Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-27T00:05:42.188Z Has data issue: false hasContentIssue false

Survival analysis of mortality in pre-weaning kids of Sirohi goat

Published online by Cambridge University Press:  18 July 2019

I. S. Chauhan*
Affiliation:
Division of Animal Genetics & Breeding, ICAR-Central Sheep and Wool Research Institute, Avikanagar-304 501, Rajasthan, India
S. S. Misra
Affiliation:
Division of Animal Genetics & Breeding, ICAR-Central Sheep and Wool Research Institute, Avikanagar-304 501, Rajasthan, India
A. Kumar
Affiliation:
Division of Animal Genetics & Breeding, ICAR-Central Sheep and Wool Research Institute, Avikanagar-304 501, Rajasthan, India
G. R. Gowane
Affiliation:
Division of Animal Genetics & Breeding, ICAR-Central Sheep and Wool Research Institute, Avikanagar-304 501, Rajasthan, India
*
Get access

Abstract

Pre-weaning animals exit a flock through death induced by various reasons, causing significant economic losses to the goat producers. In this study, we investigated the survival from birth to weaning of Sirohi goat kids within framework of the survival analysis. Kid records were accessed from 1997 to 2017, with the information on 4417 pre-weaning animals of farmed Sirohi goat native to the Rajasthan State of India. A multivariable Cox regression was fitted to the data after checking the assumptions of regression. The explanatory variables were sex, type of birth, season of birth, birthweight, doe weight at kidding and year of birth. Model selection eliminated doe weight from the model, and sex, type of birth, season of birth, birthweight and year of birth were retained in the model. With model calibration also, these five covariates were retained in the model. The mortality on the first day after birth was 0.3%, constituting 3.5% of all pre-weaning mortality. The mortality until the end of weaning period was 7.8%. Regression analysis revealed that the higher birthweight at kidding was associated with reduced hazard of death among the kids. Male kids had higher hazards of death compared with female kids. The single-born kids had lower risks of death compared with twin-born kids after accounting for heterogeneity. The winter season had a very high adverse effect on the survival of the kids. With each passing year, risks of death decreased. The results of this study indicate that better survival of kids can be achieved by controlling both environmental and animal-related factors.

