Relationship between milk composition and some nutritional parameters in early lactation

Livestock Production Science, 14 (1986) 2 3 9 - - 2 5 4

239

Elsevier Science Publishers B.V., A m s t e r d a m -- Printed in The Netherlands

RELATIONSHIP NUTRITIONAL

BETWEEN MILK COMPOSITION AND SOME PARAMETERS IN EARLY LACTATION

D.G. G R I E V E ' , S. K O R V E R 2, Y,S. R I J P K E M A 3 and G. H O F 4

,,a Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario (Canada) 2Department o f Animal Breeding, Agricultural University, Wageningen (The Netherlands) 3Institute for Livestock Feeding and Nutrition Research, Lelystad (The Netherlands) 4Department of Animal Nutrition, Agricultural University, Wageningen (The Netherlands) (Accepted 18 N o v e m b e r 1985)

ABSTRACT Grieve, D.G., Korver, S,, Rijpkema, Y.S. and Hof, G., 1986. Relationship b e t w e e n milk c o m p o s i t i o n and some nutritional parameters in early lactation. Livest. Prod. Sci., 14 : 239--254. Ten sets of data f r o m four experiments with individual measurements on 236 cows for one to f o u r w e e k s in each of second and third m o n t h s of lactation were used to determine relationships between milk c o m p o s i t i o n and nutritional parameters. Effects o f breed and parity of cow as well as experimental rations were removed in analysis. The estimated balance b e t w e e n energy intake and energy r e q u i r e m e n t for milk and maintenance was calculated f r o m intake of roughage and concentrates together with their chemical c o m p o s i t i o n and in vitro digestibility, milk yield and milk composition. Adjusted m e a n estimated energy balances ranged f r o m - 1 7 . 6 5 to 14.35 MJ NE~ per d a y ; dry m a t t e r intake f r o m 16.1 to 20.1 kg per day and % dietary roughage from 36.6 to 67.5% across data sets. Mean milk fat % was between 3.76 and 4.11% while milk protein % was 2.79--3.15% across data sets. Dry m a t t e r intake and % dietary roughage explained relatively small a m o u n t s of variation in milk fat %, protein % or the ratio of these two c o m p o n e n t s after effects o f ration were removed in data analysis. Estimated energy balance was negatively correlated w i t h milk fat % ( - 0 . 0 7 to - 0 . 6 5 ) , positively correlated with milk protein % (0.12 to 0.47) and negatively correlated w i t h ratio of milk fat to protein ( - 0 . 3 6 to - 0 . 7 4 ) across data sets. Estimated energy balance increased the p r o p o r t i o n of explained variation of f a t : p r o t e i n ratio by an average 21% to a total of 43%. In the reverse situation, fat to protein ratio increased coefficients of d e t e r m i n a t i o n of energy balance by 19 to 52%. The ratio of c o n t e n t s of milk fat and protein was a m o r e sensitive and consistent indicator of changes in nutritional variables and also a better predictor o f energy status of the cow than either c o m p o n e n t by itself.

aCorrespondence address.

0301-6226/86/$03.50

© 1986 Elsevier Science Publishers B.V.

