Nutrient net absorption across the portal-drained viscera of forage-fed beef steers: Quantitative assessment and application to a nutritional prediction model.

This study aimed to establish the relationship between ME intake and energy and nutrient absorption across the portal-drained viscera (PDV) of forage-fed beef steers. Eight Angus (328 +/- 40 kg of BW) steers were surgically fitted with portal, mesenteric arterial, and mesenteric venous catheters, and were fed alfalfa cubes in a replicated 4 x 4 Latin square design with 4 levels of energy intake between 1 and 2 times maintenance energy requirements. On d 28 of each experimental period, p-aminohippuric acid was infused to measure blood and plasma flow across the PDV, and blood samples (1 every hour, for 6 h) were collected simultaneously from arterial and venous catheters for net absorption measurements. Oxygen utilization, and therefore energy utilization, increased (P < 0.05) linearly in relation to ME intake. Glucose net uptake was unaffected, but lactate net release increased linearly in response to ME intake (P < 0.05). Net absorption of all AA except tryptophan, glutamate, and glutamine increased linearly with ME intake (P < 0.05). The constant net absorption of glutamate and glutamine indicated increased net utilization of these AA when dietary supply was increased. These data provide quantitative measures of the PDV effects on energy and AA availability for productive tissues, and suggest that the greater net utilization of some AA when ME intake is increased could relate to their catabolism for energy production. Prediction estimates of small intestinal AA absorption, based on the Cornell Net Carbohydrate and Protein System (CNCPS), exceeded observed net AA PDV absorption. Mean bias represented the greatest proportion (87 to 96%) of the deviation between individual AA absorption and observed net AA PDV absorption, suggesting that the CNCPS model may be used to predict AA net absorption when factors describing AA utilization by the PDV are applied to model predictions.