Selection for Economic Efficiency of Dairy Cattle Using Information on Live Weight and Feed Intake: A Review1

GENETICS AND BREEDING Selection for Economic Efficiency of Dairy Cattle Using Information on Live Weight and Feed Intake: A Review1 R. F. VEERKAMP2 Genetics and Behavioural Sciences Department, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, Scotland

ABSTRACT The objective of this study was to review some of the latest evidence on genetic variation in feed intake and feed utilization and to determine how this variation might be used. The most important sources of genetic variation in gross efficiency are likely to be the quantities of feed eaten and used for yield or maintenance and the extent to which body tissue is mobilized. Accounting for just one of these components when selection is for improved feed efficiency might result in undesirable genetic changes. For example, in an ad libitum feeding system, the heritability of body condition score is reported to be 0.43 for heifers; genetic correlations of body condition score with milk production and live weight were –0.46 and 0.66, respectively. Also, the genetic correlation between milk yield and live weight depends on lactation stage. For example, over the first 26 wk of lactation, this correlation was reported to be –0.09, but, after genetic adjustment for body condition score, the correlation was 0.29. When economic values are being derived, energy norms or genetic correlations can be used, and double counting of the feed costs needs to be avoided. An index that contained linear type traits, however, gave high accuracy of selection. Hence, although there appears to be great potential to improve economic efficiency by selecting for feed intake and live weight or by possible indicator traits, there is still uncertainty about some of the genetic parameters, especially among traits related to health, reproduction, and energy balance. ( Key words: feed intake, live weight, selection index, body condition score) INTRODUCTION The potential significance of feed intake for dairy cattle breeding is demonstrated by the relative impor-

Received June 25, 1997. Accepted November 14, 1997. 1Invited paper. 2Current address: Institute for Animal Science and Health, PO Box 65, 8200 AB Lelystad, The Netherlands. 1998 J Dairy Sci 81:1109–1119

tance of costs associated with growing and purchasing feed for milk yield. For example, for a herd of Holstein cows in the United Kingdom, mean milk returns were £1331, and costs associated with feed, health, and reproduction were £577, £33, and £140, respectively, per cow over the first 38 wk of lactation (46). Reproduction costs might be relatively high in this study because an opportunity cost for a prolonged calving interval was included, but feed costs were based on the actual intakes of individual animals fed a total mixed ration. Hence, feed costs reflect a considerable part of the variable costs that are associated with milk yield, and, therefore, genetic improvement potentially can be of considerable economic importance. Another reason why interest in feed intake is justified is that the feed eaten by a cow is used for several functions other than milk production, such as maintenance, growth, reproduction, and fetal growth. Hence, the consequences of genetic selection for higher yield on these associated traits are of interest, especially because several studies have suggested the addition of live weight in the breeding objective to account for increasing maintenance cost of heavier cows (6, 13, 15, 53, 61). Furthermore, genetic correlations between intake and yield suggest that the correlated response in feed intake from selection on yield alone can cover only 40 to 48% of the extra requirements for the increased yield (51, 60). A particularly relevant question appears to be whether this apparent improvement in gross efficiency is due to improved feed efficiency or whether the improvement is just due to a greater negative energy balance during early lactation. Selection index theory ( 1 7 ) is widely accepted as a method to combine several traits of economic importance. In dairy cattle breeding, estimated breeding values are generally available for selection, which lead to further simplification of index calculations (19, 44). Thus, in principle, the construction of a selection index is straightforward. However, when feed intake and related traits are considered, consideration must also be given to the decision of how to account for feed costs. Should genetic correlations or

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well-established energy norms be used? A further complication is the potential risk of double counting costs associated with feed. Both of these factors need to be considered carefully when selection indices including feed intake or live weight information are being derived. The objective of this study is to review some of the latest evidence on genetic variation in feed efficiency, feed intake, and feed utilization and to determine how this variation might be combined in a selection index. In the mid-1980s (and before), some of these aspects were reviewed extensively (2, 4, 9, 12, 26, 29), and, therefore, the emphasis in this paper is on developments since then. Definitions of economic efficiency (e.g., perspectives from which profit is viewed and the unit that should be used to calculate profit) have been reviewed by Goddard ( 1 1 ) and, therefore, is not discussed here. NET EFFICIENCY One of the most obvious ways of improving economic performance by selection for intake and live weight is to improve the efficiency of feed utilization, which appears to be easily accomplished. There is abundant evidence of genetic variability in feed efficiency and, thus, in gross energetic efficiency measured between breeds (10, 37) or selection lines (40, 59) or expressed as heritabilities (24, 25, 39, 47, 48, 51, 55). Published heritabilities for gross efficiency are moderately large and often are very similar to the heritability of milk yield. However, the problem with the use of gross efficiency is that it does not distinguish between the energy used for the separate functions of maintenance, lactation, and body tissue gain or loss; therefore, selection for feed efficiency might not improve the efficiency of feed utilization directly. A review of sources of variation in gross efficiency ( 5 6 ) suggested that there are probably no large genetic differences among cows in the ability to digest or metabolize a given feed at a constant level of feeding. Also, it is unclear whether genetic variation exists in the partial efficiency of feed conversion into valuable product. Results from energy chambers (often on a limited number of cows) suggest no genetic differences for partial efficiencies, but estimates from field data (often after crude adjustments or imprecise measurement for some of the components involved) suggest that some genetic variation exists in the apparent partial efficiency of energy utilization, especially with ad libitum feeding of a single feed. Overall, however, Veerkamp and Emmans ( 5 6 ) concluded that it is difficult to demonstrate genetic variation in net effiJournal of Dairy Science Vol. 81, No. 4, 1998

