Abstract/Summary

There is growing interest in developing integrated post-absorptive metabolism models for dairy 30 cattle. An integral part of linking a multi-organ post-absorptive model is the prediction of nutrient 31 fluxes between organs, and thus blood flow. It was the purpose of this paper to use a multivariate 32 meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow 33 (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF = 34 HEPBF – PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop 35 equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef 36 cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 37 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. 38 Both databases had information on DMI, MEI, body weight and a basic description of the diet 39 including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg 40 BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg BW0.75/d) with linear and quadratic 41 fits were examined. Equations were developed using cow within experiment and experiment as 42 random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation 43 database, equations based on DMI typically resulted in lower root mean square prediction errors, 44 expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient 45 (CCC) values than equations based on MEI. Quadratic equation terms were frequently non-46 significant, and the quadratic equations did not out-perform their linear counterparts. The best 47 performing blood flow equations were: PORBF (L/h) = 202 (± 45.6) + 83.6 (± 3.11) × DMI (kg/d) and HEPBF (L/h) = 186 (± 45.4) + 103.8 (± 3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 49 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted – observed) for 50 PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for 51 3 PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h) = 210 52 (± 51.0) + 82.9 (± 6.43) × Forage (kg DM/d) + 82.9 (± 6.04) × Concentrate (kg DM/d), and 53 HEPBF (L/h) = 184 (± 50.6) + 92.6 (± 6.28) × Forage (kg DM/d) + 114.2 (± 5.88) × Concentrate 54 (kg DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, 55 respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian 56 Information Criterion (BIC) value for PORBF and HEPBF (BIC = 6512 vs. 7303), as well as 57 slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite 58 minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow 59 well, and could easily be used within a post absorptive model of nutrient metabolism. Results also 60 suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting 61 for this difference resulted in improved ARTBF predictions.