THE BLOOD PROFILES OF SOME EUROPEAN
CATTLE BREEDS
by
BRUCE JAMES McGILLIVRAY
B.Sc. (Agr.), University of British Columbia, 1975
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in
THE FACULTY OF GRADUATE STUDIES (The Department of Animal Science)
We accept this thesis as conforming
to the required standard
THE UNIVERSITY OF BRITISH COLUMBIA
September, 1981
(c) Bruce James McGillivray, 1981 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.
Bruce J. McGILLIVRAY
Department of Animal Science
The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5
September, 1981
DE-6 (2/79) ii
ABSTRACT
Blood samples drawn from 147 bulls representing ten
European breeds from four artificial insemination centres were analysed for fourteen blood serum traits. These were calcium, inorganic phosphorus, glucose, blood urea nitrogen, uric acid, cholesterol, total protein, albumin, total bilirubin, alkaline phosphatase, lactic dehydrogenase, serum glutamic-oxaloacetic transaminase, sodium, and potassium.
The blood profiles compiled from these analyses were used to see if the readily apparent phenotypic differences such as coat colour, size, carcass characteristics, etc., which occur between different breeds of cattle were also accompanied by differences in their blood trait levels. Several other effects were viewed as having a potentially significant influence On the blood traits. These included the reaction to handling stress (temperament), the effect of management unit (stud), and the individuality of each animal's profile. Age at time of sampling was included as a covariable. All effects were subjected to analysis by least squares techniques. Repeatabilities were calculated which provided an indication of the influence of genotype on the blood trait levels.
The blood profiles of twelve half sib groups were compared to see if blood traits under possible genetic control would exhibit less variation within groups of related animals than among unrelated animals.
Correlating the blood profiles to several traits of economic importance provided insight into whether the profiles could be useful in the selection of breeding stock. The profiles of thirty-one Hoi stein bulls iii
were correlated to their average daughter milk production variables..
The relationships between the blood profiles and the growth traits were examined by comparing the mean blood profiles of several breeds to their respective growth trait means.
The ranges and means of the blood traits compared quite favourably to the literature reported means for cattle. Temperament was excluded from the model because it was insignificant for all the blood traits. Age, through significant for several blood traits, was not biologically important. Its contribution to the model was very small.
Breed was not a major contribution to the variation of the blood traits. Of the four blood traits with significant breed effects only urea-N, and SGOT look promising. Glucose and lactic dehydrogenase have low repeatabilities which indicated that their significant breed effect probably occurred by chance. Between and within individual variation accounted for the majority of the blood trait variation with environmental factors being an important component. The individuality of the blood profiles may prove useful in the selection of breeding stock.
Urea-N and uric acid which had moderately high repeatabi1ities correlated to several growth traits. Alkaline phosphatase was included in the regression equations of milk yield and predicted difference fat.
More extensive breed differences in the blood profiles of cattle may come to light in subsequent studies if sorting of the breeds
into genetically similar groups is performed. Expansion of the profile
to include other enzymes and protein fractions might also prove fruitful. TABLE OF CONTENTS
ABSTRACT
LIST OF TABLES
LIST OF ABREVIATIONS
ACKNOWLEDGEMENTS
INTRODUCTION
LITERATURE REVIEW
MATERIALS AND METHODS
RESULTS AND DISCUSSION
SUMMARY AND CONCLUSIONS
BIBLIOGRAPHY
APPENDIX V
LIST OF TABLES
TABLE PAGE
1 BREAKDOWN OF ANIMALS BY BREED, STUD, AND SAMPLING 31
2 BREAKDOWN OF ANIMALS AFTER SCREENING DATA 32
3 MODEL 1 EMS 33
4 MODEL 2 EMS 33
5 COEFFICIENTS OF DETERMINATION (R2) FOR MODEL 1. 34 6 COEFFICIENTS OF DETERMINATION (R2) FOR MODEL 2. 3b
7 BREED LEAST SQUARES CONSTANTS OF BLOOD TRAITS AND GROWTH TRAIT INDICES. 36 8 HOLSTEIN PROGENY TESTS RESULTS 37
9 RANGES AND MEANS FOR AGE AND THE BLOOD TRAITS 39
10 BLOOD TRAIT LEVELS FROM THE LITERATURE FOR BULLS
AND STEERS 40
11 BLOOD TRAIT LEVELS FROM THE LITERATURE FOR COWS 41
12 AGE REGRESSION COEFFICIENTS AND REPEATABILITYS 42
13 REPEATABILITYS FROM THE LITERATURE 43
14 CORRELATIONS BETWEEN ALL CONTINUOUS VARIABLES ^
15 GROWTH TRAIT - BLOOD PROFILE CORRELATIONS 47 vi
LIST OF ABBREVIATIONS
ALB Albumi n ALKP Alkaline phosphatase BILI Total bilirubin BURLINGTON All - West breeders, Burlington, Wash. B-WT Birth weight Ca Calcium \ CALGARY B.C. Artificial Insemination, Calgary, Alta. CHOL Cholesterol F-WT Final weight GLU Glucose I/B/S Individual within breed within stud ICC Improved Contemporary Comparison K Potassium LDH Lactic dehydrogenase MILNER B.C. Artificial Insemination, Milner, B.C. ME Metabolizable energy Na Sodium P Inorganic phosphorus POSADG Post-weaning average daily gain PREADG Pre-weaning average daily gain SGOT Serum glutamic-oxaloacetic transaminase TDN Total digestible nutrients T.PROT total protein UREA-N Blood urea nitrogen URIC Uric acid WESTERN Western Breeders, Balzac, Alta. W-WT Weaning weight ACKNOWLEDGEMENTS
I wish at this time to acknowledge those people whose efforts were of particular importance in the completion of this thesis. To
Dr. J. Hodges, I express my gratitude for initiating this study and the supervision provided. To Dr. R. Peterson and Mrs. M. Striker, I owe my thanks for the considerable advice and aid given during the analysis of the data. I am also indebted to Dr. M. Hoque, Dr. J. Nyama, Mr. A. Gregson the staff of the A.I. Centres, and the Health of Animals Veterinarians without whose help the blood sampling could not have occurred.
Finally, I wish to thank my family and friends for their encouragement and support. 1
INTRODUCTION
There are many cattle breeds in existence today. They have evolved by natural or artifical selection to exhibit a wide diversity
in their phenotypic traits. Artificial selection was based on the principle of like begets like (Rouse 1970). Crossing selected animals of each breed fixed the desired characteristics of both breeds in their cross bred progeny. Breeding stock was then developed by inbreeding and selection based on the desired traits, thereby, yielding a new breed of cattle.
It would be of interest to know if the wide phenotypic variations which exist between the different breeds for such traits as body colour, shape, size, growth rate, carcass characteristics, etc., are accompanied by breed differences in the blood constituents. In most blood studies of cattle, breed is usually a minor effect reported as an aside to the main objectives (Tumbleson et al., 1973 a,b; MacDonald et al. ,1956; Kitchenham and Rowlands (1976). Two or three breeds, usually cows or young stock, are often the source of data. Relatively few researchers have reported on the blood profiles of bulls, particularly, in regards to differences between breeds (Russoff et al., 1954; Rowlands et al., 1977; Kunkel et al.,
1953). This study attempted to investigate this aspect of the phenotypic variation between breeds by comparing the blood profiles of several European cattle breeds. 2
Significant differences between the blood profiles of the breeds would indicate some sort of genetic control operating on the blood traits. Nash (1978) reported moderate heritabilities for several blood traits in dairy cows. In the present study repeatabilities were calculated for each blood trait to give an indication of the extent of genetic control. They also provided an upper limit of the possible hereitability of each blood trait.
If a blood trait is under genetic control then closely related animals should have blood trait levels grouping closer together than would be evident in unrelated animals. The research of Ageraard and Katholm (1978), Ageraard (1978), Roubicek and Ray (1972), and Lane et al.,(1968) lend support to this hypothesis. The Hoi stein bulls in the present study provided a number of half sib groups which were used to test the hypothesis.
Those blood traits which show some degree of genetic control could prove useful in the selection of breeding stock if they correlate to other heritable traits which are of economic importance such as those pertaining to milk production, and growth. Stark et al., (1978) reported significant correlations between the blood profiles of Friesian bulls and their daughter milk production variables. Several researchers have found significant correlations between the blood and growth traits in cattle (Price et al.,1959; El-Sabban et al.,1971; Sengonca, 1977).
Average daughter milk production data were available for some of the Hoi stein bulls in this study. This allowed for correlation of the blood profiles of those bulls to their average daughter milk production. 3
variables. Data on the growth traits of several breeds were gleaned
from the literature and correlated to the mean blood profiles of these
breeds to look for possible relationships between the growth and blood
traits. 4
LITERATURE REVIEW
Breed
The reports from the literature in regards to the presence or absence of breed differences in the blood traits of cattle are relatively few.
Russoff and Piercy (1946) were unable to detect any breed differences between Hoi stein and Jersey cows for calcium and phosphorus.
Long et al. (1952) had similar results with Angus, Hereford, and
Shorthorn cows and heifers as did Russoff et al. (1954) with Jersey,
Guernsey, and Holstein bulls. Kitchenham and Rowlands (1976) found significant differences between the blood levels of calcium, potassium, and total protein of Ayrshire and Friesian cows. Sikes (1963) reported a significant breed effect for the phosphorus levels of Guernsey, Jersey and Holstein cattle. Tumbleson et al. (1973 a) found no significant differences between Holstein and Guernsey cows for phosphorus, sodium,
potassium and urea-N but calcium levels were significantly higher in the
Guernsey breed than the Holstein. Using Afrikaner and Friesland cattle,
Heyns (1971) found no breed differences for potassium and sodium but did
find lower calcium and glucose levels in Afrikaner cattle.
MacDonald et al. (1956) reported that Angus calves generally
had higher levels of uric acid than Hereford calves. Working with Devon,
Sussex, Hereford, and Lincoln Red bulls Rowlands et al. (1977) found
significant breed differences for glucose, urea-N, calcium, and sodium. 5
Hereford had calcium, urea-N, and albumin levels lower than the other breeds while their phosphorus levels were higher.
Tumbleson etal.(1973b) in a study involving Guernsey and
Holstein female dairy cattle reported no significant breed differences for serum total protein, albumin, urea-N, lactic dehydrogenase, and
SGOT. The level of alkaline phosphatase was significantly higher in Guernseys than Holsteins. Heyns (1971) also reported breed differences for alkaline phosphatase. Albumin and alkaline phosphatase were higher in Afrikaner cattle than Friesland. Russoff et al., (1954) found a significant breed effect for alkaline phosphatase among Jersey,
Guernsey, and Holstein bulls. Kunkel et al, (1953), on the other hand, found no significant difference between Angus, Hereford, Jersey, and
Holstein cattle for alkaline phosphatase or between beef and dairy types. Price (1959) found that Hereford male calves had lower averages for albumin than Angus males, there were no breed differences in plasma proteins: for female cattle. Tumbleson et al., (1973 a) reported no breed differences between Holstein and Guernsey for urea-N. Kitchenham and
Rowlands(1976) found no breed differences between Ayrshires and
Friesfan cows for urea-N.
Kitchenham and Rowlands (1976) reported that levels of total protein were significantly different between Ayrshire and Friesian cows.
Albumin levels were not significantly different. Tumbleson et al.
(1973 b) could find no differences between Holsteins and Guernseys for total protein and albumin, while Rowlands et al., (1977) found breed differences among several beef breeds for albumin. 6
Individuals,
Kitchenham and Rowlands (1976) and Rowlands et a1., (1975) reported significant differences between individual dairy cows for glucose, calcium, potassium, sodium, phosphorus, urea-N, albumin, and total protein. They concluded that animals have individual patterns of blood chemistry which may change with age. Variation between herds accounted for most of the variation of glucose, urea-N, phosphorus, calcium, sodium, potassium and albumin in a study reported by Payne et al.,
(1973). The results of a study by Rowlands et al. (1974 a) suggested that calves have individual blood profiles. Differences among calves were significant for all blood traits (glucose, urea-N, albumin, phosphorus, calcium, sodium, potassium). A major part of the variation was within calves; showing a need for repeated samples.
Stark et al. (1978) in a study with 172 Friesian bulls found significant differences between bulls within studs and age groups for glucose, urea-N, albumin, phosphorus, calcium and potassium. Sodium was not significant. Kitchenham et al. (1977) reported similar results for albumin and sodium in a study with bulls and steers. Gartner et al. (1966) working with grazing Hereford cattle found sodium and potassium to be insignificant for individuals and phosphorus and calcium to have a high sampling variance. Russoff and Piercy (1946) also reported significant monthly variation for calcium and phosphorus within dairy cows as well as significant individual variation. Lennon and Mixner (1957) reported that the variation among animals accounted for 90.07% of the total 7 variation of cholesterol in dairy cattle. Daily variation accounted for less than 1% and diurnal 2,79%. Crookshank et al. (.1952), Russoff et al.
