 Milk fat composition varies with the season (summer vs. winter) but also with the milking time (AM vs. PM).  These observations could allow the diversification.

Slides:



Advertisements
Similar presentations
ABSTRACT The objective of this research was to derive factors to predict daily milk yield when milk is sampled once per d for cows milked three times (3x)
Advertisements

Factors affecting milk ELISA scores of cows tested for Johne’s disease H. D. Norman 1, J. R. Wright 1 *, and T. M. Byrem 2 1 Animal Improvement Programs.
Relationship of somatic cell score with fertility measures Poster 1390 ADSA 2001, Indiannapolis R. H. Miller 1, J. S. Clay 2, and H. D. Norman 1 1 Animal.
Extreme Deviations Herds with large percentage of their cows on Elite List may have two management systems in their herds: High producers Low producers.
Materials and Methods Abstract Conclusions Introduction 1. Korber B, et al. Br Med Bull 2001; 58: Rambaut A, et al. Nat. Rev. Genet. 2004; 5:
J. B. Cole 1, P. D. Miller 2, and H. D. Norman 1 1 Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD 2 Department.
CRITICAL POINTS IN THE FEEDING OF HIGH YIELDING DAIRY COWS Gergácz, Z., †Báder, E., Szűcs, E. University of West Hungary, Faculty of Agricultural and Food.
Derivation of Factors to Estimate Daily Fat, Protein, and Somatic Cell Score from One Milking of Cows Milked Three Times Daily M. M. Schutz* 1 and H. D.
Walloon Agricultural Research Center Walloon Agricultural Research Center, Quality Department Chaussée de Namur, 24 – 5030 GEMBLOUX - Tél :++ 32 (0) 81.
Extension of Bayesian procedures to integrate and to blend multiple external information into genetic evaluations J. Vandenplas 1,2, N. Gengler 1 1 University.
Principal Components Approach for Estimating Heritability of Mid-Infrared Spectrum in Bovine Milk H. Soyeurt 1,2,*, S. Tsuruta 3, I. Misztal 3 & N. Gengler.
M. Dufrasne 1,2, V. Jaspart 3, J. Wavreille 4 et N. Gengler 1 1 University of Liège, Gembloux Agro Bio-Tech, Animal Science Unit - Gembloux 2 F.R.I.A.
Capitalizing on mid-infrared to improve nutritional and environmental quality of milk H. Soyeurt *,§, F. Dehareng **, N. Gengler *, and P. Dardenne **
2007 ADSA 2007 (1)H.D. Norman Effect of service sire and cow sire on gestation length H.D. Norman,* J.R. Wright, P.M. VanRaden, and J.B. Cole Animal Improvement.
Innovation on Milk Recording New Management Indicators Decision Making and Profitability Pregnancy, Embryo loss, Ketosis, Acidosis, Methane, Energy Balance...
Abstract: This study was conducted to determine the effects of reducing rumen degradable protein (RDP) with constant rumen undegradable protein in mid-lactation.
 PTA mobility was highly correlated with udder composite.  PTA mobility showed a moderate, positive correlation with production, productive life, and.
ASAS/ADSA 2001 Conference (1) 2001 Variance of effects of lactation stage within herd by herd yield N. Gengler 1,2, B. Auvray *,1, and G.R. Wiggans 3 1.
2002 ADSA 2002 (HDN-1) H.D. NORMAN* ( ), R.H. MILLER, P.M. V AN RADEN, and J.R. WRIGHT Animal Improvement Programs.
Statistical Analysis of Milk Fatty Acids in Three Breeds of Dairy Cattle during a Lactation Melissa Bainbridge Lucy Greenberg.
2003 G.R. Wiggans,* P.M. VanRaden, and J.L. Edwards Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
Assessment of voluntary waiting period and frequency of estrus synchronization among herds R.H. Miller, 1, * H.D. Norman, 1 M.T. Kuhn, 1 and J.S. Clay.
REGRESSION MODEL y ijklm = BD i + b j A j + HYS k + b dstate D l + b sstate S l + b sd (S×SD m ) + b dherd F m + b sherd G m + e ijklm, y = ME milk yield,
Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk N. Gengler 1,2 and H. Soyeurt 1 1 Gembloux Agricultural University,
2002 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD USDA Dairy Goat.
Technical Efficiency in Milk Production of the Dual- Purpose Cattle System in El Salvador during Dry and Rainy Seasons Presenter: Angel A. Duron B. Co-Author:
2008 ADSA-ASAS Joint Annual Meeting Indianapolis, July 7-11 Genetic Parameters of Saturated and Monounsaturated Fatty Acids Estimated by Test-Day Model.
