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

McLaren, A; Brotherstone, S.; Lambe, N. R.; Conington, J.; Mrode, Raphael; Bünger, L.

doi:10.1017/s1751731115001123

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Production traits have been combined in an environmental index in dairy (Haskell et al, ) and used with fertility and production indexes for the evaluation of G × E for fertility traits (Strandberg et al, ). Clustering techniques have also been used to categorize environments for the analysis of G × E multiple traits in sheep (McLaren et al, ) and in dairy cattle (Huquet, Leclerc, & Ducrocq, ; Windig, Calus, Beerda, & Veerkamp, ). Since there are varying degrees to which phenotypes can be impacted by different environmental factors, the combination of traits to derive environmental descriptors may provide a way to better capture environmental challenges.…”

Section: Discussionmentioning

confidence: 99%

Reaction norm analysis of pig growth using environmental descriptors based on alternative traits

Guy

Thomson

et al. 2019

J Animal Breeding Genetics

View full text Add to dashboard Cite

Contemporary group (CG) estimates of different phenotypes have not been widely explored for pigs. The objective of this study was to extend the traits used to derive environmental descriptors of the growing pig, to include CG estimates of early growth between birth and start of feed intake test (EADG), growth during feed intake test (TADG), lifetime growth (ADG), daily feed intake (DFI), backfat (BF) and muscle depth (MD). Pedigree and performance records (n = 7,746) from a commercial Australian piggery were used to derive environmental descriptors based on CG estimates of these six traits. The CG estimates of growth traits described different aspects of the environment from the CG estimates of carcass traits (r < 0.10). These definitions of the environment then were used in reaction norm analysis of growth, to evaluate sire‐by‐environment interaction (Sire × E) for growth. The most appropriate reaction norm model to evaluate Sire × E for growth was dependent on the environmental descriptor used. If the trait used to derive an environmental descriptor was distinctly different from growth (e.g., BF and MD), CG as an additional random effect was required in the model. If not included, inflated common litter effect and sire intercept variance suggest there was unaccounted environmental variability. There was no significant Sire × E using any of the definitions of the environment, with estimated variance in sire slopes largest when environments were defined by BF (σfalse^bi2 = 97 ± 83 (g/day)2), followed by environments defined by DFI (σfalse^bi2 = 39 ± 101 (g/day)2). While there appears to be differences in ability to detect Sire × E, improved data structure is required to better assess these environmental descriptors based on alternative traits. The ideal trait, or combination of traits, used to derive environmental descriptors may be unique for individual herds. Therefore, multiple phenotypes should be further explored for the evaluation of Sire × E for growth in the pig.

show abstract

Section: Discussionmentioning

confidence: 99%

Reaction norm analysis of pig growth using environmental descriptors based on alternative traits

Guy

Thomson

et al. 2019

J Animal Breeding Genetics

View full text Add to dashboard Cite

show abstract

“…Sheep are generally regarded as being quite robust to variations in environmental factors, and some breeds (such as Lacaune or Eastern Friesian) are being reared in different countries under an extremely broad range of conditions. A recent investigation of sire sensitivity across diverse farm environments proved that some genotypes were sensitive whereas others were robust with respect to meat sheep production (McLaren et al, 2015). In future genetic evaluations, accounting for environmental sensitivity could enable the identification and selection of sires predicted to be best suited to specific environments (McLaren et al, 2015;Huisman et al, 2016).…”

Section: Genotype By Environment Interactions In Ruminant Speciesmentioning

confidence: 99%

Review: Towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes: I-selection goals and criteria

Phocas

Belloc

Bidanel

et al. 2016

Animal

View full text Add to dashboard Cite

Agroecology uses natural processes and local resources rather than chemical inputs to ensure production while limiting the environmental footprint of livestock and crop production systems. Selecting to achieve a maximization of target production criteria has long proved detrimental to fitness traits. However, since the 1990s, developments in animal breeding have also focussed on animal robustness by balancing production and functional traits within overall breeding goals. We discuss here how an agroecological perspective should further shift breeding goals towards functional traits rather than production traits. Breeding for robustness aims to promote individual adaptive capacities by considering diverse selection criteria which include reproduction, animal health and welfare, and adaptation to rough feed resources, a warm climate or fluctuating environmental conditions. It requires the consideration of genotype × environment interactions in the prediction of breeding values. Animal performance must be evaluated in low-input systems in order to select those animals that are adapted to limiting conditions, including feed and water availability, climate variations and diseases. Finally, we argue that there is no single agroecological animal type, but animals with a variety of profiles that can meet the expectations of agroecology. The standardization of both animals and breeding conditions indeed appears contradictory to the agroecological paradigm that calls for an adaptation of animals to local opportunities and constraints in weakly artificialized systems tied to their physical environment.

show abstract