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Adjusting for age can lead to biased genetic evaluation for body weight in cattle
Livestock Science 140 (2011) 1–7
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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Adjusting for age can lead to biased genetic evaluation for body weight in cattle G. Bittante 1, A. Cecchinato, R. Dal Zotto, M. De Marchi, M. Penasa ⁎,1 Department of Animal Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
a r t i c l e
i n f o
Article history: Received 21 September 2010 Received in revised form 18 January 2011 Accepted 2 February 2011 Keywords: Age Bias Breeding value Body weight Cattle
a b s t r a c t The aim of this study was to investigate the relationship between age (Age; 24.2 ± 8.7 days) and BW (61.0 ± 8.2 kg) of Brown Swiss male calves (n = 6826) sold at auction between 2003 and 2007. Animals were progeny of 330 sires mated to 6826 heifers. Preliminary least-squares analysis showed that herd of origin and date of auction significantly influenced Age and BW, along with age of dam at calving and age of calf for BW. Single-trait animal models highlighted that both Age and BW were heritable (0.075 ± 0.016 and 0.153 ± 0.026, respectively); therefore, under these conditions, Age cannot be regarded as a managerial factor. When included as an explanatory variable, age of calf increased the heritability of BW by 5 or 9% depending on whether the effect was introduced as discrete factor or linear covariate, respectively. Bivariate analyses with or without the inclusion of age of calf in the model for BW provided estimates of heritability similar to those from single-trait analyses (0.079 ± 0.014 for Age, and 0.136 ± 0.021 to 0.192 ± 0.031 for BW). The re-ranking of sires for BW of their calves at auction was very limited comparing bivariate models, while it was more pronounced comparing the results from single-trait models, and from single-trait and bivariate models; this is due to the strong and negative genetic correlation (−0.926 ± 0.081) between Age and BW, which reflects the tendency of farmers to sell first the fast growing and then the slow growing calves. Hence, the inclusion of age of calf as an explanatory variable in single-trait models for BW leads to biases in the estimation of breeding values. On the contrary, combining Age and BW in bivariate analyses leads to unbiased results, suggesting that both traits can be used as good predictors of growth potential in calves. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Models used to estimate genetic parameters for BW often account for the age of animals at weighing, with the purpose of adjusting BW to a fixed time (Dodenhoff et al., 1999b; Boligon et al., 2009) and estimating the growth potential of the animals, i.e., the ratio between BW and Age. The implicit assumption is that age is a nongenetic factor to be removed for improving the estimation of dispersion parameters for BW. The criteria usually adopted to demonstrate this
⁎ Corresponding author. Tel.: + 39 049 8272629; fax: + 39 049 8272633. E-mail address: [email protected] (M. Penasa). 1 These authors contributed equally to the manuscript. 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.02.001
assumption are 1) the significance of the age effect from preliminary ANOVA and 2) the increase of heritability for BW when age is included in the genetic model (Harville and Henderson, 1966). Managing field data can lead to different conclusions, especially if the weighing occurred in particular phases of the animal's life such as its weaning, selling or slaughtering. These events are usually a choice of the breeder whose decision is based on weight, appearance or other factors influenced by the genes of the animal. The complexity of decisions that should be taken by the farmer is well described in the case of the heifers management (Mourits et al., 1997). A study case to investigate this issue is to consider the age (Age, days) and BW (kg) of Brown Swiss calves sold at auction in Bolzano−Bozen (northeast Italy). The Brown Swiss is the
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most widespread dairy cattle breed in the Trentino−Südtirol region of Italian Alps, mainly because of the notable fat and protein content (Samorè et al., 2007) and coagulation properties (De Marchi et al., 2007, 2009; Cecchinato et al., 2009) of its milk, the production of high quality cheeses (De Marchi et al., 2008), and the good reproduction traits (Dal Zotto et al., 2007a). Most of the male calves are destined to veal production, as well as surplus females not used for replacement purposes in the herd. Immediately after birth, the animals are separated from the dam, fed colostrum for a few days and then a milk replacer with a small amount of roughage until they are sold, often through public auctions. The selling of male calves is a source of revenue for farmers. Hence, a good understanding of the genetic basis of BW is useful to address subsequent strategies aimed at enhancing the income of the herd. The objectives of this work are to investigate the relationship between Age and BW of calves sold at public auctions fitting different animal models and to study the impact of these models on the prediction of breeding values of sires. 2. Materials and methods 2.1. Data and editing procedure Animal Care and Use Committee approval was not obtained for this work because records were from preexisting databases. Information included in the study was age (Age, days) and BW (kg) of purebred Brown Swiss calves from first calving cows sold at auction between January 2003 and December 2007. Age of dams at calving ranged from 22 to 40 months. Identity code, date of auction, sex, and BW of animals were recorded by the Kovieh Cooperative, an important wholesale cattle organization operating in Bolzano−Bozen province (Trentino−Südtirol alpine region, northeast Italy). The Kovieh Cooperative groups purebred and crossbred calves from several dairy herds around the province, and sells them individually during weekly public auctions in a permanent livestock venue located in Bolzano− Bozen (Dal Zotto et al., 2007b, 2009; Penasa et al., 2009). Information on sire and dam identity codes, as well as date of birth and herd of origin of calves were supplied by the Breeders Association of Bolzano−Bozen province. Age of animals was calculated as difference between date of auction and date of birth. The original dataset accounted for both male and female calves, with the former representing more than 95% of the data. The reason for this imbalance is that dairy cows not used to breed replacements for the herd are usually mated to beef bulls (mainly Belgian Blue), so that almost all Brown Swiss purebred heifer calves are kept as future replacements (Dal Zotto et al., 2009). Because of the low number of records available, females sold at auction were not considered in the present study. Calves were required to have known parents, herd of origin, Age, and BW. Animals presented more than once at auction were discarded from the database, as well as records pertaining to twin births and calves produced by embryo transfer. Age at auction was restricted to be between 7 and 50 days, and BW between 32 and 96 kg (mean ± 3.5 SD). Only herds with at least three calves presented at auction and
dates of auction with at least three Brown Swiss males, were included. To avoid possible confusion between date of auction and herd of origin, only herds with animals sold during at least two different dates of auction were retained. After editing procedures, 6826 calves distributed among 237 auctions were available for subsequent analyses. Characteristics of the final dataset are shown in Table 1. Tests for normality were used to assess the deviation of the data from the normal distribution. Because of the quite large number of records available, the deviation of Age and BW was statistically significant (P b 0.05). Nevertheless, the values of skewness and kurtosis were relatively low, with the highest skewness for Age (0.58) and the highest kurtosis for BW (0.36), so that the application of linear models is justified. 2.2. Nongenetic fixed effects A preliminary least-squares analysis considering different factors progressively added to the models for Age and BW was carried out to identify nongenetic fixed effects to be included in mixed models (Table 2). The GLM procedure of SAS (SAS Inst. Inc., 2008) was used for this purpose. The effects of herd of origin (1086 levels) and date of auction (237 levels) were significant (P b 0.001) for both Age and BW, and hence were retained in the genetic models. Additionally, BW was significantly (P b 0.001) influenced by classes of age of dam at calving (class 1: b30 months; class 2: from 30 to 35 months; and class 3: N35 months), and age of calf at auction. The latter effect was added to the model first as discrete factor (class 1: b17 days; class 2: from 17 to 30 days; and class 3: N30 days), and second as continuous linear and quadratic explanatory variable. Only the quadratic regression was not significant (P N 0.05). 2.3. Genetic models Heritability for the studied traits and genetic correlation between them were investigated using single-trait and bivariate animal models. The single-trait analysis for Age (Model Age-2) included herd of origin and date of auction as fixed effects, and additive genetic animal and residual as random. For BW, three models were proposed: Model BW-3 accounted for the same factors of Model Age-2 along with age of dam at calving; Model BW-4 was Model BW-3 with the addition of classes of age of calf at auction; and Model BW-5
Table 1 Summary of data, overall means, and standard deviations (SD) for age (Age, days) and body weight (BW, kg) of calves. Item Calves, n Herds, n Auctions, n Cows, n Sires, n Records in pedigree file, n Age a, mean ± SD BW b, mean ± SD
6826 1086 237 6826 330 34,549 24.2 ± 8.7 61.0 ± 8.2
a Minimum = 7 days; maximum = 50 days; skewness = 0.58; and kurtosis = −0.01. b Minimum=34 kg; maximum=95 kg; skewness=0.36; and kurtosis=0.36.
