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Electromagnetic Induction Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface
Author(s) -
Woodbury Bryan L.,
Lesch Scott M.,
Eigenberg Roger A.,
Miller Daniel N.,
Spiehs Mindy J.
Publication year - 2009
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2008.0274
Subject(s) - feedlot , sampling (signal processing) , manure , environmental science , sampling design , statistics , sample (material) , linear regression , stratified sampling , soil science , mathematics , computer science , zoology , ecology , filter (signal processing) , chemistry , computer vision , population , demography , chromatography , sociology , biology
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 (EC a ) data. Meter data were combined with global positioning system coordinates at a rate of five 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). 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 that it can be used to map spatially variable manure accumulations. Each model was capable of explaining >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.