Development of a decision making support system to increase the productivity of dairy animal breeding

Introduction. The most important trend in modern global development is the transition to new, smart technologies based on the operational processing of large amounts of data using the most advanced mathematical methods, carried out using the latest communication tools and information technologies. A similar trend can be seen in most areas of human activity, including in dairy production - the most important branch of agriculture. The aim of the work is to develop a decision support system to increase the productivity of dairy farming. Materials and methods. As the basis of the study, the works of various authors on the study of criteria that have a key impact on livestock productivity were used, on the basis of which the interdependence between the factors ensuring the productivity of dairy farming was determined, and the possibility of controlling the productivity of dairy farming through changes in animal feed intake. An important place is given to the assessment of the microelement status of animals, which is an indicator of livestock productivity. Results. In the framework of the study, models were built for the dependence of the productivity of dairy farming on the feed base, which, in turn, depends on the yield of forage crops. The constructed models made it possible to identify the possibilities of a controlling effect on the yield of fodder crops through fertilizer application and the overall productivity of dairy farming, by regulating the diet with feed additives. At the same time, the microelement status was used as an indicator of the state of the system, which can be measured both in the feed base and in animals. Tracking changes in elemental status allows you to quickly respond to changing conditions and adjust the feed base to optimize dairy production. Conclusion. The revealed dependencies made it possible to develop a decision support system for increasing the productivity of dairy farming, including modules for assessing yield and productivity, as well as algorithms for their correction.