Predictions of ruminal outflow of essential amino acids in dairy cattle

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dc.contributor.authorFleming, A. J.en
dc.contributor.authorLapierre, H.en
dc.contributor.authorWhite, Robin R.en
dc.contributor.authorTran, H.en
dc.contributor.authorKononoff, P. J.en
dc.contributor.authorMartineau, R.en
dc.contributor.authorWeiss, W. P.en
dc.contributor.authorHanigan, Mark D.en
dc.contributor.departmentDairy Scienceen
dc.date.accessioned2020-02-20T18:10:26Zen
dc.date.available2020-02-20T18:10:26Zen
dc.date.issued2019-12en
dc.description.abstractThe objective of this work was to update and evaluate predictions of essential AA (EAA) outflows from the rumen. The model was constructed based on previously derived equations for rumen-undegradable (RUP), microbial (MiCP), and endogenous (EndCP) protein outflows from the rumen, and revised estimates of ingredient composition and EAA composition of the protein fractions. Corrections were adopted to account for incomplete recovery of EAA during 24-h acid hydrolysis. The predicted ruminal protein and EAA outflows were evaluated against a data set of observed values from the literature. Initial evaluations indicated a minor mean bias for non-ammonia, non-microbial nitrogen flow ([RUP EndCP]/6.25) of 16 g of N per day. Root mean squared errors (RMSE) of EA.A predictions ranged from 26.8 to 40.6% of observed mean values. Concordance correlation coefficients (CCC) of EAA predictions ranged from 0.34 to 0.55. Except for Leu, all ruminal EA.A outflows were overpredicted by 3.0 to 32 g/d. In addition, small but significant slope biases were present for Arg [2.2% mean squared error (MSE)] and Lys (3.2% MSE). The overpredictions may suggest that the mean recovery of AA from acid hydrolysis across laboratories was less than estimates encompassed in the recovery factors. To test this hypothesis, several regression approaches were undertaken to identify potential causes of the bias. These included regressions of (1) residual errors for predicted EAA flows on each of the 3 protein-driven EA flows, (2) observed EAA flows on each protein-driven EAA flow, including an intercept, (3) observed EAA. flows on the protein-driven EAA flows, excluding an intercept term, and (4) observed EAA. flows on IMP and MiCP. However, these equations were deemed unsatisfactory for bias adjustment, as they generated biologically unfeasible predictions for some entities. Future work should focus on identifying the cause of the observed prediction bias.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesThis research was supported by funding provided, in part, by a USDA NIFA grant (Award No: 20176701526539; Washington, USA); the Virginia Agricultural Experiment Station (Richmond, VA) and the Hatch Program of the National Institute of Food and Agriculture (Washington D.C., USA), U.S. Department of Agriculture; Agriculture and Agri-Food Canada (Sherbrooke, QC, Canada), the College of Agriculture and Life Sciences Pratt Endowment at Virginia Tech (Blacksburg, VA); and Dairy Farmers of Canada, the Canadian Dairy Network, and the Canadian Dairy Commission under the Agri-Science Clusters Initiative (Ottawa, ON, Canada). A portion of this work was carried out and supported as an activity of the National Animal Nutrition Program (NANP) to provide enabling technologies, support, and shared resources to the research community. The NANP, a National Research Support Project (NRSP-9) of State Agricultural Experiment Stations, is funded from Hatch funds administered by the National Institute of Food and Agriculture, US Department of Agriculture, Washington, DC. The NANP Coordinating Committee at the initiation of this work was composed of Gary Cromwell (University of Kentucky), Phillip Miller (University of Nebraska), Jack Odle (North Carolina State University), Mark Hanigan (Virginia Tech), William Weiss (The Ohio State University), Mary Beth Hall (USDA-ARS), Mike Galyean (Texas Tech), Todd Applegate (Purdue University), and Donald Beitz (Iowa State University). The NANP Modeling Subcommittee comprised Mark Hanigan (Virginia Tech), Brian Kerr (USDA/ARS), Ermias Kebreab (University of California-Davis), John McNamara (Washington State University), Luis Tedeschi (Texas A&M University), Mike VandeHaar (Michigan State University), Nathalie Trottier (Michigan State University), and Roselina Angel (University of Maryland).en
dc.description.sponsorshipUSDA NIFA grant (Washington, USA) [20176701526539]; Virginia Agricultural Experiment Station (Richmond, VA); Hatch Program of the National Institute of Food and Agriculture (Washington D.C., USA), U.S. Department of Agriculture; Agriculture and Agri-Food Canada (Sherbrooke, QC, Canada)Agriculture & Agri Food Canada; College of Agriculture and Life Sciences Pratt Endowment at Virginia Tech (Blacksburg, VA); Dairy Farmers of Canada (Ottawa, ON, Canada); Canadian Dairy Network (Ottawa, ON, Canada); Canadian Dairy Commission under the Agri-Science Clusters Initiative (Ottawa, ON, Canada); National Animal Nutrition Program (NANP); Hatch funds [NRSP-9]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3168/jds.2019-16301en
dc.identifier.eissn1525-3198en
dc.identifier.issn0022-0302en
dc.identifier.issue12en
dc.identifier.pmid31704011en
dc.identifier.urihttp://hdl.handle.net/10919/96954en
dc.identifier.volume102en
dc.language.isoenen
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectmechanistic modelen
dc.subjectamino aciden
dc.subjectruminal outflowen
dc.subjecttissueen
dc.titlePredictions of ruminal outflow of essential amino acids in dairy cattleen
dc.title.serialJournal of Dairy Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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