240 INTRODUCTION In a review of the effects of diet on milk composition Oldham and Sutton (1979) illustrated the negative effect of both level of feed intake and proportion of dietary concentrates on milk fat content. Fat % was depressed by increasing the proportion of dietary concentrates from 60 to 90%, the effect being more dramatic with ad libitum feeding than with generous but controlled intakes. However, the effect of dietary composition is usually less pronounced when concentrates make up less than 60% of total dry matter (Clark and Davis, 1980; Macleod et al., 1983). Indeed, Gordon (1977) found no effect on fat % from increasing the plane of nutrition by addition of concentrates while maintaining a fixed level of forage feeding. In this case the highest feeding level was 0.58 kg concentrates per kg milk produced. The effects of intake level on milk fat % are also somewhat inconsistent. For example, Zanartu et al. (1983) found no effect of ad libiturn versus restricted amounts of a 87% concentrate diet. On the other hand, increased milk protein content was observed when the plane of nutrition was improved by decreasing the forage to concentrate ratio (Macleod et al., 1983) or by increasing the level of concentrate feeding with fixed roughage (Gordon, 1977). The effect of improving energy intake on milk protein is usually considered to be curvilinear (Oldham and Sutton, 1979; Kaufmann, 1979; Journet and Remond, 1980) with a more dramatic effect occurring in underfed cows. One of the difficulties in relating nutritional status of the cow to milk composition is to distinguish among interrelated effects such as diet energy concentration, level of energy intake and level of milk yield, each of which affects both milk composition and energy equilibrium of the cow. Since milk fat % and milk protein % usually move in opposite directions in response to dietary changes but results sometimes indicate a change in only one of these milk components, it is felt that a ratio of fat to protein would be an appropriate indicator of compositional changes. Milk composition changes may also indicate other physiological effects associated with energy balance (Von Farries, 1983). Kaufmann (1979) illustrated a positive association between milk protein % in early lactation and fertility of cows, which may be attributed to the relationship of milk protein to energy supply. Additionally, Reid (1983) reported reduced reproductive performance in cows with fatty liver after calving, a part of the generalized fat mobilization syndrome that occurs in response to energy deficit in early lactation. The objectives of this study were to determine relationships between milk fat %, milk protein % or their ratio and the estimated difference between energy intake and requirement, dry matter intake or percentage dietary roughage. Since maintaining energy equilibrium is most difficult during early lactation and since milk composition during the immediate post-partum period may be affected by pre-partum feeding, data were from second and third months of lactation. Additionally, the

241

predictive value of milk composition for nutritional parameters, especially the estimated difference between energy intake and energy requirement for maintenance and milk production, were determined. MATERIALS

AND METHODS

Source o f data Ten sets of data (Table I) were subjected to similar analysis. In each data set, average daily data per cow for a one- to four-week period between the fifth and twelfth weeks o f lactation were used. The first group o f data sets comprised a series of experiments conducted by the third author at the Institute of Livestock Feeding and Nutrition Research, Lelystad, The Netherlands (Rijpkema and van Reeuwijk, 1983 and unpublished data). Roughage feeding (grass silage) was ad libitum with concentrate feeding either at a fiat rate (fixed) of 10.1 kg (1979/80, 1 9 8 0 / 8 1 ) and 9.6 kg {1981/82) dry matter per c o w daffy or adjusted weekly according to individual c o w requirement {norm) based on body weight, 4% fat-corrected milk, roughage intake and quality (Van Es, 1978). Within feeding systems and years, treatments were concentrate protein source (1979/80; fixed and norm) and protein level (1981/82, fixed). Data from fixed and norm feeding for Weeks 5 -- 8 and 9 -- 12 o f lactation were analysed separately considering treatment, year and parity effects. TABLE I

S u m m a x y o f e x p e r i m e n t s used in analysis a Experiment

Cows

Rijpkema fixed

71

Rijpkema norm

Data set

Lactation weeks

Breed b

Parity

Concentrate treatment

1 3

5--8 9--12

DF

1 >__2

79/80 79/80 80/81 81/82 81/62

42

2 4

5--8 9--12

DF

1 :>2

7 9 / 8 0 s o y protein 7 9 / 8 0 fish protein 80/81 c

ad lib

Korver (Lactation I )

91

5 7

6 9

DF HF

2--5

11 kg (high) 3 kg ( l o w )

ad lib

Korver (Lactation ID

64

6 8

6 9

DF HF

3--6

11 kg (high) 3 kg ( l o w )

ad lib

Hof

32

9 10

6 9

DF HF BF

3 4 5

14 kg 1 0 kg 6 kg

fixed

s o y protein fish protein c l o w protein high protein

aSee t e x t for further details of e x p e r i m e n t s . b D F = D u t c h Friesian; H F = ~__ 50% Holstein Frieaian; BF = ~__ 50% British Friesian. CNo further t r e a t m e n t s duzd.ng 1 9 8 0 - - 8 1 season.

