Abstract：
Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called \"sniffer technique\") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 (\"best model\"), where all significant traits were included; model 2 (\"on-farm model\"), where DMI was excluded; and model 3 (\"reduced on-farm model\"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.
摘要：
自动测量甲烷和二氧化碳的浓度比，[CH4]:[CO2],在单个动物的呼吸中(所谓的“嗅探器技术”)和估计的二氧化碳产量可用于估计CH4产量，前提是可以可靠地计算CO2产量。这将允许在大型奶牛队列中估计个体奶牛的CH4产量，由此,根据它们的CH4产量对奶牛进行排序可能成为可能,并且它们的值可用于低CH4排放动物的育种。二氧化碳产量的估计通常基于产热预测，可以根据体重（BW）计算，能量校正牛奶产量，和怀孕的日子。本研究的目的是直接从牛奶生产中预测二氧化碳的产生，饮食，和动物变量，并进一步开发用于不同场景的不同模型，取决于可用的数据。国际数据集包含2,244条来自个体泌乳奶牛的记录，包括二氧化碳产量和相关性状，作为干物质摄入量(MDI)，饮食组成，BW,牛奶生产和成分，牛奶和怀孕的日子，被编译以构成训练数据集。研究地点和嵌套在研究地点内的实验被包括为随机截距。CO2产生的测量方法（呼吸室（RC）或GreenFeed（GF））与研究地点混淆，因此被排除在模型之外。总的来说,基于当前训练数据集开发了3个模型：模型1（“最佳模型”），其中包括所有重要的特征，模型2(“农场模型”)，在那里，MI被排除在外，和模型3(“简化农场模型”)，其中DMI和BW均被排除在外。用RC数据(n=103)对测试数据集进行评估，没有添加剂的GF数据(n=478)或仅包括硝酸盐、3-硝基氧基丙醇(3-NOP),或硝酸盐和3-NOP的组合饲喂奶牛（GF+：n=295），显示了3个模型的良好精度，通过绝对值（-0.22至0.097）和均方误差（MSE）的百分比（0.049至4.89）的低斜率偏差来说明。然而,平均偏差（MB）表明，当在GF和RC测试数据集上评估模型时，系统的CO2产量过度预测和预测不足，分别。为了解决这种偏见，这3个模型是在修改后的测试数据集上进行评估的，通过从RC上的特定模型的评估中减去（测量值通过RC获得）或添加绝对MB（测量值通过GF获得）来调整CO2产量（g/d），GF,和GF+测试数据集。通过这种修改，MB的绝对值和MB占MSE的百分比变得可以忽略不计。总之,这3个模型在预测泌乳奶牛的二氧化碳产量方面是精确的。
