Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems

Public concern nowadays is an important frame of reference for thedevelopment of agricultural production systems. The development ofsuch systems, therefore, involves both society level and productionsystem level. Following Zadeh's principle of incompatibility,information obtained at production system level is interpreted atsociety level in linguistic terms. Fuzzy models promise to be avaluable tool as they link measurable information to linguisticinterpretation using membership functions. The objective of this paperis to outline a procedure which deals with criticism regarding theinherent subjectivity in the construction of membership functions whenusing expert knowledge. The procedure guarantees the selection ofappropriate expert knowledge, and provides a guideline supporting theselection of methods to elicit expert knowledge and constructmembership functions. Also on the basis of the results in anillustrative example, it is concluded that the procedure outlined inthis paper suitably deals with criticism regarding membershipfunctions and, therefore, enables a practical implementation of fuzzyevaluation of agricultural production systems. Current researchimplements the procedure to build a fuzzy model which evaluates eggproduction systems in relation to public concern about the welfare oflaying hens.evaluation;subjectivity;expert knowledge;fuzzy models;knowledge elicitation

Assessment of Sustainable Development

The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development

Assessment of Sustainable Development

The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development

Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems

Public concern nowadays is an important frame of reference for the development of agricultural production systems. The development of such systems, therefore, involves both society level and production system level. Following Zadeh's principle of incompatibility, information obtained at production system level is interpreted at society level in linguistic terms. Fuzzy models promise to be a valuable tool as they link measurable information to linguistic interpretation using membership functions. The objective of this paper is to outline a procedure which deals with criticism regarding the inherent subjectivity in the construction of membership functions when using expert knowledge. The procedure guarantees the selection of appropriate expert knowledge, and provides a guideline supporting the selection of methods to elicit expert knowledge and construct membership functions. Also on the basis of the results in an illustrative example, it is concluded that the procedure outlined in this paper suitably deals with criticism regarding membership functions and, therefore, enables a practical implementation of fuzzy evaluation of agricultural production systems. Current research implements the procedure to build a fuzzy model which evaluates egg production systems in relation to public concern about the welfare of laying hens

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

The goal of this study was to develop a fuzzy model to predict the occupancy rate of free-stalls facilities of dairy cattle, aiding to optimize the design of projects. The following input variables were defined for the development of the fuzzy system: dry bulb temperature (Tdb, °C), wet bulb temperature (Twb, °C) and black globe temperature (Tbg, °C). Based on the input variables, the fuzzy system predicts the occupancy rate (OR, %) of dairy cattle in free-stall barns. For the model validation, data collecting were conducted on the facilities of the Intensive System of Milk Production (SIPL), in the Dairy Cattle National Research Center (CNPGL) of Embrapa. The OR values, estimated by the fuzzy system, presented values of average standard deviation of 3.93%, indicating low rate of errors in the simulation. Simulated and measured results were statistically equal (P>0.05, t Test). After validating the proposed model, the average percentage of correct answers for the simulated data was 89.7%. Therefore, the fuzzy system developed for the occupancy rate prediction of free-stalls facilities for dairy cattle allowed a realistic prediction of stalls occupancy rate, allowing the planning and design of free-stall barns