Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier-transform infrared spectra

Abstract

Mitigating the dairy chain's contribution to climate change requires cheap, rapid methods of predicting enteric CH4 emissions (EME) of dairy cows in the field. Such methods may also be useful for genetically improving cows to reduce EME. Our objective was to evaluate different procedures for predicting EME traits from infrared spectra of milk samples taken at routine milk recording of cows. As a reference method, we used EME traits estimated from published equations developed from a meta-analysis of data from respiration chambers through analysis of various fatty acids in milk fat by gas chromatography (FAGC). We analyzed individual milk samples of 1,150 Brown Swiss cows from 85 farms operating different dairy systems (from very traditional to modern), and obtained the cheese yields of individual model cheeses from these samples. We also obtained Fourier-transform infrared absorbance spectra on 1,060 wavelengths (5,000 to 930 waves/cm) from the same samples. Five reference enteric CH4 traits were calculated: CH4 yield (CH4/DMI, g/kg) per unit of dry matter intake (DMI), and CH4 intensity (CH4/CM, g/kg) per unit of corrected milk (CM) from the FAGC profiles; CH4 intensity per unit of fresh cheese (CH4/CYCURD, g/kg) and cheese solids (CH4/CYSOLIDS, g/kg) from individual cheese yields (CY); and daily CH4 production (dCH4, g/d). Direct infrared (IR) calibrations were obtained by BayesB modeling; the determination coefficients of cross-validation varied from 0.36 for dCH4 to 0.57 for CH4/CM, and were similar to the coefficient of determination values of the equations based on FAGC used as the reference method (0.47 for CH4/DMI and 0.54 for CH4/CM). The models allowed us to select the most informative wavelengths for each EME trait and to infer the milk chemical features underlying the predictions. Aside from the 5 direct infrared prediction calibrations, we tested another 8 indirect prediction models. Using IR-predicted informative fatty acids (FAIR) instead of FAGC, we were able to obtain indirect predictions with about the same precision (correlation with reference values) as direct IR predictions of CH4/DMI (0.78 vs. 0.76, respectively) and CH4/CM (0.82 vs. 0.83). The indirect EME predictions based on IR-predicted CY were less precise than the direct IR predictions of both CH4/CYCURD (0.67 vs. 0.81) and CH4/CYSOLIDS (0.62 vs. 0.78). Four indirect dCH4 predictions were obtained by multiplying the measured or IR-predicted daily CM production by the direct or indirect CH4/CM. Combining recorded daily CM and predicted CH4/CM greatly increased precision over direct dCH4 predictions (0.96-0.96 vs. 0.68). The estimates obtained from the majority of direct and indirect IR-based prediction models exhibited herd and individual cow variability and effects of the main sources of variation (dairy system, parity, days in milk) similar to the reference data. Some rapid, cheap, direct and indirect IR prediction models appear to be useful for monitoring EME in the field and possibly for genetic/genomic selection, but future studies directly measuring CH4 with different breeds and dairy systems are needed to validate our findings.