(1954), and All croft and Folley (1941) reported alkaline phosphatase to vary widely from animal to animal but remain relatively constant within animals.
Environmental factors contribute to individual variation as evidenced by the numerous research papers published on this topic.
Handling stress, diurnal, seasonal and monthly variation, semen collection, nutrition, sample storage, water intake, and the proximity of feeding and sampling times all contribute to the variation between individuals and between repeated samples of an individual.
The effects of handling stress (temperament) have been reported by Palmer et al. (1930) and Gartner et al. (1969). Palmer et al.(1930) found phosphorus to increase after vigorous exercise followed by a marked decrease after half an hour to a point below the level prior to the exercise and remain low for at least two hours. Gartner et al. (1969) was unable to find any consistent effect of excitation and exercise on the level of phosphorus. The changes in total protein were small but signi• ficant. Potassium was unaffected.
Water deprivation affects the levels of urea-N and sodium in dairy cows. Little et al. (1976) reported the levels of urea-N and sodium increased with the degree of water deprivation. Palmer et al. 8
(1930) reported wide day to day fluctuations hut that water intake had no effect on phosphorus levels.
Spate etal. (1970) investigated the effects of storage time and temperature on the concentrations and activities of bovine serum constituents. They reported that storage for up to four weeks at
-10° did not affect the concentrations of glucose, cholesterol, bilirubin, total protein, creatinine, blood urea nitrogen, sodium, and potassium. Cloudiness caused by bacterial contamination occurred after only one week of storage at 2°. The enzymes, alkaline phosphatase,SGOT and lactic dehydrogenase, were the least stable. Spate et al., (1970) recommended that enzyme assays should be completed as soon as possible after sample collection. Calcium and phosphorus concentration exhibited a sharp increase after ten days in storage at -10°. The refrigerated samples had elevated levels even earlier (day 3).
The proximity of feeding and sampling will have had some effect on the blood traits of some animals. According to Kennedy et al. (1939) glucose levels rise after feeding then decrease. Palmer et al.(1930) reported a small but significant increase in phosphorus within the first hour of food ingestion which persisted for about two hours followed by a decrease to the level prior to eating. Coggins and Field (1976) found that the levels of glucose and urea-N were affected by feeding.
Glucose decreased rapidly after feeding followed by a slower increase.
Urea-N increased after feeding to a peak level about eight hours later. 9
Coggins and Field (.1976) reported significant diurnal variation in glucose, urea-N, albumin, and calcium, particularly for glucose and urea-N. Most of the variation was associated with feeding.
Phosphorus and total protein were not significant. Palmer et al. (1930) and Palmer and Eckles (1930) reported no significant diurnal variation discernable in dairy cattle for phosphorus and calcium but wide day to day fluctuations were apparent. A significant but small (< 1%) portion of the variation of cholesterol in dairy cattle can be attributed to diurnal variation according to Lennon and Mixner (1957).
A number of researchers have reported the existence of seasonal and monthly variation in the blood traits. Manston et al. (1977) found significant seasonal differences in calcium, sodium, potassium, phosphorus, glucose, urea-N, and albumin in bulls and steers .
Rowlands et al. (1974 b) detected seasonal patterns in urea-N. Smaller seasonal effects were observed for albumin, glucose, calcium, phosphorus,
sodium, and potassium. Lane et al. (1968) also reported seasonal effects for calcium and phosphorus. Dairy cattle in Louisiana did not exhibit seasonal effects for calcium and phosphorus (Russoff and
Piercy, 1946). The warmer climate in that area may not predispose the
cattle to as great a seasonal variation as perhaps a more northerly climate.
Van Landingham et al. (1942) also found seasonal variation in the
phosphorus levels of dairy cattle with higher levels in the warmer
months. Conversely, lactic dehydrogenase was highest in the winter
months in a study by Roussel and Stall cup (1967). In an earlier study
with Holstein bulls they reported SGOT and alkaline phosphatase to be 10 at their highest levels in the summer. In Guernsey cows, urea-N peaked in the spring and was lower in the winter (Lane and Campbell,
1966). Russoff et al. (1954) reported a significant monthly variation in calcium and alkaline phosphatase levels while phosphorus did not vary significantly from month to month in dairy cattle. Stufflebeam and
Lasley (1969) detected a small seasonal effect in the cholesterol of
Hereford bulls and cows. Cholesterol was lowest in the summer and highest in the winter.
Semen collection according to Reid et al.(1947) and Reid et al. (1948 b) has a strong influence on the plasma levels of alkaline phosphatase in bulls. They reported the following highly significant negative correlations between alkaline phosphatase and semen variables:
ALKP - # ejaculates/day - 0.71 ± 0.06
ALKP - volume/day - 0.83 ± 0.04
ALKP - # spermatozoa/day x 108 - 0.86 ± 0.03
Alkaline phosphatase levels in a bull never collected from ranged from
18.04 to 19.75 units with a mean of 18.95 units. After collection of nine samples over 16 days (193 ml or 4.14 billion spermatozoa) alkaline phosphatase dropped to 5.24 units. They concluded that the quantity or frequency of semen ejaculates, more particularly the number of spermatozoa, produced by the bulls is the major factor determining the level of alkaline phosphatase in the blood plasma.
Nutritional factors have been reported by numerous researchers to influence the levels of blood traits. Little and Manston (1972)
looked at the effects of feeding maize and lucerne silages on the
blood composition of dairy cattle. They found serum albumin and 11
urea-N to be higher in the lucerne fed cows which they attributed to
the higher levels of digestible protein. The maize fed cows had higher
phosphorus levels. Phosphorus they concluded was more available in the maize than lucerne silages. Ruppanner et al.(1978) found urea-N
to be higher in calves on feedlot fed rations higher in protein.
Belyea et al.(1975) reported urea-N in Holstein dairy cows to rise with increased grain (protein) intake. Manston et al. (1975),
Prewitt et al, (1971), and Blowey et al. (1973) had similar results.
Morris and Swan (1976) had a strong correlation between nitrogen
intake and urea-N (r = 0.80) in the Friesian cows of their study.
Glucose was not related to food intake.
Blowey et al. (1973) found glucose to vary with the starch
intake while serum albumin followed the trends of protein intake.
Hassan and Roussel (1975) reported total protein was unaffected by changes in the protein level of the diet of their Holstein cows.
Albumin and glucose increased with a rise in dietary protein. Glucose, phosphorus, and urea-N were lower in calves on a conventional system of rearing compared to a rapid rearing system (Kitchenham et al.,
1975). Lane and Campbell (1966) reported that in Guernsey cows urea-N peaked in the spring time when the cattle were on pasture and was at its lowest during the winter months. Uric acid and total protein levels increased in Hereford cattle on range when their feed supply improved. Uric acid decreased during times of nutritional stress. (Roubicek et al., 1970; Roubicek and Ray, 1972). 12
Stufflebeam et al. (1969) reported that energy intake affected the levels of the blood traits in beef heifers. Total protein, and cholesterol decreased with a lowering of the energy intake while phosphorus and potassium increased. Glucose and sodium were not greatly affected. Stufflebeam and Lasley (1969) found the cholesterol levels of Hereford cows and bulls decreased with a lower energy intake.
Ration did not have any significant effect on cholesterol levels in
Holstein cows and heifers (Arave et al., 1975).
The calcium levels of Hereford, Shorthorn and Angus cows and heifers fed mineral supplements were unaffected by diet. Their phosphorus levels were affected by diet (Long et al., 1952; Fisher et al. ,1972). McMillen and Langham (1942) reported that Hereford cattle had lower phosphorus levels on wheat pasture than when on dry lot. Van Landingham et al. (1935, 1936) concluded that a low phosphorus diet resulted in a lowering of the phosphorus level of the blood and that phosphorus intake had no effect on the calcium level of the blood of dairy cows. Devlin et al. (1969) in a.study with yearling Hereford
steers found dietary potassium to have no effect on the blood levels
of calcium, phosphorus and sodium. Serum potassium concentrations were lowered by a low potassium diet. Gahne (1967) and Reid et al.
(1948 a) both reported alkaline phosphatase to be unaffected by plane
of nutrition and feed type. Horn et al. (1977) found the total protein
of steers to be unaffected by fasting. Urea-N decreased in animals
fasting longer than twelve hours. 13
AGE
Age has been reported by a number of researchers to exert a significant influence on the levels of the blood traits.
Peterson (1974) reported a decline in alkaline phosphatase with age in growing Hereford bulls. Lactic dehydrogenase increased with age. Roussel and Stallcup (1966, 1967) reported similar findings for alkaline phosphatase in young Holstein Friesian bulls but that lactic dehydrogenase decreased with age. SGOT was not significantly affected. Sengonca (1977) in a study involving thirty-four bulls from the Angeln, Simmental, Holstein-Friesian, and Brown Swiss breeds found alkaline phosphatase to increase with age. Tumbleson et al.(1973 a and b) reported on the changes in the blood traits of 510 Guernsey and Holstein female cattle which ranged in ages from one month to sixteen years. They found alkaline phosphatase to decline with age.
Lactic dehydrogenase increased significantly from less than six months to 2 years of age. Thereafter it decreased significantly from
2 to more than 10 years of age. SGOT was not significantly affected by age. Reid et al. (1948b) were unable to detect a significant age effect for alkaline phosphatase in dairy bulls (17 to 128 months old). Reid et al. (1948a) were unable to detect an age effect on alkaline phosphatase in Holstein bulls (18 to 33 months old). 14
Arthaud et al. (1959) found no significant age effect for glucose in beef bulls (Angus, Hereford, and Shorthorn) during the ages of six months to one year. Stark et al. (1978) looked at the effects of age on the blood composition of 172 Friesion bulls from one to fourteen years old. They also found no significant age effect for glucose. Urea-N, sodium, and potassium were also not significantly affected. Tumbleson et al. (1973a) reported urea-N to increase with age in dairy cattle, and potassium to decrease. Sodium was not affected.
Calcium and phosphorus decreased with age (Stark et al., 1978;
Tumbleson et al., 1973a). Reid et al. (1948a) found that phosphorus decreased and calcium increased with age in twelve Holstein bulls
(18 to 33 months old).
Tumbleson et al. (1973b) reported total protein to increase with age. Albumin was not affected. Stark et al. (1978) found a significant quadratic effect for albumin. Albumin concentration increased fron one to five years then decreased. Nash (1978) found significant age effects for total protein, urea-N, phosphorus, calcium, alkaline phosphatase, and sodium in dairy cattle. Peterson and Waldern (1981) reported significant age effects for cholesterol, glucose, and lactic dehydrogenase in dairy cows. 15
Growth Performance Correlations
Colby et al. (1950) reported no significant correlations between the rate of gain in beef calves and their blood concentrations of urea-N and glucose. The bull calves had a correlation of 0.98 for cholesterol and rate of gain. Their limited data on heifers gave a non-significant correlation. Arave et al. (1975) had a correlation of 0.21 for cholesterol and body weight in dairy cattle.
Using Hereford bulls, Stufflebeam and Lasley (1969) found a low correlation of cholesterol to body weight but a significant genetic correlation between cholesterol and post-weaning growth rate (0.63) which indicated, in their opinion, that some of the same genes affect both traits and that environmental factors were also important
sources of variation.
Prince (1959) reported urea-N was highly correlated to gain and food utilization efficiency in Hereford and Angus calves.
In another study using Hereford and Angus calves Price et al.
(1959) found urea-N significantly correlated to rate of gain
(-0.31). Kitchenham et al. (1977) reported significant positive
correlations for growth rate and final weight (0.44 and 0.38).
Price et al. (1959) found uric acid was significantly
correlated to rate of gain at both 500 and 800 lbs for Hereford and
Angus calves (-0.30 and -0.35). Albumin was not significant.
Kitchenham et al. (1977) reported that for bulls and steers albumin
and potassium were significantly correlated to growth rate and 16 final Weight but when the data were adjusted for Initial weight the correlations were insignificant. Significant correlations of 0.36 or greater between weight gain and glucose, phosphorus, sodium and albumin in heifer calves 6-13 weeks old became insignificant when the data were adjusted for total feed intake. Kitchenham et al. (1975) had dairy calves on a conventional rearing system. Their growth rates correlated significantly with serum phosphorus whereas the calves on a rapid rearing system did not have any blood traits correlating significantly to growth. They concluded that the relationships between rate of growth and blood chemistry appeared to depend on the level of nutrition. Some relationships may occur only when the dietary intake of a particular nutrient is a limiting factor to rate of growth.
Their results and conclusions agree with those of Little et al. (1977) mentioned earlier. Little et al. (1977) concluded that feed intake was the dominant factor which accounted almost entirely for the significant correlations between weight gain and blood composition that were observed. Their correlations became insignificant when adjusted for total feed intake. They further concluded that the correlations were unlikely to be useful for the selection of stock with improved feed conversion efficiency because of the lack of any significant correlations between feed conversion ratio and the concentration of any blood trait.
