Section: Animal Science
Topic: Agricultural sciences, Applied mathematics

Quantifying growth perturbations over the fattening period in swine via mathematical modelling

Revilla, Manuel1 ; Guillaume, Lenoir1, 2  ; Loïc, Flatres-Grall2; Rafael, Muñoz-Tamayo1 ; Friggens, Nicolas C1
1 Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005 Paris, France
2 AXIOM, La Garenne, 37310 Azay-sur-Indre, France

10.24072/pcjournal.82 - Peer Community Journal, Volume 2 (2022), article no. e9.

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Background: Resilience can be defined as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. In a perspective of precision livestock farming, it is key to create informative indicators for general resilience and therefore incorporate this concept in breeding goals. In the modern swine breeding industry, new technologies such as automatic feeding system are increasingly common and can be used to capture useful data to monitor animal phenotypes such as feed efficiency. This automatic and longitudinal data collection integrated with mathematical modelling has a great potential to determine accurate resilience indicators, for example by measuring the deviation from expected production levels over a period of time. Results: This work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period, from approximately 34 kg to 105 kg of body weight. A total of 13 093 pigs, belonging to three different genetic lines were monitored (Pietrain, Pietrain NN and Duroc) since 2015, and body weight measures registered (approximately 11.1 million of weightings) with automatic feeding systems. We used the Gompertz model and linear interpolation on body weight data to quantify individual deviations from expected production, thereby creating a resilience index (ABC). The estimated heritabilities of ABC are low but not zero from 0.03 to 0.04 (+/- 0.01) depending on the breed. Conclusions: Our model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the genetic improvement of resilience of fattening pigs by providing a resilience index.

Published online:
DOI: 10.24072/pcjournal.82
Type: Research article

Revilla, Manuel 1; Guillaume, Lenoir 1, 2; Loïc, Flatres-Grall 2; Rafael, Muñoz-Tamayo 1; Friggens, Nicolas C 1

1 Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005 Paris, France
2 AXIOM, La Garenne, 37310 Azay-sur-Indre, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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     title = {Quantifying growth perturbations over the fattening period in swine via mathematical modelling},
     journal = {Peer Community Journal},
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Revilla, Manuel; Guillaume, Lenoir; Loïc, Flatres-Grall; Rafael, Muñoz-Tamayo; Friggens, Nicolas C. Quantifying growth perturbations over the fattening period in swine via mathematical modelling. Peer Community Journal, Volume 2 (2022), article  no. e9. doi : 10.24072/pcjournal.82. https://peercommunityjournal.org/articles/10.24072/pcjournal.82/

PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.animsci.100008

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

[1] Banhazi, T.; Lehr, H.; Black, J.; Crabtree, H.; Schofield, P.; Tscharke, M.; Berckmans, D. Precision Livestock Farming: An international review of scientific and commercial aspects, International Journal of Agricultural and Biological Engineering, Volume 5 (2012) no. 3

[2] Ben Abdelkrim, A.; Puillet, L.; Gomes, P.; Martin, O. Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming, Animal, Volume 15 (2021) no. 1 | DOI

[3] Berghof, T. V. L.; Poppe, M.; Mulder, H. A. Opportunities to Improve Resilience in Animal Breeding Programs, Frontiers in Genetics, Volume 9 (2019) | DOI

[4] Colditz, I. G.; Hine, B. C. Resilience in farm animals: biology, management, breeding and implications for animal welfare, Animal Production Science, Volume 56 (2016) no. 12 | DOI

[5] Enterprise S. Scilab: Free and open source software for numerical computation (OS, Version 6.0.0), 2018

[6] Friggens, N.; Blanc, F.; Berry, D.; Puillet, L. Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management, Animal, Volume 11 (2017) no. 12, pp. 2237-2251 | DOI

[7] Ge, L.; Anten, N. P.; van Dixhoorn, I. D.; Feindt, P. H.; Kramer, K.; Leemans, R.; Meuwissen, M. P.; Spoolder, H.; Sukkel, W. Why we need resilience thinking to meet societal challenges in bio-based production systems, Current Opinion in Environmental Sustainability, Volume 23 (2016), pp. 17-27 | DOI

[8] Gilmour, A.; Gogel, B.; Cullis, B.; Thompson, R. ASReml user guide release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK, 2009 (URL: https://repository.rothamsted.ac.uk/item/8w146/asreml-user-guide-release-3-)

[9] Gompertz, B. XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. F. R. S. &c, Philosophical Transactions of the Royal Society of London, Volume 115 (1825), pp. 513-583 | DOI

