Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk.

ABSTRACT

Enteric methane (CH4), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH4 emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH4CO2 ratio and CH4 production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH4CO2 ratio and CH4 production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R2Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH4CO2 ratio, and R2Val = 0.13 with RMSEP = 111 L/d for CH4 production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R2Val of 0.07 was obtained for both CH4CO2 ratio and CH4 production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH4CO2 ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R2Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH4 production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH4 production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH4 emission. Thus, it is not feasible to predict CH4 emission based on FT-IR spectra alone.