Directory UMM :Data Elmu:jurnal:L:Livestock Production Science:Vol65.Issue1-2.Jul2000:

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www.elsevier.com / locate / livprodsci

Prediction of energy balance in a high yielding dairy herd in

early lactation: model development and precision

a ,

_*

b a b a

C. Heuer

, W.M. Van Straalen , Y.H. Schukken , A. Dirkzwager , J.P.T.M. Noordhuizen

Utrecht University, Department of Farm Animal Health, Yalelaan 7, 3584 CL Utrecht, The Netherlands

CLO Institute of Animal Nutrition, De Schothorst, Meerkotenweg 26, P.O. Box 533, 8200 AM Lelystad, The Netherlands Received 5 May 1999; received in revised form 18 October 1999; accepted 9 November 1999

Abstract

This study was conducted to (1) predict herd mean EB, and (2) investigate whether herd size affects the precision of prediction. In order to achieve the first goal, it was studied to what extent milk test day information, body condition scores, and blood and milk ketones can estimate energy balance at cow level. EB was calculated in 72 Holstein–Freisian cows of one research herd (10,500 kg milk per 305-day lactation) during lactation weeks 2–12. A repeated measures model was used for multiple regression of predictors on energy balance. In addition to a base model (including lactation week, parity and milk yield), the fat–protein-ratio, milk fat and milk protein concentrations explained substantially more variability in EB than did milk lactose concentration and body condition scores, or tests for ketone bodies. A simulation of random selections of milk test day and animal subsets showed that sufficiently precise prediction of energy balance would require either herds larger than 150 cows or pooling of the results of subsequent test days. It was concluded that milk test day information without ketone levels or body condition scores is sufficient to estimate herd mean energy balance, but that herd size limits the precision of prediction. Model validation in other herds and rations would be the next phase of this research.  2000 Elsevier Science B.V. All rights reserved.

Keywords: Dairy cattle; Herd mean energy balance; Test day information; Metabolic parameters; Body condition scoring; Precision of prediction

1. Introduction 1991) and may result in increased time to first ovulation and failure to conceive (Senatore et al.,

Maximal energy intake and utilisation in the early 1996). The real intake of energy and other nutrients

lactation period is crucial for optimal health and often deviates from planned rations (Simensen et al.,

production of high yielding dairy cows (Goff and 1990) because dry matter intake depends not only on

Horst, 1997). Insufficient energy intake postpartum the ration but also on changing environmental factors

increases the risk of primary ketosis (Lean et al., such as climate, roughage quality, animal density,

housing conditions or feeding practices. Monitoring the nutrient intake of a high yielding dairy herd is

*Corresponding author. Tel.:131-30-253-1155; fax:1

31-30-therefore important for the management of nutrition.

252-1887.

E-mail address: [email protected] (C. Heuer) The difference between energy intake and energy

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requirement is the energy balance (EB). Possible 2.1. Cows, feeding, measurements, and

indicators of EB are body condition scores (BCS) calculations

and concentration of glucose, free fatty acids, and

ketone bodies in blood or milk (Gravert et al., 1986; At a dairy research farm (Institute of Animal

Magdus et al., 1988; Gustafsson and Emanuelson, Nutrition, De Schothorst, Lelystad, The

Nether-1996; Dirksen et al., 1997). However, the correla- lands), 72 Holstein–Freisian cows of parities 1–11

tions between EB and, respectively, blood glucose, were observed for a total period of 21 weeks

acetoacetate and beta-hydroxy-butyrate (BHB) ap- beginning at calving. The herd average 305-day milk

pear to be low (Garnsworthy, 1988), and an earlier production was |10,500 kg. The cows were

evaluation of our field data showed that high or low synchronised for the trial by timed insemination so

BCS of fresh cows or BCS-loss postpartum were that calving occurred within a short period (Sept.

unrelated to clinical ketosis (Heuer et al., 1999). 10–Nov. 26, 1996). This study on postpartum EB

Because milk recording is regularly done in most was part of a feeding trial in which the cows were

dairy herds, there is a growing interest to evaluate randomly allocated to six trial diets designed to

milk yield, fat and protein concentrations as in- compensate lack of roughage quality with four

dicators of EB. A ratio of percent milk fat over milk different types of concentrate. The ration consisted

protein .1.5 of the first test day increased the risk of a mixed grass–maize silage at a ratio of 3:1 at dry

of metabolic diseases (Geishauser et al., 1997; Heuer matter weight basis, and the four different trial

et al., 1999). The correlation between the fat– concentrates fed at increasing amounts from 5.5 kg

protein-ratio and EB among cows was in the range of (day 112) to 14.0 kg (after day 26). The difference

20.36 to 20.74, and the fat–protein-ratio explained in roughage quality between the control and the five

19–21% of the variance of EB in a multifactorial experimental diets turned out to be small. It only

analysis of various feeding experiments (Grieve et affected the net energy content and amounted to 0.20

al., 1986; Gravert, 1991). MJ NEL / kg DM. All experimental rations were

In addition to the fat–protein-ratio, EB is associ- formulated to meet requirements (Van Es, 1978;

ated with lactation stage, milk yield, age, BCS, and Tamminga et al., 1994). The ration composition,

metabolic parameters, such as BHB (Grieve et al., DMI and balances of energy, protein and rumen

1986; Garnsworthy, 1988; Gravert, 1991). Hence, nitrogen are shown in Table 1.

more valid estimates of EB may be obtained if The trial diets started in lactation week 6 after a

effects of age, lactation stage, milk yield, BCS, and pre-trial period of 5 weeks during which good

BHB would be considered in the analysis of milk test quality roughage and a standard concentrate were fed

day information. to all cows. The transition to moderate quality

The ultimate objective of this study was to predict roughage occurred abruptly within 1 day, the shift to

the mean EB of cows in a dairy herd during the first the different types of concentrate gradually within 1

3 months of lactation and to evaluate the effect of week (week 6–7). Roughage was mixed and fed

herd size on the precision of prediction. individually ad libitum twice a day (07:00 h, 15:00

h) facilitated by transponder operated feed-troughs for individual cows. Leftovers were weighed and

2. Materials and methods deducted from total amounts fed. They were negli-gible and therefore not analysed. Concentrates were

For the development of a prediction model at herd provided to the cows individually three times a day

level, EB was estimated at cow level in the first part (07:00 h, 14:00 h, 21:00 h).

of this study. In that, test day information, BCS, and The cows were weighed twice a day after milking

several metabolic parameters were tested using sim- (06:00 h, 16:30 h), hence the morning weight was

ple comparisons and multifactorial models. The taken before and the afternoon weight after feeding.

resulting prediction equation was used in the second Milk was tested for weight, fat, protein and lactose

part to estimate herd mean EB and to simulate loss four times per week (Monday and Wednesday

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Table 1

Ration composition, dry matter intake (DMI), energy balance (EB), protein balance, and rumen nitrogen balance (OEB) based on the individual intake of roughage and concentrates of 72 cows (weekly measurements)

Mean S.D. Min Max

Ash (g / kg) 97.44 1.71 92.22 102.09

CP (g / kg) 180.21 4.10 173.14 192.12

Fat (g / kg) 39.56 4.48 32.10 53.38

Crude fiber (g / kg) 172.99 7.50 150.34 193.34

Neutral detergent fiber (g / kg) 380.84 12.04 340.24 408.44

Starch (g / kg) 124.66 5.83 111.56 150.21

Sugar (g / kg) 67.51 5.02 58.37 90.81

Digestible organic matter (g / kg) 393.30 27.07 297.15 457.85

Energy for lactation (MJ NEL / kg) 6.86 0.10 6.70 7.32

Digestible protein (DVE / kg) 98.74 2.81 93.07 111.19

OEB / kg 24.33 5.01 14.00 34.00

Daily ration parameters:

DMI (kg / day) 23.58 4.1 5.9 32.4

Energy balance (MJ NEL / day) 22.46 14.9 280.5 45.7

Protein balance (DVE / day) 181 221 2807 954

OEB / day 585 174 0 949

DVE, true protein digested in the small intestine (Tamminga et al., 1994).

