Microclimate modeling in naturally ventilated dairy barns during the hot season: Checking the accuracy of forecasts. (October 2020)

• Record Type: Journal Article
• Authors: Mylostyvyi, Roman; Izhboldina, Olena; Chernenko, Oleksandr; Khramkova, Olga; Kapshuk, Natalya; Hoffmann, Gundula
• Is Part Of: Journal of thermal biology. Volume 93(2020)
• Publication Date: 2020-10
• Subjects: Linear regression -- Temperature-humidity index -- Heat stress -- Weather station -- Barn -- Microclimate; Thermobiology -- Periodicals; Temperature -- Periodicals; Biology -- Periodicals; Thermobiologie -- Périodiques; Thermobiology; Periodicals; 571.46
• DOI: 10.1016/j.jtherbio.2020.102720
• Languages: English
• ISSNs: 0306-4565

Abstract

Abstract: Monitoring and predicting the microclimate in naturally ventilated barns (NVB) is important given the adverse effects of high summer temperatures on dairy cows in the context of global climate change. The aim of the work was to verify the accuracy of the microclimate forecast in a NVB using linear regression (LR). Our working hypothesis suggested that multiple periodic measurements of air temperature and relative humidity outside and inside the barns at the same time will allow us to build LR models for predicting the temperature-humidity index (THI). This was done not only for a specific dairy barn based on this indicator outside, but also in other dairy barns with a similar design, located in similar weather conditions. The results of the research indicate that the use of LR had a high accuracy of forecasting (93–96%) the THI in NVB of various designs during the summer heat. At the same time, differences were found between traits (air temperature, relative humidity as well as resulting THI) provided by meteorological weather stations and these data measured simultaneously next to the dairy barns. The proposed LR models can be used to predict THI in NVBs of different designs. Highlights: Predicting microclimate in naturally ventilated dairy barns is important to assess heat stress. Linear regression models can be used for predicting the temperature-humidity index. Caution is required by predicting barns' climate based on weather station data.