Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T12:24:08.275Z Has data issue: false hasContentIssue false

The effects of different farm environments on the performance of Texel sheep

Published online by Cambridge University Press:  03 July 2015

A. McLaren*
Affiliation:
SRUC, Hill & Mountain Research Centre, Kirkton Farm, Crianlarich, FK20 8RU, UK
S. Brotherstone
Affiliation:
Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh, EH9 3JT, UK
N. R. Lambe
Affiliation:
SRUC, Hill & Mountain Research Centre, Kirkton Farm, Crianlarich, FK20 8RU, UK
J. Conington
Affiliation:
Animal &Veterinary Sciences, SRUC, Easter Bush, Midlothian, EH25 9RG, UK
R. Mrode
Affiliation:
Animal &Veterinary Sciences, SRUC, Easter Bush, Midlothian, EH25 9RG, UK
L. Bunger
Affiliation:
Animal &Veterinary Sciences, SRUC, Easter Bush, Midlothian, EH25 9RG, UK
*
Get access

Abstract

In order to assess the extent of genotype by environment interactions (G×E) and environmental sensitivity in sheep farm systems, environmental factors must be identified and quantified, after which the relationship with the traits(s) of interest can be investigated. The objectives of this study were to develop a farm environment (FE) scale, using a canonical correlation analysis, which could then be used in linear reaction norm models. Fine-scale farm survey data, collected from a sample of 39 Texel flocks across the United Kingdom, was combined with information available at the national level. The farm survey data included information on flock size and concentrate feed use. National data included flock performance averages for 21-week-old weight (21WT), ultrasound back-fat (UFD) and muscle (UMD) depths, as well as regional climatic data. The FE scale developed was then combined with 181 555 (21WT), 175 399 (UMD) and 175 279 (UFD) records from lambs born between 1990 and 2011, on 494 different Texel flocks, to predict reaction norms for sires used within the population. A range of sire sensitivities estimated across the FE scale confirmed the presence of genetic variability as both ‘plastic’ and ‘robust’ genotypes were observed. Variations in heritability estimates were also observed indicating that the rate genetic progress was dependent on the environment. Overall, the techniques and approaches used in this study have proven to be useful in defining sheep FEs. The results observed for 21WT, UMD and UFD, using the reaction norm models, indicate that in order to improve genetic gain and flock efficiency, future genetic evaluations would benefit by accounting for the G×E observed.

