J. Anim. Sci. 2005. 83:2052-2057
© 2005 American Society of Animal Science
Study of codes of disposal at different parities of Large White sows using a linear censored model
J. Arango*,1,2,
I. Misztal*,
S. Tsuruta*,
M. Culbertson
and
W. Herring
* Department of Animal and Dairy Science, The University of Georgia, Athens 30602-2771; and
and
Smithfield Premium Genetics, Roanoke Rapids, NC 27870
 |
Abstract
|
|---|
To study the genetic relationship between three grouped reasons for sow removal (SR) in consecutive parities, accounting for censoring, 13,838 records from Large White sows were analyzed. Data were from seven pure-line farms having, on average, 5.9% unknown SR. Three traits were subjectively defined, each corresponding to a classification of SR (reproductive [RR], nonreproductive [RN], and others [RO]). Records for each trait could take one of five categories, according to parity at removal (0 to 4 or later). A multivariate linear censored model was implemented. The model to estimate (co)variance components and parameters included the effects of year-season, region, contemporary group, and additive genetic effects. The most common SR was related to reproduction (48.5%). Diseases of different origin and cause, old age/parity, and sow death or loss accounted for about 18, 7, and 4% of total culls, respectively. Estimates of variance components showed heterogeneity of additive genetic and residual variances for the three traits. Estimates of heritability were 0.18, 0.13, and 0.15 for RR, RN, and RO, respectively. Genetic correlations between removal codes were high (
0.90). Results suggest sizeable additive genetic variances exist for parity at removal and different codes of removal. Different SR reasons seem to operate similarly or as a closely related genetic trait associated with fitness. In particular, RN and RO seem to be genetically indistinguishable. Data structure, definition, and volume are major limitations in studies of sow survival. A multiple-trait censored model is preferred to evaluate reasons of sow disposal. Grouped removal causes seem to be strongly genetically correlated but with heterogeneous variances, suggesting that combining all removal causes and treating the trait as parity at disposal is an alternative approach.
Key Words: Sow Culling • Sow Disposal • Sow Removal • Survival • Swine
|
Introduction
|
|---|
In typical sow herds, mean parity at disposal is less than four (Dijkhuizen et al., 1989
; Lucia et al., 2000). High sow replacement rate increases costs of production. Therefore, there is interest to improve sow longevity by various means, including selection. Most analyses of sow disposal have used survival analysis (Yazdi et al., 2000a,b; Rodriguez-Zas et al., 2003; Serenius and Stalder, 2004) or stayability (Tholen et al., 1996; Lopez-Serrano et al., 2000), ignoring causes of disposal.
Disposal can be due to many reasons, for instance reproduction or disease. These reasons can be split further (Dijkhuizen et al., 1989; Lucia et al., 2000; Heusing et al., 2003). If one analyzes each reason (or their clusters) as separate traits, the information from other reasons is ignored. In particular, if an animal was disposed for one reason at a given parity, the remaining reasons can be treated as censored.
There is interest in estimating genetic correlations among removal reasons. If correlations are low, one needs to select for each reason separately. If correlations are high, they indicate one combined underlying reason as fitness. Decreased fitness as a result of strong selection on production in major species was reported by van der Waaij (2004).
Survival analysis, as currently developed, does not accommodate multiple-trait analysis. Some correlations among survival and other traits obtained from single-trait analyses were reported by Yazdi et al. (2000a) and Serenius and Stalder (2004). Multivariate models for normal censored traits with a Gibbs sampling implementation were proposed by Korsgaard et al. (1999, 2003) and used in a single-trait analysis of length of productive life in sows by Guo et al. (2001). Analysis of polychotomous categorical traits in its censored version has been outlined by Gelfand et al. (1992).
Our objective was to estimate genetic parameters for groups of sow removal (SR) codes using a multiple-trait linear model accounting for censoring.
|
Materials and Methods
|
|---|
Data Origin and Frequency of Removal Codes
Field data were provided by Smithfield Premium Genetics for disposal codes of Large White sows in 22 pure-line multiplication farms located throughout the United States and Mexico. Different recording systems and software were used across farms. Data included sow identification and cause, date, and parity of disposal from the herd. In a preliminary data exploration, 22.3% of records across all farms had an unknown reason for disposal out of a total of 143 possible codes of removal, which indicated a need for a better system of classification and recoding of data. Similar codes were pooled, and only a subsample of farms with a better recording system was kept for the study. The selected group was formed by seven farms with 95 codes of disposal on record and, on average, 5.9% of records with an unknown cause of removal. The most common causes of removal were related to reproductive functions including the following: not in heat (20.0%), return to service (12.5%), fail to conceive (8.1%), and abortion (7.2%). In general, reasons for disposal directly related to reproduction accounted for approximately one-half (48.5%) of the removals. Total diseases of different origin and cause accounted for 17.8% of disposals. Old age/parity and sow death or loss accounted for 6.7 and 3.6% of the total discards, respectively. There was a total of 13,754 animals with records, and 19,726 animals in the pedigree file, using all genealogical information available.
