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This is to certify that the

thesis entitled

THE ASSOCIATION OF PREPARTUM
NON-ESTERIFIED FATTY ACIDS AND BODY CONDITION
WITH PERI PARTUM HEALTH PROBLEMS
ON 95 MICHIGAN DAIRY FARMS

presented by

Paul Brian DYk

has been accepted towards fulfillment
of the requirements for

”.5. , Animal Science
degree in

 

 

Major rofessor

Date /'/"‘”a 5/

 

0-7639 MS U it an Affirmative Action/Equal Opportunity Institution

 

LIBRARY
Michigan State
University

 

 

 

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PLACE IN RETURN BOXto mouthi- chockout from your record.
To AVOID FINES Mum on or baton date duo.

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

THE ASSOCIATION OF PREPARTUM NON-ESTERIFIED FATTY ACIDS
AND BODY CONDITION WITH PERIPARTUM HEALTH PROBLEMS
ON 95 MICHIGAN DAIRY FARMS

By

Paul Brian Dyk

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Animal Science

1995

ABSTRACT
THE ASSOCIATION OF PREPARTUM NON-ESTERIFIED FATTY ACIDS
AND BODY CONDITION WITH PERIPARTUM HEALTH PROBLEMS
ON 95 MICHIGAN DAIRY FARMS
By

Paul Brian Dyk

Ninety-five dairy farms above Michigan DHIA average of 8760 kg of
milk/cow/year were visited four times within a 6 week period. At each visit, a body
condition score (BCS) and a blood sample were taken from each Holstein animal that was
within 35 days of the expected date of parturition. Plasma from the blood sample was
analyzed for non-esterified fatty acids (NEFA). Higher prepartum NEFA concentrations
were associated with a higher incidence of dystocia, retained placenta, ketosis, displaced
abomasum, and mastitis but not milk fever. Animals with higher BCS scores had a higher
incidence of ketosis and displaced abomasum but not dystocia, retained placenta, milk
fever, or mastitis. Prepartum NEFA concentrations were elevated in animals with higher
BCS, and lower predicted transmitting ability for milk. BCS were lower in animals that
had higher predicted transmitting ability for milk. Decreasing prepartum lipid

mobilization may result in fewer peripartum health problems.

To God, the Creator and Sustainer of All.

iii

ACKNOWLEDGMENTS

This project would not have been possible without the advice, patience, and
assistance of many people. I would first like to thank Dr. Michael VandeHaar for acting
as my major professor. His guidance and insightful questions were very helpful
throughout my M. S. program. A special thank-you goes to Dr. Roy Emery who was
essential to the completion of this project and was always a great resource. I also thank
my other committee members, Dr. Tom Herdt and Dr. Roy Fogwell, for serving on my
committee.

I thank Dr. Herb Bucholtz and Dr. Richard Cameron for assisting with the many
farm visits. Thank-you to Jim Liesman for his statistical prowess and patience. Thank-
you to Dr. Bal Sharma for his laboratory expertise and guidance.

Thank-you to Erinn Dempsey, and Janice Rumph for their lab and computer help
that was invaluable for the completion of this project. I also thank Brad, Randel,
Christine, and Corey for their assistance on farm visits.

Thank-you to my parents for their guidance and support throughout my education.

Finally, thank-you Neva for your constant cheer and encouragement.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................ vii
LIST OF FIGURES ...................................................................................................... viii
INTRODUCTION ........................................................................................................... 1
LITERATURE REVIEW ................................................................................................ 3
Relationship of parity and peripartum health problems .......................................... 3
Relationship of BCS and peripartum health problems ............................................ 3
Relationship of NEF A and peripartum health problems ......................................... 5
Plasma NEF A concentrations around parturition ....................................... 6

Causes of NEFA elevation during the last week prepartum ....................... 8

Plasma NEF A through Fatty Liver as a cause of health problems .............. 9

Other possible connections of NEFA to health problems ......................... 11

MATERIALS AND METHODS ................................................................................... 13
Farm selection .................................................................................................... 13

Farm visits ......................................................................................................... 13

Lab method for NEF A analysis ........................................................................... 14

Raw data editing and summarization ................................................................... 19

The Four Databases used for statistical analysis .................................................. 20

WT Database .......................................................................................... 21

WT&PTA.. Database .............................................................................. 21

NT Database .......................................................................................... 22

NT&PTA Database ................................................................................ 22

Statistical Analysis ............................................................................................. 23
Disease analyses using Cochran-Mantel-Haenszel Statistics ..................... 23

Analysis of relationship of Parity to Disease ............................................ 25

Analyses of relationship of BCS Group and NEFA Group to Disease ...... 25

Analysis of relationships of NEF A, BCS, PTA”, Herd, and Parity ........... 25

Analysis of twinning ............................................................................... 26

RESULTS ..................................................................................................................... 27

Database summary ............................................................................................. 27
Prepartum NEFA concentrations in plasma ......................................................... 27
Relationship of BCS, PTA“, Parity, Herd and Day prepartum to plasma NEFA .. 29
Relationship of PTA”, Parity, Herd and Day prepartum to BCS ......................... 34
Time frame of diseases ....................................................................................... 35
Association of Disease and Parity ....................................................................... 37
Association of Disease and NEFA Group among Parities .................................... 38
Association of Disease and NEF A Group within Parity ....................................... 39
Association of BCS Group and Disease Incidence .............................................. 41
Association of PTA... and Disease ....................................................................... 44
Results from twin analysis .................................................................................. 45
DISCUSSION ............................................................................................................... 48
Effects of Parity on NEFA, BCS and Disease ..................................................... 48
Relationship of NEF A concentration and Disease ............................................... 50
Relationship of BCS and Disease ........................................................................ 52
Causes of elevated prepartum plasma NEFA ...................................................... 53
Effect of PTA.I on disease, BCS, and NEFA ...................................................... 53
Potential problems with this study ...................................................................... 54
Practical recommendations from this study ......................................................... 55
SUMMARY & CONCLUSIONS .................................................................................. 58
APPENDICES .............................................................................................................. 59
Appendix A ........................................................................................................ 59
Appendix B ........................................................................................................ 60
Appendix C ........................................................................................................ 71
Appendix D ........................................................................................................ 73
Appendix E ........................................................................................................ 75
BIBLIOGRAPHY ......................................................................................................... 79

LIST OF TABLES

Table 1. Factors affecting NEFA in plasma at week -2 and week -1 (NT Database) ........ 31

Table 2. LS Means for NEFA concentration by parity BCS, and PTA... .......................... 32
Table 3. Factors affecting NEF A at week -2 and week -1 (NT &PTA... Database) ........... 33
Table 4. Effect of PTA... Group and parity on BCS for the 2 weeks prepartum. PTA... and
parity had a significant effect (P<.Ol) on BCS. ....................................................... 34
Table 5. The association of disease incidence and NEF A Group by parity ...................... 40
Table 6. The association of disease incidence and BCS Group by parity. ....................... 43
Table 7. Plasma NEFA and BCS in cows that did and did not have twins ....................... 45

vii

LIST OF FIGURES

Figure l. Periparturient DMI and plasma NEF A concentrations of multiparous cows
(adapted from Grummer, 1993) ................................................................................ 9

Figure 2. Layout of nricrotiter plate for each plasma NEFA assay. Working standards
were in Column 1, internal standards were in wells C12 to H12 and samples were in

remaining wells. ..................................................................................................... 17

Figure 3. Plasma NEFA in NT Database. Each sample from each cow was expressed as
percent of the average of all animals on the same day prepartum ............................. 24

Figure 4. Plasma NEF A concentration before parturition averaged for animals in WT

Database. ............................................................................................................... 28
Figure 5. Plasma NEFA concentration before parturition by parity in WT Database. ...... 29
Figure 6. Number of cases of mastitis, ketosis, and displaced abomasum in the first 21

days after calving. .................................................................................................. 36
Figure 7. Incidence of disease by parity. ......................................................................... 37
Figure 8. Disease incidence within NEF A Group across parities ..................................... 38
Figure 9. Incidence of disease by BCS Group across parities ......................................... 42
Figure 10. Incidence of disease in animals (n=1655) after giving birth to single or twin

calves ..................................................................................................................... 46
Figure 11. Incidence of disease in parity 3+ (n=63 8) after giving birth to single or twin

calves ..................................................................................................................... 47
Figure 12. Relationships in the present study and proposed mechanisms for these

relationships. .......................................................................................................... 57
Figure A. 1. Initial questionnaire sent to farms ............................................................... 59

viii

Figure B. 1. First version of nutrition and management sheets ........................................ 60

Figure C. 1. First version of health sheet given to farmers .............................................. 71
Figure C.2. Second version of health sheet given to farmers .......................................... 72
Figure D.2. Sheet 2 for NEFA analysis .......................................................................... 74
Figure B. 1. Program for CMH Analysis ........................................................................ 75

ix

INTRODUCTION

The time around calving is a critical period in the life of a dairy cow. The initiation
of lactation, the stress of calving, and a high incidence of health problems make it a key
transition phase in the life of a dairy cow. There is great economic incentive to make this
transition period smooth and free of problems. Currently, peripartum health problems
result in losses of 28850 million/year in Michigan due to increased veterinary costs and loss
of milk production (Ferris and F ogwell, 1984).

A challenge faced by the dairy animal in the last couple of weeks prepartum is
maintaining a positive nutrient balance. Poor nutrient balance may result fiom a
combination of decreased feed intake in the last couple weeks prepartum and increased
energy requirements due to lactogenesis and increasing fetal needs.

Prepartum non-esterified fatty acids (NEFA) were used in this study as an
indicator of nutrient balance. When a cow consumes less energy than required, body fat is
mobilized and concentration of plasma NEFA increases. Many of these NEFA are taken
up by the liver and reesterified to TG. Excess TG accumulation results in fatty liver,
which may cause more health problems such as ketosis and displaced abomasum after
calving. In addition, poor nutrient balance as indicated by elevated NEFA may suppress

the immune system and thus lead to more health problems such as mastitis.

2
Body condition score, which is a measure of body fatness in the dairy animal, was

also examined in this study. High BCS has been reported to reduce feed intake and
increase the incidence of health problems such as ketosis and mastitis.

The hypothesis of this study is that elevated concentrations of NEF A in plasma
and higher BCS before calving will increase the incidence of peripartum health problems in
dairy cattle.

Our specific objectives were:

1) To determine if elevated prepartum NEF A concentrations are associated with

more health problems in the first 60 days after calving.

2) To determine if higher BCS is associated with more health problems in the

first 60 days after calving.

3) To determine what factors affect NEFA concentration and BCS in dairy cattle

before calving.
The specific health problems examined were: dystocia, retained placenta, milk fever,

ketosis, displaced abomasum, and mastitis.

LITERATURE REVIEW

Relationship of parity and peripartum health problems

There is a significant relationship between disease and parity. As shown already in
1923 (Sjollema and Van Der Zande), and again in 1956 (Shaw), the incidence of ketosis
increases as parity increases. In Swedish Red and White and Swedish F riesian cattle, there
was an increase in retained placenta, ketosis, and mastitis as parity increased (Emanuelson
et al., 1993). In another Scandinavian study with Finnish Ayrshire cattle, the incidence of
dystocia, milk fever, and ketosis was greater during the second lactation than the first

lactation (Mantysaari et al., 1991).

Relationship of BCS and peripartum health problems

Body condition score (BCS) is a measure of subcutaneous lipid stores based on a
visual appraisal (Wildman et al., 1982). Domecq et al. (1994) showed that BCS was
positively correlated (ranging from .49 to .73) to ultrasound measurements of
subcutaneous fat at the lumbar, thurl, and tailhead regions. Otto et al. (1991) did a
carcass study to evaluate the correlation of BCS and fat content of the carcass. Afier

slaughter, 9th to 11th rib sections were analyzed for ether extract content. Each unit

4
increase of body condition score was associated with a 12.7% increase in ether extract.