Roughage

ad lib

242 The second group of data sets was from a two-lactation experiment conducted by Korver (1982) at the experimental farm of the Agricultural University (Ir. A.P. Minderhoudhoeve), The Netherlands. Experimental design was a breed (Dutch vs Holstein-Friesian crossbreds) by ration (low vs high concentrate) factorial over two lactations with rations reversed within cow between first and second lactations. Data for Weeks 6 and 9 of lactation were analysed separately for the first and second years of the experiment considering breed, parity and ration effects. In these experiments, Weeks 6 and 9 were the only weeks for which measurements were taken within the second and third months of lactation. The third group of data sets was from an experiment conducted by the fourth author at the experimental farm (Ir. A.P. Minderhoudhoeve) in which cows were fed fixed quantities of concentrates at three different levels and a fixed a m o u n t of roughage (grass silage). Data from lactation weeks 6 and 9 were analysed considering effects o f breed, parity and ration. Feed intake and milk yield were measured daily and milk composition (fat, protein) on two consecutive milkings was measured weekly in all data except those of Korver (1982), where milk yield was on two milkings (one day) per week. Body weight was measured weekly. CALCULATIONS From the original data, energy requirements per cow (for maintenance and milk production (MJ NE~) was calculated according to Van Es {1978). Energy intake (MJ NE~) from roughage was estimated from roughage dry matter intake, in vitro digestible organic matter (Tilley and Terry, 1963 as modified by Van der Koelen and Dijkstra, 1971) and estimated digestible crude protein (Centraal Veevoeder Bureau, 1977) while concentrates were assumed to contain 6.49 MJ NE per kg. Energy balance (ENB) was estimated from intake minus required amounts. STATISTICAL ANALYSIS Data were analyzed by the least square methods of Harvey {1977) with the following general model: Y i j k l = t~ + g i + P j + rk +

independent variable(s) + e i j k l

w here: Yijkl

year k; p gi

=

the characteristic of the /th cow with breed i, parity j and ration-

= overall mean; effect of the ith breed (i = 0 Rijpkema; i = 1,2 Korver; i = 1,3

=

Hof); pj = effect o f the j t h parity (j = 1,2 Rijpkema; j = 2,5 or 3,6 Korver; j = 3,5 Hof); rk = effect of kth ration (within year) (k = 1,5 Rijpkema fixed, k = 1,3 Rijpkema norm, k = 1,2 Korver, h = 1,3 Hof); and eijkl = error term.

243

0

4NN~dd6~ddN e~ 0

t

0

,6

t"-

II

m

e~

~ 0

~

,ge

244 II

Ib- t ~

LO

,5 ~5,5

z

,5,5,5 ¢!

I

0

0

~

I

I I I

0

0

,1= e~

m

I

i

I

I ~ 1

H

.~

~5,5,5

e~

~ oo. V 0

I

I

It

II

e£.~ r~

'~

~,~ 0

°

~.~

oV

245 Analysis o f milk composition data (fat %, protein % and fat:protein ratio, FP) included the linear effects of ENB, dry matter intake (DMI) and roughage as a percentage of DMI (ROP) individually and jointly as independ e n t variables. Similarly, ENB, DMI and ROP data were analysed using fat % (FAT), protein % (PRO) and FP as independent variables. In the data of Korver, breeding values for fat % and protein % o f the sire of the cow were also used as covariates in an attempt to remove some of the genetic difference for milk components among cows. In general, relationships contained both genetic and environmental components. In this study we were interested only in the environmental relationship. Since associations between milk composition and nutritional parameters were determined in both directions, regression coefficients were calculated from the geometric mean of one coefficient and the reciprocal of the other. When measurements of both variables were subject to an error this method provides a better estimate of regression (Ricker, 1973). RESULTS

Data The adjusted means and standard deviations for the 10 sets of data analysed are in Table II. For FAT and FP, considerably more variation was present in the data of Hof. Variation in ROP was dependent largely on the experimental design. Adjusted correlation coefficients among the six variables studied are in Table III. FAT and PRO were generally positively correlated but the magnitude of the coefficients was small. In correlations between milk composition and nutritional parameters, FAT and FP were negatively associated with ENB while PRO was positively correlated. Correlations of milk composition parameters with DMI and ROP were variable in both sign and magnitude, reaching significance in only a few cases. The most consistent values were between the ratio of FAT to PRO (FP) and ENB, all of which were significant, ranging from -0.36 to - 0 . 7 4 among data sets.