Rowlands et al. (1974a) reported a negative correlation between potassium and growth rate and body weight in Holstein-
Friesian calves but in a study involving bulls, Rowlands et al.(1977) 17 were unable to detect any significant correlations to rate of growth within breed.
El-Sabban et al.(1971) found that SGOT correlated signi• ficantly with body weight (-0.28) and metabolic weight (-0.21).
Peterson (1974) said lactic dehydrogenase was not important in explaining the variation of weight or weight gain.
Alkaline phosphatase was reported by several researchers to correlate significantly to the growth traits. Alkaline phosphatase correlated significantly to rate of gain (0.54) in Holstein-Friesian calves in a study done by Alexander et al.(1958). Another by Ageraard
(1978) involving calves had a correlation of 0.30 between plasma alkaline phosphatase and daily gains with the highest gains shown by calves with highest alkaline phosphatase activities. Kruger and
Lakanc (1968) found indications that Black Pied Lowland bull calves with high initial alkaline phosphatase activities had higher daily weight gains in the subsequent fattening period (r = 0.81). Female calves had a negative correlation (-0.29). Sengonca (1977) found significant correlations of alkaline phosphatase to average daily gain, carcass weight, dressing percentage and loin eye area in
Simmental, Holstein-Friesian, and Brown Swiss cattle. Kunkel et al.
(1953) reported significant correlations with feed efficiency (-0.63) feed intake per pound of body weight (-0.60) and rate of gain (-0.56) in Hereford and Angus bulls. 18
Progeny Tests
In a study involving blood samples drawn from 172 Friesfan bulls, Stark et al. (1978) reported significant correlations to exist among the blood concentrations of urea-N, phosphorus, and potassium and the Improved Contemporary Comparisons (ICC's) for milk yield. Their multiple regression analysis showed that the inclusion of globulin and potassium in the regression equation was significant. They found no significant relationships between ICC for butter fat yield, butter fat percentage or protein yield and blood concentrations.
Related Animals
Lane et al.(1968) reported significant sire effects in
Guernsey cows for phosphorus, sodium, and potassium. Calcium was not
significantly affected. Kitchenham et al. (1975) working with dairy
calves also found significant sire effects for sodium, potassium,
and calcium but phosphorus was not significantly affected. Glucose
was. significantly affected while urea-N and albumin were not. Their
results agree with those of Lane and Campbell (1966) for urea-N but
not those of Plum and Schultze (1958) for glucose and those of Roubicek
and Ray (.1972) for albumin. Roubicek and Ray (1972) also found total
protein to be affected significantly by sire. Working with Hereford
cattle Roubicek et al. (1970) concluded genetic influence to be of
little importance on the levels of uric acid under the uncontrolled
environmental conditions of cattle on range. Wilson and Dinkel (1968 b) 19 reported similar conclusions with regards, to the blood composition of
Hereford steers. They concluded that ranch effects were much more important than sire differences, thus indicating that permanent environmental deviations have a larger effect on most traits of blood composition than additive gene differences.
Arave et al. (1975) and Stufflebeam and Lasley (1969) reported significant sire effects for cholesterol in Holstein cows and heifers, and beef cattle respectively. Ageraard and Katholm
(1978) and Ageraard (1978) both found significantly smaller variation in alkaline phosphatase activity of calves within half sib groups.
Sengonca (1977) reported that alkaline phosphatase was not signi• ficantly affected by genotype in Holstein-Friesian, Simmental, Brown
Swiss and Angeln bulls. 20
MATERIALS AND METHODS
Data Collection
The source of blood for this study was the bulls of four
A.I. centers. These were:
a. All - West Breeders of Burlington, Washington,
b. Western Breeders of Balzac, Alberta,
c. BCAI of Milner, B.C., and
d. BCAI of Calgary, Alberta.
These studs will be referred to as Burlington, Western, Milner, and
Calgary, respectively, for the remainder of this paper. The A.I. centers offered the ohly readily accessible source of different breeds of cattle.
Other advantages offered included:
1. Some degree of uniformity of management between centres,
2. Pure bred animals with records of birth, health, and ancestry, and
3. The blood profiles of bulls are not affected by lactation, pregnancy, and estrous as in the case of cows.
Samples were drawn from 277 bulls representing 23 breeds during the spring and summer of 1976. A breakdown of the samples by breed, stud, and single and paired samples is presented in Table 1. Two visits to each stud were made about a month apart. Approximately 50 ml of blood were drawn from the jugular vein or coccygeal vein and/or artery into 20 ml vacutainer tubes. The site of blood sampling was dependent on the veterinarian drawing the blood. The veterinarians at Burlington and Milner bled the bulls from the neck while at Western and Calgary 21 bulls were bled from the tail. Site of sampling was therefore confounded with stud effects. This may have had some effect on the results for phosphorus which according to Parker and Blowey (1974) and Teleni et al .(1976) is significantly higher (12%) in the coccygeal vessels than the jugular vein.
The blood was allowed to clot upright in racks prior to centrifuging for twenty minutes at about 3,000 rpm in plastic centrifuge tubes. The serum was then pipetted into a second centrifuge tube,spun for another ten minutes and stored in plastic scintillation vials. The samples were refrigerated at the A.I. centre until transfer to freezing facilities was possible which in the case of Burlington and Milner was the same day. The samples from the other studs were refrigerated during the sampling period of each trip and frozen prior to departure for Vancouver. The samples were packed in ice during the twelve hour return trip.
Concurrent with sampling, each animal was assessed on a scale of one to three for its temperament, i.e. degree of calmness in
response to sampling. A calm animal scored a one while an excited
animal received a three. A two was reserved for animals judged to be
somewhere between calm and excited. Birth date and weight at sampling were recorded. Many of the weights were estimates provided by the
staff of the A.I. centre. Weighing of the dairy breeds is an infrequent
occurence. It was later decided not to include weight as a covariable
for this reason. 22
Blood profiles were compiled from the analysis performed by the technicians of B.C. Bio-Medical Laboratory in Burnaby on a multichannel sequential analyser. The levels of fourteen blood traits were deter• mined by the procedures described by Peterson and Waldern (1981), and
Nash (1978). These were:
1. Calcium (Ca)
2. Inorganic Phosphorus (P)
3. Glucose (GLU)
4. Blood Urea Nitrogen (Urea-N)
5. Uric Acid (URIC)
6. Cholesterol (CHOL)
7. Total Protein (T. PROT)
8. Albumin (ALB)
9. Total Bilirubin (BILI)
10. Alkaline Phosphatase (ALKP)
11. Lactic dehydrogenase (LDH)
12. Serum glutamic-oxaloacetic Transaminase (SGOT)
13. Sodium (Na)
14. Potassium (K)
Statistical Technique
The data were screened for outliers and missing observations.
Two or three animals were lacking test results on some of their blood traits while-another six were found upon plotting the data to have five or more of their blood traits showing levels considerably higher 23 or lower than the rest. Also excluded were animals with a single blood sample because weighting of single and paired samples would have been necessary, thereby greatly increasing the complexity of the analysis, and contributing nothing to the repeatability calculations or residual.
Breeds which had five or fewer animals with repeated samples were not included. This was an arbitrary limit designed to maximize the number of breeds, yet still include those which have enough animals to yield a somewhat representative sample of the breed. Ten breeds were left in the study (Table 2).
At least squares analysis of variance technique for unequal subclass numbers as described by Harvey (1975) was used to analyse the data.
Temperament proved to be insignificant for all the blood
traits and was therefore excluded from the model. Breed, stud, and
individual effects were fitted. Age was included as a covariable.
In order to fit individuals it was necessary to screen the data again
to remove animals that changed stud between sampling and those which
were the only animal of a breed within a stud. Table 2 displays the
results of this final screening of the data. As a result Angus had
been reduced to five individuals. The model is given below. 24
Model 1
Yijkl a + Bi + Sj + 1 k(ij 1 + bi Mm + Eejkl where: Y. the 1 th observation of the i th breed, j th stud, and ijkl k th individual;
cx = the overall mean when -j^ is equal to zero;
the breed, i = 1, 2, 3, ... 10; Bf= S, = the stud, j = 1, 2 ... 4;
the k th individual nested within the i th breed and Lk(ij) the j th stud;
the regression of the dependent variable on the
independent continuous variable A^-j each time holding ,
the fitted discrete variables, breed (B.)» stud (S.)
and individuals (1^) constant;
Mjkl the age in days at time of sampling for the corresponding
Yijkl; and the random error, NID (0,v2) (1 = 1, 2). ijkl
Breed and stud were assumed to be fixed effects. Individuals was a random effect. The testing term for breed and stud was individuals.
Individuals was tested against the residual. When individuals was insignificant it was pooled with the residual and the pooled term used as the testing term for breed and stud. This subset of model 1 is given below. It is called model 2 for convenience sake. 25
Model 2
Yijk = a + Bi + sj + Bi: (A)fjk + eijk Where: Y... = the k th observation in the i th breed and the j th stud; ijk
a - the overall mean when A... is equal to zero; IJK
Bi = the breed, i = 1, 2, 3, ... 10;
S. = the stud, j = 1, 2 ... 4; J b. = the regression of the dependent variable Y... on the inde-
pendent continuous variable A. .. holding the fitted 1 J K discrete variables, breeds (B.) and stud (S.) constant; Aijk = the a9e in ^ays at tne time of samPlin9 f°r the corresponding Y... ; and e.-i, = the random errorI ,J K NID (0,v2) (k = 1 , 294) ijk
The expected mean squares for Models 1 and 2 are displayed in Table 3" and 4, respectively, where B = # of breeds, S = # of studs, n = # of samples and I is a summation of degrees of freedom for each breed stud class; i.e., summing the number of individuals, less one, for each breed stud class. There were 21 breed stud classes, therefore, 21 degrees of freedom were lost for individuals. The K value was 2 because these were two samples per animal (Becker, 1975). 26
Repeatabi1i ti es
Repeatabilities were calculated from Model 1 by the method given by Becker (1975).
v2 Repeatability = R = vw + E
ANOVA SOURCE df SS MS EMS ,
(I/B/S) Between ? 9 Individuals N SS MS,, v£ + kv^ w w E w (Residual) Between measurements within 2
individuals M SS^ MS£ v£
N = df for individuals
M = df for residual
k = 2 2 v^= the difference between measurements within individuals 2 MSw " MSE v^= the difference between individuals = ^
v estimates all the geneti3 c variance and the environmental variance w arising from permanent or non-localised circumstances and which contributes to the variance between individuals.
Growth Performance Correlations
Simple correlations were calculated to determine if there are any significant relationships between the average of several growth performance traits and the blood trait levels of different breeds of cattle. Do breeds with higher levels of a blood trait exhibit higher or lower performance for a particular growth trait? 27
The first step towards answering the ahove question was to scan the literature for papers reporting the growth performance results of various cattle breeds. Prior to deriving an average for each growth trait of each breed it was necessary to standardize the data drown from various sources. An indexing technique as described by Pringle (1973) was employed (Appendix I). Angus was assigned an
index of 100 because this breed was common to many studies. The
indices were weighted to account for unequal numbers of animals in each study and an average index for each growth trait of each breed
calculated.
Average indices were obtained from seven breeds for birth
weight, weaning weight, final weight, pre- and post-weaning average
daily gain. Average indices for feed efficiency expressed as
metabolizable energy and total digestible nutrients were also available.
Table 7 displays the average indices for each growth trait by
breed. The least squares constants of each blood trait for each breed
are listed in Table 7. Simple correlations were calculated according
to Steel and Torrie (1960) and listed in Table 15.
Multiple Range Tests
Duncan's multiple range tests were performed on the least
squares constants of breed and stud as described by Steel and Torrie
(1960) with a minor modification to the calculation of the standard
error of each treatment mean. The standard errors were calculated
by the technique given by Harvey (1975). 28
The Effect of Groups of Related Animals on the Variance of Blood Traits
To test the hypothesis that a group of related animals would exhibit less variation in their blood trait levels than a group of unrelated animals, the pedigrees of Holstein bulls from the Burlington stud were compiled to two generations (i.e. grandparents). The
Holsteins were chosen because of the availability of ancestral information and because they comprised the largest group of bulls of the same breed from the same stud, thereby, eliminating breed and stud effects.
Inspection of the pedigrees revealed that at least 58 of the 60
Holstein bulls at Burlington were related to at least one other animal in the stud. There were a set of twins, a number of half sibs, and other relationships, such as, bulls being half uncles to other bulls in the stud. More complex relationships may be found upon further inspection.
Twelve groups of paternal half sibs were selected for a least squares
analysis.
A break down of the groups follows:
#/Group # Groups
2 * 5 = 10
3 * 3=9
4 * 1=4
5 * 2 = 10
6 * 1 = 6 12 groups 39 Bulls 29
The model fit was:
Y = + G + + b ijk <> i ^(i) i tA)1jk + e 1jk where: y = the k th observation of the i th group and j th individual; 1 J K a = the overall mean when A. is equal to zero;
G.j = the paternal h sib group, i = 1, 2, 3 ... 12;
lj(i)= the j th individual nested within the i th group;
b- = the regression of the dependent variable Y... on the
independent continuous variable A., each time holding
the fitted discrete variables, groups and individuals
constant;
A... = the age in days at the time of sampling for the
corresponding Y...; and 1 J K eijk = the random em3r> NID (0»v2)» k = 1, 2.
