A study was initiated to test the validity of using electromagnetic induction (EMI) survey data, a prediction-based sampling strategy, and ordinary linear regression modeling to predict spatially variable feedlot surface manure accumulation. A 30- by 60-m feedlot pen with a central mound was selected for this study. A Dualem-1S EMI meter (Dualem Inc., Milton, ON, Canada) pulled on 2-m spacing was used to collect feedlot surface apparent electrical conductivity (ECa) data. Meter data were combined with global positioning system coordinates at a rate of fi ve readings per second. Two 20-site sampling approaches were used to determine the validity of using EMI data for prediction-based sampling. Soil samples were analyzed for volatile solids (VS), total N (TN), total P (TP), and Cl−. A stratified random sampling (SRS) approach (n = 20) was used as an independent set to test models estimated from the prediction-based (n = 20) response surface sample design (RSSD). Th e RSSD sampling plan demonstrated better design optimality criteria than the SRS approach. Excellent correlations between the EMI data and the ln(Cl−), TN, TP, and VS soil properties suggest that it can be used to map spatially variable manure accumulations. Each model was capable of explaining \u3e90% of the constituent sample variations. Fitted models were used to estimate average manure accumulation and predict spatial variations. The corresponding prediction maps show a pronounced pen design effect on manure accumulation. This technique enables researchers to develop precision practices to mitigate environmental contamination from beef feedlots
