DESIGNING EFFICIENT SOIL SURVEY SCHEMES WITH A KNOWLEDGE-BASED SYSTEMUSING DYNAMIC-PROGRAMMING

Soil sampling and measurement often consume a significant portion of t he budget available for a project. On a national or worldwide basis th ese activities require large investments, which are justified if the s oil information leads to better decisions on land use or environmental issues to an extend which more than counterbalances the costs. This d epends on both the costs and the quality of the information. At presen t soil sampling schemes are designed an hoc or according to a protocol . In either case the available prior information on soil variability a nd statistical knowledge on spatial sampling is often not fully exploi ted. This may lead to unnecessarily high costs or low quality of the i nformation. Therefore, sampling schemes should be designed such that e ither the costs are minimized under quality requirements related to th e aim of the survey, or the quality is maximized for a given budget. I mportant aspects of quality are accuracy and precision, which can be q uantified as sampling and measurement error. In this paper we describe a knowledge-based system that assists in the design of soil survey sc hemes. The system facilitates the full use of prior information as wel l as pedological, operational and statistical knowledge. Part of the k nowledge will be formalized as decision rules that guide the user to s uitable types of sampling designs. In addition, models and algorithms are prc,posed to predict the accuracy and the costs of the information , taking into account differences in spatial variability or sampling c osts between sub-regions. Finally, given a stratification of the area, dynamic programming is used to determine the optimal allocation to th e strata of sample