ciency (i.e., conflict between size of the data set and detail of recording), but evidence is too weak to allow scientists to assume large genetic differences in net efficiencies, which agrees with the conclusion of others ( 2 ) . This conclusion is an important one for animal breeders (and nutritionists) because the most important components to improve feed utilization by genetic selection are then the capacity for feed intake; the amount of energy needed for yield, maintenance, and body condition changes; and any differences in partitioning among these components. GENETIC PARAMETERS Yield, Intake, Live Weight, and Live Weight Change An overview of some recent studies presenting genetic parameters for intake, live weight, or live weight change is given in Table 1. Generally, the number of records is small in most studies, and, therefore, estimates for the genetic parameters are subject to large sampling errors. Also, populations and trait definitions (i.e., time of recording and number of measurements involved) differ among the studies described in Table 1. For these reasons, genetic parameters are not expected to be identical. Nonetheless, estimates are consistent in that, in most studies, the heritability for intake is similar to the heritability for yield. Heritability estimates for live weight were generally high, especially when weight was based on an average of more than one measurement. Heritabilities were lowest for live weight change during lactation. There is close agreement among the genetic correlations reported between yield and intake, which ranged from 0.46 to 0.65 (Table 2). Values outside this range were artificially high because they were estimated from experiments in which cows were fed according to yield or in which intake was predicted partially from yield. Correlations between intake and live weight or live weight change and correlations between yield and live weight are more variable than between yield and intake, and the feeding system has no obvious effect on these correlations. Apart from normal sampling variation, a likely explanation for these variable genetic correlations is that different definitions of live weight were used in the studies included in Table 2. Despite high genetic correlations between live weight measured at different points during lactation (48, 52), the correlation between milk yield and weight depends more on when live weight is measured. Van Elzakker and van Arendonk ( 5 2 ) reported that the genetic correlation between yield

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TABLE 1. Overview of some recent studies presenting heritabilities for feed intake, live weight (LW), or LW change (LWC) for lactating Holstein cows.1

Source

Type of record

Records (no.)

Ahlborn and Dempfle ( 1 ) Hietanen and Ojala ( 2 0 ) Jensen et al. ( 2 4 ) Lee et al. ( 3 1 )

Heifers Cows Heifers Heifers

Madgwick et al. ( 3 3 ) Moore et al. ( 3 5 )

Heifers Heifers

Persaud et al. ( 3 9 ) Svendsen et al. ( 4 8 )

Heritability LWC

Yield

Feeding system2

0.23 0.20

0.27 0.27 0.50 0.35

Field data Field data TMR ad lib. Conc. according to yield, ad lib. roughage

0.16 0.32

Field data Field data, no individual intake TMR ad lib. Fixed conc., ad lib. roughage Field data Fixed conc., ad lib. roughage

DMI

LW

7345 9664 295 1266

0.16 0.27

0.24 0.28 0.35 0.37

ca.3000 >80,000

0.16

0.40 0.23

Lactations Heifers

475 353

0.45 0.49

0.32 0.64

0.14

0.19 0.20

Tveit et al. ( 5 0 ) Van Arendonk et al. ( 5 1 )

Heifers Heifers

334 360

0.31

0.65 0.88

0.17 0.27

0.35 0.48

Veerkamp and Brotherstone ( 5 5 ) Veerkamp et al. ( 5 7 )

Lactations Lactations

1157 377

0.44 0.36

0.44 0.71

0.10

0.34 0.45

TMR ad lib. TMR ad lib.

1Traits have been grouped under these general headings, but time of recording and trait definitions differ among these studies. Estimates of similar traits in the same study have been pooled. 2Conc. = Concentrate; ad lib. = fed for ad libitum intake.

and live weight changed from 0.29 in lactation wk 3 to –0.25 in wk 13, and Veerkamp and Brotherstone ( 5 5 ) found that the genetic correlations between yield and live weight measured at first calving or as an average during lactation were positive and negative, respectively. One of the most likely reasons for the change in genetic correlation between yield and live weight is that stored body fat is an important contributor to live weight during some parts of the lactation, and body tissue mobilization is closely related to milk

yield. This hypothesis is confirmed by the large genetic correlations between average live weight and body condition score. Madgwick et al. ( 3 3 ) reported a genetic correlation between live weight and body condition score of 0.48, and values ranging between 0.27 and 0.67 were reported by Veerkamp and Brotherstone (55). Hence, body condition score explains a significant part of the genetic variation in weight. The effect of body condition score on the genetic correlation between live weight and yield was demonstrated in this last study by adjusting live weight genetically

TABLE 2. Overview of genetic correlations between intake, yield, live weight (LW), and LW change (LWC) from some recent studies. Genetic correlations DMI Source Ahlborn and Dempfle ( 1 ) Hietanen and Ojala ( 2 0 ) Jensen et al. ( 2 4 ) Lee et al. ( 3 1 ) Madgwick et al. ( 3 3 ) Moore et al. ( 3 5 ) Persaud et al. ( 3 9 ) Svendsen et al. ( 4 8 ) Tveit et al. ( 5 0 ) Van Arendonk et al. ( 5 1 ) Veerkamp and Brotherstone ( 5 5 ) Veerkamp et al. ( 5 7 )

Yield

LW

Yield LWC

0.50 0.95

0.34 0.23

–0.05 –0.45

0.84 0.60 0.46

0.36 0.35 0.86

0.10

0.65 0.64 0.44

0.65 0.23 0.30

0.21 0.23

LW 0.20 0.05 0.18 –0.01 0.20 –0.18 –0.41 0.22 0.45 0.02 0.01 –0.10

LWC

–0.67 –0.37

–0.50 –0.77 –0.84 –0.65

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VEERKAMP TABLE 3. Overview of heritability and genetic correlation with yield for some measures indicating energy balance.1

Source

Description

Madgwick et al. ( 3 3 ) Van Arendonk et al. ( 5 1 )

Body condition score Residual feed intake (intake minus yield, LW,2 and LWC) Svendsen et al. ( 4 8 ) Energy balance (intake minus yield and maintenance) Veerkamp et al. ( 5 6 ) Residual feed intake (intake minus yield, LW, and LWC) Jensen et al. ( 2 4 ) Residual feed intake (intake adjusted for yield, LW, and LWC as ratio) Minimum weight during lactation Veerkamp and Brotherstone ( 5 5 ) Body condition score

h2

Genetic correlation with yield

0.28

–0.05

0.19

–0.12

0.20

–0.70

0.34

–0.18

0.69 0.23 0.35

–0.91 –0.67 –0.37

1Signs of the genetic correlation have been changed so that a negative value indicates a more negative energy balance rather than a higher efficiency. 2LW = Live weight; LWC = LW change.

