PCI Animal Science

Title

Quantifying growth perturbations over the fattening period in swine via mathematical modellinguse asterix (*) to get italics

Authors

Manuel Revilla, Guillaume Lenoir, Loïc Flatres-Grall, Rafael Muñoz-Tamayo, Nicolas C FriggensPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year

2021

Picture

Abstract

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.

Indicate the full web address (DOI or URL) giving public access to these data (if you have any problems with the deposit of your data, please contact contact@animsci.peercommunityin.org). In case all raw data are included in the preprint, indicate the DOI or URL of the preprint.

http://doi.org/10.5281/zenodo.4109395You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://

Indicate the full web address (DOI or URL) giving public access to these scripts (if you have any problems with the deposit of your scripts, please contact contact@animsci.peercommunityin.org). In case all raw scripts are included in the preprint, indicate the DOI or URL of the preprint.

You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

Indicate the full web address (DOI, SWHID or URL) giving public access to these codes (if you have any problems with the deposit of your codes, please contact contact@animsci.peercommunityin.org). In case all raw codes are included in the preprint, indicate the DOI or URL of the preprint.

You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

Keywords

modelling, perturbation, resilience, robustness, body weight, big data, pig, precision livestock farming

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields

Animal genetics, Animal health, Farming systems, Mathematical modelling, Precision livestock farming

Suggested reviewers - Suggest up to 10 reviewers (provide names and Email addresses). (Optional)

No need for them to be recommenders of PCI Anim Sci. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

Opposed reviewers - Suggest up to 5 people not to invite as reviewers. (Optional)

e.g. John Doe [john@doe.com]

Submission date

2020-10-26 11:47:08

Recommender

Mohammed Gagaoua

Reviewers

or Register
Submit a preprint

MANAGING BOARD

MEMBER OF

INDEXED BY