Transcript Somatic cells in milk

Seasonal changes
in somatic cell content
in milk of Polish
Holstein-Friesian cows
Maciej Gierdziewicz
The Faculty of Animal Sciences
Agricultural University in Cracow
Somatic cells in milk
In cattle breeding, the number
of somatic cells in milk
(Somatic Cell Count - SCC)
is a commonly accepted
indicator of subclinical
mastitis.
Somatic cells in milk (2)
• Healthy cow – SCC
up to 100 000 /ml
SCC depends on
lactation number:
• 1st lactation cow –
up to 35 000 /ml
• 2nd lactation cow –
up to 50 000 /ml
• 3rd lactation cow –
up to 60 000 /ml
• Cow with
(sub)clinical
mastitis – SCC
500 000
5 000 000
50 000 000
?
COUNCIL DIRECTIVE 92/46/EEC,
16 June 1992:
Health rules for the production and placing
on the market of raw milk, heat-treated milk
and milk-based products
„ [...]
CHAPTER IV Standards to be met for collection
of raw milk [...]
A. Raw cow's milk
[...]”
COUNCIL DIRECTIVE 92/46/EEC
16 June 1992
(continued)
„Somatic cell count (per ml)
400 000 (b)
(b) Geometric average over a period of three
months, with at least one sample a month, or,
where production levels vary considerably
according to season, method of calculating
results to be adjusted [...]”
Rozporządzenie Ministra Rolnictwa
i Gospodarki Żywnościowej
z dnia 29 września 1999 r.
(Polish regulations)
A.Wprowadzone w normie zmiany obejmują:
[...]
2. Ograniczenie ilości klas jakościowych mleka.
[...] klasa Ekstra, klasa I i klasa II
Wymagania szczegółowe dla poszczególnych klas mleka:
Ekstra: Komórki somatyczne
- liczba w 1 ml
(SCC/ml limit)→
100 000
Transformation of SCC to SCS
Somatic Cell Count is very variable:
0-1-10-100- ... 10 000 000 - 100 000 000 ...
logarithmic transformation:
SCS - Somatic Cell Score
SCS = log2(SCC/100 000) + 3
SCC
SCS
6 250
12 500 25 000 50 000 100 000 200 000 400 000 800 000
-1
0
1
2
3
4
5
6 ...
Selected references
I. General
Svendsen M., B. Heringstad, 2006:
Somatic cell count as an indicator of sub-clinical
mastitis. Genetic parameters and correlations with
clinical mastitis. (Interbull Bulletin)
Correlation coefficients between clinical
and sub-clinical mastitis: 0,3 - 0,6
Selected references
II. Age, test month, % of HF genes
Effect of age:
Kennedy B.W. et al., 1982: Environmental Factors
Influencing Test-Day Somatic Cell Counts in Holsteins
(Journal of Dairy Science)
slow decrease with age; steep increase in VI and XII
Effect of HF genes percentage:
Philipsson J., G. Ral, B. Berglund, 1995. Somatic cell
count as a selection criterion for mastitis resistance in
dairy cattle. (Livestock Production Science)
—
HF share may influence SCS;
Selected references
III. Days in milk (in Polish cattle)
Strabel T., Jamrozik J. 2006. Genetic analysis of milk
production traits of Polish Black and White cattle using
large-scale random regression test-day models.
(Journal of Dairy Science)
Ptak E., Brzozowski P., Jagusiak W., Zdziarski K.,
2007: Genetic parameters for somatic cell score for
Polish Black-and-White cattle estimated with a random
regression model.
(Journal of Animal and Feed Sciences )
The goal
The aim of the work was to apply nonlinear
regressions on month of calving and on month
of test to investigate seasonal changes in somatic
cell content in milk of Polish HF cows.
