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. Geostatistics can provide a basis for making these decisions more soundly. Secondly, many growers want to know why plants in a particular field, or area of the field, are healthier than others. They also want ways to predict where in the field a problem may occur. Conventional research designs have determined many factors that affect plant health and productivity, but they are able to study only a limited number of variables at a time.