Type
Research Article
Copyright
© The Animal Consortium 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, PD 1997. Survival analysis using the SAS system: a practical guide, 2nd edition. Statistical Analysis Systems Institute Inc., Cary, NC, USA.Google Scholar
Bangar, YC, Pachpute, ST and Nimase, RG 2016. The survival analysis of the potential risk factors affecting lamb mortality in deccani sheep. Journal of Dairy, Veternary and Animal Research 4, 266270.Google Scholar
Barazandeh, A, Moghbeli, SM, Vatankhah, M and Hossein-Zadeh, NG 2012. Lamb survival analysis from birth to weaning in Iranian Kermani sheep. Tropical Animal Health and Production 44, 929934. doi: 10.1007/s11250-011-9990-2CrossRefGoogle ScholarPubMed
Baxter, EM, Jarvis, S, Palarea-Albaladejo, J and Edwards, SA 2012. The weaker sex? The propensity for male-biased piglet mortality. PLoS One 7, e30318.CrossRefGoogle ScholarPubMed
Casellas, J, Caja, G, Such, X and Piedrafita, J 2007. Survival analysis from birth to slaughter of Ripollesa lambs under semi-intensive management. Journal of Animal Science 85, 512517.CrossRefGoogle ScholarPubMed
Chowdhury, SA, Bhuiyan, MSA and Faruque, S 2002. Rearing Black Bengal goat under semi-intensive management. 1. Physiological and reproductive performances. Asian-Australasian Journal of Animal Sciences 15, 477484.CrossRefGoogle Scholar
Cox, D 1972. Regression models and lifetables (with discussion). Journal of the Royal Statisticial Society, Series B 34, 187220.Google Scholar
Dwyer, CM, Conington, J, Corbiere, F, Holmøy, IH, Muri, K, Nowak, R, Rooke, J, Vipond, J and Gautier, J-M 2016. Invited review: improving neonatal survival in small ruminants: science into practice. Animal 10, 449459.CrossRefGoogle Scholar
Dwyer, CM, Lawrence, AB, Bishop, SC and Lewis, M 2003. Ewe-lamb bonding behaviours at birth are affected by maternal undernutrition in pregnancy. British Journal of Nutrition 89, 123136.CrossRefGoogle ScholarPubMed
Ellen, ED, Ducrocq, V, Ducro, BJ, Veerkamp, RF and Bijma, P 2010. Genetic parameters for social effects on survival in cannibalistic layers: combining survival analysis and a linear animal model. Genetics Selection Evolution 42, 27. doi: 10.1186/1297-9686-42-27.CrossRefGoogle Scholar
Fragkou, IA, Mavrogianni, VS and Fthenakis, GC 2010. Diagnostic investigation of cases of deaths of newborn lambs. Small Ruminant Research 92, 4144.CrossRefGoogle Scholar
Gowane, GR, Swarnkar, CP, Prince, LLL and Kumar, A 2018. Genetic parameters for neonatal mortality in lambs at semi-arid region of Rajasthan India. Livestock Science 210, 8592.CrossRefGoogle Scholar
Harrell, FE Jr 2018. rms: Regression Modeling Strategies. R package version 5.1-2. Retrieved on 28 July 2018 from https://CRAN.R-project.org/package%20=%20rmsGoogle Scholar
Harrell, FE, Lee, KL and Mark, DB 1996. Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15, 361–87.3.0.CO;2-4>CrossRefGoogle Scholar
Kaplan, EL and Meier, P 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457481.CrossRefGoogle Scholar
Kim, J and Bang, H 2016. Three common misuses of P values. Dent Hypotheses 7, 7380. doi: 10.4103/2155-8213.190481.CrossRefGoogle ScholarPubMed
Kraemer, S 2000. The fragile male. The British Medical Journal 321, 16091612.CrossRefGoogle ScholarPubMed
Kumar, U, Sharma, SK, Nagda, RK and Rajawat, BS 2010. Replacement index and mortality pattern of Sirohi goats under field condition. Indian Journal of Small Ruminants 16, 274276.Google Scholar
Meira-Machado, L, Cadarso-Suárez, C, Gude, F and Araújo, A 2013. smoothHR: an R package for pointwise nonparametric estimation of hazard ratio curves of continuous predictors. Computational and Mathematical Methods in Medicine, Article ID 745742, 111. doi: 10.1155/2013/745742.CrossRefGoogle Scholar
Nguti, R 2003. Random effects survival models applied to animal breeding data. PhD thesis, Limburgs Universitair Centrum, Diepenbeek, Belgium. Retrieved on 8 April 2019 from https://ibiostat.be/publications/phd/rosemarynguti.pdfGoogle Scholar
R Core Team 2018. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved on 28 July 2018 from URL https://www.R-project.org/.Google Scholar
Royston, P and Altman, DG 2013. External validation of a Cox prognostic model: principles and methods. BMC Medical Research Methodology 13, 33.CrossRefGoogle ScholarPubMed
Sawalha, RM, Conington, J, Brotherstone, S and Villanueva, B 2007. Analyses of lamb survival of scottish blackface sheep. Animal 1, 151157.CrossRefGoogle ScholarPubMed
Schneider, JW 2015. Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations. Scientometrics 102, 411432.CrossRefGoogle Scholar
Serrano, N 2012. Calibration strategies to validate predictive models: is new always better? Intensive Care Medicine 38, 12461248. doi 10.1007/s00134-012-2579-zCrossRefGoogle ScholarPubMed
Sharma, SK, Nagda, RK, Kumar, U and Khadda, BS 2007. Mortality pattern in Sirohi goats under field conditions. Indian Journal of Small Ruminants 13, 210212.Google Scholar
Singh, MK, Rai, B and Sharma, N 2008. Factors affecting survivability of Jamunapari kids under semi-intensive management system. Indian Journal of Animal Sciences 78, 178181.Google Scholar
Southey, BR, Rodriguez-Zas, SL and Leymaster, KA 2001. Survival analysis of lamb mortality in a terminal sire composite population. Journal of Animal Science 79, 22982306.CrossRefGoogle Scholar
Southey, BR, Rodriguez-Zas, SL and Leymaster, KA 2003. Discrete time survival analysis of lamb mortality in a terminal sire composite population. Journal of Animal Science 81, 13991405.CrossRefGoogle Scholar
Steyerberg, EW, Harrell, FE Jr., Borsbooma, GJJM, Eijkemansa, MJCR, Vergouwea, Y, Habbemaa, JDF 2001. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology 54, 774781.CrossRefGoogle ScholarPubMed
Subramaniyan, M, Thanga, TV, Subramanian, M and Senthilnayagam, H 2016. Factors affecting pre-weaning survivability of kids in an organized goat farm. International Journal of Livestock Research 6, 8392.CrossRefGoogle Scholar
Therneau, T 2015. A package for survival analysis in S. version 2.38. Retrieved on 28 July 2018 from https://CRAN.R-project.org/package%20=%20survival.Google Scholar
Therneau, TM and Grambsch, PM 2000. Modeling survival data: extending the Cox model. Springer-Verlag, New York, NY, USA.CrossRefGoogle Scholar
Thiruvenkadan, AK and Karunanithi, K 2007. Mortality and replacement rate of Tellicherry and its crossbred goats in Tamil Nadu. Indian Journal of Animal Sciences 77, 590594.Google Scholar
Vatankhah, M and Talebi, MA 2009. Genetic and non-genetic factors affecting mortality in Lori-bakhtiari lambs. Asian Australian Journal of Animal Science 22, 459464.CrossRefGoogle Scholar
Wickham, H 2016. ggplot2: elegant graphics for data analysis, 2nd edition. Springer-Verlag, New York, NY, USA.CrossRefGoogle Scholar
Zhang, Z, Reinikainen, J, Adeleke, KA, Pieterse, ME and Groothuis-Oudshoorn, CGM 2018. Time-varying covariates and coefficients in Cox regression models. Annals of Translational Medicine 6, 12.CrossRefGoogle ScholarPubMed
Supplementary material: File

Chauhan et al. supplementary material

Chauhan et al. supplementary material 1

Download Chauhan et al. supplementary material(File)
File 2.3 MB