for the variation in body condition score. Adjustment changed a slightly negative genetic correlation between milk yield and live weight (–0.09) to a moderately positive correlation of 0.29 between yield and adjusted live weight. This value of 0.29 seemed more in line with other genetic correlations between measures of size (e.g., stature, chest width, and body depth) and yield. For example, Brotherstone ( 5 ) reported correlations between stature and yields of milk, fat, and protein of 0.22, 0.16, and 0.25, respectively. Hence, the genetic association between yield and size is positive, but the association between yield and live weight is more dynamic because both are closely associated with body mobilization. Energy Balance and Body Tissue Mobilization Even though changes in body composition during lactation are normal for mammals (41), from genetic parameters, evidence is unequivocal that selection for yield has a strong negative effect on live weight change during lactation; genetic correlations between yield and live weight change range from –0.37 to –0.84 in recent studies (Table 2). Interpretation of this relationship is difficult because animals with high genetic merit for milk production might grow less during lactation or might mobilize more body tissue. Assuming that genetic variation in partial efficiencies is relatively unimportant (as discussed previously), measurements of feed intake adjusted for milk yield (and maintenance or live weight change) can been used as a measure of the negative energy balance rather than improved efficiency. Although genetic correlations between this measure of energy Journal of Dairy Science Vol. 81, No. 4, 1998

balance (often referred to as residual feed intake) and other traits depend on the method of adjusting feed intake for the predicted energy requirements (27, 57), the genetic correlations between energy balance and yield are all negative (Table 3). This relationship indicates that the higher the genetic merit is for milk yield, the higher is the difference between energy eaten and predicted energy requirements and, thus, the more negative is the energy balance. Body condition score might also be used to indicate energy balance. Heritability estimates for body condition score are as high as those for milk yield, and Madgwick et al. ( 3 3 ) reported a correlation of –0.05 between yield and body condition score in Australia. Veerkamp and Brotherstone ( 5 5 ) reported a much larger value of –0.46 in a high yielding herd of Holsteins when body condition score was measured during lactation (Table 4). At first calving, the correlation between body condition score and milk yield was only –0.18, indicating that the lower body condition score for cows of high genetic merit is indeed a consequence of greater mobilization of body tissue during lactation. Hence, the genetic correlation between yield and condition score is negative and, with all of the other evidence, indications seem to be that selection for higher yield alone leads to a more negative energy balance during some parts of the lactation. Type Traits and Other Predictors Measurement of the performance of an individual cow for live weight and feed intake is not the common practice of most breeding programs; therefore, there

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TABLE 4. Heritabilities for different measures of body condition score1 and its genetic correlations with several other traits measured during the first 26 wk of lactation or at calving. Heifers Body condition score at calving ( n = 910) 2 Heritability Milk yield Fat yield Protein yield DMI LW3 Calving LW Calving body condition score

Heifers + cows Average body condition score ( n = 410)

0.34

0.43

–0.18 –0.07 –0.06 0.18 0.41 0.36

–0.46 –0.33 –0.29 0.23 0.66 0.27 0.67

Body condition score at calving ( n = 3399)

0.24 Genetic correlations –0.38 –0.38 –0.33 –0.08 0.64 0.51

Average body condition score ( n = 1157) 0.35 –0.46 –0.31 –0.35 0.00 0.67 0.41 0.88

1A

high body condition score indicates more fat (55). = Number of lactation records. 3Live weight. 2n

is great interest in other traits that may help to predict these potential goal traits. To overcome the high costs of measuring feed intake, measurements can be restricted to part of the lactation ( 3 8 ) as is done in the nucleus herd in the United Kingdom. However, measurement of the intake of individual cows is not feasible for most breeding programs, which depend on progeny testing of bulls via daughter records from many dispersed commercial herds rather than from a nucleus herd. Nieuwhof et al. ( 3 6 ) suggested using measurements of feed intake of growing bulls and heifers, and Persaud et al. ( 3 9 ) suggested that selection on an index of fat and protein yields and live weight would be about 85 to 95% as accurate as selection on breeding value for efficiency. Leuthold et al. ( 3 2 ) discussed the use of blood chemical parameters for indirect selection on feed efficiency; however, the use of those parameters is still unclear. As an alternative, body measures might be used to predict live weight and feed intake. Body measures can explain nearly all of the phenotype variation in body weight (18), and some evidence exists that body measures might be useful to predict feed intake or feed efficiency as well. Sieber et al. ( 4 5 ) found negative correlations between estimated efficiency and seven body measurements. Gravert ( 1 2 ) reported that chest circumference is an accurate predictor of feed intake. Madgwick et al. ( 3 3 ) found a genetic correlation between height and live weight of 0.55. Thus, benefits might be possible from the inclusion of linear type traits in a selection index for the prediction of DMI, body condition score, and live weight. Investigating this option further, Veerkamp and Brotherstone ( 5 5 ) found that genetic correlations be-

tween live weight and stature, chest width, body depth, and rump width were consistently high, and chest width and body depth were slightly to moderately correlated with DMI. Angularity and chest width were correlated also with body condition score. When these traits were combined in one index, the accuracies of predicting DMI, live weight (adjusted for body condition score), and body condition score were 0.65, 0.84, and 0.88, respectively. When milk, fat, and protein yields were included in the index, this genetic correlation with DMI increased to 0.90. As another alternative measure, visual assessment of body condition score by type classifiers is under investigation in the United Kingdom. Although mean score and variation among scorers differed, initial (Veerkamp and Brotherstone, 1997, unpublished) results demonstrated that the heritability for body condition is as high as the values reported in this study. Therefore, this trait might be potentially useful for inclusion when selection is for feed intake, live weight, or energy balance. Hence, selection for live weight and, perhaps, for feed intake and energy balance can be relatively inexpensive because linear type traits are measured and standardized in most national and international breeding programs and appear to have high genetic correlations with the traits of economic interest. The optimal direction of selection for each of the type traits depends on the economic values for live weight, feed intake, and body condition score. Strong genetic correlations between some of the traits make it difficult to anticipate what the optimal index weights for each of the individual body traits might be. Journal of Dairy Science Vol. 81, No. 4, 1998

