• No results found

Statistical analysis

III. MATERIALS AND METHODS

3. Statistical analysis

Statistical analysis was conducted using REML in the MIXED procedure in SAS (SAS SAS, 2002). Standard model for a repeated measurements experiment is:

π‘Œπ‘–π‘—π‘˜=Β΅+ π‘”π‘Ÿπ‘œπ‘’π‘π‘–+ π‘‘π‘˜+ cow𝑗(π‘”π‘Ÿπ‘œπ‘’π‘π‘–) + (π‘”π‘Ÿπ‘œπ‘’π‘π‘–Γ— π‘‘π‘˜) +π‘’π‘–π‘—π‘˜

Yijk: parameter of cow j in group i at time k.

Β΅: overall mean

groupi: fixed effect of group i

dk: fixed effect of day in lactation k cowj(groupi): random effect of cow j in group i

groupiΓ— dk: fixed interaction effect of group i with time k eijk: random error at time k on cow j in group i

As measurements of different animals were independent, covariance structure referred to variances at different time points and to correlation between measurements of the same animal. This

correlation consists firstly of the fact that two measurements of the same animal are correlated just because it is the same animal (variation between animals, RANDOM statement) and secondly it consists of covariation within the same animal (measurements close in time tend to be more highly correlated than those far apart, REPEATED statement). For every parameter, three covariance structures were evaluated: unstructured, compound symmetry or autoregressive order one. Used was the covariance structure that was closest to zero in the Akaike information criterion or Sawa's Bayesian information criterion (Littell et al., 1998). Day of lactation and group as well as their interaction (day Γ— group) were used as fixed effects, whereas cow within treatment was the random effect. Furthermore, cow was the repeated subject. PDIFF function was used to determine the differences between treatments. Data was considered to differ significantly at P < 0.05.

Specific calculations or statistic calculations differing from above mentioned approach are explained below.

3.1.

Milk parameters during experimental period

Energy balance (EB) was calculated using the formula 𝐸𝐡= (𝐷𝑀𝐼𝑑𝑖𝑒𝑑×𝑁𝐸𝐿𝑑𝑖𝑒𝑑) + (π·π‘€πΌπ‘π‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘’π‘  Γ— π‘πΈπΏπ‘π‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘’π‘ )βˆ’ (0.293 Γ—π‘π‘œπ‘‘π‘¦π‘€π‘’π‘–π‘”β„Žπ‘‘0.75βˆ’ [(0.38 Γ—

π‘šπ‘–π‘™π‘˜π‘“π‘Žπ‘‘π‘π‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›)βˆ’(0.21 Γ—π‘šπ‘–π‘™π‘˜π‘π‘Ÿπ‘œπ‘‘π‘’π‘–π‘›π‘π‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›) + 0.95] Γ—π‘šπ‘–π‘™π‘˜π‘¦π‘–π‘’π‘™π‘‘ as described by Kamphues et al. (2004). ECM was calculated with the formula 𝐸𝐢𝑀= (π‘šπ‘–π‘™π‘˜π‘¦π‘–π‘’π‘™π‘‘ Γ— 0.327) + (π‘“π‘Žπ‘‘π‘¦π‘–π‘’π‘™π‘‘ Γ— 12.86) + (π‘π‘Ÿπ‘œπ‘‘π‘’π‘–π‘›π‘¦π‘–π‘’π‘™π‘‘Γ— 7.65) and FCM with 𝐹𝐢𝑀= (π‘šπ‘–π‘™π‘˜π‘¦π‘–π‘’π‘™π‘‘ Γ— 0.4) + (π‘“π‘Žπ‘‘π‘¦π‘–π‘’π‘™π‘‘ Γ— 15).

Prior to statistical analysis, repeated end point measurements (daily milk yield, milk composition and hydrocortisone) were pooled to weekly means. Days 21, 23 to 31, 127 and 138 to 146 pp were excluded from calculation of weekly means because FR and ivGTT were conducted at these days. For better illustration, milk parameters were subsumed to 6 time periods: d 1 to 22 pp (period 1), d 33 to 56 pp (period 2), d 57 to 84 pp (period 3), d 85 to 112 pp (period 4), d 113 to 134 pp (period 5) and d 148 to 155 pp (period 6). Calculation of statistical differences were performed using REML in the MIXED procedure in SAS. To conserve the effect of different intervals between measurements, SAS received the first day of each period as day of interest (e.g. 1 for period 1 or 85 for period 4).

3.2.

Feed restrictions in early and mid-lactation

Repeated end point measurements of the last day before, the last day during and the last day after restricted feeding were compared within groups for milk parameters including hydrocortisone and Ξ²-hydroxybutyrate (d 25, 28 and 31 pp as well as d 140, 143 and 146 pp), blood parameters (d 26, 29 and 32 pp as well as d 141, 144 and 147 pp) and for feed intake and EB (d 25, 28 and 31 pp as well as d 140, 143 and 146 pp) using REML in the mixed procedure in SAS, like mentioned above. Differences between groups within each FR and differences between similar days of early and mid-lactation FR were evaluated correspondingly.

3.3.