Figure 1 shows the distribution of removal reasons for the two more important groups: reproduction (48.5%) and diseases (17.8%). Reproduction was the main cause of removal across parities, being more frequent in early parities, for which data were more abundant. When sow disposal was evaluated at particular parities, old age/parity became an important culling code in later parities (fifth and later), leading "others" as the most important culling reason; however, "others" accounted for a low proportion of total culls.
View larger version (19K):
[in this window]
[in a new window]
|
Figure 1. Distribution of codes of sow removal (1 = reproduction, 2 = disease, and 3 = others) across parities (Panel A; 0 = gilts) and at each parity (Panel B).
|
|
Differentiation among removal codes related to reproduction and diseases is difficult because some diseases had a direct effect on the reproductive ability of the sow. Therefore, pooled codes of sow disposal were classified into three groups: reproductive (RR = 58.4%), non-reproductive (RN = 29.3%), and other (RO = 12.3%), which were used as traits for analysis. "Reproductive" reasons for disposal included reasons that impaired a sow to produce a normal litter, including, but not restricted to, some infectious and noninfectious diseases. "Nonreproductive" reasons for disposal included general diseases, as well as anatomic and conformation problems, and eliminations because of old age or parity and death. The third group (RO) included all other codes that could not be classified into the previous two groups.
Table 1 shows the frequency of clustered reasons of disposal, pooling parities equal to or larger than four. "Reproductive" was the main code group of removal across parities, being more frequent in early parities when data were more numerous. At late parities (fourth or later), old age/parity was an important culling cause. Therefore, when pooling fourth and older parities, RR and RN codes contributed similarly (approximately 14%), whereas RO only contributed approximately 3% to total culls.
Analysis
A multiple-trait analysis of ordered categorical variables defined as different clustered reasons for removal at different parities was proposed. Ordered categorical data were created for each record and each trait (removal reason). Categories were assigned according to culling parity of the sow or to add information to censor records. Data inspection determined that most SR occurred before the fourth parity; thus, Parity 4 was used as the upper limit. The main categories were 1 to 5 (1 = removed as a gilt, 2 = removed in the first parity, ..., 5 = removed in Parity 4 or later). A sow was removed from the herd at a given parity and for a given reason (RR, RN, or RO); additional categories were assigned to each record for the other traits (other grouped reasons of culling) to account for the fact that if the sow had not been culled for that reason, she could be removed from the herd at a later parity and for other reasons, which allows incorporation of information from other reasons for culling as censored. A summary of categorical codes related to parity and reason for removal or censoring is shown in Table 2. For example, an animal that was removed as a gilt for Reason 1 will be assigned to Category 1 in the first trait (RR) and to Category 1+ in the other two traits (i.e., 1+ indicates that if the sow had not been culled as a gilt because of RR, she could be removed by other grouped reasons up to four parities later, following the first parity), and so on. Each record was assigned to a contemporary group (farm-year-season).
The multiple-trait model to analyze the three clustered classes of SR was as follows:
 | [1] |
where yi = record of SR caused by RR (i = 1), RN (i = 2), or RO (i = 3); ysi = systematic effect of year-season of disposal; ri = systematic effect of region; cgi = random effect of contemporary group (CG); ai = animal additive genetic effect for trait I; and ei = residual effect. Model [1] can be represented in terms of the standard system of mixed-model equations (Henderson, 1975). The only important difference is for the structure of residuals, which are diagonal in [1], and are caused by zero residual covariance between traits. That is because only one trait is realized for each animal (i.e., the sow is removed for a given reason and parity). Data were augmented by censoring to complete the vector of records.