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ECONOMIC CONSIDERATIONS Economic Values As various researchers ( 2 1 ) have pointed out, increased yield automatically increases economic efficiency per unit of milk because less of the total energy consumed is needed for maintenance. Obviously, this relationship only holds when live weight does not increase at a rate that offsets these gains in efficiency. The second option to improve feed efficiency is to reduce maintenance requirements for a cow with decreasing live weight. Holmes et al. ( 2 1 ) demonstrated that, at a common live weight, a 25% yield increase in milk solids causes efficiency to improve by 10 to 15%, and that, at a common yield, a 25% decrease in live weight causes efficiency to improve by 10 to 12%. Hence, everything else being equal, the economic value of live weight is reported to be negative in many studies (6, 13, 15, 22, 53, 61). It is important to note, however, that the economic value for live weight is often calculated using norms that describe the maintenance cost for a cow assuming a constant for body composition. These costs are related primarily to the fat-free content of the body ( 8 ) , and, hence, applying the economic value across cows in which a large part of variation in weight is due to variation in body condition score might not give an accurate reflection of the actual differences in maintenance cost between cows. Maintenance costs might be more closely related to measures of size other than live weight. Obviously, there are feed costs associated with higher body condition score also, but these costs are primarily associated with repletion or depletion rather than being a constant cost for maintenance. A third option to improve economic efficiency is to increase feed intake or feed intake capacity. Two examples of scenarios are presented in which this increase might be beneficial. First, in the situation in which additional concentrates are needed to supplement energy from forage, an increase in intake per liter of milk means that relatively more forage can be fed, and the amount of concentrate in the diet at a given milk yield can be reduced (14, 64). Second, an increase in feed intake from genetic selection might be advantageous when feed is limited. In that situation, if intake per cow were to be increased, then stocking rates could decrease, and fewer cows would be needed to convert all of the available feed into milk. Hence, on a given farm, less feed would be partitioned toward maintenance because there would be fewer cows. Obviously, in terms of selection criteria, there is an inherent conflict with a system Journal of Dairy Science Vol. 81, No. 4, 1998

that reduces maintenance costs by breeding a smaller cow but that might need to be compensated by increasing stocking rates to use all available feed. Selection for milk yield alone does not necessarily fit any of these scenarios because the correlation between intake and yield is smaller than unity. Impact on Health and Reproduction Another consideration when selection is for feed intake and live weight is the effect of selection on energy balance and body condition score. This trait seems to have an implicit economic value because negative energy balance is generally related to poorer health and fertility (3, 16), and the magnitude of the energy deficit during the first 2 to 3 wk after calving is closely correlated with the interval to first estrus (42). However, most studies relating reproductive performance and body condition score do not measure genetic relationships because cows are deliberately fed to reach a certain body condition score ( 4 9 ) or cows are fed on different feeding levels (7, 23, 30); hence, effects observed in these studies are environmental only. For the same reasons, use of the phenotypic relationships between health or fertility and body condition score (62, 63) to make inferences about genetic improvement might not be appropriate. If dairy form (i.e., angularity) is taken as indicator of body condition score, then large negative genetic correlations were reported with diseases other than mastitis, either adjusted or unadjusted for PTA for milk (43). Hence, potentially negative energy balance or body condition score might be important factors when selection is for improved feed utilization because these factors have an implicit economic value. To quantify this economic value, genetic associations with traits of economic importance, such as health and fertility, need to be investigated further. Selection for Feed Intake, Live Weight, and Feed Utilization An interesting question is what the consequences are of different selection criteria following the options just described. When all traits are expressed in genetic standard deviation units, standard selection index theory and parameter estimates from Veerkamp and Brotherstone ( 5 5 ) can be used to illustrate the consequences of selection on different indices. Table 5 shows that one standard deviation selection on a yield index increases intake (within the same 26-wk period) by only 0.67 genetic standard deviations; however, yields of milk, fat, and protein are

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expected to increase by 0.70, 0.83, and 0.98 standard deviation units, respectively. Live weight is not expected to change, primarily because of a strong reduction in body condition score. The response for adjusted live weight, however, demonstrates that size is still expected to increase by 0.29 standard deviations. When live weight was included with a negative economic value of –0.4 (relative to a value for protein yield of 1.0), the increase in intake was reduced, and the negative responses for live weight and body condition score were exacerbated. These effects became weaker when intake was included in the index, although average body condition score is expected to decrease regardless of which index was used. When live weight adjusted for body condition score was included, then the response in body condition score was hardly affected compared with selection for yield alone, although DMI is still reduced. The loss in yield is greater than the loss with selection on unadjusted weight. With an increase in importance of intake relative to live weight, live weight would be expected to increase rather than decrease, an effect that would be stronger when adjusted live weight is used. These results illustrate the difficulty of finding appropriate weighting factors for feed intake, live weight, and yield in dairy cattle breeding goals. Genetic associations among these traits are strong, and, therefore, economic and genetic arguments should be combined to evaluate the economic consequences of changes in the whole complex of traits (i.e., intake, live weight, and energy balance) simul-

taneously. Otherwise, what might be perceived to be gained by including one of these traits might in fact be lost because the other traits are changed as a consequence of the correlated response in the other direction. SELECTION INDEX DERIVATION Selection Index Method The final aim when selection is for many correlated traits is to combine the traits in a selection index to aid decision making. To do so, it currently seems appropriate to assume that all index traits are PTA from a complete multivariate BLUP analysis (i.e., all traits are analyzed together). In that situation, optimal index weights are the sum of the partial genetic regression coefficients of each goal trait on each index trait, weighted by the economic value of the goal traits (19, 44, 58). The partial coefficients for genetic regression can be calculated from estimated genetic variances and covariances: b = G–1 Gig v, where b = vector containing the index weights, the matrix Gig ( m × n ) = genetic covariances contained between the m goal and n index traits, the symmetric matrix G ( n × n ) = genetic (co)variance matrix between the index measurements, and v = vector with the economic weights for the goal traits (i.e. the returns for the yield traits and the costs for DMI). This method gives optimal index weights for multivariate breeding values and also for single-trait EBV

TABLE 5. Consequences of selection on six different indices combining yield, DMI, live weight ( L W ) and LW adjusted for body condition score (LWa). All traits are expressed in genetic standard deviation units. Selection on