Individuals was the testing term for groups. Groups was
considered to be a fixed effect because of the restrictions imposed
by the inclusion of Holstein bulls from only one stud. It was felt that
the groups did not constitute a random selection of all possible
Holstein half sib groups because the animals in the stud defined the
groups available for this study. Individuals within groups was taken
as a random effect since the Hoi steins of each group were assumed to
be a representative sample of all possible Holsteins of that group.
When individuals was insignificant it was pooled with the residual to
become the testing term for groups. 30
The Relatlons of Si,re BIood Composition to Progeny Test Results
The progeny test results for 31 Holstein bulls were available
from the sire summaries produced by the Holstein Friesfan Association
of America (1977) (HFA) and the U.S. Department of Agriculture (USDA)
(Dickinson, 1978; Dickinson et al., 1975). The summaries provided
daughter averages for pounds of milk, pounds of fat, and percent fat,
plus their respective predicted differences. (Predicted differences
are the expected average production of a bull's future daughters above or below breed average herdmates). The HFA summary did not include pounds of fat, which therefore had to be calculated using the following formula: lbs FAT = % FAT * lbs MILK/100. Table 8 contains the progeny test results. A series of stepwise multiple regressions were performed setting the blood traits and age of the sire as the independent variables with each progeny test variable in turn as the dependent variable. 31
TABLE 1
BREAKDOWN OF ANIMALS BY BREED, STUD, AND SAMPLING
STUr ) TOTAL BREED BURL WEST CAL MIL OVER P S P s P S P S P S ALL
Maine Anjou 1 7 2 2 3 13 2 15 Simmental 2 8 9 2 1 5 1 17 11 28 Chianina 6 2 1 2 1 8 4 12
Charolais 1 3 3 3 1 7 4 11 Tarantaise 2 2 2 Angus 2 1 2 1 3 7 2 9
MEUSE-RHINE-YSSEL 2 1 2 2 4 3 7 Hereford 3 3 3 3 6 BlondeD'Aquitaine 1 6 3 2 8 4 12
Limousin 1 2 1 2 3 3 6 Pinzgaur 1 1 r 2 2 Gelbvieh 3 5 1 9 9
Brahman 4 2 4 2 6 Hays Converter 3 1 3 1 4 Brown Swiss 1 1 1 1 2
Romagnola 1 1 1 3 3 Short Horn 2 1 2 1 3 Marchiana 1 1 1
Beefalo 1 1 1 Beefmaster 1 1 1 Guernsey 6 6 6
Jersey 10 10 10 Holstein 62 5 7 1 2 2 42 73 48 121 186 91 277
P = Animals from which a second sample was received
S = Animals from which only one sample was received
BURL = Burlington WEST = Western CAL = Calgary MIL = Milner 32
TABLE 2
BREAKDOWN OF ANIMALS AFTER SCREENING DATA
S T U D BREED BREED 1 2 3 4 TOTALS BURLINGTON WESTERN MILNER CALGARY
1. Maine Anjou 7 3 10
2. Simmental 2 6 5 2 15
3. Chianina 6 2 8
4. Charolais 3 3 6
5. Angus 2 3 5
6. Blonde D'Aquitaine 2 6 8
7. Gelbvieh 3 5 8
8. Guernsey 6 6
9. Jersey 10 10
10.Holstein 62 •7 2 71
STUD TOTALS 82 32 20 13 147
294 Samples (2 samples/animal) 33
TABLE 3
MODEL 1 EMS
Source DF SS MS EMS 2 2 SS MS 6f + K 6 j + Breed B-1 9 B B e I
2 2 Stud S-l 3 ss MSS 6 + K 67 + BKA s e I
2 2 I 126 Individuals SSj MSj e I
1 Age SS MSA 6g+ X (A) A 154
Residual N-B-S-I SS MSR R
293 TOTAL N-l ssT
TABLE 4
MODEL 2 EMS
Source DF SS MS EMS
B-1 9 ss MSB + S0 Breed B *e B
Stud S-l 3 ss MSS 6" + B0Q s e ^>
Age 1 SS MS 6^ +X(A) A A 280
Residual N-B-S SS MS R R 293
TOTAL N-l ssT TABLE 5
COEFFICIENT OF DETERMINATION^2) FOR MODEL 1
BLOOD . R^ TRAIT BREED STUD I/B/S AGE RESIDUAL TOTAL
Calcium 0.0542 0.0317* 0.4383 0.0204* 0.5034 0.4965
Phosphorus 0.0482 0.0059 0.5090* 0.0065* 0.2506 0.7494 0.7031 Glucose 0.0467* 0.1023* 0.2827 0.0076* 0.2969 0.8791 Urea-N 0.0582* 0.1244* 0.2921* 0.0011 0.1208
Uric Acid 0.0398 0.1871* 0.3495* 0.0061 0.2864 0.7136
Cholesterol 0.0567* 0.0193 0.3979 0.0128 0.5044 0.4955
Total Protein 0.0300 0.0039 0.4688* 0.0041 0.2961 0.7038
Albumin 0.0634 0.0158 0.5875* 0.0001 0.3328 0.6671
Bilirubin 0.0365 0.0468* 0.2771 0.0022 0.5770 0.4230
ALKP 0.0314 0.0073 0.4854* 0.0315* 0.1264 0.8736
LDH 0.1636* 0.0231 0.3822 0.0193* 0.4234 0.5766
SGOT 0.1063* 0.0384* 0.4663* 0.0055* 0.2096 0.7903
Sodium 0.0184 0.0078 0.4457 0.0001 0.5113 0.4886
Potassium 0.0459 0.0330* 0.4045 0.0090 0.4618 0.5381
* Significant at P<0.05 TABLE 6
COEFFICIENTS OF DETERMINATION (R2) FOR MODEL 2.
R2 BLUUU AGE RESIDUAL TOTAL TRAIT BREED STUD
Calcium 0.0401 0.0165 0.0030 0.9418 0.0582
Glucose 0.0476* 0.1062* 0.0271* 0.5796 0.4203
Cholesterol 0.0539 0.0256* 0.0190* 0.9023 0.0976
Bilirubin 0.0433 0.0755* 0.0351* 0.8541 0.1458
LDH 0.1531* 0.0449* 0.0070 0.8056 0.1943
Sodium 0.0175 0.0082 0.0034 0.9571 0.0429
Potassium 0.0430 0.0273* 0.0051 0.8663 0.1336
•Significant at P< 0.05 TABLE 7 BREED LEAST SQUARES CONSTANTS OF BLOOD TRAITS AND GROWTH TRAIT INDICES
BREED 1 2 3 4 5 6 7
99 89 88 90 89 93 92 Ca 68 63 67 66 74 66 64 P 66 60 61 57 69 67 62 GLUCOSE 13 14 17 14 19 16 13 urea-N 15 13 13 13 13 13 14 URIC CHOL 143 125 119 117 126 107 116 79 75 73 76 76 74 76 TPROT 38 36 36 36 36 37 36 ALB 3 3 3 3 3 3 2 BILI 100 63 75 79 73 78 66 ALKP 1155 1209 1034 970 1124 856 1066 LDH 115 117 122 116 120 125 116 SGOT 141 140 140 140 141 139 140 Na 45 42 44 43 42 43 44 K 100 126 123 131 133 118 74 BIRTH WT 100 143 133 129 123 124 84 WEANING WT 100 134 128 115 110 117 84 FINAL WT 100 106 104 106 115 111 86 PRE-WEAN ADG 100 112 113 115 115 110 82 POST-WEAN ADG 100 90 90 99 102 102 103 TDN 100 90 90 99 102 102 103 ME
BREEDS 1. Angus 2. Charolais 3. Simmental 4. Maine Anjou 5. Chi anina 6. Gelbvieh 7. Jersey 37
TABLE 8
HOLSTEIN PROGENY TESTS RESULTS
DAUGHTER AVERAGES
SIRE ID(a) MILK(b) % FAT FAT(b) PD MILK(c) PD % FAT PD FAT
2102 16049 3.86 619 -397 15 6
2047 16867 3.61 608 270 -1 8
2050 15314 3.63 555 -660 -2 -27
2036 16258 3.77 612 -450 11 -1
2038 17364 3.58 621 482 -4 11
2028 17181 3.59 616 335 -2 10
2142 17259 3.61 623 620 -2 20
2037 17374 3.57 , 620 467 -2 14
2136 17510 3.66 640 417 3 19
2039 16034 3.58 574 76 -2 0
2143 16150 3.46 558 174 -9 -7
2100 17034 3.53 601 180 -10 -8
2025 17550 3.60 631 421 5 22
2042 18947 3.50 663 1003 -4 31
2133 17299 3.56 615 20 4 7
2105 17374 3.53 613 488 -9 4
2032 16197 3.60 583 -588 2 -18
2048 15566 3.58 557 -626 -5 -29
2134 16214 3.63 588 97 -3 -1
2104 17113 3.63 621 238 -6 0
2135 16152 3.60 581 -18 2 2
2168 15518 3.60 565 63 -11 -14 38
TABLE 8 - CONTINUED
DAUGHTER AVERAGES SIRE ID(a) MILK(b) % FAT FAT(b) PD MILK(c) PD % FAT PD FAT
2111 17489 3.60 636 898 -4 26
2163 16619 3.70 616 192 0 7
2138 17802 3.50 623 272 -4 4
2178 16078 3.90 630 437 8 28
2139 16635 3.60 607 390 -3 10
2162 17319 3.80 658 726 8 38
2144 18332 3.60 660 357 -3 9
2153 15942 3.70 585 81 -2 0
2164 18935 3.60 680 1406 -9 37
(a) ID # GIVEN BULL BY STUD
(b) POUNDS
(c) PREDICTED DIFFERENCE
References:
1. Dickinson (1978) 2. Dickinson et al. (.1975) 3. Holstein Friesian Association of America (1977) 39
TABLE 9
RANGES AND MEANS FOR AGE AND THE BLOOD TRAITS
RANGE ARITHMETIC LEAST SQUARE MEAN ± SD MEAN ± SE
AGE (Days) 299 4407 1418± 678
CALCIUM mg/dl 4.9 10.9 9.1± 0.9 9.2 ±0.05
PHOSPHORUS mgP/dl 4.3 9.8 6.6± 0.9 6.7 ±0.05
GLUCOSE mg/dl 35 110 69± 12 64 ±0.70
UREA-N mg/dl 6 43 14± 5 15 ±0.29
URIC ACID mg/dl 0.7 2.1 1.2± 0.3 1.3 ±0.03
CHOLESTEROL mg/dl 53 236 120± 27 120 ±1.6
T. PROTEIN gm/dl 3.8 9.2 7.4± 0.8 7.5 ±0.05
ALBUMIN gm/dl 2.7 4.2 3.6± 0.3 3.6 ±0.02
BILIRUBIN mg/dl 0.1 0.5 0.3± 0.08 0.3 ±0.005
ALKP U/l 27 262 77± 40 79 ±2.3
LDH U/l 280 1660 943± 245 1008 ±14
SGOT U/l 69 237 113± 24 117 ±1.4
SODIUM meq/1 121 151 140± 4 140 ±0.23
POTASSIUM meq/1 3.4 6.3 4.3± 0.3 4.4 ±0.02
U/l = Micromoles per Litre dl = Decalitre = 100 ml
SD = Standard Deviation SE = Standard Error of Mean n = 294 TABLE 10
1 2 2 1 3 3 1 1 1 1 T.PROT ALB BI Ll Na K REFERENCE Ca P GLU UREA-N URIC CH0L
Arthaud et al.(1969) 72.0
Boling et al .(1972) 17.2 8.41
Diven et al .(1958) 27.1 2.50 80 7.20
Galbraith & Watson (1978) 85.4 29.5 7.20 3.54
Galbraith et al.(1978) 88.5 16.9 6.41 3.56
Heuschele & Barber (1966) 15.0 6.82 4.50 .342 4.74
MacDonald et al.(1956) 64.0 14.3 1.92 o 5.58 Manston et al.(1977) 9.98 9.03 63.0 12.0 2.82 138
Reid et al. (1948 a) 11.52 7.34 6.90 4.02
Rowlands et al.(1977) 10.50 8.00 3.26 138 5.37
Rusoff et al.(1954) 10.31 4.64
5.95 Stark et al.(1978) 9.60 6.20 48.1 2.91 139
Taylor et al.(1966) 120
Wilson and Dinkel (1968a) 6.59
1. mg/100 ml 2. g/100 ml 3. meq/1iter 41
TABLE 11 BLOOD TRAIT LEVELS FROM LITERATURE FOR COWS
REFERENCE
1 2 3 4 5 Ca(mg/100ml) 9.15 9.6 9.59 9.54 9.90
P (mg/1OOml) 5.8 5.59 4.88 5.75
Glucose(mg/100ml) 42.5 65.6 54.1
Urea-N (mg/1OOml) 17.8 16.1 14.1 16.1
Uric Acid(mg/100ml) 1.06 1.07 0.91
Cholesterol(mg/1OOml) 143 195 206 195
T. Protein(g/100ml) 7.37 8.24 7.62 7.41
Albumin(g/100ml) 3.63 3.4 4.00 3.66 2.12
Bilirubin(mg/100ml) .170
ALKP (p/£) 35.0 40.7 72.9
LDH(yA) 733
SGOT(y/£) 127 136 86
Sodium(meq/£) 140 137 141
Potassium(meq/)i) 5.0 4.24
1. Ross and Halliday (1976) 2. Payne et al .(1974) 3. Peterson and Waldern (1981) 4. Nash (1978) 5. Basuthakur (1973) TABLE 12
AGE REGRESSION COEFFICIENTS AND REPEATABILITYS
REGRESSION SE REPEATABI
Calcium -0.00008 0.00009 0.0310
Phosphorus -0.00036 0.00018 0.4257
Glucose -0.00332 0.00092 0.0758
Urea-N 0.00089 0.00075 'o.4943
Uric Acid -0.00011 0.00006 0.1973
Cholesterol -0.00630 0.00260 0.0000
Total Protein 0.00025 0.00017 0.3185
Albumin 0.00001 0.00006 0.3661
Bilirubin 0.00003 0.00001 0.0000
ALKP -0.03520 0.00568 0.6488
LDH -0.03443 0.02199 0.0491
SGOT 0.00864 0.00431 0.4622
Sodium -0.00035 0.00035 0.0316
Potassium -0.00004 0.00003 0.0340 43
TABLE 13
REPEATABILITYS FROM THE LITERATURE {%)
1 2 3 4 5 6
Calcium 38 13 20 35 3 12
Phosphorus 51 17 28 42 43 20
Glucose 50 14 35 32 8 14
Urea-N 35 23 12 25 49 30
Uric Acid 20 19
Cholesterol 0 20
Total Protein 43 32 26
Albumin 67 33 52 68 37 36
Bilirubin 0
ALKP 65 74
LDH 5 34
SGOT 46 22
Sodium 23 11 2 2 3
Potassium 47 29 31 21 3
1. Rowlands et al-(1974a) 2. Kitchenham and Rowlands (1976) 3. Kitchenham et al .(1977) 4. Stark et al TTW8J 5. The results of this study 6. Peterson and Waldern (1981) 44
TABLE 14
CORRELATIONS BETWEEN ALL CONTINUOUS VARIABLES
AGE CALCIUM PHOSPHORUS GLUCOSE UREA-N.