OEB, rumen nitrogen balance (Tamminga et al., 1994).

the milk was tested every Wednesday evening imme- Missing BCS were interpolated from adjacent weeks.



diately after milking with the Ketolac test (KET; If clinical disease occurred it was observed and



Ketolac BHB, Hoechst, Unterschleissheim, Ger- recorded by qualified technical staff. Once a week a

many) to estimate the concentration of beta-hydroxy- roughage sample was taken. The samples of 3

butyrate (BHB), and with the sodium nitroprusside subsequent weeks were pooled and stored for

analy-test (NP; Utrecht powder (Geishauser et al., 1998)) sis after the end of the trial. Four times during the

to detect acetoacetate. Colour changes in the reaction trial a sample of each of the trial concentrates was

zone of KET were visually compared with the colour collected for testing. Both roughage and concentrate

plates provided with the product and interpolated to samples were tested in duplicate for water, ash, crude

obtain milk BHB at a linear scale in mmol / l. protein, fat, crude fibre, starch, sugar, and in vitro

Samples without apparent colour change were scored digestibility according to standard methods (Tilley

zero. A slight pink appearance in the white NP-milk and Terry, 1963; Bedrijfslaboratorium voor Grond en

mixture was recorded as a doubtful reaction (scored Gewas (BLGG), Oosterbeek, NL).

0.5), a clear change to red as a positive reaction, The calculations of energy requirement and energy

scored 1 or 2 depending on colour intensity, and intake were based on the Dutch ‘VEM’ standard

otherwise as negative reaction (scored 0). Blood was (Van Es, 1978) for net energy lactation (NEL). The

collected in weeks 2, 3, 4, 5, 7, and 9 approximately energy requirement included maintenance, fat–

3–4 h after the Wednesday morning feeding. The protein-corrected-milk (FPCM), and a correction for

blood was drained in heparinised vacuum containers level of production and growth. Energy intake was

and tested for plasma-BHB, and hemolysis (Jacobs et calculated from dry matter contents and nutrient

al., 1992). Plasma samples were analysed if they digestibility corrected for energy losses from rumen

contained less than 0.25 mmol / l haemoglobin. Once (methane), faeces and urine (Van Es, 1978). EB was

a week at the time of blood sampling all cows were the difference between intake and requirement. It

visually scored for body condition (BCS) at a scale was converted from VEM to MJ NEL by the formula

of 1–5, 1 being emaciated and 5 obese (Edmonson et 1 MJ NEL56.907 kVEM. Protein and rumen

nitro-al., 1989). The three persons taking the BCS stan- gen balances were calculated according to the Dutch

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The measurements on individual cows were aver- the combined effects of several predictors of EB

aged per week. After it had become apparent that the using the MIXED procedure in SAS (1992). Because

EB was at its maximum at the end of the third month repeated weekly determinations on the same cow

of lactation and changed very little thereafter, all were likely correlated, this correlation within cows

observations of week 13 postpartum and later were was controlled in the analysis by a repeated

measure-excluded from further analysis. Data of the first week ments model. The model was of the following form

of lactation were excluded because of the possible (Diggle et al., 1994):

effect of colostrum on milk composition. Thus, only

lactation weeks 2 to 12 were evaluated. All analyses Y5Xb 1e(R)

were done on a weekly basis.

where Y is the vector of EB measurements for each

2.2. Model development cow and week, X is the matrix of variable values of

fixed effects (constant, week, experimental diet, par,

If scatter plots suggested simple linear relation- milk yield, percent milk solids, the fat–protein-ratio,

ships, Pearson correlation coefficients were com- BCS, KET, NP, BHB), b is the vector of estimated

puted to examine direct associations between EB and coefficients for the fixed effects, e(R) is the vector of

diagnostic variables. Cut-off values were defined for residuals adjusted for correlation between subsequent

the analysis of sensitivity and specificity of the weekly measurements within cow (SAS, 1992) and

diagnostic variables and EB. Sensitivity was defined R is the correlation matrix containing the estimated

as the proportion of low calculated EB which was correlation of predicted EB between weeks.

correctly estimated as low EB by a diagnostic The model contained week postpartum (week),

parameter, and specificity was the proportion of the experimental diet (diet) and parity (par), as fixed

remaining ‘normal’ calculated EB which was cor- effects at the categorical scale and daily milk yield

rectly estimated as ‘normal’. Cut-off values of and the diagnostic variables at the continuous scale.

estimated EB were for the fat–protein-ratio .1.4, The restricted maximum likelihood (REML)

es-.1.5, and .1.6, for BCS $3.5, for BHB .1.2 timator was used as criterion for the fit of the model

mmol / l plasma, and for KET $100 and .200 (Diggle et al., 1994). The REML is adjusted for the

mmol / l milk. These values were based on critical number of parameters in the model. The difference

levels defined in other studies (Nielen et al., 1994; between two models of the quantity 223REML is

Geishauser et al., 1998; Heuer et al., 1999). Cut-off approximately distributed as a chi-square with

de-values of estimated EB for milk fat, protein, and grees of freedom of the additional parameter(s) in the

lactose concentrations were chosen from observed larger model. A good model is characterised by a

data distributions. In these, estimated low negative high REML relative to a null model with only the

EB was defined as the approximate upper or lower intercept. The REML-difference between the fitted

15% of the data, i.e. .4.8% fat, ,2.9% protein, model and the null model divided by the REML of

,4.5% lactose. Selected thresholds for low calcu- the null model is equivalent to an R of least squares

lated EB were the lower 10% (, 220.5 MJ5 analysis of variance. The difference in the R of two

severe negative EB) and the lower 25% (, 211.4 models is attributable to the parameters in the model.

MJ5moderate negative EB) of the weekly EB This R was therefore used as criterion for the

averages. Additionally, the test sensitivity and spe- relative contribution of each parameter to the

expla-cificity of KET, NP and the fat–protein-ratio to nation of variability in EB.

detect hyperketonemia was examined. This analysis A series of models containing several

combina-used BHB as gold standard for hyperketonemia at tions of fixed effects were fitted. Initially, the relative

the cut-off point .1.2 mmol / l (Nielen et al., 1994). impact of experimental diet, lactation week, parity

Differences in test sensitivity or specificity were (1, 2, 3, $4), milk yield, percent milk fat, percent

tested for significance by the McNemar method for milk protein, percent milk lactose, and the fat–

paired observations (Thrusfield, 1995). protein-ratio about EB was evaluated. Extra

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consid-ering BCS, milk ketone bodies (NP, KET), or BHB cows in the sample based on the 200 runs were

in blood in addition to a selected model from the computed for each of the lactation weeks 2–12.

initial analysis. Interactions between week and the Subsequently, the herd sample was further

de-fat–protein-ratio, BCS, KET, NP, or BHB were also creased by a similar random sampling procedure. In

tested because these parameters could have a differ- the second simulation, one milk test was selected

ent association with EB in different lactation weeks. from all weekly tests of each cow. This resembled

One ‘best model’ was selected for prediction of the situation that milk test day information was

herd mean EB within lactation week. The residual available for all cows from only one single test day.

plot of that model was used as criterion for the In two further simulations for estimating impact of

model fit regarding the homogeneous variance as- herd size, it was assumed that only 40 or 20 cows

sumption as well as to describe the variability of EB provided one milk test to the prediction of mean EB.

prediction of individual cows. The ‘raw’ residuals Each of these sampling patterns was repeated 200

were defined as difference between observed and times, as before, and population standard deviations

predicted EB. and average number of cows in the sample were

calculated for each week of lactation. Table 2 shows

2.3. Prediction of herd level EB the sampling schemes for the four simulations.

Cow-test-days were randomly selected to simulate

decreasing herd size and to describe the expected 3. Results

difference between the predicted herd mean EB of all

test days and the herd mean EB of a reduced set of 3.1. Model development

predictions. The expected difference was expressed

as 1 S.D. of the herd means resulting from repeated Complete feed and milk test data were available

random selections. from 52 cows in week 2, from 71 cows in week 3,

Under conditions of milk recording in the field, and from 72 cows in weeks 4–12 postpartum. Hence,

only one milk test result per month is available from 771 weekly observations were available for the

each lactating cow. To examine the loss of precision prediction of EB by test day information. Because

in predicted herd mean EB when only one instead of BCS, KET and NP were not available from all milk

four milk tests were available, one milk test (i.e. one test days, a second data set with 711 complete

weekly average of four individual tests) was random- observations was used in the analysis. After further

ly selected from the four tests of each cow each exclusion of data from those weeks during which

month. Thus, every cow contributed three instead of blood was not collected, a further reduced third data

11 subsequent milk tests to the prediction of herd set with 354 observations contained the previous

mean EB. This random selection was repeated 200 information plus data on BHB and hemolysis from

times, which was regarded sufficient for stable weeks 2–5, 7, and 9, respectively.

standard deviations of the resulting herd means. Means and variability between cows of DMI,