Waltner et al. (1994) slaughtered 23 animals and showed that the percent of empty body
fat (does not include fat fi'om internal organs) could be accurately estimated (r2=.7 8) fi'om
body weight and body condition score. Based on these ultrasound and carcass studies,
BCS is reliable as a relative indicator of body fatness in dairy cattle.

The link between overconditioned animals and disease has been quoted by some
individuals; however, the evidence is not conclusive. In an often referenced paper, Fronk
et al. (1980) conclude that the overconditioned animals in their study had more health
problems; however, there is no statistical analysis to back this claim. In addition, there
are no clear descriptions of diseases and there is no indication as to whether cases of a
disease were counted twice if they occurred in one animal. Fronk et al. conclude that
there are more cases of ketosis, milk fever and mastitis in overconditioned animals but the
limited number of cows (44 animals) in this study make it difficult to extrapolate this to
the Holstein population. A review by Morrow (1975) on “Fat cow syndrome” says that
overconditioned animals have a higher incidence of milk fever, ketosis, displaced
abomasum, indigestion, mastitis, etc.; however, the paper again does not give evidence to
support this claim. Although these two papers are often cited as the basis for the idea that
overconditioned animals have more health problems, they clearly should not be. More
recently, Ruegg (1995) showed that higher BCS at calving was not associated with
disease in 429 animals in 13 herds. Gearhart et al. (1990) reported that overconditioned
animals (>=4 on a scale of 1 to 5) did not have a higher incidence of ketosis or displaced

abomasum in 561 animals.

5
Despite a lack of evidence of a direct link between BCS and disease, the

mechanism for such a link may be through the effect of BCS on DMI and thus on energy
balance. Some investigators have shown lower DMI in overconditioned animals
(Garnsworthy and Topps, 1982; Treacher et al., 1986) and concluded that this may result
in more health problems; however, Holter et al. (1990) did not see lower DMI in
overconditioned animals. Grummer sums up the effect of BCS on intake quite well in his
1993 review of ruminant lipid metabolism: “The importance of body condition as a

determinant of prepartum feed intake has not been examined critically.”

Relationship of NEFA and peripartum health problems

An important indicator of whether or not a cow will develop a peripartum health
problem may be the concentration of plasma non-esterified fatty acids (NEF A) in the
prepartum period. The concentration of plasma NEFA is proportional to the rate of lipid
mobilization in the cow, and lipid mobilization is proportional to the shortfall of dietary
energy to meet an animal’s requirements (Mills et al., 1986). Plasma NEFA are taken up
by the liver proportional to their concentration in plasma and the rate of blood flow to the
liver (Bell, 1980). Because the ruminant liver does not export very low density
lipoproteins (VLDL) in significant quantities, uptake of NEF A may lead to fatty liver.
Fatty liver has been connected to ketosis which may indirectly lead to other health
problems such as displaced abomasum (Curtis et al., 1985). However, fatty liver may not

be the only mechanism whereby elevated NEF A could be associated with more health

6
problems. A higher concentration of plasma NEFA is an indication of negative energy

balance or poor nutrient balance, which may cause a general suppression of the immune

system and consequently lead to more health problems such as mastitis.

Plasma NEFA concentrations around parturition

Prepartum plasma NEF A are the focus of my study, not postpartum plasma
NEFA, because prepartum plasma NEFA are more likely to cause fatty liver. In the
prepartum period, some plasma NEFA will be used by tissues as an energy source while
the rest will be taken up by the liver. Thus, if plasma NEF A concentrations are high,
triglycerides will accumulate in the liver and eventually result in “fatty liver”. After
calving, however, if plasma NEF A concentrations are high, the mammary gland will
remove much of the plasma NEF A for use in milk synthesis. This is supported by reports
that the liver is already infiltrated by TG at the time of calving (Grummer et al., 1993;
Gerlofi‘ et al., 1986).

Immediately around the time of calving (+/- 24 hrs), plasma NEF A may increase
from 300 M to over 900 M (Bertics et al., 1992; Studer et al., 1993). Four possible
causes for this increase are: l) a decreased intake of energy, 2) an increased energy need
by the animal or fetus, 3) hormonal shifts related to lactogenesis and calving, or 4) a
combination of these possibilities. Although DMI drops over the last 10 days prepartum,
the drop 1 day before calving likely is not the cause of high NEF A at parturition, because

the NEFA surge occurred even when cows were force fed through a rumen cannula to

7
maintain DMI (Bertics et al., 1992). The possibility that the surge of plasma NEF A is

caused by a sudden need for more energy associated with lactogenesis seems more likely,
but there is little evidence in the literature to support or refute this hypothesis. Perhaps the
most likely cause of the NEFA surge at calving may be the increase in plasma epinephrine
or norepinephrine occurring around parturition (Grummer, 1993). Finally, the increase in
NEFA at calving is probably caused by a combination of these factors. However, the
possibility that this large increase in NEF A on the day before calving could by itself cause
fatty liver seems unlikely.

In addition to the NEFA surge at calving, a moderate increase in NEFA occurs
over the last week or two before calving (Grummer, 1993). And in fact, this moderate
increase over the last week seems more important than the surge at calving. Drenching
animals with propylene glycol, a glucose precursor, once daily fiom about 10 days
prepartum until parturition significantly lowered plasma NEFA concentrations (234 M vs
403 uM) from 6 to 1 days prepartum compared to those of control animals (Studer et al.,
1993). However, NEFA increased similarly on the day before calving in both drenched
and control animals, and DMI decreased similarly in both groups beginning 10 days before
calving. Irnportantly however, drenched animals had 32% less TG in liver at 1 day after
calving. Thus, the apparent decrease in the rate of lipolysis as indicated by plasma NEFA
for 1 week prepartum resulted in a decrease in liver TG concentration. This indicates that
the NEFA surge immediately around calving does not account for all the TG infiltration
and subsequent problems. Precisely how many days before calving plasma NEF A

concentration may be important has not been determined.

8
Causes of NEFA elevation during the last week prepartum

The increase in NEFA concentration during the last week before calving may be
caused by the general decrease in DMI observed over the last 2 weeks (Figure 1). In a
study by Bertics et al. (1992), animals were force fed through a rumen cannula to maintain
DMI prior to calving. In control animals, liver TG increased 227% in last 17 days of
gestation; however, the increase was only 75% in force-fed animals, which suggests that
maintaining DMI reduces TG infiltration of the liver but does not eliminate it. NO
difi'erences in plasma NEF A were observed between the two groups but authors point out
that there was a tremendous amount of variation between cows so that they were unable
to statistically detect a difference. In both groups, NEFA rose exponentially within a day
of calving.

The cause of the drop in DMI around calving is unclear but may be related to
space in the abdominal cavity, hormonal shifts occurring around calving, or the stresses of

calving and lactogenesis and the management changes associated with them.

 

NEFA (uM)

 

*NEFA
‘hDMI ~~ 10

 

 

 

 

 

 

 

 

Days relative to calving

Figure 1. Periparturient DMI and plasma NEF A concentrations of multiparous cows
(adapted from Grummer, 1993)

Another possible reason for the increase in NEFA around calving may be the
increased needs of the animal and fetus. As the fetus continues to grow throughout the
dry period, more energy may be needed for its maintenance and development. In addition,
as the mammary gland prepares for the upcoming lactation in the last week of gestation,
there may be an increased need for energy. Currently the National Research Council
(NRC) does not account for these possible changes in energy requirements in their

recommendations for the dry cow (NRC, 1989).

Plasma NEFA through Fatty Liver as a cause of health problems

A fatty liver is one that has become infiltrated with triglycerides. The liver may

take up a minor amount of TG from chylomicra in blood. In sheep, 10% of the plasma

10
pool of chylomicra is removed by the liver and 20% is removed in dogs (Bruss, 1993).

Thus, in a dry cow fed a typical diet (<5% fat), chylomicra likely are a minor source of
liver TG.

Because chylomicra are only a minor source of liver TG, a fatty liver is caused by
an increase of TG that come from the reesterification of NEFA There is very little fatty
acid synthesis within the ruminant liver, therefore the ruminant TG are formed from
plasma NEFA. Uptake of plasma NEF A by the liver is proportional to the NEFA
concentration in the blood (Bell et al., 1980) with a removal rate of 7-25% of the NEFA
presented to the liver (Emery et al., 1992). NEFA in the liver can be completely oxidized
to C02, incompletely oxidized to ketone bodies, or reesterified to TG (Bruss, 1993).
Because the ruminant liver does not export VLDL in appreciable amounts, the balance of
NEFA uptake and NEFA oxidation in the liver determines the degree of TG accumulation
in the liver. This leads to the conclusion that NEFA in plasma is the best indicator of the
accumulation of liver fat (Reid and Roberts, 1983; Roberts et al., 1981).

The mechanism whereby fatty liver might afi‘ect peripartum health is not clear,
except in the case of ketosis, because an increase in liver lipids is considered part of the
etiology of ketosis (Baird, 1982; Littledike et al.,1981). The best evidence for a link
between ketosis and fatty liver is the work conducted at Iowa State University using an
induced ketosis model (V eenhuizen et al., 1991). To induce ketosis, animals were fed at
restricted intake and fed 1,3-butanediol as a source of ketone bodies. Induction of ketosis
began 15 days postpartum and clinical ketosis occurred by 45 days postpartum. In

animals with induced ketosis, liver triglycerides on a wet weight basis increased fiom 2%

1 1
at 5 days after calving, to 10% at 14 days before ketosis occurred. The idea that fatty

liver precedes ketosis is supported by findings that showed animals that developed ketosis
during lactation already had a fatty liver at 1 day after calving (Grummer, 1993; Baird,
1982)

One possible means by which fatty liver may cause ketosis is through impaired
liver function (Mills et al., 1986). Impaired liver function is indicated by mitochondria that
seem malformed and cristae that are disordered and less distinct in the cells of a fatty liver
(Reid and Collins, 1980). Increased fat also decreases difi‘usion of cell metabolites
through the cell (Bruss, 1993 ), decreases the activity of gluconeogenic enzymes (Mills et
al., 1986), and decreases liver glycogen concentrations (Young et al., 1990). Perhaps

these changes in glucose metabolism are key in the etiology of ketosis.

Other possible connections of NEFA to health problems

Displaced abomasum may occur more frequently in animals that have ketosis
(Curtis et al., 1985; Markusfeld, 1986), but whether ketosis causes displaced abomasum
or if displaced abomasum causes ketosis, or both are caused by some other phenomenon is
not known. When ketotic animals reduce their intake, gut motility may be decreased,
which could result in displaced abomasum. On the other hand, an animal with a displaced
abomasum also would reduce intake and consequently might then become ketotic. In any
case, it seems likely that if prepartum NEFA are elevated in animals that have ketosis, then

animals that have displaced abomasum will also have higher prepartum NEFA.

12
Elevated prepartum plasma NEF A may also be related to a high incidence of some

peripartum health problems because high NEF A indicate that an animal is in poor nutrient
balance. Animals that are in poor nutrient balance may have a suppressed immune system
(Rhoads, 1980; Keusch, 1981; Dreizen, 1979; Gross and Newbeme, 1980). The immune
system is suppressed around parturition (W eigel et al., 1992), but whether nutrition plays
a role in this suppression is not known. Plasma cortisol which is elevated around
parturition (Horst and Jorgensen, 1982) and suppresses immune function (Burton et al.,
1995; Batcman et al., 1989; Roth, 1983), is increased during long-term malnutrition
(Dwyer and Stickland, 1992). Ifan animal is under stress as in the case of malnutrition,
perhaps the associated immunosuppression is a result of elevated of cortisol (Elvinger et
al., 1992).

