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Intra-flock variability in the body reserve dynamics of meat sheep by analyzing BW and body condition score variations over multiple production cycles

Published online by Cambridge University Press:  22 January 2019

T. Macé ,
E. González-García Open the ORCID record for E. González-García [Opens in a new window] ,
F. Carrière ,
S. Douls ,
D. Foulquié ,
C. Robert-Granié  and
D. Hazard
T. Macé*
Affiliation:
GENPHYSE UMR1388, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
E. González-García
Affiliation:
SELMET, INRA, CIRAD, Montpellier SupAgro, Univ Montpellier, Montpellier, France
F. Carrière
Affiliation:
INRA La Fage UE321, 12250 Roquefort-sur-Soulzon, France
S. Douls
Affiliation:
INRA La Fage UE321, 12250 Roquefort-sur-Soulzon, France
D. Foulquié
Affiliation:
INRA La Fage UE321, 12250 Roquefort-sur-Soulzon, France
C. Robert-Granié
Affiliation:
GENPHYSE UMR1388, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
D. Hazard
Affiliation:
GENPHYSE UMR1388, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
*
†E-mail: tiphaine.mace@inra.fr
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Abstract

Breeding for resilience requires a better understanding of intra-flock variability and the related mechanisms responsible for robustness traits. Among such traits, the animals’ ability to cope with feed fluctuations by mobilizing or restoring body reserves (BR) is a key mechanism in ruminants. The objective of this work was to characterize individual variability in BR dynamics in productive Romane ewes reared in extensive conditions. The BR dynamics profiles were characterized by combining individual longitudinal measurements of BW and body condition scores (BCS) over several production cycles. Historical data, including up to 2628 records per trait distributed in 1146 ewes, underwent cluster analysis. Two to four trajectories were observed for BW depending on the cycle, while three trajectories were found for BCS, whatever the cycle. Most trajectories suggested that BR dynamics were similar but the level of BR may differ between ewes. Nevertheless, some trajectories suggested that both BR dynamics and levels were different for a proportion of ewes. Clustering on BW and BCS profiles adjusted for individual level trends, resulted in differences only in the level of BW or BCS, rather than differences in trajectories. Thus, the overall shape of trajectories was not changed considering or not the individual level trend across cycles. In addition to individual variability, the ewe’s age at first lambing and litter size contributed to the distribution of the ewes between the trajectories. Regarding the entire productive life, three trajectories were observed for BW and BCS changes over three productive cycles. Increase in BW at each cycle suggested that ewes kept growing up until 3 to 4 years old in our conditions. Similar alternation of BCS gains and losses across cycles suggested BR dynamics might be repeatable. Many individual trajectories remained the same throughout a ewe’s life, whatever the age at first lambing, parity or litter size. Our results demonstrate the relevance of using BW and BCS changes for characterizing the diversity of BR mobilization–accretion profiles in sheep in a long timespan perspective.

Keywords

body reservescluster analysislongitudinal trajectorieseweRomane
Type
Research Article
Information
animal , Volume 13 , Issue 9 , September 2019 , pp. 1986 - 1998
Copyright
© The Animal Consortium 2019 

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Footnotes

a

These are co-senior authors as they contributed equally to the design and development of the study and to manuscript preparation.

