This paper is devoted to the development of predictive models for decision support systems applied in precision farming. Application of predictive models makes it possible to use resources effectively, which reduces the cost of production and increases the e􀂨ciency of agricultural production. In addition, the forecast makes it possible to reach a long-term agronomic and ecological effect due to more careful tillage and reduced use of fertilizers. The algorithms using knowledge base for creating models of grain yield are described and the results of applying these models are presented.