An animal with a suppressed immune system is more susceptible to mastitis
(Weigel et al., 1992). In addition, a suppressed immune system may cause a higher
incidence of retained placenta (Cai et al., 1994 and Gunnink, 1984). The calf and the
associated fetal membranes are immunologically foreign to the animal, and suppression of
the immune system during pregnancy prevents the rejection of the calf (Billingham and
Beer, 1984; Jacoby et al., 1984). However after calving, the placenta and fetal membranes
must be rejected. If the immune system is suppressed at the time of calving, proper
immunological rejection and expulsion of these foreign tissues may not occur. If these
tissues are not expelled within 24 hours after calving, a case of retained placenta is said to
have occurred. If poor nutrient balance causes immunosuppression, then a positive
correlation of plasma NEFA and the incidence of mastitis and retained placenta might be

expected.

MATERIALS AND METHODS

Farm selection

On June 19, 1993, letters were sent to over 300 farms across Michigan to solicit
their involvement in a dry cow study. Letters were only sent to farmers that were enrolled
in the Dairy Herd Improvement Association (DHIA) and had a current herd average above
the Michigan DHIA average of 8760 kg (19300 lb.) of milk per cow per year. The
farmers were asked to fill out and return a questionnaire (Appendix A, Figure A. 1.) if they
wanted to participate in the study. Of the over 300 letters sent out, 118 questionnaires

were returned. Of these, 104 farms were visited.

Farm visits

The 104 farms were divided between the investigators Roy Emery (42), Paul Dyk
(3 2), Herb Bucholtz (23 ), and Richard Cameron (7). Each investigator was responsible
for collecting and recording information on the assigned farms. Each farm was visited 4
times within a 6 week period with a minimum of 6 days between each visit. The farm
visits occurred between October 27, 1993, and January 25, 1995.

At the first visit, the investigator recorded management and nutrition information
on standardized sheets (Appendix B, Figure B. 1.). However, for farms (73 of the 104
farms) visited for the first time after May 1, 1994, an improved version of this sheet which

was more specific was used (Appendix B, Figure 3.2.).

13

14
At the first visit, all Holstein cows and heifers that were within 5 weeks of calving

were identified and restrained. At some farms, identification or restraint of some animals,
usually heifers, was not possible; these animals were excluded fi'om the trial. A body
condition score (BCS) was determined on a scale of 1 to 5, with 5 being very fat and 1
being very thin (Wildman et al., 1982). Prior to the initiation of the trial, investigators met
to standardize their body scoring technique using a chart fiom Elanco Products Company
(Indianapolis, IN, USA). In addition to BCS, blood was sampled via a tail vessel.
Samples were put into ice within 15 minutes, brought back to the lab, and centrifuged at
3000 rpm for 15 minutes. Plasma was siphoned Off and put into labeled plastic tubes and
stored at -20 °C.

At the conclusion of the first visit the farmers were given sheets to record health
information on animals involved in the study. These sheets were also revised (after the
first 31 farms) to improve recording of dates for diseases (compare Appendix C, Figures
Cl. and C2).

At the second, third, and fourth visit, BCS was evaluated and blood was sampled
from animals within 5 weeks of expected date of calving. The animals on the second, third
and fourth visit included animals from the previous visit that had not calved, and any other
animals that were within 5 weeks of expected date of calving. Changes in management or

nutrition were also recorded.

Lab method for NEFA analysis

Samples were analyzed for NEF A using a commercially available kit (NEFA-C kit,

Wako Chemicals USA, Richmond, VA) with modifications by McCutcheon and Bauman

1 5
(1986), Sechen et al. (1990), and Johnson and Peters (1993). The kit was an enzymatic,

colorimetric test, where NEFA in the presence of adenosine triphosphate (ATP),
coenzyme A (COA) and acyl-COA synthetase (ACS) forms Acyl-COA and the by-products
adenosine monophosphate (AMP) and pyrophosphate (PPi). Acyl-COA is then oxidized
by acyl-COA oxidase (ACOD) to produce 2,3-trans-enoyl-COA and hydrogen peroxide
(HzOz). H202 is combined with 3-methyl-N-ethyl-N-(B-hydroxyethyl)—aniline (MEHA), 4-
aminoantipyrine and peroxidase (POD) to give the final purple product which is a quinone.
This final product can be measured colorimetrically at 550 nm. The chemical basis is

represented by the following reactions:

NEFA + ATP + CoA AC5 > Acyl-CoA + AMP + PPi

 

 

Acyl-COA + 02 ACOD > 2,3-trans-enovl-COA + H202

2H202 + MEHA + 4-aminoantipyl'ine __.POD 121:“ P538201; )(Purple Qumone)

The NEFA kit included the following:

1) 6 vials of Color Reagent A (CRA) - ACS (3 U/vial), AOD (30 U/vial), CoA (7
mg/vial), ATP (30 mg/vial), 4-aminoantipyrine (3 mg/vial)

2) 1 bottle of CRA diluent - phosphate buffer (pH 6.9, SOmM), magnesium
chloride (3000 OM), surfactant, stabilizer

3) 6 vials of Color Reagent B (CRB) - ACOD (132 U/vial), POD (150 U/vial)

4) 1 bottle of CRB diluent - MEHA (1200 DM), surfactant

5) NEFA Standard - Oleic acid (1000 UM), surfactant, stabilizers

16
Each vial of Color Reagent A was diluted with 10 mL of Color Reagent A diluent and

13.3 mL of phosphate buffer (50 mM, pH 6.9) and stored at 4°C. Each vial of Color
Reagent B was diluted with 20 mL of Color Reagent B diluent and 33.3 mL of phosphate
bufl‘er and stored at 4°C. Working standards (1000, 500, 250 and 0 OM) were prepared
by diluting the NEFA standard provided in the kit with phosphate buffer.

The NEFA analysis used 96—well, flat-bottomed, polystyrene microtiter plates
(Corning Glass Works, Corning, NY). Five ILL of plasma or standard was pippetted into
the wells using a positive displacement pippetter. Figure 2 shows the layout of each plate
in the analysis. The left side (A1 to H1) was used for the working standards while the
bottom right cells (C12 to H12) contained internal standards of the analysis. These
internal standards represented one animal that had low plasma NEFA (Cow A or Cow C)
and one animal that had high plasma NEFA (Cow B or Cow D). The internal standards
were used as a check on intra-assay and inter-assay variation; the values from the internal
standards were not used to adjust NEFA concentrations. All standards were pippetted
after all the plasma samples had been pippetted. Plasma samples were pippetted into the
plate in duplicate starting at well A2, proceeding to H2, then A3 to H3, A4 to H4, etc.

Plates were placed in 4°C while other plates were being pippetted.

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Before each assay, specific farms were chosen and all samples fi'om each farm

were removed from the freezer and thawed at 4°C overnight or under cold tap water
(15°C). Samples were kept at 4°C during the pippetting. Two data sheets were generated
for the assay. One sheet (see example in Appendix D, Figure D. 1.) was identical to layout
of the plate and contained all the information for each sample for each well. This
information was identical to the information on the label of the plasma sample. The other
sheet (Appendix D, Figure D.2.) was placed underneath the plate during the pippetting
and only had the cow number for each well. This procedure was used to ensure that the
proper sample was placed in the appropriate well.

Afier all samples were pippetted, 100 uL of the Color Reagent A was added to
each well. The plate was then shaken in the microtiter plate reader and placed at room
temperature. After 30 minutes, 200 uL Color Reagent B was added to each well, and the
plate again was shaken in the microtiter plate reader (Biolog Microstation, Biolog Inc.,
Hayward, CA) and placed at room temperature. After 30 minutes, the absorbence of light
at 550 nm for each sample was measured in the microtiter plate reader. A regression
equation was set up using the working standards from all the plates in the assay from that
day. From this equation, light absorbence of the plasma samples were used to estimate
NEFA concentrations. Any plasma samples that had replicates with a coefficient of
variation greater than 20% were reanalyzed.

Some blood samples had hemolyzed because of the extreme cold conditions in the

winter of 1993-94. The ice chests that held the samples at the farms became so cold that

19
the blood samples froze. The NEFA of these samples were analyzed and adjusted using

procedures outlined by the NEFA kit.

Raw data editing and summarization

Information from the farms and NEF A analysis was entered into Microsoft Foxpro
2.6a for Windows? Data were entered into three databases in Foxpro. One database was
for the health and DHIA information for individual cows; the database was set up like the
health sheets for ease of data entry. A second database had descriptive parameters of the
farm such as date of visits, DHIA farm numbers etc. The third database contained
information pertinent to the date of the visit such as BCS, NEFA, date of lab analysis etc.

The health problems that were monitored in this study were: twins, dystocia,
retained placenta, milk fever, ketosis, displaced abomasum, and mastitis. Health problems
were defined in the following way:

a) an animal was considered to have had twins, a retained placenta, milk fever,
ketosis, or a displaced abomasum if it was reported on the health sheet the farmer filled
out (Appendix C). Whether or not the farmer reported when the disease occurred did not
matter.

b) an animal was considered to have had dystocia if the farmer reported a 4 or 5 in
the calving difficulty column of the health sheet (Appendix C). A 4 was designated as a
hard pull and a 5 was designated as surgery.

c) an animal was considered to have had peripartum mastitis if the farmer reported
that the animal had mastitis within the first 10 days. If the farmer did not report when a

case of mastitis occurred, the mastitis case was ignored.

20
d) an animal was considered to have general mastitis if the farmer reported that the

animal had mastitis within the first 60 days. Ifthe farmer did not report when a case of

mastitis occurred, the mastitis case was ignored.

The Four Databases used for statistical analysis

For the statistical analysis there were four sets of data that were used. The raw
data were edited to develop a general database of all animals. Animals that had twins
were removed to generate another database. From these two databases, the other two
databases were derived by removing animals that did not have PTA... value (predicted

transmitting ability for milk). The relationships of the databases can be summarized as

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

follows:
Raw Data
Initial
editing
WT Database
(With Twins Database)
Twins
Animals with no Removed
PTA... removed
NT Database
WT&PTA... Database (N 0 Twins Database)
(With Twins & PTA Database)
Animals with
no PTA...
NT&PTA... Database

 

 

(N o Twins & PTA Database)

 

2 1
WT Database

This database included all animals with and without twins. This database will be
designated as the “WT Database” which stands for With Twins Database, To arrive at
the WT database, the following groups of animals were removed from the raw database:

a) animals without a reported calving date

b) animals without a plasma sample between -14 and 0 days prepartum

c) animals that did not have a DHIA animal identification number

(1) animals that were not the following ages at calving (N gwerume, 1994):

1) between 18 and 36 months for first parity animals
2) between 30 and 48 months for second parity animals
3) greater than 42 months for third or greater parity animals

c) any animals that had identical DHIA cow numbers

WT&PTA.. Database

The next database was formed by using the WT Database and removing any
animals that did not have a calculated or parent average for PTA... This database, made up
of only animals with valid PTA... values, will be designated as “WT&PTA... Database”
which stands for With Twins & PTA... Database. The calculated and parent averages of
PTA... were obtained from the last DHIA records before 100 days postpartum. Parent
averages were not calculated in cases where the dam and/or sire were missing a PTA...
value. An additional 1095 lb. was added to the PTA... values that were reported after
January 4, 1995, because of the USDA genetic base change (from New and Improved

Genetic Evaluations January 1995, Pamphlet of National Association of Animal Breeders

22
P. O. Box 1033, Columbia, Missouri, 65205-1033, USA). All animals that did not have a

calculated or parent average for PTA... greater then -174 lb. (20 animals) were removed
because they represented outlying animals that could not be incorporated into the
statistical analysis. The WT Database and WT&PTA... Database were only used for

analyses studying twins directly.

NT Database

For all other statistical analyses, animals that had twins were excluded. This led to
the third database where animals bearing twins were removed from the WT Database.
This database was designated as the “NT Database” which stands for N o Twins
Database. The objective of the study was to determine nutritional and managerial factors
in the peripartum period that would affect the incidence of health problems. Animals
bearing twins would have skewed results and led to incorrect conclusions because they

had much higher NEF A in plasma and lower BCS.

