|DOREA, JOAO - University Of Wisconsin|
|DANES, MARINA - University Of Lavras(UNILAVRAS)|
|ARMENTANO, LOUIS - University Of Wisconsin|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/2017
Publication Date: 8/30/2017
Citation: Dorea, J.R., Danes, M.A., Zanton, G.I., Armentano, L.E. 2017. Urinary purine derivatives as a tool to estimate dry matter intake in cattle: a meta-analysis. Journal of Dairy Science. 100: 8977-8994.
Interpretive Summary: Feed intake is the single parameter that most strongly influences animal performance such as milk or meat production. A variety of methods has been developed to estimate dry matter intake, but the wide variation among and within methods has been identified as the primary limitation of the currently available approaches. We used a meta-analytical approach to develop equations that predict feed intake based on an animal characteristic, the waste products of purine metabolism. We identified an equation with the greatest precision, emerging as a potential estimator of nutrient intake in dairy cows. This research will benefit researchers and producers by predicting feed intake from animal characteristics.
Technical Abstract: The objectives of this study were: 1) to investigate the relationship between dry matter intake (DMI) and urinary purine derivatives (PD) excretion in order to develop equations to predict DMI, and 2) to determine the endogenous excretion of PD for beef and dairy cattle using a meta-analytic approach. To develop the models, 63 published papers for both dairy (46 papers) and beef cattle (17 papers) were compiled. Seventeen models were tested using DMI (kg/d) and digestible DMI (dDMI, kg/d) as response variable and PD:Creatinine (PD:C), Allantoin:Creatinine (ALLA:C), metabolic body weight (BW^0.75, kg), milk yield (MY, kg/d), and their combination as explanatory variables for dairy and beef (except for MY) cattle. A second set of models was developed to estimate the endogenous PD excretion. In all evaluated models, the effect of PD (either as PD:C or ALLA:C) was significant (P < 0.01), supporting our hypothesis that PD are in fact correlated to DMI. Despite the BW-independent relationship between PD and DMI, the inclusion of BW0.75 in the models with PD:C and ALLA:C as predictors slightly decreased the values of root mean square error (RMSE) and Akaike Information Criteria (AIC) for the models of DMI. Our models suggest that both DMI and dDMI can be equally well predicted by PD-related variables; however, predicting DMI seems more useful from a practical and experimental standpoint. The inclusion of milk yield into the dairy models substantially decreased RMSE and AIC values, and further increased the precision of the equations. The model including PD:C, BW^0.75 and MY had the smallest AIC and the greatest R2 for both DMI and dDMI, emerging as a potential powerful estimator of nutrient intake in dairy cows. Endogenous PD excretion was estimated by the intercept of the linear regression between DMI (g/ kg of BW^0.75) and PD excretion (mmol/ kg of BW^0.75) for beef (0.404 mmol/kg of BW^0.75, P = 0.04) and dairy cattle (0.651 mmol/kg of BW^0.75, P = 0.03). Based on the very close agreement between our result for beef cattle and the literature, the linear regression appears to be an adequate method to estimate PD endogenous excretion.