Water Content Effect on Soil Salinity Prediction: A Geostatistical Study Using Cokriging

A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract (EC3 and water content of soil samples supplemented by surface measurements of apparent electrical conductivity (EM”). Prediction of soil salinity at unsampled points by cokriging log@C3 and EMa is worthwhile because EM” measurements are quicker than soil sampling. This work studies how patterns of loa predicted by cokriging with EMa are influenced by variation in gravimetric water content (W). The data are mean EM” = 1.00 f 0.13 dS m-’ for 2378 locations, mean @(EC.) = 1.40 f 0.29 dS m-l, and mean gravimetric W = 0.260 f 0.003, both averaged for four samples from 0.3-m intervals to 1.2-m depth for 315 locations. The coefficient of determination (R*) for EMa vs. &(EC.) increased with depth from 0.05 to 0.54 whereas the R2 for EMa vs. W decreased from 0.48 to 0.28. A gray-scale EM” map contained nine out of 56 quarter-section boundaries coinciding with step variations in EM”. The t-statistics for differences in mean W were six of nine significant at 0.001 and nine of nine at 0.05, but mean log@C,) had only two of nine at 0.05, implying that W caused EMa steps. Water-affected EMa impaired prediction of EC. at depth by cokriging, because near-surface variations in Wmasked EC,. Two subareas were defined, one where management factors, such as irrigation, controlled EMH, causing steps, and one where near-surface W varied less, making cokriging predictions