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Variation in Milk Fat, Protein, and Somatic Cell Count from Four Dairy Herd Improvement Laboratories1
OUR INDUSTRY T O D A Y Variation in Milk Fat, Protein, and Somatic Cell Count from Four Dairy Herd Improvement Laboratories 1 D. T. V I N E S , B. F. J E N N Y , R. E. W R I G H T , and L . W . G R I M E S z Department of Dairy Science Clemson University Clemson, SC 29634-0363 ABSTRACT
Official DHI records also are used to merchandize cattle. Milk fat is the most important constituent of milk in determining blend price. In some market areas, component pricing o f milk has placed increased emphasis on protein or solids-not-fat. Dairy producers always question differences in milk fat tests between their plant and DHI laboratory with the usual conclusion that the lowest test was wrong. Increased availability of instrumentation for routine somatic cell counting has placed greater emphasis on the relationship between somatic cell count, mastitis, and reduced milk production (1, 2, 4, 7, l l L It is becoming more important that guidelines derived from analysis of samples in one laboratory can also be applied accurately to results from samples submitted to other laboratories. Different instrumentation and calibration procedures, methods of preservation, and interval from sample collection to analysis can cause variation in component testing of milk (2, 4, 8, 9, 10, 12). Objectives of this study were to compare results from different laboratories of milk samples obtained and handled in a consistent manner and analyzed for fat, protein, and somatic cell count.
Information on variability in milk fat, protein, and somatic cell count among four Dairy Herd Improvement laboratories was studied. Approximately 395 individual cow samples from four herds were shipped in blind duplicate via common carrier to four Dairy Herd Improvement laboratories for analysis. One herd was sampled twice and one of the four laboratories was not equipped for protein determinations. As expected, samples differed among herds, laboratory, and interaction of herd by laboratory for the constituents analyzed. Reasons for the interactions could not be fully explained. However, delay in shipment of one set o f herd samples to one laboratory probably contributed to the interaction for somatic cell count. Means from laboratories 1, 2, 3, and 4 across herds were 3.99, 3.90, 3.89, and 3.88 for percent fat, 3.45, 3.31, and 3.16 for percent protein and 364, 430, 322, and 380 for somatic cell count ( x l 0 0 0 ) . Variation among laboratories could not be explained by type of analytical instrument or time in shipment. These data indicate a need for use of uniform standards by testing laboratories to reduce variation in laboratory results.
MATERIALS AND METHODS
INTRODUCTION
Dairy producers depend on Dairy Herd Improvement (DHI) records to provide information needed to make management decisions.
Received September 30, 1985. 1Technical Contribution Number 2468. Published with the approval of the Director, South Carolina Agricultural Experiment Station. 2Department of Experimental Statistics, Clemson University. 1986 J Dairy Sci 69:2219--2223
Milk samples were obtained from four dairy herds, with one herd being sampled twice over 5 wk. A t least 300 ml of milk was obtained from individual cows during their routine evening milking. Milk samples were collected in flasks from weigh jars or DHI approved weigh meters. Samples were agitated continuously b y hand and immediately dispensed into WhirlPak ® bags in aliquots o f 30 to 35 ml. All aliquots were preserved with a potassium dicromate tablet (approximately 40 mg potassium dicromate per tablet). A p p r o x i m a t e l y 395 individual cow samples were taken during the 5 wk. All samples were collected on Mondays. On
2219
2220
VINES ET AL.