G. Bittante et al. / Livestock Science 140 (2011) 1–7
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Table 2 Results from ANOVA for fixed effects of Age (Age, days) and body weight (BW, kg) of calves (F-values and significance). Model
R2
ΔR2
RMSE e
2.96 NS
0.420 0.468 0.469
– 0.048 0.001
7.25 7.09 7.09
14.83 ⁎⁎⁎ 19.63 ⁎⁎⁎ 20.42 ⁎⁎⁎ 20.47 ⁎⁎⁎
0.456 0.495 0.498 0.528 0.539 0.539
– 0.039 0.003 0.030 0.011 0.000
6.57 6.46 6.45 6.25 6.18 6.18
Source of variation
Age Age-1 Age-2 Age-3 BW BW-1 BW-2 BW-3 BW-4 BW-5 BW-6
Herd
Auction
3.84 ⁎⁎⁎ 3.75 ⁎⁎⁎ 3.74 ⁎⁎⁎
2.09 ⁎⁎⁎ 2.03 ⁎⁎⁎
4.44 ⁎⁎⁎ 4.39 ⁎⁎⁎ 4.36 ⁎⁎⁎ 4.01 ⁎⁎⁎ 4.02 ⁎⁎⁎ 3.97 ⁎⁎⁎
1.82 ⁎⁎⁎ 1.88 ⁎⁎⁎ 1.58 ⁎⁎⁎ 1.52 ⁎⁎⁎ 1.52 ⁎⁎⁎
Age of dam
a
Age of calf
176.68 ⁎⁎⁎
b
Age of calfLC
c
487.32 ⁎⁎⁎ 40.27 ⁎⁎⁎
Age of calfQC
1.56 NS
d
a
Three classes (class 1: b30 months; class 2: from 30 to 35 months; and class 3: N35 months). Three classes (class 1: b17 days; class 2: from 17 to 30 days; and class 3: N 30 days). c Linear covariate. d Quadratic covariate. e Root mean square error. ⁎⁎⁎ P b 0.001. NS Not significant. b
was Model BW-3 with the addition of age of calf at auction as linear covariate. Three bivariate models were also fitted. Model Age-2/BW3 was a combined analysis of Models Age-2 and BW-3; Model Age-2/BW-4 was a combined analysis of Models Age-2 and BW-4; and Model Age-2/BW-5 was a combined analysis of Models Age-2 and BW-5. The general form of linear models for the bivariate approach, in matrix notation, was the following:
y1 y2
=
X1 0
0 β1 Z1 + · X2 0 β2
0 u1 e1 + ; · Z2 u2 e2
where y1 and y2 are vectors of Age and BW observations, respectively; β1 and β2 denote vectors of fixed effects; u1 and u2 are vectors of additive genetic effects of animals; e1 and e2 are vectors of random residuals; X1 and X2 are known design matrices associating fixed effects in β1 and β2 with y1 and y2, respectively; and Z1 and Z2 are known design matrices associating additive genetic effects in ul and u2 with y1 and y2, respectively. For all models, the expected values were: y X1 0 β E 1 = · 1 ; 0 X2 y2 β2 and the variance−covariance structure of the random terms was: 2 2 Aσu1 3 u1 6 6 Aσ 6 u2 7 u1u2 6 7 Var6 4 e1 5 = 6 6 0 4 e2 0 2
Aσu1u2
0
2 Aσu2
0
0
Iσe1
0
Iσe1e2
2
0
3
7 7 7 7; Iσe1e2 7 5 0
2 Iσe2
where I is an identity matrix of order equal to the number of calves; A is the additive relationship matrix among animals in the pedigree file; σ2u1 and σ2u2 are additive genetic variances for Age and BW, respectively; σ2e1 and σ2e2 are residual
variances for Age and BW, respectively; σu1u2 is the additive genetic covariance between Age and BW; and σe1e2 is the residual covariance between the traits. (Co)variance components and related parameters were obtained with the program package VCE (Neumaier and Groeneveld, 1998), which uses restricted maximum likelihood (REML) implementing a quasi-Newton optimization algorithm on the Cholesky factor of the covariance matrices. A 5generation pedigree file was traced back using the original information provided by the Italian Brown Swiss Cattle Breeders Association (ANARB, Verona, Italy). This resulted in 34,549 animals in additive relationship matrix, which accounted for all individuals with Age and BW data and their ancestors. The prediction of breeding values was then obtained with the software package PEST (Groeneveld et al., 1990) using the (co)variance components from VCE (Neumaier and Groeneveld, 1998).
3. Results and discussion 3.1. Mean and variation Age and BW of calves averaged 24 days and 61 kg, respectively (Table 1), and both traits showed a medium to high variability, with SD of 8.7 days and 8.2 kg, respectively. These statistics are consistent with values reported by Dal Zotto et al. (2007b, 2009) in a study on the effect of different breeds and breed crosses on age, BW, price, and market value of purebred and crossbred calves sold for veal and beef production, and Penasa et al. (2009) in a study on the effect of sire on BW, price, and market value of Belgian Blue × Brown Swiss calves. Mc Hugh et al. (2010a) reported that the age of calves sold at livestock marts in Ireland ranged from 2 to 84 days, with 91% of all animals sold before 42 days; however, no data on BW was available on the animals. In Barham and Troxel (2007), the BW of feeder calves sold at Arkansas weekly livestock auctions in 2005 was much larger than the mean value from our research and ranged from 136 to 362 kg.
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y = 0,3355x + 52,905 2 R = 0,1286
90
BW (kg)
80
70
60
50
40
30 0
10
20
30
40
50
60
Age (days) Fig. 1. Phenotypic relationship between BW (kg) and Age (Age, days) of calves at auction.
