Stink bug spatial distribution and sampling optimization in North Carolina and Virginia soybeans

Area of Science:

• Agricultural Entomology
• Pest Management
• Spatial Statistics

Background:

• Aggregated pest populations, such as stink bugs, present challenges for accurate detection and economic threshold determination in crop fields.
• Understanding the spatial distribution of pests is crucial for developing effective scouting strategies and minimizing sampling errors.

Purpose of the Study:

• To characterize the spatial distribution of stink bug species within soybean fields in North Carolina and Virginia.
• To estimate the optimal sample size needed for precise and accurate stink bug population density estimations.

Main Methods:

• A standardized sweep sampling protocol was employed across 154 commercial soybean fields over three years (2022-2024).
• Taylor's Power Law was utilized to analyze the within-field spatial distribution patterns of stink bugs across different life stages.
• Spatial aggregation parameters were calculated to determine optimal sample sizes for various precision levels.

Main Results:

• A strong spatial aggregation was confirmed for stink bug species and their life stages within soybean fields.
• Significant differences in aggregation patterns were observed among common stink bug species.
• Increased sampling effort was found to enhance the accuracy of stink bug density estimations for a specified precision level.

Conclusions:

• The findings highlight the significant spatial aggregation of stink bugs in Southern US soybean agroecosystems.
• This research provides valuable insights into optimal sampling strategies for stink bug monitoring, aiding in more informed pest management decisions.
• The study contributes to improved scouting guidance for soybean producers, helping to reduce false positives and negatives in pest assessments.