Milk composition and nutritional parameters Changes in coefficients o f determination of milk composition variables due to inclusion of ENB, DMI or ROP in the model are shown in Figs. 1--3. For each o f FAT, PRO and FP, the greatest increase in explained variation was from ENB with three exceptions. Two of these were in Rijpkema's norm Weeks 5--8 data, where DMI produced a larger change in R2 for FAT and FP. The third case was in Korver's Week 9 lactation 1 FAT data, but here none of the regression variables reached significance. The only uniformly significant improvement in R 2 across the 10 data sets was for FP with ENB as the independent variable.

246

.6

~ o

.5

.4

.i

m ImH

Data set:

]

3

2

4

6

5

7

F~(RZ~BPR

LEGEND,

8

9

I0

/ E N B

Fig. I. Proportion of variation in fat % (FAT), protein % (PRO) and fat to protein ratio (FP) explained by breed, parity, ration (BPR) and energy balance (ENB). For explanation of data set codes see Table I. .5

.4

i -~ I

II

.i

oe

p.

Data set:

l

2

3

LEGEND,

4

5

6

7

BPR

8

~

9

DMI

10

247

.4

4

.3

1 Data set:

1

2

3

LEGEND,

4

I

5

6

~

7

BPR

8

I

9

1{3

ROP

Fig. 3. Proportion of variation in fat % (FAT), protein % (PRO) and fat to protein ratio (FP) explained by breed, parity, ration (BPR) and roughage percentage (ROP). F o r explanation of data set codes see Table I.

When various combinations of ENB, DMI and ROP were used as independent variables in analysis of milk composition data, they explained respectively an additional 14, 5 and 5% of variation in FAT; 8, 2 and 2% in PRO; and 22, 7 and 5% in FP, on average. Adding DMI to ENB in the model resulted in (on average) a further increase in R : of 3, 1 and 5% while ROP added 5, 2 and 4% variation in FAT, PRO and FP, respectively. Nutritional parameters and milk composition ENB was analysed as the dependent variable in Rijpkema data, using both linear and quadratic terms for milk composition. Quadratic terms were non-significant (P > 0.05). FP increased R 2 by 10--29%. Adding FAT or PRO to the model containing FP explained small (0--7%) additional amounts of variation in ENB. In only one data set for FAT and Fig. 2. Proportion of variation in fat % (FAT), protein % (PRO) and fat to protein ratio (FP) explained by breed, parity, ration (BPR) and dry matter intake (DMI). F o r explanation of data set codes see Table I.

248 n o n e f o r PRO was this additional variation significantly greater than that explained by FP alone. In Korver's data, addition o f the fat index o f the cow's sire improved prediction o f fat % in one o u t of four data sets. The protein index improved protein % also in one case, while for FP, t he fat index improved prediction twice {P < 0.05). For FP, indexes improved R ~ by 4% and reduced residual standard deviations by 0.002 to 0.124 on average. Indexes improved the R 2 o f ENB by 1% and reduced residual standard deviations by 0.02 MJ NE~ to an average of 10.12. F u r t h e r results t h e r e f o r e deal only with linear effects o f individual i n d e p e n d e n t variables. When milk composition parameters were used as independent variables in analysis o f ENB, a similar finding was that FP gave a consistent significant imp r o v ement in R 2 and in all but one case (Rijpkema's norm Weeks 5--8) a c c o u n t e d f o r more variation in ENB than did FAT or PRO (Fig. 4). The average imp r ove m ent was 19%. Since the strongest relationships in the analysis were between ENB and FP, f u r th er details on changes in residual standard deviations and the respective regression coefficients are presented in Table IV. Regressions o f FP on ENB were - 4 1 4 7 × 10 -6 to - 6 9 9 6 × 1 0 - 6 units per MJ NE~ change

.8

.6

.4

-

.2

Data set:

!