These standard deviations and the average number of nutrient balances and the variables used for

predic-Table 2

Sampling schemes for the simulation of herd mean prediction of energy balance

Simulation No. of fresh No. of tests Test weeks Approximate Evaluation

run cows herd size period

1 72 3 of 11 2–12 280 3 months

2 72 1 of 11 2–12 280 1 month

3 40 1 of 11 2–12 160 1 month

4 20 1 of 11 2–12 80 1 month

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Table 3

Mean, standard deviation (S.D.) and range (min, max) of the study variables

Mean S.D. Min Max

First data set (771 weekly measurements):

Milk production, kg / day 38.2 7.9 21.9 57.8

Milk fat (%) 4.29 0.50 2.9 6.4

Milk protein (%) 3.16 0.22 2.6 3.9

Milk lactose (%) 4.62 0.13 4.1 4.9

Fat / protein ratio 1.36 0.15 0.9 2.0

Fat–protein ratio %.1.4 (per week) 35.8 7.3 29.2 49.3

Fat–protein ratio %.1.5 (per week) 16.6 4.8 9.7 25.4

Fat–protein ratio %.1.6 (per week) 6.4 3.1 1.4 11.3

Second data set (711 weekly measurements):



Ketolac %.100mmol / l (per week) 10.7 5.7 1.7 21.5



Ketolac %$200mmol / l (per week) 1.3 1.3 0 9.2

Nitroprusside doubtful or positive % (per week) 8.2 7.7 0 18.5

Nitroprusside positive % (per week) 3.8 3.6 1.7 4.3

Body condition score 3.1 0.4 1.8 4.3

Body condition score %$3.5 (per week) 23.9 4.7 12.5 41.5

Third data set (354 weekly measurements):

Bloodb-OH-butyrate %.1.0 mmol / l (per week) 9.1 4.9 1.8 14.3

Bloodb-OH-butyrate %.1.2 mmol / l (per week) 6.3 3.6 0 10.0

tion of EB are summarised in Tables 1 and 3. Most cificity between the fat–protein-ratio and KET, NP,

cows were in negative EB for up to 8 weeks or BHB were significant (P,0.05), with the

excep-postpartum. No cow was treated for clinical ketosis tion that the specificity difference between KET and

within the first 21 lactation weeks. Protein supply the fat–protein-ratio was not significant for the

was below requirements only in week 2; its increas- detection of moderate EB (lower 25% EB).

ing trend was almost parallel to that of EB (r50.80). The results of the multivariate prediction models

Optimal detection of ketonemia, defined as plas- are shown in Table 5. Lactation week, parity and

ma-BHB .1.2 mmol / l, was achieved by KET at milk yield were significant factors that increased the

cut-off $100mmol / l milk with 88% sensitivity and R of the model to 18.9%. This ‘base model’ was

91% specificity, and by a positive NP (doubtful further improved by 6.2% due to the inclusion of

results regarded positive) with 83% sensitivity and milk solids. Milk fat concentration and the fat–

92% specificity. At the linear scale, BHB and KET protein-ratio had profound, milk protein small but

correlated well (r50.81) and BHB and NP moder- significant, and milk lactose concentration small and

ately (r50.67). Sensitivity (67%) and specificity insignificant effects on the EB model. Because the

(84%) were lower for the fat–protein-ratio at cut-off fat–protein-ratio was strongly correlated with percent

.1.5 (r50.38). However, the sensitivity for the milk fat (r50.81) and the correlation between the

detection of EB by those test parameters showed an fat–protein-ratio and milk protein was small (r5

inverse trend (Table 4). Low EB was better detected 0.19), the model including the fat–protein-ratio and

by the fat–protein-ratio than by KET, NP or BHB. percent milk protein was selected as final model for

At the cut-off value .1.5, the fat–protein-ratio had prediction of EB at cow level (R 525.1%, Table 6).

sensitivities of 41–51% (r5 20.43) while the other The different experimental diets affected EB

dur-test parameters only achieved sensitivities of 14– ing transition to the experimental diets in week 6 and

31% (r5 20.32 to 20.40). At the same cut-off onwards, but not during the pre-trial period in which

values, the test specificity of KET (91–93%), NP negative EB was most severe (weeks 2–5). The

(94–97%) or BHB (97–98%) was 4–10% higher inclusion of a diet3week interaction caused a 4%

than the specificity of the fat–protein-ratio (87–91%, increase in R compared to the selected model, and

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Table 4

Sensitivity and specificity of milk test data, body condition score, milk ketone bodies (Ketolac, Nitroprusside), and blood metabolites to detect negative energy balance

Cut-off n Negative energy balance

sensitivity / specificity (%)

Lower 10% Lower 25%

Percent milk fat .4.8 771 39 / 87 28 / 89

Percent milk protein ,2.9 771 17 / 85 18 / 86

Percent milk lactose ,4.5 771 27 / 87 22 / 88

Fat:protein-ratio .1.4 771 66 / 68 61 / 73

.1.5 771 51 / 87 41 / 91

.1.6 771 29 / 96 19 / 98



Ketolac ,mmol / l $100 726 29 / 91 24 / 93

$200 726 14 / 98 10 / 99

Nitroprusside Doubtful1pos. 733 31 / 94 24 / 97

Clearly pos. 733 18 / 98 12 / 99

Body condition score $3.5 759 23 / 77 23 / 77

Bloodb-OH-butyrate, mmol / l .1.0 376 28 / 93 19 / 96

.1.2 376 25 / 97 14 / 98

Total number of 9–11 weekly measurements per cow 2–12 weeks postpartum (72 cows).

by 2%. Because a high fat–protein-ratio during perimental diets in weeks 6–7. However, the

addi-weeks 3–7 of lactation coincided with more severe tion of KET did not provide a better fit to the model

negative EB than in other weeks, the interaction despite a significant week3KET interaction. Body

between the fat–protein-ratio and lactation week condition scoring and NP decreased the standard

(P,0.001) increased the R by 1.3%. An additional deviation of the residuals by 3.5–4.5%, thus,

in-interaction between the fat–protein-ratio and parity creased precision to a small extent.

was only significant for first calving cows with little After milk protein and the fat–protein-ratio had

improvement of the model fit (R 526.7%, Table 5). been added to the base model, BHB did not provide

Stratified for parity, predicted EB was lowest for more information about EB. But increased BHB was

cows in first and fourth or higher lactation, and cows associated with lower EB in week 7 (P,0.001)

in second, and to a lesser extent in third lactation had similar to NP and KET. BHB appeared unrelated to

a higher apparent energy status (Fig. 1); they re- EB in any other week. Nevertheless, this interaction

turned to positive EB 2–3 weeks earlier than cows of was significant and increased the R by 1.2% (Table

parity 1 or 4. 5), but improved the precision of prediction only

The addition of KET, BCS or NP to a selected slightly.

cow model already including milk protein and the The residual plot (Fig. 2) indicated a

homoge-fat–protein-ratio did not increase the model fit neous variance. With the exception of two outliers,

significantly. However, higher BCS within the range the differences between observed and predicted EB

of 1.75–4.25 was associated with higher EB in were normally distributed around zero. The average

weeks 2 and 3 (P,0.01), and with slightly lower EB standard deviation of the differences between

ob-in weeks 6–9 (P,0.05). This interaction improved served and predicted EB was 9.31 MJ (8.83 MJ

the model fit by 1% (R from 25.0% to 26.0%). Also excluding outliers) with little variation between

NP interacted significantly with week postpartum weeks postpartum.

and that interaction increased the R by 0.9%. NP

test results were associated with lower EB during 3.2. Prediction of herd level EB

weeks 6–8. Similarly, EB was lower when KET was

high in weeks 3, 6 and 7. The effects of higher NP The calculated herd mean EB started low in week

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equilib-Table 5

Proportion variation (R ) of energy balance explained by covariates and interactions in comparison to the null model containing only the

intercept

R N5771

Intercept*** (null model)

Week*** 17.5%

Week***, parity** 18.0%

Week***, parity***, milk yield*** (5base model) 18.9%

Base model1protein 19.0%

Base model1lactose** 19.1%

Base model1FP*** 24.5%

Base model1fat*** 24.7%

Base model1protein , lactose* 19.2%

Base model1lactose , FP*** 24.5%

Base model1fat***, lactose 24.8%

Base model1fat***, protein** 24.8%

Base model1fat***, FP*** 24.9%

Base model1protein***, FP*** (5selected model) 25.1%

Selected model1week3FP*** 26.4%

Selected model1week3FP***, FP3parity*** 26.9%***

b d

Selected model1week3treat*** 29.1%***

N5711

Selected model 25.0%

Selected model1BCS3week*** 26.0%***



Selected model1Ketolac 3week*** 24.1%

Selected model1Nitroprusside3week*** 25.9%***

N5354

Selected model 20.8%

Selected model1BHB3week* 22.0%***

Protein, % milk protein; lactose, % milk lactose; fat, % milk fat; FP, fat–protein-ratio; treat, experimental diet; BCS, body condition score; BHB, plasma beta-hydroxy-butyrate.

