Koekompas: In what way is the transition
cow reflected?
A.G.M. van Diepen
3382087
February- April 2014
Supervisor: G.A. Hooijer
1
Contents
3
Abstract
4
Introduction Koekompas
6
PI’s representing transition cows in Koekompas
13
Materials and methods
14
Results
19
Discussion
22
Conclusion
23
References
2
Abstract
Koekompas is an instrument to investigate risks on farms to prevent disease and to optimize the
environment for the cows and the production of milk. The transition period is a hazardous period for cows
for developing metabolic and/or infectious diseases. The following performance indicators (PI’s) are
associated with transition cows: feed dry cows, water dry cows, body condition score (BSC), disease
incidence of endometritis, retained fetal membranes, metabolic diseases and housing. Koekompas reports
from 76 farms were collected and analyzed for trends in data and for relations between PI’s. From the
current PI’s and reports one cannot get a complete impression about the transition cows, because the PI’s
are not specifically split out for transition cows. The PI incidence of metabolic diseases included both ketosis
and hypocalcemia although their pathogenesis and thus their preventive strategy differs. Also for housing
calving barn and dry cow housing is not separately valued. In literature, housing, difference in cation-anion
balance (DCAB) in dry cow feed and the specific BCS in the dry period were seen as PI. Further research
which compares Koekompas results with the current situation on the farm in more detail is necessary.
3
Introduction Koekompas
Since more than ten years Dutch farmers are compelled to have a periodic farm visit of a
veterinarian to warrant the quality of Dutch dairy products. The veterinarian visits the farm four
times a year and makes an assessment of the number of cow disorders at the specific moment of
visit. Aspects as animal welfare or production process are not included in these visits. Moreover,
intensive livestock farming became less accepted by society and in politics during the last decade.
Consumers value the way their food is produced more and more and especially animal welfare and
sustainability became critical factors. Therefore, it is useful to monitor cow health in dairy herds
more continuously.
In 2009, CONO, a relatively small dairy corporation in the Netherlands, introduced a new
instrument ( Koekompas) to measure the risks for cow health and animal welfare on their farms.
CONO focuses mainly on a sustainable and fair production. Koekompas helps to investigate riskfactors on a specific farms. CONO farmers were able to test this tool in a pilot study and the results
of this pilot were satisfying. Compared to the periodic farm visits, Koekompas gives insight in
factors that influence disease prevention and that optimizes the environment for the cows and the
production of milk.
Figure 1. Build-up of the koekompas score-list 6
Since 2011, farmers are allowed to use Koekompas instead of the mandatory periodical farm visits.
Every six months, the veterinarian visits the CONO farms for Koekompas. Koekompas is build-up
from seven critical success factors (KSF’s): milking, feed and water, housing, animal welfare, disease
incidence, working routine and young stock rearing (figure 1). Each KSF is build-up of several
performance indicators ( PI’s) and these PI’s are build-up from over a hundred management control
points (MCP’s). At the end of a Koekompas visit the veterinarian makes a report with a septagram,
a list with the grades of the PI’s and a report with advices which are discussed with the farmer. The
septagram is the risk profile of the farm and a farmer has to strive to a score of three for all the
KSF’s. A score 1 is seen as high risk, score 5 as low risk.
4
Figure 2. Example of a septagram as result of Koekompas6
Transition Cows
The transition period is defined as the period three weeks prior and the three weeks after calving. In this
period, dairy cows shift from the non-lactating gestational phase to the birth and lactation phase1,2. The
decrease in dry matter intake (DMI), the increased need of nutrients and the inevitable negative energy
balance (NEB) forms a challenge for dairy cattle 13. Conditions associated with NEB are milk fever, ketosis,
metritis, displaced abomasums (DLA) retained placenta13and sub-acute rumen acidosis (SARA)14.
The effects of disease during the transition period can extend during the following lactation, for example in
developing fertility problems, claw problems, mastitis and a continuing NEB and thus can be a serious cause
of animal welfare problems and a cause of economic loss8.
Transition cows and Koekompas.
Whereas Koekompas is a tool to investigate health risks on the farms of dairy cattle6, the transition period
should be included in it. However, the main cause of the health problems is the onset of lactation in
combination with environmental factors8.
