Skip to main content
Fig. 8 | Animal Microbiome

Fig. 8

From: An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Fig. 8

Hypothetical selection of feed efficient cows for residual feed intake (RFI) and the interplay of the rumen microbiome, genomic predicted transmitting ability (PTA), and phenotypic RFI. 8.A. Comparison of daily performance and carbon footprint between the most efficient cows (n = 50) and the least efficient cows (n = 50) based on residual feed intake and gross feed efficiency. Methane production (g/d per cow) was assessed based on the predictive model (Eq. 27) from Nielsen, et al. (70) validated for cows in North America (71). The equation was as follow: CH4 (g/d per cow) = ([1.23 x DMI (kg/d)– 1.45 x dietary fatty acid content (% of DM) + 0.120 x neutral detergent fiber content (NDF; % of DMI)]/0.05565). The corrections for CH4 yield (g/kg DMI) and intensity (g/kg NESec) were calculated based on measured production traits. A correction of CH4 (g/d) for gross feed efficiency (GFE; g of milk produced per g DMI) was performed to access the CH4 production reduction potential when considering both CH4 yield and intensity. Numeric values are significant differences in selecting the most efficient cows when P ≤ 0.05; Methane results, including group means, SEM, and P-values are shown in Supplementary Table 2. 8.B. Outcome of the rumen microbiome and genomic PTA predictions for RFI showing their potential interplay in determining the phenotypic RFI of the studied lactating dairy cows

Back to article page