Treat represented six experimental diets applied from week 6 onwards.

c 2

The lower R of the selected model in this reduced data set was attributed to the fact that BHB in blood was not determined in lactation weeks 6, 8, 11, and 12.

Comparison with the selected model, ***P,0.001.

*P,0.05, **P,0.01 and ***P,0.001; ns, not significant (P$0.05).

rium after 8 weeks of lactation. Weekly calculated Table 7 summarises the results of the simulation

mean EB ranged from 220.0 MJ in the second to examining the precision of herd level prediction. In

111.4 MJ in the twelfth week of lactation. the first sampling scheme when three of 11 milk tests

The cow model including lactation week, parity, were randomly selected (n5209), the herd means of

milk yield, milk protein concentration, and the fat– 200 runs resulted in standard deviations of 1.9–2.4

protein-ratio was selected for prediction and exami- MJ for the 11 lactation weeks. Reducing the sample

nation of EB at herd level (Table 6). Calculated and further to one selected from 11 milk tests per cow

predicted means of EB were almost identical from (n570) resulted in standard deviations of 4.1–4.8

week 3 to week 12 (Fig. 3). There was a small MJ. The same sampling scheme but with 40 instead

difference between predicted and observed means in of 72 cows in the herd increased the standard

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Table 6 _{4. Discussion} Coefficients of the final model selected for prediction of herd

mean energy balance

This study primarily investigates the hypothesis

Parameter Coefficient 95% Conf. interval P _{that EB in early lactation calculated from cow side}

Constant 217.77 195.20, 240.34 0.0001 data and averaged at the level of the herd can be

Week 2 231.92 235.68, 228.16 0.0001 _{used to evaluate the performance of a common dairy} Week 3 220.64 223.07, 218.21 0.0001 _{ration to provide energy for lactation. For the} Week 4 215.56 217.89, 213.23 0.0001

purpose of this study we assumed that the energy

Week 5 211.53 213.67, 29.39 0.0001

content of the common ration of this experiment

Week 6 27.98 29.85, 26.11 0.0001

Week 7 210.62 212.73, 28.51 0.0001 (grass–maize silage plus concentrate) calculated

Week 8 27.21 28.69, 25.73 0.0001 _{according to the current feed evaluation system (Van} Week 9 25.29 26.86, 23.72 0.0001 _{Es, 1978) was an appropriate ‘gold-standard’ for true} Week 10 23.95 25.19, 22.70 0.0001

EB. Even though ration components in 1996 may

Week 11 22.66 23.94, 21.38 0.0001

differ from those used before 1978, energy intake

Week 12 0.00

Parity 1 234.91 240.39, 229.43 0.0001 and energy required for lactation appeared to

corre-Parity 2 27.20 211.65, 22.75 0.0023 _{late well (r}_._{0.66) when the evaluation system was} Parity 3 26.67 211.74, 21.60 0.0120 _{applied to today’s rations (Saama and Mao, 1995).} Parity 41 0.00

Therefore, the chosen energy evaluation system was

Milk 22.11 kg 22.34, 21.87 0.0001

likely to be a valid standard to estimate prediction

Protein 215.36% 219.76, 210.96 0.0001

FP 249.24 253.01, 245.47 0.0001 coefficients for the model parameters. The work of

a 2 Saama and Mao (1995) also indicated that there may

n5771, R 50.25; milk, milk yield; protein, % milk protein;

be a constant difference between the calculated

fat, % milk fat; FP, fat–protein-ratio.

energy intake based on the system and calorimetric measurements. This might have affected the intercept of our model, but the parameter coefficients would most likely be unaffected.

4.1. Model development

Calculated energy balance is the difference be-tween energy intake and energy requirement. The calculation of energy requirement was based on body weight, parity, milk yield, and concentrations of milk fat and milk protein. Hence, the total energy

require-2

ment correlated well with milk yield (r 50.90) and

fairly with body weight (r 50.53). Since milk yield

and milk solids represented the requirement part in both calculated and predicted EB, a close correlation was expected between calculated and predicted EB,

Fig. 1. Predicted energy balance by lactation number (parities 1,

merely by definition. However, the correlation

co-2, 3 or 4 and higher). The horizontal line indicates the median, the

2 box 25% to 75% of the data, the vertical lines typical values, and _{efficients between EB and energy requirement (r} 5

stars depict possible outliers. _{0.06), EB and milk yield (r} ₅_{0.04) or EB and milk} 2

solids (r 50.001 to 0.06) were small. Therefore, the

quantity of milk and milk solids could only explain a

the herd size was 20 cows the standard deviations small part of the association between milk test day

were 6.7–8.2 MJ. Fig. 3 shows the loss of precision information and EB. A larger part must have been

in relation to mean predicted EB by week of due to energy intake, the generally unknown

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Fig. 2. Plot of residuals (observed minus predicted energy balance) versus predicted values of model coefficients from Table 5; standard deviation indicated as horizontal band above the zero line (771 weekly measurements, 72 cows).

Fig. 3. Herd means of post partum energy balance: observed and predicted means of the total of 771 observations (72 cows), and predicted means minus standard deviations of reduced sample or herd sizes. Standard deviations calculated from the means of 200 repeated random selections.

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Table 7

Precision of predicted herd mean energy balance (EB as MJ NEL) at decreasing sample (n) and herd size in comparison to observed values; population standard deviation of 200 predicted herd means obtained by repeated random sampling from the pool of 771 milk tests (72 cows, 11 weekly tests, 2–12 weeks p.p.)

2 3 4 5 6 7 8 9 10 11 12

Mean observed EB (n5771) 220.0 213.7 210.7 26.5 23.7 24.1 0.3 2.7 4.9 7.5 11.4 Mean predicted EB (n5771) 222.6 214.0 210.9 26.7 23.9 24.3 0.1 2.5 4.7 7.3 11.2 Standard deviation of predicted herd level EB:

3 months, 72 cows (n5209) 2.0 1.9 1.9 2.2 2.2 2.4 2.2 2.3 2.2 1.9 2.1 1 month, 72 cows (n570) 4.3 4.1 4.5 4.8 4.7 4.5 4.0 3.9 4.0 4.0 4.2 1 month, 40 cows (n540) 6.2 6.1 4.4 5.9 5.8 6.5 5.3 5.6 5.9 5.1 5.4 1 month, 20 cows (n520) 6.9 8.2 6.7 9.3 8.1 7.6 8.1 7.3 8.3 7.7 7.8 Average number of cows in the sample:

3 months, 72 cows (n5209) 17.3 23.7 24.0 17.7 18.3 18.0 17.9 18.1 18.1 17.8 18.1 1 month, 72 cows (n570) 4.7 6.5 6.5 6.6 6.6 6.5 6.5 6.5 6.6 6.5 6.6 1 month, 40 cows (n540) 2.8 3.6 3.8 3.9 3.5 3.6 3.8 3.7 3.7 3.9 3.9 1 month, 20 cows (n520) 1.5 1.9 1.9 1.8 1.8 2.0 1.7 1.9 1.8 1.9 1.9

The level of EB, time of return to positive EB, and ketonemia with lower reliability than KET or NP.

time to maximal dry matter intake were similar as in But it detected calculated low EB more reliably than

the study of Gravert et al. (1986) who evaluated KET or NP. In the classification of ‘normal’ EB,

ketone bodies as indicators of EB. Senatore et al. however, BHB, KET and NP performed slightly

(1996) used a total mixed ration resulting in a much better (higher specificity) than the fat–protein-ratio.

higher and longer energy deficit in a study of the Poor relationships between BHB and EB were also

effect of energy deficit on fertility. Both studies reported by Erfele et al. (1974). BCS was a poor

calculated the diets to meet requirements. The energy diagnostic aid for calculated low or normal EB in

deficit of the cows in our study may therefore be this study (low sensitivity, low specificity).