3.2. Nongenetic effects Results from ANOVA are reported in Table 2. Different models were fitted to identify the effects significantly influencing the traits. Herd was the most relevant source of variation both for Age (R2 = 0.420) and BW (R2 = 0.456), denoting the existence of different feeding and management strategies adopted by farmers. Date of auction was highly significant (P b 0.001), but explained a lower proportion of variability than the herd effect (Table 2). Age of dam at calving was not important for Age, while it was relevant for BW (P b 0.001), despite the small proportion of variability explained. When introduced in the model for BW, age of calf was highly significant (P b 0.001), both as discrete factor and linear covariate. Differently from results obtained in beef calves at weaning (Rossi et al., 1992), in this study quadratic covariate was not significant (P N 0.05). 3.3. Relationship between Age and BW The relationship between Age and BW of calves at auction is shown in Fig. 1. The phenotypic correlation between the traits was low (36%) and the regression coefficient was
0.34 kg/day, i.e., about one third of the expected average daily gain (assuming a mean value of 38 kg for weight of calves at birth and mean values of 24 days and 61 kg for Age and BW at auction, respectively). Also, the intercept was approximately 15 kg higher than the expected birth weight. A possible explanation arises considering two opposite theoretical herd management strategies adopted by breeders. In the first one, farmers sell calves at a fixed age and growth potential is then expressed by differences in BW at auction; under this situation, we expect BW to be heritable and Age not. In the second one, farmers sell calves at a fixed BW and growth potential is then expressed by differences in age at auction; therefore, we expect Age to be heritable and BW not. Currently, farmers adopt different and intermediate selling criteria, according to the specific situations they have to face. Moreover, they are influenced by other factors such as market fluctuations, season and health status of the animal. 3.4. Heritability of Age and BW from single-trait analyses Estimates of genetic parameters for Age and BW obtained under single-trait analyses are shown in Table 3. Despite the low value (0.075 ± 0.016), results indicate that age of calf at selling is heritable, also confirming that farmers are partly influenced by the genes of the animal (mainly growth rate potential) when deciding the right moment for selling calves at auction. No data is currently available on heritability of age at sale of young calves that the authors are aware of. However, Everling et al. (2001) and Lopes et al. (2009) provided information on the age at which suckling beef calves achieve a given BW, and found higher heritability estimates than the present research, but lower than the values obtained for weaning weight. For comparison, it is worth noting that heritability of age at puberty of beef heifers is generally medium to high (Morris et al., 1992; Gregory et al., 1995). The estimate of heritability for BW from Model BW-3 was 0.153 ± 0.026. The inclusion of age of calf as class effect (BW4) or linear covariate (BW-5) increased the value to 0.205 ± 0.030 and 0.245 ± 0.033, respectively (Table 3). In a recent study, Mc Hugh et al. (2010b) reported a heritability estimate of 0.25 for BW of weanlings in Ireland, i.e., similar to results from Models BW-4 and BW-5. However, no estimates of heritability of BW from very young calves are currently
Table 3 Estimates a of variance components and related parameters (SE) from different models for age (Age) and body weight (BW) of calves. Age Model
b
2
σ
p
BW σ2e
2
σ
a
2
h (SE)
Single-trait Age-2
50.543
46.771
3.772
0.075 (0.016)
Bivariate Age-2/BW-3 Age-2/BW-4 Age-2/BW-5
50.660 50.662 50.660
46.668 46.655 46.668
3.992 4.007 3.992
0.079 (0.014) 0.079 (0.014) 0.079 (0.012)
Age-BW
Model
σ2p
σ
Single-trait BW-3 BW-4 BW-5
42.266 39.945 39.255 42.098 41.820 39.160
2
σ2a
h (SE)
35.811 31.748 29.651
6.455 8.197 9.604
0.153 (0.026) 0.205 (0.030) 0.245 (0.033)
36.368 35.952 31.654
5.730 5.868 7.506
0.136 (0.021) 0.140 (0.021) 0.192 (0.031)
e
2
σg
rg (SE)
−4.427 −4.485 −5.165
−0.926 (0.081) −0.925 (0.080) −0.944 (0.066)
a The term σp2 is the phenotypic variance, σe2 is the residual variance, σa2 is the additive genetic variance, h2 is the heritability, σg is the genetic covariance between Age and BW, and rg is the genetic correlation between Age and BW. b Model Age-2 = herd + auction + animal; Model BW-3 = herd + auction + age of dam + animal; Model BW-4 = Model BW-3 + age of calf(classes); and Model BW-5 = Model BW-3 + age of calf(linear covariate).
G. Bittante et al. / Livestock Science 140 (2011) 1–7 350
350
y = 0.9906x + 1.5604 2 R = 0.9812
y = 0.9745x + 4.2183 2 R = 0.9497
300
300
Rank Model BW-5
250
Rank Model BW-4
5
200
150
250
200
150
100
100
50
50
0
0 0
50
100
150
200
250
300
350
0
50
100
Rank Model BW-3
150
200
250
300
350
Rank Model BW-4
Fig. 2. Relationship between sire rankings based on EBV for BW from single-trait Models BW-4 (herd + auction + age of dam + animal + age of calf(classes)) and BW-3 (herd + auction + age of dam + animal).
Fig. 4. Relationship between sire rankings based on EBV for BW from single-trait Models BW-5 (herd + auction + age of dam + animal + age of calf(linear covariate)) and BW-4 (herd + auction + age of dam+ animal + age of calf(classes)).
available that the authors are aware of. Data found in literature often deals with growth curves obtained from repeated observations of BW on dairy calves reared on experimental farms, or birth weight and weaning weight of beef calves (adjusted by age). Heritability from our research is lower than the value found in dairy calves reared on experimental farms (Bushra et al., 1989; Groen and Vos, 1995; Coffey et al., 2006; Brotherstone et al., 2007). Also, heritabilities of birth and weaning weight of beef cattle are generally high, especially from experimental or very large commercial farms (Van Vleck et al., 1996; Goyache et al., 2003; Boligon et al., 2009). Only considering large populations (Albuquerque and Meyer, 2001; Shojo et al., 2006), estimates for weaning weight of beef calves are more similar to those obtained in our research. It is worth noting that heritability of weaning weight changes according to several
factors. Dodenhoff et al. (1999b) reported that direct heritability varied from 16 to 50% depending on the statistical model and sample. Also, the breed seems to be an important factor in explaining the results of the genetic analysis and the different selected lines within a breed can show variation in estimates for weaning weight, even in the case of animals reared within the same herd (Dodenhoff et al., 1998). Heritability for this trait in calves from nine different breeds varied from 11% (Charolais) to 34% (Pinzgauer; Dodenhoff et al., 1999a). In the same study, heritability for Braunvieh ranged from 27 to 30% depending on the statistical model. Genetic parameters for pre- and post-weaning traits in Braunvieh cattle were also investigated by Cucco et al. (2009, 2010) who reported heritabilities between 0.16 (weight at 550 days of age) and 0.41 (weaning weight). In particular, estimates for birth weight and weight at 120 days
350
300
y = 0.8249x + 28.977 2 R = 0.6805
300
Rank Model Age-2/BW-3
Rank Model BW-5
350
y = 0.9633x + 6.0802 R2 = 0.9279
250
200
150
100
50
250
200
150
100
50
0 0
50
100
150
200
250
300
350
Rank Model BW-3 Fig. 3. Relationship between sire rankings based on EBV for BW from single-trait Models BW-5 (herd + auction + age of dam + animal + age of calf(linear covariate)) and BW-3 (herd + auction + age of dam + animal).
0 0
50
100
150
200
250
300
350
Rank Model BW-3 Fig. 5. Relationship between sire rankings based on EBV for BW from bivariate Model Age-2/BW-3 (herd + auction + age of dam + animal) and single-trait Model BW-3 (herd + auction + age of dam + animal).