With regard to the transition cow, the following PI’s can be taken in account: feed dry cows, water dry cows,
body condition score, disease incidence of endometritis, retained fetal membranes, metabolic disease and
housing. In this study, we would like to investigate what the PI’s exactly tell us about transition, if and how
they influence each other and which MCP’s are possibly missing in Koekompas.
5
Data Description
Feed Dry cows.
Table 1: model for criticizing Feed6.
The PI feed is build out of three MCP’s.
The first one is quantity. At least 0,6 meter feed bunk space per cow and a feed rest of 5% should be
provided6. Limited feed space can cause competitions at the feed bunk, this can reduce feeding activity and
increases the number of displacements of cows at the feed bunk. When cows have to wait to gain access to
the feed bunk, their standing time increases. Reduction in feed bunk space alters cow behavior around the
transition period and possibly increases the risk for lameness and disease24. Also, the number of aggressive
interactions between cows increases which is a problem in animal welfare. Especially for dry cows, because
their group is not stable. The second one is quality, also point of importance. Dry cows should not be fed the
same diet as lactation cows. Their energy requirement is lower and when cows gain too much weight during
the dry period the risk for metabolic diseases increases, due to a greater lipid mobilization post-calving17.
In dry cow feeding, risks for developing NEB and thus ketosis, fatty liver and DLA can be decreased by
creating a close-up diet for cows twenty-one days pre-calving and a far away group.
The third MCP is ‘apropriate for the group’. Building up the concentrate intake during the last three weeks of
the dry period makes cows more sensitive for insulin and lowers NEFA concentration in blood7. This
increases dry matter intake (DMI) post-calving and the buildup of the lactation curve is steeper7. Providing
dry cow minerals containing magnesium is a strategy to prevent hypocalcemia, because magnesium is
necessary for PTH release and the sensitivity of bone and kidney to PTH16 (see section hypocalcemia later). A
diet high in concentrates and/or low in fiber post-calving increases the risk for SARA because the rumen
fibers are not adapted yet to the new diet14.
As the decision tree in table 1 shows, when quantity is inappropriate one cannot get a score higher then 1,
reflecting high risk. Also quality should be at least moderate to score 3 reflecting medium risk. To score 4 or
5, quality and quantity should be good and the type of feed has to be appropriate for dry cows.
6
Water Dry-cows.
Table 2: model for criticizing water6
.
Water is an essential nutrient for cows. It can come from different sources and the quality does not always
reach the accepted standard. The first MCP is, like for feed, quantity. Cows can lose up to 60L of fluid at
calving. Combined with reduced water and feed intake 24 hours prior to calving this can cause dehydration.
Cows should drink about 40L after calving; a sufficient provision of water is thus necessary11. So-called Fastdrinkers can provide up to 30 liters of water per minute and can give a proper provision of water.
The second one is quality. The micro-ecology of some water sources is not fully controlled or understood.
Biofilms can serve as a reservoir for potentially pathogenic bacteria but the primary source of contamination
is manure. The water quality is good when the water is clear and there is no sludge on the bottom of the
drinking bins. Also the water should not smell as this influences the palatability. Poor quality may have
adverse effect on the appetite, water intake, cow health and productivity12.
The third one is ‘appropriate for the group’. Also, one should keep an eye on the accessibility of water for
the dry cows. When water is not easy accessible, dominant cows can block the access to fresh water which
causes aggressive interactions between cows22. Overstocking can cause a comparable effect2 and this can
possibly reduce water intake. In fresh cows, low water intake reduces rumen contractions, body weight and
milk yield22.
The decision tree is quite similar to the one for feed intake. Quantity and quality has to be at least moderate
to get score 3 and with an inappropriate quality of quantity one can only get grade 1 reflecting high risk.
7
BCS.
Body condition score (BSC) gives an impression of the fat proportion in cows. Score 1 reflects emaciation and
score 5 reflects obesity. Body weight alone is a not a good indicator of body reserve in dairy cows because it
is highly influenced by size of the cow, parity, stage of lactation and rumen filling. Body condition score has
its limits as well, because it mostly reflected subcutaneous fat and not inter- and intra-muscular fat
reserves17. In Koekompas, the accent lies on the distribution of BSC among the cattle. Big differences
between cows could possibly indicate an insufficient feeding strategy or overstocking.
