Table 4: Root mean square error between field observations and predicted soil attributes for multiple regression and IDW and using different densities of observations.

Predicted soil attributesR_180 (A) I_180 (B)R_60 (C)I_60 (D)B-AD-C

Soil depth (cm)26.4 32.64 33.7 43.37 6.249.67
Clay (%) 12.616.6412.7020.514.047.81
Silt (%)7.310.258.5914.642.956.05
Sand (%)9.411.4710.2415.442.075.2
Organic matter (%)1.391.531.551.820.140.27
Bulk density (g cm−3)0.18 0.25 0.24 0.36 0.070.12
pH0.380.540.460.830.160.37
Total N (%)0.290.460.090.340.170.25
Available P (mg kg−1)19.41 20.35 17.12 21.22 0.944.1
Surface stone cover (%)12.214.5314.1520.622.336.47
Stone in the soil (%)12.415.7117.1326.383.319.25

R_180 (A): root mean square error calculated from those predicted using 180 observations by multiple regression models, I_180 (B): root mean square error calculated from spatial interpolation (IDW: inverse distance weighted) using 180 observations, R_60 (C): root mean square error calculated from those predicted using 60 observations by multiple regression models, I_60 (D): root mean square error calculated from spatial interpolation (IDW: inverse distance weighted) using 60 observations, B-A: the difference in root mean square errors between those interpolated from 180 observations and those predicted using 180 observations by multiple regression models, and D-C: the difference in root mean square errors between those interpolated from 180 observations and those predicted using 180 observations by multiple regression models.