Department of Animal Breeding and Genetics, National Institute for Agricultural and Food Research and Technology (INIA-CSIC), Madrid, Spain.
Department of Animal Breeding and Genetics, National Institute for Agricultural and Food Research and Technology (INIA-CSIC), Madrid, Spain.
Animal. 2022 Nov;16(11):100662. doi: 10.1016/j.animal.2022.100662. Epub 2022 Oct 8.
The search for criteria that allow the quantification of the level of thermotolerance of an animal is a major challenge in animal production. Different criteria have been proposed to date, mainly the use of routine milk recording and weather information or the collection of physiological measures related with heat stress. This study aimed at quantifying the association between indicators of heat tolerance derived from productive and physiological traits. For this purpose, two physiological traits, rectal temperature (RT) and respiratory rate (RR), and nine productive traits (milk yield, fat, protein and lactose yields and contents, casein and urea contents) were measured from June to September of 2018 in three flocks of Manchega sheep. A total of 462 lactating ewes participated in the study. Air temperature (Ta), relative humidity (RH) and associated temperature and humidity index (THI) were recorded inside the barn and also obtained from the closest weather station from the national meteorological network, and used to produce several measurements of heat load on animals. Based on the results of fits for quadratic and cubic regressions on the alternative heat load measures, the cubic regression on Ta and THI obtained inside the barn at time of recording yielded the best fit for physiological and productive parameters. The use of weather information taken from the official weather station closest to the farm also produced similar estimates and could be considered as a good alternative when on-farm meteorological data are not available. Two-trait random regression models that involved individual intercept and slope of response to heat load were used to obtain correlations between basal levels and heat tolerance within and across traits. Estimated correlations showed that animals with smaller vs larger basal levels of RT and RR tend to be more vs less heat tolerant (correlations up to 0.46) and that slopes of increase for RR and RT under heat stress were highly correlated (0.82). Estimated correlations between tolerance criteria from production vs physiology were up to -0.5 (between milk yield and RT), indicating that animals that show less increase in body temperature also tend to show a smaller decrease in production under heat stress. However, because of the non-unity correlation between the two types of indicators of heat tolerance, both sources of information, productive and physiological ought to be taken into account to ensure the long-term sustainability of selection programmes aiming at improving productive levels when heat stress is a concerning issue.
寻找能够量化动物耐热水平的标准是动物生产中的一个主要挑战。迄今为止，已经提出了不同的标准，主要是使用常规的牛奶记录和天气信息或收集与热应激相关的生理测量值。本研究旨在量化耐热性指标与生产和生理特征之间的关联。为此，在 2018 年 6 月至 9 月期间，在三个曼彻格羊群中测量了直肠温度（RT）和呼吸率（RR）这两个生理特征以及 9 个生产特征（牛奶产量、脂肪、蛋白质和乳糖产量和含量、酪蛋白和尿素含量）。共有 462 只哺乳期母羊参与了这项研究。在畜舍内记录了空气温度（Ta）、相对湿度（RH）以及相关的温度和湿度指数（THI），并从国家气象网络中最近的气象站获得了这些数据，用于对动物的热负荷进行了多项测量。基于对替代热负荷测量的二次和三次回归拟合结果，在记录时畜舍内 Ta 和 THI 的三次回归拟合对生理和生产参数的拟合效果最佳。使用来自离农场最近的官方气象站的天气信息也产生了类似的估计值，并且在没有农场气象数据时可以作为一个很好的替代方案。使用涉及个体截距和对热负荷响应斜率的两特质随机回归模型，获得了基础水平和耐热性在特质内和特质间的相关性。估计的相关性表明，RT 和 RR 的基础水平较小的动物比基础水平较大的动物更耐热（相关性高达 0.46），并且在热应激下 RR 和 RT 的增加斜率高度相关（0.82）。生产与生理耐热性指标之间的估计相关性高达-0.5（在牛奶产量和 RT 之间），这表明在热应激下，体温升高幅度较小的动物的产奶量也倾向于下降较小。然而，由于耐热性的两种类型的指标之间的非统一相关性，生产和生理这两种信息来源都应该被考虑，以确保在热应激是一个关注问题的情况下，旨在提高生产水平的选择计划的长期可持续性。
