State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China.
State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China.
J Dairy Sci. 2020 Jun;103(6):5466-5484. doi: 10.3168/jds.2019-16411. Epub 2020 Apr 8.
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), 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 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 = 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 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, which could be linearized by the quadratic term of T. 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 = 0.836): MRR in 0630-0800 h was determined to 56.28 + (-3.40 + 0.11 × T + 0.02 × RH) × T - 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.
产奶量和时间效应被认为与热应激有关，但它们尚未在预测模型中结合。本研究分为两部分，旨在通过输入环境温度（T）、相对湿度（RH）、风速（WS）、产奶量（MY）和时间块等预测因子，开发预测泌乳奶牛热应激（直肠温度和呼吸率）的新模型。在研究的第一部分，我们为第二部分建立了定量基础，包括呼吸率和直肠温度之间的回归关系（将预测呼吸率转换为预测体温），以及热应激触发时直肠温度和呼吸率之间的关系（以识别牛群是否处于应激状态）。在第二部分，我们建立了结合上述预测因子预测呼吸率的模型。在第一部分中，我们从 45 头高产荷斯坦奶牛的数据中获得了 9.5 到 30.8°C 的 T 范围。我们发现平均呼吸率（MRR）和平均直肠温度（MRT）之间存在很强的相关性，其中 MRT = 0.021 × MRR + 37.6（R = 0.925），这表明 MRR 每增加 4.8 次/分钟（bpm），MRT 预计会增加 0.1°C。当触发热应激时，直肠温度被确定为 38.6°C，对应的呼吸率为 48 bpm。在第二部分中，在 3 个牛舍中获得了 6.9 到 33.3°C 的 T 范围的数据，所有数据均在前奶时间（0630-0800、1230-1400 和 1830-2000 h）的 90 分钟内获得。我们发现 MRR 对 T 的非线性反应，可以通过 T 的二次项线性化。MRR 的反应在 0630-0800 h 块中最高，其次是 1230-1400 h，最后是 1830-2000 h。我们提出了一个结合 3 个时间块的模型（R = 0.836）：0630-0800 h 的 MRR 被确定为 56.28 +（-3.40 + 0.11 × T + 0.02 × RH）× T - 0.21 × RH - 2.82 × WS + 0.62 × MY；1230-1400 h 和 1830-2000 h 的 MRR 分别比 0630-0800 h 低 4.6 和 10.3 bpm（降低了 0630-0800 h 表达的截距）。与温度湿度指数方程相比，所提出的模型在抑制预测误差方面表现更好，并且在识别是否触发热应激方面具有更好的敏感性和准确性。
