Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level

The milk Fourier-transform infra-red spectrometry (FTIRS) can be used to predict the enteric methane emissions (EME) at population level. In this study, the variability in FTIRS predicted EME traits due to the breed of cow, farm, the production level of individual cows within herds, and their interactions were evaluated. A dataset obtained from milk recordings, which covered four breeds (Holstein, Brown Swiss, Simmental and Alpine Grey), 6,430 herds, 115,819 cows, and 1,759,706 test-day milk/spectra records was used. The herds were stratified into 5 production levels considering their average daily milk energy production; individual cows within herd were similarly stratified considering their individual production levels. The EME traits were predicted directly from milk FTIR spectra or indirectly from six informative milk FA predicted from milk spectra. The statistical model included, separately for each trait and method, breed, herd intensiveness level, cow production level, and their interactions, year, month, parity, and lactation stage. The direct and indirect methods yielded similar results in predicting CH4 yield per kg of DMI and CH4 intensity per kg of fat-protein corrected milk. The indirect method was reliable in predicting daily EME production per cow, the indirect one did not. EME was affected by the breed x herd production intensiveness interaction, and to a lesser degree, by the breed x cow production level interaction. A better understanding of the complex interactions influencing EME in dairy herds was achieved. This would be useful for the genetic improvement, the environmental certification of farms, and for setting prices in milk payment schemes.

Automated summary

The study evaluated the variability in predicting enteric methane emissions (EME) using milk Fourier-transform infra-red spectrometry (FTIRS) due to the breed of cow, farm, the production level of individual cows within herds, and their interactions. A dataset obtained from milk recordings of four breeds, 6,430 herds, 115,819 cows, and 1,759,706 test-day milk/spectra records was used. The results showed that both direct and indirect methods yielded similar results in predicting CH4 yield and intensity, but the indirect method was reliable in predicting daily EME production per cow. The study achieved a better understanding of the complex interactions influencing EME in dairy herds, which has practical implications for genetic improvement, environmental certification, and milk payment schemes.