During late pregnancy and early lactation endocrine profiles change due to homeorhetic changes in the
somatotrophic axis. This makes peripheral tissues less sensitive to insulin for 50 to 100 days post-calving.
This lipolytic state occurs naturally in mammals post-calving. They nurture their calves from stored adipose
tissue17. The ideal BCS for dry-cows is 3-3,5. Post-calving, the BCS profile of cows is similar to the inverted
lactation curve17. The cows with a high (>3,5) BCS during the dry period have a higher mass of lipid which
can be mobilized compared to cows with a normal ( 2,5 >< 3,5). Their decrease in DMI is also higher, so cows
with a higher BCS have higher risks for developing the metabolic diseases related to decline in DMI1,14,
especially ketosis1 but also milk fever16 . The best way to get a better BCS during dry-period is managing the
BCS during the whole lactation.
8
Disease Incidence.
In Koekompas ketosis, milk-fever and displaced abomasum are noted together in the incidence of metabolic
disease6. The score reflects the percentage of the herd that has suffered from metabolic disease during the
last six months as followed6:
Score 5:
< 5%
Score 4:
5-10%
Score 3:
10-15%
Score 2:
15-20%
Score 1:
>20%
A proper registration of diseases is retrospective but can be used to react faster to problems in the future.15
If the farmer has no disease registration, he maximally scores a 3.
Displaced abomasums.
Displaced abomasums ( left or right) is a result of hypomotility and gas-production of the abomasums.
Important contributing factors in developing displaced abomasums are already existing diseases as
hypocalcemia, metritis and mastitis. Also recently giving birth and the associated changes in the position of
the abdominal organs is predisposing15. Reduction in DMI en thus a lighter rumen mass can contribute to this
process as well1. Cows with displaced abomasums are also at higher risk for ketosis due to the reduction in
DMI1. In Koekompas, the incidence of displaced abomasums for the past six months is registered. Together
with ketosis and milk fever, this gives insights in the incidence of metabolic disease.
Ketosis.
Depression of DMI around calving can lead to NEB, especially in fresh cows due to their high energy
requirement. Increased lipid mobilization results in a higher concentration of NEFA. When the maximum
capacity of the liver is reached, ketone bodies will be produced 1
Cows can mobilize adipose tissue as a source of energy. Non esterified fatty acids ( NEFA’s) are cleaved from
the adipose tissue, bounded to albumin and transported to liver. NEFA’s can be stored as TAG or undergo
partial or total oxidation to ketone bodies or to acetyl-CoA. 1,2,5 Ketone bodies are transported back to the
circulation and can be used as an alternative for glucose by heart and brain. The ability of a ruminant to
transport TAG as VLDL is very low. Thus, when the concentration of NEFA’s increases, it may exceed the
capacity of the liver. Excessive storage of NEFA’s as TAG compromises liver function, while the oxidation of
NEFA can decrease appetite. The liver automatically takes up all the NEFA’s from the blood, so limiting
lipolysis is necessary for a successful transition1 .
Pre-calving, gestation itself is a sign for lipolysis and thus NEFA release. The increased NEFA concentration is
required to increase the fat concentration of the colostrum, the fuel of the calve. Combined with an appetite
suppression , a decrease in DMI and a reduced insulin sensitivity of tissue1,2,5 all cows suffer from a certain
degree of NEB13.
As the cow starts lactation, most of the glucose and gluceneogenic precursors as propionate, oxaloacetate
and lactate are used for lactose for milk production in the udder. The amount of glucose used by the udder is
significant: the production of 1 liter milk requires 72 grams of glucose. The total glucose requirement of the
cow is 2,5 times higher compared with the last week as dry-cow. The transport of glucose to the udder is
9
independent of insulin concentration, thus the uptake by the udder continues even with very low
concentration in the blood4.