Implementation of models with censoring was by modification of the GIBBSF90 (Misztal et al., 2002) using the MCMC approach. Attempts to develop a multiple-trait analysis with censoring using the threshold model were unsuccessful because simulated parameters could not be recovered. Thus, clustered removal reasons were treated as linear traits. The Gibbs sampler was run as a single chain of 100,000 cycles, with a conservative burn-in period of the first 10,000. Convergence to a stationary stage was confirmed by graphical inspection, tracing plots of the sampled values vs. iterations (Kass et al., 1998). Every 10th sample was stored thereafter for a total of 9,000 samples kept to compute posterior means, SD, and credible regions, using the POSTGIBBSF90 program (Misztal et al., 2002). Point estimates of parameters were calculated as the posterior mean of the respective variance components.
|
Results and Discussion
|
|---|
Clustered Reasons of Removal
Frequency and parity distribution for clustered reasons of removal presented herein agree with previous studies with sows (Dijkhuizen et al., 1989; Lucia et al., 2000; Heusing et al., 2003). In typical sow herds, removal rates are between 40 and 55%, which relates to a mean parity of three to four at removal (Dijkhuizen et al., 1989; Lucia et al., 2000). For a large sample (148,568 sows) of commercial operation (32 herds) in the U.S. Midwest, the average replacement rate, culling rate, and death rate were 59.8, 41.6, and 9.7% (Rodriguez-Zas et al., 2003). The present study includes removal from the time the gilts are included in the breeding herd (Parity 0) up to late parities in their productive life. At each stage, RR was the main cause of disposal, except for older sows (Parity 4 or later), for which RN causes were equal in importance to those for RR (14%). Pooling all parities, reproduction and related codes accounted for more than one-half (58.4%) of all SR, whereas RN reasons were approximately twofold less frequent (29.3%), and the cluster of RO approximately fourfold less frequent (12.3%), than those related to reproduction. Sow removal by parity was 27, 22, 12, 9, and 30% for Parities 0, 1, 2, 3, and 4 to 11, respectively, with RR reasons being responsible for 64, 65, 64, 60, and 45% of total culls at the respective parities. Approximately one-half of the females were culled before their second parity. Only 3.6% of total SR were due to death, sudden death, or loss, which is approximately twofold less than the 7.4% reported by Lucia et al. (2000) in a large sample of herds with high-quality data (a maximum of 10% fluctuation in breeding-herd inventory) in the United States and Canada.
Similar results to ours were reported in previous studies that presented reproductive problems as the most important cause of sow culls (Dijkhuizen et al., 1989; Lopez-Serrano et al., 2000; Lucia et al., 2000; Heusing et al., 2003). For the Dutch system, Dijkhuizen et al. (1989) reported that reproductive problems were approximately twice as frequent as any other cause of removal, accounting for 34.2% of total culls. Other important causes had similar frequency, with low productivity (old age and small litter), sickness/accident, and mother characteristics representing 17, 16, and 14% of total culls. In a study of 28 herds, spanning 5 yr of records in North America, Lucia et al. (2000) determined that reproductive disorders were the most common removal reason (33.6%) for sows. Other leading causes of removal were litter performance, miscellaneous, locomotion, and old age, with 20.6, 13.3, 13.2, and 8.7%, respectively. Similarly, Heusing et al. (2003) identified fertility (15 to 20%) and diseases (11 to 13%) as the two most important causes of SR for German Large White, German Landrace, and Pietrain sows. A third cause was related to old age for the maternal breeds (9 and 10%), but was due to performance (low fattening and low efficiency in carcass yield, 8%) for Pietrain sows. Lopez-Serrano et al. (2000) summarized literature reports and concluded that reproduction was the primary removal reason in productive sows, whereas for first-parity sows, the second most important culling reason was leg weakness, with an incidence >10%. Only one study reported leg problems as the second main sow culling reason (20 and 31%) after miscellaneous (34 and 33%) in two Northern German crossbred herds (Kirchner et al., 2004). Particular attention has been placed on studies of osteochondrosis or osteochondral disease, a bone development and joint ossification problem, which is a common cause of leg weakness and other health problems in pigs (i.e., Jorgensen and Andersen, 2000; Yazdi et al., 2000a; Kadarmideen et al., 2004). In our study, leg weakness or osteochondrosis were not listed codes of SR; however, a category that pooled foot injuries, lameness, spraddle, and other injuries accounted for approximately 8% of total culls. That is close to the 10.5% reported by Dijkhuizen et al. (1989) for lameness/leg weakness, which represented the main cause for economic loss of premature disposal, representing 3 and 16% of gross product and income for a typical farmer (Dijkhuizen et al., 1989). In the present study, diseases of different origin and cause accounted for approximately 18% of culls, and by adding the 49% for RR reasons (not associated to diseases), this illustrates the economic importance of improving sow health and fertility.