Type
Research Article
Copyright
© The Animal Consortium 2015 

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

Bradshaw, AD 1965. Evolutionary significance of phenotypic plasticity in plants. Advances in Genetics 13, 115155.Google Scholar
Bryant, J, Lopez-Villalobos, N, Holmes, C and Pryce, J 2005. Simulation modelling of dairy cattle performance based on knowledge of genotype, environment and genotype by environment interactions: current status. Agricultural Systems 86, 121143.Google Scholar
Calus, MPL, Groen, AF and de Jong, G 2002. Genotype × environment interaction for protein yield in Dutch dairy cattle as quantified by different models. Journal of Dairy Science 85, 31153123.Google Scholar
Cardoso, FF and Tempelman, RJ 2012. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction. Journal of Animal Science 90, 21302141.Google Scholar
Clark, D 1975. Understanding canonical correlation analysis. Concepts and techniques in modern geography 3. University of East Anglia, Norwich, England.Google Scholar
de Jong, G and Bijma, P 2002. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livestock Production Science 78, 195214.Google Scholar
Falconer, DS 1990. Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genetical Research 56, 5770.Google Scholar
Fikse, WF, Rekaya, R and Weigel, KA 2003. Assessment of environmental descriptors for studying genotype by environment interaction. Livestock Production Science 82, 223231.Google Scholar
Gilmour, AR, Cullis, BR, Welham, SJ and Thompson, R 2002. ASReml reference manual. NSW Agriculture, Orange, NSW, Australia.Google Scholar
Hair, JF, Tatham, RL, Anderson, RE and Black, W 2006. Multivariate data analysis vol. 2, Pearson Prentice Hall, Upper Saddle River, New Jersey, USA.Google Scholar
Haskell, MJ, Brotherstone, S, Lawrence, AB and White, IMS 2007. Characterization of the dairy farm environment in Great Britain and the effect of the farm environment on cow life span. Journal of Dairy Science 90, 53165323.CrossRefGoogle ScholarPubMed
Hill, WG and Zhang, XS 2004. Effects on phenotypic variability of directional selection arising through genetic differences in residual variability. Genetical Research 83, 121132.Google Scholar
Jones, HE, Lewis, RM, Young, MJ and Simm, G 2004. Genetic parameters for carcass composition and muscularity in sheep measured by X-ray computer tomography, ultrasound and dissection. Livestock Production Science 90, 167179.Google Scholar
Knap, PW and Su, G 2008. Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis. Animal 2, 17421747.Google Scholar
Kolmodin, R, Strandberg, E, Madsen, P, Jensen, J and Jorjani, H 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agriculturae Scandinavica, Section A, Animal Science 52, 1124.Google Scholar
Macfarlane, JM and Simm, G 2007. Genetic improvement programme meat type sheep: an experience from the United Kingdom. Proceedings of the 3rd International Symposium about Goat and Sheep Meat Type – 3rd SINCORTE, Joao Pessoa, Paraiba, Brazil.Google Scholar
Mattar, M, Silva, LOC, Alencar, MM and Cardoso, FF 2011. Genotype×environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis. Journal of Animal Science 89, 23492355.Google Scholar
McLaren, A, Lambe, NR, Morgan-Davies, C, Mrode, R, Brotherstone, S, Conington, J, Morgan-Davies, J and Bunger, L 2014. Characterisation of UK terminal sire sheep farm systems, based on a range of environmental factors: a case study in the context of genotype by environment interactions using Charollais lambs. Animal 8, 867876.CrossRefGoogle ScholarPubMed
Mulder, HA, Bijma, P and Hill, WG 2007. Prediction of breeding values and selection responses with genetic heterogeneity of environmental variance. Genetics 175, 18951910.CrossRefGoogle ScholarPubMed
Mulder, HA, Veerkamp, RF, Ducro, BJ, van Arendonk, JAM and Bijma, P 2006. Optimization of dairy cattle breeding programs for different environments with genotype by environment interactions. Journal of Dairy Science 89, 17401752.Google Scholar
Pollot, GE and Greeff, JC 2004. Genotype x environment interactions and genetic parameters for fecal egg count and production traits of Merino sheep. Journal of Animal Science 82, 28402851.Google Scholar
Ravagnolo, O and Misztal, I 2000. Genetic component of heat stress in dairy cattle, parameter estimation. Journal of Dairy Science 83, 21262130.Google Scholar
SanCristobal-Gaudy, M, Bodin, L, Elsen, JM and Chevalet, C 2001. Genetic components of litter size variability in sheep. Genetics Selection Evolution 33, 249271.Google Scholar
Santana, ML, Eler, JP, Cardoso, FF, Albuquerque, LG and Ferraz, JBS 2013a. Phenotypic plasticity of composite beef cattle performance using reaction norms model with unknown covariate. Animal 7, 202210.Google Scholar
Santana, ML, Bignardi, AB, Eler, JP, Cardoso, FF and Ferraz, JBS 2013b. Genotype by environment interaction and model comparison for growth traits of Santa Ines sheep. Journal of Animal Breeding and Genetics 130, 394403.Google Scholar
Schaeffer, LR and Dekkers, JCM 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, Guelph, Canada, pp. 443446.Google Scholar
Simm, G and Dingwall, WS 1989. Selection indices for lean meat production in sheep. Livestock Production Science 21, 223233.Google Scholar
Strandberg, E 2006. Analysis of genotype by environment interaction using random regression models. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13–18 August, pp. 25–05. Retrieved November 3, 2013, from http://www.cabi.org/cabdirect/FullTextPDF/2006/20063170059.pdf.Google Scholar
Strandberg, E, Brotherstone, S, Wall, E and Coffey, P 2009. Genotype by environment interaction for first-lactation female fertility traits in UK dairy cattle. Journal of Dairy Science 92, 34373446.Google Scholar
Strandberg, E, Kolmodin, R, Madsen, P, Jensen, J and Jorjani, H 2000. Genotype by environmental interaction in Nordic dairy cattle studied by the use of reaction norms. Interbull Bulletin 25, 4145.Google Scholar