To support this energy need, cows start to mobilize NEFA. Since gluconeogenic precursors are used for milk
production, most of the acetyl-CoA will be oxidized to ketone bodies due to absence of gluceogenic
precursors1. The excessive store of NEFA as TAG in the liver compromises gluconeogenenis more and
extends the lipolytic state1,2,5,17. If the ketone body production exceeds the use of the body, cows develop a
condition named ketosis1,2,5
An increase in NEFA concentration causes (subclinical) ketosis is an important cause of decreased milk
production, increase of uterine and metabolic disease and decreased reproductive problems1 and thus forms
an important risk factor on dairy farms.
Hypocalcemia
Before calving, calcium requirement is approximately 30g a day, after calving only the loss of calcium in milk
can exceed 50 grams a day. To get the amount of calcium required, calcium absorption from the bones and
the gut should increase. In response to lower blood calcium levels ( norm: 2,0-2,5 mmol/L), PTH is released
from the parathyroid glands. To activation of 25-hydroxy-D3 to 1,25(OH)2D3
Increases calcium absorption from the bones, increased renal reabsorption en increased absorption of
calcium from the intestines.
It takes a cow about three days to adapt for proper absorption and the onset of bone resorption. All cows
have a certain amount of hypocalcemia during the onset of the lactation. Milk fever, when calcium
concentration is below 1,6 mmol/L, is the clinical manifestation of hypocalcemia. This is combined with some
of the following signs: muscle weakness and not being able to stand up, cold ears, a lethargic appearance.
Subclinical hypocalcemia is defined as a calcium concentration lower then 1,8 mmol/L without the clinical
signs.10
Dry cow-feeding, specifically point three: appropriate for group can show the risk factor for hypocalcemia.
Dry-cow minerals mostly contain magnesium, which is important for the tissue sensitivity to PTH10.
Hypocalcemia can cause ketosis, because weak cows were not able to stand up and walk to the food barn.
Milk fever predisposes for additional health problems as dystocia, retained fetal membranes and metritis.10
Metritis, endometritis and retained placenta.
The incidence of metritis and retained placenta is reported in Koekompas as followed6:
Score 5:
> 15%
Score 4:
12-15%
Score 3:
9-12%
Score 2:
6-9%
Score 1
<6%
During late pregnancy, parturition and the onset of lactation, dairy cows are in oxidative stress through high
energy metabolism. This will cause a high concentration of reactive oxygen species (ROS). ROS causes cell
damage and lipid peroxidation. Especially immune cells are sensitive to ROS because they have lipid
10
membranes containing high amounts of polyunsaturated fatty acids and are thus typically sensitive to
peroxidase2, 5. Due to the earlier mentioned compromised liver function paraoxanase (PON) activity is lower.
Also, the concentration of high density lipoprotein (HDL) and blood cholesterol to which PON is bound is
lower which will decrease its function further5.
Round parturition cows suffer from neutropenia and eosinopenia and low levels of IgM and IgG. 10 Milk fever
influences the immune system by disturbing the activity of the mononuclear cells10. Cows with low
concentration neutrophils are predisposed for retained placenta9.
Due to negative energy balance, NEFA’s are released. NEFA influences not only liver function, but they also
causes an up-regulation of inflammatory immune genes and down-regulation of the acquired immune
response genes2,13. If cows develop ketosis, the chemotactic differentials of the leukocytes are lower
compared to non-ketotic cows. The chemotactis capacity decreases in an environment with ketone bodies,
thus ketotic cows have a lower leukocyte function18.
The decrease of glucose concentration in blood of the transition cows causes a deficit function of the
immune cells because they do not have enough fuel to work properly2.
The primary immune response from the uterus comes from polymorphonuclear cells ( PMN’s). An impaired
function of PMN is associated with retained placenta, metritis and endometritis9,18. Due to inevitable
trauma during partiruation most cows have bacterial infection and exposure of the endometrium to LPS.
There is an essential immune response to this bacterial contamination for tissue repair and as part of the
involution process, but this response can cause disease if the inflammation persists18. What exactly causes
and sustains the inflammation is not clear although ketosis, hypocalcemia and the oxidatieve stress are
predisposing factors.
So, the incidence of retained placenta and metritis reflect the start-up of a cow. Having a high incidence of
metritis and retained placenta reflects problems around the transition due to compromised liver function
due to NEB, milk fever or birth difficulties.
11
Housing.
