The information predictive model of creating temperature and humidity conditions in cow barns

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.