Genetic correlations between first and later parity calving ease in a sire-maternal grandsire model G. R. Wiggans*, C. P. Van Tassell, J. B. Cole, and.
36th ICAR Session and Interbull Meeting Niagara Falls, June 2008 Potential Estimation of Minerals Content in Cow Milk Using Mid- Infrared Spectrometry.
2005 Paul VanRaden Animal Improvement Programs Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA Selection for.
Genetic Evaluation of Lactation Persistency Estimated by Best Prediction for Ayrshire, Brown Swiss, Guernsey, and Milking Shorthorn Dairy Cattle J. B.
Synchronization Effects on Parameters for Days Open M. T. Kuhn, J. L. Hutchison, and R. H. Miller* Animal Improvement Programs Laboratory, Agricultural.
Factors affecting heifer fertility in U.S. Holsteins M. T. Kuhn* and J. L. Hutchison Animal Improvement Programs Laboratory, Agricultural Research Service,
Effects of dam’s dry period length on calf M. T. Kuhn,* J. L. Hutchison, and H. D. Norman Animal Improvement Programs Laboratory, Agricultural Research.
Interbull Meeting – Dublin 2007 Genetic Parameters of Butter Hardness Estimated by Test-Day Model Hélène Soyeurt 1,2, F. Dehareng 3, C. Bertozzi 4 & N.
Accuracy of reported births and calving dates of dairy cattle in the United States Poster 1705 ADSA 2001, Indiannapolis H. D. Norman *,1, J. L. Edwards,
 Objective 7.03: Apply the Use of Production Records.
Factors that affect abortion frequency in dairy herds in the United States R.H. Miller,* M.T. Kuhn, H.D. Norman, J.R. Wright Animal Improvement Programs.
John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Best prediction.
J. B. Cole 1,*, P. M. VanRaden 1, and C. M. B. Dematawewa 2 1 Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville,
Factors affecting death rate of lactating cows in Dairy Herd Improvement herds R. H. Miller, H. Duane Norman, M. T. Kuhn* and J. R. Wright Animal Improvement.
Paul VanRaden and John Cole Animal Improvement Programs Laboratory Beltsville, MD, USA 2004 Planned Changes to Models and Trait Definitions.
Genetic and environmental factors that affect gestation length H. D. Norman, J. R. Wright, M. T. Kuhn, S. M. Hubbard,* and J. B. Cole Animal Improvement.
2007 John B. Cole USDA Animal Improvement Programs Laboratory Beltsville, MD, USA 2008 Data Collection Ratings and Best Prediction.
1 Dairy Cattle Production (95314) Dr Jihad Abdallah Factors affecting milk composition.
ADSA 2002 (RHM-P1) 2002 R.H. Miller, ,1 H.D. Norman, 1 and J.S. Clay 2 1 Animal Improvement Programs Laboratory, Agricultural Research Service, USDA,
Ashley H. Sanders and H. Duane Norman Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
2004 P.M. VanRaden, M.E. Tooker*, and J.B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
Lactation Number Effects on the Genetic Variability of the Stearoyl Coenzyme-A Desaturase 9 Activity Estimated by Test-Day Model V. M.-R. Arnould 1, N.
LOGO Effects of Adding Extruded Flaxseed on Milk Fatty Acids in Holstein Dairy Cows Ali Mahdavi Faculty of Veterinary Medicine, Semnan University, Iran.
2001 ASAS/ADSA 2001 Conference (1) Simultaneous accounting for heterogeneity of (co)variance components in genetic evaluation of type traits N. Gengler.
Strategies to Incorporate Genomic Prediction Into Population-Wide Genetic Evaluations Nicolas Gengler 1,2 & Paul VanRaden 3 1 Animal Science.
Walloon Agricultural Research Center Genetic Variability of Lactoferrin Content Predicted by MIR Spectrometry : MIR Spectrometry + Quantitative Models.
Dairy Cattle Production (95314)
Steinshamn, H.1, Inglingstad, R. Aa.2, Nymo, M.3 and Jørgensen, M.4
Individual fatty acid measurements in milk from Danish dairy cows
C. Maroteau, I. Palhière, H. Larroque, V. Clément,
J. Vandenplas 1,2 and N. Gengler 1,2
Methods to compute reliabilities for genomic predictions of feed intake Paul VanRaden, Jana Hutchison, Bingjie Li, Erin Connor, and John Cole USDA, Agricultural.