categorised as moderate. We deem the data base In the multivariate prediction model, cow was not

suitable for model development because a moderate included as fixed or random effect, but was corrected

post partum energy deficit may be expected in most for in the error structure. We intended to develop a

high yielding dairy herds. The model is supposed to population (not cow) specific prediction equation for

be applied for such herds. EB (Diggle et al., 1994). Modelling criteria were a

As in similar studies (Gravert, 1991; Dirksen et good fit (high R , low residual variation), a low

al., 1997; Geishauser et al., 1998), the variance of degree of collinearity among covariates, and a low

sensitivity and specificity of ketonemia might have number of parameters. Increasing the number of

been biased because test results of subsequent sam- parameters in the model would increase the R but

ples from the same cow were not independent. make the model too similar to the data and

supposed-Correlated samples are likely to produce smaller ly less applicable to other herds and rations. In fact,

variances (McDermott et al., 1994). Nevertheless, additional interaction terms yielded only marginal

hyperketonemia, indicated by blood BHB .1.2 gains for the precision of prediction. Therefore,

mmol / l, was detected by field tests (KET, NP) interaction terms were excluded from the final

slightly more reliably (higher sensitivity) than and prediction model. A high correlation between milk

normal ketonaemia as reliable (similar specificity) as fat and the fat–protein-ratio was the reason for

reported earlier (Dirksen and Breitner, 1993; Nielen substantial collinearity. Because the fat–protein-ratio

et al., 1994; Geishauser et al., 1998). Sensitivity and predicted EB better than milk fat, the latter was

specificity of the fat–protein-ratio in milk were omitted.

slightly higher than reported (Duffield et al., 1997). The final model showed that lactation stage,

(12)

fat–protein-ratio were strong predictors of EB. The considefat–protein-ration parity and week of lactation. However, the

coeffi-of the fat–protein-ratio almost fully replaced further cient indicated a drop in EB of 2.11 MJ NEL with

tests of ketone bodies in blood or milk, despite every additional kg standard milk (provided FP

6.9–10.0% sub-clinical ketosis in the first 5 weeks of remains constant). Applying the prediction model to

lactation. The level of ketonemia in this study was, herds with higher milk production would therefore

however, somewhat lower than in other studies of result in more negative EB compared to herds with

high producing dairy herds (Dohoo and Martin, lower production. And vice versa, a herd that

1984; Nielen et al., 1994; Duffield et al., 1997). A decreased milk production, for example as a

conse-mild state of negative EB may more often or more quence of a severe energy deficit, would exhibit high

readily result in an elevated fat–protein-ratio than in EB. Consequently, the model predictions cannot be

solely increased ketone body levels in blood or milk. reasonably interpreted without simultaneous

consid-Ketone bodies may rise when negative EB is increas- eration of the average level of production. Only if a

ingly de-compensated. All cows in this study were herd maintains its average production level could a

able to compensate negative EB, because ketone lower model prediction be regarded as a relative

bodies rarely accumulated to high concentrations. An decrease of EB.

instant increase of the fat–protein-ratio in response As the diagnosis of EB by single test parameters

of low EB is in agreement with earlier observations was affected by lactation stage, parity, and milk

(Grieve et al., 1986; Gravert, 1991). Hence, adipose yield, the interpretation of individual results of milk

cells mobilise while mammary cells synthesise tri- or blood tests with respect to EB should take these

glycerides in response to negative EB. The differ- effects into account. Nevertheless, even after

consid-ence in response was called ‘acetate partitioning’ by eration of these effects, some 75% of the variability

Kronfeld (1982) who suggested that, in early lacta- in EB remained unexplained (R 525%). However,

tion, acetate is converted to milk fat to a greater given that microbial fermentation, rumen absorption

extent by the mammary gland than by adipose of volatile fatty acids, digestive interactions between

tissues, and that it is reversed in later lactation. The rumen and abomasum, intestinal absorbtion,

regula-suggestion was confirmed by MacNamara et al. tory mechanisms of the intermediate metabolism,

(1995) who estimated that, in early lactation, the passage from blood to mammary parenchyma, and

mammary gland synthesised milk fat while adipose synthesis of lactose, fat and protein by the mammary

tissue mobilised fat in response to added dietary fat; gland may all affect the association between

feed-the opposite was observed in later lactation. NEL and milk constituents, we regard the strength of

Milk protein was also a highly significant predic- the observed association as relatively high.

More-tor but had relatively little bearing on EB. Milk over, the cow level model was only used as a tool to

protein percent correlates positively with energy identify important prediction parameters. At this

intake (Magdus et al., 1988). Therefore, low energy step, a moderate model fit may be sufficient. There

intake results in a higher fat–protein-ratio. Conse- was no specific interest in cow-level prediction of

quently, part of the variation due to milk protein was negative EB in this study. More crucial was the

probably represented by the fat–protein-ratio in our certainty (or uncertainty) of herd level prediction

data. Because of its known relationship with energy because the model was developed to estimate

aver-intake, we included milk protein in the final model age herd EB in order to provide a monitoring tool for

even though it only contributed 0.1% to the model the provision of net energy through the ration.

R of this study. The model coefficient for milk yield Because Fig. 2 suggests that the 75% of the

remain-was negative indicating lower EB with increasing ing variation in EB not explained by the predictors

yield. This negative effect was partly compensated was random variability, the precision of the predicted

for by an inverse relationship between milk yield and herd means was expected to be considerably more

milk protein (r5 20.37). The impact of milk yield precise than the predictions for individual cows.

on the variation of EB was relatively small (R 5 With the exception of week 7, observed and

(13)

The dip in week 7 was presumably caused by feed November 26. Therefore, the difference in week 2

changes due to the experimental diets starting in was the sum of two possible effects: a smaller

week 6. We did not correct the week 7 coefficient for sample and calving seasonality. Seasonal effects

this effect because the herd simulation intended to were probably small because the cows were

estimate the precision of prediction, and this was synchronised, kept indoors and received the same

unlikely to change after correction. However, before ration throughout the study period. We therefore

applying the prediction equation of Table 4 to other believe that the difference was most likely caused by

herds and rations, it may be reasonable to adjust the sampling variation.

coefficient for week 7, e.g. by replacing it with the The standard deviations (S.D.) of herd means

average of the adjacent weeks 6 and 8. increased with decreasing number of cows in the

Cows in first lactation were more severely and sample. When EB prediction was based on only one

longer in negative EB than cows in lactation 2 or 3 of 11 available weekly milk tests per cow (n570),

(Fig. 1). It was previously reported that EB de- the weekly means were based on 4–7 cows and the

creased with increasing parity (Lean et al., 1991; herd means varied by an equivalent of 61.5 kg

Duffield et al., 1997), but in agreement with our standard milk (Table 5). In order to have 70 cows

findings, Dirksen and Breitner (1993) observed 2–12 weeks in milk at one point in time, the total

higher BHB levels postpartum in primiparous than in herd size would have to be|280 cows (4370 cows,

multiparous cows. A potentially greater risk of first non-seasonal calving). Compared to the predictions

calving heifers than older cows for severe negative for individual cows (S.D.59.3 MJ / day), the

vari-EB renders more investigative work. ability of the herd means of these 72 cows was less

It was not surprising that BCS was a poor than half as large (S.D. |4.3 MJ / day).

predictor of EB in our study. Although the propor- A test day was defined as one weekly average

tion of cows in relatively obese condition (BCS$ milk test result randomly selected from four

avail-3.5) was 41.5% in lactation week 2 and decreased to able results within a month. Pooling 3 subsequent

12.5% in week 11, the variability in BCS throughout test days improved the precision further by over

the first 12 weeks of lactation was probably too small 100% (S.D. |62 MJ, 0.7 kg standard milk). On the

to reveal significant associations with EB. Using other hand, EB prediction based on 1 test day of 40

body condition loss instead of BCS did not improve cows (projected total herd size of 160 cows) reduced

the model either (data not shown). Moreover, the the precision by 33% compared to 70 cows with 1

relationship between BCS at calving and milk pro- test day. A herd with 20 fresh cows, equivalent to a

duction in the first 12 weeks postpartum is not clear total herd size of 80, did often not have enough cows

(Garnsworthy, 1988), the risk of ketonemia and to compute weekly EB means for each of the 11

ketosis was the same for cows in obese or in normal weeks (S.D.567.5 MJ, 2.5 kg standard milk). If

condition at calving (Smith et al., 1997), and BCS mean EB was based on one or two cows, individual

was regarded as not sufficiently sensitive to reveal cow effects made the mean highly variable. Hence,

substantial changes in EB (Senatore et al., 1996). herd size was a limitation to herd level prediction of

EB based on test day information. Although pooling

4.2. Prediction of herd level EB subsequent test days efficiently increased precision,

it may not be desirable to evaluate new rations as

Overall means of observed and predicted EB were late as 2–3 months after the feed change. Collapsing

nearly identical, indicating that the prediction was the initial 12 weekly into 3 monthly means may not

unbiased. However, predicted EB was slightly lower be advisable either, because EB was particularly

than observed EB in lactation week 2 when only 52 critical from week 2 to 7, and our analysis suggested

cows were in the study. The 20 cows with missing that stratification by week postpartum had profound

observations in week 2 had calved between Sep- effects on the prediction of EB. The solution will

tember 10 and October 3, and the other 52 cows with therefore have to be a compromise between pooling

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Observed and predicted EB were in this study References

from one herd. Whether prediction may be

extrapo-lated to other herds under different conditions (ra- Diggle, P.J., Liang, K.-Y., Zeger, S.L., 1994. Analysis of

Longi-tudinal Data, Clarendon Press, Oxford.

tions, seasons) cannot be concluded from this study.

