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Producing probability maps to assess risk of exceeding critical threshold value of soil EC using geostatistical approach  Suresh Tripathi Technical Director, Geosun Pty Ltd P O Box 207, Boronia, VIC, 3155, Australia Email: s.tripathi@geosun.net Abstract: Improved management decision and delineating high and low risk area of soil land involved good understanding of the modeling of spatial variation and spatial distribution of electrical conductivity exceeding certain certain threshold value. The central objective is how to quantify and model soil variation and perform interpolation at location where no measurment were collected and to delimit zones that need some remedial treatment to produce economic yield of agriculture crops. Current approach in this paper is based on the advanced non-linear geostatistical techniques, and assuming that the spatial distribution of the electrical conductivity is a function of distance. Recent advances in non linear geostatistcal approach have provided a new approach in solving contamination problem of soil quality. The main advantage of this approach is that orginal data is converted to binary number zero and one. The number is converted to zero if the values are less than threshold value and one if values are exceeding threshold value. This type of conversion of the original data is useful when data do not follow stationary assumptions and linear geostatistical approach do not provide best optimal result. A non-linear geoststistical approach (indicator kriging) is used to generate probability maps of high or low risk area of soil properties exceeding a certain threshold value. Such approach is useful to quantify spatial dependence of Electrical Conductivity and associated uncertainties arising from sampling, measurement, modeling, and interpolation. Probability maps generated using linear and non-linear geostatistical techniques provides improved prediction at unknown location together with associated error variance and help delimiting zones of high and low risk area of any soil and environmental properties. The increase understanding of quantifying such spatial dependence of spatial variables leads to improve management decision practice that either boost the yield of crop production or reduce cost. For example, if EC exceeds maxima, the recommendation may take the form of restricting the use of land, remedial cleansing or controlling affected area. However, if data show any trend or violate Gaussian distribution then additional precaution such as detrending or transforming of the data is required. The objective of using such technique is to minimize the uncertainty about whether the critical threshold has exceeded or not. |