Aim of the project was to evaluate behavioral and productive variations of dairy cows detected by automatic monitoring systems, during different seasons. Environmental data (Temperature (T, °C), Relative Humidity, (RH, %) and Temperature and Humidity Index (THI)), were recorded inside the pens by electronic probes during 4 periods over 2 years, for a total of 473 days. Fans and sprinklers were activated at THI >60. Hundred lactating and dry cows were equipped with monitoring tag (Heatime-Pro, SCR Engineers Ltd.) that recorded continuously time (min/d) spent ruminating (RT), panting (PT) and feeding (FT). Daily PT and FT data were recorded as group average [lactating (LC) or dry], while RT and milk production were extracted as individual data. Four classes of environmental stress were identified based on maximum THI: comfort (C, THI < 60, 101d), moderate stress (MS, 60< THI >69, 92d), stress (S, 70< THI >75, 102d), severe stress (SS, THI >75, 178d). Behavioral and productive data were compared between classes. Data were analyzed by mixed model with repeated measures, with environmental class, group (dry or lactating), parity and interactions as fixed effect. Cow was included as random effect for RT and milk production. Means comparison was performed by Tukey post hoc test. All behavioral outcomes were influenced by heat stress (P < .001, table 1). Panting increased linearly with maximum THI recorded, being highest during SS days in LC (49.6 min/d, P < .001). FT and RT (min/d) were lowest (P < .001) during S days (206.9, FT and 473.1, RT) and RT reached the lowest values in cows within 15 DIM (456.4). Milk production was affected in multiparous cows, with a linear reduction from C to SS days (P < .001). Heat stress deeply affected cows behavior and performances, despite cooling systems. Automatic monitoring of these parameters can effectively help in detecting heat stress and consequently adopt strategies to improve animal welfare.

504 Late-Breaking: Automatic Monitoring Systems to Detect Behavioral and Productive Variations during Heat Stress in Dairy Cows

L. M. E. Mammi;D. Cavallini;A. Palmonari;A. Concolino;F. Ghiaccio;G. Buonaiuto;G. Visentin;A. Formigoni
2021

Abstract

Aim of the project was to evaluate behavioral and productive variations of dairy cows detected by automatic monitoring systems, during different seasons. Environmental data (Temperature (T, °C), Relative Humidity, (RH, %) and Temperature and Humidity Index (THI)), were recorded inside the pens by electronic probes during 4 periods over 2 years, for a total of 473 days. Fans and sprinklers were activated at THI >60. Hundred lactating and dry cows were equipped with monitoring tag (Heatime-Pro, SCR Engineers Ltd.) that recorded continuously time (min/d) spent ruminating (RT), panting (PT) and feeding (FT). Daily PT and FT data were recorded as group average [lactating (LC) or dry], while RT and milk production were extracted as individual data. Four classes of environmental stress were identified based on maximum THI: comfort (C, THI < 60, 101d), moderate stress (MS, 60< THI >69, 92d), stress (S, 70< THI >75, 102d), severe stress (SS, THI >75, 178d). Behavioral and productive data were compared between classes. Data were analyzed by mixed model with repeated measures, with environmental class, group (dry or lactating), parity and interactions as fixed effect. Cow was included as random effect for RT and milk production. Means comparison was performed by Tukey post hoc test. All behavioral outcomes were influenced by heat stress (P < .001, table 1). Panting increased linearly with maximum THI recorded, being highest during SS days in LC (49.6 min/d, P < .001). FT and RT (min/d) were lowest (P < .001) during S days (206.9, FT and 473.1, RT) and RT reached the lowest values in cows within 15 DIM (456.4). Milk production was affected in multiparous cows, with a linear reduction from C to SS days (P < .001). Heat stress deeply affected cows behavior and performances, despite cooling systems. Automatic monitoring of these parameters can effectively help in detecting heat stress and consequently adopt strategies to improve animal welfare.
ASAS Annual 2021 Meeting Abstracts
180
181
L.M.E. Mammi, D. Cavallini, A. Palmonari, A. Concolino, F. Ghiaccio, G. Buonaiuto, G. Visentin, A. Formigoni
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/870729
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