## Application of Fuzzy Sets within Measuring and Managing certain Agricultural Risks

The aim of this paper is to set-up the algorithm for determining the degree of workability of the soil, to help the owners of family farms to plan working hours of agricultural machines, i.e. with the machine park management. The plans, which would be made by use of these algorithms and based on the accurate information of the cultivation conditions, would result in the appropriate use of time and capacity of the agricultural machines. In some sectors, such as agriculture or certain industries, chance that certain event occurs plays a very important role. The influence of random events, however, can be significantly reduced if decision makers are counting on them, reveal their nature and gather as much information about them as possible. Justification of new testing methods applied in the economy follows from the fact that, according to economic policy, the growing burden of risk that comes from the uncertainty is borne by farmers. The method that was used in the study is based on the Fuzzy mathematical modeling. Treating uncertain, vague and linguistically described phenomena and situations is facing difficulties in classical mathematics. In fact, a large degree of uncertainty is primarily resulting from uncertain external events. Fuzzy mathematical modeling can satisfactorily treat those parameters that are uncertain, vague and subjectively evaluated. The algorithm of risk assessment should be based on the opinion of economic experts, on the experience of the makers of planned decisions and on all the available data. Solving this problem can be approached in three ways: a) conventional method, b) applying the expert system, c) applying the theory of fuzzy sets. The main characteristic of the traditional way of solving the problem of evaluation is the almost exclusive reliance on measurable economic effects (time and money). Only in some rare cases, additional criteria are taken into account. Because of the importance of additional criteria, it is possible to develop a prototype of an expert system. Modeling problems in which the interdependence between the variables is very complex, fuzzy logic can be successfully applied. The complete review and analysis of the problem relying only on knowledge, experience of experts, without the fuzzy logic, would be impossible.

Keywords:
Issue Date:
2010
Publication Type:
Conference Paper/ Presentation
Record Identifier:
http://ageconsearch.umn.edu/record/109383
PURL Identifier:
http://purl.umn.edu/109383
Total Pages:
15