Tuesdays, Whirl-Pak ® bags were boxed in blind duplicate and shipped via United Parcel Service to four DHI state or regional laboratories. All laboratories analyzed samples for percentage milk fat and somatic cell count; three laboratories also determined percentage milk protein. Instrumentation utilized in the four DHI laboratories is presented in Table 1. Data were analyzed by least squares analysis of variance for a factorial arrangement of treatments among laboratories and herds with duplicates as blocks. Least-squares means were compared using linear functions. RESULTS A N D DISCUSSION
Samples were received and analyzed in three of the four DHIA laboratories on Thursday or Friday following shipment on Tuesday. In the other laboratory, samples were not received and analyzed until Monday or, in one instance, Wednesday of the week following shipment. Analysis of variance indicated significant effect of herd, laboratory, and interaction of herd and laboratory on milk fat, protein, and somatic cell counts. Effects due to duplicate and interaction between duplicates and herd or laboratory were not significant. The significant effect of herd was expected because four different herds were used in the study. Due to the significant (P<.01) interaction of herd and laboratory for fat percent, protein percent, and somatic cell count, means from each laboratory for each herd are presented in Figures 1, 2, and 3. Least squares means for percent fat, percent protein, and somatic cell count for the four laboratories are presented in Table 2. The low means obtained b y laboratory 4 for herd C contributes strongly to the interaction
of herd and laboratory for percent fat (Figure 1). Values for percent fat showed the greatest variability in herd C and the least in herds B and D. Laboratories 2 and 3, which used the same instrumentation (Table 1), were the most consistent across all herds. Means for percent fat were higher (P<.05) for laboratory i than for each of the other three laboratories (Table 2). Studies (8, 9) have indicated that storage for as little as 3 d can affect determinations of percent fat in samples preserved with potassium dicromate, although others (10, 12) have noticed no difference in determination of percent fat in samples stored up to 14 d. Differences in instrumentation and amount of preservative used may have contributed to the discrepancy in the studies cited. Although laboratory 1 received samples 3 to 4 d after the other three laboratories, it is doubtful that this was the main cause of differences in overall percent fat (Table 2). Given the variation in percent fat shown in Figure 1, instrumentation may have resulted in the most variation in percent fat. All instruments were standardized before analysis and rechecked at periodic intervals throughout the day; however, the standards used were not the same at all four laboratories. The interaction o f herd and laboratory for percent protein is most likely the result of lower values obtained on samples from herds B and D by laboratory 3. This also contributed to the difference in mean percent protein for the three laboratories (Table 2). Laboratory 1 was consistently higher in percent protein than laboratory 2, for all herds, whereas laboratories 2 and 3 obtained consistent results on samples from three of the five herds (Figure 2). Although interval between milk sample collec-
TABLE 1. Instrumentation used by cooperating state and regional Dairy Herd Improvement Association laboratories.
Laboratory
Fat
1 2 3 4
Milko-Scan Multi-Spec Multi-Spec Milko-O-Tester 1SCC = Somatic cell counts.
Journal of Dairy Science Vol. 69, No. 8, 1986
Determination used Protein Milko-Scan Multi-Spec Multi-Spec ...
SCC~ Foss~O-Matic Coulter Coulter Foss-O-Matic
OUR INDUSTRY TODAY
2221
Laboratory @---4
4.6
II---,,,I
//.
4.4
~\
b..---~
\
x
x
4.2
4.0
r~
3.8
3.6
3.4 "
3.2
% X
3.0
,
J
,
,
,
A
B
C
O
E
Figure 1. Mean milk fat percentage determined by individual Dairy Herd Improvement laboratories for each herd sampled.
TABLE 2, Least squares means and standard errors for milk fat, protein and somatic cell count (SCC) determinations from four Dairy Herd Improvement Association laboratories. Laboratory
Milk fat
Protein
SCC (× 1000)
(%) SE
SE
X
SE
3.45 a
.01
364 bc (409
20 20) d
1
3.99 a
.03
2
3.90 b
.03
3.31 b
.01
430 a
20
3
3.89 b
.03
3,16 c
,01
322 b
20
4
3.88 b
.03
380 ac
20
.,.
a'b'CMeans in a column with different superscripts differ (P<.05). d Mean somatic cell count with herd B samples deleted.
Journal of Dairy Science Vol. 69, No. 8, 1986
2222
VINES ET AL. 31.8
Laboratory
/ lx \
3.7
*--,*=
,
3.6
/
\ \
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3•4
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3.3
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/
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2.9
2.8
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B
C
D
E
Herd
Figure 2. Mean protein percentage determined by individual Dairy Herd Improvement laboratories for each herd sampled.
