Enteric methane (CH4) is a main driver to greenhouse gas emission (GHGE) in dairy herds, but its direct measurement is complex. This study aimed at comparing estimations of enteric CH4 due to lactating cows obtained with equations based on 1) milk fatty acids (FA) predicted by mid-infrared spectra (MIR) and 2) estimated cows’ feed intake and diet composition. Data originated from 174 farms enrolled in the Parmigiano Reggiano Consortium. For each farm, data on milk yield (MY), feed allowance and composition of diets fed to lactating cows were collected. Individual milk composition, milk MIR spectra (MilkoScan FT6000), days in milk (DIM) and parity were retrieved from the official Italian milk recording systems over a period of almost two years. Editing of spectra included outliers’ detection by mahalanobis distance. Prediction of six informative FA were obtained using equations previously developed to calculate enteric CH4 emissions. Farm-based least square means were obtained using a linear model (fixed effects: farm, DIM (12 classes of 30 days) and parity (5 classes); random effects: individual cow, test-day). These were compared with the values obtained from eight diet-based equations retrieved from the literature using Pearson correlation analysis. On average, farms herded 148±135 cows, with a MY of 27.8±5.7 kg/cow/d (3.38% protein, 3.76% fat). Feed allowance averaged 25.2±2.3 kg dry matter/cow/d (mean forage-concentrate ratio of 58:42). MIR-based CH4 values averaged 405±79 g/cow/d, whereas estimates from diet-based equations ranged from 361±37 to 478±40 g, with an overall average of 432±36 g. Correlation factors between MIR-based and diet-based CH4 ranged from 0.08 to 0.58: three equations showed low values (0.08-0.37) and five equation (plus the overall average) higher and significant ones (0.48-0.58, P<0.01). In conclusion, enteric CH4 estimated using milk MIR spectra could be an interesting alternative method to compute farm GHGE.
Comparison of procedures for estimating enteric methane emissions in dairy herds
M. Berton
;M. A. Ramirez Mauricio;A. Cecchinato;H. Toledo Alvarado;L. Gallo;E. Sturaro
2026
Abstract
Enteric methane (CH4) is a main driver to greenhouse gas emission (GHGE) in dairy herds, but its direct measurement is complex. This study aimed at comparing estimations of enteric CH4 due to lactating cows obtained with equations based on 1) milk fatty acids (FA) predicted by mid-infrared spectra (MIR) and 2) estimated cows’ feed intake and diet composition. Data originated from 174 farms enrolled in the Parmigiano Reggiano Consortium. For each farm, data on milk yield (MY), feed allowance and composition of diets fed to lactating cows were collected. Individual milk composition, milk MIR spectra (MilkoScan FT6000), days in milk (DIM) and parity were retrieved from the official Italian milk recording systems over a period of almost two years. Editing of spectra included outliers’ detection by mahalanobis distance. Prediction of six informative FA were obtained using equations previously developed to calculate enteric CH4 emissions. Farm-based least square means were obtained using a linear model (fixed effects: farm, DIM (12 classes of 30 days) and parity (5 classes); random effects: individual cow, test-day). These were compared with the values obtained from eight diet-based equations retrieved from the literature using Pearson correlation analysis. On average, farms herded 148±135 cows, with a MY of 27.8±5.7 kg/cow/d (3.38% protein, 3.76% fat). Feed allowance averaged 25.2±2.3 kg dry matter/cow/d (mean forage-concentrate ratio of 58:42). MIR-based CH4 values averaged 405±79 g/cow/d, whereas estimates from diet-based equations ranged from 361±37 to 478±40 g, with an overall average of 432±36 g. Correlation factors between MIR-based and diet-based CH4 ranged from 0.08 to 0.58: three equations showed low values (0.08-0.37) and five equation (plus the overall average) higher and significant ones (0.48-0.58, P<0.01). In conclusion, enteric CH4 estimated using milk MIR spectra could be an interesting alternative method to compute farm GHGE.
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