Map Quality for Site‐Specific Fertility Management

Abstract

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 ≤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.