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300
Rank Model Age-2/BW-5
Differently from the single-trait approach, the inclusion of age of calf as class effect or linear covariate in the model for BW did not increase largely the heritability of this trait. Additive genetic and residual variances somewhat changed after including age of calf as linear covariate in the model for BW (Age-2/BW-3 vs. Age-2/BW-5; Table 3); nevertheless, the relationship between the ranking of sires obtained from different models was very high (R2 N 0.99; Fig. 6).
y = 0.9998x + 0.0253 2 R = 0.9997
250
200
150
4. Conclusion 100
50
0 0
50
100
150
200
250
300
350
Rank Model Age-2/BW-3 Fig. 6. Relationship between sire rankings based on EBV for BW from bivariate Models Age-2/BW-5 (herd + auction + age of dam + animal + age of calf(linear covariate)) and Age-2/BW-3 (herd + auction + age of dam + animal).
of age were 0.23 and 0.25, respectively, similar to the results from Models BW-4 and BW-5. Figs. 2, 3, and 4 show the relationships between rankings of sires based on EBV for BW of their calves from single-trait models. The ranking changed according to the model used. The correlation between sire ranking based on EBV for BW obtained with Models BW-5 and BW-4 was higher (R2 = 0.98; Fig. 4) than with Model BW-3 (R2 = 0.95 between BW-4 and BW-3, and 0.93 between BW-3 and BW-5; Figs. 2 and 3); this outcome was expected as Model BW-3 dealt with the analysis of BW of calves at auction while Models BW-4 and BW-5 dealt with BW adjusted to a fixed age, i.e., with the estimation of the growth potential of calves. Also, the change in sire ranking was not due to the presence of many sires having a low number of progeny because in a preliminary analysis sires with less than 20 sons were excluded, and resulting coefficients of determination for BW-4 vs. BW-3, BW-5 vs. BW-3, and BW-5 vs. BW-4 were very similar (0.95, 0.92, and 0.97, respectively) to those presented in this study. It is clear that, if Age is heritable, adjusting BW for age of calf leads to biased results; this is particularly true if the two traits are genetically correlated.
3.5. Heritability of BW from bivariate analyses A better understanding of the relationship between Age and BW of calves was possible by fitting the bivariate models (Table 3). The first model (Age-2/BW-3) estimated heritability values consistent with those obtained under the corresponding single-trait analyses. Despite comparable results, the correlation between sire rankings based on EBV for BW of calves from bivariate and single-trait models was moderate. Fig. 5 shows the large re-ranking of sires (R2 = 0.68) when Models Age-2/ BW-3 and BW-3 are compared. This was the consequence of the strong and negative genetic correlation between Age and BW (−0.926 ± 0.081; Table 3), and revealed the importance of taking into account the information from Age.
Results from this study provide a better understanding of the relationship between Age and BW of calves sold at auction. As there is a tendency of farmers to sell first the fast growing and then the slow growing calves, Age is a heritable trait and exhibits a strong and negative genetic correlation with BW. As a consequence, its inclusion as explanatory variable in single-trait analysis for BW leads to biases in the prediction of breeding values. In particular, EBV for sires whose progeny has fast growing rates is underestimated, while it is overestimated for sires whose progeny has slow growing rates. Combining Age and BW in bivariate analyses allows to account for the strong and negative genetic correlation between them and to correctly predict breeding values of animals. As a consequence, both traits can be regarded as good predictors of growth rate in calves. Moreover, the high genetic correlation between Age and BW suggests that it would be enough to include one of them in a breeding program aiming at improving the growth rate and the economic revenue of the dairy farm. Acknowledgment The authors wish to thank the Kovieh Cooperative (Bolzano−Bozen, Italy) and the Breeders Association of Bolzano−Bozen province for providing field data, the Superbrown Consortium of Bolzano and Trento, and Trento Province for financial support, and the Italian Brown Swiss Cattle Breeders Association (ANARB, Verona, Italy) for supplying pedigree information. The useful comments and suggestions provided by anonymous reviewers are gratefully acknowledged. References Albuquerque, L.G., Meyer, K., 2001. Estimates of direct and maternal genetic effects for weights from birth to 600 days of age in Nelore cattle. J. Anim. Breed. Genet. 118, 83–92. Barham, B.L., Troxel, T.R., 2007. Factors affecting the selling price of feeder cattle sold at Arkansas livestock auctions in 2005. J. Anim. Sci. 85, 3434–3441. Boligon, A.A., Albuquerque, L.G., Mercadante, M.E.Z., Lobo, R.B., 2009. Heritability and correlations between weights from birth to maturity in Nellore cattle. Rev. Bras. Zootec. 38, 2320–2326. Brotherstone, S., Coffey, M.P., Banos, G., 2007. Genetic parameters of growth in dairy cattle and associations between growth and health traits. J. Dairy Sci. 90, 444–450. Bushra, O.E. El, Wilcox, C.J., Wing, J.M., Littell, R.C., 1989. Genetic effects on dairy calf growth. J. Dairy Sci. 72, 162–166. Cecchinato, A., De Marchi, M., Gallo, L., Bittante, G., Carnier, P., 2009. Midinfrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk. J. Dairy Sci. 92, 5304–5313. Coffey, M.P., Hickey, J., Brotherstone, S., 2006. Genetic aspects of growth of Holstein−Friesian dairy cows from birth to maturity. J. Dairy Sci. 89, 322–329.
G. Bittante et al. / Livestock Science 140 (2011) 1–7 Cucco, D.C., Ferraz, J.B.S., Pinto, L.F.B., Eler, J.P., Balieiro, J.C.C., Mattos, E.C., 2009. Genetic parameters for pre-weaning traits in Braunvieh cattle. Genet. Mol. Res. 8, 291–298. Cucco, D.C., Ferraz, J.B.S., Eler, J.P., Balieiro, J.C.C., Mattos, E.C., Varona, L., 2010. Genetic parameters for postweaning traits in Braunvieh cattle. Genet. Mol. Res. 9, 545–553. Dal Zotto, R., De Marchi, M., Dalvit, C., Cassandro, M., Gallo, L., Carnier, P., Bittante, G., 2007a. Heritabilities and genetic correlations of body condition score and calving interval with yield, somatic cell score, and linear type traits in Brown Swiss cattle. J. Dairy Sci. 90, 5737–5743. Dal Zotto, R., Penasa, M., Povinelli, M., Bittante, G., 2007b. Effect of crossbreeding on market value of calves from dairy cows. Ital. J. Anim. Sci. 6 (Suppl. 1), 102–104. Dal Zotto, R., Penasa, M., De Marchi, M., Cassandro, M., López-Villalobos, N., Bittante, G., 2009. Use of crossbreeding with beef bulls in dairy herds: effect on age, body weight, price, and market value of calves sold at livestock auctions. J. Anim. Sci. 87, 3053–3059. De Marchi, M., Dal Zotto, R., Cassandro, M., Bittante, G., 2007. Milk coagulation ability of five dairy cattle breeds. J. Dairy Sci. 90, 3986–3992. De Marchi, M., Bittante, G., Dal Zotto, R., Dalvit, C., Cassandro, M., 2008. Effect of Holstein Friesian and Brown Swiss breeds on quality of milk and cheese. J. Dairy Sci. 91, 4092–4102. De Marchi, M., Fagan, C.C., O'Donnell, C.P., Cecchinato, A., Dal Zotto, R., Cassandro, M., Penasa, M., Bittante, G., 2009. Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy. J. Dairy Sci. 92, 423–432. Dodenhoff, J., Van Vleck, L.D., Kachman, S.D., Koch, R.M., 1998. Parameter estimates for direct, maternal, and grandmaternal genetic effects for birth weight and weaning weight in Hereford cattle. J. Anim. Sci. 76, 2521–2527. Dodenhoff, J., Van Vleck, L.D., Gregory, K.E., 1999a. Estimation of direct, maternal, and grandmaternal genetic effects for weaning weight in several breeds of beef cattle. J. Anim. Sci. 77, 840–845. Dodenhoff, J., Van Vleck, L.D., Wilson, D.E., 1999b. Comparison of models to estimate genetic effects of weaning weight of Angus cattle. J. Anim. Sci. 77, 3176–3184. Everling, D.M., Ferreira, G.B.B., Rorato, P.R.N., Roso, V.M., Marion, A.E., Fernandes, H.D., 2001. Heritability estimates and genetic correlation of growth characteristics in the preweaning period and scrotal circumference measurement at yearling for Angus−Nelore beef cattle. Rev. Bras. Zootec. 30, 2002–2008. Goyache, F., Fernandez, I., Royo, L.J., Alvarez, I., Gutierrez, J.P., 2003. Factors affecting actual weaning weight, preweaning average daily gain and relative growth rate in Asturiana de los Valles beef cattle breed. Arch. Tierz. 46, 235–243.
