No abstract

Semivariograms are used to quantitatively assess spatial variability of depth to mottles, depth to gravels, and thickness of loamy sand and/or coarser-textured layers, which are definitive criteria for classification of soils derived from alluvium on the Canterbury Plains near Lincoln College, New Zealand. The three properties vary anisotropically with the anisotropy ratio being highest for depth to mottles (k -5.84), lowest for thickness of loamy sand and/or coarser-textured layers (k = 1.58), and intermediate for depth to gravels (k = 2.43). Directions of maximum variation for depth to mottles and depth to gravels are NE-SW across an abandoned channel hollow. This pattern is reflected in the soil map of the study area and in the smaller-scale soil map of the adjacent region. Such variation reflects the past regional drainage patterns of channels flowing predominantly in a NW-SE direction. The appropriate field configuration of a sampling scheme for future survey of similar adjacent soils would be rectangular with a sample spacing in the direction of least variation k (anisotropy ratio) times that in the direction of maximum variation. Suitable sample numbers and sampling intervals to achieve desired levels of precision in the direction of maximum variation are determined. These are obtained from graphs showing relationships between kriging standard error, sample spacing and sample number. This geostatistical approach is more efficient than conventional statistical methods in designing sampling strategies: less samples are needed for kriging than for the conventional method to achieve the same level of precision.

Co‐kriging was used to interpolate values of topsoil 0.5 M NaHCO3‐extractable P at 234 locations in West Sumatra, Indonesia, by exploiting its spatial covariance with a more densely sampled property, 25% HCl‐extractable P. NaHCO3‐P and HCl‐P had been sampled at 52 and 107 locations, respectively, in a nongeometric pattern across the 106 650 has region. Isotropic semivariograms of NaHCO3‐P and HCl‐P and their cross semivariogram showed spatial dependence over 6.3, 4.2, and 10.4 km, respectively, at the sampling scale used. The map of co‐kriged values for NaHCO3‐P showed a similar regional pattern but more local detail than that achieved by kriging from NaHCO3‐P samples alone. Co‐kriging reduced estimation variances (relative to kriging) by up to 40% in areas where sampling density of NaHCO3‐P was lowest. Co‐kriging variances exceeded those of kriging by up to 10% in areas where sampling density of NaHCO3‐P was high. In such areas, the covariate HCl‐P had little effect on the interpolated value but still added a component to the estimation variance. Co‐kriging could not be used to interpolate values for extreme locations where there were no NaHCO3‐P samples within the radius of the kriging neighborhood. Despite local improvement in estimation precision the full benefits of co‐kriging interpolation are not universally obtained from nongeometric variate‐covariate sampling patterns.

Spatial and temporal interaction of soil‐forming factors and processes determine the distribution of soil properties within the landscape. This study relates anisotropic spatial dependence of particle size fractions, pH and 25% HCl‐extractable P to directional differences in the main soil‐forming factors in Sitiung, West Sumatra, Indonesia. Linear geometric anisotropic models were fitted to pooled directional semivariances for each of these properties. Directions of maximum variation coincided with the main southwest to northeast axis of volcanic tuff fallout, deposition of alluvium and the general sequence of soil weathering in the region. Anisotropy ratios ranged from 1.5 for subsoil pH to 5.2 for subsoil HCl‐P. Topsoil textural components and pH were more variable than in subsoils, having larger sample variances and larger anisotropy ratios. Punctual kriging of topsoil sand content using the linear geometric anisotropic model resulted in low estimation variances in densely sampled areas where weighting of neighbor samples by direction as well as distance took most effect. Quantitative analysis of anisotropic spatial dependence of soil properties can aid interpretation of soil genesis. Estimates of anisotropic spatial dependence can also be incorporated into kriging interpolation for optimal, unbiased spatial estimation with minimum variance.

Crop yields on cleared forest land of the humid tropics are often highly variable due to spatial heterogeneity of soil properties. This study aimed to characterize spatial variation of soil chemical properties and yield components of upland rice (Oryza sativa) on a 0.1‐ha field of recently cleared land in West Sumatra, Indonesia. Plants were taller, stover and grain yields higher on sites where forest rash had been piled and burned, compared to the surrounding soils and other sites where topsoils had been removed. This pattern was largely caused by lower levels of Al saturation and higher concentrations of exchangeable cations in burned sites compared to other parts of the plot. The range of spatial dependence for soil acidity and exchange characteristics was 3 to 4 m, but increased to 7 m for organic C, total N, and extractable P. Semivariograms of crop components were better structured and had longer ranges (up to 20 m) than the soil chemical properties. Block kriging gave more precise local estimation of grain yield at unsampled locations than terrain unit means because it utiliized the inherent structure of variation determined from quantitative spatial analysis of observed values. Block kriged values of grain yield ranged from 11 to 355 g/m2 with estimation standard deviations ranging from 9 to 24 g/m2, depending on distance of interpolated cells from the 122 sample locations.