tion and analysis may affect the protein test (10), this does not appear to be a major factor in our study. Lack of a uniform standard could explain differences in our results in laboratories utilizing similar or different instrumentation. Individual laboratory analysis for somatic cell counts from each herd are presented in Figure 3. The low somatic cell count by laboratory 1 on herd B samples contributes to the significant interaction of herd by laboratory. Laboratory 1 did not receive and analyze samples from herd B until 8 d after shipment, but the other laboratories received samples in 2 d. This delay may have contributed to the lower somatic celi count obtained by laboratory 1 on herd B samples. With this point deleted, mean somatic cell count obtained by laboratory 1 would be 409 + 20 rather than the 364 + 20 as presented in Table 2. No other differences in Journal of Dairy Science Vol. 69, No. 8, 1986
sample shipment or consistency with instrumentation can help explain variations in herd somatic cell counts in our study, particularly as relates to herd A, Use of common standards could reduce differences due solely to different standards and thus eliminate one area of potential variation. Variation in laboratory determinations can make it difficult to apply guidelines established in one state to producers in another state• This may be of great importance when dealing with somatic celt counts, because it is well accepted that somatic cell counts can be related directly to lost milk production (1, 3, 5, 6, 7, 11). Use of a linear score (3, 5) has improved the practical application of somatic cell count data. In most cases, dissolution and thorough mixing of preservatives should control variation in components due to the interval of time between
O U R INDUSTRY TODAY
2223
Laboratory 0---4 bOO
X 550
~
. . . . . . .
=
• ----0
=
A~.- --Lk
=
~
~. =
500 o 450
//
400
350
•
\, .-/ /
300
250
200
\ /
\/ b
I
I
I
I
I
A
B
C
D
E
Herd
Figure 3. Mean somatic cell count determined by individual Dairy Herd I m p r o v e m e n t laboratories for each herd sampled.
sample collection and analysis. Use of uniform standards could reduce variation caused by lack of uniform standards for calibration of instrumentation at various laboratories. REFERENCES
1 Blosser, T. H. 1979. Economics losses from and the national research program on mastitis in the United States. J. Dairy Sci. 62:119. 2 Bodoh, G. W., W. J. Battista, L. H. Schultz, and R. P. Johnson. 1976. Variation in somatic ce/l counts in Dairy Herd I m p r o v e m e n t milk samples. J. Dairy Sci. 59:1428. 3 Dabdoub, S.A.M., and G. E. Shook. 1984. Phenotypic relationships among milk yield, somatic cell count and clinical mastitis. J. Dairy Sci. 67 (Suppl. 1) :163. (Abstr.) 4 Eberhart, R. J., H. C. Gilmore, L. J. Hutchinson, and S. B. Spencer. 1979. Somatic cell c o u n t s in DHI samples. Page 32 in Proc. 18th Annu. Mtg., Natl. Mastitis Counc., Inc. 5 Heald, C. W. 1983. Use o f linear scale for reporting somatic cells. Page 24 in Proc. 10th A n n u . Dairy Conf., Dairy Sci. Ser. No. 6, Clernson Univ. 6 Jones, G. M., R. E. Pearson, C. W. Heald, and W. E. Vinson. 1982. Milk loss, somatic cell counts and udder infections in Virginia herds. Page 31 in Proc.
21st Annu. Mtg., Natl. Mastitis Counc., Inc. 7 Kirk, J. H. 1984. Programmable calculator program for linear somatic cell scores to estimate mastitis yield losses. J. Dairy Sci. 76:441. 8 Kroger, M. 1971. Instrumental milk fat determination. I. Effects of potassium dichromate concentration and sample storage time on Milko-Tester results. J. Dairy Sci. 54:735. 9 Michalak, W., H. Cynalewska, W. Michalakowa, H. Oczkowicz, and H. Siuda. 1979. A central system o f calibrating apparatus for determining t h e main milk c o m p o n e n t s and its effects on the comparability o f results obtained in different laboratories. III. Effect of storage time (from test milking to analysis) on results of protein and fat determination. Dairy Sci. Abstr. 41:807. 10 Ng-Kwai-Hang, K. F., and J. F. Hayes. 1982. Effects of potassium dichromate and sample storage time on fat and protein by Milko-Scan and on protein and casein by a modified Pro-Milk MK II m e t h o d . J. Dairy Sci. 65:1895. 11 Shook, G., E., and A. Saeman. 1983. The new DHI linear score for somatic cell counts. Technol. Transfer Session, 22nd A n n u . Mtg., Natl. Mastitis Counc., Inc. 12 Thomasow, J. 1976. Simultaneous determination o f the milk fat and protein c o n t e n t by m e a s u r e m e n t o f IR absorption with the Milko-Scan instrument. Milchwissenschaft 31:149. Journal of Dairy Science Vol. 69, No. 8, 1986
Official DHI records also are used to merchandize cattle. Milk fat is the most important constituent of milk in determining blend price. In some market areas, component pricing o f milk has placed increased emphasis on protein or solids-not-fat. Dairy producers always question differences in milk fat tests between their plant and DHI laboratory with the usual conclusion that the lowest test was wrong. Increased availability of instrumentation for routine somatic cell counting has placed greater emphasis on the relationship between somatic cell count, mastitis, and reduced milk production (1, 2, 4, 7, l l L It is becoming more important that guidelines derived from analysis of samples in one laboratory can also be applied accurately to results from samples submitted to other laboratories. Different instrumentation and calibration procedures, methods of preservation, and interval from sample collection to analysis can cause variation in component testing of milk (2, 4, 8, 9, 10, 12). Objectives of this study were to compare results from different laboratories of milk samples obtained and handled in a consistent manner and analyzed for fat, protein, and somatic cell count.