2

B LEGEND;

4

5 ~

6

13PR

7

8 I

9

]D

FAT, PRO OR FP

Fig. 4. Proportion of variation in energy balance explained by breed, ration, parity (BPR) and fat % (FAT), protein % (PRO) or fat to protein ratio (FP). For explanation of data set codes see Table I.

249

e~ t~

I

r

0 0

z

I

~0

I

Z

0

~0 0 C3

0

g~

i .0 0

°

~ e~ ~.x ~ , ~ ~

.<

•

,.., ;~ ~ ..,, ~ ,~ + ~

~

250

in ENB in Rijpkema and Korver's data but about twice that level in Hof's data. Regression of ENB on FP was -27.17 to -52.46 MJ NE~ per unit change in FP (Table IV) but without a clear pattern of difference among experiments. Lactation week apparently was not a major factor in magnitude of regression nor was the level of ENB, since the data sets with highest (14.35 MJ NE~) and lowest (-17.6 MJ NEll) mean energy balances had similar regressions on FP (-44.64 and -43.70). When the geometric mean of the two regressions was calculated, differences reflected those noted earlier with those for Hof's data being lowest and the range being -59.26 to -104.65 MJ NE~ per unit change of FP (average of -80.27). DISCUSSION

General relationships Within cows in early lactation the relationship of milk fat to milk protein content was generally positive in this study but of low magnitude. Similarly, Korver (1982) found correlations o f - 0 . 0 9 to 0.21 in early lactation compared to 0.30 to 0.48 for total lactations. This may be due to the general positive association between these two components being offset by the opposing effects that nutritional forces exert (Von Farries, 1983). In our data fat % was negatively associated with estimated energy balance, while protein % was positively associated with this variable. Since fat % was used as one c o m p o n e n t in the calculation o f energy requirement, an increase in fat content would result in a lower energy balance at a given energy intake. In the Rijpkema-fixed and Korver data sets, fixed level concentrate feeding together with ad libitum roughage was practised. Therefore a high positive correlation between total dry matter intake and proportion of roughage in the diet would be expected and with the exception o f Week 6 of Lactation I in Korver's data this was true. In the same data sets, the relationship between estimated energy balance and either dry matter intake or proportion of roughage was also positive and moderately high. In the Hof data, where feeding of both roughage and concentrate was at fixed levels, the relationship between estimated energy balance and dry matter intake was positive but other relationships among nutrition variables were inconsistent. Inclusion of ration effects in data analysis models generally resulted in low associations between milk composition variables and either dry matter intake or percentage diet roughage. For example, in the Korver data milk protein was correlated with dry matter intake 0.29 to 0.62 before and -0.06 to 0.18 after removal of ration effects. Similarly correlations o f milk protein with % roughage were reduced from -0.26 to - 0 . 6 1 (before) to -0.01 to 0.06 (after). Increasing % dietary roughage has resulted in increased fat % (Oldham and Sutton, 1979; Grieve et al., 1982; Banks et al.,

