Mesgaran Sadjad Danesh, Eggert Anja, Höckels Peter, Derno Michael, Kuhla Björn
1Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany.
2Institute of Genetics and Biometry, Leibniz Institute for Farm Anih8mal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany.
J Anim Sci Biotechnol. 2020 May 7;11:43. doi: 10.1186/s40104-020-00455-0. eCollection 2020.
Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows.
Heat production was computed based on the animal's consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression.
Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5 kJ/kg BW. The ratio of performance to deviation (RPD) was 1.56.
The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.
反刍动物摄入的饲料能量转化为牛奶的过程中，会通过粪便、尿液排泄、发酵气体和热量产生能量损失。尽管奶牛的产奶量和体重相当，但产热情况可能有所不同。因此，产热可被视为代谢效率的一个指标，可在呼吸室中直接测量。后者是一种准确但耗时的技术。相比之下，牛奶傅里叶变换中红外（FTIR）光谱分析是一种廉价的高通量方法，用于估计奶牛的不同生理特征。因此，本研究旨在利用呼吸室中的产热测量、牛奶FTIR光谱和奶牛的产奶量测量数据，建立一个产热预测模型。
根据动物在呼吸室中消耗的氧气、产生的二氧化碳和甲烷来计算产热。产热数据包括来自64头德国荷斯坦奶牛和20头兼用西门塔尔奶牛的168次24小时观测数据。动物在呼吸室中每天于07:00和16:30挤奶两次。测定产奶量以使用线性回归预测产热。每次挤奶时采集牛奶样本，并用MilkoScan FT 6000获得FTIR光谱。计算上午和下午挤奶的吸收光谱的平均值或产奶量加权平均值，以获得计算光谱。每个光谱共288个波数以及相应的产奶量用于使用偏最小二乘法（PLS）回归建立产热模型。
所研究动物的实测产热范围在712至1470kJ/kg体重之间。产奶量与产热之间线性回归的决定系数为0.46，而基于FTIR光谱的PLS模型的决定系数为0.23。使用加权平均光谱和产奶量的PLS预测模型的交叉验证方差为57%，预测均方根误差为86.5kJ/kg体重。性能与偏差比（RPD）为1.56。
与仅应用FTIR光谱或产奶量的模型相比，使用加权平均FTIR光谱和产奶量的PLS模型在预测奶牛产热方面具有更高的潜力。