Age 1.0000
Calcium 0.0030 1.0000
Phosphorus -0.3956 0.1185 1.0000
Glucose -0.2826 0.2268 0.2101 1.0000
Urea-N 0.3449 0.0231 0.0504 -0.2966 1.0000
Uric Acid 0.0843 -0.0499 0.0709 -0.2539 0.4842
Cholesterol -0.1462 -0.0105 0.0274 -0.0425 0.0876
T. Protein 0.4496 0.6322 -0.1420 -0.0245 0.1571
Albumin 0.0287 0.5736 -0.0467 -0.0092 -0.0218
Bilirubin 0.0552 -0.0251 -0.2042 0.1480 -0.0326
ALKP -0.5761 0.0900 0.4320 0.1935 -0.1813
LDH -0.0239 0.1577 0.0650 -0.1232 0.0506
SGOT 0.2816 0.1818 0.0367 -0.1268 0.4593
Sodium 0.1064 0.5640 0.1409 0.0634 0.0557
Potassium -0.0464 0.1405 0.2182 -0.1634 -0.0089 45
TABLE 14
CORRELATIONS CONTINUED
URIC ACID CHOLESTEROL T.PROTEIN ALBUMIN BILIRUBIN
AGE
Calcium
Phosphorus
Glucose
Urea-N
Uric Acid 1.0000
Cholesterol 0.2914 1.0000
T. Protein -0.0542 -0.1460 1.0000
Albumin 0.0179 0.0994 0.5152 1.0000
Bilirubin 0.3030 0.2790 0.0451 0.1834 1.0000
ALKP 0.0187 0.1655 -0.3251 -0.0861 -0.0597
LDH 0.0834 0.0618 0.1348 0.1882 -0.2537
SGOT 0.3585 0.1124 0.3262 0.1169 -0.1076
Sodium 0.0072 0.0089 0.4498 0.4076 -0.0994
Potassium 0.2005 0.0023 0.0011 0.1427 -0.0684 46
TABLE 14
CORRELATIONS CONTINUED
ALKP LDH SGOT SODIUM POTASSIUM
AGE
Calcium
Phosphorus
Glucose SIGNIFICANCE LEVELS Urea-N I Uric ACid rO.05(2),2^2 = 0.113 Cholesterol rO.01(2),292 = 0.148 T. Protein
Albumin
Bilirubin
ALKP 1.0000
LDH 0.0316 1.0000
SGOT -0.1439 0.3411 1.0000
Sodium -0.0474 0.2552 0.1865 1.0000
Potassium 0.1305 0.1310 0.0111 0.3282 1.0000 TABLE 15 GROWTH TRAIT - BLOOD PROFILE CORRELATIONS
B-WT W-WT F-WT PREADG POSADG TDN ME B-WT 1.0000 W-WT 0.9056 1.0000 F-WT 0.8073 0.9721 1.0000 PREADG 0.9090 0.7499 0.6547 1.0000 POSADG 0.9907 0.9082 0.8323 0.9124 1.000 TDN -0.4022 -0.6837 -0.7962 -0.1275 -0.4301 1.0000 ME -0.4022 -0.6837 -0.7962 -0.1275 -0.4301 1.0000 1.000 C3 -.0.5279 -0.6187 -0.5409 -0.3037 -0.4616 0.4702 0.4702 P 0.3768 0.0078 -0.1034 0.5513 0.3583 0.3476 0.3476 GLU -0.0532 -0.2631 -0.2682 0.3422 -0.0345 - 0.5168 0.5168 UREA-N 0.6120 0.4415 0.3639 0.7198* 0.5994 -0.0392 -0.0392 URIC -0.7116* -0.7713* -0.6752 -0.5927 -0.6704 0.3388 0.3388 CHOL -0.0791 -0.1941 -0.1435 -0.0653 -0.0654 -0.0973 -0.0973 T.PROT -0.3565 -0.5656 -0.5942 -0.2640 -0.3697 0.4671 0.4671 ALB -0.2706 -0.3560 -0.2604 -0.0228 -0.1783 0.3011 0.3011 BILI -0.8554* 0.7657* 0.7477* 0.8542* 0.9043* -0.3918 -0.3918 ALKP -0.0662 -0.2751 -0.2354 0.0671 0.0202 0.2685 0.2685 LDH -0.0752 -0.0317 0.0115 -0.1642 -0.1212 -0.3503 -0.3503 SGOT 0.3659 0.3940 0.4142 0.5436 0.4170 -0.0477 -0.0477 Na -0.0000 -0.2418 -0.2805 -0.0000 -0.0344 0.0858 0.0858 K -0.6520 -0.6450 -0.5079 -0.6287 -0.5646 0.1331 0.1331 * Significant at P<0.05 48
RESULTS AND DISCUSSION
Ranges and Means
Table 9 displays the ranges and means of the blood traits measured in this study. Tables 10 and 11 convey the blood trait means reported in the literature. Table 10 has the results of studies on bulls and steers; Table 11 the results of cows. Comparing the two tables (10 and
11) one can see that male and female cattle have a similar blood levels for calcium, phosphorus, glucose, urea-N, total protein, albumin, sodium, and potassium. The bulls have a wider range of means for phosphorus and glucose. The mean of 9.2 mg/dl for calcium in this study is in agreement with that reported by Ross and Halliday (1976) in their study of 200,000 cattle in Scotland but is lower than the means of the other papers. The mean for phosphorus is higher than the cow means but within the range of the bull and steers means. Glucose, urea-N, total protein, albumin, sodium, andpotassium are within the ranges of the means for cows and bulls.
The bull means for uric acid (2.50, 1.92 mg/dl) are higher than the means given for cows (1.06, 1.07, 0.91 mg/dl). The uric acid mean of this study (1.3 mg/dl) falls between the two sets of means. The higher levels of uric acid reported by Diven et al. (1958) and McDonald et al.
(1956) are based on blood samples from five yearling Hereford steers and eighteen Hereford and Angus 800 lb male calves, respectively. Their results may not be representative of bulls in general and adult animals in particular. Uric acid may be lower in older animals. The effect of age on uric acid in bulls has not been reported in the literature. 49
There was no significant age effect for uric acid in this study.
This study's cholesterol mean agrees with that of Taylor et al.
(1966) for Hereford bulls. The cholesterol means of the cow reports are higher than the means reported by Diven et al.(1958) and Taylor et al.
(1966) for bulls but the results of Taylor et al.(1966) based on 86
Hereford bulls (710 days old) and this study are fairly close to those of Ross and Halliday (1976). A possible explanation for the higher levels of cholesterol in dairy cows could be their energy intake. Stufflebeam et al.(1969) found a positive relationship between cholesterol in beef heifers and their energy intake. Serum cholesterol levels increased with a higher energy intake. Stufflebeam and Lasley (1969) reported a
similar relationship between cholesterol and energy intake in Hereford cows and bulls. Dairy cows fed for milk production may well have a
higher energy intake than bulls in a stud which would result in a
higher cholesterol level.
This study's mean of 0.3 mg/dl is similar to the mean of 0.342
found by Heuschele and Barber (1966) in Hereford steers for bilirubin
but higher than the 0.170 mg/dl reported by Nash (1978) for dairy
cattle. The mean of SGOT from this study is within the range of means
given for cows by Peterson and Waldern (1981), Nash (1978),
and Basuthakur (1973). The alkaline phosphatase mean is similar to
that of Basuthakur (1973) and higher than those of Peterson and
Waldern (1981) and Nash (1978). The lactic dehydrogenase mean of this study
is higher than the mean of Peterson and Waldern (1981). 50
Temperament
Temperament was excluded from the model used in this study because it was insignificant for all the blood "traits. It accounted for less than one percent of the total variation of any one blood trait. Palmer et al. (1930) reported a significant handling stress effect for phosphorus in dairy cows. Phosphorus levels increased after vigorous exercise followed by a marked decrease after half an hour to a point below the level prior to the exercise and remained low for at least two hours. Gartner et al. (1969) was unable to find any consistent effect of excitation and exercise on the level of phosphorus in forty
Australian Illawara shorthorn yearling cattle. The changes in total protein were small but significant. Potassium was not affected.
The results of this study are not surprising because the majority of the animals were quite calm when bled, especially those bled from the tail. Neck bleeding required tying the bull's head off to one side whch tended to cause more stress in the animal. Another factor which contributed to the calmness of the animals was that they were accustomed to handling by humans, in particular the staff at the studs which aided in the bleeding.
Also, the assigning of a temperament rating to an animal was dependent on the judgement of the recorder. It was not always clear as to what rating an animal deserved. There were no definite boundaries between the categories: calm, moderately excited, and very excited, Inconsistencies would have occurred between the different 51 studs. An animal's behaviour was judged relative to the behaviour of the other animals in the stud sampled before him and to the experience of the recorder.
Stud
The effect of stud on the blood traits will have included the environmental influences relating to the individual feeding and management programs of the stud. Also included are environmental factors peculiar to each stud.
Stud was significant for glucose, urea-N, uric acid, cholesterol, bilirubin, lactic dehydrogenase, SGOT and potassium. It accounted for
10, 12, and 19% of total variation in glucose, urea-N, and uric acid, respectively, but less than 8% in the remaining blood traits (Table 5 and 6). The site of blood withdrawal on the animal was confounded within stud effects. The veterinarians at Burlington and Milner bled the bulls from the neck while at Western and Calgary bulls were bled from the tail.
Parker and Blowey (1974) and Teleni et al. (1976) both reported significant differences between phosphorus levels of the jugular vein and the coccygeal artery and vein. Phosphorus was considerably higher
in the coccygeal vessels (12%, Teleni et al., 1976) than the jugular vein. Parker and Blowey (1974) attributed the results to saliva
production withdrawing phosphorus from the arterial blood of the head.
Teleni et al.(1976) had similar conclusions and recommended tail
sampling when testing for phosphorus. Phosphorus was not significant 52 for stud effects in this study. Other environmental effects may have overshadowed the sampling effect.
Factors pertaining to feed and management were not measured for this study and therefore will not be included in this discussion. Stud was included in the analysis as a source of variance to be accounted for and removed from the total variance of a blood trait.
Individuals
The statistical model was efficient for all the blood traits.