Yield1

Yield –0.4 LW

Milk yield Fat yield Protein yield DMI Average LW Average body condition score

0.70 0.83 0.98 0.67 0.00 –0.32

0.69 0.77 0.92 0.52 –0.37 –0.54

Milk yield Fat yield Protein yield DMI Average LWa Average body condition score

Yield1 0.70 0.83 0.98 0.67 0.29 –0.32

Yield –0.4 LWa 0.61 0.73 0.90 0.55 –0.11 –0.33

1Yield

Yield –0.3 LW +0.1 DMI

Yield –0.2 LW +0.2 DMI

Yield –0.1 LW +0.3 DMI

0.72 0.82 0.96 0.62 –0.24 –0.47 Yield –0.3 LWa +0.1 DMI 0.66 0.80 0.94 0.65 0.03 –0.31

0.73 0.85 0.97 0.71 –0.12 –0.39 Yield –0.2 LWa +0.2 DMI 0.70 0.84 0.96 0.73 0.15 –0.29

0.73 0.87 0.97 0.77 –0.01 –0.32 Yield –0.1 LWa +0.3 DMI 0.72 0.86 0.96 0.78 0.25 –0.27

Yield +0.4 DMI 0.72 0.87 0.95 0.82 0.09 –0.24 Yield +0.4 DMI 0.72 0.87 0.95 0.82 0.34 –0.24

index = –0.2 milk + 0.2 fat + 1.0 protein. Journal of Dairy Science Vol. 81, No. 4, 1998

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with high accuracies. Therefore, the selection index method is applicable to most progeny-tested bulls. Even in some other scenarios (depending on the correlations and how many records are recorded for each trait), there might be little loss in accuracy by ignoring the environmental covariances when index weights are being calculated (58). Norms Versus Genetic Correlation to Derive Economic Values Economic values are important in determining the direction and relative importance of traits and are needed to establish selection index weights. Among other methods, there are two contrasting methods that can be used to derive economic values associated with feed costs; one is based on genetic correlations, and the other is based on norms. Traditionally, the method to account for the cost of feeding animals is to include it with all the returns in the breeding objective. Let H be the additive genetic value for economic merit; then, H = rm MY + rf FY + rp PY – cDMI where r = returns for the yield traits (MY, FY, and PY are milk, fat, and protein yields, respectively), and c = cost for 1 kg of feed (DMI). Optimal index weights are then the sum of the partial genetic regression coefficients of each goal trait on each index trait, weighted by the economic values of the goal traits. A practical application is given, for example, when only milk, fat, and protein yields are available as index measures; then, the index becomes I = bm MY + bf FY + bp PY where b = index weights: the difference between the returns ( r ) for each of the yield traits and a function of the partial genetic regression of DMI on MY, FY, and PY and the cost of 1 kg of DMI. These index weights can be derived from the estimated genetic (co)variances, as described previously. In contrast to this empirical method using the genetic correlations to derive feed costs, most selection indices in use are based on feeding norms to calculate feed cost. For example, ITEM in the United Kingdom uses the effective energy system described by Emmans ( 8 ) . These norms are used to calculate the extra energy needed for 1 kg of protein, for example, and subsequently a ration is formulated to derive the feed costs. For practical purposes, this means that, for example, when only milk, fat, and protein Journal of Dairy Science Vol. 81, No. 4, 1998

yields are available as index measures, the index becomes the difference between the returns and calculated feed costs associated with 1 kg of protein. Most often, these norms are hidden in a larger bioeconomic model. These two methods yield different weights even for such a simple index as that just described (54). Four reasons are given as to why these weights are different for these two methods: 1. Estimates of genetic parameters may be inaccurate in comparison with well-established norms. 2. Genetic parameters are limited, for example, by the part of the lactation for which feed intake is measured, but norms can be used to calculate costs over a full lifetime. 3. Norms for the amount of energy needed for 1 kg of protein have been calculated as the heat of combustion value of that milk divided by the net efficiency. This result is in contrast to genetic parameters that are not adjusted for all other traits. Consequently, weights based on the genetic regression are not partial weights on traits absent from the breeding goal (as it should be), but weights based on norms assume that no other traits change. 4. Norms are generally developed using data from a within-animal experiment and, therefore, are often based on environmental differences (e.g., the same animal fed different amounts of feed). There is no reason why environmental effects should change in the same direction as genetic effects. Overall, it is difficult to generalize which of these two methods is preferred. However, it seems crucial to quantify how compatible they are, especially when more complicated bioeconomic models are developed. For example, live weight could be treated in a manner similar to any of the milk yield traits, either using norms to calculate the feed costs or using the genetic correlation with DMI. The consistency between these norms and the genetic parameters should be checked. Double Counting of Feed Costs If genetic variation in feed efficiency contributes to economic efficiency, then DMI (or a predictor of DMI) needs to be included in the index. It then becomes more crucial to determine which of the two methods (norms or genetic parameters) is used to derive the index weights for the yield traits. Because norms for the energy content of yield are well established com-

REVIEW: SELECTION FOR LIVE WEIGHT AND FEED INTAKE

pared with the genetic correlations between yield and intake, it is tempting to use the economic values for the milk production traits from these norms. However, as soon as DMI is included in the index and the genetic regressions are calculated (as discussed earlier), then feed costs related to yield are accounted for twice. It seems logical, when feed costs have already been included for the yield traits (by using the norms), that only the feed intake component, which is independent of milk production, should be accounted for in the goal. Hence, as described in more detail by Veerkamp (54), the trait DMI should be converted into the trait-adjusted DMI, which has a genetic correlation of 0 with the yield traits and a correlation of less than unity with DMI. DISCUSSION The objective was to review some of the latest evidence on genetic variation of feed efficiency, feed intake, and feed utilization and to determine how this variation might be combined in an index to select for improved economic performance. Because there is little evidence for genetic variation in partial efficiency, selection for improved feed efficiency has to focus on improving the partitioning of feed eaten into valuable components while ensuring that the energy supply for other important functions is not sacrificed. Heritabilities are moderately high for several of the components for which feed is used, but large genetic correlations among feed intake, yield, live weight, live weight change, energy balance, and body condition score make selection more complicated. Complications also arise from the dynamics of body tissue mobilization and yield during lactation because correlations between yield and live weight vary, depending on when live weight is measured. Adjustment for genetic variation in body condition score at the time that live weight was measured might provide a solution. Linear type traits proved to have moderate genetic correlations with some of the traits of economic importance discussed here and, therefore, might be used as indicator traits in a selection index, which obviously would be a great advantage to measuring weight or intake. Linear type traits are measured in most national and international breeding programs, which makes selection relatively cheap and easy. There are several options to use live weight and feed intake in genetic selection for improved economic efficiency. Selection for increased yield and lower live weight are obvious, and there is no doubt that smaller cows have lower feed requirements for maintenance. However, improved intake or reduced energy deficit in early lactation might be of interest as well. Which combination of these scenarios would improve eco-

