Geostatistical techniques attempt to quantify and predict the variation of these spatial properties. Advances in global positioning systems and geographic information systems have resulted in the development of research initiatives in the area of spatial statistics applied to agricultural systems. The objective of this study was to examine several sampling and spatial interpolation techniques for precision farming technologies. Chapter 1 is an introduction to spatial interpolation techniques.; The objective of chapter 2 was to evaluate an alternative procedure for principal component kriging. My hypothesis was that principal components constructed with a spatial correlation matrix are more effective than 'linear' principal components. The spatial correlation matrix was constructed with a Moran's I statistic. Based on the goodness-of-fit statistic and back-transformation results, both techniques were equally effective.; The objective of chapter 3 was to examine an alternative spatial analysis tool called the cumulative correlogram. Evaluation of point autocorrelation coefficients and cumulative correlograms indicated that two factors contribute to the poor performance of the kriging models: anomalous characteristics present in the typical sampling designs, and, large separation distances among samples.; The objective of chapter 4 was to develop an alternative sampling approach to capture small scale variability of soil parameters. The design utilizes auxiliary data and an alternative cluster-sampling approach to data collection. The alternative sampling design is compared to a traditional sampling design. The alternative design improved all parameter estimates.; The objective of chapter 5 was to analyze four techniques for delineating soil-productivity management zones. Each of the methods uses a unique set of soils, yield, and or remotely sensed data. Analysis of variance indicated yields among management zones were different. A non-parametric analysis of crop yields also provided evidence to conclude that management zone delineation techniques resulted in yield patterns that were different from random yield patterns. Overall, delineation techniques that combined secondary soils information and soil-sample analysis results were the most effective techniques.
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