Work

Neural Network based Ranking of Feed Additives according to Milk Productivity and Cow Blood Biochemical Parameters

Year: 2025

Type: article

Abstract: Cows' potential milk productivity, their health, conditional lactation age, and the intensity of internal biochemical processes in their bodies require constant monitoring to respond in time to age-related and infectious changes. It is proposed to use computational neural networks to control biochemical processes in cows' bodies. Processing blood counts using computational neural networks allows one to determine the potential milk productivity of cows and draw reasonable conclusions regarding the effect of feed and feed additives on their milk productivity. Based on these findings, there is a unique opportunity to form a daily diet that has a positive effect on cows' health and productive longevity. The study introduces a new information technology based on the GeorgyNN neural network, which calculates the LactCSI and MilkCSIp indices representing conditional lactation age and potential milk productivity, respectively. Using the values of the MilkCSIp vs LactCSI indices for each cow, one can conduct a two-dimensional ranking of cows and present the ranking results in the form of a Scale1 matrix; using the values of the MilkCSIf vs MilkCSIp indices, one can present the results in the form of a Scale2 matrix. The analysis showed that brewer's spent grain (BSG) reduced the intensity of internal biochemical processes, while the Cellobacterin probiotic increased it. These findings suggest that the impact of these feed additives is primarily influenced by the cows' genetic and microbiome specificity. This approach enables the formulation of daily diets that improve cows' health, longevity, and productivity. The priority in the impact of these external biotic and abiotic factors on biochemical processes is likely determined primarily by the genetic specificity of cows and the specificity of the intestinal microbiome for the digestion of feed and probiotic supplements. Keywords: Cow health, biochemical process, pathogenic infections, active feed additives, lactation, blood counts.

Source: Journal of Global Innovations in Agricultural Sciences

DOI: 10.22194/jgias/26.1490

Authors: N.I. Vorobyov, G. Yu. Laptev

Institutions: All-Russian Research Institute of Agricultural Microbiology, Moscow State Academy of Veterinary Medicine and Biotechnology named after Skryabin, Saint-Petersburg State Agrarian University

Language: English

Topic: Ruminant Nutrition and Digestive Physiology

Subfield: Agronomy and Crop Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $200