The aim of the research is to compare and study the economic and biological characteristics of animals of the Kalmyk and Mongolian breeds of cattle, to identify related parameters and evidence of a common historical origin. Methods. The method of evaluating and comparing the characteristics of two breeds of cattle (Kalmyk and Mongolian) with a common origin has been improved, and methods of zootechnical, graphic and comparative analysis. Results. The authors made a comparative assessment of the economic and biological characteristics of the Kalmyk and Mongolian cattle breeds bred in the South of Russia, in the Bayangol-Mongol and Xinjiang Uygur autonomous regions of China. According to the results of the assessment, similar characteristics of the studied animal breeds were revealed, and their common origin from a single ancestor was proved. The factors that influence the high adaptive abilities of the two breeds and the similarity in their production and reproduction abilities are revealed. Studies of Kalmyk breed of cattle was carried out in the territory SPK “Prolific” of the Republic of Kalmykia, the Mongol breed cattle was held on the territory of the farm Baingol-Mongol Autonomous Region of China. The study of these cattle breeds will allow us to understand the origin of the Kalmyk breed of cattle, which was migrated with the Mongolian-Kalmyk tribes from the Western part of China more than 400 years ago.
Organic agriculture is a dynamically developing area of the global agro-industrial complex. Global climate problems and the depletion of natural resources across the planet dictate the need to review the food production technologies used. Excessive intensification of agricultural production through soil mineralization and fertilization, hormonal stimulation of animal and plant growth has led to the deterioration of water, soil, air quality and overall health. Therefore, the problem of reducing the anthropogenic impact on the environment is the main trend of implementing the principles of organic production. The paper developed recommendations for the strategic development of the Russian market of organic production within the framework of the current EAEU agreement. Conclusions were made on the expansion of instruments of state incentives for the development of organic agricultural production in a changing world economic relations and integration.
The article presents a study on steers’ biological growth patterns that are possible to be considered for increasing the meat productivity of steers. To enhance the live weight gain of Holstein steers, we used a variable feeding system that is based on the physiological characteristics of metabolism and takes into account growing periods and the nature of nutrient absorption by periods, corresponding to the S-shaped growth curve. Analyzing growth records of young cattle, he paid attention to some patterns of the live weight gain. These data showed that the live weight gain was uneven along the S-curve and subject to periodic fluctuations that were characterized by decreases and increases in growth; a period took 12 days, that is, the amplitude of the increase in growth made 12 days and then the decrease was also 12 days. We also divided the growth cycle into 2 periods, 12 days each. In the increase period, if the amount of feed was reduced by 20%, there was no decrease in average daily gain in steers, and vice versa, in the 12-day decrease period, a 20% increase in feed compensated for the low level of metabolism in the body with a stable rate of average daily gains. Therefore, similar amount of feed can help the traditional cattle breeding system increase the average daily gain by 8.5% and the carcass weight by 10.5% and reduce feed costs per 1 kg of gain by 7.1%.
Neural networks have proven to be highly adaptable to various tasks associat-ed with large data sets and their processing in order to obtain new knowledge and data for subsequent planning of the development of various systems. Neural networks are used not only in the processing of large data sets, but also in the construction of predictive models. In this article, we built a neural net-work model for calculating and forecasting profit index of the agro-industrial complex (AIC) of Russia, on the basis of aggregated input factor parameters, reflecting the potential of the industries. In addition to the neural network forecast, the article builds a profit forecast using the method of regres-sion-correlation analysis, which has long been used by economists. For fore-casting purposes, the analysis of the dynamics of development of the branches of agro-industrial complex was carried out and the main factors determining their future opportunities were selected. Using the online platform Deductor Studio Academic assessed the dependence and impact of input indicators on the derived profit indicator and checking the correlation coefficients between the parameters were calculated. The obtained forecasted profit values were com-pared with the actual profit value and the difference in the accuracy of the forecasts was calculated.
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