Estimating the nutritional quality of milk fat in cow milk
S. Vanderick1,2, F.G. Colinet1, A. Mineur1, R.R Mota1,
Correlations Among Measures of Dairy Cattle Fertility and Longevity
A National Sire Fertility Index
Abstr. M65 Test-day milk loss associated with elevated test-day somatic cell score R.H. Miller, H.D. Norman, G.R. Wiggans, and J.R. Wright Animal Improvement.
Use of a threshold animal model to estimate calving ease and stillbirth (co)variance components for US Holsteins.
Abstr. M4 Merit of obtaining genetic evaluations of milk yield for each parity on Holstein bulls H.D. Norman, J.R. Wright,* R.L. Powell, and P.M. VanRaden.
Effectiveness of genetic evaluations in predicting daughter performance in individual herds H. D. Norman 1, J. R. Wright 1*, C. D. Dechow 2 and R. C.
Genetic Evaluation of Milking Speed for Brown Swiss Dairy Cattle
Multiplicative Factors
MILK YIELD AND COMPOSITION VARIATIONS OF LATVIAN BREED GOATS DEPENDING ON THE LENGTH OF THE KIDS SUCKLING PERIOD Kristīne Piliena, Daina Jonkus Latvia.
Presentation transcript:

 Milk fat composition varies with the season (summer vs. winter) but also with the milking time (AM vs. PM).  These observations could allow the diversification of dairy products by season and milking time Differences in milk composition between morning and evening milk are documented for major milk components. This study extended research to milk fat composition. Milk samples were collected between October 2007 and November 2011 in 491 Luxembourg farms and analyzed by MIR spectrometry. The milk contents of saturated (SFA) and unsaturated fatty acids (UFA) were predicted from the recorded MIR spectral data. As expected, the milk composition and, especially, the milk fat composition, were affected by AM / PM milking during the lactation. Therefore adjustments for morning / evening milking are required before using it jointly. Differences in composition could allow different uses of milk. V. M.-R. Arnould 1,2,*, N. Gengler 2,3, and H. Soyeurt 2,3 1 CONVIS s.c., Ettelbruck, Luxembourg; 2 University of Liège, Gembloux Agro Bio-Tech, Animal Science Unit, Animal Breeding and Genetics Group, Gembloux, Belgique; 3 Fond National pour la Recherche Scientifique (F.N.R.S.), Bruxelles, Belgique. * Financed by National Research Fund, Luxembourg (AFR PHD RE) The first author acknowledges the financial support of AFR/FNR (Aide à la Formation Recherche/Fonds National de la Recherche Luxembourg) PhD grant (AFR PHD RE). CONVIS s.c. and Luxembourg farms are acknowledged for providing data. Abstract Milk fat composition Contact: Objective To Analyse the milking-to-milking variability in milk and milk fat quality.  Data.  Holstein cows (n=32,339 cows from 491 herds) in first lactation (5 < DIM < 366).  Data set:  130,997 records in morning milking data (AM)  125,384 records in evening milking data (PM)  Fatty acids predicted from FOSS milk spectrum (Mid-Infared spectrometry): saturated (SAT) and unsaturated fatty acids (UFA) Material and method  There were no significant difference between AM and PM milk fat percentage  BUT: SAT was higher in the evening milking (fig. 2 and 3).  SFA/UFA was lower during the evening milking (fig. 3).  There were also some variations in milk fat composition according to the milking time and the season. Figure 3. Evolution of SAT (g/dl milk) during the first lactation and according to the milking time and the season. Figure 2. SAT (g/dl milk) and UFA (g/dl milk) contents during the first lactation and according to the milking time and the season. Figure 4. Evolution of SAT daily heritability values accros lactation and according to the milking time  Heritability is defined as proportion of observable differences between individuals that is due to genetic differences  Daily heritability values for SAT were: AM: 0.33 ± 0.01 PM: 0.38 ± 0.01 Conclusion NS  Model:  Fixed effects: Herd * date of test Classes of 15 days in milk Classes of age at calving  Random effects:Additive genetic effect Permanent environment Residual effects  Variances components estimated by REML (Restricted Maximum Likelihood )  The significance of differences was evaluated by Student t- test Figure 1. Difference in milk fat percentage according the milking time and the season during the first lactation. *** NS Milk fat Variability Among Morning And Evening Milk Compositions During The Lactation