Map Quality for Site‐Specific Fertility Management

Maps are fundamental to SSFM because they repThe quality of soil fertility maps affects the efficacy of site-specific resent either the spatial state of a condition of soil fertility management (SSFM). The purpose of this study was to interest, the prescription of inputs needed to manage a evaluate how different soil sampling approaches and grid interpolation particular condition site-specifically, or a record of inschemes affect map quality. A field in south central Michigan was puts or outputs (Pierce and Nowak, 1999). In his review soil sampled using several strategies including grid-point (30- and 100- of variable rate technology (VRT), Sawyer (1994) conm regular grids), grid cell (100-m cells), and a simulated soil map cluded that the success of VRT depends to a large extent unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation-exchange on the quality of fertility management maps. Although capacity (CEC)] data were predicted using ordinary kriging, inverse methods exist for measuring map quality, in general, distance weighted (IDW), and nearest neighbor (NN) interpolations maps used in SSFM are rarely examined for quality for the various data sets. Each resulting map was validated against (Sawyer, 1994; Pierce and Nowak, 1999). Thus, poor an independent data (n 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal map quality may explain why results of some SSFM (prediction efficiencies 48%) at high sample densities and poor at agronomic and economic studies have produced mixed lower densities (i.e., 61- and 100-m grids; prediction efficiencies or negative results (Wibawa et al., 1993; Wollenhaupt 21%). The average optimal distance exponent at each scale of mea- and Bucholz, 1993; Snyder et al., 1996; Lowenberg-Desurement was 1.5. The performance of kriging relative to IDW meth- boer and Swinton, 1997). Map quality consists of two ods (with a distance exponent of 1.5) improved with increasing sam- components, map precision and map accuracy. The forpling intensity (i.e., IDW was superior to kriging for 100% of cases mer is a measure of residual variability in map predicwith the 100-m grid, 79% of the cases with the 61.5-m grid scale, and tion, whereas the latter measures the closeness to the 67% of the cases with the 30-m grid). Practically, there was little true conditions. Map quality is quantified as the mean difference between these interpolation methods. Grid sampling with square error (MSE) of residuals (predicted measured) a 100-m grid, grid cell sampling, and simulated soil map unit sampling obtained with an independent validation data set. yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.

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