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Gregory, K.E., Cundiff, L.V., Koch, R.M., 1995. Genetic and phenotypic (co) variances for production traits of intact male populations of purebred and composite beef cattle. J. Anim. Sci. 73, 2227–2234. Groen, A.F., Vos, H., 1995. Genetic parameters for body weight and growth in Dutch Black and White replacement stock. Livest. Prod. Sci. 41, 201–206. Groeneveld, E., Kovac, M., Wang, T., 1990. PEST, a general purpose BLUP package for multivariate prediction and estimation. Proc. 4th World Congr. Genet. Appl. Livest. Prod., Edinburgh, Scotland, pp. 488–491. Harville, D.A., Henderson, C.R., 1966. Interrelationships among age, body weight, and production traits during first lactations of dairy cattle. J. Dairy Sci. 49, 1254–1261. Lopes, J.S., Rorato, P.R.N., Weber, T., de Almeida Dornelles, M., Comin, J.G., de Araujo, R.O., 2009. Genetic parameters and genetic and phenotypic trends for growing traits for a Brangus breed population. Rev. Bras. Zootec. 38, 662–669. Mc Hugh, N., Fahey, A.G., Evans, R.D., Berry, D.P., 2010a. Factors associated with selling price of cattle at livestock marts. Animal 4, 1378–1389. Mc Hugh, N., Evans, R.D., Amer, P.R., Berry, D.P., 2010b. Genetic parameters for cattle prices routinely collected at livestock marts. Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany, p. ID-657. Morris, C.A., Baker, R.L., Cullen, N.G., 1992. Genetic correlations between pubertal traits in bulls and heifers. Livest. Prod. Sci. 31, 221–234. Mourits, M.C.M., Dijkhuizen, A.A., Huirne, R.B.M., Galligan, D.T., 1997. Technical and economic models to support heifers management decisions: basic concepts. J. Dairy Sci. 80, 1406–1415. Neumaier, A., Groeneveld, E., 1998. Restricted maximum likelihood estimation of covariances in sparse linear models. Genet. Sel. Evol. 30, 3–26. Penasa, M., De Marchi, M., Dal Zotto, R., Cecchinato, A., Cassandro, M., Bittante, G., 2009. Influence of the sire on market value of Belgian Blue× Brown Swiss crossbred calves. Ital. J. Anim. Sci. 8 (Suppl. 3), 113–115. Rossi, D.J., Kress, D.D., Tess, M.W., Burfening, P.J., 1992. Correcting bias from the standard linear adjustment of weaning weight to an age-constant basis for beef calves. J. Anim. Sci. 70, 1333–1341. Samorè, A.B., Romani, C., Rossoni, A., Frigo, E., Pedron, O., Bagnato, A., 2007. Genetic parameters for casein and urea content in the Italian Brown Swiss dairy cattle. Ital. J. Anim. Sci. 6 (Suppl. 1), 201–203. SAS Inst. Inc., 2008. SAS® User's Guide, 2008 Version 9.1.3 Edition. SAS Inst., Inc, Cary, NC. Shojo, M., Okanishi, T., Anada, K., Oyama, K., Mukai, F., 2006. Genetic analysis of calf market weight and carcass traits in Japanese Black cattle. J. Anim. Sci. 84, 2617–2622. Van Vleck, L.D., Gregory, K.E., Bennett, G.L., 1996. Direct and maternal genetic covariances by age of dam for weaning weight. J. Anim. Sci. 74, 1801–1805.
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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Adjusting for age can lead to biased genetic evaluation for body weight in cattle G. Bittante 1, A. Cecchinato, R. Dal Zotto, M. De Marchi, M. Penasa ⁎,1 Department of Animal Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
a r t i c l e
i n f o
Article history: Received 21 September 2010 Received in revised form 18 January 2011 Accepted 2 February 2011 Keywords: Age Bias Breeding value Body weight Cattle
a b s t r a c t The aim of this study was to investigate the relationship between age (Age; 24.2 ± 8.7 days) and BW (61.0 ± 8.2 kg) of Brown Swiss male calves (n = 6826) sold at auction between 2003 and 2007. Animals were progeny of 330 sires mated to 6826 heifers. Preliminary least-squares analysis showed that herd of origin and date of auction significantly influenced Age and BW, along with age of dam at calving and age of calf for BW. Single-trait animal models highlighted that both Age and BW were heritable (0.075 ± 0.016 and 0.153 ± 0.026, respectively); therefore, under these conditions, Age cannot be regarded as a managerial factor. When included as an explanatory variable, age of calf increased the heritability of BW by 5 or 9% depending on whether the effect was introduced as discrete factor or linear covariate, respectively. Bivariate analyses with or without the inclusion of age of calf in the model for BW provided estimates of heritability similar to those from single-trait analyses (0.079 ± 0.014 for Age, and 0.136 ± 0.021 to 0.192 ± 0.031 for BW). The re-ranking of sires for BW of their calves at auction was very limited comparing bivariate models, while it was more pronounced comparing the results from single-trait models, and from single-trait and bivariate models; this is due to the strong and negative genetic correlation (−0.926 ± 0.081) between Age and BW, which reflects the tendency of farmers to sell first the fast growing and then the slow growing calves. Hence, the inclusion of age of calf as an explanatory variable in single-trait models for BW leads to biases in the estimation of breeding values. On the contrary, combining Age and BW in bivariate analyses leads to unbiased results, suggesting that both traits can be used as good predictors of growth potential in calves. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Models used to estimate genetic parameters for BW often account for the age of animals at weighing, with the purpose of adjusting BW to a fixed time (Dodenhoff et al., 1999b; Boligon et al., 2009) and estimating the growth potential of the animals, i.e., the ratio between BW and Age. The implicit assumption is that age is a nongenetic factor to be removed for improving the estimation of dispersion parameters for BW. The criteria usually adopted to demonstrate this
⁎ Corresponding author. Tel.: + 39 049 8272629; fax: + 39 049 8272633. E-mail address: [email protected] (M. Penasa). 1 These authors contributed equally to the manuscript. 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.02.001
assumption are 1) the significance of the age effect from preliminary ANOVA and 2) the increase of heritability for BW when age is included in the genetic model (Harville and Henderson, 1966). Managing field data can lead to different conclusions, especially if the weighing occurred in particular phases of the animal's life such as its weaning, selling or slaughtering. These events are usually a choice of the breeder whose decision is based on weight, appearance or other factors influenced by the genes of the animal. The complexity of decisions that should be taken by the farmer is well described in the case of the heifers management (Mourits et al., 1997). A study case to investigate this issue is to consider the age (Age, days) and BW (kg) of Brown Swiss calves sold at auction in Bolzano−Bozen (northeast Italy). The Brown Swiss is the
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G. Bittante et al. / Livestock Science 140 (2011) 1–7