Dirksen, G., Breitner, W., 1993. A new quick test for

semiquantita-Therefore, the validation of EB prediction using data

tive determination of beta-hydroxybutyric acid in bovine milk.

of other feeding trials is an important next step.

J. Vet. Med. A 40, 779–784.

Dirksen, G.U., Hagert-Theen, C., Alexander-Katz, M., Berger, A.,

¨ ¨

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Hochlakta-5. Conclusions tion anhand von Milchparametern. II. Azeton-, Azetazetat- und ¨

Beta-Hydroxybutyratkonzentration. Tierarztl. Umschau 52, 476–484.

Elevated blood levels of BHB were reliably

Dohoo, I.R., Martin, S.W., 1984. Subclinical ketosis: prevalence

detected by cow side tests for ketone bodies in milk

and associations with production and disease. Can. J. Comp.

(KET, NP). But when ketone bodies in blood or milk _{Med. 48, 1–5.}

and the fat–protein-ratio were compared with EB Duffield, T.F., Kelton, D.F., Leslie, K.E., Lissamore, K.D.,

Lumsden, J.H., 1997. Use of test day milk fat and milk protein

calculated from measured energy intake and

esti-to detect subclinical keesti-tosis in dairy cattle in Ontario. Can. Vet.

mated requirement, the fat–protein-ratio detected

J. 38, 713–718.

low EB more reliably than BHB or cow side tests.

Edmonson, A.J., Lean, I.J., Weaver, L.D., Farver, T., Webster, G.,

Moreover, after consideration of lactation stage, _{1989. A body condition scoring chart for Holstein dairy cows.}

parity and milk yield, the fat–protein-ratio was to an _{J. Dairy Sci. 72, 68–78.}

even greater extent related to EB than any other test Erfele, J.D., Fisher, L.J., Sauer, F.D., 1974. Interrelationship

between blood metabolites and an evaluation of their use as

parameter.

criteria of energy status of cows in early lactation. Can. J.

Using the information of one milk test per month,

Anim. Sci. 54, 293–303.

the prediction of herd mean EB stratified by lactation

Garnsworthy, P.C., 1988. The effect of energy reserves at calving

week resulted in precise estimates of observed EB _{on performance of dairy cows. In: Nutrition and Lactation in}

when 70 cows were evaluated (equivalent total herd _{the Dairy Cow, Butterworths, ISBN 0-408-00717-6.}

size of 280 cows). The described method of predict- Geishauser, T., Leslie, K., Duffield, T., Edge, V., 1997. Fat / protein

ratio in first DHI test milk as test for displaced abomasum in

ing EB in early lactation was effectively limited to

dairy cows. J. Vet. Med. A 44, 265–270.

large dairy herds (.160 cows). Pooling the

in-Geishauser, T., Leslie, K., Kelton, D., Duffield, T., 1998.

Evalua-formation of 3 subsequent test days more than

tion of five cowside tests for use with milk to detect subclinical

doubled the precision. Hence, small herds may pool _{ketosis in dairy cows. J. Dairy Sci. 81, 438–443.}

milk test day information to maintain adequate _{Goff, J.P., Horst, R.L., 1997. Physiological changes at parturition}

precision. We conclude that, with precision limits, and their relationship to metabolic disorders. J. Dairy Sci. 80,

1260–1268.

milk test data aggregated at herd level indicated

Gravert, H.O., 1991. Indikatoren zur Beurteilung der

Ener-energy intake from feed in the study herd.

giebilanz der Milchkuh. Mh. Vet. Med 46, 536–537. Gravert, H.O., Langner, R., Diekmann, L., Pabst, K.,

Schulte-¨ ¨

Coerne, H., 1986. Ketokorper in Milch als Indikatoren fur die

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Grieve, D.G., Korver, S., Rijpkema, Y.S., Hof, G., 1986.

Relation-The study was carried out with permission and

ship between milk composition and some nutritional

parame-support of the Director of the Institute of Animal

ters in early lactation. Livest. Prod. Sci. 14, 239–254.

Nutrition, De Schothorst, Lelystad (NL). Without the _{Gustafsson, A.H., Emanuelson, U., 1996. Milk acetone as an}

help of his staff in the cattle shed, the milking indicator of hyperketonemia in dairy cows: the critical value

parlour, the laboratory and the data processing unit revised. Anim. Sci. 63, 183–188.

Heuer, C., Schukken, Y.H., Dobbelaar, P., 1999. Postpartal body

this study would not have been possible. The authors

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acknowledge the contributions of all those

col-disease, fertility, production, and culling in commercial dairy



laborators. Ketolac tests were provided free by _{herds. J. Dairy Sci. 82, 295–304.}

Hoechst Russel Veterinary GmbH (Unterschleiss- Jacobs, R.M., Lumsden, J.H., Grift, E., 1992. Effects of

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volume, mammary efficiency, and spontaneous ketosis in dairy Senatore, E.M., Butler, W.R., Oltenacu, P.A., 1996. Relationship cows. J. Dairy Sci. 65, 2204–2212. between EB and post-partum ovarian activity and fertility in Lean, I.J., Bruss, M.L., Baldwin, R.L., Troutt, H.F., 1991. Bovine first lactation dairy cows. Anim. Prod. 62, 17–23.

ketosis: a review. I. Epidemiology and pathogenesis. Vet. Bull. Simensen, E., Halse, K., Gillund, P., Lutnaes, B., 1990. Ketosis 61 (12), 1209–1218. treatment and milk yield in dairy cows related to milk acetone MacNamara, J.P., Harrison, J.H., Kincaid, R.L., Waltner, S.S., levels. Acta Vet. Scand. 31, 433–440.

1995. Lipid metabolism in adipose tissue of cows fed high fats Smith, T.R., Hippen, A.R., Beitz, D.C., Young, J.W., 1997. diets during lactation. J. Dairy Sci. 78, 2782–2796. Metabolic characteristics of induced ketosis in normal and Magdus, M., Fekete, S., Frenyo, L.V., Miskucza, O., Kotz, V., obese dairy cows. J. Dairy Sci. 80, 1569–1581.

1988. Milk production and certain parameters of energy Tamminga, S., Van Straalen, W.M., Subnel, A.P.J., Meijer, R.G.M., metabolism in dairy cows fed rations of varying energy and Steg, A., Wever, C.J.G., Blok, M.C., 1994. The Dutch protein crude protein contents and fat. Acta Vet. Hungarica 36 (1–2), evaluation system: the DVE / OEB-system. Livest. Prod. Sci.

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McDermott, J.J., Schukken, Y.H., Shoukri, M.M., 1994. Study Thrusfield, M., 1995. In: 2nd Edition, Veterinary Epidemiology, design and analytic methods for data collected from clusters of Blackwell Science, London, p. 214.

animals. Prev. Vet. Med. 18, 175–191. Tilley, J.M., Terry, R.E., 1963. A two stage technique for the in Nielen, M., Aarts, M.G.A., Jonkers, A.G.M., Wensing, T., Schuk- vitro digestion of forage crops. J. Br. Grassland Soc. 18,

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Table 7

2 3 4 5 6 7 8 9 10 11 12

Mean observed EB (n5771) 220.0 213.7 210.7 26.5 23.7 24.1 0.3 2.7 4.9 7.5 11.4 Mean predicted EB (n5771) 222.6 214.0 210.9 26.7 23.9 24.3 0.1 2.5 4.7 7.3 11.2 Standard deviation of predicted herd level EB:

3 months, 72 cows (n5209) 2.0 1.9 1.9 2.2 2.2 2.4 2.2 2.3 2.2 1.9 2.1 1 month, 72 cows (n570) 4.3 4.1 4.5 4.8 4.7 4.5 4.0 3.9 4.0 4.0 4.2 1 month, 40 cows (n540) 6.2 6.1 4.4 5.9 5.8 6.5 5.3 5.6 5.9 5.1 5.4 1 month, 20 cows (n520) 6.9 8.2 6.7 9.3 8.1 7.6 8.1 7.3 8.3 7.7 7.8 Average number of cows in the sample:

