Autori: Messeri A, Mancini M, Bozzi R, Parrini S, Sirtori F, Morabito M, Crisci A, Messeri G, Ortolani A, Gozzini B, Orlandini S, Fibbi L, Cristofori S, Grifoni D.

Rivista: International Journal of Biometeorology. 2023 Oct;67(10):1555-1567

DOI: 10.1007/s00484-023-02510-7

Abstract:

Many studies have reported that the impact of high temperatures affects physiology, welfare, health, and productivity of farm animals, and among these, the dairy cattle farming is one of the livestock sectors that suffers the greatest effects. The temperature–humidity index (THI) represents the state of the art in the evaluation of heat stress conditions in dairy cattle but often its measurement is not carried out in sheds. For this reason, the aim of this study was the monitoring of the THI in three dairy cattle farms in Mugello (Tuscany) to understand its influence on dairy cows. THI values were calculated using meteorological data from direct observation in sheds and outdoor environments. Data relating to the animal’s behavior were collected using radio collars. The Pearson test and Mann–Kendall test were used for statistical analysis. The results highlighted a significant (P < 0.001) upward trend in THImax during the last 30 years both in Low Mugello (+ 1.1 every 10 years) and in High Mugello (+ 0.9 every 10 years). In Low Mugello sheds, during the period 2020–2022, more than 70% of daytime hours during the summer period were characterized by heat risk conditions (THI > 72) for livestock. On average the animals showed a significant (P < 0.001) decrease in time spent to feeding and rumination, both during the day and the night, with a significant (P < 0.001) increase in inactivity. This study fits into the growing demand for knowledge of the micro-climatic conditions within farms in order to support resilience actions for protecting both animal welfare and farm productivity from the effects of climate change. This could also be carried out thanks to estimation models which, based on the meteorological conditions forecast, could implement the thermal stress indicator (THI) directly from the high-resolution meteorological model, allowing to get a prediction of the farm’s potential productivity loss based on the expected THI.