Those methods are appropriate for studying the effects of experimental treatments, assuming that the measurements taken in different plots are spatially independent. Common sense tells us, however, that measurements taken nearby one another in a field might be related. It is an overview and introduction to the concepts and some of their applications, rather than a thorough guide to using these method. Most growers are aware of the variability of their fields, and may therefore be skeptical of the applicability of some experimental results. Researchers are aware of this variability too, but they haven't had the tools to include it in their experiments.
Geostatistical methods provide additional tools to describe and analyze spatial variability.
These methods involve intensive sampling along a line or in a grid pattern, rather than in randomized, replicated treatments. Geostatistics is important to sustainable agriculture in two ways. First, geostatistical methods can improve researchers' ability to lay out experimental plots and take samples more effectively. Researchers usually try to put their field experiments in representative areas in a field. They also choose plot sizes that seem large enough to ensure independence between plots, yet are not so large that each plot contains too much variability.