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DIVISION S-6 - SOIL & WATER MANAGEMENT & CONSERVATION

Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data Across Irrigated Cotton

Australian Cotton Cooperative Research Centre, Dep. of Agricultural Chemistry and Soil Science, The Univ. of Sydney, NSW 2006, Australia

Corresponding author (johnt{at}acss.usyd.edu.au)

Various approaches have been used to estimate soil salinity (EC_e) at unsampled locations. Some of these approaches are briefly discussed. Of these, geostatistical methods such as ordinary kriging (OK1 and OK2), regression kriging (RK), three-dimensional kriging (3-DK), and cokriging (Co-K), provide best linear unbiased estimates (BLUE). These methods were tested with a raw electromagnetic induction instrument (Type EM38) in a soil electrical conductivity (EC_a) survey, and calibrated EC_e data were obtained from an irrigated cotton (Gossypium hirsutum L.)-growing area in the Edgeroi district of the lower Namoi valley, northern New South Wales, Australia. We compared these methods, on the basis of precision and bias of estimation, and found that RK was the best performer. This is because of the incorporation of regression residuals within the kriging system. Mean and standard deviation of ranks (SDR) showed that Co-K performed best against these criteria.

Abbreviations: BLUE, best linear unbiased estimates • Co-K, cokriging • EC_a, soil electrical conductivity • EC_e, soil salinity • EM_0,H, soil conductivity measurements made in horizontal mode • EM_0,V, soil conductivity measurements made in vertical mode • ME, mean error • OK1, ordinary kriging of raw EM_0,H data • OK2, ordinary kriging based on EC_e estimates • RK, regression kriging • RMSE, root mean square error • SDR, standard deviation of ranks • 3-DK, three-dimensional kriging

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