Assessment of the risks of implementing a big data processing system in animal husbandry

The aim of the study is to describe the concept of a system for collecting and processing a large dataset from each livestock farm in a regional data center, as well as the possible risks associated with its operation, and to predict the economic effect of implementing the system in the industry. The concept involves collecting data from each sensor and machine regarding health, productivity, conformation, and the quality of all technological processes; further pre-processing of the data on the livestock complex’s server; and transmitting the information to the regional data center for processing and storage, with the aim of subse-quently providing the data to consumers. A table is presented with a description of the data and the methods used to collect it. The data include 88 automatic parameters (87.1 % of the total, such as rumen pH, temperature, and activity, with a reliability of >93 %) and 13 manual parameters (12.9 %, such as the results of genetic tests, with an input error of <8 %), collected using sensors, milking robots, monitoring systems, and manual input. Four stages of system implementation are described, with each stage in-volving an assessment of the factors affecting the final reliability of the information stored on the regional server, along with a description of the actions that increase the reliability of the information. Possible risks and the likelihood of their occurrence during the implementation and operation of the system are described, using an assessment of the measure of uncertainty of the possible states of the system, based on Claude Shannon’s formula for calculating the amount of information in a message about non-equiprobable events. Using the Monte Carlo method to model events associated with an uncertain probability, the entropy distribution of the risk system was estimated based on 10,000 possible scenarios. A model of the relationship between determinants (factors) and risks is presented, expressed as an effect with an assessment of its level of influence on the success of system imple-mentation. The obtained results are superimposed on a theoretically constructed productivity growth curve for a livestock farm, which could potentially implement systems with the four described development scenarios. Under the baseline scenario accounting for risks, the projected milk yield is expected to reach 10,000–11,000 kg per cow per year after three years and 13,000–14,000 kg after ten years, compared with the initial level of 8,100 kg. For a herd of one thousand cows, this corresponds to an additional annual milk output of 1,900–2,900 tonnes after three years and 4,900–5,900 tonnes after ten years.

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