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nomic efficiency fastest is not obvious, but, given that none of the genetic correlations was unity, there is potential for any of these options (Table 5). A suggestion that might merit further investigation is to use the consequences of selection described in Table 5 with current population means as input parameters in a bioeconomic model. Using such a model, profit contours can be established that might help to establish the index that is economically most important, both over the short and long term (34). One of the difficulties is in how to account for the conflicting economic values between feed intake and weight when the correlation between these is positive. Without economic evidence of the superiority of one of these scenarios, increased intake seems especially appealing, given the large negative correlation between yield and energy balance that already exists. This relationship is not surprising, given that the correlated increase in intake from selection for yield is expected to cover less than 50% of the requirements needed for the extra milk yield (51, 60). Hence, the rate of genetic progress in milk yield is faster than can be supported by increased intake alone. Selection for lower live weight unadjusted for body condition score increases the negative energy balance even further because selection is for a lower body condition score directly. Furthermore, increasing intake might become more important in the future as a selection goal. Results from Veerkamp et al. ( 6 0 ) suggested that, with a higher percentage of forage in the diet, animals of high genetic merit were not capable of eating much more than control line animals; on high concentrate diets, animals of high genetic merit have higher intake and more body tissue mobilization. Hence, some evidence exists of an interaction of genotype and feed, suggesting an even lower genetic correlation between yield and intake for diets based on forage. The second difficulty in defining economic weights and selection indices is how to account for the economic importance of the buffering capacity of body tissue mobilization and the related energy balance or lower body condition score. For this reason, as an alternative to the economic indices discussed thus far, it could be argued that an increase in milk production during early lactation should be accompanied by a sufficient increase in intake capacity to accommodate any additional energy required. Hence, selection should be for increased yield and decreased live weight with the condition that intake increases sufficiently to supply the extra energy required. Another option might be to use body condition score as a measure of energy balance during early lactation and use an index that constrains genetic progress so that body condition score is not further reduced. In the Journal of Dairy Science Vol. 81, No. 4, 1998

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long run, compared with a simple increase in the energy deficit during early lactation, these two indices might be more sustainable. It could also be argued that, if the energy density of a diet can be increased, problems coming from a negative energy balance might be reduced by changes in management. However, this option might be impossible because there is a limit to how much concentrate can be included in the diet or because this option might be economically undesirable in the future, also an increase in energy density of the diet may not compensate sufficiently for the extra loss in condition during early lactation. Koenen and Veerkamp ( 2 8 ) investigated body condition score and live weight patterns during the first 26 wk of lactation for selected and control line heifers on a forage and concentrate diet. Those researchers demonstrated how selected heifers lost more body condition than did control heifers, an effect that seemed unaffected by the concentrate-based diet fed during the first 15 wk of lactation. Selected animals put the extra dietary energy into milk yield rather than in reducing the energy gap during early lactation. Thus, changes in management or nutrition might not be sufficient to compensate for the increasing negative energy balance as a consequence of genetic selection. Hence, although there appears to be great potential to improve economic efficiency by selection for feed intake and live weight or possible indicator traits, there is still uncertainty about some of the genetic parameters, especially for traits related to health, reproduction, and energy balance. This limitation is a major hindrance to establishing which scenario (i.e., increasing yield, decreasing weight, increasing intake, or improving energy balance) improves economic importance fastest. However, all traits clearly should be considered simultaneously; otherwise, the perceived gain from improvement of one trait might in fact be lost by correlated responses in other traits. ACKNOWLEDGMENTS P. Amer, M. E. Goddard, J. K. Oldenbroek, G. Simm, and P. M. Visscher are acknowledged for several helpful suggestions on earlier versions of this manuscript. Discussions with many colleagues in Edinburgh have been very much appreciated over the past 7 yr. Financial support of the Ministry of Agriculture, Food and Fisheries, the Milk Development Counsel, the Holstein Friesian Society for Great Britain and Ireland, and the Scottish Office Agriculture and Fisheries Department is acknowledged. Journal of Dairy Science Vol. 81, No. 4, 1998