Information on variability in milk fat, protein, and somatic cell count among four Dairy Herd Improvement laboratories was studied. Approximately 395 individual cow samples from four herds were shipped in blind duplicate via common carrier to four Dairy Herd Improvement laboratories for analysis. One herd was sampled twice and one of the four laboratories was not equipped for protein determinations. As expected, samples differed among herds, laboratory, and interaction of herd by laboratory for the constituents analyzed. Reasons for the interactions could not be fully explained. However, delay in shipment of one set o f herd samples to one laboratory probably contributed to the interaction for somatic cell count. Means from laboratories 1, 2, 3, and 4 across herds were 3.99, 3.90, 3.89, and 3.88 for percent fat, 3.45, 3.31, and 3.16 for percent protein and 364, 430, 322, and 380 for somatic cell count ( x l 0 0 0 ) . Variation among laboratories could not be explained by type of analytical instrument or time in shipment. These data indicate a need for use of uniform standards by testing laboratories to reduce variation in laboratory results.
MATERIALS AND METHODS
INTRODUCTION
Dairy producers depend on Dairy Herd Improvement (DHI) records to provide information needed to make management decisions.
Received September 30, 1985. 1Technical Contribution Number 2468. Published with the approval of the Director, South Carolina Agricultural Experiment Station. 2Department of Experimental Statistics, Clemson University. 1986 J Dairy Sci 69:2219--2223
Milk samples were obtained from four dairy herds, with one herd being sampled twice over 5 wk. A t least 300 ml of milk was obtained from individual cows during their routine evening milking. Milk samples were collected in flasks from weigh jars or DHI approved weigh meters. Samples were agitated continuously b y hand and immediately dispensed into WhirlPak ® bags in aliquots o f 30 to 35 ml. All aliquots were preserved with a potassium dicromate tablet (approximately 40 mg potassium dicromate per tablet). A p p r o x i m a t e l y 395 individual cow samples were taken during the 5 wk. All samples were collected on Mondays. On
2219
2220
VINES ET AL.