NT&PTA Database
For the final database, animals that did not have valid PTA... values were removed
from the NT Database. This fourth database was designated “NT&PTA... Database” which

stands for No Twins & PTA Database. The criteria for the PTA... values were the same

as in the development of the WT&PTA database.

23

Statistical Analysis
Disease analyses using Cochran-Mantel-Haenszel Statistics

Cochran-Mantel-Haenszel (CMH) statistics in Statistical Analysis System (SAS,
1991) were used to study the relationship of parity, NEFA concentration, BCS, and PTA...
to health problems because health data was categorical. However, because CMH statistics
make use only of categorical data, it was necessary to categorize any continuous data.
Parity was a) l, b) 2, c) 3 and greater as derived from DHIA records. The BCS from each
animal over the last 2 weeks prepartum was averaged and grouped in the following way:

a) Very Low BCS Group - BCS S 2.75

b) Low BCS Group - BCS >2.75 and 53.25

c) Medium BCS Group - BCS>3.25 and BCS<4

d) High BCS Group 4 - BCSZ4
For all analyses with PTA... the following PTA... Groups were used:

a) Low PTA... Group = PTA... 2-174 lb. and <826 1b.,

b) Medium PTA... Group = PTA... 2826 lb. and <1826 1b., and

c) High PTA... Group = PTA... 21826 1b..

NEFA values could not be categorized the same way because NEFA values were
strongly affected by days prepartum. To compare NEFA values, each plasma NEFA value
for each animal was expressed as a percentage of the average NEFA for all animals from
the same day prepartum (Figure 3). If an animal had two samples from the last 2 weeks,
the average of these percentages was used as the value for that animal. Three NEFA

groups were then formed:

24
a) Low NEFA Group - cows with average NEFA percent below 75.

b) Medium NEFA Group - cows with average NEFA percent between 75 and 125.

c) High NEFA Group - cows with average NEFA percent above 125.

Mix

.41

NEFA in plasma (uM)

 

-14 -12 -10 -8 -6 -4 -2 0
Days Prepartum

Figure 3. Plasma NEFA in NT Database. Each sample from each cow was expressed as
percent of the average of all animals on the same day prepartum.

25
Analysis of relationship of Parity to Disease

Using the NT Database, the relationship of parity to disease was studied with
CMH statistics in SAS. NEFA Group and BCS Group were controlled in one analysis and

not controlled in the subsequent analysis.

Analyses of relationship of BCS Group and NEFA Group to Disease

To look at the relationship of BCS Group and NEFA Group to disease incidence,
the NT Database was used. When analyzing the relationship of NEF A Group to disease
within parity, BCS Group was controlled; for the association of NEF A Group to BCS
across parities, BCS Group and parity were controlled. The association of BCS to disease
incidence across parities was also analyzed, controlling for NEFA Group and parity. (See
Appendix E, Figure B. l. for SAS program.)

To look at the relationship of PTA... Group and disease incidence, the NT&PTA...
Database was used. When analyzing the relationship of PTA... to disease, NEFA Group,
BCS Group, and parity were controlled; the relationship was also analyzed controlling
only for NEF A and BCS Group. Finally the relationship of PTA... and disease incidence

was analyzed, controlling for no other factors.

Analysis of relationships of NEFA, BCS, PTA.., Herd, and Parity
To look at the effect of BCS group, herd and parity on NEFA, the NT Database
was analyzed using the GLM (General Linear Models) option in SAS. Week -1 and week
-2 prepartum were analyzed separately. The concentration of plasma NEFA was used as

the dependent variable, and BCS Group, herd, and parity as independent variables. Day

26
prepartum was used as a covariate because of the sharp rise in plasma NEFA at calving.

To look at the efl‘ect of PTA... Group on NEFA, the same model was used except PTA...
Group was used as additional independent variable, and the NT&PTA Database was used.
To look at the effect of parity and herd on BCS, the GLM option in SAS was used
with the NT Database. BCS was used as a dependent variable with herd and parity as
independent variables. The effect of PTA. Group on BCS was studied using the
NT&PTA Database and the same model except PTA... was put in as an additional

independent factor.

Analysis of twinning

To look at the effect of twins on NEF A and BCS, the WT Database was used.
When the effect of twins on NEF A was analyzed the GLM procedure in SAS was used.
NEF A was the dependent variable, and BCS Group, herd, parity, and twins were put in as
independent variables. When the effect of twins on BCS was analyzed, BCS was used as
the dependent variable, and parity, twins, and herd were independent variables. To look at
the relationship of PTA. and twins, the CMH option in SAS was used on the WT&PTA
Database. Finally, to determine if disease incidence was higher in animals with twins, the

WT Database was analyzed using the Chi-Square analysis of SAS.

RESULTS

Database summary

Of the 104 farms visited, only 95 farms were used in the data analyses for this
thesis. The other farms were not used because health sheets were not returned or because
no animals fit the criteria for our analysis. The raw health database contained 2577
animals. Of these, 26 did not have calving dates, 192 did not have any blood samples, 119
could not be linked to a DHIA cow identification number, 26 animals did not meet parity
criteria, and 19 had insuflicient or incorrect DHIA information, such as multiple animals
with identical DHIA numbers. Of the remaining 2195 animals, 1655 had at least one
blood sample in the last 2 weeks (day -14 to day -1) prior to parturition. These 1655
cows were the base of the analyses (WT Database) for this thesis. Ninety-nine of these
animals had twins and were not used for analyses involving single births (NT Database).
Of the remaining 1556 animals, 1093 (z70%) had valid PTA... values (WT &PTA...

Database).

Prepartum NEFA concentrations in plasma

In analyzing the raw data, plasma NEFA concentration did not vary until about 2
weeks before calving (Figure 4). From -14 to -4 days prepartum, plasma NEFA

concentration increased fi'om 300 1AM to 400 M. Then from day -4 to day 0, plasma

27

28
NEFA concentration doubled to 800 M. Plasma NEFA concentration was lowest for

parity 2 animals but increased significantly in the last days before calving for all parities
(Figure 5).

900

 

800

 

700
g...)

500

 

 

 

 

 

 

§

 

 

 

NEFA in plus
or»
8

 

 

 

100

 

 

 

UU‘U'TYYf1 VVVVVVVVVVVVVVVVVV

-35 -28 -21 -14 -7 0

Figure 4. Plasma NEFA concentration before parturition averaged for animals in WT
Database.

29

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

900
800
+1 1234 les
700 ( sump )
-o- 2 (1545 samples)
v 600 + 3+ (1977 samples)
I’m.
/\ . /
€w“~ ’ ............................
-14 -7 0

 

Figure 5. Plasma NEF A concentration before parturition by parity in WT Database.

Relationship of BCS, PTA... Parity, Herd and Day prepartum to plasma NEFA

The efl‘ects of parity, herd, BCS Group, and day prepartum on the concentration of
NEFA in plasma were examined with the GLM procedure in SAS. NEFA was the
dependent variable and BCS Group, parity, and herd were blocks and number of days
prepartum was used as a covariate to account for the increase in plasma NEFA as animals
approached calving. Day prepartum was significant as a covariate only at week -1 (Table
1). Herd, parity, and BCS Group were significant at week -2 and week -1. It should be
noted that the variation in NEFA associated with the term “herd” accounts for variation
not only in on-farm management and diets, but also for interassay variation among NEFA

assays, time of the year, and time the sample was taken relative to feeding. Prirniparous

30
animals had higher NEF A than the other parity groups while parity 2 animals had the

lowest plasma NEF A in both week -2 and week -1 (Table 2). The lowest and highest
BCS Groups had the highest NEFA in plasma in both week -2 and week -1 (Table 2).

In another analysis, the GLM procedure was used with the NT&PTA... Database.
Again, NEF A was the dependent variable. Parity, BCS Group, PTA... were used as blocks
and day prepartum was used as a covariate. For week -2, PTA... was not significant in the
model (Table 3). At week -1, the means for PTA“ showed the same trend as week -2
(Table 2) and approached statistical significance at P=.09 (Table 3). In this model, BCS
group was not significant at week -2 or week -1; possibly because there were fewer

animals in the dataset.

31

Table 1. Factors afl‘ecting NEFA in plasma at week -2 and week -1 (NT Database)

 

 

 

 

Source Degrees of Type 111 SS Mean Square Probability
Freedom

Week -2
Days prepartum 1 1551 1551 0.8398
Lactation 2 602366 301183 0.0004
Herd 94 18650324 198408 0.0001
BCS Group 3 320492 106831 0.03 82
Lactation*BCS Group 6 284899 47483 0.2776
Error 957 36318777 37951
Total 1063 56178409

Week -1
Days prepartum 1 4179062 4179062 0.0001
Lactation 2 1773207 886604 0.0001
Herd 94 26457305 281461 0.0001
BCS Group 3 587288 195763 0.0650
Lactation*BCS Group 6 467257 77876 0.4503
Error 1035 83845951 81011
Total 1141 117310070

* Probability of a greater F value occuning by chance

32

Table 2. LS Means for NEF A concentration by parity BCS, and PTA...

 

 

Week -2 Week -1
n NEFA n NEFA
(LSMeans) (LSMeans)
Effect of Parity
Parity 1 304 338 360 442
Parity2 351 244 335 319
Parity 3+ 409 297 447 425
Effect of BCS
Very Low BCS 102 312 107 440
Low BCS 246 274 259 353
Medium BCS 448 270 493 374
High BCS 268 317 283 415
Eject of PTA
Low PTA 183 31 1 188 426
Medium PTA 47 5 287 51 l 371

High PTA 100 272 106 3 52

33

Table 3. Factors afl‘ecting NEF A at week -2 and week -1 (NT&PTA... Database)

 

 

 

 

Source Degrees of Type IH SS Mean Square Probability
Freedom

Week -2
Days prepartum 1 829 829 0.8841
Parity 2 663066 331533 0.0002
Herd 82 13871243 169162 0.0002
BCS Group 3 151591 50530 0.275
PTA Group 2 80170 40085 0.3585
Error 667 26020052 3901 1
Total 757 40786951

Week -1
Days prepartum 1 1867329 1867329 0.0001
Parity 2 1901948 950974 0.0001
Herd 84 18146779 216033 0.0001
BCS Group 3 190283 63428 0.4925
PTA Group 2 373 92 18696 0.093 1
Error 712 56254902 79010
Total 804 78398633

* Probability of a greater F value occurring by chance

34

Relationship of PTA.., Parity, Herd and Day prepartum to BCS

To look at the effect of PTA..., parity, and herd on BCS, the GLM procedure in
SAS was used with BCS as the dependent variable. For one analysis the NT&PTA
Database was used with PTA... Group, parity, and herd as blocks (Table 4), all of which
were significant in the model (P<.01). Animals in the Low PTA... Group had significantly
higher BCS than animals in the other PTA... Groups. In a second analysis, the NT
Database was used with BCS as the dependent variable, and parity and herd as blocks; this
analysis was done to take advantage of the higher number of animals in the NT database.
In this analysis, herd and parity were significant (P<.01), and primiparous animals had the

highest BCS at 3.60, then parity 3+ animals at 3.37, and finally parity 2 animals at 3.23.

Table 4. Effect of PTA... Group and parity on BCS for the 2 weeks prepartum. PTAm and
parity had a significant effect (P<.01) on BCS.

n Mean BCS SEM
(LSMeans) (LSMeans)

Effect of PTA Group
Low PTA Group 264 3.50 .037
Medium PTA Group 755 3.37 .023
High PTA Group 152 3.31 .045
Effect of Parity
Parity 1 492 3.60 .024
Parity 2 525 3.23 .023

Parity 3+ 638 3.37 .021

35
Time frame of diseases

At the beginning of the study, many farmers were not recording the date on which
a health problem occurred. This led to a revision of the health sheet (Appendix C).
Farmers recorded the date of a disease incident 62% of the time for retained placenta,
67% of the time for milk fever, 79% of the time for ketosis, and 83% of the time for
displaced abomasum. These diseases were reported to have occurred within 60 days after
parturition. Of the 192 cases of mastitis, 160 had a date reported. Of these 160 cases
with reported dates, 136 occurred within the first 60 days and 102 occurred within the
first 10 days. Using animals from the WT Database, ketosis, displaced abomasum, and
mastitis occurred an average of 7 i 0.5, 12 i 0.7, and 47 i 4 days postpartum

respectively (see Figure 6).

