Salt-affected soil maps for Northeast Thailand focus on the percentage of salt crusts. Investigation was done to find the field-scale spatial variability of the electrical conductivity of saturation extract (ECe) in salt-affected areas (percentage salt crusts: very severely = class 1; severely = class 2, and moderately = class 3). Two study sites were selected for each class (n = 6). Soil samples (n = 100) were collected at each site using stratified, systematic, unaligned sampling, and analyzed for ECe. Variations in ECe were assessed using basic statistics and geostatistics. At the field-scale, in every class, the best-fit semivariogram model generated was satisfactory (R2 > 0.8). Interpretation from the relevant model parameters (i.e., nugget, sill, and effective range), together with the interpolated (kriged) maps, demonstrated that the characteristics of spatial variability of soil ECe were inconsistent, even between different sites of the same salt-affected soil class. In general, various degrees of small-scale variation were observed, very high variation of ECe was common, spatial dependence was strong to moderate, while the spatial distribution pattern was in distinctive patches. The size of patches depended on the effective range at each site. This study also revealed that the class 1 areas were entirely, very strongly saline (ECes range, 56.70 and 433.00 dS·m-1), whereas the areas of class 3 were non-saline to moderately saline (range, 0.11 - 5.26 dS·m-1). Class 2 areas were much more complex; the soils varied from non-saline to very strongly saline (range, 0.16 - 49.00 dS·m-1). Information on the nature and characteristics in the spatial variability of soil ECe is useful for developing strategies for management of salt-affected soils in precision agriculture in this region.
We investigated GPS-derived atmospheric propagation delays and meteorological data for estimating rainwater quality by creating a regression model using the combinatorial group method of the datahandling algorithm (COMBI GMDH). The dependent variable was the daily wet deposition, while the independent variables were zenith hydrostatic delay, zenith wet delay, daily rainfall, and daily average wind speed. The model had a coeffi cient of determination (R -day. Rainwater quality could be estimated using GPS-derived atmospheric propagation delay (APD) and meteorological data.
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