RESUMEN
Dietary carbohydrate manipulation can be used to reduce enteric CH4 emission, but there is a lack of studies on the interaction of different types of carbohydrates that can affect feed intake and ruminal fermentation. Understanding this interaction is necessary to make the most out of CH4 mitigation feeding strategies using different dietary carbohydrates. The aim of this study was to test the effect on enteric CH4 emission, feed intake and milk production response when cows were fed either grass-clover (GCS) or corn silage (CS) as the sole forage source (55% of dry matter, DM), in combination with either barley (BAR) or dried beet pulp (DBP) as a concentrate (21.5% of DM). Twenty-four (half first and half second parity) cows were used in a crossover design with 2 periods of 21 d each, receiving 2 of 4 diets obtained from a 2 × 2 factorial arrangement of the experimental diet. Feed intake, CH4 emission metrics and milk production were recorded at the end of the experimental periods. The diets had NDF concentrations between 258 and 340 g/kg of DM, and starch concentrations between 340 and 7.45 g/kg of DM (CS-BAR and GCS-DBP, respectively). The effects of silage and concentrate on dry matter intake (DMI) were additive, with the highest feed intake in cows fed COR-BAR, followed by cows fed COR-DBP, GCS-BAR, and GCS-DBP (21.2, 19.9, 19.1, and 18.3 kg/d). Energy corrected milk (ECM) yield was not affected by silage source in first parity cows, but it was higher for cows fed CS than cows fed GCS in second parity. The effects of silage and concentrate on CH4 production (g/d), yield (g/kg of DMI) and intensity (g/kg of ECM) were not additive as cows fed GCS had similar responses regardless of the concentrate used, but cows fed CS had lower CH4 production, yield and intensity, when fed BAR instead of DBP. The lower CH4 production, yield and intensity in cows fed CS-BAR compared with other diets could be partially explained by the nonlinear relationship between ruminal VFA and carbohydrates (NDF and starch) concentration reported in literature, however, we observed a linear relationship between acetate:propionate ratio and CH4 yield, suggesting possible other effects. The effects of silage and concentrate on the ruminal VFA were additive in first parity cows, but not in second parity cows. The interaction between dietary CHO type and parity might indicate an effect of feed intake or the energy balance of the cow. Feeding cows silage and concentrate both rich in starch can result in the lowest enteric CH4 emission.
RESUMEN
Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.