251

1983; Macleod et al., 1983) and sometimes (Macleod et al., 1983) but not always (Oldham and Sutton, 1979; Grieve et al., 1982) reduced milk protein %. Most of these effects have been observed in diets of less than 40--50% roughage. Also, a change in roughage to concentrate ratio is often conf o u n d e d with plane of nutrition, although not in the data reported by Oldham and Sutton (1979) where increasing intake of constant diets also resulted in decreasing fat % with little effect on protein %. On the other hand, Gordon (1977) reported an increase in milk protein % but no effect on fat % from feeding increasing amounts of concentrate, which could increase both dry matter intake and plane of nutrition and decrease roughage percentage. Journet and R e m o n d (1980) concluded that milk protein content increases with improved energy status of the cow, especially when feeding is below recommended levels. In a summary of 13 experiments, Emery (1978) found a positive correlation o f 0.42 between milk protein and energy intake. This value was even higher when the proportion of diet concentrate was held constant. Thus the literature is not unanimous on the effects o f level of intake or diet roughage percentage on milk composition and caution is advised when attempting to predict milk composition from these dietary factors, especially when large differences in feeding systems are accounted for as in this study. Using an estimate of energy balance has the advantage of combining effects of diet composition, level of intake and milk energy o u t p u t in one value related to the plane of nutrition. Similarly a ratio o f milk fat to prorein will reflect a shift in either or both of these milk components in response to changes in plane of nutrition. Thus, in these data the highest average within-ration relationships among variables were between estimated energy balance and fat to protein ratio. For FP, ENB increased proportion of explained variation by an average of 21 to 43% for the complete model. However, considerable variation was left unexplained, as evidenced by residual standard deviations of 0.08 -- 0.16 on means of 1.28 -- 1.41. Other factors that could influence this variable include genetic differences among cow and level of production. As stated earlier, inclusion of sire indexes to reduce genetic variation failed to consistently reduce variation. The inverse relationship of ENB and FP was also calculated to determine the predictive value of milk composition for estimating the cow's energy status. Similar results were obtained, with FP increasing R 2 values by an average of 19 to 52%. Residual standard deviations of 6.65--10.77 MJ NE~ remained. While the predictive value of FP lacks precision, it nevertheless gives an indication of the general energy status of the cow. When the geometric mean of the regressions of ENB on FP and the reciprocal of FP on ENB was determined, ENB decreased by 27.17--52.46 MJ NE~ per unit increase in FP. The lowest regression coefficient and highest increase in R 2 were in data where both roughage and concentrate feeding were at fixed levels. In this situation energy balance would be largely influenced by milk yield and composition since, with ration effects accounted for,

252

there was little variation in level o f intake or diet composition. Indeed one would generally expect a high relationship between milk solids yield and energy balance since t hey are used to calculate energy requirement. In situations o f this study where roughage was fed ad libitum, explained variation was lower and regression coefficients (especially FP on ENB) were in a narrower range. It is concluded that the ratio of milk fat to milk protein is a more sensitive indicator and a better predictor of nutritional status of the cow than either c o m p o n e n t by itself. These relationships were f o u n d in experiments where cows were fed m o derat e amounts of concentrates and in the absence o f added dietary fat. The fat : vrot ei n ratio was more closely related to an estimate o f energy balance than to either dry m at t er intake or percentage roughage in diet when major dietary differences were also acco u n ted for. Relationships were similar whether concent rat e feeding was fixed o r according to requirement so long as roughage feeding was ad libitum.