The coefficients of determination for the model are displayed in
Table 5. The portion of the overall variation of the blood traits attributed to the fitted effects ranged from 0.42 to 0.87. The variation between individuals received the largest share of this partitioned variation for each blood trait (0.27 to 0.58). The other effects generally had much smaller contributions (< 0.10); the exceptions being 0.16 and 0.11 for the breed effects of lactic dehydrogenase and
SGOT, and the stud effects of glucose, urea-N, and uric acid (0.10, 0.12, and 0.19, respectively). The residual variation which in this study was the variation between repeated samples from an individual was also an important source of variability in the levels of the blood traits
(0.12 to 0.57).
Between individuals variation was significantly greater than the within individuals variation for seven of the fourteen blood traits. These were phosphorus, urea-N, uric acid, total protein, 53 albumin, alkaline phosphatase, and SGOT. The results indicate a definite individuality among bulls in their blood profiles. This conclusion is supported by the findings of other researchers. Stark et al. (1978) found significant differences between individual Friesian bulls for glucose, urea-N, albumin, phosphorus, calcium, and potassium.
Sodium was not significant. Kitchenham et a1.(1977) reported similar results for albumin and sodium in a study with bulls and steers.
Kitchenham and Rowlands (1976) and Rowlands et al. (1975) concluded that dairy cows have individual patterns of blood chemistry which may change with age. Individual cow profiles were different for glucose, calcium, potassium, sodium, phosphorus, urea-N, albumin, and total protein.
Rowlands et al. (1974 a)reported individual blood profiles in calves for glucose, urea-N, albumin, phosphorus, calcium, sodium, and potassium.
Crookshank et al. (1952), Russoff et al.(1954), and Allcroft and Folley
(1941) reported that alkaline phosphatase levels varied ..widely between animals but remained relatively constant within animals.
Alkaline phosphatase had a high repeatability (0.65) in this study which indicates a much larger between individuals variance than within individuals variance.
The within individual component is entirely environmental in origin caused by temporary differences in environmental conditions between successive samples. The between individual component is partly environmental and partly genetic, the environmental part being caused by circumstances that affect the animal permanently (Falconer,
1960). This between individuals component is expressed as a proportion of the variance of single measurements (within individuals 54 variance) by the repeatability calculated for each blood trait
(Table 12).
The repeatabilities ranged from 0 to 65%. The zero repeata-
bi 1 ities of cholesterol and bilirubin indicate a negligible input of
genetic and permanent environmental factors. Other blood traits with
a small genetic input were calcium, glucose, lactic dehydrogenase,
sodium and potassium. These blood traits are very susceptable to
temporary environmental influences. These temporary environmental
influences might include such factors as handling stress, diurnal,
seasonal and monthly variation, semen collection, sample storage,
water intake, and the proximity of feeding and sampling time.
Handling stress (temperament) was not an important contribution
to within individuals variation in this study. Handling stress has
already been discussed in the temperament section. The work done by
Spate et al. (1970) indicated that the time spent in storage
by the blood samples prior to analysis affected the levels of the blood
traits.
Spate et al. (1970) were unable to find changes in the
concentrations of cholesterol, bilirubin, glucose, total protein,
urea-N, sodium, and potassium brought about by storage at -10°C for
a month. Calcium and phosphorus increased sharply after ten days at
-10°C. The enzymes, alkaline phosphatase, lactic dehydrogenase, and
SGOT, were the least stable. Spate et al. (1970) found that within
theone month limit of their study SGOT tended to decrease then 55
gradually increase. Lactic dehydrogenase increased then remained stable therafter. Alkaline phosphatase increased for a few days then gradually decreased. Spate et al.(1970) recommended that enzyme assays should be completed as soon as possible after collection.
Sampling occurred over a three month period in this study which necessitated variable storage times of well over the one month time period looked at by Spate et al. (1970). Spate et al. (1970) found the enzymes unstable in storage but in this study the enzymes SGOT and alkaline phosphatase had repeatabilities of 0.46 and 0.65, respectively which indicate that some sort of equilibrium state is attained after a month in storage. This hypothesis is supported by the results of lactic dehydrogenase which increased then remained stable (Spate et al. 1970). Phosphorus which exhibited a sharp increase after ten days at -10°C also had a high repeatability (0.43) in the present study. Even if some state of equilibrium was reached it is important to know if the process by which the equilibrium came about has altered the relationships of the blood traits between samples of an individual, eg., if phosphorus concentrations have increased to an equilibrium point has the process affected both samples of an individual equally such that differences between the samples for phosphorus are proportionately the same as at the time of sampling. If not, then the within individuals variance will be altered for that blood trait. The same question applies to samples from different individuals. If the process is not consistent for all individuals then the variance between individuals for a blood 56 trait will be affected by storage time.
It seems unlikely that the equilibrium process will proceed equally for all samples because the chemical reactions occurring in the serum which affect the blood trait levels measured by the present study will be fueled by substances, themselves which are in variable amounts in each sample. These reactions will proceed for varying lengths of time and at varying rates to eventually stop after increasing or decreasing the blood trait levels by variable amounts for each sample.
It seems quite reasonable to assume that time spent in storage will have contributed to the sample variance of several blood traits.
The importance of this contribution could vary among the blood traits which Spate et al. (1970) reported to be affected by storage. The repeatabilities of phosphorus, SGOT, and alkaline phosphatase suggest it may not be as important an influence to the sample variance for these blood traits as it is for calcium and lactic dehydrogenase which have very low repeatabilities.
If time spent in storage brings about an inflation or deflation in either the between or within individual variances it could have affected the results of this study. The inflation of individual variances would make it less likely for the other effects to appear significant in the analysis of variance. The deflation of the variance could have effects appearing significant when they should not be.
An interesting study of this problem would be one where the blood traits are measured shortly after samples are drawn from the animals and analysed again after a few months in frozen storage. 57
It would be useful not only as a check on how the blood trait levels have altered but also as to what effect these changes would have had on the results and conclusions of a study such as this. Would the
results for the main effects and the covariable be the same for both
sets of data? If not, then storage time of blood samples is an important effect external to the study proper which must be considered in the
planning of experiments, otherwise, the results are a reflection of
the handling procedure of the samples rather than the actual experiment
itsef.
Water intake and water deprivation seem unlikely causes of
variation in this study since the bulls would have had free access to water in their stalls. None of the animals were subjected to physical
stress prior to bleeding such that would induce substantial intake of
water.
Even though the feeding programs of the studs were confounded
with stud effects, nutrition will have contributed to individual
variation. Each animal on a particular feeding program will not have
responded in exactly the same manner as his companions. Feed intake
will have varied from animal to animal. They will not all grow and
mature at the same rate. It is to be expected that those blood
traits susceptible to nutritional influences will have had some
contribution to the between individual variation by the feeding
program of the animal. Numerous papers discuss the effects of
nutrition on the blood traits. The literature review gives the results
of a number of these. 58
The proximity of feeding and sampling also influences the levels of the blood traits. Kennedy et al.(1939) and Coggins and
Field (1976) reported an increase in glucose levels after feeding.
Coggins and Field (1976) also noted an increase in urea-N. Palmer et al. (1930) found a small but significant increase in phosphorus.
This factor will have contributed to the within individuals variance because the second sample drawn from an animal will not have been taken at a time post-feeding identical to the first sample. The blood trait levels which change after feeding will have been at different stages of their post-feeding cycles for the first and second samplings.
Coggins and Field (1976) associated most of the significant diurnal variation in glucose, urea-N, albumin, and calcium with feeding.
This is not surprising since feeding which occurs at specific intervals during the day will cause a regular pattern of changes in the blood traits which it affects. Seasonal variation was not likely to have been of much consequence in this study because blood sampling occurred during the spring. The literature indicates that a seasonal effect would be relevant if a winter - summer program of sampling had been used. Russoff et al.(1954) detected significant monthly variation in calcium and alkaline phosphatase. Monthly variation if present within this study would have contributed to within individual variation.
Reid et al. (1947) and Reid et al.(1948 b)found semen collection
to exert a strong influence on the plasma levels of alkaline phosphatase 59 in bulls. They concluded that the quantity or frequency of semen ejaculates, more particularity the number of spermotozoa, produced by the bulls.was the major factor in determining the level of alkaline phosphatase in the blood plasma. Semen collection will have contributed to the between individuals variance because bulls wi11 not have been on identical collection programs. Alkaline phosphatase has a high repeatability (0.65) which indicates a large between individuals variance.
Semen collection could well be an important factor influencing the repeatability of alkaline phosphatase through the permanent environment component of the between individuals variance. Blood sampling occurred over a relatively short period of time. The second sample was taken about a month after the first during which time the semen collection program of a bull was unlikely to have altered. As such it would appear as a permanent environmental effect in this study because it would have kept the levels of alkaline phosphatase relatively constant between repeated samples.
The environmental factors mentioned above are no doubt but a few of the many forces which operate both internally and externally to affect
in some way the blood profile of an animal. The repeatabilities of
phosphorus, urea-N, uric acid, total protein, albumin, alkaline phosphatase,
and SGOT indicate the possibility of some genetic input (Table 12). Uric
acid has a repeatability of 0.20. The others range from 0.32 up to
0.65. Table 13 displays the repeatabilities of this study and those of
other researchers reported in the literature. The repeatabilities from
the literature are from studies involving young calves (Rowlands et al. ,
1974 a),cows (Kitchenham and Rowlands, 1976), and bulls and steers
(Stark etal. , 1978; Kitchenham et al., 1977). There is a fairly 60 broad range of repeatabili.ti.es for each blood trait which most likely re• flect the different environmental influences acting within each study.
The permanent and temporary environmental factors which contribute, respectively, to the between and within individuals variances will not all be the same nor of the same importance in the different types of animals; ie. bulls, cows, calves. In cows lactation and pregnancy influence the blood profile while in young calves growth and development are important factors. Factors peculiar to each study will also contri• bute to different repeatabilities occurring in different studies on the same type of animal. *
Peterson and Waldern (1981) reported that cows in different physiological stages have different repeatabilities for their blood traits.
The physiological stages were lactating non-pregnant, lactating-pregnant, and dry. They suggested that genetic differences may be masked in part by physiological effects. This could be a partial explanation for the different repeatabilities reported for cows, calves, and bulls.
These animals fall into different physiological groupings.
Unfortunately the variance between individuals cannot be partitioned into the variance caused by permanent or general environ• mental effects and that caused by genotype. The high repeatability of alkaline phosphatase (0.65) may largely be a result of semen collection.
The repeatabilities set the upper limits of the heritabilities of the blood traits. They are useful in that they indicate directions in which further research may prove promising. 61
The individuality of the blood traits and the blood profile of an animal could prove useful in the selection of breeding stock if any strong relationships between the blood profile and the traits of economic importance in the livestock industry can be detected.
It was with this in mind that three additional studies were under• taken with the limited amount of data that could be gathered from various sources. Daughter production and bull pedigree information were available on a number of the Holstein bulls from the Burlington stud. The correlations with the growth traits are included in the breed discussion.
The pedigree information allowed an investigation into whether groups of related animals (half sibs) would exhibit less variation in their blood traits levels than a group of unrelated animals. If a blood trait is under genetic control related animals should have blood trait levels grouping closer together than would be found in unrelated animals. The results of other researchers lend support to this hypothesis. Ageraard and Katholm (1978) and Ageraard
(1978) found significantly less variation in alkaline phosphatase activity in calves within half sib groups. Lane et al. (1968) reported significant sire effects in Guernsey cows for phosphorus, sodium, and potassium. Roubicek and Ray (1972) had significant sire effects for albumin and total protein. The literature review contains the results of other relevant studies.
This study was unable to detect any significant sire effects upon the blood traits of half sib groups of Holstein bulls from the Burlington stud other than for bilirubin which was very close 62 to being insignificant. The variances between and within half sib groups may not have been significantly different because of the many inter-relationship between animals of different groups. The animals of one group were not completely unrelated to those in the other groups. They had common ancestors in many instances. A more vigorous test would ensure that members of a half sib group were unrelated to members of the other half sib groups in the study.
Average daughter production data were available for thirty- one of the Holstein bulls from the Burlington stud. This permitted a search for possible relationships between the blood profiles of the sires and their daughter's production variables which could be useful in the selection of superior sires. The study by Stark et al.
(1978) was the only paper found in the literature to look at this aspect. With a much larger number of bulls with average daughter production data than was possible in this study Stark et al. (1978) were able to report significant correlations between the blood trait levels of urea-N, phosphorus, and potassium, and the improved contemporary comparisons (ICC's) for milk yield. Their multiple regression analysis included globulin and potassium in the regression equation for milk yield. They found no significant relationships between the ICC's for butter fat yield, butter fat percentage, or protein yield and the blood trait levels.
In the present study there were no significant correlations between the blood traits and the average daughter production variables.
Alkaline phosphatase was the only blood trait included in the regression 63 equations of milk yield and predicted difference fat. None of the blood traits were included in the regression equations for fat, percent fat, predicted difference milk, and predicted difference percent fat. The alkaline phosphatase - milk yield relationship possibly warrants further attention because alkaline phosphatase had a high repeatability (0.65) which indicates possible genetic control of the blood trait. Further investigation may prove it useful in sire selection for daughter milk production.
