Abstract：
Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH4) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH4 emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH4 spot measurements with GF were taken as the basic measurement unit (BMU) of CH4. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH4 reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH4 measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH4 measurement by GF performed better than short duration: the coefficient of determination of validation (R2V) for CH4 emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH4 emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH4 measurement period gave better predictions than using day spectra (R2V = 0.70 vs. 0.60 for CH4 as g/d on 4 BMU). Correcting the day spectra by DIM improved R2V compared with the equivalent DIM-uncorrected models (R2V = 0.67 vs. 0.60 for CH4 as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH4 measurement period (R2V = 0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R2V = 0.13 for CH4 as g/d on 1 BMU) or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH4 prediction when cows receive chemical inhibitors of CH4 emissions not affecting milk composition.
摘要：
在来自饲喂影响产甲烷和乳合成的饮食的奶牛的乳样品上测试了各种方法学方案,以确定用于通过乳中红外(MIR)光谱法预测GreenFeed系统(GF)测量的甲烷(CH4)排放的最佳方法。开发的模型还在饲喂CH4排放化学抑制剂[3-硝基氧基丙醇（3NOP）]的奶牛的数据集上进行了测试，这些数据仅对牛奶成分有轻微影响。总共考虑了129头初产和多胎荷斯坦奶牛，饲喂具有不同产甲烷潜力的日粮。每天记录个体产奶量（MY）和干物质摄入量，而每周两次记录脂肪和蛋白质校正牛奶（FPCM）。每周两次收集来自2次连续挤奶的MIR光谱。用GF测量20个CH4点作为CH4的基本测量单位(BMU)。通过将数据库分成校准和验证数据集(不包括3NOP样品),使用偏最小二乘回归建立方程。通过挤奶开发了牛奶MIR光谱模型，并通过平均2次连续挤奶的光谱获得了日光谱。通过使用CH4参考数据针对1、2、3或4个BMU的测量持续时间校准基于日光谱的模型。从在相应的CH4测量期间收集的日光谱的平均值建立模型。按天校正牛奶中的光谱（DIM）和包括奇偶校验，我的,和FPCM作为解释变量进行了测试，作为提高模型性能的工具。建立在牛奶MIR光谱上的模型比使用一次挤奶光谱开发的模型具有更好的性能。通过GF进行的长时间CH4测量比短时间更好：以克/天为单位的CH4排放的确定系数（R2V）为0.60，而不是0.60。4和1BMU为0.52，分别。当CH4排放量表示为每公斤干物质摄入量的克数时，每公斤我的克，或每公斤FPCM克，长时间的性能也有所提高。将GF参考数据与在整个相应的CH4测量期间收集的牛奶MIR光谱的平均值相结合，可以得出比使用日光谱更好的预测（R2V=0.70vs.在4个BMU上，CH4为0.60g/d)。与等效的DIM未校正模型相比，通过DIM校正日光谱改善了R2V（R2V=0.67与在4个BMU上，CH4为0.60g/d)。添加其他表型信息作为解释变量并没有进一步改善建立在单日DIM校正光谱上的模型的性能，而包括MY（或FPCM）改善了在CH4测量期间记录的光谱平均值（未通过DIM校正）建立的模型的性能（R2V=0.73vs.在4个BMU上，CH4为0.70g/d)。在3NOP数据集上验证模型时，在模型中没有3NOP数据的积分(1个BMU上CH4的R2V=0.13g/d)或(1个BMU上CH4的R2V=0.31g/d)预测效果不佳。因此，当奶牛接受不影响牛奶成分的CH4排放的化学抑制剂时，CH4预测需要特定的模型。