36

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37

Association of Disease and Parity

Cochran-Mantel-Haenszel (CMH) statistics in SAS and the NT Database were
used to examine the relationship of parity and disease. Primiparous animals had a
significantly higher incidence of dystocia while multiparous animals had a significantly
higher incidence of retained placenta, milk fever, ketosis, and general mastitis (Figure 7).
Parity and disease were not significantly associated for displaced abomasum and

peripartum mastitis.

20
I Parity 1 (n=486)

DPari 2 n=480
P1‘<.01 ty ( )

P <_o 1 =
16 P2b<m 1 1 P1<.01IPar1ty 3+ (n 590)
IA11(n=1556)

P2<.01 p2=.()9

v—Ir—o
Nb

Disease Incidence (%)
oo 8

NACh

 

Dystocia Retained Milk fever Ketosis Displaced Peripartum General
Placenta Abornasum Mastitis Mastitis

Figure 7. Incidence of disease by parity.

' P value for the association between disease and parity, controlling for NEFA Group and
BCS Group.

b P value for the association between disease and parity, controlling for no other factors.

38

Association of Disease and NEFA Group among Parities

The incidence of disease and association with NEFA Group was investigated using

CMH statistics and the NT Database. Across parities, the incidence of dystocia, retained

placenta, ketosis, displaced abomasum, and mastitis was higher in animals in the High

NEFA Group (Figure 8). The incidence of milk fever across all parities was not different

 

 
 

 

among NEFA Groups.
20
p 1<.01 I Low NEFA Group (n=741)
18 - ------------------------------- P1=-03 -pz<.01 . El Medium NEFA Group(n=435) ----------
16 ”=04 High NEFA Group (n=380)

H
A

"’P1“<.01
«P2b<.01

fl
ON

Disease Incidence (%)
m lid

 

Dystocia
Placenta

  
     
     
   

Retained Milk fever

 

I All (n=1556)

 

P1<.01

 

Ketosis

P2<.01
P1<.01

"""" P2<.01

 

Displaced Peripartum General

Figure 8. Disease incidence within NEFA Group across parities

" P value for the association between disease and NEFA Group, controlling for Parity and

BCS Group.

b P value for the association between disease and NEF A Group, controlling for no other

factors.

39

Association of Disease and N EFA Group within Parity

Primiparous animals in the high NEF A Group had a significantly higher incidence
of dystocia than animals in the low NEF A Group. However, for parity 2 and 3+, dystocia
was not significantly different among NEF A Groups (Table 5).

When sorted by parity, retained placenta and NEF A Group were associated
significantly only for parity 3+; animals in the high NEFA group had an incidence of
22.5% for retained placenta while the medium and low NEFA Groups had incidences of
13.0% and 13.7%, respectively.

The high NEF A Group for primiparous animals had a significantly higher incidence
of milk fever than the other NEF A Groups. But primiparous animals had very few cases
of milk fever compared to multiparous animals. For multiparous animals, NEFA Group
and the incidence of milk fever was not significantly associated.

For parity 1 and 2 animals, the high NEF A Group had an incidence of ketosis
about 2.5 times that of the low NEFA Group. For parity 3+, the incidence of ketosis also
increased as plasma NEF A increased, but this association was not statistically significant.
The high incidence of displaced abomasum in the high NEFA Group was 4 times that of
the low NEFA Group for parity 2 and 3+. However for primiparous animals, the
incidence of displaced abomasum was not different among NEFA Groups. The incidence
of mastitis was significantly higher in the high and medium NEF A Groups in all parities

than in the low NEFA Group for all parities.

40

Table 5. The association of disease incidence and NEFA Group by parity.

 

 

 

 

 

 

 

 

Dystocia Retained Milk Ketosis Displaced Peripartum General
Placenta fever Abomasum Mastitis Mastitis
Parity 1
Incidence of Disease (% of NEFA Group)
Low NEFA 8.1 8.7 0.0 5.8 4.1 3.5 3.5
Medium NEFA 15.2 10.1 0.0 8.9 6.3 6.6 7.6
High NEFA 20.0 10.3 1.3 13.6 7.7 9.0 9.7
Significance of General Association
P1 " P=.01 P=.9 P=.06 P=.04 P=.5 P=.09 P=.06
P2 5 P=.01 P=.9 P=.12 P=.05 P=.4 P: 11 P=.07
Parity 2
Incidence of Disease (% of NEFA Group)
Low NEFA 2.4 8.3 1.4 8.9 3.1 3.1 4.8
Medium NEFA 3.5 10.4 0.9 13.0 7.8 8.7 10.4
High NEFA 2.7 12.2 1.4 23.0 13.5 8.1 10.8
Significance of General Association
P1 P=.8 P=.5 P=.9 P=.01 P<.01 P=.04 P=.05
P2 P=.8 P=.5 P=.9 P<.01 P<.01 P=.04 P=.06
Parity 3+
Incidence of Disease (% of NEFA Group)
Low NEFA 2.9 13.7 14.4 11.9 3.3 4.7 6.9
Medium NEFA 4.3 13.0 13.6 13.0 3.1 6.2 10.5
High NEFA 4.6 22.5 11.9 17.2 11.3 12.6 15.9
Significance of General Association
P1 P=. 7 P=. 02 P=. 7 P=.3 P<.01 P=. 01 P=. 01
P2 P=.6 P=.03 P=.8 P=.3 P<.01 P=. 01 P=. 01

 

' P values for the general association between disease incidence and NEFA Group,
controlling for BCS Group

b P values for the general association between disease incidence and NEFA Group,
controlling for no other factors.

 

41

Association of BCS Group and Disease Incidence

The association of BCS and Disease was studied using CMH statistics and the NT
Database. Across parities, the incidence of ketosis, and displaced abomasum was
significantly associated with BCS Group (Figure 9). As BCS increased, the incidence of
ketosis and displaced abomasum increased. There was no relationship between BCS
Group and the incidence of milk fever, dystocia, retained placenta, and mastitis. The
incidence of dystocia was significantly higher in the high BCS group when NEFA Group
and parity were not controlled; this significance is due to the fact that primiparous animals

have significantly higher BCS and a significantly higher incidence of dystocia. (Table 6)

42

20
l Very Low (n= 155)
18 [3 Low (n= 396)
g 16 Medium (n= 653)
.5 I High (n= 352)
8 14 I A11(n=1566)
ca
«5 12
e\° P1=.04
3 1° P2=.03
g s
E
o 6
a
2
0

 

Dystocia Retained Milk fever Ketosis Displaced Peripartum General
Placenta Abomasum Mastitis Mastitis

Figure 9. Incidence of disease by BCS Group across parities

' P value for the general association between disease and BCS Group, controlling for
Parity and NEFA Group.

b P value for the general association between disease and BCS Group, controlling for no
other factors.

43

Table 6. The association of disease incidence and BCS Group by parity.

 

 

 

 

 

 

 

 

Dystocia Retained Milk Ketosis Displaced Peripartum General
Placenta fever Abomasum Mastitis Mastitis
Parity 1
Incidence of Disease (% of BCS Group)
Very Low BCS 0.0 15.4 0.0 0.0 15.4 7.7 7.7
Low BCS 10.7 9.3 2.7 8.0 1.3 12.0 13.3
Medium BCS 14.8 9.3 0.0 4.9 6.3 5.5 5.5
High BCS 16.2 9.9 0.0 9.3 6.8 5.0 5.6
Significance of General Association
Pl “ P=.3 P=.9 P<.01 P=.6 P=. 20 P=.14 P=.08
P2 ‘ P=.3 P=.9 P=. 01 P=.6 P=.16 P=.18 P=.11
Parity 2
Incidence of Disease (% of BCS Group)
Very Low BCS 2.9 11.4 1.4 10.0 1.4 4.3 7.1
Low BCS 2.4 10.2 0.0 9.6 5.4 4.8 7.2
Medium BCS 2.7 9.1 1.1 13.9 7.0 5.9 7.5
High BCS 3.6 5.4 5.4 16.1 8.9 5.4 5.4
Significance of General Association
P1 P=.9 P=.6 P=. 02 P=.6 P=.4 P=.9 P=.9
P2 P=.9 P=. 7 P=. 02 P=.4 P=.3 P=.9 P=.9
Parity 3+
Incidence of Disease (% of BCS Group)
Very Low BCS 4.2 12.5 12.5 5.6 2.8 6.9 6.9
Low BCS 1.3 20.8 7.8 9.1 2.6 3.9 8.4
Medium BCS 3.5 15.7 17.0 14.9 5.7 9.2 11.4
High BCS 6.7 11.9 14.8 20.7 8.9 7.4 11.9
Significance of General Association
P1 P=.14 P=.10 P=.09 P<.01 P<.10 P=.3 P= 6
P2 P=.12 P=.] 7 P=. 21 P=. 02 P<.08 P=.3 P=. 6

 

‘ P values for the general association between disease incidence and BCS Group,
controlling for NEF A Group

b P values for the general association between disease incidence and BCS Group,
controlling for no other factors.

 

Association of PTA. and Disease

To study the relationship of PTA... and disease, CMH statistics were used once
again. In a series of analyses, the relationship between PTA... and disease was studied,
controlling for parity, NEFA Group, and/or BCS Group. Ketosis and PTA..., controlled
for NEFA Group and BCS Group, were significantly related at P=.08 in primiparous
animals. The low PTA... Group (n=6) had an incidence of ketosis of 11%, the medium
PTA... Group (n=202) had an incidence of 8.4%, and the high PTA... Group (n=62) had an
incidence of 17.7%. Ketosis and PTA..., controlled for NEFA Group and BCS Group,
were not significantly related in multiparous cows. The remaining diseases and PTA...,
controlled for BCS Group and NEFA Group, were not significantly associated within a
parity group or across all parities.

When the relationship between PTA... and disease was tested within and across all
parities, controlling only for BCS group, no significant associations were found. When
the relationship between PTA... and the incidence of disease was studied, controlling for no

other factors, no significant associations were found within or across parities.

45
Results from twin analysis

Of the 1655 animals in the WT Database, there were 99 animals that had twins
(6.0% incidence). The incidence of twins among parity 1, 2, and 3+ animals was 1.2%,
8.5% and 7.5% respectively. Using the Chi-square statistic, first parity animals had
significantly (P<.01) fewer cases of twins than second and greater parity animals.

To determine the effect of twins on BCS and NEF A, the GLM procedure of SAS
was used. In one analysis, NEFA was the dependent variable, herd and parity were
blocks, and day prepartum was used as a covariate. In the second analysis, BCS was the
dependent variable, and herd and parity were blocks. Animals with twins had significantly

higher plasma NEFA and lower BCS (Table 7).

Table 7. Plasma NEFA and BCS in cows that did and did not have twins

 

 

 

 

 

 

 

 

 

 

 

 

Week
-2 f -1
n [NEFA lacs n “NEFA [BCS
[(LSMeans) 1(LSMeans)
NoTwins 1064 ml 3.41 1142 382[ 3.39]
Twins 66 534| 320' 79 741] 3.11]
P< 0.01| 0.01| 0.01| 0.01|

 

 

 

Figure 10 shows the incidence of disease in all animals, comparing animals that had
twins to animals that did not have twins using a Chi-Square analysis. Animals that had
twins had a significantly higher incidence of retained placenta, milk fever, ketosis, and

displaced abomasum. However, because multiparous cows have a higher incidence of

46
twins, and diseases in general, the higher disease incidence may only be related to parity

and not the incidence of twins. This is the case for milk fever. Figure 11 shows the
incidence of disease in parity 3+ animals separated into twins and no twins. In the parity
3+ group, animals that had twins had a significantly higher incidence of retained placenta,
ketosis, and displaced abomasum; however, the incidence of milk fever was not difl‘erent.