Estimates of (Co)variance Components
Posterior estimates of (co)variance components are presented in Table 3. With the data structure defined for this analysis, there was only one realized uncensored trait (i.e., the one corresponding to the actual reason of removal); therefore, the analysis excluded residual covariances between traits. Posterior estimates of additive genetic variances were acceptably symmetric and quasi-normal (distribution not presented), showing good agreement between the mean and the median for the three traits. Estimates of additive variance were different for the three grouped causes of removal. The estimate for removal reasons related to RR (0.59) was more than two times larger than those for RN (0.26) or RO (0.28). Heritability estimates were 0.18, 0.13, and 0.15 for RR, RN, and RO, respectively. These estimates are sizeable for traits related to survival and fitness and indicate that selection could be effective to improve fitness, especially relative to reproduction, in this population. Genetic correlations between codes of removal were approximately 0.90 for RR-RN and RN-RO and >0.99 for RN-RO. This result indicates that RR and RN codes have high genetic association and that RN and RO are almost the same trait. The correlations could be inflated because of the imperfect assignment of disposal codes at culling and by imperfect clustering of those codes into RR, RN, and RO traits.
View this table:
[in this window]
[in a new window]
|
Table 3. Estimates of (co)variance components and genetic parameters for three codes of disposal of Large White sows using a linear-censored modela,b
|
|
High genetic correlations between RN and RO indicate that they could be combined (i.e., RN*) and a two-trait model implemented for RR and RN codes (RR-RN*). This was attempted. However, although similar estimates of genetic variance were obtained, variances for contemporary groups and solutions for fixed effects differed, and heritability estimates decreased slightly (0.15 and 0.10 for RR and RN*). Thus, the original three-trait model seemed to be more useful. A possible alternative would be to combine codes, perhaps as simply as to define a single trait (parity at disposal) and use different adjustments to reflect differences in variances and fixed effects for the component traits.
The contemporary group variance was larger than the additive genetic variance, but less than the residual variance, and represented approximately one-third of the total variance for all traits (Table 3
). Fitting CG as random effects with linear models has been discussed for many livestock species, including pigs (Estany and Sorensen, 1995; Frey et al., 1997; Bidanel, 1998). Correctly specifying environmental effects for fitness-related traits is difficult, and they can be poorly estimated if considered as systematic effects in the model. With CG random, this problem is partially alleviated, especially for field data, such as in the present study, in which size of CG vary widely, leading to better prediction and parameter estimation. Therefore, implementation of linear models with random CG for these traits tends to be favored (Frey et al., 1997; Bidanel, 1998).
Literature estimates of heritability for causes of SR in three German breeds were low, ranging from 0.01 to 0.16 according to type of removal reason and breed of sow using linear models and single-trait REML analyses (Heusing et al., 2003). Estimates were from 0.04 to 0.08 for poor fertility, 0.01 to 0.09 for low performance (fattening and carcass yield), 0.02 for reproductive problems, 0.01 to 0.05 for diseases, and 0.05 to 0.16 for miscellaneous reasons. For German Large White sows, estimates of heritability for different culling reasons ranged from 0.02 to 0.06 (Heusing et al., 2003). Estimates of heritability for osteochondrosis using a linear model were low to moderate, ranging from 0.08 to 0.39 in Danish and Swedish Landrace and Yorkshire (Jorgensen and Andersen, 2000; Yazdi et al., 2000a).
Analyses of field data have discovered sizeable genetic variation for disease incidence on livestock species (i.e., Henryon et al., 2001 for growing pigs; Abdel-Azim et al., 2005 for dairy cows), which is related to RN removal causes in our study. It seems that heritabilities for separate traits were higher than those for combined ones for incidence of various infectious and noninfectious diseases (Abdel-Azim et al., 2005) and for immune response (Bishop et al., 2002). Generalized immunity is an indicator of broad health status, welfare, and performance of groups of animals, which refers to protection against infections (Stear et al., 2001; Bishop et al., 2002). Feasibility of selecting for generalized immunity has been explored in swine (Mallard et al., 1998; Wilkie and Mallard, 2000). Correlations between breeding values for any clinical or subclinical disease and breeding values for particular diseases (lameness, respiratory disease, diarrhea, and others) were positive and moderate to large in growing pigs (Henryon et al., 2001). Abdel-Azim et al. (2005) recommended grouping infectious diseases into categories, but not noninfectious diseases, for genetic studies in U.S. Holsteins.
