Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.

中文翻译：

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牛奶中红外光谱预测泌乳早期个体奶牛氮利用效率的潜力。

出于环境和经济方面的原因，提高个体母牛和畜群水平的氮利用效率（NUE）已成为乳制品生产系统中的关键目标。为了通过基因选择以及饲喂和管理策略改善NUE，需要具有成本效益的大规模表型分析方法。这项研究的目的是评估使用牛奶的中红外（MIR）光谱预测泌乳早期个体奶牛NUE的可能性。从3头研究牛群（丹麦，爱尔兰和英国）的129头荷斯坦奶牛（从产犊到产奶50天）收集了数据。在其中的两个牛群中，饮食被设计为以代谢方式挑战母牛，而在第三个牛群中提供了反映当地管理实践的饮食。每天计算氮摄入量（kg / d）和牛奶中排泄的氮含量（kg / d）。氮的利用效率以乳中氮与氮摄入量之比计算，并以百分比表示。每天的NUE值介于9.7至81.7％之间，平均值为36.9％，标准偏差为10.4％。牛奶MIR光谱每周记录两次，并标准化为通用格式，以避免仪器或采样周期之间出现偏差。使用偏最小二乘或支持向量机回归方法对1,034个观测值建立了使用牛奶MIR光谱预测NUE的回归模型。然后通过以下方式对模型进行评估：（1）使用10个子集进行交叉验证；（2）排除25％的母牛作为验证集的母牛验证；（3）不包括每种饮食的饮食验证一个用作验证集。当使用支持向量机方法时，可获得最佳的统计性能。将牛奶产量和泌乳数作为预测指标，并结合光谱，也可以改善校准。在交叉验证中，最佳模型预测NUE，交叉验证的确定系数为0.74，相对误差为14％，适用于区分低NUE和高NUE的奶牛。进行奶牛验证时，相对误差保持在14％，而在日粮验证过程中，相对误差在12％至34％之间。在饮食验证中，模型显示出缺乏鲁棒性，表明在预测饮食和未在校准数据集中表示的样品的NUE方面存在困难。因此，需要在模型中集成更多数据，以涵盖有关品种，饮食，泌乳阶段，管理实践，季节，MIR仪器和地理区域的最大可变性。尽管需要对模型进行验证和改进以用于常规条件，但这些初步结果表明，可以通过牛奶MIR光谱获得有关NUE的信息。这有可能允许进行大规模预测，以帮助进行进一步的遗传和基因组研究，以及开发农场管理工具。