2025/08/11 by Richard S González Aquino, Sandip Mondal, Kendall Lovejoy +1 · 1 voice
Agricultural and Biological Sciences · #Nematode management and characterization studies
paper · doi:10.1094/pdis-09-24-2033-re
openalex publication_date 2025/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11
Effective soybean cyst nematode (SCN) management decisions start with accurate soil sampling to assess population densities. Manual sampling is common but time-consuming and less effective over large areas, and it may overlook spatial aggregation. This project aimed to optimize and compare the accuracy and efficiency of an automated precision soil sampler with manual soil sampling. Soil samples were collected from one SCN-infested field in Clark County (Western Agricultural Research Station [WARS]) and two in Fulton County (Fulton-1 and Fulton-2), Ohio, using both manual and automated grid-pattern methods. The two sampling methods were evaluated using two approaches: direct SCN egg count and pixel-based counting derived from surface interpolation predicted by inverse distance weighting (IDW) model. Lin’s concordance correlation coefficient (ρ c ) was used to assess agreement between the two methods. The egg count approach showed moderate positive ρ c values in WARS (ρ c = 0.53) and Fulton-1 (ρ c = 0.77), indicating agreement between manual and automated sampling methods. However, a lower correlation was observed for Fulton-2 (ρ c = 0.47). Using the IDW interpolation approach, which accounts for spatial aggregation, both sampling methods detected similar SCN egg population densities (ρ c = 0.99) for all fields. Moreover, removing 50% of the automated sampling points resulted in similar spatial resolution of SCN distribution across the fields, offering a significant cost-benefit advantage. The feasibility of on-farm trials using this automated approach was also demonstrated. Integrating automated sampling with a geospatial approach enhances understanding of SCN distribution, streamlines soil sampling, and identifies areas with SCN population densities exceeding economic thresholds, helping growers make informed management decisions.