Heritability for length of productive life using a linear mixed model with censoring was 0.25 for the actual level of censoring (15.5%), and it tended to decrease to 0.18 and 0.16 as simulated censoring increased to 25 and 35%, respectively, for sows of a Landrace nucleus herd (Guo et al., 2001) using the Gibbs sampler as proposed by Korsgaard et al. (1999, 2003). Length of productive life analyzed using a linear model in different breeds of German sows was reported by Heusing et al. (2004). Estimates of heritability were 0.12, 0.10, and 0.19 for Large White, Landrace, and Pietrain, respectively. Heritabilities for number of piglets born alive during the entire life of a sow were 0.14, 0.12, and 0.17 for the same breeds; corresponding values were the same for number of piglets weaned in during the life of a sow. Serenius and Stalder (2004) used a survival model approach and a linear model to estimate heritability for length of productive life of Finnish Landrace and Large White sows. Estimates were lower (0.05 to 0.10) with the linear model approach than with the survival model (0.16 to 0.19).
|
Implications
|
|---|
Different grouped reasons for sow disposal can be analyzed jointly in a multiple-trait censored model. These reasons seem to be strongly genetically correlated; however, they had different variance estimates. For practical purposes, a multiple-trait model is preferred. An alternative option would be to combine all removal codes and treat the resulting trait as parity at disposal in a univariate model, but using different adjustments for the component traits.
|
Footnotes
|
|---|
2 On leave from Facultad de Ciencias Veterinarias, Universidad Central de Venezuela, Apartado. 4563, Maracay 2105, Aragua, Venezuela. 
1 Correspondence: 306 Dept. of Anim. and Dairy Sci. (phone: 706-583-0250; fax: 706-583-0274; e-mail: arangoj{at}uga.edu).
Received for publication April 8, 2005.
Accepted for publication June 10, 2005.
|
Literature Cited
|
|---|
Abdel-Azim, G. A., A. E. Freeman, M. E. Kehrli Jr., S. C. Kelm, J. L. Burton, A. L. Kuck, and S. Schnell. 2005. Genetic basis and risk factors for infectious and noninfectious diseases in US Holsteins. I. Estimates of genetic parameters for single diseases and general health. J. Dairy Sci. 88:1199–1207.[Abstract/Free Full Text]
Bidanel, J. P. 1998. Benefits and limitations of increasingly sophisticated models for genetic evaluation: The example of pig breeding. Proc. 6th World Cong. Genet. Appl. Livest. Prod. Armidale, Australia. 25:577–584.
Bishop, S. C., J. Chenais, and M. J. Stear. 2002. Breeding for disease resistance: Issues and opportunities. Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France. Communication No. 13–01.
Dijkhuizen, A. A., R. M. M. Krabbenborg, and R. B. M. Huirne. 1989. Sow replacement: A comparison of farmers’ actual decisions and model recommendations. Livest. Prod. Sci. 23:207–218.
Estany, J., and D. Sorensen. 1995. Estimation of genetic parameters for litter size in Danish Landrace and Yorkshire pigs. Anim. Sci. 60:315–324.
Frey, M., A. Hofer, and N. Kunzi. 1997. Comparison of models with a fixed or a random contemporary group effect for the genetic evaluation for litter size in pigs. Livest. Prod. Sci. 48:135–141.
Gelfand, A. E., A. F. M. Smith, and T.-M. Lee. 1992. Bayesian analysis of constrained parameter and truncated data problems. J. Am. Stat. Assoc. 87:523–532.
Guo, S. F., D. Gianola, R. Rekaya, and T. Short. 2001. Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livest. Prod. Sci. 72:243–252.
Henderson, C. R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447.[Medline]
Henryon, M., P. Berg, J. Jensen, and S. Andersen. 2001. Genetic variation for resistance to clinical and subclinical diseases existing in growing pigs. Anim. Sci. 73:375–387.
Heusing, M., H. Hamann, and O. Distl. 2003. Culling reasons and their effect on lifetime productivity of sows of the breeds German Large White, German Landrace and Pietrain. Arch. Tierz. Dummerstorf 46:569–583.
Heusing, M., H. Hamann, and O. Distl. 2004. Genetische Analyse der Lebensund Nutzungsdauer sowie der lebenszuchtleistung bei Sauen der Rassen Deutsches Edelschwein, Deutsches Landrasse und Pietrain. Züchtungskunde 76:18–35.
Jorgensen, B., and S. Andersen. 2000. Genetic parameters for osteochondrosis in Danish Landrace and Yorkshire boars and correlations with leg weakness and production traits. Anim. Sci. 71:427–434.