Tuesdays, Whirl-Pak ® bags were boxed in blind duplicate and shipped via United Parcel Service to four DHI state or regional laboratories. All laboratories analyzed samples for percentage milk fat and somatic cell count; three laboratories also determined percentage milk protein. Instrumentation utilized in the four DHI laboratories is presented in Table 1. Data were analyzed by least squares analysis of variance for a factorial arrangement of treatments among laboratories and herds with duplicates as blocks. Least-squares means were compared using linear functions. RESULTS A N D DISCUSSION
Samples were received and analyzed in three of the four DHIA laboratories on Thursday or Friday following shipment on Tuesday. In the other laboratory, samples were not received and analyzed until Monday or, in one instance, Wednesday of the week following shipment. Analysis of variance indicated significant effect of herd, laboratory, and interaction of herd and laboratory on milk fat, protein, and somatic cell counts. Effects due to duplicate and interaction between duplicates and herd or laboratory were not significant. The significant effect of herd was expected because four different herds were used in the study. Due to the significant (P<.01) interaction of herd and laboratory for fat percent, protein percent, and somatic cell count, means from each laboratory for each herd are presented in Figures 1, 2, and 3. Least squares means for percent fat, percent protein, and somatic cell count for the four laboratories are presented in Table 2. The low means obtained b y laboratory 4 for herd C contributes strongly to the interaction
of herd and laboratory for percent fat (Figure 1). Values for percent fat showed the greatest variability in herd C and the least in herds B and D. Laboratories 2 and 3, which used the same instrumentation (Table 1), were the most consistent across all herds. Means for percent fat were higher (P<.05) for laboratory i than for each of the other three laboratories (Table 2). Studies (8, 9) have indicated that storage for as little as 3 d can affect determinations of percent fat in samples preserved with potassium dicromate, although others (10, 12) have noticed no difference in determination of percent fat in samples stored up to 14 d. Differences in instrumentation and amount of preservative used may have contributed to the discrepancy in the studies cited. Although laboratory 1 received samples 3 to 4 d after the other three laboratories, it is doubtful that this was the main cause of differences in overall percent fat (Table 2). Given the variation in percent fat shown in Figure 1, instrumentation may have resulted in the most variation in percent fat. All instruments were standardized before analysis and rechecked at periodic intervals throughout the day; however, the standards used were not the same at all four laboratories. The interaction o f herd and laboratory for percent protein is most likely the result of lower values obtained on samples from herds B and D by laboratory 3. This also contributed to the difference in mean percent protein for the three laboratories (Table 2). Laboratory 1 was consistently higher in percent protein than laboratory 2, for all herds, whereas laboratories 2 and 3 obtained consistent results on samples from three of the five herds (Figure 2). Although interval between milk sample collec-
TABLE 1. Instrumentation used by cooperating state and regional Dairy Herd Improvement Association laboratories.
Laboratory
Fat
1 2 3 4
Milko-Scan Multi-Spec Multi-Spec Milko-O-Tester 1SCC = Somatic cell counts.
Journal of Dairy Science Vol. 69, No. 8, 1986
Determination used Protein Milko-Scan Multi-Spec Multi-Spec ...
SCC~ Foss~O-Matic Coulter Coulter Foss-O-Matic
OUR INDUSTRY TODAY
2221
Laboratory @---4
4.6
II---,,,I
//.
4.4
~\
b..---~
\
x
x
4.2
4.0
r~
3.8
3.6
3.4 "
3.2
% X
3.0
,
J
,
,
,
A
B
C
O
E
Figure 1. Mean milk fat percentage determined by individual Dairy Herd Improvement laboratories for each herd sampled.
TABLE 2, Least squares means and standard errors for milk fat, protein and somatic cell count (SCC) determinations from four Dairy Herd Improvement Association laboratories. Laboratory
Milk fat
Protein
SCC (× 1000)
(%) SE
SE
X
SE
3.45 a
.01
364 bc (409
20 20) d
1
3.99 a
.03
2
3.90 b
.03
3.31 b
.01
430 a
20
3
3.89 b
.03
3,16 c
,01
322 b
20
4
3.88 b
.03
380 ac
20
.,.
a'b'CMeans in a column with different superscripts differ (P<.05). d Mean somatic cell count with herd B samples deleted.
Journal of Dairy Science Vol. 69, No. 8, 1986
2222
VINES ET AL. 31.8
Laboratory
/ lx \
3.7
*--,*=
,
3.6
/
\ \
sstlJS~l~
3•4
~ •~
," v
3.3
~..--. :
/
3.5
"
o"
\\ °,
•,• ~.
3.2
. "*.
//
\
/
.
~'~,~ .
\../"
l~
\ \
•
---... /
3.1
3.0
,.
..L./"
2.9
2.8
I
I
!
!
|
A
B
C
D
E
Herd
Figure 2. Mean protein percentage determined by individual Dairy Herd Improvement laboratories for each herd sampled.
