THE MODEL-BASED APPROACH TO GEOSTATISTICAL ANALYSIS:A Case Study of Micro-Nutrient Content in the Soils of the Witwatersrand Area Mzabalazo Z. Ngwenya a, Christien Thiart b & Linda M. Haines b a Biometry Unit, Agricultural Research Council (ARC), b Department of Statistical Sciences, University of Cape Town (UCT)
OUTLINE1. Introduction o Geostatistics o Model-based geostatistics1. Spatial prediction & Kriging o Model-based approach o Kriging predictor & variance o Estimation of parameters1. Case Study o Description o Motivation for study o Analysis1. Conclusions
INTRODUCTION 1.1 Geostatistics: The branch of spatial statistics concerned with continuous spatial variation Traditional geostatistics developed largely independently outside mainstream spatial statistics hence the approach lacks statistical rigor 1.2 Model-based Geostatistics: The application of formal statistical methods of modeling and inference to geostatistical problems; Analyses are carried out under explicitly assumed stochastic models
CASE STUDY 3.1 Description• 214 soil samples collected at 1000 locations between 2005 & 2008• Various micronutrients measured; Fe, Zn, Pb, Cd, Co, Cr, Ni, Mn 3.2 Motivation for study• Iron (Fe) has important role in plant health. Plays major role in Energy transfer within plant Nitrogen fixation Plant respiration & metabolism Chlorophyll development & function• However its accumulation within plant cells can be toxic
CONCLUSIONS• Clear methodology• None of the subjectivity of empirical semivariogram construction• Models for kriging selected on the basis of established statistical criterion• Can obtain confidence intervals for parameters in models• Methods extendable to multivariate and non-Gaussian cases
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