Synopsis
Many methods of statistical analysis are available for examining experimental data observed at different points in time and space relative to describing and understanding soil-plant-atmospheric processes within the landscape (e.g. Cahil et al., 1999). For observations that are temporally or spatially independent, parametric and nonparametric statistical methods are available. For those that manifest temporal or spatial dependence, methods derived from regionalized variable analysis and applied time series may be selected.
Hence, the question arises, "How can these regionalized variable and applied time series theories frequently being used successfully in other scientific disciplines be applied to agricultural research?" This book is intended to introduce such concepts and theories to scientists already familiar with classical statistics and one or more disciplines of agricultural science. Each chapter introduces one concept and its application to several sets of field-measured data.
Examples of data from various field studies observed by colleagues and ourselves are used as a frame for explaining basic concepts of spatial statistics and how to apply them within and between fields. The original data, the analysis and the interpretation are followed by a dicussion of issues and concerns associated with the underlying assumptions of the analysis.
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