REFERENCES 1 Ahlborn, G., and L. Dempfle. 1992. Genetic parameters for milk production and body size in New Zealand Holstein Friesian and Jersey. Livest. Prod. Sci. 31:205–219. 2 Bauman, D. E., S. N. McCutcheon, W. D. Steinhour, P. J. Eppard, and S. J. Sechen. 1985. Sources of variation and prospects for improvement of productive efficiency in the dairy cow: a review. J. Anim. Sci. 60:583–592. 3 Berglund, B., and B. Danell. 1987. Live weight changes, feed consumption, milk yield, and energy balance in dairy cattle during the first period of lactation. Acta Agric. Scand. 37: 495–509. 4 Blake, R. W., and A. A. Custodio. 1984. Feed efficiency: A composite trait of dairy cattle. J. Dairy Sci. 67:2075–2083. 5 Brotherstone, S. 1994. Genetic and phenotypic correlations between linear type traits and production traits in HolsteinFriesian dairy cattle. Anim. Prod. 59:183–188. 6 Dempfle, L. 1986. Increasing the efficiency of the dairy cow with regard to body size. Res. Bull. No. 4. Livest. Improvement Co., N.Z. Dairy Board, Hamilton, New Zealand. 7 DeRouen, S. M., D. E. Franke, S. G. Morrison, W. E. Wyatt, D. F. Coombs, T. W. White, P. E. Humes, and B. B. Greene. 1994. Prepartum body condition and weight influences on reproductive performance of first-calf beef cows. J. Anim. Sci. 72:1119–1125. 8 Emmans, G. C. 1994. Effective energy: a concept of energy utilisation applied across species. Br. J. Nutr. 71:801–821. 9 Freeman, A. E. 1975. Genetic variation in nutrition of dairy cattle. Pages 19–46 in The Effect of Genetic Variation on Nutrition of Animals. Natl. Acad. Sci., Washington, DC. 10 Gibson, J. P. 1986. Efficiency, and performance of genetically high and low milk-producing British Friesian and Jersey cattle. Anim. Prod. 42:161–182. 11 Goddard, M. E. 1997. Consensus and debate in the definition of breeding objectives. J. Dairy Sci. 80 (Suppl. 1):144.(Abstr.) 12 Gravert, H. O. 1985. Genetic factors controlling feed efficiency in dairy cows. Livest. Prod. Sci. 13:87–99. 13 Groen, A. F. 1989. Economic values in cattle breeding: II Influences of production circumstances in situation with output limitations. Livest. Prod. Sci. 22:17–30. 14 Groen, A. F., and S. Korver. 1989. The economic value of feed intake capacity of dairy cows. Livest. Prod. Sci. 22:269–281. 15 Groen, A. F., J.A.M. Van Arendonk, M.H.A. Steverink, and P.B.M. Berentsen. 1994. The economic value of body weight in dairy cattle: influences of farm intensity and environmental legislation. 45th Annu. Mtg. Eur. Assoc. Anim. Prod., Edinburgh, United Kingdom. 16 Harrison, R. O., S. P. Ford, J. W. Young, A. J. Conley, and A. E. Freeman. 1990. Increased milk production versus reproductive and energy status of high producing dairy cows. J. Dairy Sci. 73:2749–2758. 17 Hazel, L. N. 1943. The genetic basis for constructing selection indexes. Genetics 28:476–490. 18 Heinrich, A. J., G. W. Rogers, and J. B. Cooper. 1992. Predicting body weight and wither height in Holstein heifers using body measurements. J. Dairy Sci. 75:3577–3581. 19 Henderson, C. R. 1984. Application of Linear Models in Animal Breeding. Can. Cat. Publ. Data, Univ. Guelph, Guelph, ON, Canada. 20 Hietanen, H., and M. Ojala. 1995. Factors affecting body weight and its association with milk production traits in Finnish Ayrshire and Friesian cows. Acta Agric. Scand. A. Anim. Sci. 45: 17–25. 21 Holmes, C. W., A.W.F. Davey, and C. Grainger. 1981. The efficiency with which feed is utilised by the dairy cow. Proc. N.Z. Soc. Anim. Prod. 41:16–27. 22 Holmes, C. W., G. F. Wilson, W. Kuperus, S. Vuvaneshwa, and B. Wickham. 1993. Live weight, feed intake and feed conversion efficiency of lactating cows. Proc. N.Z. Soc. Anim. Prod. 53: 95–99.

REVIEW: SELECTION FOR LIVE WEIGHT AND FEED INTAKE 23 Holter, J. B., M. J. Slotnick, H. H. Hayes, C. K. Bozak, W. E. Urban, and M. L. McGilliard. 1990. Effect of prepartum dietary energy on body condition score, postpartum energy, nitrogen partition, and lactation production response. J. Dairy Sci. 73: 3502–3511. 24 Jensen, J., W. D. Hohenboken, P. Madsen, and B. B. Andersen. 1995. Sire × nutrition interactions and genetic parameters for energy intake, production and efficiency of nutrient utilization in young bulls, heifers and lactating cows. Acta Agric. Scand. A Anim. Sci. 45:81–91. 25 Jensen, J., P. Madsen, and B. B. Andersen. 1991. Sire by rearing interactions on feed intake, efficiency, and production of bulls, heifers and cows. Proc. 42nd Annu. Mtg. Eur. Assoc. Anim. Prod., Berlin, Germany. 26 Kennedy, B. W. 1984. Breeding for efficiency: swine and dairy cattle. Can. J. Anim. Sci. 64:505–512. 27 Kennedy, B. W., J.H.J. van der Werf, and T.H.E. Meuwissen. 1993. Genetic and statistical properties of residual feed intake. J. Anim. Sci. 71:3239–3250. 28 Koenen, E.P.C., and R. F. Veerkamp. 1997. Genotype by diet interactions for live weight and body condition score during lactation in heifers. Page 38 in Proc. Br. Soc. Anim. Sci., Scarborough. Br. Soc. Anim. Sci., Edinburgh, United Kingdom. 29 Korver, S. 1988. Genetic aspects of food intake and feed efficiency in dairy cattle: a review. Livest. Prod. Sci. 20:1–13. 30 Laflamme, L. F., and M. L. Conner. 1992. Effect of postpartum nutrition and cow body condition at parturition on subsequent performance of beef cattle. Can. J. Anim. Sci. 72:843–851. 31 Lee, A. J., D. A. Boichard, A. J. McAllister, C. Y. Lin, K. Nadarajah, T. R. Batraa, G. L. Roy, and J. A. Vesely. 1992. Genetics of growth, feed intake, and milk yield in Holstein cattle. J. Dairy Sci. 75:3145–3154. 32 Leuthold, G., U. Muller, P. Reinecke, C. Brunch, and W. Leucht. 1993. Suitability of biochemical markers of energy exchange in indirect selection for nutrient intake and residual feed intake in dairy cattle. Arch. Tierz. 36:299–314. 33 Madgwick, P. P, K. A. Beard, and M. E. Goddard. 1991. Possibilities for incorporating selection for feed conversion efficiency into the national Dairy Herd Improvement Program. Pages 45–48 in Proc. 9th Mtg. Assoc. Adv. Anim. Br. Gen., Australia. 34 Moav, R., and W. G. Hill. 1966. Specialised sire and dam lines IV Selection within lines. Anim. Prod. 8:375–390. 35 Moore, R. K., B. W. Kennedy, L. R. Schaeffer, and J. E. Moxley. 1992. Relationships between age and body weight at calving, feed intake, production, days open, and selection indexes in Ayrshires and Holsteins. J. Dairy Sci. 75:294–306. 36 Nieuwhof, G. J., J.A.M. van Arendonk, H. Vos, and S. Korver. 1992. Genetic relationships between feed intake efficiency and production traits in growing bulls, growing heifers and lactating heifers. Livest. Prod. Sci. 32:189–202. 37 Oldenbroek, J. K. 1988. Food intake and energy utilisation in dairy cows of different breeds. Ph.D. Diss., Res. Inst. Anim. Prod. Schoonoord, Zeist, The Netherlands. 38 Persaud, P., and G. Simm. 1991. Genetic and phenotypic parameters for yields, food intake and efficiency of dairy cows fed ad libitum. 2. Estimates for part lactation measures and their relationship with ‘total’ lactation measured. Anim. Prod. 52:445–450. 39 Persaud, P., G. Simm, and W. G. Hill. 1991. Genetic and phenotypic parameters for yields, food intake and efficiency of dairy cows fed ad libitum. 1. Estimates for total measures and their relationship with live weight traits. Anim. Prod. 52: 435–444. 40 Persaud, P., G. Simm, H. Parkinson, and W. G. Hill. 1990. Relationships between sires’ transmitting for production and daughters’ production, food intake and efficiency in a highyielding herd. Anim. Prod. 51:245–253. 41 Robinson, J. J. 1986. Changes in body composition during pregnancy and lactation. Proc. Nutr. Soc. 45:71–80. 42 Robinson, J. J. 1996. Nutrition and reproduction. Anim. Reprod. Sci. 42:25–34. 43 Rogers, G. W., G. Banos, and U. Sander Nielsen. 1997. Genetic correlations among US traits and diseases other than mastitis