most widespread dairy cattle breed in the Trentino−Südtirol region of Italian Alps, mainly because of the notable fat and protein content (Samorè et al., 2007) and coagulation properties (De Marchi et al., 2007, 2009; Cecchinato et al., 2009) of its milk, the production of high quality cheeses (De Marchi et al., 2008), and the good reproduction traits (Dal Zotto et al., 2007a). Most of the male calves are destined to veal production, as well as surplus females not used for replacement purposes in the herd. Immediately after birth, the animals are separated from the dam, fed colostrum for a few days and then a milk replacer with a small amount of roughage until they are sold, often through public auctions. The selling of male calves is a source of revenue for farmers. Hence, a good understanding of the genetic basis of BW is useful to address subsequent strategies aimed at enhancing the income of the herd. The objectives of this work are to investigate the relationship between Age and BW of calves sold at public auctions fitting different animal models and to study the impact of these models on the prediction of breeding values of sires. 2. Materials and methods 2.1. Data and editing procedure Animal Care and Use Committee approval was not obtained for this work because records were from preexisting databases. Information included in the study was age (Age, days) and BW (kg) of purebred Brown Swiss calves from first calving cows sold at auction between January 2003 and December 2007. Age of dams at calving ranged from 22 to 40 months. Identity code, date of auction, sex, and BW of animals were recorded by the Kovieh Cooperative, an important wholesale cattle organization operating in Bolzano−Bozen province (Trentino−Südtirol alpine region, northeast Italy). The Kovieh Cooperative groups purebred and crossbred calves from several dairy herds around the province, and sells them individually during weekly public auctions in a permanent livestock venue located in Bolzano− Bozen (Dal Zotto et al., 2007b, 2009; Penasa et al., 2009). Information on sire and dam identity codes, as well as date of birth and herd of origin of calves were supplied by the Breeders Association of Bolzano−Bozen province. Age of animals was calculated as difference between date of auction and date of birth. The original dataset accounted for both male and female calves, with the former representing more than 95% of the data. The reason for this imbalance is that dairy cows not used to breed replacements for the herd are usually mated to beef bulls (mainly Belgian Blue), so that almost all Brown Swiss purebred heifer calves are kept as future replacements (Dal Zotto et al., 2009). Because of the low number of records available, females sold at auction were not considered in the present study. Calves were required to have known parents, herd of origin, Age, and BW. Animals presented more than once at auction were discarded from the database, as well as records pertaining to twin births and calves produced by embryo transfer. Age at auction was restricted to be between 7 and 50 days, and BW between 32 and 96 kg (mean ± 3.5 SD). Only herds with at least three calves presented at auction and
dates of auction with at least three Brown Swiss males, were included. To avoid possible confusion between date of auction and herd of origin, only herds with animals sold during at least two different dates of auction were retained. After editing procedures, 6826 calves distributed among 237 auctions were available for subsequent analyses. Characteristics of the final dataset are shown in Table 1. Tests for normality were used to assess the deviation of the data from the normal distribution. Because of the quite large number of records available, the deviation of Age and BW was statistically significant (P b 0.05). Nevertheless, the values of skewness and kurtosis were relatively low, with the highest skewness for Age (0.58) and the highest kurtosis for BW (0.36), so that the application of linear models is justified. 2.2. Nongenetic fixed effects A preliminary least-squares analysis considering different factors progressively added to the models for Age and BW was carried out to identify nongenetic fixed effects to be included in mixed models (Table 2). The GLM procedure of SAS (SAS Inst. Inc., 2008) was used for this purpose. The effects of herd of origin (1086 levels) and date of auction (237 levels) were significant (P b 0.001) for both Age and BW, and hence were retained in the genetic models. Additionally, BW was significantly (P b 0.001) influenced by classes of age of dam at calving (class 1: b30 months; class 2: from 30 to 35 months; and class 3: N35 months), and age of calf at auction. The latter effect was added to the model first as discrete factor (class 1: b17 days; class 2: from 17 to 30 days; and class 3: N30 days), and second as continuous linear and quadratic explanatory variable. Only the quadratic regression was not significant (P N 0.05). 2.3. Genetic models Heritability for the studied traits and genetic correlation between them were investigated using single-trait and bivariate animal models. The single-trait analysis for Age (Model Age-2) included herd of origin and date of auction as fixed effects, and additive genetic animal and residual as random. For BW, three models were proposed: Model BW-3 accounted for the same factors of Model Age-2 along with age of dam at calving; Model BW-4 was Model BW-3 with the addition of classes of age of calf at auction; and Model BW-5
Table 1 Summary of data, overall means, and standard deviations (SD) for age (Age, days) and body weight (BW, kg) of calves. Item Calves, n Herds, n Auctions, n Cows, n Sires, n Records in pedigree file, n Age a, mean ± SD BW b, mean ± SD
6826 1086 237 6826 330 34,549 24.2 ± 8.7 61.0 ± 8.2
a Minimum = 7 days; maximum = 50 days; skewness = 0.58; and kurtosis = −0.01. b Minimum=34 kg; maximum=95 kg; skewness=0.36; and kurtosis=0.36.
G. Bittante et al. / Livestock Science 140 (2011) 1–7
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Table 2 Results from ANOVA for fixed effects of Age (Age, days) and body weight (BW, kg) of calves (F-values and significance). Model
R2
ΔR2
RMSE e
2.96 NS
0.420 0.468 0.469
– 0.048 0.001
7.25 7.09 7.09
14.83 ⁎⁎⁎ 19.63 ⁎⁎⁎ 20.42 ⁎⁎⁎ 20.47 ⁎⁎⁎
0.456 0.495 0.498 0.528 0.539 0.539
– 0.039 0.003 0.030 0.011 0.000
6.57 6.46 6.45 6.25 6.18 6.18
Source of variation
Age Age-1 Age-2 Age-3 BW BW-1 BW-2 BW-3 BW-4 BW-5 BW-6
Herd
Auction
3.84 ⁎⁎⁎ 3.75 ⁎⁎⁎ 3.74 ⁎⁎⁎
2.09 ⁎⁎⁎ 2.03 ⁎⁎⁎
4.44 ⁎⁎⁎ 4.39 ⁎⁎⁎ 4.36 ⁎⁎⁎ 4.01 ⁎⁎⁎ 4.02 ⁎⁎⁎ 3.97 ⁎⁎⁎
1.82 ⁎⁎⁎ 1.88 ⁎⁎⁎ 1.58 ⁎⁎⁎ 1.52 ⁎⁎⁎ 1.52 ⁎⁎⁎
Age of dam
a
Age of calf
176.68 ⁎⁎⁎
b
Age of calfLC
c
487.32 ⁎⁎⁎ 40.27 ⁎⁎⁎
Age of calfQC
1.56 NS
d
a
Three classes (class 1: b30 months; class 2: from 30 to 35 months; and class 3: N35 months). Three classes (class 1: b17 days; class 2: from 17 to 30 days; and class 3: N 30 days). c Linear covariate. d Quadratic covariate. e Root mean square error. ⁎⁎⁎ P b 0.001. NS Not significant. b
was Model BW-3 with the addition of age of calf at auction as linear covariate. Three bivariate models were also fitted. Model Age-2/BW3 was a combined analysis of Models Age-2 and BW-3; Model Age-2/BW-4 was a combined analysis of Models Age-2 and BW-4; and Model Age-2/BW-5 was a combined analysis of Models Age-2 and BW-5. The general form of linear models for the bivariate approach, in matrix notation, was the following:
y1 y2
=
X1 0
0 β1 Z1 + · X2 0 β2
0 u1 e1 + ; · Z2 u2 e2
where y1 and y2 are vectors of Age and BW observations, respectively; β1 and β2 denote vectors of fixed effects; u1 and u2 are vectors of additive genetic effects of animals; e1 and e2 are vectors of random residuals; X1 and X2 are known design matrices associating fixed effects in β1 and β2 with y1 and y2, respectively; and Z1 and Z2 are known design matrices associating additive genetic effects in ul and u2 with y1 and y2, respectively. For all models, the expected values were: y X1 0 β E 1 = · 1 ; 0 X2 y2 β2 and the variance−covariance structure of the random terms was: 2 2 Aσu1 3 u1 6 6 Aσ 6 u2 7 u1u2 6 7 Var6 4 e1 5 = 6 6 0 4 e2 0 2
Aσu1u2
0
2 Aσu2
0
0
Iσe1
0
Iσe1e2
2
0
3
7 7 7 7; Iσe1e2 7 5 0
2 Iσe2
where I is an identity matrix of order equal to the number of calves; A is the additive relationship matrix among animals in the pedigree file; σ2u1 and σ2u2 are additive genetic variances for Age and BW, respectively; σ2e1 and σ2e2 are residual
variances for Age and BW, respectively; σu1u2 is the additive genetic covariance between Age and BW; and σe1e2 is the residual covariance between the traits. (Co)variance components and related parameters were obtained with the program package VCE (Neumaier and Groeneveld, 1998), which uses restricted maximum likelihood (REML) implementing a quasi-Newton optimization algorithm on the Cholesky factor of the covariance matrices. A 5generation pedigree file was traced back using the original information provided by the Italian Brown Swiss Cattle Breeders Association (ANARB, Verona, Italy). This resulted in 34,549 animals in additive relationship matrix, which accounted for all individuals with Age and BW data and their ancestors. The prediction of breeding values was then obtained with the software package PEST (Groeneveld et al., 1990) using the (co)variance components from VCE (Neumaier and Groeneveld, 1998).