A larger number of bulls with average daughter production data may yield more promising results than those which occurred in this study, in light of the results reported by Stark et al. (1978).
Thirty-one bulls do not provide a particularly large data set to work with.
AGE
The contribution of age to the model is very small for all
the blood traits. Cholesterol, glucose, alkaline phosphatase, and
bilirubin were assigned 1.90, 2.71, 3.15, and 3.51%, respectively, of
the total variation. The other ten blood traits had less than 1%
(.01 to .70%) of the total variation attributed to age. The bulls 64 in this study ranged from approximately one year to twelve years of age with a mean of four years. Nash (1978) in a study with dairy cows from a similar age range also reported very low coefficients of determination ( < 1.0%) for age with regards to the variation of the blood traits. The notable exceptions in his study were 7.2% for phosphorus and 15.4% for total protein. Phosphorus and total protein had coefficients of determination of age of 0.6 and 0.4%, respectively
in the present study. The reasons for these wide discrepancies are
not apparent.
Phosphorus, glucose, cholesterol, bilirubin, alkaline
phosphatase, and SGOT had significant age effects in this study. The
regression coefficients can be found in Table 12; the partial
correlation coefficients in Table 14. There were negative relation•
ships between age and the four blood traits: phosphorus, glucose,
cholesterol, and alkaline phosphatase. Positive relationships
occurred between age and bilirubin, and, age and SGOT.
The reports in the literature on the effects of age on the
blood traits of bulls are few in number. Most are studies of young
animals less than three years old (Reid et al., 1948a; Arthaud et al.,
1959; Roussel and Stall cup, 1966, 1967). The results of Reid et al.
(1948b) and Stark et al. (1978) are from bulls of a wide range of
ages (1 to 14 years). The animals in the present study predominate 65 in the two to six year range but do extend from 0.82 years to 12.07 years.
The papers by Peterson and Waldern (1981) and Nash (1978) present results for female dairy cattle local to the region of this study. Tumbleson et al. (1973 a, b) studied a large number of female dairy cattle spread over an age range of one to sixteen years. For these reasons reference will be made to these papers reporting age effects in female dairy cattle.
Calcium, urea-N, uric acid, total protein, albumin, lactic dehydrogenase, sodium, and potassium were not significant for age effects in this study. These results are not in total agreement with the literature. Stark et al. (1978) found no age effects for glucose, urea-N, sodium, and potassium but did report that calcium and phosphorus decreased with age. Reid et al. (1948 a) found phosphorus to decrease with age while calcium increased in twelve Holstein bulls (18-33 months old). The results of Tumbleson et al. (1973 a) agree with Stark
et al. (1978) for calcium, phosphorus, and sodium. Sodium was not
significantly affected by age (Tumbleson et al., 1973 a). Calcium and
phosphorus were not affected by age according to the results of
Peterson and Waldern (1981) but were affected in the study by Nash (1978).
Phosphorus decreased with age in the present study. Calcium was not
significant for age effects. Sodium and potassium were not affected
by age in this study nor that by Stark et al. (1978). Tumbleson et al. 66
(1973 a) reported a decrease in potassium with age, Sodium was not affected. Nash (.1978) found a significant age effect on sodium but not potassium.
Nash (1978), Stark et al. (1978), and Arthaud et al. (1959)
reported glucose was not significantly affected by age. Glucose levels
decreased with age in the present study. Peterson and Waldern (1981)
also found a significant age effect for glucose in dairy cows. Urea-N,
uric acid, total protein, and albumin were insignificant for age affects
in this study and that by Peterson and Waldern (1981). Urea-N was not
affected by age according to Stark et al. (1978), where as, Tumbleson
et al. (1973 a) and Nash (1978) reported significant age effects.
Urea-N Increased with age reported Tumbleson et al. (1973 a). Age was
not significant for uric acid or albumin but total protein was
(Nash, 1978). Reid et al. (1948 a) reported insignificant age effects
for albumin and total protein in young Holstein bulls. Tumbleson et al.
(1973 b) found total protein levels increasing with age with no
significant difference in albumin levels whereas Stark et al.(1978)
reported albumin increased between one and five years of age then
declined thereafter.
Cholesterol and bilirubin were not significant for age
effects (Nash, 1978). Cholesterol decreased with age while bilirubin
increased in the present study. Peterson and Waldern (1981) reported
cholesterol significant for age effects. The results of the present
study agree with those of Peterson (1974), Roussel and Stall cup (1966), 67 and Tumbleson et al. (1973 b) that alkaline phosphatase levels decrease with age. Sengonca (1977) reported alkaline phosphatase levels increased with age in his study of bulls. Reid et al. (1948 b), and Petersen and Waldern (1981) found no significant relationship between age and alkaline phosphatase levels.
Tumbleson et al. (1973 b) reported that lactic dehydrogenase
in female dairy cattle increased until about two years of age then
decreased after until about ten years of age. These results do not
agree with those of Roussel and Stallcup (1967) from forty-two Holstein
Friesian bulls which were less than two years old. They found lactic
dehydrogenase levels decreased with age in their animals. Peterson
(1974) had lactic dehydrogenase levels increasing with age in his
growing Hereford bulls. Peterson and Waldern (1981) found a significant
negative age effect for lactic dehydrogenase in dairy cows. Lactic
dehydrogenase did not have a significant age effect in the present study.
SGOT was not significant for age effects in the studies of
Roussel and Stallcup (1967), Tumbleson et al. (1973b), Peterson and
Waldern (1981) and Nash (1978). Boots etal. (1969) detected a signi•
ficant quadratic age effect in seventy-four calves (5 to 480 days old).SGOT
increased with age in the present study.
The variable results reported in the literature on the
relationships between age and the various blood traits indicate the
possibility of outside influences, (which were not taken into account 68
fay the researchers), affecting the levels of the blood traits.
Tumbleson et al. (1973a) have suggested that relationships hetween age and some of the blood traits may be dependent upon environmental, managerial, and nutritional factors inherent in the different studies.
Kitchenham et al. (1975) supported this view with their work on dairy calves reared under conventional and rapid-growth systems. They reported that rearing systems can influence changes in the concentrations of blood traits with age. For example, inorganic phosphate levels decreased from 9.0 mg/1OOml at three months to 7.9 mg/100 ml at nine months for
the conventionally reared calves, but there was no corresponding
decrease for the rapidly reared calves. Calcium concentrations appeared
to follow opposite trends for the different rearing systems.
Age is a measure of the passage of time from some fixed point.
The age effect indicates if some sort of pattern in the changes of the
blood trait levels is present across an interval of time. Age, itself
does not cause any deviation in the levels of the blood traits. As the
factors which are the underlying cause of age effects, eg. nutritional
programs which alter during the life of an individual from birth to
adulthood, are isolated or grouped with other related factors, eg.
individual effects, management unit, herd, etc., the contribution of
age effect diminishes: in importance. In this study and that by Nash
(1978) the very low coefficients of determination for age effects
indicated that age, though statistically significant, had little
importance from a biological standpoint. 69
The factors, which contributed to the variation between and within individuals exerted much, more influence on the blood traits.
With coefficients of determination of less than one percent age was not important in the partitioning of the total variance of any of the blood traits. It did not reduce the amount of the variation assigned to the residual term by an appreciable amount. The age effect's primary function in this study was to indicate if any patterns existed
in the changes of the blood trait levels across time. The causes of the
age effects appear to have been mostly accounted for by the other
effects fitted in the model.
BREED
Breed was a significant effect for glucose, urea-N, lactic
dehydrogenase, and SGOT. It accounted for 5, 6, 15 and 11 percent of
the total variation, respectively. The only blood traits the multiple
range tests were able to distinguish separate breed groupings for were
lactic dehydrogenase and SGOT. The Blonde D' Aquitaine bulls had
significantly higher levels of SGOT than the Holstein and Guernsey bulls.
Charolals and Angus bulls had significantly higher levels of lactic
dehydrogenase than the Holstein and Guernsey bulls. Gelbvieh bulls
had lower levels of lactic dehydrogenase than the Charolais bulls.
The multiple range tests indicate a grouping of breeds into beef and
dairy types by their blood profiles may be possible. 70
The literature reports, are not tn total agreement as to the existence of breed differences in the blood traits. Calcium and phosphorus levels were not significantly different between Holstein and Jersey cows, nor were they in Angus, Shorthorn, and Hereford cows and heifers, and again not different in Jersey, Guernsey, and Holstein bulls according to the results of studies of Russoff and Piercy
(1946), Long et al. (1952), and Russoff et al. (1954), respectively.
Other researchers have found breed effects for the minerals.
Kitchenham and Rowlands (1976) had significant breed differences between the levels of calcium and potassium in Ayrshire and Friesian cows.
Sikes (1963) reported a significant breed effect for the phosphorus levels of Guernsey, Jersey, and Holstein cattle. Tumbleson et al.
(1973a) had significantly higher calcium levels in Guernsey cows than in Holstein cows but no breed difference for phosphorus, sodium,
and potassium. Rowlands ejt_al_J_ (1977) detected significant breed differences in Devon, Sussex, Hereford, and Lincoln Red bulls for calcium, and sodium. Breed was not significant for the minerals in
the present study.
Kitchenham and Rowlands (1976), and Tumbleson et al. (1973 a)
reported that no breed differences existed between Ayrshire and
Friesian cows and between Holstein and Guernsey cows, respectively,
for urea-N, whereas Rowlands et al. (1977) did find breed differences
in Devon, Sussex, Hereford, and Lincoln Red bulls. Urea-N was
significant for breed in the present study. MacDonald et al. (1956) 71 were the only researchers to look for breed differences In uric acid.
They found Angus calves generally had higher levels of uric acid than Hereford calves. Breed was not significant in the present study for uric acid.
Glucose had a significant breed effect in this study and also
in those of Rowlands et al. (1977) and Heyns (1971). Kitchenham and
Rowlands (1976) reported that levels of total protein were significantly
different between Ayrshire and Friesian cows but that albumin levels were not. Tumbleson et al. (1973b) could find no differences between
Holstein and Guernsey cows for total protein and albumin, whereas,
Rowlands et al. (1977) did find breed differences between several beef
breeds and Herefords for albumin. Albumin and total protein did not
have significant breed effects in the present.
Kunkel et al. (1953) were unable to show significant breed
differences between four European cattle breeds whereas Russoff et al.
(1954) and Tumbleson et al. (1973 b) did find significant breed
differences between the dairy breeds for alkaline phosphatase. Alkaline
phosphatase was not significant for breed in the present study but
lactic dehydrogenase and SGOT were. Tumbleson et al. (1973 b)
reported no significant breed effects for lactic dehydrogenase and
SGOT in Holstein and Guernsey cattle.
As the above discussion indicates the literature reports are
contradictory as to whether breed effects do exist in the various
blood traits. What may in fact be occurring in some studies is a 72 masking of breed effects by other effects, which influence the blood trait levels in a more direct manner. Peterson and Waldern (1981) suggest that masking of genetic differences in dairy cattle by physiological effects may occur. Other researchers (eg. Tumbleson et al., 1973 a; Kitchenham et al. ,1975) have reported that environmental, managerial, and nutritional factors inherent in different studies influence the degree and direction of the age - blood trait relation• ships which appear in the study. Variation in the environmental inputs and the physiological states of the animals of different studies may have some influence as to what breed differences in the blood traits, if any, are detected by researchers.
Breed differences in the blood traits may be more prevalent
in young stock. The factors which control the rate and to what degree animals grow and develop are at their strongest in immature animals.
It is these factors which may contribute the most to differences in
the blood profiles of different breeds. Breed differences in the blood
profiles may tend to disappear as the animals attain maturity. It may
be that in order to obtain consistent and strong breed effects the
researchers may have to strictly control environmental inputs as much
as possible, standardize the sampling and storage procedures, and use
animals of uniform ages and physiological states.
Breed differences in the blood traits may not be prevalent
because of genetic similarities between the European breeds. Rouse
(1970) discusses the origins of the European breeds. They have evolved
from two ancient types of cattle, Bos primigenius and Bos longifrons 73
(or Bos, orachycerps). BOs longjfrons, itself may have had its ancestors in the Bos primigenius according to some authorities. Over the centuries tribal migrations and conquests have caused the mixing of the early cattle types of Europe as they became differentiated by either natural or artificial selection.
The Introduction of cattle into Britain from the European continent has occurred on several occasions. The Romans brought their cattle when they invaded. Cattle from northern Germany arrived during various invasions from the fifth to the seventh centuries.
Invasions by the Norsemen and the Normans brought cattle from
Scandinavia and Normandy to the island. The cattle breeds of Guernsey and Jersey were developed from cattle of north western France.
