Neural Network Visualization of Stochastic Dependence of Weight Gain Processes on Dairy Productivity of Cows

The study aimed to determine the optimal dose of a humic feed additive that balances weight gain and milk-forming processes in cows using neural network analysis. The productivity of cows can be increased by regulating the nutritional diet. It is possible to disrupt the balance of weight gain and milk-forming processes, which leads to an increase in the risks of susceptibility of cows to pathogenic infections. A novel computational system, NONN, was developed to calculate indices representing weight gain (CSImass) and milk production (CSImilk) based on blood parameters. As a result, it was possible to determine the maximum intensity of weight gain processes with a slight decrease in dairy productivity of cows when they were fed humic supplement No. 3 containing 70% humic acid. Thus, the proposed NONN computing system allows monitoring the effect of feed additives on the balance of weight gain, milk-forming, and immune protective processes based on cow blood parameters, which can be applied in developing new technologies for keeping farm animals. This approach is adaptable to other feed additives and livestock species, providing a scalable solution for sustainable livestock management and improving resource efficiency in farming practices Keywords: Milk-forming biochemical processes, humic acids, computational neural network, veterinary, food industry.