3. Results and discussion 3.1. Mean and variation Age and BW of calves averaged 24 days and 61 kg, respectively (Table 1), and both traits showed a medium to high variability, with SD of 8.7 days and 8.2 kg, respectively. These statistics are consistent with values reported by Dal Zotto et al. (2007b, 2009) in a study on the effect of different breeds and breed crosses on age, BW, price, and market value of purebred and crossbred calves sold for veal and beef production, and Penasa et al. (2009) in a study on the effect of sire on BW, price, and market value of Belgian Blue × Brown Swiss calves. Mc Hugh et al. (2010a) reported that the age of calves sold at livestock marts in Ireland ranged from 2 to 84 days, with 91% of all animals sold before 42 days; however, no data on BW was available on the animals. In Barham and Troxel (2007), the BW of feeder calves sold at Arkansas weekly livestock auctions in 2005 was much larger than the mean value from our research and ranged from 136 to 362 kg.
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y = 0,3355x + 52,905 2 R = 0,1286
90
BW (kg)
80
70
60
50
40
30 0
10
20
30
40
50
60
Age (days) Fig. 1. Phenotypic relationship between BW (kg) and Age (Age, days) of calves at auction.
3.2. Nongenetic effects Results from ANOVA are reported in Table 2. Different models were fitted to identify the effects significantly influencing the traits. Herd was the most relevant source of variation both for Age (R2 = 0.420) and BW (R2 = 0.456), denoting the existence of different feeding and management strategies adopted by farmers. Date of auction was highly significant (P b 0.001), but explained a lower proportion of variability than the herd effect (Table 2). Age of dam at calving was not important for Age, while it was relevant for BW (P b 0.001), despite the small proportion of variability explained. When introduced in the model for BW, age of calf was highly significant (P b 0.001), both as discrete factor and linear covariate. Differently from results obtained in beef calves at weaning (Rossi et al., 1992), in this study quadratic covariate was not significant (P N 0.05). 3.3. Relationship between Age and BW The relationship between Age and BW of calves at auction is shown in Fig. 1. The phenotypic correlation between the traits was low (36%) and the regression coefficient was
0.34 kg/day, i.e., about one third of the expected average daily gain (assuming a mean value of 38 kg for weight of calves at birth and mean values of 24 days and 61 kg for Age and BW at auction, respectively). Also, the intercept was approximately 15 kg higher than the expected birth weight. A possible explanation arises considering two opposite theoretical herd management strategies adopted by breeders. In the first one, farmers sell calves at a fixed age and growth potential is then expressed by differences in BW at auction; under this situation, we expect BW to be heritable and Age not. In the second one, farmers sell calves at a fixed BW and growth potential is then expressed by differences in age at auction; therefore, we expect Age to be heritable and BW not. Currently, farmers adopt different and intermediate selling criteria, according to the specific situations they have to face. Moreover, they are influenced by other factors such as market fluctuations, season and health status of the animal. 3.4. Heritability of Age and BW from single-trait analyses Estimates of genetic parameters for Age and BW obtained under single-trait analyses are shown in Table 3. Despite the low value (0.075 ± 0.016), results indicate that age of calf at selling is heritable, also confirming that farmers are partly influenced by the genes of the animal (mainly growth rate potential) when deciding the right moment for selling calves at auction. No data is currently available on heritability of age at sale of young calves that the authors are aware of. However, Everling et al. (2001) and Lopes et al. (2009) provided information on the age at which suckling beef calves achieve a given BW, and found higher heritability estimates than the present research, but lower than the values obtained for weaning weight. For comparison, it is worth noting that heritability of age at puberty of beef heifers is generally medium to high (Morris et al., 1992; Gregory et al., 1995). The estimate of heritability for BW from Model BW-3 was 0.153 ± 0.026. The inclusion of age of calf as class effect (BW4) or linear covariate (BW-5) increased the value to 0.205 ± 0.030 and 0.245 ± 0.033, respectively (Table 3). In a recent study, Mc Hugh et al. (2010b) reported a heritability estimate of 0.25 for BW of weanlings in Ireland, i.e., similar to results from Models BW-4 and BW-5. However, no estimates of heritability of BW from very young calves are currently
Table 3 Estimates a of variance components and related parameters (SE) from different models for age (Age) and body weight (BW) of calves. Age Model
b
2
σ
p
BW σ2e
2
σ
a
2
h (SE)
Single-trait Age-2
50.543
46.771
3.772
0.075 (0.016)
Bivariate Age-2/BW-3 Age-2/BW-4 Age-2/BW-5
50.660 50.662 50.660
46.668 46.655 46.668
3.992 4.007 3.992
0.079 (0.014) 0.079 (0.014) 0.079 (0.012)
Age-BW
Model
σ2p
σ
Single-trait BW-3 BW-4 BW-5
42.266 39.945 39.255 42.098 41.820 39.160
2
σ2a
h (SE)
35.811 31.748 29.651
6.455 8.197 9.604
0.153 (0.026) 0.205 (0.030) 0.245 (0.033)
36.368 35.952 31.654
5.730 5.868 7.506
0.136 (0.021) 0.140 (0.021) 0.192 (0.031)
e
2
σg
rg (SE)
−4.427 −4.485 −5.165
−0.926 (0.081) −0.925 (0.080) −0.944 (0.066)
a The term σp2 is the phenotypic variance, σe2 is the residual variance, σa2 is the additive genetic variance, h2 is the heritability, σg is the genetic covariance between Age and BW, and rg is the genetic correlation between Age and BW. b Model Age-2 = herd + auction + animal; Model BW-3 = herd + auction + age of dam + animal; Model BW-4 = Model BW-3 + age of calf(classes); and Model BW-5 = Model BW-3 + age of calf(linear covariate).