More recent research by Kidd et al. (1974) corroborates the statements of Rouse that there is common ancestry between the cattle breeds of Europe. Kidd et al. (1974) reported that based on a comparison of ten immunagenetic loci of Gelbvieh and South Devon cattle,
these breeds are nearly as genetically similar as red and black Angus.
The results indicated that Gelbvieh and South Devon had a common
ancestry on the continent and are distinct from the other British
breeds such as Hereford, Angus and Jersey.
The sorting of breeds into groups of common ancestry may yield
stronger breed effects in the blood traits than when comparing
individual breed profiles. The multiple range test results also
indicate the possibility of grouping breeds into beef or dairy breeds
may accentuate breed effects. 74
A number of researchers have examined the growth traits of various breeds of cattle. In particular, the U.S. Meat Animal Research
Center in Nebraska has a very large ongoing study involving a large number of exotic breeds of cattle and their crossbreeds. They have gathered a large amount of information on the growth traits of these breeds. Other reports of smaller studies have come from Hutchinson
(1957), Fredeen et al.(1972), Barton (1977) and Pringle (1973).
Breed differences have been detected in the growth traits of animals in these studies. It is not surprising to find differing growth and development patterns between the beef and dairy breeds, and between breeds within these groups. These breeds were developed by selection based on the phenotypic traits of growth rate, size, shape, colour, carcass characteristics, milk production, etc. It is these traits which distinguish one breed from another.
The literature review gives the results of research into the relationships between the blood and growth traits. Price (1959),
Alexander et al.(1958), Kruger and Lakanc (1968), Kunkel et al.(1953) and others have reported significant correlations between several blood traits, and growth traits, such as feed efficiency, rate of gain,and final weight.
If high correlations between blood traits and growth traits do exist then it may be possible to find significant correlations between the averages of the blood traits and the averages of the growth traits 75 for several breeds of cattle. Do breeds with higher levels of a blood trait exhibit higher or lower performance for a growth trait?
Data on the growth traits of several breeds in this study were available from the literature. The data provided an average index
for each growth trait of each breed. The growth trait indices were correlated to the average blood profiles of the breeds. Table 7 has the
average blood profile of each breed and its corresponding growth trait
indices. Table 15 has the simple correlations calculated between the
blood and growth traits.
There were eight significant correlations between the blood and
growth traits. Five of these included bilirubin which has a repeatability
of zero. This indicates a lack of genetic input. Bilirubin levels are
influenced by environmental factors. Without genetic control these
results would appear to have occurred by chance. This conclusion is
supported by inspection of the data (Table 7) which shows all the breeds
to have the same bilirubin levels except Jersey. Jersey is also lower
for the growth traits which is not surprising because it is a much smaller
breed of cattle. Further inspection reveals that the other six breeds
exhibit fairly wide variation in their growth trait indices yet the
bilirubin levels are the same for all. This occurred because bilirubin
results are rounded to one significant figure by the bio-medical laboratory
which analyzed the blood samples. The significant correlations occurred
because Jersey bilirubin was rounded to 2 mg/100ml whereas the others
rounded to 3 mg/100 ml. There were no bilirubin levels between two and
three mg/100 ml. 76
Urea-N had a significant positive correlation with pre-weaning average daily gain (0.72) in this study. Price (1959) and Price et al.(1959) reported urea-N was highly correlated with rate of gain and feed utilization efficiency in Hereford and Angus calves. They had a negative correlation with rate of gain (-0.31). Kitchenham et al.
(1977) had positive correlations between urea-N and growth rate (0.44) and final weight (0.38). Colby et al.(1950) found no significant correlations between rate of gain in beef calves and their concentrations of urea-N. Uric acid had significant negative correlations with birth weight (-0.71) and weaning weight (-0.77) in the present study. Price et al .(1959) reported uric acid significantly correlated to rate of gain at both 500 and 800 lbs. (-0.30 and -0.35) in Hereford and Angus calves.
None of the other blood traits had significant correlations with the growth traits in this study. The literature review reveals that other researchers have encountered significant relationships. Alkaline phosphatase was reported to be correlated to rate of gain; SGOT to body weight; and lactic dehydrogenase to weight gain.
Kitchenham et al.(1977) and Little et al.(1977) found that all significant correlations became insignificant when the data were adjusted for total feed intake. Little et al. (1977) concluded that feed intake was the dominant factor which accounted almost entirely for the
significant correlations between weight gain and blood composition that were observed for glucose, phosphorus, sodium, and albumin. It would
seem that the most promising blood traits would be those which correlate 77
to the growth traits and are least affected by nutritional factors.
The enzymes may be worth further study in this regard because Gahne
(1967) and Reid et al.( 1948a) reported alkaline phosphatase to be unaffected by the plane of nutrition and feed type. Alkaline phosphatase correlated significantly with rate of gain (Alexander et al.,1958;
Ageraard, 1978; Kruger and Lakanc, 1968; Sengonca, 1977; and
Kunkel et al., 1953).
The enzymes were significant for breed effects in this study but did not correlate significantly to the growth traits even though
SGOT had quite high correlations (0.36 to 0.54). This could be because of the small amount of data available. There were only seven pairs of means used in each correlation. With only seven pairs of means per correlation, the correlations had to be very large before they were significant ( >.707). With a larger number of breeds the cut off point for significance would drop. Also, a better picture of the blood-growth trait relationships would appear because the correlations would be based on more data.
As the results stand in this study, the breeds with the higher levels of urea-N tend to have higher preweaning average daily gains. The breeds with the higher uric acid levels have lower birth weights and weaning weights. 78
SUMMARY AND CONCLUSIONS
Blood samples drawn from 147 hulls representing ten breeds were analysed for fourteen blood traits. The blood profiles compiled from these analyses were used in this study to see if the readily apparent phenotypic differences such as coat colour, size, carcass characteristics, etc., which occur between different breeds of cattle were also accompanied by differences in their blood trait levels.
Repeatabilities were calculated for each blood trait.
To test the hypothesis that blood traits under possible genetic control would show less variation within groups of related animals than among unrelated animals, the blood profiles of twelve half sib groups were compared. Correlating the blood profiles to several traits of economic importance provided insight into the possible usefulness of the profiles in the selection of breeding stock.
The blood profiles of thirty-one Holstein bulls were correlated to their average daughter milk production variables. The relationships between the blood profiles and the growth traits were looked at by comparing the mean blood profiles of several breeds to their respective growth trait means.
The ranges and means of the blood traits of this study compared quite favourably to the literature reported means for cows and bulls. Inspection of the tables revealed similar profiles in male and female cattle. 79
Age was significant for phosphorus, glucose, cholesterol, bilirubin, alkaline phosphatase, and SGOT. Phosphorus, glucose, cholesterol, and alkaline phosphatase levels decreased with age, whereas, SGOT and bilirubin increased with age. The very low coefficients of determination for age effect in this study indicate that age, though statistically significant, had little importance biologically. It did not appreciably reduce the residual variation.
The underlying causes of the changes of the blood trait levels over time appear to have been mostly accounted for by the other effects fitted in the model.
The statistical model was efficient for all the blood traits.
The portion of the overall variation of the blood traits attributed to the fitted effects ranged from 0.42 to 0.87. The variation between individuals received the largest share of this partioned variation for each blood trait (0.27 to 0.58). The other effects generally had much smaller contributions. The variation between repeated samples from an individual (residual) was also important
(0.12 to 0.57).
The variation between individuals was significantly greater than the within individuals variation for phosphorus, urea-N, uric acid, total protein, albumin, alkaline phosphatase, and SGOT which
indicates the definite individuality of the blood profiles of bulls.
The repeatabilities of the blood traits ranged from 0 to
65 percent. The low repeatabilities of cholesterol, bilirubin, 80 calcium, glucose, lactic dehydrogenase, sodium and potassium reveal their susceptability to temporary environmental influences. Genetic influences would appear to be of little importance in these traits.
The other seven blood traits: phosphorus, urea-N, uric acid, total protein, albumin, alkaline phosphatase, and SGOT, had moderate (0.20) to high (0.65) repeatabilities. The high repeatability of alkaline phosphatase (0.65) may reflect the influence of semen collection during blood sampling.
The individuality of the blood profiles and the high repeata• bilities of several blood traits lend support to the possibility that the blood profile could be useful in the selection of breeding stock if any strong relationships between the blood profile and the traits of economic importance could be found.
Even though other researchers have reported significantly less variation among related animals for several blood traits, this study was unable to do so. The many inter-relationships between animals of different half sib groups was likely a major factor contributing to the lack of significant results. The ideal situation, whereby all the animals of each half sib group are not related to members of other half sib groups, was unfortunately not possible. 81
There were no significant correlations between the blood and milk traits. Alkaline phosphatase was the only blood trait included in the regression equations of milk yield and predicted difference fat. The regression equations of the other milk variables did not include any blood traits. The high repeatability of alkaline phosphatase (0.65) which indicates some degree of genetic control of the blood trait suggests that further study of the alkaline phosphatase- milk yield relationships is warranted. A large data set may have yielded better insight into the relationships of sire blood profile to daughter milk production.
There were eight significant blood trait - growth trait correlations. Five of the eight significant correlations involved bilirubin which had a repeatability of zero. These correlations appear to have occurred by chance because the low repeatability of bilirubin indicates a lack of genetic control on the blood trait. The only other significant correlations were: urea-N to pre-weaning average daily gains (0.72), uric acid to birth weight (-0.71), and uric acid to weaning weight (-0.77). With only seven pairs of means per correlation, the correlations had to be very large before they were significant
(>0.707). With a larger data set the cut off point for significance would drop and a better picture of the blood - growth trait relationships would appear. As the results stand, the breeds with the higher levels of urea-N tend to have higher pre-weaning average daily gains. The breeds with the higher uric acid levels have lower -82- birth and weaning weights.
Breed was not a major contributor to the variation of the blood traits. Of the four blood traits with significant breed effects only urea-N and SGOT look promising. Glucose and lactic dehydrogenase have low repeatabilities which indicates that their significant breed effect probably occurred by chance. The multiple range tests were able to distinguish separate breed grouping of lactic dehydrogenase and SGOT. The multiple range tests indicated a grouping of breeds into beef and daily types by their blood profiles may be possible.
Variation in the environmental inputs and the physiological states of the animals of different studies may have some influence as to what breed effects, if any, are detected by researchers. One aspect of this is that breed differences may be more prevalent in young stock. The factors which control growth and development may contribute the most to differences in the blood profiles of different breeds. These differences may tend to disappear as the animals mature.
Breed differences in the blood traits may not be prevalent because of genetic similarities between the European breeds. More extensive breed differences in the blood profiles of cattle may come to light in subsequent studies if sorting of the breeds into genetically similar groups is performed. Expansion of the profile to include other enzymes and protein fractions might also prove fruitful. 83
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APPENDIX I
INDEXING COMPUTATION
To illustrate the indexing procedure an example is given below.
ORIGINAL DATA
Breed # Weaning Wt. (kg)
Angus 1003 201
Charolais 1237 290
Limousin 173 239
Simmental 627 282
The above data is from one reference. The weights were divided by the weaning weight of Angus and multiplied by 100 to yield the indices for weaning weight.
Breed # Index
Angus 1003 (201/201)xl00 = 100
Charolais 1237 (290/201)xl00 = 144
Limousin 173 (239x201)x!00 = 119
Simmental 627 (282/201)xl00 = 140
This indices had to be weighted to take into account the
number of animals contributing to each index. The weighting was
calculated by the formula: 93
n.i x n.2 W = n.i •+ n.2 where W = the weighting
m = the number of animals in the non-Angus breed
n2 -= the number of Angus cattle
Breed Weighting Index
Angus 1003 100
1237x1003 Charolais 1237 554 144 1237+1003
173x1003 148 119 Limousin 173 173+1003
627x1003 627 140 Simmental 627+1003 386
The weightings are multiplied by their corresponding indices from all the sources of data. For each breed the weightings are added
up, as are the "weightings times the index", and the total "weighting
times the index" divided by the total weighting to yield the average
index.
CHAROLAIS WEANING WEIGHT
Index Weighting Wxl
144 554 79776
110 87 9570
105 35 3675
108 46 4968 772 97989 TOTAL
Average Index = 97989 * 772 = 136 94
The average index for charolais weaning weight was 136.
This procedure was repeated for the weaning weights of the other
breeds and for the remaining growth traits.
For those references which had information only on crossbred
animals the indices were adjusted for 5% heterosis (Preston and Willis
(1974)) to obtain estimates for the purebred performance. All the
crossbred references contained purebred Angus and Angus crossbreds.
An example follows.
ORIGINAL DATA
Breed # Birth Wt.(kg)
Angus x Angus 66 73.5
Jersey x Angus 71 68.5
The indices were calculated.
Breed Index
Angus x Angus 100
Jersey x Angus (68.5/73.5)xl00 = 93
The crossbred index was adjusted with the formula:
Where JJ = purebred Jersey index
JA = Jersey x Angus index 95
JJ ^n^yp) •100 = 77
The birthweight index for purebred Jersey was 77. The weighting was calculated and included with the adjusted index in the average index calculation.