In an attempt to overcome the double problem of storage and 
retrieval of fuzzy information a special sub-system (FUZZ)-was 
designed and incorporated into the SLEMS. The FUZZ module 
can recognize and process a number of fuzzy expressions such 
as "slightly more than y" or "more or less y", where "y" isa 
number. Others recognized by the system include "about y", 
"roughly equal to y", "greater than y", "less than y", "much 
greater than y", "much less than y", and "from y to x^". The 
FUZZ sub-system therefore makes it possible for the LEARN 
sub-system to query a semantic network of fuzzy object triplets. 
The fuzzy knowledge processing operator is based on a new 
method for processing fuzzy expressions based on the concept 
of fuzzy geometric partition of the search space(Mtalo, 1990). 
During a query session the FUZZ module parses and tests both 
the query and examined database objects against a limited set of 
fuzzy expressions. If no fuzzy expression is found, the module 
passes the query to the normal query processor, otherwise, it 
performs a fuzzy object comparison in order to locate the 
matching database object. 
Using this mechanism it is possible to store and query non- 
precise information provided by soil erosion domain experts 
without the loss of information associated with attempts to 
translate fuzzy expressions into exact or precise facts. 
4. CONCLUDING REMARKS. 
This paper has explored the utility of the knowledge based 
systems approach in the solution of soil erosion problems. The 
paper discussed briefly data requirements and information 
processing issues relevant to the introduction of the technology 
in soil loss estimation and modelling. A feasible method for the 
representation and manipulation of fuzzy information in the soil 
erosion domain was also developed. 
An experimental knowledge based system prototype was also 
developed from basic principles and its application demonstrated 
in the soil loss estimation and modelling applications. The 
system, consisting of a unique combination of four easily 
accessible information management tools, demonstrates the 
viability of the knowledge based approach in general. 
Although there has not been much progress in the development 
and use of expert systems in the soil erosion domain, the 
complexity of the problem beggars the adoption of knowledge 
based systems in this area. Also, because of the broad nature of 
the soil erosion problem, a multi-disciplinary approach to the 
development of soil erosion expert systems is strongly 
recommended. 
In conclusion SLEMS four sub-systems offer easily manageable 
functions for solving simple soil erosion related problems. Its 
ability to manipulate vague information provides a partial 
solution to the problem of handling fuzzy data and fuzzy 
queries. In addition its is strongly argued that knowledge based 
systems are a useful vehicle for inter-disciplinary technology 
transfer. Finally, on the basis of the response from a multi- 
disciplinary group of experts from Tanzania, the use of the 
expert systems technology in developing countries is not only 
viable but desirable. 
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