In natural environments, cows separate themselves close to calving20. With this in view, a separate calving
barn is recommended. Around calving, cows change a lot from standing to lying position and back21. A
comfortable bedding is thus preferred by cattle, especially sand or straw bedding are preferred 21 .
Cow cowfort, bunkspace and grouping are points for attention for transition cows. The incidence of E.coli
mastitis decreases when there is more feed bunk space per cow and thus when the animal density
decreases23 . The overall incidence of clinical mastitis decreases when dry cows are not housed together with
pregnant heifers. The hygiene of the calving parlor may be important as well23.
Proper housing decreases stress. Catecholamines, the so-called stress-hormones, are lipolytic. An increase in
stress-hormones can result in higher plasma NEFA concentrations and thus a greater and extended period of
reduced feed intake and prolongation of the period of NEB. A suited environment reduces stress and thus
lowers the risk of metabolic disease1.
In Koekompas, housing for dry cows and cows at calving is not reported separately from lactating dairy cows.
12
Materials and methods.
After every visit, the vets had to save their findings online. The list with grades for every PI together with the
septagram and the report of the veterinarian are saved together. In this study, Koekompas datasets from 76
different farms in the Dutch province *** were used. All the farms produce milk for CONO. In the period
from March 2013 to March 2014 the visits to the farms occurred. From every farm, the most recent
Koekompas visit was included. When farms happened to have more than one location only the location with
dry cows was included. The farms were all served by one Veterinary Practice, Dierenkliniek ******, where
five different vets are working and are carrying out Koekompas. Also different cow-breeds and housings
were included.
The seven PI’s relevant for transition cows: BCS, feeding dry cows, water dry cows, metabolic disease,
retained placenta, endometritis, and housing, were collected and exported to Microsoft Excel.
The reports were checked for relevant observations for the transition period and those observations were
noted behind the number of the corresponding farm. The farms were anonymized by giving them numbers.
The comments of the veterinarians about transition period were categorized in different categories as
housing, water and diseases and displayed in a cirkeldiagram. The PI’s were transported to SPSS for the
analyzes.
13
Results.
In graphic 1 the different comments on transition management from the veterinarians were displayed.
The three most common comments include an insufficient water supply or none or non-sufficient minerals
for dry-cows.
14
Housing with/without separate dry-cow and/or calving area.
Descriptives
Statistic
Mean
3,3227
95% Confidence Interval for
Lower Bound
3,1856
Mean
Upper Bound
3,4598
5% Trimmed Mean
3,3035
Median
3,2000
Grade in housing with
Variance
separate calving and/or dry
Std. Deviation
cow housing
Minimum
1,80
Maximum
5,00
Range
3,20
Std. Error
,06865
,311
,55769
Interquartile Range
,63
Skewness
Kurtosis
,665
,295
1,362
,582
Table 1: Mean grade for housing with a separate calving barn and dry cow area. Amount of farms: 67
Descriptives
Statistic
Mean
3,7200
95% Confidence Interval for
Lower Bound
3,2780
Mean
Upper Bound
4,1620
5% Trimmed Mean
3,6889
Median
3,7500
Grade in housing without a
Variance
separate area for dry-cows
Std. Deviation
and/or calving cows
Minimum
3,00
Maximum
5,00
Range
2,00
Interquartile Range
Std. Error
,19539
,382
,61788
,78
Skewness
,830
,687
Kurtosis
,751
1,334
Table 2 Mean grade for housing without a separate calving barn of dry cow area. Amount of farms: 9
15
Distribution of data
BCS
Feed
3,00
4,00
5,00
Count
Count
Count
1,00
0
0
1
2,00
0
2
9
3,00
0
5
31
4,00
1
0
17
5,00
0
1
9
Table 3. Distribution of grades for BCS and feed and 76 farms. No correlation found.
Thirty-one (48%) of the seventy-six farms with a score 5 for BCS had a score 3 for dry cow feeding (table 3).
Still ten (13,15%) farms with a score 5 for BCS scored 1 or 2 for dry-cow feeding which means that there are
risks in this area. If we search further in the reports, we found out that five of the ten farms reported score 5
for the incidence of metabolic disease, retained placenta, mastitis and endometritis. Three farms reported
score 3 of 4 for incidence of mastitis, one farm reported score 1 for endometritis and one farm reported
score 3 for metabolic disease.
