The use of region partitioning to improve the representation of geo statistically mapped soil attributes

SUMMARY An attempt to improve the representation of a geo statistically mapped soil attribute, clay content of the surface soil, through partitioning of the study area into two new regions was made. A topographic boundary divided the study area into hill and plain regions. Possible global non-stationarity or non-stationarity within the two newly defined regions was dealt with through the use of intrinsic random functions (IRF) of order k. Cross-validation of generalized covariance functions suggested that ordinary kriging might also have been appropriate. Exponential variogram models were subsequently fitted to the experimental variograms for each region. IRF-k block kriging and ordinary block kriging were then used as the primary methods of estimation. Both IRF-k and ordinary kriging performed badly in the vicinity of the topographic boundary when global models were used. This discontinuity was removed, at the expense of the introduction of some additional edge effects, when the hill and plain regions were kriged using models appropriate to each region. Independent zero-order generalized covariance functions with nugget and linear terms and exponential variogram models produced similar representations of clay content within each region, when used with their respective estimators. Splitting the region resulted in a 6% reduction in mean absolute deviation and a 14% reduction in mean squared deviation of predicted clay contents compared with a global model.