Updated July 15, 2024:
The project continues to build off the work previously funded by KSC. For Objective 1, we continue to sample counties across of northeast Kansas for the presence of soybean gall midge (SGM). In 2023, SGM was observed in two counties (Marshall and Nemeha). It is vital that we continue to sample and educate various stakeholders (farmers, agents, industry, etc.) so that we can have an effective communication strategy in place to respond to infestations in a timely manner. In 2024, we are sampling 30 sites across NE KS, including Marshall and Nemeha counties. 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 the efficacy of various stink bug pheromones to improve stink bug monitoring. Results from 2023 showed that the lures are effective in attracting several species of economically important stink bugs to the traps, including Brown marmorated stink bug. We are currently using these traps to survey stink bug numbers in fields across northeast KS. 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 only in fields in northeast KS. In contrast, bean leaf beetles were negatively associated the amount of soybean and corn in the landscape but only in east-central KS. 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 and we plan to receive feedback in Fall 2024. 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 July 2024, we will submit 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 2023, 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 Sept 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 in the process of collecting more field data, refining models, gathering farmer input and will use future funding to complete project goals.
Our project had three objectives: (1) Document the distribution of new and established pests in Kansas and adapt existing monitoring technologies to manage insect pests in soybean, (2) Create landscape model to predict pest densities and damage to soybean plants using existing and new pest distribution data, and (3) Expand web pages and other educational materials associated with soybean insects. Throughout the course of the 3-year study, we have made steady progress in collecting field data, conducting lab experiments, and communicating with farmers about their concerns and disseminating results. Specifically, we have surveyed soybean fields for the distribution of new pests such as the soybean gall midge, and established pests such as podworms, and pests of growing concern such as Japanese beetles, across many counties in north-east KS. With these surveys, we are also testing the efficacy of trapping methods for perennial pests such as stink bugs through the use of pheromone traps and the efficacy of new insecticides that are more specific to pests (mostly lepidopterans) and less harmful to non-target organisms such as beneficials. We are using these field data to create predictive models on pest distribution based on location, surrounding landscape, management practices, and climate. Although commercial insecticides can be used to treat many insect pests, other practices such as cultural control can reduce costs and minimize insecticide resistance. The models generated from this study, will help identify fields and locations that are likely to be infested with pests and will be a valuable tool for pest monitoring and effective pest management. This 3-year project supports 1 PhD student and several undergraduate students that have been extensively involved in all 3 objectives. We have shared results through field days, extension newsletters, social media platforms, incorporated results to pest management databases (e.g., myFields), presented results at grower and scientific meetings, and are also in the process of publishing results in scientific journals. Furthermore, we are leveraging information gained from this study to apply for larger federal grants. Specifically, we are working with neighboring states to broaden the scope of the current study and integrate sustainable farming practices such as double cropping to predict pest risk. Additionally, we are assessing whether technologies such as drones and mobile devices can be effectively used for automated pest monitoring in the field. Throughout the course of the study, we have engaged with soybean farmers on various levels from help with on-farm sites selection to creating surveys about their pest concerns.