REFERENCES Banks, W., Clapperton, J.L. and Steele, W., 1983. Dietary manipulation of the content and fatty acid composition of milk fat. Proc. Nutr. Soc., 42 : 399--406. Clark, J.H. and Davis, C.L., 1980. Some aspects of feeding high producing cows. J. Dairy Sci., 63: 873--885. Centraal Veevoeder Bureau, 1977. Handleiding voor de berekening van de voederwaarde van ruwvoermiddelen. Centraal Veevoeder Bureau in Nederland, Lelystad, The Netherlands. Emery, R.S., 1978. Feeding for increased mill protein. J. Dairy Sci., 61: 825--828. Gordon, F.J., 1977. The effect of protein content on the response of lactating cows to level of concentrate feeding. Anita. Prod., 25: 181--191. Grieve, D.G., Burton, J.H., Braun, H.E. and Frank, R., 1982. Voluntary intake of shredded newsprint by dairy cattle. Can. J. Anim. Sci., 62: 799--806. Harvey, W.R., 1977. Mixed model least-squares and maximum likelihood computer program. Users guide for LSML 76, Ohio State University. Journet, M. and Remond, B., 1980. Influence de l'alimentation et de la saison sur les fractions azot~es du lair de vache. Lait, 60: 140--159. Kaufmann, W., 1979. Protein utilization. In: W.H. Broster and H. Swan (Editors), Feeding Strategy for the High Yielding Dairy Cow, Granada, London, pp. 90--113. Korver, S., 1982. Feed intake and production in dairy breeds dependent on the ration. Ph.D. Thesis, Agricultural University, Wageningen, The Netherlands. Macleod, G.K., Grieve, D.G. and McMillan, I., 1983. Performance of first lactation dairy cows fed complete rations of several ratios of forage to concentrate. J. Dairy Sci., 66: 1668--1674. Oldham, J.D. and Sutton, J.D., 1979. Milk composition and the high yielding cow. In: W.H. Broster and H. Swan (Editors), Feeding Strategy for the High Yielding Dairy Cow. Granada, London, pp. 114--117. Reid, I.M., 1983. Reproductive performance and fatty liver in Guernsey cows. Anita. Reprod. Sci., 5: 275--279. Ricker, W.E., 1973. Linear regressions in fishery research. J. Fish. Res. Board Can., 30: 409--434. Rijpkema, Y.S. and van Reeuwijk, L., 1983. Voederproeven bij mellvee met vaste krachtvoergiften. I. Verslag van een proef met twee krachtvoeders waarin vismeel respectievelijk sojaschroot de belangrijkste eiwitbronnen vormden. I.V.V.O. rapport No. 156, Lelystad, The Netherlands (summary in English).

253 Tilley, J.M.A. and Terry, R.A., 1963. A two-stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Soc., 18: 104--111. Van Es, A.J.H., 1978. Feed evaluation for ruminants. I. The system in use from May 1977 onwards in The Netherlands. Livest. Prod. Sci., 5 : 331--345. Van der Koelen, C.J. and Dijkstra, N.D., 1971. Bepaling van de verteerbaarheid in-vitro als hulpmiddel bij de schatting van de voederwaarde van ruwvoeders. Landbouwk. Tijdschr., 83: 494--499. Von Farries, E., 1983. Stoffwechselstorungen und ihr Einfluss auf die Zusammensetzung der Milch. Zuchtungskunde, 55 : 265--274. Zanartu, D., Polan, C.E., Ferreri, L.E. and McGilliard, M.L., 1983. Effect of stage of lactation and varying available energy intake on milk production, milk composition and subsequent tissue enzymatic activity. J. Dairy Sci., 66: 1644--1652. RESUME Grieve, D.G., Korver, S., Rijpkema, Y.S. et Hof, G., 1986. Relations entre la composition du lait et quelques param~tres nutritionnels en d~but de lactation. Livest. Prod. Sci., 1 4 : 2 3 9 - - 2 5 4 (en anglais). On a d~termin~ les relations entre la composition du lait et les param~tres nutritionnels partir de 10 s~ries de donees obtenues dans 4 experiences avec des mesures individuelles sur 236 vaches pendant une ~ quatre semaines au second et au troisi4me mois de lactation successivement. Les effets de la race, du num4ro de lactation, de m~me que ceux des rations exp~rimentales, ont ~t~ ~limin~s. On a calcul~ le bilan 4nerg4tique d partir des quantitgs de fourrages et de concentr~s ing~r~es, compl~t4es par leur composition chimique et leur digestibilit4 in vitro, et de la quantit4 et de la composition du lait produit. Les moyennes ajust~es de ce bilan ont vari~ de - - 1 7 . 6 5 ~ 14.35 MJ d'~nergie lait par jour. Pour l'ensemble des 10 s~ries de mesures, la quantit4 de mati~re s~che ing~r~e a vari~ de 16.1 ~ 20.1 kg par jour, avec une proportion de fourrages de 36.6 fi 67.5%, et les teneurs du lait de 3.76 fi 4.11% de mati~res grasses et de 2.79 ~ 3.15% de mati~res azot4es. Une fois enlev~s les effets de la ration au cours de l'analyse, la mati~re s~che ing~r4e et la proportion de fourrages n'expliquent qu'une pattie relativement faible des variations de la composition du lait. Le brian ~nerg~tique calcul~ a 4t~ en relation n4gative avec la teneur en mati~res grasses (-0.007 fi -0.65), positive avec la teneur en mati~res azot~es (0.12 ~ 0.47 ) et n~gative avec le rapport mati~res grasses: mati~res azot4es (-0.36 ~ -0.74). I1 a augment~ la proportion de la variation expliqu~e de ce rapport en moyenne de 21%. A l'oppos~, ce rapport a accru le coefficient de d~termination du bilan 4nerg~tique de 19 d 52%. Le rapport mati4res grasses : mati~res azot~es du lait a ~t~ une crit~re, plus sensible et plus constant, que chacune des deux teneurs des modifications des variables nutritionneUes et aussi un meilleur pr4dicteur de l'4tat ~nerg~tique de la vache.

