Local Wheat Price Prediction Models

Abstract

The paper presents an approach to intelligent models development for predicting different parameters of agricultural management. Intelligent predictive models based on knowledge can be built into decision support systems for agricultural producers. Such systems, in turn, allow agricultural producers to make plans and spend their resources more effectively. Based on intelligent analysis of the data, identification models are developed for agricultural commodity price prediction. Modeling results are presented that demonstrate high efficiency of presented approach. Predicted values of agricultural production market price can be used for predicting agricultural enterprise profit and, with using other parameters’ predicted values, could help farmers achieve their business goals more successfully.