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Quantifying growth perturbations over the fattening period in swine via mathematical modellinguse asterix (*) to get italics
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"
<p>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.</p> <p>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.</p> <p>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.</p> 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://
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modelling, perturbation, resilience, robustness, body weight, big data, pig, precision livestock farming
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Animal genetics, Animal health, Farming systems, Mathematical modelling, Precision livestock farming
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
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2020-10-26 11:47:08
Mohammed Gagaoua