BCS
Metabolic Disease
3,00
4,00
5,00
Count
Count
Count
3,00
0
0
1
4,00
0
2
6
5,00
1
6
60
Table 4. Relation between incidence of metabolic disease and body condition score in 76 farms. Score 5 for BCS correlates with score 5 for Incidence
of metabolic disease and vice versa.
Sixty (78,9%) of the farms had a 5 for metabolic disease with a score 5 for BCS (table 4). Only one farm had a
score 3 for BCS with a score 5 for metabolic disease and vice versa. One can conclude that there is a positive
relation between the score for metabolic disease and the BCS score when it is score 4 or 5. For the lower
scores there’s no data, thus this correlation is only valid for score 4 or 5.
16
Housing
Mean
Metabolic Disease
Count
3,00
3,20
1
4,00
3,60
8
5,00
3,41
67
Table 5. Relation between incidence of metabolic disease and housing in 76 farms. No positive or negative relation found.
For a score 3 for metabolic disease the mean housing score van 3.2, for a score 4 for metabolic disease the
mean housing score was 3.6 and for score 5 for metabolic disease the mean housing score was 3,41 (table
5). Eventhough the mean grade for housing is higher by farms with a relatively low risk for metabolic disease
compared to the housing grade for low risk of metabolic disease, but this only differ 0.21.
Feed
Metabolic Disease
1,00
2,00
3,00
4,00
5,00
Count
Count
Count
Count
Count
3,00
0
1
0
0
0
4,00
0
0
4
2
2
5,00
1
10
32
16
8
Table 6. Relation between incidence of metabolic disease and feed dry cows of 76 farms. No correlation found.
Sixty-four (84%) of the farms had score 3-4-5 for dry cow feed with a 3-4-5 for metabolic disease (table 6).
Ten ( 13,16%) of the farms had score 2 for dry cow feed with score 5 for incidence of metabolic disease. One
(1,3%) of the farms had a score 1 for dry cow feed with a score 5 for incidence of metabolic disease. One can
conclude that even when there are high risks taken for feed, the incidence of metabolic disease can still be
low, because this includes 11 of the 76 farms.
BCS
Metabolic Disease
3,00
4,00
5,00
Count
Count
Count
3,00
0
0
1
4,00
0
2
6
5,00
1
6
60
Table 7. Relation between the incidence of metabolic disease and BCS on 76 farms. Relation is found for BCS score 4 and 5 with incidence of
metabolic disease score 4 or 5.
All the farms scored 3-4-5 for metabolic disease with 3-4-5 for BCS. Sixty ( 78,9%) of the farms scored 5 for
BCS with 5 for metabolic disease( Table 7). Six (7,9%) of the farms had a score 4 for BCS with a 5 for
metabolic disease. Six ( 7,9%) of the farms had a score 4 for incidence of metabolic disease compared with a
17
5 for BCS. One (1,3%) of the farms had a score 5 for incidence of metabolic disease with a score 3 for BCS.
One ( 1,3%) of the farms had a score 5 for BCS with a score 3 for metabolic disease. One can conclude that
there is a positive correlation between the incidence of metabolic disease and BCS when the score is 4 or 5.
For lower scores there is no data available is this study so the correlation is only valid for score 4 or 5.
Mastitis
1,00
Mean
Housing
3,90
3,00
Count
Mean
1
3,40
4,00
Count
Mean
2
3,11
5,00
Count
Mean
15
3,50
Count
58
Table 8. Housing grade related with the incidence of mastitis on 76 farms. No relation found.
Fifty-eight (76,3%) of the farms had score 5 for mastitis (percentage of treated cows <10%) with a mean of
3,5 for housing( table 8). Fifteen (19,7%) of the farms had score 4 for mastitis ( percentage of treated cows
10%-20%) with a mean of 3,11 for housing. Two ( 2,6%) of the farms had a score 3 for mastitis ( percentage
of treated cows 20%-30%) with a mean of 3,4 for housing. One (1,3%) farm had a score of 1 for mastitis
( percentage of treated cows >40%) with a mean of 3,9 for housing. This is the highest mean score for
housing, with means that, looking at this table, the highest grade for housing correspondents with the
highest incidence of mastitis. This relation is not significant, it reflects just one farm.
