Effects of the density of extruded pellets on starch digestion kinetics, rumen fermentation, fiber digestibility and enteric methane production in dairy cows.

Dynamics of starch digestion in dairy cows fed extruded pellets differing in physical functional properties were investigated by measuring starch digestibility, post-prandial rumen fermentation patterns, and post-prandial duodenal starch appearance. Additionally, starch digestion effects on neutral detergent fiber (NDF) digestibility and methane (CH4 ) emission were studied. Pure barley was extruded to produce three treatments having pellets of either low-density (LD), medium-density (MD) or high-density (HD). The experiment was conducted in a 3 × 3 Latin square design using three lactating Danish Holstein cows fitted with ruminal, duodenal and ileal cannulas. After the allocation of experimental concentrate directly into the rumen through the rumen cannula, cows were fed a basal diet low in starch. Eight samples were collected on equal time intervals (9 h) from duodenal digesta, ileal digesta and feces (grab sample) to determine digestibility. For post-prandial rumen fermentation patterns, four sample sets of rumen dorsal, medial and ventral fluid were taken from each cow, whereas for post-prandial duodenal starch appearance, 14 samples of duodenal chyme were obtained from each cow relative to morning feeding of experimental concentrate at 07:00 h. Ruminal, small intestinal, hindgut and total tract digestibility of starch did not differ among treatments. Similarly, NDF digestibility and CH4 emission also remained unaffected by treatments. However, compared with the LD and MD treatments, the HD treatment showed higher acetate: propionate ratio at all positions in the rumen and a higher post-prandial duodenal starch appearance. This indicates lower ruminal starch degradation (RSD) and higher starch flow into the small intestine for HD treatment. In conclusion, the current study indicates that pellets' physical properties can manipulate RSD, where pellets with high density and fluid stability can partly shift starch digestion from the rumen to the small intestine. Indeed, further investigations are needed.

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.