Using CMH statistics and the WT&PTA... Database, PTA... was associated
significantly with twins, when controlling for parity (P=.064). In parity 2, the incidence of
twins was 5.6%, 9.3%, and 9.3% in PTA... Groups 1, 2, and 3, respectively. In parity 3,
the incidence of twins was 4.8%, 10.4%, and 10.6% in PTA... Groups 1, 2, and 3,

respectively.

60

 

P<.01
50

 

 

 

INo Twins (n=1556)
I Twins (n=99)

 

 

40

 

 

 

 

30

 

 

Disease incidence

20

 

 

 

10 -—

 

 

 

 

Dystocia Retained Milk fever Ketosis Displaced Peripartum General
Placenta Abomasum Mastitis Mastitis

Figure 10. Incidence of disease in animals (n=1655) after giving birth to single or twin
calves.

deuce

rsease' mcr

D

Figure 11. Incidence of disease in parity 3+ (n=63 8) after giving birth to single or twin

calves.

47

 

P<.01

 

 

 

 

 

E No Twins (n=590)

I Twins (n=48)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DISCUSSION

Peripartum health problems have caused economic losses on dairy farms for many
years. Despite many years of research, the causes of most health problems are still unclear.
This study attempted to shed some light on the critical transition period around
parturition. The hypothesis of this study was that elevated prepartum NEF A
concentration in plasma and fatter body condition are positively associated with more
health problems. The results of this study are consistent with this hypothesis. In addition
to relationships of NEFA and BCS to health problems, relationships among parity, BCS,

and PTA... were analyzed and integrated into our understanding of the peripartum period

(Figure 12).

Effects of Parity on NEFA, BCS and Disease

Plasma NEF A concentration increased over the last few weeks of gestation with a
large surge at the time of calving as seen by others (Grummer, 1993; VandeHaar et al.;
1995b; Simmons, 1993). In this study, mean plasma NEFA concentration was difl‘erent
among parities; however, all parities showed a similar surge at calving. Parity 2 animals
had the lowest NEFA concentration for the 2 weeks before calving followed by parity 3+,

and parity 1 animals.

48

49
Interestingly, parity 2 animals which had the lowest NEF A, also had the lowest

BCS. Parity 1 animals which had the highest NEFA, also had the highest BCS. How
parity influenced BCS is not clear from this study; however, there are several possibilities.
Lactating animals may partition less energy toward adipose tissues than non-lactating
animals; this would explain why primiparous animals in this study had higher BCS.
Perhaps parity 2 animals had lower BCS because they were still growing, as well as
producing milk, in the previous lactation. Parity 3+ animals, although they generally
produce more milk, are not growing, and because of their larger body size, they may have
had higher feed intake and thus partitioned more energy to adipose tissue in the previous
lactation.

Not only did NEFA and BCS difi‘er among parities, but the incidence of disease
was also different among parities. Primiparous animals had a greater incidence of dystocia
as shown by Manfredi et al. (1991). We also found that multiparous animals have a higher
incidence of retained placenta, ketosis, and milk fever as others have observed
(Emanuelson et al., 1993; Sjollema and Van Der Zande, 1923; Shaw, 1956; Mantysaari et
al., 1991). However, multiparous animals did not have a higher incidence of displaced
abomasum or mastitis as previously observed (Constable, 1992; Emanuelson, 1993).
Perhaps the incidence of displaced abomasum was not different among parities in our
study because we did not account for corrective surgeries in previous lactations. Previous
corrective surgery probably caused us to underestimate the normal risk of multiparous

animals for displaced abomasum.

50

Relationship of NEFA concentration and Disease

Within and among parities, animals with concentrations of plasma NEF A 25%
above average had a higher incidence of health problems. The mechanism for this
relationship may be through triglyceride (TG) accumulation in the liver or through
immunosuppression associated with poor nutrient balance.

High NEFA levels lead to fat infiltration of the liver (Bell, 1980), so animals with
higher NEF A in this study probably had more TG in their livers. Whether this excess fat
in the liver caused the higher incidence of ketosis is not clear. Although there have been
many studies that associate ketosis and fatty liver, a clear cause and effect relationship has
not been established (Grummer, 1993). The most likely explanation for how fatty liver
could cause ketosis is the that fatty liver impairs liver function. Proper liver firnction,
especially gluconeogenesis, is critical for a cow in early lactation in which glucose flux is
4-5 kg/day (V andeHaar, 1988). Data from Mills et al. (1986), showing decreased
gluconeogenic potential in the liver of ketotic animals, supports the idea that fatty liver is
the direct cause of ketosis.

In general, when plasma NEFA are taken up by the liver they can be incompletely
oxidized to ketones, completely oxidized to C02, or reesterified to TG. Since VLDL
synthesis is limited in the ruminant, any TG formed in the liver tends to accumulate. Our
study shows that there is a moderate rise in NEFA concentration over the last 2 weeks
before parturition. This suggests that the development of fatty liver may occur over this 2
week period. This is supported by VandeHaar et al. (1995b) who found that feeding

higher energy diets 3 weeks before calving resulted in greater energy intake, lower plasma

5 1
NEFA, and less TG in the liver at calving. On the other hand, cows that eat less before

calving may simply eat less after calving and thus be more susceptible to ketosis.

Although our data shows that prepartum NEFA concentrations were higher in
animals that later developed displaced abomasums, this may not be a direct relationship.
One indirect way that the incidence of displaced abomasum could be related to prepartum
NEF A concentrations is through the effects of prepartum NEFA on the incidence of
ketosis. Displaced abomasum is positively related to the incidence to the incidence of
ketosis (Markusfeld, 1986; Curtis et al., 1985). Descriptors of ketosis include decreased
appetite and impaired nerve function (Baird, 1982; Littledike et al., 1981; Sjollema and
Van Der Zande, 1923). Either of these could perhaps lead to decreased gut motility which
may lead to a higher incidence of displaced abomasum. This is supported by our study
where displaced abomasum happened an average of 5 days after the incidence of ketosis
and over 50% of the animals that had a displaced abomasum also had a case of ketosis.
Of the 40 animals with displaced abomasum, ketosis, and a date for each disease, 36 cases
of ketosis happened on the same day or before the day that displaced abomasum was
diagnosed.

The high NEFA concentration was also highly associated with retained placenta.
One possible mechanism for this is through the effects of nutrient balance on immune
system. In this case, the elevated NEFA may have no direct effect on immune function
but rather serve as an indicator of poor nutrient balance. Recently, retained placenta has
been correlated to a decrease in immune function of the animal (Cai et al., 1994; Gunnink,

1984). Ifthe animals that developed retained placenta were in poor nutrient balance

52
before calving (as indicated by higher plasma NEFA), their immune systems might have

been compromised at the critical time for ejecting fetal membranes alter parturition.
Irnmunosuppression has been observed at the time of calving (Detilleux et al., 1994b), but
this may be due to altered hormone concentrations, especially increased cortisol (Burton et
al., 1995). Whether the suppression is greater in cows that are in poorer nutrient balance
before calving is unknown, but it seems likely because malnutrition causes
immunosuppression in other species (Keusch, 1981; Rhoads, 1980; Driezen, 1979; Gross
and Newberne, 1980). Possible effects of prepartum nutrition on immune function at the
time of calving also explain the association between prepartum NEFA and mastitis.
Mastitis which is an infectious disease is more likely to occur when the immune system is
suppressed (W eigel, 1992), and in fact, the increased incidence of mastitis of animals with
elevated NEF A in my study is a good indication that poor prepartum nutrient balance does
cause immunosuppression. This connection to the immune system may be why animals
with retained placenta are more likely to develop mastitis (Schukken et al., 1989;
Emanuelson et al., 1993).

In the case of dystocia, it is difficult to understand the link between increased
prepartum plasma NEF A and a higher incidence of problems. The simplest and probably
most likely explanation is that an animal in poor nutrient balance has a more difficult time

making it through the stressful birthing process.

Relationship of BCS and Disease

Animals with higher BCS had a higher incidence of ketosis and displaced

abomasum, even when we controlled for NEFA Group in this analysis. This suggests that

53
BCS affects disease incidence independent of prepartum lipid mobilization. Perhaps cows

with high BCS ate less after calving and thus had more cases of ketosis and displaced
abomasum. An association of high BCS and low postpartum DMI has been shown by
some researchers (Treacher et al., 1986; Garnsworthy and Topps, 1982) but not by others

(Holter et al., 1990).

Causes of elevated prepartum plasma NEFA

There are several factors that affect prepartum NEFA concentrations. One such
factor is BCS. We found that, within parity, animals with higher BCS had higher NEFA.
Furthermore, BCS and NEFA were both lowest in parity 2 cows and highest in parity 1.
The reason that cows with high BCS also had high NEFA is likely that they ate less. High
BCS is associated with reduced feed intake postpartum (Treacher et al., 1986;
Garnsworthy and Topps, 1982) and prepartum (V andeHaar et al., 1995b). This decline in
DMI would increase lipid mobilization resulting in elevated plasma NEFA.

Certainly factors in addition to BCS also affect the plasma NEFA concentration.
Some other factors that may alter prepartum NEFA include feeding management, diet

quality, housing, temperature, and stress in the last 3 weeks before calving.

Effect of PTA. on disease, BCS, and NEFA

To ensure that the relationship between NEFA and disease was not an artifact of
genetics in our dataset, we looked at the efl‘ect of PTA... on disease, BCS, and NEFA.
On the one hand I expected a positive relationship between PTA... and incidence of

disease. Many studies show a link between high disease incidence and high level of milk

54
production (Jones et al., 1994; Detilleux et al., 1994a; Sirnianer et al., 1991). However,

the only relationship of disease and PTA... that was seen in this study was that primiparous
animals with high PTA... developed more ketosis. This relationship is supported by a study
with 28,000 Finnish Ayrshire primiparous cows, in which ketosis and milk yield were
correlated positively. As in our study, no relationship between ketosis and milk yield
existed in multiparous animals (Deluyker, 1991). Perhaps high-producing primiparous
animals are more likely to eat insufficient feed for their requirements for both milk
production and body growth, and thus develop more ketosis. Despite the fact that the
high PTA... group had significantly lower BCS and lower NEFA than the low PTA... group,
no relationship between PTA... and the incidence of any disease was observed, perhaps

because of the smaller database (NT&PTA Database) with the PTA... analyses.

Potential problems with this study

One possible weakness of this study is that health problems were diagnosed by
individual farmers rather than veterinarians or researchers. Despite the possibility of more
incorrect diagnoses, we expected the farmer diagnosis to be a more accurate estimate of
the incidence of health problems because farmers do not contact a veterinarian for many
health problems. In retrospect, this may have been a problem for ketosis, because farmers
did not have a clear description for diagnosis on the health sheets.

Farmer diagnosis of health problems also may have caused problems in
understanding the chronology of ketosis and displaced abomasum. Where animals had
ketosis and a displaced abomasum, most cases of ketosis were reported to have happened

before the occurrence of displaced abomasum. However, this may not be true. For

55
example, if a displaced abomasum occurred on April 1, the farmer may not have

recognized or recorded the displaced abomasum until April 5 (when a veterinarian
diagnosed the displaced abomasum). In the period between the actual occurrence of the
displaced abomasum and diagnosis/recording of the displaced abomasum, ketosis may
have developed. The farmer may have recorded the case of ketosis before April 5, thus
indicating that ketosis preceded displaced abomasum, when the opposite may have been
true.

