Abstract
Key words
Introduction
Materials and Methods
Experimental Setup, Animals, and Housing
Item | Glucogenic | Lipogenic |
---|---|---|
Ingredient (g/kg) | ||
Rapeseed meal | 112.9 | 170.4 |
Corn | 530.8 | |
Palm kernel, expeller | 212.6 | |
Sugar beet pulp | 72.3 | 321.0 |
Wheat | 39.0 | 61.2 |
Soybean hulls | 4.9 | |
Soybean meal | 86.6 | 16.9 |
Soybean meal, formaldehyde treated | 22.4 | 18.4 |
Rapeseed meal, formaldehyde treated | 34.6 | 59.4 |
Energizer RP10 | 20.1 | |
Molasses | 57.0 | 47.0 |
Vinasses | 39.2 | |
Palm oil | 1.0 | 11.9 |
Calcium carbonate | 19.3 | 10.9 |
Magnesium oxide | 7.1 | 4.5 |
Sodium chloride | 7.5 | 5.1 |
Mineral-vitamin mixture | 2.3 | 2.4 |
Calculated chemical composition | ||
DM (g/kg of product) | 873 | 879 |
CP | 181 | 194 |
Crude fat | 33 | 71 |
NDF | 178 | 379 |
ADF | 79 | 222 |
ADL | 18 | 50 |
Starch | 417 | 28 |
Sugars | 77 | 103 |
Ash | 79 | 90 |
DVE | 120 | 120 |
OEB | 12 | 12 |
NEL (MJ/kg of DM) | 7.7 | 7.7 |
Diet | Prepartum ration | Postpartum ration | ||
---|---|---|---|---|
Dry | Lactating | Glucogenic | Lipogenic | |
Ingredient | ||||
Grass silage | 391 | 484 | 338 | 338 |
Corn silage | 245 | 321 | 227 | 227 |
Soybean meal | 38 | 68 | 46 | 46 |
Rapeseed meal | 79 | 52 | 36 | 36 |
Rapeseed straw | 2 | 1 | 10 | 10 |
Wheat straw | 245 | 13 | 5 | 5 |
Concentrate | 0 | 56 | 338 | 338 |
Chemical composition | ||||
DM (g/kg of product) | 532 | 453 | 561 | 566 |
CP (g/kg of DM) | 116 | 159 | 167 | 169 |
Crude fat | 26 | 31 | 31 | 37 |
NDF | 527 | 384 | 318 | 389 |
ADF | 326 | 229 | 182 | 224 |
Starch | 65 | 117 | 215 | 106 |
Sugars | 67 | 80 | 82 | 85 |
Ash | 74 | 76 | 76 | 80 |
DVE | 47 | 80 | 87 | 84 |
OEB | 10 | 21 | 17 | 17 |
NEL (MJ/kg of DM) | 5.26 | 6.48 | 6.55 | 6.52 |
FA composition (g/kg of FAME) | ||||
C12:0 | 0.83 | 5.26 | 0.53 | 40.0 |
C14:0 | 1.55 | 3.15 | 1.13 | 15.6 |
C16:0 | 136 | 164 | 133 | 370 |
C18:0 | 160 | 155 | 139 | 85.6 |
C18:1 cis-9 | 124 | 127 | 215 | 149 |
C18:2 cis-9,cis-12 | 146 | 155 | 261 | 93.8 |
C18:3 cis-9,cis-12,cis-15 | 397 | 354 | 248 | 169 |
Milk Analysis
Blood Plasma Metabolites
Statistical Analysis, Calculations, and Generation of Functions
Descriptive Statistics
NEFA Threshold Definition
Discriminant Function
Empirical Cumulative Probability Distributions for Diagnosis of Detrimental Blood NEFA
Empirical Cumulative Probability Distributions for Early Warning of Detrimental Blood Plasma NEFA
Logistic Curve Fitting
Economic Cost Function for Prevention of Detrimental Blood Plasma NEFA
- a = Financial loss of an ill cow (€) due to a decrease in reproductive performance of detrimental blood plasma NEFA cows. Costs were estimated at €25 per case (Geishauser et al., 2001). For the current simulation, costs of €15, 25, and 35 were considered.
- b = Financial cost of pre-emptive treatment (€) based on the use of propylene glycol (PEG), which is reported to allow effective treatment or prevention of elevated blood plasma NEFA development with application rates varying between 200 and 500 g/d during 3 wk ((Johnson, 1954;Grummer et al., 1994;Formigoni et al., 1996;Miyoshi et al., 2001) and costs for PEG of €1.1 /kg.
- I = The event a cow suffers from elevated blood plasma NEFA.
- R = Milk fat C18:1 cis-9 concentration for that cow.
If the treatment is fully effective to treat detrimental blood plasma NEFA, then P(I|R > θ) = 0 and P(I) reduces to
Results
Classification Based on Blood Plasma NEFA Thresholds and Influence of Experimental Factors (Dietary and Dry Period Management)

Identification of Milk FA Predominantly Linked with Blood NEFA Concentrations: Discriminant Analysis and Correlations

Empirical Cumulative Probability Distributions for Diagnosis of Detrimental Blood NEFA

Empirical Cumulative Probability Distributions for Early Warning of Detrimental Blood Plasma NEFA
Economic Cost Function

Discussion
Blood Plasma NEFA Threshold Value
Milk FA as an Indicator of Elevated Blood Plasma NEFA
Potential of Milk Fat Characteristics as Biomarkers for Elevated Blood Plasma NEFA as Compared with Other Cow-Side Tests
Townsend, J. 2011. Cowside tests for monitoring metabolic disease. Accessed Nov. 2013. http://tristatedairy.osu.edu/Proceedings%202011/Townsend%20paper.pdf
Cost Effectiveness of Selective Treatment Based on Biomarker Monitoring
Conclusions
Acknowledgments
References
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