Data (1)
over 12 600 000 test milking results
about 880 000 cows
first, second and third lactation
5 984 860, 4 139 104, 2 519 366 test results
calving in 1997-2007, 1998-2007, 1999-2007
average SCS: 3.48, 3.92, 4.19
Data (2)
average SCS by month of calving - lactations I - III
mean I
4,4
mean II
mean III
4,2
4
SCS
3,8
3,6
3,4
3,2
3
I
II
III
IV
V
VI
VII VIII IX
Month of the year
X
XI
XII
Data (3)
average SCS by month of test - lactations I - III
mean I
4,4
mean II
mean III
4,2
4,0
SCS
3,8
3,6
3,4
3,2
3,0
I
II
III
IV
V
VI
VII VIII
IX
Month of the year
X
XI
XII
Methods (1)
Equation to fit to the data
(each lactation separately):
 2

y  a 0  a 1 sin 
t  a 2   error
 12

y - SCS (dependent variable)
t - time (independent variable); 0 ≤ t ≤ 12
time scale - months of the year
(month of calving or month of test milking)
a0 - lactation mean of SCS
a1 – amplitude of seasonal changes of SCS
a2 – phase shift in months
Methods (2)
Curve fitting - grid search phase
Calving month:
a0 = 3.0 to 4.5 by 0.1
a1 = -0.2 to 0.2 by 0.05
a2 = 1.0 to 4.0 by 0.5
Test month:
a0 = 3.4 to 4.3 by 0.1
a1 = 0.0 to 0.20 by 0.05
a2 = -6 to 6 by 1;
Curve fitting - Iterative phase
Gauss-Newton method
Hardware
IBM BladeCenter HS21 cluster ("mars")
• One of 56 nodes - containing:
2 Intel Xeon Dual Core
processors (2.66 GHz)
8 GB memory
hard disk 36.4 GB
Software
IBM BladeCenter HS21 cluster ("mars")
• operating system : Linux RedHat
• SAS® (Statistical Analysis System)
► version 9.1.3
• documentation:
►http://support.sas.com/documentation/onlinedoc/sas9doc.html
Technical remarks
The NLIN procedure of SAS was used.
For examining the effect of the month of
calving, grid search time was:
1h26’58’’, 1h00’02’’ and 0h35’13’’
for lactations 1, 2 and 3, respectively.
For estimating the effect of test month:
0h56’19’’, 0h39’03’’ and 0h23’2’’
Without iterative phase, about 3’30’’ in
both cases.
Results for 1st lactation (1)
2,90E+07
2,80E+07
2,70E+07
2,60E+07
2,50E+07
2,40E+07
2,30E+07
2,20E+07
2,10E+07
2,00E+07
1,90E+07
1,80E+07
1,70E+07
1,60E+07
1,50E+07
1,40E+07
1,30E+07
1,20E+07
1,10E+07
1,00E+07
9,00E+06
8,00E+06
7,00E+06
6,00E+06
5,00E+06
4,00E+06
3,00E+06
2,00E+06
1,00E+06
0,00E+00
0,1
-0,0
AM PLITUDE
4,4
4,2
4
M EAN
3,8
3,6
3,4
3,2
3
RESIDUAL SUM OF SQUARES
grid search - residual sum of squares for phase shift = +1month
-0,2
28000000-29000000
27000000-28000000
26000000-27000000
25000000-26000000
24000000-25000000
23000000-24000000
22000000-23000000
21000000-22000000
20000000-21000000
19000000-20000000
18000000-19000000
17000000-18000000
16000000-17000000
15000000-16000000
14000000-15000000
13000000-14000000
12000000-13000000
11000000-12000000
10000000-11000000
9000000-10000000
8000000-9000000
7000000-8000000
6000000-7000000
5000000-6000000
4000000-5000000
3000000-4000000
2000000-3000000
1000000-2000000
0-1000000
Results for 1st lactation (2)
2,90E+07
2,80E+07
2,70E+07
2,60E+07
2,50E+07
2,40E+07
2,30E+07
2,20E+07
2,10E+07
2,00E+07
1,90E+07
1,80E+07
1,70E+07
1,60E+07
1,50E+07
1,40E+07
1,30E+07
1,20E+07
1,10E+07
1,00E+07
9,00E+06
8,00E+06
7,00E+06
6,00E+06
5,00E+06
4,00E+06
3,00E+06
2,00E+06
1,00E+06
0,00E+00
0,2
0,1
-0,1
4,4
4,2
4
3,6
0
3,8
MEAN
3,4
3,2
3
RESIDUAL SUM OF SQUARES
