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Map Quality for Site-Specific Fertility Management

^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

View larger version (167K): [in a new window]   Fig. 1. A digital orthophotograph overlaid by the 30.5-m grid data set (G30, black squares), the 100-m grid data set (G100, at the intersections of the straight black dashed lines), the check data set (Gchk, white circles) and NRCS soil types (CaA represents Capac loam with 0 to 4% slope; MeA represents Metamora–Capac sandy loam with 0 to 4% slope; MoB represents Morley Loam with 2 to 6% slope; and WbA represents Wasepi sandy loam with 2 to 6% slope). The solid white lines represent the soil types boundaries. Some of the grass water ways in the field (dashed white lines) and the boundaries of the NRCS soil map units were used to define directed sampling zones (DS 1-11). The dashed black lines also indicate the boundaries of the Gcell based sampling.

View larger version (60K): [in a new window]   Fig. 2. Soil fertility values for the 30-m grid (G30) data set overlain by NRCS soil types (CaA represents Capac loam with 0 to 4% slope; MeA represents Metamora-Capac sandy loam with 0 to 4% slope; MoB represents Morley Loam with 2 to 6% slope; and WbA represents Wasepi sandy loam with 2 to 6% slope).

View larger version (33K): [in a new window]   Fig. 3. Omnidirectional (omni) and directional experimental semivariograms and omnidirectional semivariogram models for the combination(Gcomb) data set. The directions indicate the anisotropic axis.

View larger version (28K): [in a new window]   Fig. 4. Predicted vs. measured for P and K for cross-validation with replacement and with an independent validation data set.

View larger version (39K): [in a new window]   Fig. 5. Predicted vs. measured for the 100-m grid (G100) data set.

View larger version (22K): [in a new window]   Fig. 6. Omnidirectional empirical semivariograms and fitted exponential semivariogram models for pH, P, and K using the full (FULL), combination (Gcomb), 30.5-m grid (G30), 61-m grid (G61), and 100-m grid (G100) data sets. The lag distance at which semivariogram models were cutoff was used as the search radius for ordinary kriging.

View larger version (29K): [in a new window]   Fig. 7. Predicted vs. measured for ordinary kriging at each scale of measurement of pH, P, and K.

View larger version (11K): [in a new window]   Fig. 8. Prediction efficiency vs. sampling intensity for pH, P, and K.

View larger version (48K): [in a new window]   Fig. 9. Performance of IDW, ordinary kriging, and log-normal ordinary kriging as a function of sampling scale and design.

View larger version (28K): [in a new window]   Fig. 10. Predicted vs. measured for IDW with a distance exponent of 1.5 at each scale of measurement of pH, P, and K.

View larger version (21K): [in a new window]   Fig. 11. Prediction efficiency for IDW vs. prediction efficiency for ordinary kriging at each scale of measurement.

View larger version (22K): [in a new window]   Fig. 12. Individual semivariogram pairs (small circles) and average semivariograms for each lag class (large circles; plotted in Fig. 2) for soil P at the combination (Gcomb) scale.