References

Akaike, H 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
Álvarez-Rodríguez, J, Estopañan, G, Sanz, A, Dervishi, E, Govoni, N, Tamanini, C and Joy, M 2012. Carry-over effects of body condition in the early pregnant ewe on peri-partum adipose tissue metabolism. Journal of Animal Physiology and Animal Nutrition 96, 985–992.CrossRefGoogle ScholarPubMed
Bauman, DE and Currie, WB 1980. Partitioning of nutrients during pregnancy and lactation: a review of mechanisms involving homeostasis and homeorhesis. Journal of Dairy Science 63, 1514–1529.CrossRefGoogle ScholarPubMed
Bocquier, F and González-García, E 2010. Sustainability of ruminant agriculture in the new context: feeding strategies and features of animal adaptability into the necessary holistic approach. Animal 4, 1258–1273.CrossRefGoogle ScholarPubMed
Brown, DJ, Savage, DB, Hinch, GN and Hatcher, S 2015. Monitoring liveweight in sheep is a valuable management strategy: a review of available technologies. Animal Production Science 55, 427–436.CrossRefGoogle Scholar
Dai, X, Hadjipantelis, PZ, Ji, H, Mueller, HG and Wang, JL 2017. fdapace: functional data analysis and empirical dynamics. R package version 0.3. 0: CRAN. Retrieved on 9 January 2017 from https://github.com/functionaldata/tPACE/Google Scholar
De La Torre, A, Recoules, E, Blanc, F, Ortigues-Marty, I, D’Hour, P and Agabriel, J 2015. Changes in calculated residual energy in variable nutritional environments: an indirect approach to apprehend suckling beef cows’ robustness. Livestock Science 176, 75–84.CrossRefGoogle Scholar
Dumont, B, González-García, E, Thomas, M, Fortun-Lamothe, L, Ducrot, C, Dourmad, JY and Tichit, M 2014. Forty research issues for the redesign of animal production systems in the 21st century. Animal 8, 1382–1393.CrossRefGoogle ScholarPubMed
Edmonson, AJ, Lean, IJ, Weaver, LD, Farver, T and Webster, G 1989. A body condition scoring chart for Holstein dairy cows. Journal of Dairy Science 72, 68–78.CrossRefGoogle Scholar
Friggens, NC, Blanc, F, Berry, DP and Puillet, L 2017. Review: deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management. Animal 11, 2237–2251.CrossRefGoogle ScholarPubMed
González-García, E, Gozzo de Figuereido, V, Foulquie, D, Jousserand, E, Autran, P, Camous, S, Tesniere, A, Bocquier, F and Jouven, M 2014. Circannual body reserve dynamics and metabolic profile changes in Romane ewes grazing on rangelands. Domestic Animal Endocrinology 46, 37–48.CrossRefGoogle ScholarPubMed
González-García, E and Hazard, D 2016. Growth rates of Romane ewe lambs and correlated effects of being mated as hoggets or two-tooth ewes on first offspring performance. Livestock Science 189, 63–69.CrossRefGoogle Scholar
González-García, E, Tesniere, A, Camous, S, Bocquier, F, Barillet, F and Hassoun, P 2015. The effects of parity, litter size, physiological state, and milking frequency on the metabolic profile of Lacaune dairy ewes. Domestic Animal Endocrinology 50, 32–44.CrossRefGoogle ScholarPubMed
Kenyon, PR, Maloney, SK and Blache, D 2014. Review of sheep body condition score in relation to production characteristics. New Zealand Journal of Agricultural Research 57, 38–64.CrossRefGoogle Scholar
Kharrat, M and Bocquier, F 2010. Impact of indoor feeding at late lactation stage on body reserves recovery and reproductive performances of Baladi dairy goats fed on pastoral system. Small Ruminant Research 90, 127–134.CrossRefGoogle Scholar
Kitano, H 2004. Biological robustness. Nature Reviews Genetics 5, 826–837.CrossRefGoogle ScholarPubMed
Klopcic, M, Reents, R, Philipsson, J and Kuipers, A 2009. Breeding for robustness in cattle. Wageningen Academic Publishers, Wageningen, The Netherlands.CrossRefGoogle Scholar
Langrognet, F, Lebret, R and Poli, C 2016. Rmixmod: supervised, unsupervised, semi-supervised classification with MIXture MODelling (Interface of MIXMOD Software). R package version 2.1.1. Retrieved on 9 January 2017 from https://cran.r-project.org/web/packages/Rmixmod/index.htmlGoogle Scholar
Lebret, R, Iovleff, S, Langrognet, F, Biernacki, C, Celeux, G and Govaert, G 2014. Rmixmod: the R package of the model-based unsupervised, supervised and semi-supervised classification mixmod library. Journal of Statistical Software 67, 241–270.Google Scholar
Macé, T, González-García, E, Pradel, J, Parisot, S, Carrière, F, Douls, S, Foulquié, D and Hazard, D 2018b. Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time. Journal of Animal Science 96, 450–4511.CrossRefGoogle ScholarPubMed
