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Soil Science Society of America Journal 65:1547-1558 (2001)
© 2001 Soil Science Society of America


DIVISION S-8 - NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Map Quality for Site-Specific Fertility Management

T. G. Mueller*,a, F. J. Pierceb, O. Schabenbergerc and D. D. Warncked

a Dep. of Agronomy, Univ. of Kentucky N-122 Ag. Science North, Lexington, KY 40546-0091
b Center for Precision Agricultural Systems, Washington State Univ., Irrigated Agricultural Research and Extension Center, 24106 N. Bunn Road, Prosser, WA 99350-8694
c Dep. of Statistics, Virginia Polytechnic Institute and State Univ., 211 Hutcheson Hall, Blacksburg, VA 24061-0439
d Dep. of Crop and Soil Science, Michigan State Univ., E. Lansing, MI 48824-1325

* Corresponding author (mueller{at}pop.uky.edu)

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.

Abbreviations: A30, prediction for the field average approach for 30.5-m grid data set • A100, prediction for the field average approach for 100-m grid data set • CEC, cation-exchange capacity • CV, coefficient of variance • GFULL, full data set • G30, 30-m grid data set • G61a, G61b, G61c, G61d, 61-m grid data set (a total of four • a,b,c,d) • G100, 100-m grid data set • Gchck, check data set • Gcomb, combination of G30 and G100 grid data set • IDW, inverse distance weighted • NN, nearest neighbor • MSE, mean square error • RMSE, root mean square error • RSV, relative structural variability • SSFM, site-specific fertility management • VRT, variable rate technologies




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