Another potential problem with this study is that NEFA may not be the best
indicator of energy status and lipid mobilization. One concern is that NEFA
concentrations do not remain constant throughout a day. The time relative to feeding
affects NEFA concentrations in plasma. Shortly after feeding the concentration of NEFA
in plasma are lower than after feeding (VandeHaar et al., 1995a). In our analysis of the
efl‘ect of BCS and PTA... on plasma NEF A using the GLM procedure in SAS, herd was
used as a block. Using herd as a block probably accounted for some of the differences in
NEFA caused by the time of feeding relative to the time of sampling blood. However, in
analyses where CMH statistics were used, herd effect was not taken into account and we
could not account for time of blood sampling relative to feeding. This increased variation
in NEF A that had no relationship with actual daily nutrient balance may have masked

some relationships and caused them to be statistically insignificant.

Practical recommendations from this study

Although we did not measure energy intake in this study, energy intake is inversely

related to plasma NEFA levels. VandeHaar et al. (1995b) and Sharma et al. (1995)

56
showed that dry cows that received a diet higher in energy than NRC recommendations

tended to have lower plasma NEF A concentrations. This could point to the
recommendation that farmers should try to maximize energy intake of the dry cow. This
can be done by minimizing the drop in feed intake around calving and/or by increasing the
energy density of the feed. Minimizing the drop in DMI is difficult because the causes of
the spontaneous decline in DMI are not known. However, maintaining fresh feed in front
of the animals for most of the day may help alleviate this problem. Probably a more
effective strategy would be for farmers to increase the energy density of dry cow diets in
the last 2 or 3 weeks before parturition. The creation of a “close-up” group of dry cows
would enable closer supervision of feed and diet management during this critical time.
This should result in animals that are in better nutrient balance just before calving, which
in turn should decrease the incidence of dystocia, retained placenta, ketosis, displaced

abomasum, and peripartum mastitis.

57

 

Management & Environment
- Diet
- Feeding Management POOI' Energy Status
- Housing —— (TPlasma NEFA)
- Temperature
- Stress

 

 

 

 

 

  
 

Immunosuppression
Fatty Liver
Other?

E:

It PTA...

 

 

 

 

 

It BCS

 

 

    

l DMI
Postoartum??

T Health Problems

 

Figure 12. Relationships in the present study and proposed mechanisms for these
relationships.

 

SUMMARY & CONCLUSIONS

In our study higher prepartum NEFA concentrations were associated with a higher
incidence of dystocia, retained placenta, ketosis, displaced aborrrasum, and mastitis but not
milk fever. Animals with higher BCS scores had a higher incidence of ketosis and
displaced abomasum but not dystocia, retained placenta, milk fever, or mastitis. Prepartum
NEFA concentrations were elevated in animals with higher BCS and lower PTA... and in
cows in parity 2 compared to parities 1 and 3+. BCS were lower in animals that had
higher PTA...

In conclusion, increased lipid mobilization prepartum, as indicated by higher NEFA
concentrations in plasma, is associated with more health problems postpartum. Increasing
the energy intake of cows prepartum may result in fewer peripartum health problems and

lead to more profit for the farmer.

58

APPENDICES

Appendix A

DECRESING HEALTH AND REPRODUCTIVE PROBLEMS IN DAIRY COWS m CALVING

Wilmpbanhhafswminutotomtbfolbwinqwfiouwfichmrhdgudtomtw
mstuiyawofdrycowumgemsnt eonditbrl. “mums. unflafid.

Howunyeowsanyoumflking?_
Descrbsdrycowfssdirgprogm: (pm circb m1)
Drycowsf‘sdwihmflk‘agcows
Oneyoupofdryeows
Twoyoupsof'rhyeows
Dryeowsfiidsxtnyaincbse-up
Cbso-updrycowspdwihmilkcows

Howmanycowsluw hadths folbw'rgproblemsinths lastém?

D'Qhesd bonanza Milk fever
Ketos'l Rataimd Wing——
Msuit'u Brusdingprobbrn

110de youmincows tbronmimtimor matron?

 

 

Win ishra your wterim'mis)?

 

 

Telsplmc Nunbor:
Who istm yourfbedrhalsrandnutritioncomultant. Cowmil’mon‘s was:

 

Do You ages to release your DHIA records to Michian State University fhr tie durat'nn ofmis tr’nl?
Yes No

Name (PM Print) Sigmtrne
Telephone No:

 

 

 

Fax No.

 

 

Figure A1. Initial questionnaire sent to farms

59

Appendix B

 

 

DRY COD FEEDIIG FIELD TRIAL CHECK LIST Date: Tine:
Bane: DHIA Io:
Tine blood saapled Tine last red

 

 

 

Recorder (s):
Cows within 5 weeks or calving:
Huaber & fl DAYS DRY Health

 

 

 

 

 

 

 

 

 

 

 

 

Take blood Sanple tron each cost. keep on ice.

-ING 2mm (DI! Ml) msrr) tine lat tad:
Sanple TllR for dry and fresh cows and/or individual forages tor chenical

analysis.
Collect labels tor conercial reeds and copy any analysis reports.

 

1 haunts and tines ted:
DRY C035; teedstutt lb as ted/day renoval s. wastage

 

 

 

 

 

 

 

Figure B. 1. First version of nutrition and management sheets

60

61

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CLOSE-UP: teeditutt lbs as tedgdaz renoval s wastage
CLLVING PEIS: Tine last ted:

1
Fresh: Tine last ted:

 

 

 

 

 

 

Figure B. 1. (cont’d)

62

 

 

Formula for nixestrron Conputer it available otherwise list as accurately
as possibly):

Bunk Space per cow:

 

Close-up:

 

Calvinq pen:

 

Fresh:

 

Pen lssignnent Criteria:

1 Dry:

 

i Close-up:

 

Calving Pen:

 

Fresh:

 

Describe teed Storage s laount:

Hay:

 

 

Haylaqe:

 

 

Corn silage:

 

 

Figure B. 1. (cont’d)

63

Describe Bunk magenent:

Hours bunk tull:

 

Tiles ted/day:

 

Ho' otten Dunks cleaned:

 

Is storage consistent with anounts ted?

 

Describe teed nixing and delivery:

 

 

nascent minis mums:

Square teat/cow:

 

Floor type:

 

Bedding:

 

Cons/pen:

 

Avg. days in pen:

 

Criteria tor putting in and holding in pan:

 

 

roman-sir rm IOLIC! (All cous?, it not how selected):

museum .1 mar: Ill) .3016:

Voluntary Baiting period:

 

1 Who observes:

 

Observed tines or day: Heat detection device?

LI policy relative to heat:

 

nanagenent or lnestrus CoustPG!’ and/or ERRED):

 

Figure B. 1. (cont’d)

    

     

 

DOST-III!!! min: List all cows in sanple, Calving Date, Disease Dates
(nil! tever lll', Dystocia DIS, Placenta retained >24 hr ll, lletritis It,
Cystic ovaries cars, lastitis ns, displaced abonasun D1, Ketosis Er,
Laneness, Detected Beat near, 11 dates, Other)

Docunent it can'has previously'been surgically treated tor Dl.

List heat and.Breeding Dates and any drugs used as aids. 0r, attach torn

lett with producer.
a PIES! amiss! mu COMES

 
       
   
    
    
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

m m ”503121108 no COWTS 0" rant:

 
 

Figure B. 1. (cont’d)

65

DRY COU FEEDING FIELD TRIAL CHECK LIST Date:

 

lane: DHIA:

 

Recorder (s):

 

1. Fan Perspective

A. Dry Group
Average tine in dry group: (days)
than do they go in?
ihen do they go out?
Is there more than 1 dry group (not including CU)?
ihat deternines which dry group they are in?

 

 

 

Are the heiters in a separate group than the dry cows
or sinilar gestation?
Other conents

 

 

 

B. Close-Up Group

Does it exist?

Average tine in close-up group: (days)
when do they go in?
Uhen do they go out?
Is it part or lactating group?

Uhich one?

Are the heiters in a separate group than the dry cows
or sinilar gestation?
Other conents

 

 

 

 

 

 

 

C. Calving Pen
Does it exist? L 1
Is it part or the Close-Up Group?
when do they go in?
Uhen do they go out?
Other connents

 

 

 

 

 

D. Fresh Cow Group
Is there a separate group tor trash cows?
Is it part of a lactating group?
Which one?

When do they go in?
When do they go out?
How are the fresh heifers handled?
Other conents

 

 

 

 

 

 

 

 

Figure B.2. Second version of nutrition and management sheets

E. lilting Group
Describe brietly the groups and when trash cows enter the groups

 

 

Are the heifers handled ditterently than the cows? How?

 

Other conents

 

 

I. nanagenent Policies
A.Reproductlon - tor cows on trial (ex. not heiter policy)
1. voluntary Felting'Period betore breeding (days)
2. Heat Observation
a. how nany tines per day
-average length of! observation (Iin)
b. who observes
c. when (tines at day)
d. in conjunction with another activity?
- what else are they doing?
3. Breeding policy
a. AI (tarner or other) or natural
b. what sign is breeding tine based.on
(l) standing heat
(2) other
c.how'nany hours after heat are the cows bred __
d. policy tor anestrus cows (1-5)
(1) vs: check and then.treatnent with
(2) prostaglandins without vet check
(3) GIRL-I without vet check
(4) leave that or sell then
(5) other
e. any heat detection devices?

- type:

 

 

 

 

 

8. Feed
1. Storage - Type 0: Storage and Aaount
a. Bay -

 

 

b. Haylage -

 

 

c. Corn Silage -

 

 

d. Other -

 

 

2. Anionic Salts: Are dry cows ted anionic salts?
C. DST
Is it currently being used?
Eben did.BST use start?
Chat is use based on?

 

 

 

 

Have any cows on our trial received EST?
Uhich cows?

 

 

 

Figure B.2. (cont’d)

67

III. Grow Descriptions

These descriptions are to be done for each group - dry. close-w,
naternity pen, fresh cow and wilt cow. (Conbined grows need only be
described once but this should be indicated).

A. Grow lane
1. Housing
a. Type (l-6)
(l) Freestall
(2) Covered nanure pack with access to
outside lot
Covered nanure pack with no access to
outside lot
Dry Lot - no cover
Pen - size of pen (ft')
Other
b. Rooniness (l of cows/l stalls)

 

 

(l of cows/pen)

 

c. Quality
(1) type of bedding (a-e)
(a) straw
(b) sand
(01 paper
(d) wood shavings
(e) other
(2) Cleanliness of resting areas (a-c)
(a) very clean (nostly bedding)
(b) noderate (soae Ianure in bedding)
(0) unclean (lot of nanure in bedding)
(3) Cleanliness of cows (a-c)
(a) very clean
(b) noderate
(c) very dirty
(4) General Condition or State of Repair (a-d)
(a) excellent - well naintained
facilities (no broken freestalls)
(b) noderate
to) poor - state of disrepair or inappropriate
conditions (ex. very snall stalls for big
heifers)
(5) Floor Rating - (a-e)
(a) grooved floor
(b) very snooth cenent floor
(c) rough ceaent
(d) straw or nanure pack
(e) other
(6) Ease of novenent (access to food) (a-c)
(a) very good - good cow flow. cows
close to feed
(b) noderate
to) poor - probleas getting to feed,
narrow alleys, great distance to
feed

 

 

 