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in Denmark and Sweden. J. Dairy Sci. 80(Suppl. 1): 255.(Abstr.) 44 Schneeberger, M., S. A. Barwick, G. H. Crow, and K. Hammond. 1992. Economic indexes using breeding values predicted by BLUP. J. Anim. Breed. Gen. 109:180–187. 45 Sieber, M., A. E. Freeman, and D. H. Kelley 1988. Relationships between body measurements, body weight, and productivity in Holstein dairy cows. J. Dairy Sci. 71:3437–3445. 46 Simm, G., R. F. Veerkamp, and P. Persaud. 1994. The economic performance of dairy cows of different predicted genetic merit for milk solids production. Anim. Prod. 58:313–320. 47 Svendsen, M., P. Skipenes, and I. L. Mao. 1993. Genetic parameters in the feed conversion complex of primiparous cows in the first two trimesters. J. Anim. Sci. 71:1721–1729. 48 Svendsen, M., P. Skipenes, and I. L. Mao. 1994. Genetic correlations in the feed conversion complex of primiparous cows at a recommended and a reduced plane of nutrition. J. Anim. Sci. 72:1441–1449. 49 Treacher, F. J., I. M. Reid, and C. J. Roberts. 1986 Effect of body condition at calving on the health and performance of dairy cows. Anim. Prod. 43:1–6. 50 Tveit, B., M. Svendsen, and K. Hove. 1991. Heritability of hypocalcemia at first parturition in Norwegian cattle: genetic correlations with yield and weight. J. Dairy Sci. 74:3561–3567. 51 Van Arendonk, J.A.M., G. J. Nieuwhof, H. Vos, and S. Korver. 1991. Genetic aspects of feed intake and efficiency in lactating dairy heifers. Livest. Prod. Sci. 29:263–275. 52 Van Elzakker, P.J.M., and J.A.M. Van Arendonk. 1993. Feed intake, body weight and milk production: genetic analysis of different measurements in lactating dairy heifers. Livest. Prod. Sci. 37:37–51. 53 Van Raden, P. M. 1988. Economic value of body size in Holsteins. J. Dairy Sci. 71(Suppl. 1):238.(Abstr.) 54 Veerkamp, R. F. 1996. Live weight and feed intake in dairy cattle breeding goal. Proc. Int. Workshop on Functional Traits in Cattle, Gembloux, Belgium, January, 21–23. Int. Bull. Eval. Serv., Uppsala, Sweden. 55 Veerkamp, R. F., and S. Brotherstone. 1997. Genetic correlations between linear type traits, food intake, live weight and body condition score in Holstein Friesian dairy cattle. Anim. Sci. (Pentcaitland) 64:385–392. 56 Veerkamp, R. F., and G. C. Emmans. 1995. Sources of genetic variation in energetic efficiency of dairy cows: a review. Livest. Prod. Sci. 44:87–97. 57 Veerkamp, R. F., G. C. Emmans, A. R. Cromie, and G. Simm. 1995. Variance components for residual food intake in dairy cows. Livest. Prod. Sci. 41:111–120. 58 Veerkamp, R. F., W. G. Hill, S. Brotherstone, A. W. Stott, and G. Simm. 1995. Selection for longevity and yield in dairy cattle using transmitting abilities for yield and type. Anim. Sci. (Pentcaitland) 61:189–197. 59 Veerkamp, R. F., G. Simm, and J. D. Oldham. 1994. Effects of interaction between genotype and feeding system on milk production, feed intake, efficiency and body tissue mobilisation in dairy cows. Livest. Prod. Sci. 39:229–241. 60 Veerkamp, R. F., G. Simm, and J. D. Oldham. 1995.Genotype by environment interaction—experience from Langhill. Pages 59–66 in Breeding and Feeding the High Genetic Merit Dairy Cow. Occas. Publ. 19. T.L.J. Lawrence, F. J. Gordon, and A. Carson, ed. Br. Soc. Anim. Sci., Edinburgh, United Kingdom. 61 Visscher, P. M., P. J. Bowman, and M. E. Goddard. 1994. Breeding objectives for pasture based dairy production systems. Livest. Prod. Sci. 40:123–137. 62 Waltner, S. S., J. P. McNamara, and J. K. Hillers. 1993. Relationships of body condition score to production variables in high producing Holstein dairy cattle. J. Anim. Sci. 76:3410–3419. 63 Wildman, E. E., G. M. Jones, P. E. Wagner, H. F. Troutt, and T. N. Lesch. 1982. A dairy cow body condition scoring system and its relationship to selected production characteristics. J. Dairy Sci. 65:495–501. 64 Zeddies, J. 1985. Die wirtschaftliche Bedeutung der Leistungsmerkmale beim Rind nach der Milchkontingentierung. Polykopie. Ins. Landwirtschaftliche Betriebslehre, Univ. Hohenheim, Stuttgart, Germany. Journal of Dairy Science Vol. 81, No. 4, 1998