18
Discussion
Koekompas is a more or less subjective tool, because most of the MCP’s have space for individual
interpretation and evaluation. The MCP’s are given in a relatively small scale: 1-5. Giving a score 1 or 2 could
possibly feel drastic to the farmer who wants a good ‘report’. This small scale also decreases a variation of
the data, which makes it difficult to see trends.
Different veterinarians reported that Koekompas is a quite expensive tool. Although it is a very suitable tool
to help veterinarians build-up their advices and to focus on disease prevention, most farmers do not
voluntary participate to Koekompas because of the price. Another often heard opinion is that after two years
of visits for Koekompas nothing new is scored or only already reported problems are rehearsed. This should
not be the case, for example silage quality differs every year. Also indoor or outdoor season influences the
cows. There lies a big challenge for veterinarians in giving advices for optimizing cow health and welfare
because society and dairy corporations mandate more guarantees in this field from their farmers.
To assure that veterinarians have a more or less common interpretation, they have to be integral cow
management (IKM)-certified before they are qualified to make a Koekompas. During a six-day training in
small groups, the IKM-trainers help them to create the right vision and view to make a proper Koekompas.
The advices and reports are also topics in the course. This tunes the common opinion but there is still space
for individual, possibly different, interpretation of the MCP’s. Still, the data used in this study were collected
from one Veterinary Center by five different vets in a relatively small area. For example, the reported
problems with water resource can be area-bounded, and probably not representative for all the farms in the
Netherlands using Koekompas. All vets are working in the same veterinary center, so it is possible that they
are all tuned for the same opinion and that this opinion is not representative for other vets in The
Netherlands.
The dataset is checked for observations about the transition period. Because there is no specific area in
Koekompas to report observations about the transition period, it can be possible that there are actually
problems in the transition period but they are not reported or not checked. Even the distinction between
the two cannot be made from Koekompas reports. From the collected report comments, one vet actually
mentioned the problem that he does not know where to rapport the transition problems. Sensitivity and
specificity are not warrant for transition cows, because they are not a separate KSF but included in several
MCP’s.
Koekompas is used as a tool to investigate risk-factors on a specific farm. The transition period is a hazardous
period for cows for developing metabolic and/or infectious diseases. Problems starting in the transition
period could persist for a whole lactation and it is thus important that the hazards among the transition
period are minimized. But for minimizing hazards, they have to be acknowledged.
For transition cows for example, it is very important to have a BCS between 3-3,51, 17. In Koekompas, only the
distribution of BCS is reported6. Big variations between cows will be reported, but one does not know the
BCS profile of the dry-cows specifically. Having a BCS > 3,5 at calving is known as a risk for developing
metabolic disease because it makes the cow less sensitive for insulin. This extends the period of changes in
the somatotrophic axis which reduces the DMI more. This together increases and extends the lipolytic state
of the cow which is a risk for ketosis, fatty liver and displaced abomasum. Thus for transition cows
specifically, one is not interested in the variation in BCS, but the specific BCS of this group of cows.
19
The mean grade for housing without a separate calving barn or separate housing for dry-cows (WSC) is
higher than the grade for housing with a separate calving barn (SC). Although the n for the WSC is smaller
than for SC, respectively nine versus sixty-seven, the mean grade should not be higher. Calving in a
suboptimal environment causes stress and increases risk for dystocia, mastitis and metabolic disease. In a
tool designed to stimulate animal welfare and reduce risks a clean, spacious and separate calving barn
should be valued higher then when this is not provided. A hygienic and separate calving barn is associated
with a lower incidence of mastitis, specifically the ones caused by Escherichea Coli, Streptococcus Uberis and
Staphylococcus Aureus.23
In table 8, the percentage of animals treated for mastitis is set next to the mean score for housing. When the
percentage of treated animals for mastitis increases (and thus scored lower) the mean score for housing
decreases as well ( from 3,5 to 3,11). There is one farm with a score 1 for percentage of animals treated for
mastitis which correspondents with a percentage of >40 treated animals for mastitis. Unfortunately, this
farm scored a 3,9 for housing which is higher than the mean score for housing for farms with a score 5 for
mastitis ( score 5 correspondents with a percentage of treated animals <10%).
