Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free:

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site:, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Google Analytics

Targeted advertising cookies


The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at or by post at:

24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal

Home page

Accounting for individual variability in modelling the response of pigs to the nutrient supply

Inra Prod Anim 25(1) 17-28


1INRA, UMR1348 Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage,

F-35590 Saint-Gilles, France

2Agrocampus Ouest, UMR Physiologie, Environnement et Génétique pour l'Animal et les Systèmes

d'Elevage, F-35000 Rennes, France

3IFIP- Institut du Porc, Pôle Techniques d'Elevage, BP 35104, F-35651 Le Rheu, cedex, France


Knowledge of amino acid requirements in growing pigs is required to formulate diets that optimize the feed efficiency and potentially limit the environmental impact of pig production. However, not all pigs have the same amino acid requirements, and thus the same responses to a defined nutrient supply, resulting from the variation in growth potential between pigs. The objective of this review was to discuss the tools available to assess the variation in amino acid requirements in growing pigs. We also demonstrate how the integration of variability in modelling tools is possible and allows optimizing feeding strategies. Current experimental techniques allow defining the requirement of individual animals, but the extrapolation of the results obtained is limited because few animals can be studied simultaneously. Although a sufficient number of data will be required, accounting for individual variability in pig growth models allows for a dynamic analysis of requirements of large populations. Using these models to simulate populations instead of a single average pig, feeding strategies can be defined that are adapted to the studied populations and optimize performance and economical results while reducing environmental impact. Accounting for individual variability in growth modelling is also essential to develop new technologies, such as precision feeding.

Download documents