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DOI: 10.15507/2658-4123.031.202102.241-256
The Information Predictive Model of Creating Temperature and Humidity Conditions in Cow Barns
Valeryij F. Vtoryi
Senior Researcher, Institute for Engineering and Environmental Problems in Agricultural Production – Branch of Federal Scientific Agroengineering Center VIM (3 Filtrovskoye Shosse, Saint Petersburg 196625, Russian Federation), D.Sc. (Engineering), Researcher ID: Z-1809-2019, ORCID: https://orcid.org/0000-0003-0026-6979, This email address is being protected from spambots. You need JavaScript enabled to view it.
Sergei V. Vtoryi
Senior Researcher, Institute for Engineering and Environmental Problems in Agricultural Production – Branch of Federal Scientific Agroengineering Center VIM (3 Filtrovskoye Shosse, Saint Petersburg 196625, Russian Federation), Cand.Sc. (Engineering), Researcher ID: Z-1812-2019, ORCID: https://orcid.org/0000-0002-7169-1625, This email address is being protected from spambots. You need JavaScript enabled to view it.
Vladislav V. Gordeev
Head of the Research Departament for Technologies and Technical Means in Animal Husbandry, Institute for Engineering and Environmental Problems in Agricultural Production – Branch of Federal Scientific Agroengineering Center VIM (3 Filtrovskoye Shosse, Saint Petersburg 196625, Russian Federation), Cand.Sc. (Engineering), Researcher ID: Y-9104-2019, ORCID: https://orcid.org/0000-0001-6181-396X, This email address is being protected from spambots. You need JavaScript enabled to view it.
Introduction. Information-predictive modeling is an effective tool for optimizing the indoor climatic variables to make full use of cow potential. Noncompliance with barn climate requirements may result in 10–30% lower lactation capacity. The research aimed at creating an information model of indoor climate formation based on experimental findings.
Materials and Methods. A 24-hour measuring system of relevant climate variables with a 10-minutes data recording interval was designed. It included nine sensor units, three data recording/storing devices and a common power unit. Measurements took place in a dairy cow barn for 200 head in the Leningrad Region.
Results. According to the summer study results, certain areas in the cow barn at high relative humidity had Temperature Humidity Index >75, i.e. were unfavorable for animals. This period may last up to 18 hours a day. In the daytime at Temperature Humidity Index >80, the indoor environment might become critical and be accompanied by a drastic decrease in milk cow productivity. Correlation models for temperature conditions in a cow barn are obtained and their dependence on indoor and outdoor temperature and air humidity are calculated.
Discussion and Conclusion. An information predictive model was created to describe the formation of temperature and humidity conditions inside cow barns, depending on weather conditions. Under constant real-time database updating, the model allows monitoring the temperature and humidity in cow barns and forecasting these variables for the next few days. The relevant data are visualized in real-time on monitors and information panels for personnel and specialists supporting the timely managerial decisions to prevent critical situations associated with overheating or hypothermia of animals.
Keywords: cattle, cow barn, indoor climate, air temperature, air humidity, Temperature Humidity Index
Conflict of interest: The authors declare no conflict of interest.
For citation: Vtoryi V.F., Vtoryi S.V., Gordeev V.V. The Information Predictive Model of Creating Temperature and Humidity Conditions in Cow Barns. Inzhenerernyye tekhnologii i sistemy = Engineering Technologies and Systems. 2021; 31(2):241-256. DOI: https://doi.org/10.15507/2658-4123.031.202102.241-256
Contribution of the authors:
V. F. Vtoryi – literature review and analysis, mission statement.
S. V. Vtoryi – conducting research and processing the results.
V. V. Gordeev – describing the research methodology, analyzing the results.
All authors have read and approved the final manuscript.
Submitted 02.03.2021; approved after reviewing 20.04.2021;
accepted for publication 28.04.2021
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