Predicting rectal temperature and respiration rate responses in lactating dairy cows exposed to heat stress

Abstract

Milk production and time effects are considered related to heat stress but they have not yet been combined in predictive models. In two parts, this study aimed to develop new models to predict heat stress (rectal temperature and respiration rate) of lactating dairy cows by inputting predictors, including ambient temperature (T a ), relative humidity (RH), wind speed (WS), milk yield (MY), and time blocks. In the first part of the study, we built the quantitative foundation for the second part, including the regression relation between respiration rate and rectal temperature (to convert predicted respiration rate to predicted body temperature), as well as between rectal temperature and respiration rate when heat stress was triggered (to recognize whether herds were under stress). In the second part, we built models that combined the abovementioned predictors to predict respiration rate. In part I, data were obtained from 45 high-producing Holstein cows within a T a range of 9.5 to 30.8°C. We found a very strong correlation between mean respiration rate (MRR) and mean rectal temperature (MRT), where MRT = 0.021 × MRR + 37.6 (R 2 = 0.925), suggesting that for each 4.8 breaths per minute (bpm) increase of MRR, MRT would be expected to increase by 0.1°C. Rectal temperature was determined to be 38.6°C when heat stress was triggered, which corresponded to a respiration rate of 48 bpm. In part II, data were obtained in 3 stalls within a T a range of 6.9 to 33.3°C over 3 time blocks, all of which were the 90 min preceding milking (0630-0800, 1230-1400, and 1830-2000 h). We found a nonlinear response of MRR to T a , which could be linearized by the quadratic term of T a . The response of MRR was the highest in the 0630-0800 h block, followed by 1230-1400 h, and finally 1830-2000 h. We proposed a model combining 3 time blocks (R 2 = 0.836): MRR in 0630-0800 h was determined to 56.28 + (−3.40 + 0.11 × T a + 0.02 × RH) × T a − 0.21 × RH − 2.82 × WS + 0.62 × MY; MRR in 1230-1400 h and 1830-2000 h were 4.6 and 10.3 bpm lower than that in 0630-0800 h, respectively (reducing the intercept of the expression in 0630-0800 h). Compared with temperature-humidity index equations, the proposed model performed better at suppressing prediction error, and had better sensitivity and accuracy in recognizing whether heat stress was triggered.

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

“…Eigenberg et al [117] reported that RR fluctuated less significantly during the thermoneutral period (18 ± 7 • C) (45 ± 16 BPM) compared with the heat stress period (>25 • C) (78 ± 26 BPM), indicating a nonlinear response of RR to AT. A consistent trend was found by Li et al [47] in a field study with 45 healthy high-producing Holstein cows in Beijing where the AT averaged 22.0 • C (9.5-30.8 • C) and RH averaged 61.8% (37.9-82.2%). They found a greater R 2 (0.916) after introducing a quadratic term of AT.…”

“…In a study using 45 high-producing Holstein cows, Li et al [47] applied a broken-line model and determined the threshold of RT as 38.6 • C when heat strain was triggered at a THI of 70. Similarly, Pinto et al [19] demonstrated a slightly lower threshold of RT (38.4 • C) at the same THI of 70 using 139 multiparous lactating Holstein-Friesian cows.…”

Nutritional Physiology and Biochemistry of Dairy Cattle under the Influence of Heat Stress: Consequences and Opportunities

“…However, each aforementioned method has some advantages and drawbacks, for example, vaginal temperature is accurate, milk temperature monitoring is easy [18], and infrared thermography give best results on forehead and eye area [27]. Rapidly evolving temperature and activity monitoring technology produce big data, which can be affectively used for modeling to predict accurate HS [29][30][31] and at the same time thermo-tolerant animals can be identified for possible future breeding. Identification of thermo-tolerant cows based on the physiological exhibits [32], defining their phenotypes together with the integration of molecular techniques [33] can help to achieve thermo-tolerance breeding in the cattle.…”

Glucose Metabolism and Dynamics of Facilitative Glucose Transporters (GLUTs) under the Influence of Heat Stress in Dairy Cattle

“…In order to secure dairy production under the influence of ever-increasing events of heat stress, sustainable long-term mitigation strategies include achieving thermo-tolerance among dairy cows. Modern data collection technologies of heat stress and activity monitoring give a huge amount of data that could be used for modeling to identify thermo-tolerant cows [ 105 , 130 , 131 ]. Crossbreeding with thermo-tolerant animals, preferably within the breed, is a way forward in this context [ 132 , 133 ].…”