Intravenous glucose tolerance tests

Area under the curve for insulin (AUCI) and glucose (AUCG) from -20 to 120 minutes after infusion were calculated with the trapezoid-rule:

π΄π‘ˆπΆ β‰ˆ οΏ½ �𝑑𝑖2βˆ’ 𝑑𝑖1οΏ½Γ— (𝐢𝑖1+𝐢𝑖2) Γ— 1 2 𝑖𝑛=120 𝑖1=βˆ’20 t: minute of sampling

C: concentration of glucose [mmol/L] or insulin [Β΅U/mL] i: sampling time point

III. Materials

and methods

31 Values for basal insulin and basal glucose were obtained by calculating arithmetic means of the four measurements before glucose infusion (-20, -15, -10 and immediately before infusion). Afterwards, basal AUCI and AUCG (bAUCI and bAUCG) were calculated as π‘π΄π‘ˆπΆ β‰ˆ π‘π‘Žπ‘ π‘Žπ‘™π‘–π‘›π‘ π‘’π‘™π‘–π‘›orπ‘”π‘™π‘’π‘π‘œπ‘ π‘’ Γ— 140, assuming that the base is a rectangle with height of basal insulin or glucose concentrations and length of 140 minutes. Then net AUCI and AUCG (nAUCI and nAUCG) were calculated by subtraction of bAUCI from AUCI and bAUCG from AUCG, respectively.

According to Kerestes et al. (2009), clearance of glucose was calculated using the formula 𝐢𝑅= ln π‘”π‘™π‘’π‘π‘œπ‘ π‘’π‘–5βˆ’ln π‘”π‘™π‘’π‘π‘œπ‘ π‘’π‘–60

60βˆ’5 Γ— 100. Consistent with Radziuk (2000), homeostatic model

assessment of insulin was calculated as 𝐻𝑂𝑀𝐴 βˆ’ 𝐼𝑅 = π‘π‘Žπ‘ π‘Žπ‘™π‘”π‘™π‘’π‘π‘œπ‘ π‘’22 Γ—.5π‘π‘Žπ‘ π‘Žπ‘™π‘–π‘›π‘ π‘’π‘™π‘–π‘›.

Statistical differences were calculated using REML in the MIXED procedure in SAS, like mentioned before.

3.4.

Major milk proteins in skim milk

Data was provided by the software as percentage amount of total protein concentration. Due to technical reasons, the absolute amount of individual proteins were constantly over- or underestimated, so all following calculations were conducted with the percentage amount of protein concentration in the sample. To obtain comparable results between chips, every protein fraction was corrected by multiplication with a correction factor on the basis of the results of the respective proteins in the protein mix sample. Correction factor (CF) was calculated as 𝐢𝐹= % π‘œπ‘“π‘π‘Ÿπ‘œπ‘‘π‘’π‘–π‘›π‘“π‘Ÿπ‘Žπ‘π‘‘π‘–π‘œπ‘›20% π‘–π‘›π‘π‘Ÿπ‘œπ‘‘π‘’π‘–π‘›π‘šπ‘–π‘₯. After correction, percentage amount of each protein fraction within the sum of all protein fractions was determined. Results were subsumed to six time periods like done before with milk parameters. Differences between groups and time points were estimated with the above described model in SAS.

IV.

R

ESULTS

1.

Animal experiment

1.1.

Cows and classification

Approximately four weeks before parturition 26 cows were transported from a farm in Saxony to the research farm Veitshof. One cow died during the course of parturition due to severe calving difficulties caused by an oversized calf. Another cow had to be euthanized shortly after parturition due to downer-cow-syndrome and slipping injuries. Additionally, all data of one animal had to be omitted because of recurring inflammations which were caused by a perforating foreign body in the reticulum.

Classification of remaining 23 cows was done based on mean FCM yield and mean milk protein content during d 23, 24 and 25 pp and resulted in four groups: 6 cows with high FCM (52.66 Β± 2.91 kg/d) and high protein content (3.28 Β± 0.07%; MP-cows), 5 cows showing low FCM (40.49 Β± 1.15 kg/d) and low protein content (2.84 Β± 0.06%; mp-cows), 7 cows with high FCM (48.98 Β± 2.12 kg/d) and low protein content (2.90 Β± 0.06%; Mp-cows) and 5 cows showing low FCM (39.08 Β± 0.60 kg/d) and high protein content (3.40 Β± 0.05%; mP-cows; table 4). Furthermore MP- and Mp-cows showed higher FCM compared to mp- and mP-cows (P < 0.01), whereas mp- and Mp-cows showed lower milk protein content compared to MP- and mP-cows (P < 0.001). Parity amongst all groups was comparable (MP-cows 2.8 Β± 0.3, mp-cows 2.4 Β± 0.2, Mp-cows 2.3 Β± 0.3, and mP-cows 2.8 Β± 0.4; P = 0.35).

Table 4: Parameters of classification (mean FCM yield, kg/d and mean milk protein content, %) of 23 cows during d 23, 24 and 25 pp.

cow parity group FCM, milk kg/d

mean FCM, kg/d

milk protein

content, % mean protein content, %

14024 57758 4 MP 58.73 52.66 Β± 2.91a 3.07 3.28 Β± 0.07a 14027 34346 3 MP 51.04 3.06 14027 34439 3 MP 60.16 3.50 14030 03463 3 MP 55.78 3.42 14030 03870 2 MP 40.97 3.29 14031 15582 2 MP 49.25 3.33 14026 25564 3 mp 44.25 40.49 Β± 1.15b 3.02 2.84 Β± 0.06b 14027 34303 3 mp 37.70 2.70 14031 15366 2 mp 38.64 2.86 14032 20073 2 mp 41.59 2.73 14032 20330 2 mp 40.24 2.89 14026 25242 4 Mp 54.13 48.98 Β± 2.12a 3.00 2.90 Β± 0.06b 14028 63689 2 Mp 46.72 3.00 14030 03642 2 Mp 55.40 2.71 14030 03827 2 Mp 41.13 2.95 14030 03863 2 Mp 44.10 3.02 14031 15265 2 Mp 54.23 2.95 14031 15625 2 Mp 47.17 2.67

Download now "Gellrich, Katharina ..."

Outline

Related documents