3 months, 72 cows (n5209) 17.3 23.7 24.0 17.7 18.3 18.0 17.9 18.1 18.1 17.8 18.1 1 month, 72 cows (n570) 4.7 6.5 6.5 6.6 6.6 6.5 6.5 6.5 6.6 6.5 6.6 1 month, 40 cows (n540) 2.8 3.6 3.8 3.9 3.5 3.6 3.8 3.7 3.7 3.9 3.9 1 month, 20 cows (n520) 1.5 1.9 1.9 1.8 1.8 2.0 1.7 1.9 1.8 1.9 1.9

The level of EB, time of return to positive EB, and ketonemia with lower reliability than KET or NP. time to maximal dry matter intake were similar as in But it detected calculated low EB more reliably than the study of Gravert et al. (1986) who evaluated KET or NP. In the classification of ‘normal’ EB, ketone bodies as indicators of EB. Senatore et al. however, BHB, KET and NP performed slightly (1996) used a total mixed ration resulting in a much better (higher specificity) than the fat–protein-ratio. higher and longer energy deficit in a study of the Poor relationships between BHB and EB were also effect of energy deficit on fertility. Both studies reported by Erfele et al. (1974). BCS was a poor calculated the diets to meet requirements. The energy diagnostic aid for calculated low or normal EB in deficit of the cows in our study may therefore be this study (low sensitivity, low specificity).

categorised as moderate. We deem the data base In the multivariate prediction model, cow was not suitable for model development because a moderate included as fixed or random effect, but was corrected post partum energy deficit may be expected in most for in the error structure. We intended to develop a high yielding dairy herds. The model is supposed to population (not cow) specific prediction equation for be applied for such herds. EB (Diggle et al., 1994). Modelling criteria were a

As in similar studies (Gravert, 1991; Dirksen et good fit (high R , low residual variation), a low al., 1997; Geishauser et al., 1998), the variance of degree of collinearity among covariates, and a low sensitivity and specificity of ketonemia might have number of parameters. Increasing the number of

been biased because test results of subsequent sam- parameters in the model would increase the R but ples from the same cow were not independent. make the model too similar to the data and supposed-Correlated samples are likely to produce smaller ly less applicable to other herds and rations. In fact, variances (McDermott et al., 1994). Nevertheless, additional interaction terms yielded only marginal hyperketonemia, indicated by blood BHB .1.2 gains for the precision of prediction. Therefore, mmol / l, was detected by field tests (KET, NP) interaction terms were excluded from the final slightly more reliably (higher sensitivity) than and prediction model. A high correlation between milk normal ketonaemia as reliable (similar specificity) as fat and the fat–protein-ratio was the reason for reported earlier (Dirksen and Breitner, 1993; Nielen substantial collinearity. Because the fat–protein-ratio et al., 1994; Geishauser et al., 1998). Sensitivity and predicted EB better than milk fat, the latter was specificity of the fat–protein-ratio in milk were omitted.

slightly higher than reported (Duffield et al., 1997). The final model showed that lactation stage, In our study, the fat–protein-ratio detected hyper- parity, milk yield, and especially the

(3)

fat–protein-ratio were strong predictors of EB. The considefat–protein-ration parity and week of lactation. However, the coeffi-of the fat–protein-ratio almost fully replaced further cient indicated a drop in EB of 2.11 MJ NEL with tests of ketone bodies in blood or milk, despite every additional kg standard milk (provided FP 6.9–10.0% sub-clinical ketosis in the first 5 weeks of remains constant). Applying the prediction model to lactation. The level of ketonemia in this study was, herds with higher milk production would therefore however, somewhat lower than in other studies of result in more negative EB compared to herds with high producing dairy herds (Dohoo and Martin, lower production. And vice versa, a herd that 1984; Nielen et al., 1994; Duffield et al., 1997). A decreased milk production, for example as a conse-mild state of negative EB may more often or more quence of a severe energy deficit, would exhibit high readily result in an elevated fat–protein-ratio than in EB. Consequently, the model predictions cannot be solely increased ketone body levels in blood or milk. reasonably interpreted without simultaneous consid-Ketone bodies may rise when negative EB is increas- eration of the average level of production. Only if a ingly de-compensated. All cows in this study were herd maintains its average production level could a able to compensate negative EB, because ketone lower model prediction be regarded as a relative bodies rarely accumulated to high concentrations. An decrease of EB.

instant increase of the fat–protein-ratio in response As the diagnosis of EB by single test parameters of low EB is in agreement with earlier observations was affected by lactation stage, parity, and milk (Grieve et al., 1986; Gravert, 1991). Hence, adipose yield, the interpretation of individual results of milk cells mobilise while mammary cells synthesise tri- or blood tests with respect to EB should take these glycerides in response to negative EB. The differ- effects into account. Nevertheless, even after consid-ence in response was called ‘acetate partitioning’ by eration of these effects, some 75% of the variability

Kronfeld (1982) who suggested that, in early lacta- in EB remained unexplained (R 525%). However, tion, acetate is converted to milk fat to a greater given that microbial fermentation, rumen absorption extent by the mammary gland than by adipose of volatile fatty acids, digestive interactions between tissues, and that it is reversed in later lactation. The rumen and abomasum, intestinal absorbtion, regula-suggestion was confirmed by MacNamara et al. tory mechanisms of the intermediate metabolism, (1995) who estimated that, in early lactation, the passage from blood to mammary parenchyma, and mammary gland synthesised milk fat while adipose synthesis of lactose, fat and protein by the mammary tissue mobilised fat in response to added dietary fat; gland may all affect the association between feed-the opposite was observed in later lactation. NEL and milk constituents, we regard the strength of Milk protein was also a highly significant predic- the observed association as relatively high. More-tor but had relatively little bearing on EB. Milk over, the cow level model was only used as a tool to protein percent correlates positively with energy identify important prediction parameters. At this intake (Magdus et al., 1988). Therefore, low energy step, a moderate model fit may be sufficient. There intake results in a higher fat–protein-ratio. Conse- was no specific interest in cow-level prediction of quently, part of the variation due to milk protein was negative EB in this study. More crucial was the probably represented by the fat–protein-ratio in our certainty (or uncertainty) of herd level prediction data. Because of its known relationship with energy because the model was developed to estimate aver-intake, we included milk protein in the final model age herd EB in order to provide a monitoring tool for even though it only contributed 0.1% to the model the provision of net energy through the ration.

R of this study. The model coefficient for milk yield Because Fig. 2 suggests that the 75% of the remain-was negative indicating lower EB with increasing ing variation in EB not explained by the predictors yield. This negative effect was partly compensated was random variability, the precision of the predicted for by an inverse relationship between milk yield and herd means was expected to be considerably more milk protein (r5 20.37). The impact of milk yield precise than the predictions for individual cows.

on the variation of EB was relatively small (R 5 With the exception of week 7, observed and 0.9%) when compared to a model only including predicted herd means increased steadily (Table 4).

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The dip in week 7 was presumably caused by feed November 26. Therefore, the difference in week 2 changes due to the experimental diets starting in was the sum of two possible effects: a smaller week 6. We did not correct the week 7 coefficient for sample and calving seasonality. Seasonal effects this effect because the herd simulation intended to were probably small because the cows were estimate the precision of prediction, and this was synchronised, kept indoors and received the same unlikely to change after correction. However, before ration throughout the study period. We therefore applying the prediction equation of Table 4 to other believe that the difference was most likely caused by herds and rations, it may be reasonable to adjust the sampling variation.

coefficient for week 7, e.g. by replacing it with the The standard deviations (S.D.) of herd means average of the adjacent weeks 6 and 8. increased with decreasing number of cows in the Cows in first lactation were more severely and sample. When EB prediction was based on only one longer in negative EB than cows in lactation 2 or 3 of 11 available weekly milk tests per cow (n570), (Fig. 1). It was previously reported that EB de- the weekly means were based on 4–7 cows and the creased with increasing parity (Lean et al., 1991; herd means varied by an equivalent of 61.5 kg Duffield et al., 1997), but in agreement with our standard milk (Table 5). In order to have 70 cows findings, Dirksen and Breitner (1993) observed 2–12 weeks in milk at one point in time, the total higher BHB levels postpartum in primiparous than in herd size would have to be|280 cows (4370 cows,

multiparous cows. A potentially greater risk of first non-seasonal calving). Compared to the predictions calving heifers than older cows for severe negative for individual cows (S.D.59.3 MJ / day), the vari-EB renders more investigative work. ability of the herd means of these 72 cows was less

It was not surprising that BCS was a poor than half as large (S.D. |4.3 MJ / day).

