Differentiating soil types using electromagnetic conductivity and crop yield maps

Variable rate technology enables management of individual soil types within fields. However, correct classification of soil types for mid-Atlantic coastal plain soils are currently impractically expensive using an Order I Soil Survey, yet variable rate fertilizer application based on soil type can be highly effective. The objectives of this study were to determine if apparent electromagnetic conductivity (ECa) alone or combined with previous year crop yields using global positioning system technology can provide a useful alternative to detailed soil mapping. The site contained alluvial soils ranging from Bojac 1 and 2 (coarse-loamy, mixed, thermic, Hapludults) to Wickham 3 and 4 (fine-loamy, mixed, thermic, Ultic Hapludalfs). The two fields totaled approximately, 24 ha. A statistical nonparametric classification method, called recursive binary classification trees, was used to determine how well soil types could be classified. Electromagnetic conductivity readings and crop yields were positively correlated. Broad patterns in the relationship between soil types and ECa readings and crop yields existed for all crop combinations considered. Lower ECa readings and crop yields corresponded to the Bojac soils, while higher ECa readings and crop yields were categorized as Wickham soils. Electromagnetic induction alone correctly classified the soils into broad categories of Bojac or Wickham with over 85% accuracy. When ECa was combined with crop yield data, correct classification rose to over 90%. More precise classification into Bojac 1, Bojac 2, and Wickham soils yielded slightly lower correct classifications ranging from 62.6 to 81.2% for ECa alone, and 80.3 to 91.5% when combined with various crop yields.