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

Abstract

Aggregated pest populations in crop fields pose a significant challenge for efficient detection and economic threshold decisions. Stink bugs are one common pest group that tends to be spatially aggregated and cause localized crop injury within fields. Understanding elements of spatial distribution is important to provide scouting guidance that limits false positive and negative rates when monitoring stink bug populations. The objectives of this study were to characterize the distribution of stink bug species in North Carolina and Virginia soybean fields and to estimate the optimal sample size required for accurate and precise sampling. A standardized sweep sampling protocol was used to measure stink bug populations in 154 commercial soybean fields over three years (2022–2024). We used Taylor’s Power Law to characterize the within-field spatial distribution of stink bugs and found a strong spatial aggregation for stink bug species life stages. Furthermore, the estimated aggregation patterns differed among the common species. We used the spatial aggregation parameters to calculate the optimal sample size required to estimate stink bug density at four precision levels. Results showed that increased sampling improved accuracy of density estimations for a given precision, which is an expected outcome of pest sampling analysis focused on aggregated pests. This information on spatial aggregation and sample size will be helpful to improve stink bug scouting in soybeans in the Southeast United States.