tion and analysis may affect the protein test (10), this does not appear to be a major factor in our study. Lack of a uniform standard could explain differences in our results in laboratories utilizing similar or different instrumentation. Individual laboratory analysis for somatic cell counts from each herd are presented in Figure 3. The low somatic cell count by laboratory 1 on herd B samples contributes to the significant interaction of herd by laboratory. Laboratory 1 did not receive and analyze samples from herd B until 8 d after shipment, but the other laboratories received samples in 2 d. This delay may have contributed to the lower somatic celi count obtained by laboratory 1 on herd B samples. With this point deleted, mean somatic cell count obtained by laboratory 1 would be 409 + 20 rather than the 364 + 20 as presented in Table 2. No other differences in Journal of Dairy Science Vol. 69, No. 8, 1986
sample shipment or consistency with instrumentation can help explain variations in herd somatic cell counts in our study, particularly as relates to herd A, Use of common standards could reduce differences due solely to different standards and thus eliminate one area of potential variation. Variation in laboratory determinations can make it difficult to apply guidelines established in one state to producers in another state• This may be of great importance when dealing with somatic celt counts, because it is well accepted that somatic cell counts can be related directly to lost milk production (1, 3, 5, 6, 7, 11). Use of a linear score (3, 5) has improved the practical application of somatic cell count data. In most cases, dissolution and thorough mixing of preservatives should control variation in components due to the interval of time between
O U R INDUSTRY TODAY
2223
Laboratory 0---4 bOO
X 550
~
. . . . . . .
=
• ----0
=
A~.- --Lk
=
~
~. =
500 o 450
//
400
350
•
\, .-/ /
300
250
200
\ /
\/ b
I
I
I
I
I
A
B
C
D
E
Herd
Figure 3. Mean somatic cell count determined by individual Dairy Herd I m p r o v e m e n t laboratories for each herd sampled.
sample collection and analysis. Use of uniform standards could reduce variation caused by lack of uniform standards for calibration of instrumentation at various laboratories. REFERENCES
1 Blosser, T. H. 1979. Economics losses from and the national research program on mastitis in the United States. J. Dairy Sci. 62:119. 2 Bodoh, G. W., W. J. Battista, L. H. Schultz, and R. P. Johnson. 1976. Variation in somatic ce/l counts in Dairy Herd I m p r o v e m e n t milk samples. J. Dairy Sci. 59:1428. 3 Dabdoub, S.A.M., and G. E. Shook. 1984. Phenotypic relationships among milk yield, somatic cell count and clinical mastitis. J. Dairy Sci. 67 (Suppl. 1) :163. (Abstr.) 4 Eberhart, R. J., H. C. Gilmore, L. J. Hutchinson, and S. B. Spencer. 1979. Somatic cell c o u n t s in DHI samples. Page 32 in Proc. 18th Annu. Mtg., Natl. Mastitis Counc., Inc. 5 Heald, C. W. 1983. Use o f linear scale for reporting somatic cells. Page 24 in Proc. 10th A n n u . Dairy Conf., Dairy Sci. Ser. No. 6, Clernson Univ. 6 Jones, G. M., R. E. Pearson, C. W. Heald, and W. E. Vinson. 1982. Milk loss, somatic cell counts and udder infections in Virginia herds. Page 31 in Proc.
21st Annu. Mtg., Natl. Mastitis Counc., Inc. 7 Kirk, J. H. 1984. Programmable calculator program for linear somatic cell scores to estimate mastitis yield losses. J. Dairy Sci. 76:441. 8 Kroger, M. 1971. Instrumental milk fat determination. I. Effects of potassium dichromate concentration and sample storage time on Milko-Tester results. J. Dairy Sci. 54:735. 9 Michalak, W., H. Cynalewska, W. Michalakowa, H. Oczkowicz, and H. Siuda. 1979. A central system o f calibrating apparatus for determining t h e main milk c o m p o n e n t s and its effects on the comparability o f results obtained in different laboratories. III. Effect of storage time (from test milking to analysis) on results of protein and fat determination. Dairy Sci. Abstr. 41:807. 10 Ng-Kwai-Hang, K. F., and J. F. Hayes. 1982. Effects of potassium dichromate and sample storage time on fat and protein by Milko-Scan and on protein and casein by a modified Pro-Milk MK II m e t h o d . J. Dairy Sci. 65:1895. 11 Shook, G., E., and A. Saeman. 1983. The new DHI linear score for somatic cell counts. Technol. Transfer Session, 22nd A n n u . Mtg., Natl. Mastitis Counc., Inc. 12 Thomasow, J. 1976. Simultaneous determination o f the milk fat and protein c o n t e n t by m e a s u r e m e n t o f IR absorption with the Milko-Scan instrument. Milchwissenschaft 31:149. Journal of Dairy Science Vol. 69, No. 8, 1986