G. Bittante et al. / Livestock Science 140 (2011) 1–7 350
350
y = 0.9906x + 1.5604 2 R = 0.9812
y = 0.9745x + 4.2183 2 R = 0.9497
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Fig. 2. Relationship between sire rankings based on EBV for BW from single-trait Models BW-4 (herd + auction + age of dam + animal + age of calf(classes)) and BW-3 (herd + auction + age of dam + animal).
Fig. 4. Relationship between sire rankings based on EBV for BW from single-trait Models BW-5 (herd + auction + age of dam + animal + age of calf(linear covariate)) and BW-4 (herd + auction + age of dam+ animal + age of calf(classes)).
available that the authors are aware of. Data found in literature often deals with growth curves obtained from repeated observations of BW on dairy calves reared on experimental farms, or birth weight and weaning weight of beef calves (adjusted by age). Heritability from our research is lower than the value found in dairy calves reared on experimental farms (Bushra et al., 1989; Groen and Vos, 1995; Coffey et al., 2006; Brotherstone et al., 2007). Also, heritabilities of birth and weaning weight of beef cattle are generally high, especially from experimental or very large commercial farms (Van Vleck et al., 1996; Goyache et al., 2003; Boligon et al., 2009). Only considering large populations (Albuquerque and Meyer, 2001; Shojo et al., 2006), estimates for weaning weight of beef calves are more similar to those obtained in our research. It is worth noting that heritability of weaning weight changes according to several
factors. Dodenhoff et al. (1999b) reported that direct heritability varied from 16 to 50% depending on the statistical model and sample. Also, the breed seems to be an important factor in explaining the results of the genetic analysis and the different selected lines within a breed can show variation in estimates for weaning weight, even in the case of animals reared within the same herd (Dodenhoff et al., 1998). Heritability for this trait in calves from nine different breeds varied from 11% (Charolais) to 34% (Pinzgauer; Dodenhoff et al., 1999a). In the same study, heritability for Braunvieh ranged from 27 to 30% depending on the statistical model. Genetic parameters for pre- and post-weaning traits in Braunvieh cattle were also investigated by Cucco et al. (2009, 2010) who reported heritabilities between 0.16 (weight at 550 days of age) and 0.41 (weaning weight). In particular, estimates for birth weight and weight at 120 days
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Differently from the single-trait approach, the inclusion of age of calf as class effect or linear covariate in the model for BW did not increase largely the heritability of this trait. Additive genetic and residual variances somewhat changed after including age of calf as linear covariate in the model for BW (Age-2/BW-3 vs. Age-2/BW-5; Table 3); nevertheless, the relationship between the ranking of sires obtained from different models was very high (R2 N 0.99; Fig. 6).
y = 0.9998x + 0.0253 2 R = 0.9997
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Rank Model Age-2/BW-3 Fig. 6. Relationship between sire rankings based on EBV for BW from bivariate Models Age-2/BW-5 (herd + auction + age of dam + animal + age of calf(linear covariate)) and Age-2/BW-3 (herd + auction + age of dam + animal).
of age were 0.23 and 0.25, respectively, similar to the results from Models BW-4 and BW-5. Figs. 2, 3, and 4 show the relationships between rankings of sires based on EBV for BW of their calves from single-trait models. The ranking changed according to the model used. The correlation between sire ranking based on EBV for BW obtained with Models BW-5 and BW-4 was higher (R2 = 0.98; Fig. 4) than with Model BW-3 (R2 = 0.95 between BW-4 and BW-3, and 0.93 between BW-3 and BW-5; Figs. 2 and 3); this outcome was expected as Model BW-3 dealt with the analysis of BW of calves at auction while Models BW-4 and BW-5 dealt with BW adjusted to a fixed age, i.e., with the estimation of the growth potential of calves. Also, the change in sire ranking was not due to the presence of many sires having a low number of progeny because in a preliminary analysis sires with less than 20 sons were excluded, and resulting coefficients of determination for BW-4 vs. BW-3, BW-5 vs. BW-3, and BW-5 vs. BW-4 were very similar (0.95, 0.92, and 0.97, respectively) to those presented in this study. It is clear that, if Age is heritable, adjusting BW for age of calf leads to biased results; this is particularly true if the two traits are genetically correlated.
3.5. Heritability of BW from bivariate analyses A better understanding of the relationship between Age and BW of calves was possible by fitting the bivariate models (Table 3). The first model (Age-2/BW-3) estimated heritability values consistent with those obtained under the corresponding single-trait analyses. Despite comparable results, the correlation between sire rankings based on EBV for BW of calves from bivariate and single-trait models was moderate. Fig. 5 shows the large re-ranking of sires (R2 = 0.68) when Models Age-2/ BW-3 and BW-3 are compared. This was the consequence of the strong and negative genetic correlation between Age and BW (−0.926 ± 0.081; Table 3), and revealed the importance of taking into account the information from Age.
Results from this study provide a better understanding of the relationship between Age and BW of calves sold at auction. As there is a tendency of farmers to sell first the fast growing and then the slow growing calves, Age is a heritable trait and exhibits a strong and negative genetic correlation with BW. As a consequence, its inclusion as explanatory variable in single-trait analysis for BW leads to biases in the prediction of breeding values. In particular, EBV for sires whose progeny has fast growing rates is underestimated, while it is overestimated for sires whose progeny has slow growing rates. Combining Age and BW in bivariate analyses allows to account for the strong and negative genetic correlation between them and to correctly predict breeding values of animals. As a consequence, both traits can be regarded as good predictors of growth rate in calves. Moreover, the high genetic correlation between Age and BW suggests that it would be enough to include one of them in a breeding program aiming at improving the growth rate and the economic revenue of the dairy farm. Acknowledgment The authors wish to thank the Kovieh Cooperative (Bolzano−Bozen, Italy) and the Breeders Association of Bolzano−Bozen province for providing field data, the Superbrown Consortium of Bolzano and Trento, and Trento Province for financial support, and the Italian Brown Swiss Cattle Breeders Association (ANARB, Verona, Italy) for supplying pedigree information. The useful comments and suggestions provided by anonymous reviewers are gratefully acknowledged. References Albuquerque, L.G., Meyer, K., 2001. Estimates of direct and maternal genetic effects for weights from birth to 600 days of age in Nelore cattle. J. Anim. Breed. Genet. 118, 83–92. Barham, B.L., Troxel, T.R., 2007. Factors affecting the selling price of feeder cattle sold at Arkansas livestock auctions in 2005. J. Anim. Sci. 85, 3434–3441. Boligon, A.A., Albuquerque, L.G., Mercadante, M.E.Z., Lobo, R.B., 2009. Heritability and correlations between weights from birth to maturity in Nellore cattle. Rev. Bras. Zootec. 38, 2320–2326. Brotherstone, S., Coffey, M.P., Banos, G., 2007. Genetic parameters of growth in dairy cattle and associations between growth and health traits. J. Dairy Sci. 90, 444–450. Bushra, O.E. El, Wilcox, C.J., Wing, J.M., Littell, R.C., 1989. Genetic effects on dairy calf growth. J. Dairy Sci. 72, 162–166. Cecchinato, A., De Marchi, M., Gallo, L., Bittante, G., Carnier, P., 2009. Midinfrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk. J. Dairy Sci. 92, 5304–5313. Coffey, M.P., Hickey, J., Brotherstone, S., 2006. Genetic aspects of growth of Holstein−Friesian dairy cows from birth to maturity. J. Dairy Sci. 89, 322–329.
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