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 logo remix UE.Logo

ReMIX H2020 - Intercrops

Several models have been developed, improved and calibrated to identify the best management strategies

Meta-analysis studies are underway or finalized, some of them already published

Significant progress was made in modelling the role of plant traits in maize/soybean intercropping. For this, a novel functional-structural plant model was developed and parameterized, and simulation studies were carried out. Experiments were conducted on six different species combinations to explore the role of major species traits, such as height, photosynthesis mechanism, phenology, and ability for nitrogen capture as a factor in overyielding in strip (relay) intercropping under western European conditions. Above ground processes such as light capture and photosynthesis were measured in detail.

The model IPSIM for qualitative modelling of pest injury profiles was finalized and calibrated. The model showed potential for identification for circumstances enabling pest and disease control in barley/pea intercropping. A meta-analysis on disease control in wheat-fava bean intercropping was finished, while two other meta-analyses on disease control are still underway. The IPSIM results and three meta-analyses provide a strong basis for assessing the potential of intercropping to suppress diseases.

Solid progress was made to further develop and strengthen the crop-weed modelling environment FLORSYS. Progress was especially made in modelling below-ground processes. Literature on weed suppression in crop species mixtures was mined to assemble the globally available data on the relationships between species combinations, species traits, management, crop densities and weed suppression.

ReMIX team worked on the further development of the crop model STICS to predict the performance of multi-species mixtures under climate change and climate variability. Algorithms for modelling nitrogen capture were improved and calibrated to improve the predictive quality of the model. Furthermore, a model without name was developed for simulating maize/wheat intercropping for water-limited conditions, and two models (APSIM and M3) were further developed and parameterized in collaboration with the DIVERSify project to model cereal/legume intercropping under nitrogen limited conditions in Europe.