Location: Environmental Management Research
Title: EMI-Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface Authors
Submitted to: Symposium on Application of Geophysics to Engineering and Environmental Problems Proceedings
Publication Type: Proceedings
Publication Acceptance Date: January 9, 2009
Publication Date: April 2, 2009
Citation: Woodbury, B.L., Lesch, S.M., Eigenberg, R.A., Miller, D.N., Spiehs, M.J. 2009. EMI-Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface. In: Proceedings Expanding Horizons for Near-Surface Geophysics. 22nd Symposium on the Application of Geophysics to Engineering and Environmental Problems,Ft. Worth, TX, Mar.29-Apr. 2, 2009, p. 850-861. 2009 CDROM. Interpretive Summary: Accumulated feedlot manure negatively affects the environment. The objective of this study was to test the validity of a method for measuring manure accumulation on the feedlot surface. Also, a directed sampling design was compared with a random sampling design to determine which approach provided greater insight to manure accumulation patterns on feedlot surfaces. It was determined the new method was able to measure the variability of manure accumulation on the feedlot surface. Also, the directed sampling design provides an understanding of manure accumulation that is as good as or better than that provided by the random sampling design. This allows for measurement of manure accumulation using fewer sampling sites than traditional methods. This method will be used for future studies aimed at minimizing the impact on the environment from feedlots.
Technical Abstract: 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 m × 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 was combined with GPS coordinates at a rate of five readings per second. 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). These 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 nitrogen (TN), total phosphorus (TP) and chloride (Cl). The 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 it can be used to map spatially variable manure accumulations. Each model was capable of explaining more than 90% 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.