HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Kronfeld, D. S. Articles by Engle, R. H. Search for Related Content PubMed PubMed Citation Articles by Kronfeld, D. S. Articles by Engle, R. H.

Nutritional Status of Dairy Cows Indicated by Analysis of Blood¹

Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, Kennet Square 19348
and Damon Medical Laboratory, Trevose, PA 19047

ABSTRACT

A study of 395 cows in 21 herds tested the suitability of applying the normal distribution to 23 blood variables in setting reference ranges and the potential of multiple regression equations for predicting energy and nutrient intakes, milk and fat production, and reproductive variables from blood measurements. After adjustment for herd and lactational group within herd, deviations from the normal distribution were significant for nine blood variables: glucose, cholesterol, glycerol, triglyceride, acetoacetate, urea, uric acid, creatinine, and chloride. Multiple regressions were calculated for group-averages of ration variables on group-medians of blood variables, and for production and reproductive variables on blood variables in individual cows. Coefficients of determination were .06 to .14 for milk production and reproductive variables and .55 to .79 for ration variables. Best serum predictors of ration variables were glutamic-oxaloacetic and glutamic-pyruvic transaminases and cholesterol, phosphorus, triglyceride, and globulin. These results suggest that the normal distribution should not be assumed for blood variables: instead it should be tested. Multiple regressions on blood variables may be useful in predicting energy and nutrient intakes, and regression analysis may help in selection of blood measurements for inclusion in profiles.

¹ Supported in part by the Damon Medical Laboratory, Trevose, PA, and the USDA-SEA cooperative research program (PL1433). The study was made possible and greatly facilitated by R. C. Hann, Milton Hershey School Farms, Hershey, PA.