Macé, T, Hazard, D, Carrière, F, Douls, S, Foulquié, D, Robert-Granié, C. and González-García, E 2018a. Body weight and body condition score variations in Romane ewes: intraflock variability in their dynamics and magnitude over multiple production cycles. Journal of Dairy Science 101 (suppl. 2), 236.Google Scholar
María, GA and Ascaso, MS 1999. Litter size, lambing interval and lamb mortality of Salz, Rasa Aragonesa, Romanov and F1 ewes on accelerated lambing management. Small Ruminant Research 32, 167–172.CrossRefGoogle Scholar
Martin, O and Sauvant, D 2010. A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling. Animal 4, 2030–2047.CrossRefGoogle ScholarPubMed
Mendizabal, JA, Delfa, R, Arana, A and Purroy, A 2011. Body condition score and fat mobilization as management tools for goats on native pastures. Small Ruminant Research 98, 121–127.CrossRefGoogle Scholar
Molénat, G, Foulquie, D, Autran, P, Bouix, J, Hubert, D, Jacquin, M and Bibe, B 2005. Pour un élevage ovin allaitant performant et durable sur parcours : un système expérimental sur le Causse du Larzac. INRA Productions Animales 18, 323–338.Google Scholar
Molina, A, Gallego, L, Torres, A and Vergara, H 1994. Effect of mating season and level of body reserves on fertility and prolificacy of Manchega ewes. Small Ruminant Research 14, 209–217.CrossRefGoogle Scholar
Molotsi, A, Dube, B, Oosting, S, Marandure, T, Mapiye, C, Cloete, S and Dzama, K 2017. Genetic traits of relevance to sustainability of smallholder sheep farming systems in South Africa. Sustainability (Switzerland) 9, 1–18.Google Scholar
Morel, PCH, Schreurs, NM, Corner-Thomas, RA, Greer, AW, Jenkinson, CMC, Ridler, AL and Kenyon, PR 2016. Live weight and body composition associated with an increase in body condition score of mature ewes and the relationship to dietary energy requirements. Small Ruminant Research 143, 8–14.CrossRefGoogle Scholar
Nielsen, HM, Friggens, NC, Løvendahl, P, Jensen, J and Ingvartsen, KL 2003. Influence of breed, parity, and stage of lactation on lactational performance and relationship between body fatness and live weight. Livestock Production Science 79, 119–133.CrossRefGoogle Scholar
Phocas, F, Belloc, C, Bidanel, J, Delaby, L, Dourmad, JY, Dumont, B, Ezanno, P, Fortun-Lamothe, L, Foucras, G, Frappat, B, González-García, E, Hazard, D, Larzul, C, Lubac, S, Mignon-Grasteau, S, Moreno, CR, Tixier-Boichard, M and Brochard, M 2016. Towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes: I-selection goals and criteria. Animal 10, 1749–1759.CrossRefGoogle ScholarPubMed
Puillet, L and Martin, O 2017. A dynamic model as a tool to describe the variability of lifetime body weight trajectories in livestock females. Journal of Animal Science 95, 4846–4856.CrossRefGoogle ScholarPubMed
Ricordeau, G, Tchamitchian, L, Brunel, JC, Nguyen, TC and François, D 1992. La race ovine INRA 401: un exemple de souche synthétique. INRA Productions Animales hs (hs), 255–262.Google Scholar
Rojas-Downing, MM, Nejadhashemi, AP, Harrigan, T and Woznicki, SA 2017. Climate change and livestock: impacts, adaptation, and mitigation. Climate Risk Management 16, 145–163.CrossRefGoogle Scholar
Russel, AJF, Doney, JM and Gunn, RG 1969. Subjective assessment of body fat in live sheep. Journal of Agricultural Sciences 72, 451–454.Google Scholar
Schwarz, G 1978. Estimating the dimension of a model. The Annals of Statistics 6, 461–464.CrossRefGoogle Scholar
Smith, GL, Friggens, NC, Ashworth, CJ and Chagunda, MGG 2017. Association between body energy content in the dry period and post-calving production disease status in dairy cattle. Animal 11, 1590–1598.CrossRefGoogle ScholarPubMed
Thorup, VM, Edwards, D and Friggens, NC 2012. On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score. Journal of Dairy Science 95, 1784–1793.CrossRefGoogle ScholarPubMed
Walkom, SF, Brien, FD, Hebart, ML, Fogarty, NM, Hatcher, S and Pitchford, WS 2014. Season and reproductive status rather than genetics factors influence change in ewe weight and fat over time. 2. Spline analysis of crossbred ewes. Animal Production Science 54, 814–820.CrossRefGoogle Scholar
Yao, F, Mueller, HG and Wang, JL 2005. Functional linear regression analysis for longitudinal data. Annals of Statistics 33, 2873–2903. (Dense data).CrossRefGoogle Scholar
Zygoyiannis, D, Stamataris, C, Friggens, NC, Doney, JM and Emmans, GC 1997. Estimation of the mature weight of three breeds of Greek sheep using condition scoring corrected for the effect of age. Animal Science 64, 147–153.CrossRefGoogle Scholar
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