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Division S-4—Soil Fertility & Plant Nutrition

Mapping Soil pH

Accuracy of Common Soil Sampling Strategies and Estimation Techniques

^a Agronomy Dep., Purdue Univ., 915 W. State Street, West Lafayette, IN, 47907
^b Biological and Agricultural Engineering Dep., 123 E.B. Doran Bldg., Louisiana State University, Baton Rouge, LA 70803

^* Corresponding author (sbrouder{at}purdue.edu)

The theoretical profitability of variable rate (VR) lime management has driven adoption of intensive soil sampling strategies used with complex statistical techniques without demonstration of approach efficacy. Our objective was to compare the accuracy of spatially continuous pH and lime requirement (LR) maps derived from commercially used approaches to sampling and LR prediction at unsampled locations. We evaluated point (P) sampling on 0.1-, 0.4-, and 1.0-ha grids and area composite (AC) sampling by 1-ha grids, soil type (ST), and whole field (WF). Inverse distance (ID) weighting and ordinary kriging were applied to water pH and LR data from 11 fields. Modeling of semivariance identified range parameters of 100 m. For intensive P sampling (0.1- or 0.4-ha grids), kriging was occasionally more accurate than ID weighting but mean absolute error (MAE) differences were small (0.01 pH units and 0.13 Mg lime ha^–1), suggesting little practical consequence to prediction method selection. One-hectare point data were too sparse to produce variograms and applying ID weighting to these data found only small advantages over WF compositing. Lime use was either unaltered or minimally reduced (10%) by 1-ha P as compared with WF compositing. When compared with WF composites, map prediction efficiencies (PEs) based on mean square error (MSE) analysis ranged from 7 to 51, –13 to 40, and –6 to 54% for ST compositing, 1- and 0.4-ha P sampling, respectively. These results suggest ST compositing remains viable and cost-effect for pH management, especially where ancillary information exists to verify distinct soil series boundaries.

Abbreviations: AC, area composite • CP, center point • CV, coefficient of variation • DPAC, Davis Purdue Agricultural Center • ID, inverse distance • IQR, interquartile range • LR, lime requirement • MAE, mean absolute error • MSE, mean square error • NEPAC, Northeast Purdue Agricultural Center • P, point • PA, precision agriculture • PE, prediction efficiency • SMP, Shoemaker-McLean-Pratt • ST, soil type • TF, true farm • VR, variable rate • WF, whole field