Kadarmideen, H. N., D. Schwörer, H. Ilahi, M. Malek, and A. Hofer. 2004. Genetics of osteochondral disease and its relationship with meat quality and quantity, growth, and feed conversion traits in pigs. J. Anim. Sci. 82:3118–3127.[Abstract/Free Full Text]
Kass, R. E., B. P. Carlin, A. Gelman, and R. M. Neal. 1998. Markov Chain Monte Carlo in practice: A roundtable. Am. Stat. 52:93–100.
Kirchner, K., K. H. Tölle, and L. Krieter. 2004. Decision tree technique applied to pig farming datasets. Livest. Prod. Sci. 90:191–200.
Korsgaard, I. R., M. S. Lund, D. Sorensen, D. Gianola, P. Madsen, and J. Jensen. 1999. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling, Uppsala, Sweden. Interbull Bull. 20:79–92.
Korsgaard, I. R., M. S. Lund, D. Sorensen, D. Gianola, P. Madsen, and J. Jensen. 2003. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling. Genet. Sel. Evol. 35:159–183.[Medline]
Lopez-Serrano, M., N. Reinsch, H. Looft, and E. Kalm. 2000. Genetic correlations of growth, backfat thickness and exterior with stayability in Large White and Landrace sows. Livest. Prod. Sci. 64:121–131.
Lucia, T., Jr., G. D. Dial, and W. E. Marsh. 2000. Lifetime reproductive performance in female pigs having distinct reasons of removal. Livest. Prod. Sci. 63:213–222.
Mallard, B. A., B. N. Wilkie, B. W. Kennedy, J. Gibson, and M. Quinton. 1998. Immune responsiveness in swine: Eight generations of selection for high and low immune response in Yorkshire pigs. Proc. 6th World Cong. Genet. Appl. Livest. Prod., Armidale, Australia. 27:257–264.
Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet, and D. H. Lee. 2002. BLUPF90 and related programs (BGF90). Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France. Communication No. 28–07.
Rodriguez-Zas, S. L., B. R. Southey, R. V. Knox, J. F. Connor, J. F. Lowe, and B. J. Roskamp. 2003. Bioeconomic evaluation of sow longevity and profitability. J. Anim. Sci. 81:2915–2922.[Abstract/Free Full Text]
Serenius, T., and K. J. Stalder. 2004. Genetics of length of productive life and lifetime prolificacy in the Finnish Landrace and Large White populations. J. Anim. Sci. 82:3111–3117.[Abstract/Free Full Text]
Stear, M. J., S. C. Bishop, B. A. Mallard, and H. Raadsma. 2001. The sustainability, feasibility and desirability of breeding livestock for disease resistance. Res. Vet. Sci. 71:1–7.[Medline]
Tholen, E., K. L. Bunter, S. Hermsch, and H. U. Graser. 1996. The genetic foundation of fitness and reproduction traits in Australian pig populations 1. Genetic parameters for weaning to conception interval, farrowing interval, and stayability. Aust. J. Agric. Res. 47:1261–1274.
van der Waaij, E. H. 2004. A resource allocation model describing consequences of artificial selection under metabolic stress. J. Anim. Sci. 82:973–981.[Abstract/Free Full Text]
Wilkie, B. N., and B. A. Mallard. 2000. Genetic aspects of health and disease in pigs. Pages 379–396 in Breeding for Research Resistance in Farms Animals. 2nd ed. R. F. E. Axford, S. C. Bishop, F. W. Nicholas, and J. B. Owen, eds. CAB International, Oxford, U.K.
Yazdi, M. H., N. Lundeheim, L. Rydhmer, E. Ringmar-Cederberg, and K. Johansson. 2000a. Survival of Swedish Landrace and Yorkshire sows in relation to osteochondrosis: A genetic study. Anim. Sci. 71:1–9.
Yazdi, M. H., L. Rydhmer, E. Ringmar-Cederberg, N. Lundeheim, and K. Johansson. 2000b. Genetic study of longevity in Swedish Landrace sows. Livest. Prod. Sci. 63:255–264.
This article has been cited by other articles:
|
|
|
|
 
T. Serenius, K. J. Stalder, and R. L. Fernando
Genetic associations of sow longevity with age at first farrowing, number of piglets weaned, and wean to insemination interval in the Finnish Landrace swine population
J Anim Sci,
December 1, 2008;
86(12):
3324 - 3329.
[Abstract]
[Full Text]
[PDF]
|
|
|
Top
Abstract
Introduction