predictor of EB in our study. Although the propor- A test day was defined as one weekly average tion of cows in relatively obese condition (BCS$ milk test result randomly selected from four avail-3.5) was 41.5% in lactation week 2 and decreased to able results within a month. Pooling 3 subsequent 12.5% in week 11, the variability in BCS throughout test days improved the precision further by over the first 12 weeks of lactation was probably too small 100% (S.D. |62 MJ, 0.7 kg standard milk). On the

to reveal significant associations with EB. Using other hand, EB prediction based on 1 test day of 40 body condition loss instead of BCS did not improve cows (projected total herd size of 160 cows) reduced the model either (data not shown). Moreover, the the precision by 33% compared to 70 cows with 1 relationship between BCS at calving and milk pro- test day. A herd with 20 fresh cows, equivalent to a duction in the first 12 weeks postpartum is not clear total herd size of 80, did often not have enough cows (Garnsworthy, 1988), the risk of ketonemia and to compute weekly EB means for each of the 11 ketosis was the same for cows in obese or in normal weeks (S.D.567.5 MJ, 2.5 kg standard milk). If condition at calving (Smith et al., 1997), and BCS mean EB was based on one or two cows, individual was regarded as not sufficiently sensitive to reveal cow effects made the mean highly variable. Hence, substantial changes in EB (Senatore et al., 1996). herd size was a limitation to herd level prediction of EB based on test day information. Although pooling 4.2. Prediction of herd level EB subsequent test days efficiently increased precision, it may not be desirable to evaluate new rations as Overall means of observed and predicted EB were late as 2–3 months after the feed change. Collapsing nearly identical, indicating that the prediction was the initial 12 weekly into 3 monthly means may not unbiased. However, predicted EB was slightly lower be advisable either, because EB was particularly than observed EB in lactation week 2 when only 52 critical from week 2 to 7, and our analysis suggested cows were in the study. The 20 cows with missing that stratification by week postpartum had profound observations in week 2 had calved between Sep- effects on the prediction of EB. The solution will tember 10 and October 3, and the other 52 cows with therefore have to be a compromise between pooling complete data had calved between October 4 and and wider precision limits.

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Observed and predicted EB were in this study References from one herd. Whether prediction may be

extrapo-lated to other herds under different conditions (ra- Diggle, P.J., Liang, K.-Y., Zeger, S.L., 1994. Analysis of Longi-tudinal Data, Clarendon Press, Oxford.

tions, seasons) cannot be concluded from this study.

Dirksen, G., Breitner, W., 1993. A new quick test for semiquantita-Therefore, the validation of EB prediction using data

tive determination of beta-hydroxybutyric acid in bovine milk. of other feeding trials is an important next step.

J. Vet. Med. A 40, 779–784.

Dirksen, G.U., Hagert-Theen, C., Alexander-Katz, M., Berger, A.,

¨ ¨

1997. Stoffwechseluberwachung bei Kuhen in der Hochlakta-5. Conclusions tion anhand von Milchparametern. II. Azeton-, Azetazetat- und

Beta-Hydroxybutyratkonzentration. Tierarztl. Umschau 52, 476–484.

Elevated blood levels of BHB were reliably

Dohoo, I.R., Martin, S.W., 1984. Subclinical ketosis: prevalence detected by cow side tests for ketone bodies in milk

and associations with production and disease. Can. J. Comp. (KET, NP). But when ketone bodies in blood or milk _{Med. 48, 1–5.}

and the fat–protein-ratio were compared with EB Duffield, T.F., Kelton, D.F., Leslie, K.E., Lissamore, K.D., Lumsden, J.H., 1997. Use of test day milk fat and milk protein calculated from measured energy intake and

esti-to detect subclinical keesti-tosis in dairy cattle in Ontario. Can. Vet. mated requirement, the fat–protein-ratio detected

J. 38, 713–718. low EB more reliably than BHB or cow side tests.

Edmonson, A.J., Lean, I.J., Weaver, L.D., Farver, T., Webster, G., Moreover, after consideration of lactation stage, _{1989. A body condition scoring chart for Holstein dairy cows.} parity and milk yield, the fat–protein-ratio was to an _{J. Dairy Sci. 72, 68–78.}

even greater extent related to EB than any other test Erfele, J.D., Fisher, L.J., Sauer, F.D., 1974. Interrelationship between blood metabolites and an evaluation of their use as parameter.

criteria of energy status of cows in early lactation. Can. J. Using the information of one milk test per month,

Anim. Sci. 54, 293–303. the prediction of herd mean EB stratified by lactation

Garnsworthy, P.C., 1988. The effect of energy reserves at calving week resulted in precise estimates of observed EB _{on performance of dairy cows. In: Nutrition and Lactation in} when 70 cows were evaluated (equivalent total herd _{the Dairy Cow, Butterworths, ISBN 0-408-00717-6.} size of 280 cows). The described method of predict- Geishauser, T., Leslie, K., Duffield, T., Edge, V., 1997. Fat / protein

ratio in first DHI test milk as test for displaced abomasum in ing EB in early lactation was effectively limited to

dairy cows. J. Vet. Med. A 44, 265–270. large dairy herds (.160 cows). Pooling the

in-Geishauser, T., Leslie, K., Kelton, D., Duffield, T., 1998. Evalua-formation of 3 subsequent test days more than

tion of five cowside tests for use with milk to detect subclinical doubled the precision. Hence, small herds may pool _{ketosis in dairy cows. J. Dairy Sci. 81, 438–443.}

milk test day information to maintain adequate _{Goff, J.P., Horst, R.L., 1997. Physiological changes at parturition} precision. We conclude that, with precision limits, and their relationship to metabolic disorders. J. Dairy Sci. 80,

1260–1268. milk test data aggregated at herd level indicated

Gravert, H.O., 1991. Indikatoren zur Beurteilung der Ener-energy intake from feed in the study herd.

giebilanz der Milchkuh. Mh. Vet. Med 46, 536–537. Gravert, H.O., Langner, R., Diekmann, L., Pabst, K.,

Schulte-¨ ¨

Coerne, H., 1986. Ketokorper in Milch als Indikatoren fur die Acknowledgements Energiebilanz der Milchkuhe. Zuchtungskunde 58 (5), 309–¨ ¨

318.

Grieve, D.G., Korver, S., Rijpkema, Y.S., Hof, G., 1986. Relation-The study was carried out with permission and

ship between milk composition and some nutritional parame-support of the Director of the Institute of Animal

ters in early lactation. Livest. Prod. Sci. 14, 239–254. Nutrition, De Schothorst, Lelystad (NL). Without the _{Gustafsson, A.H., Emanuelson, U., 1996. Milk acetone as an} help of his staff in the cattle shed, the milking indicator of hyperketonemia in dairy cows: the critical value parlour, the laboratory and the data processing unit revised. Anim. Sci. 63, 183–188.

Heuer, C., Schukken, Y.H., Dobbelaar, P., 1999. Postpartal body this study would not have been possible. The authors

condition score and first milk test results as predictors of acknowledge the contributions of all those

col-disease, fertility, production, and culling in commercial dairy



laborators. Ketolac tests were provided free by _{herds. J. Dairy Sci. 82, 295–304.}

Hoechst Russel Veterinary GmbH (Unterschleiss- Jacobs, R.M., Lumsden, J.H., Grift, E., 1992. Effects of heim, Germany). bilirubinemia, hemolysis, and lipemia on clinical chemistry

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 

43–59. 40, 139–155.

ken, Y.H., 1994. Evaluation of two cowside tests for the 104–111.

detection of subclinical ketosis in dairy cows. Can. Vet. J. 35, Van Es, A.J.H., 1978. Feed evaluation for ruminants I, the system

229–232. in use from May 1977 onwards in The Netherlands. Livest.

Saama, P.M., Mao, I.L., 1995. Energy intake and gross efficiency Prod. Sci. 5, 331–345. comparisons from calorimetric and field data on the same

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Prediction of energy balance in a high yielding dairy herd in

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Directory UMM :Data Elmu:jurnal:L:Livestock Production Science:Vol65.Issue1-2.Jul2000:

Prediction of energy balance in a high yielding dairy herd in

early lactation: model development and precision

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C. Heuer

, W.M. Van Straalen , Y.H. Schukken , A. Dirkzwager , J.P.T.M. Noordhuizen

Parts

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PERSEPSI MAHASISWA TENTANG MATERI TAYANGAN SANG PEMBURU DI LATIVI ( Studi Pada Mahasiswa Jurusan Ilmu Komunikasi UMM Angkatan 2002)

APRESIASI MAHASISWA TERHADAP TAYANGAN Â“OPERA VAN JAVAÂ” DI TRANS7 (Studi Pada Mahasiswa Jurusan Ilmu Komunikasi UMM Angkatan 2008)

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FPP UMM Seleksi Sarjana Membangun Desa

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