Innovative lactation stage specific prediction of CH4 from milk MIR spectra

Previous research has shown that CH4 emissions of dairy cows are linked to milk composition and particularly to fatty acids (FA). We showed that mid-infrared (MIR) prediction equations can be used to obtain individual enteric CH4 emissions from the milk MIR spectra. However body tissue mobilisation alters milk FA and potentially links between CH4 and MIR spectra. Therefore to reflect the expected metabolic status during lactation, a method was developed to consider days in milk (DIM) in the MIR based prediction equation. A total of 446 CH4 reference data were obtained using the SF6 method on 146 Jersey, Holstein and Holstein-Jersey cows. Linear (P1) and quadratic (P2) Legendre polynomials were computed from DIM of CH4 measurements. A first derivative was applied to the MIR spectra. The calibration model was developed using as independent variables first derivative, first derivative × P1, first derivative × P2 and a modified PLS regression. The CH4 emission prediction (g CH4/day) showed a calibration coefficient of determination (R2c) of 0.75, a cross-validation coefficient of determination (R2cv) of 0.67 and the standard error of calibration (SEC) was 63 g/day. In order to check if this new equation showed an expected and biological meaningful behavior, it was applied to the milk MIR spectra database of the Walloon Region of Belgium (1,804,476 records). The resulting trend across lactation was similar to what was expected, with increasing averaged CH4 up to DIM 83 and a slight decrease after. This pattern was a clear improvement when compared to predictions from previous equations. Results indicate that this innovative approach with integration of DIM information could be a good strategy to improve the equation by taking better account of the metabolism of the cows.