provided as sticky cards and sweep samples are processed coming past winter and will analyzed. In addition, all samples from traps and sweep nets will be combined with landscape data to expand predictive modules being developed in Obj.2. 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).

The proposed studies will lead to improved crop protection and management practices that suppress losses caused by Dectes stem borer, stink bugs, Japanese beetles, and soybean podworm. Past efforts described here proposal focused on identifying plants with resistance to borer stem damage; on monitoring borer movement and distributions in and around soybean fields to refine an optimum time for insecticide application; and on determining the impact of alternate borer hosts and environmental factors to better predict the appearance of early season adult borer populations