Map quality for site-specific fertility management

The quality of soil fertility maps affects the efficacy of site-specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid-point (30- and 100-m regular grids), grid cell (100-m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation-exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies less than or equal to 48%) at high sample densities and poor at lower densities (i.e., 61- and 100-m grids; prediction efficiencies < 21 %). The average optimal distance exponent at each scale of measurement was 1.5. The performance of kriging relative to IDW methods (with a distance exponent of 1.5) improved with increasing sampling intensity (i.e., IDW was superior to kriging for 100% of cases with the 100-m grid, 79% of the cases with the 61.5-m grid scale, and 67% of the cases with the 30-m grid). Practically, there was little difference between these interpolation methods. Grid sampling with a 100-m grid, grid cell sampling, and simulated soil map unit sampling yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.