grid search - residual sum of squares for phase shift = +1.5 months
-0,2
AMPLITUDE
28000000-29000000
27000000-28000000
26000000-27000000
25000000-26000000
24000000-25000000
23000000-24000000
22000000-23000000
21000000-22000000
20000000-21000000
19000000-20000000
18000000-19000000
17000000-18000000
16000000-17000000
15000000-16000000
14000000-15000000
13000000-14000000
12000000-13000000
11000000-12000000
10000000-11000000
9000000-10000000
8000000-9000000
7000000-8000000
6000000-7000000
5000000-6000000
4000000-5000000
3000000-4000000
2000000-3000000
1000000-2000000
0-1000000
Results (3) - analysis of variance
effect of month of test - 1st lactation
Source
DF
Model
2
Error
5.98E6
Corrected Total 5.98E6
Sum of
Squares
52158.3
21769945
21822104
Parameter
A0
A1
A2
Std Error
0.000781
0.00111
0.0158
Estimate
3.4885
0.1327
5.0324
Mean
Square
26079.1
3.6375
F Value
7169.51
Pr > F
<.0001
95% Confidence Limits
3.4870
3.4900
0.1306
0.1349
5.0014
5.0633
Results (4) – SCS vs month of calving
mean I
mean III
mean II (predicted)
4,4
mean II
mean I (predicted)
mean III (predicted)
4,2
4
SCS
3,8
3,6
3,4
3,2
3
I
II
III
IV
V
VI
VII VIII
IX
Month of the year
X
XI
XII
Results (5) – SCS vs month of test
mean I
mean III
mean II (predicted)
4,4
mean II
mean I (predicted)
mean III (predicted)
4,2
4,0
SCS
3,8
3,6
3,4
3,2
3,0
I
II
III
IV
V
VIof the
VII year
VIII
Month
IX
X
XI
XII
Results(6) - Curve fitting - Iterative phase
effect of test month of SCS
Sum of
Lactation Iter
1
0
1
2
3
4
2
0
1
2
3
4
3 0
1
2
3
A0
3.5000
3.4885
3.4885
3.4885
3.4885
3.9000
3.9231
3.9231
3.9231
3.9231
4.2000
4.1975
4.1975
4.1975
A1
0.1000
0.1138
0.1314
0.1327
0.1327
-0.1000
-0.0979
-0.1261
-0.1269
-0.1269
0.1000
0.0841
0.1126
0.1127
A2
4.0000
5.3045
4.9882
5.0328
5.0324
1.0000
-0.5422
-0.2509
-0.3174
-0.3169
4.0000
5.4321
5.3767
5.3907
Squares
21785676
21771906
21769979
21769945
21769945
16078374
16065773
16063684
16063643
16063643
9917903
9911426
9910394
9910393
Results(7) – fitted curves
SCS vs month of calving
y = 3.49 + 0.14 sin [ 2/12 ( t – 0.56 ) ]
(lactation I)
y = 3.92 + 0.13 sin [ 2/12 ( t – 1.30 ) ]
(lactation II)
y = 4.20 + 0.10 sin [ 2/12 ( t – 1.26 ) ]
(lactation III)
Results(8) – fitted curves
SCS vs month of test
y = 3.49 + 0.13 sin [ 2/12 ( t – 5.03 ) ]
(lactation I)
y = 3.92 + 0.13 sin [ 2/12 ( t – 6.32 ) ]
(lactation II)
y = 4.20 + 0.11 sin [ 2/12 ( t – 5.39 ) ]
(lactation III)
Conclusions
○There were seasonal changes in SCS in milk.
○The amplitude of SCS changes varied from about 0.1
to about 0.2,
○The maximum SCS has been reached in cows calving
in April, May or June.
○ The cows milked in August or September had
maximum SCS in milk.
○Although the effect of season on SCS has been
proved, the variation is not high, and therefore
according to the EEC rules no special adjustment on
season is needed.
○ Technical remark: for fitting simple curves to the
data a good guess is better than initial grid search.
Data source: research funding for grant G-1594/KGiMDZ/06-09 (head: dr hab. Ewa
Ptak, University of Agriculture, Kraków) ”Genetic and environmental factors
influencing the number of somatic cells in milk”;
data were used in 2009; research project No. N31101131/3353