It can be concluded that the presence or absence of a calving barn is not properly valued in MCP’s in
Koekompas and cannot be extracted from the KSF housing.
Table 3 shows the BCS distribution compared with dry cow feeding. Of all farms with BCS score 5, 48%
scored 3 for dry cow feeding. This shows that even when moderate risks are taken, the variation in BCS is
not always affected. Actually, of all farms with a score 1 or 2 for dry cow feeding, 83% scored 5 for BCS and
17% scored 4. Three of ten farms with score 1 or 2 for dry cow feeding reported an incidence of 5-10% of
metabolic diseases, the other farms had an incidence of metabolic disease <5%.
In Table 4 the scores for BCS and the incidence of metabolic disease are showed. Only one ( 1,3%) farm
scored 3 for BCS but scored 5 for the incidence of metabolic disease and vice versa. In literature BCS and the
incidence of metabolic disease are highly correlated, but due to the way of scoring BCS in Koekompas one
cannot find this back in the data. The conclusion that can be made, is that scoring a grade 5 for BCS
correlates with scoring a grade 5 for incidence of metabolic disease. But this correlation only suits for score
5, what happens when one score grade 2 cannot be extrapolated from this data source.
One should keep in mind that Koekompas investigates risks on a farm. The presence of certain risks does not
mean that the farmer cannot manage this risk or equals the presence of a certain disease. Still, transition
management can be represented in a more complete way.
In literature, one can find the following MCP’s which could be included in Koekompas to make it more
suitable for risk investigation of transition cows as well. These are: A specific dry-cow BCS, exact notation of
specific diseases, a separate grade for dry cow housing and the cation/anion balance in dry cow feed.
In Koekompas, disease incidence is noted, but mostly by the farmer. It is likely that he only notes the treated
animals. Also, in the case of milk-fever, it is possible that farmers treat their cows in a preventive way, for
example with calcium intravenously. This is mostly not noted as a treated cow. Due to this process,
veterinarians can underestimate the real disease incidence in farms. Not only treated animals should be
noted, but the specific disease incidence. Although tracking the rate of clinical disease is retrospective, it can
be used to react fast to problems in the future3. Treated animals are ‘tip of the iceberg’ and the potential
performance limiting health conditions can thus be underestimated.3
20
In Koekompas, the metabolic diseases ketosis, fatty liver, hypocalcemia are all caught in the incidence of
metabolic disease. But the pathogenesis of these diseases is different, thus the prevention strategies differ
as well. It should be relevant to split out the incidence of metabolic disease more for an adapted advice.
In feeding dry cows, a MCP for the negative cation/anion balance in dry cow feed can be an addition. Most
diets fed to dry cow are naturally high in potassium. This causes a metabolic alkalosis in cattle which causes a
conformation in the PTH receptor, making it less responsive to PTH. Feeding a diet with a negative
cation/anion balance, one can cause a metabolic acidosis thus an increase in the response to PTH, which can
reduce the risk for hypocalcemia. An easy method to measure the pH of a cow is measuring pH in the
urine19. This can be included in the MCP for dry cow feed ‘appropriate for group’.
21
Conclusion.
The transition cows are not a KSF in Koekompas, but they are included in the following PI’s: feed dry cows,
water dry cows, BCS, disease incidence of endometritis, retained fetal membranes, metabolic disease and
housing. Feed and water are very specific for dry cows, but BCS is measured as the distribution among the
herd, although literature reports a specific BCS for dry cows: 2,5-3,5. Disease incidence reflects only treated
animals The metabolic disease hypocalcemia and ketosis are caught in the same PI although their
pathogenesis and thus the preventive strategy is different. SARA is not included in the incidence of
metabolic disease. Dry cow housing is in literature named as a risk but is not a PI in Koekompas just like
DCAB.
The current PI’s do not give a complete impression of the risks for transition cows on the specific farms.
In this study only plane data were analyzed and reports were checked but no farms were visited to compare
the Koekompas rapports with the present situation on the farm. Further research in this field is necessary
with PI’s dry cow housing, DCAB balance, displaced abomasums and SARA incidence and real disease
incidence included or possibly a new KSF transition cows.
22
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