Ifigunefi2.(conftD

(7) Ventilation (a-c)
(a) excellent (outdoors/open sidewalls)
(b) questionable (closed barn)
(c) poor (little air novenent)
(8) Lighting __ (a-c)
(a) very light (outdoors/open sidewalls)
(b) noderate lighting
(C) poor lighting - darn barn
2. Feeding
a.Dunkspace (l-3)
(l) lft or less/cow
(2) l to 2 ft/cow
(3) >2ft per cow
b.Raised Bunk (l or 2)
(l) 1ft or higher above level of cow's feet
(above cow's hooks)
(2) ground level
c.Covered bunk - is bunk protected fron the
elenents such as rain. snow. or sun?
d.Uater availability
(1) Plentiful (easy access and nunerous troughs)
{2 noderate
3 Poor (linited access and troughs)
e.Bunk nanagenent (a-e)
(1) how often cleaned
(a) twice per day
(b) once per day
(0) once every other day
(d) every 3 days
(e) other
(2) how nany hours/day do cows have access to

feed bunk
(3) how nany hours7day is there feed in the
bunk

 

f. Tine of feeding
(1) how nany tines per day cows fed
(2) how nany tine per day is feed pusfid up
to cows
(3) what tines are cows fed (6an,6pn etc)

 

 

g.'1‘ype of Ration (1-7)
(1) Straight; True 11R. - one feed thoroughly nixed
(2) TIER ad lib and linited hay
(3) 111R restricted and ad lib bay
(4) THR and grain separate
(5) 'l'llR and grain and hay
(6) Individuel Feeds Fed separately
(7) Other
h. Quality of Ration
(1) Does the feed in front of the cows seen
fresh? _
(2) Describe the particle sire. (a-c)
(a) very course
(b) noderate
(c) very fine

 

 

Figure B.2. (cont’d)

69

i.Conponents of Ration, anticipated consunption
and actual consunption

Ration sheets are acceptable but it should be double checked to see if the
actual anount fed is the sane as what the ration sheet indicates. The
following are sone of the feed values that we are interested in.
(l) 'I'IIR and other feeds (not in 'I'llR)
(a) constituents
(b) individual values of individ. ingred
i)lD!' or ADP (if possible)
ii)’cCP
iii)%UIP (if possible)
iv)l!l
v)Cation-Anion Values (if possible)
vi)Vit!:. and Se (if possible)
(c) total ‘l'lR values
(d) anounr actually fed

 

Ration details, anounts and connents for this grow. Peed sanples. or feed
tags need to be ta_k_e_n of feed_s that have not been sanpled before end are
not indiceted in the fegl sheets.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure B.2. (cont’d)

70

on : o -- "a on onnu: .
DRY COU FIELD TRIAL - BCS AID BLOOD SllPLIlG

Date: Fern:

 

Tine Sanpled:

 

 

 

 

 

 

 

Grow l - Tine a Day Grow 1 last fed:
Grow 2 - Tine a Day Grow 2 last fed:
Grow 3 - Tine a Day Grow 3 last fed:
Grow 4 - Tine 6. Day Grow 4 last fed:

 

 

(Cows and heifers within 5 weeks of calving are sanpled.)

Grow Cowl BCS Health Connents (ex. laneness)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sanples are to be on put ice innediately.

Figure B.2. (cont’d)

208.3 8 52m 80% 530: we cone?» “CE 4.0 oSmE

£56 8.6 .m
wast... 122.!

 

 

 

 

 

0 £525.

71

moo—Ea 2 :03» 805 .28: mo :38? 983m NO “SEE

 

 

 

 

Appendix D

NEFA PLATE Pb R M I“

1 2 8 4 5 4 'l 8 8
A ms 111' 15 71 14 78 15“ 1284 1447 1544 1.287
s: 82 a 82 82 n 82 82
um 11.04 m 11.04 1111404 1111404 110.404 1112804
I 1. 1.11! 13 11 14‘. 1544 1284 1441 1544 1187
82 82 82 82 82 82 82 82
11.04 11.04 11.04 11” 110404 11M 110.404 110104
C m 12. 15,5 153 111' 1287 14" 1552 1352
82 82 82 82 s2 s2 82 82
11,04 11.04 11.04 1111404 1111404 1111404 1111404 111304
I) ”I 12. 1285 15’ 111! 1281 14 “I 1552 1558
82 82 D 82 82 s: 82 82
11.04 11.04 11.04 114404 1111404 1111404 110.404 1112304
I ”I 1184 14” 15” 12. 1585 15” 12” 1.895
82 82 n s: 82 a 82 82
11.04 11.04 11” 1M 11I1404 11l1404 11mm 110504
I" 88 1284 14” 15” 12. 15,5 15” 1.2. 1395
82 82 82 82 82 s: 82 82
11.04 11.04 m 1111404 1111404 1.1/1404 1112804 1112304
0 0 1287 1447 1541 N8 14” 1541 1284 14”
82 82 82 82 82 82 82
11M 11’04 “H04 110404 1111404 1112904 1112304
I! 0 1287 1447 1541 N8 14” 1541 1284 14”
82 82 82 82 82 82 82
1.13.04 11’04 M04 11I1404 1111404 110804 110304

FHwanhphk 1119 . “WC"-tbphk

  
  

harsh-unflAwnsdhd:

 

   
 
  
 
    

I. ,. hslsdtbph. DI-hvwhqpnces-tut
fmthwhkhwwhmtssbwuhsfli

thrwnyh‘snbdthldbrw

mummluafld:

 

n. ,. bdsdtbph. hound-wen“?
{mt-wihwwh-wunlwuuuhf
thrwqfiisnwdtbflfiw

Innofthhlm

 

 

Figure D.l. Sheet 1 for NEFA analysis

73

15”

11M

15”

11 12
1541 1.418
82 82

um 110304

1541 1518

1.1/”04 11’304

1544CCC

1112304

1544 CIC

110304

1551 Cut:

11,304

1551 CHD

um

1552 Cub

110504

1552 wal)

1112304

74

 

 

 

 

Figure D.2. Sheet 2 for NEFA analysis

Appendix E

ptions ps=63 ls=75 pageno=1;

. ata one; infile'c:\proj\sapfld\JULY23 .prn'missover;

pi=hooooocxx'; bc=‘iooorxroor';

input hdhia herd cowid $14. cowno @34 calve date9. date date10.
bcs nefa pi $ @72 visit] date9. andate date10.
da rp dyst twins ket mfmas60d died lact ptarnilk maled;

daypp=date~calve;

if lact>2 then lact=3;

if bcs=0 then delete; if twins=1 then delete;

if -14<=daypp<0 then output;

. ata nefa; set one; keep daypp nefa;
I. roc sort data=nefa; by daypp;
I. roc means data=nefa noprint; by daypp; var nefa;
output out=nefa mean=nefa r1=n min=min max=max range=range std=stdl
skewness=skew kurtosis=kurtosis;
*proc print data=nefa;

. ata nefa; set nefa; keep daypp nefa; rename nefa=avnefa;

proc sort data=one; by daypp;

data one; merge one nefa; by daypp;
pernefa=nefalavnefa* 100;

proc sort data=one; by herd cowno lact;
. roc means data=one noprint; by herd cowno lact;
var pernefa bcs da rp dyst twins ket mf maled mas60d ptamilk;
output out=two mean=pernefa bcs da rp dyst twins ket mf
mas10d mas60d ptamilk;
. ata two; set two; h=125; l=75;
if bcs<=2.75 then bc='lLow_BC';
if 2.75<bcs<=3.25 then bc='2M_BC';
if 3.25<bcs<4 then bc='3MH_BC';
if bcs=>4 then bc='4High_BC';
if ptamilk=9999 or ptamilk<-200 then ptamilk=.;
if -l74<=ptamilk<826 then pta=l;
if 826<= tamilk<l826 then ta=2'

Figure E.1. Program for CMH Analysis

75

76

. ptami1k>=1826 then pta=3;
if ptamilk=9999 or ptarnilk<-200 then pta=.;

if pernefa>h then fagr='3_hfa';
else if pernefa<l then fagr=' 1_lfa'; else if l<=pernefa<=h then fag1='2_mga';
else fagr='xxxx';

I. roc sort data=two; by fagr;

1 roc means data=two; by fagr;
var pemefa bcs da rp dyst twins ket mfmaled ptamilk;
title'MEAN S FOR NEFA (FAGR) CLAS SIFICATION';

1 roc sort data=two; by bc;

proc means data=two; by bc;
var pemefa bcs da rp dyst twins ket mf maled ptamilk;
title'MEAN S FOR BC CLASSIFICATION;

. roc sort data=two; by lact;

proc means data=two; by lact;
var pemefa bcs da rp dyst twins ket mf mas10d ptamilk;
title'MEAN S FOR LACT CLASSIFICATION;

. roc sort data=two; by pta;

. roc means data=two; by pta;
var pernefa bcs da rp dyst twins ket mf maled ptamilk;
title'MEAN S FOR PTA CLASSIFICATION;

proc sort data=two; by lact herd cowno;

I.roc freq; by lact;

tables bc*fagr*(ket da rp mf dyst maled mas60d)/cmh noprint;
proc freq; by lact; tables fagr*(ket da rp mf dyst maled mas60d )lcmh;
Proc freq;

tables lact*bc*fagr"‘(ket da rp rnf dyst maled mas60d)/cmh noprint;
proc fieq; tables fagr*(ket da rp mf dyst maled mas60d)/cmh;

roc freq; by lact;

tables fagr*bc*(ket da rp mf dyst maled mas60d)/cmh noprint;
proc freq; by lact; tables bc*(ket da rp mf dyst maled mas60d )/cmh;
proc freq;

tables fa r*1act*bc* ket da - mf (1 st maled mas60d /cmh no orint;

Figure E. 1. (cont’d)

77
I-roc freq; tables bc*(ket da rp mfdyst maled mas60d)/cmh;

Iroc freq;
tables fagr'bc‘lact*(ket da rp mf dyst maled mas60d)/cmh noprint;
proc fi'eq; tables lact*(ket da rp mf dyst maled mas60d)/cmh;

1' too freq; by lact;

tables fagr*bc*pta*(ket da rp mf dyst maled mas60d)/cmh noprint;

oroc freq; by lact; tables pta*(ket da rp mf dyst maled mas60d)/cmh;

. roc freq;

tables fagr*bc‘lact*pta*(ket da rp mf dyst maled mas60d)/cmh noprint;

oroc freq; by lact;

tables bc*pta*(ket da rp mf dyst maled mas60d)/cmh noprint;
I-roc fi'eq; by lact; tables pta*(ket da rp mf dyst mas10d mas60d)/cmh;
I' we freq;

tables bc‘lact‘pta*(ket da rp mf dyst maled mas60d)/cmh noprint;

I~roc freq; by lact;
tables pta“ (ket da rp mf dyst maled mas60d)/cmh noprint;
.roc freq; by lact; tables pta*(ket da rp mf dyst maled mas60d)/cmh;
. roc freq;
tables lact‘pta*(ket da rp mf dyst maled mas60d)/cmh noprint;
I. roc fi'eq; tables pta*(ket da rp mf dyst maled mas60d)/cmh;

Ioroc freq; tables bc’pta*lact*fagr/cmh noprint;
. roc fi'eq; tables lact'fagr/cmh;

. roc freq; by lact; tables bc*pta*fagr/cmh noprint;
proc freq; by lact; tables pta*fagr/cmh;

proc freq; tables bc*lact*pta*fagr/cmh noprint;

. roc freq; tables pta*fagr/cmh;

. roc freq; by lact; tables bc*fagr/cmh noprint;
. roc fi'eq; by lact; tables bc*fagr/cmh;

proc freq; tables lact‘bc*fagr/cmh noprint;

I. roc freq; tables bc‘fagr/cmh;

Figure E. 1. (cont’d)

78

 
  
 

proc freq; tables fagr‘lact*bc/cmh noprint;
proc freq; tables lact‘bc/cmh;

proc freq; by lact; tables fagr‘pta‘bc/cmh noprint;
proc freq; by lact; tables pta‘bc/cmh;

proc freq; tables fagr‘lact*pta*bc/cmh noprint;
proc freq; tables pta‘bc/cmh;

proc fi'eq; tables bc‘fagr‘lacflpta/cmh noprint;
proc fieq; tables lact‘pta/cmh;

run;

 

Figure E. 1. (cont’d)

 

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l.

   

"uniuni: