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

J. Triantafilis, I.O.A. Odeh and A.B. McBratney

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



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Fig. 1. Location of Auscott farm and layout of eight surveyed fields

 


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Fig. 2. Location of electromagnetic induction instrument (Type EM38) measurement and validation sites for Fields 18 to 20

 


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Fig. 3. Soil electrical conductivity (ECa) plot of a) EM0,V (electromagnetic vertical-mode measurements) vs. EM0,H (electromagnetic horizontal-mode measurements), and frequency distribution of b) EM0,V and c) EM0,H

 


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Fig. 4. Plot of prediction root mean square error (RMSE) and mean error (ME) for the various kriging methods used to interpolate raw electromagnetic induction instrument (Type EM38) data and converted ECe data across all eight fields. Co-K = cokriging, OK1 = ordinary kriging of raw EM0,H data, OK2 = ordinary kriging based on ECe estimates, RK = regression kriging, 3-DK = three-dimensional kriging

 


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Fig. 5. Mean rank plotted against the standard deviation of ranks (SDR) resulting from various prediction methods used to validate the survey data across all eight fields. Co-K = cokriging, OK1 = ordinary kriging of raw EM0,H data, OK2 = ordinary kriging based on ECe estimates, RK = regression kriging, 3-DK = three-dimensional kriging

 


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Fig. 6. Plot of prediction root mean square error (RMSE) and mean error (ME) for the various prediction methods used to validate the survey data in Field a) 18 and b) 20. Co-K = cokriging, OK1 = ordinary kriging of raw EM0,H data, OK2 = ordinary kriging based on ECe estimates, RK = regression kriging, 3-DK = three-dimensional kriging

 


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Fig. 7. Maps of the spatial distribution of soil salinity (ECe) in Fields 18 to 20 using regression kriging (RK) at depths of a) 0.90 to 1.20 m and b) 1.80 to 2.00 m

 





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