[10] Jourdain, C.; Gueblez, R.; Le Henaff, G. Adjustement of age and backfat thickness to a constant liveweight in the French Large-White and Landrace. 21es Journées de la Recherche Porcine; Paris, France, 1989

[11] Knap, P. W. Robustness., Resource allocation theory applied to farm animal production, CABI, Wallingford, 2008, pp. 288-301 | DOI

[12] Lawrence, A. B.; Conington, J.; Simm, G. Breeding and animal welfare: practical and theoretical advantages of multi-trait selection, Animal Welfare, Volume 13 (2004) no. Supplement 1 (https://agris.fao.org/agris-search/search.do?recordID=US20130098274)

[13] Mulder, H. A.; Rashidi, H. Selection on resilience improves disease resistance and tolerance to infections1, Journal of Animal Science, Volume 95 (2017) no. 8, pp. 3346-3358 | DOI

[14] Neethirajan, S. The role of sensors, big data and machine learning in modern animal farming, Sensing and Bio-Sensing Research, Volume 29 (2020) | DOI

[15] Nguyen-Ba, H.; van Milgen, J.; Taghipoor, M. A procedure to quantify the feed intake response of growing pigs to perturbations, Animal, Volume 14 (2020) no. 2, pp. 253-260 | DOI

[16] Nguyen-Ba, H.; Taghipoor, M.; van Milgen, J. Modelling the feed intake response of growing pigs to diets contaminated with mycotoxins, Animal, Volume 14 (2020) | DOI

[17] Piñeiro, C.; Morales, J.; Rodríguez, M.; Aparicio, M.; Manzanilla, E. G.; Koketsu, Y. Big (pig) data and the internet of the swine things: a new paradigm in the industry, Animal Frontiers, Volume 9 (2019) no. 2, pp. 6-15 | DOI

[18] Putz, A. M.; Harding, J. C. S.; Dyck, M. K.; Fortin, F.; Plastow, G. S.; Dekkers, J. C. M. Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs, Frontiers in Genetics, Volume 9 (2019) | DOI

[19] R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2017 (https://www.R-project.org)

[20] Rauw, W. M.; Gomez-Raya, L. Genotype by environment interaction and breeding for robustness in livestock, Frontiers in Genetics, Volume 6 (2015) | DOI

[21] Revilla, M.; Friggens, N.; Broudiscou, L.; Lemonnier, G.; Blanc, F.; Ravon, L.; Mercat, M.; Billon, Y.; Rogel-Gaillard, C.; Le Floch, N.; Estellé, J.; Muñoz-Tamayo, R. Towards the quantitative characterisation of piglets’ robustness to weaning: a modelling approach, Animal, Volume 13 (2019) no. 11, pp. 2536-2546 | DOI

[22] SAS Institute Inc. SAS/STAT User Guide, Release 8.0; Cary; NC, USA, 2002 (http://v8doc.sas.com/sashtml/stat/index.ht)

[23] Schulin-Zeuthen, M.; Kebreab, E.; Dijkstra, J.; Lopez, S.; Bannink, A.; Darmani Kuhi, H.; Thornley, J.; France, J. A comparison of the Schumacher with other functions for describing growth in pigs, Animal Feed Science and Technology, Volume 143 (2008) no. 1-4, pp. 314-327 | DOI

[24] Slader, R.; Gregory, A. An automatic feeding and weighing system for ad libitum fed pigs, Computers and Electronics in Agriculture, Volume 3 (1988) no. 2, pp. 157-170 | DOI

[25] Stratz, P.; Wimmers, K.; Meuwissen, T.; Bennewitz, J. Investigations on the pattern of linkage disequilibrium and selection signatures in the genomes of German Piétrain pigs, Journal of Animal Breeding and Genetics, Volume 131 (2014) no. 6, pp. 473-482 | DOI

[26] Suzuki, K.; Shibata, T.; Kadowaki, H.; Abe, H.; Toyoshima, T. Meat quality comparison of Berkshire, Duroc and crossbred pigs sired by Berkshire and Duroc, Meat Science, Volume 64 (2003) no. 1, pp. 35-42 | DOI

[27] Wand, M.; Jone, M. Kernel Smoothing, Monographs on Statistics & Applied Probability, Chapman & Hall/CRC, 1994

[28] Wellock, I. J.; Emmans, G. C.; Kyriazakis, I. Describing and predicting potential growth in the pig, Animal Science, Volume 78 (2004) no. 3, pp. 379-388 | DOI

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