KUR ZF AS S UN G Grieve, D.G., Korver, S., Rijpkema, Y.S. und Hof, G., 1986. Beziehungen zwischen Milchinhaltsstoffen und einigen Ffitterungsparametern am Anfang der Laktation. Livest. Prod. Sci., 1 4 : 2 3 9 - - 2 5 4 (auf englisch). Um die Beziehungen zwischen Milchzusammensetzung und Ftitterungsparametern zu bestimmen, wurden 10 Datens~tze aus 4 Versuchen mit Individualdaten yon 236 Kfihen verwendet. Die Datenerhebung erfolgte im 2 und 3. Monat der Laktation jeweils innerhalb eines Zeitraumes von 1 bis 4 Wochen. In der Analyse wurde eine Korrektur der Daten auf die Effekte Rasse, Laktationszahl und Versuchsration durchgefiihrt. Die

254 gesch~/tzte Bilanz der Energieaufnahme und des Energiebedarfs fiirMilch und Erhaltung wurde anhand der A u f n a h m e yon Rauhfutter und Kraftfutter unter Berticksichtigung ihrer chemischen Zusammensetzung und der in-vitro-Verdaulichkeit sowie der Milchleistung und der Milchinhaltsstoffe berechnet. Die ermittelte, mittlere und korregierte Energiebilanz schwankte fiber alle Datens~tze zwischen -17.65 und 14.35 M J N E I pro Tag; die Trockensubstanzaufnahme zwischen 16.1 und 20.1 kg pro Tag und der Anteil des Rauhfutters in der Ration zwischen 36.6 und 37.5%. Der Milchfettgehalt bewegte sich tiber alle Datens~'tze zwischen 3.76 und 4.11%, w~/hrend der Eiweissgehalt zwischen 2.79 und 3.15% variierte.Nach Korrektur auf die Effekte der Rationen erkl~rte die Trockensubstanzaufnahme und der Anteil des Rauhfutters in der Ration relativgeringe Teile der Variation im Milchfett- und Milcheiweissgehalt sowie im Verh~/Itnis dieser beiden zueinander. Die gesch~'tzte Energiebilanz war tiber alle Datens~itze negativ mit d e m Fettgehalt (--0.07 bis --0.65), positiv mit d e m Eiweissgehalt (0.12 bis 0.47 ) sowie negativ mit d e m Verh~/Itnis von Fett und Eiweiss der Milch korreliert (-0.36 bis -0.74). Die gesch~/tzte Energiebilanz erhShte den Anteil der erkl~'rten Variation irn Fett:EiweissVerh~Itnis durchschnittlich urn 2 1 % auf insgesamt 43%. Urngekehrt steigerte das Fett: Eiweiss-Verh~itnis der Milch das Bestimmtheitsmass der Energiebilanz von 19 auf 52%. Das Verh~iltnis yon Fett zu Eiweiss in der Milch war ein sensibler und zutreffender Indikator ffir die Ver~'nderungen von Ftitterungsparametern; gleichzeitig sch~/tzte es den Energieversorgungsstatus der K u h besser voraus, als eine der K o m p o n e n t e n allein.