Know more

About cookies

What is a "cookie"?

A "cookie" is a piece of information, usually small and identified by a name, which may be sent to your browser by a website you are visiting. Your web browser will store it for a period of time, and send it back to the web server each time you log on again.

Different types of cookies are placed on the sites:

  • Cookies strictly necessary for the proper functioning of the site
  • Cookies deposited by third party sites to improve the interactivity of the site, to collect statistics

Learn more about cookies and how they work

The different types of cookies used on this site

Cookies strictly necessary for the site to function

These cookies allow the main services of the site to function optimally. You can technically block them using your browser settings but your experience on the site may be degraded.

Furthermore, you have the possibility of opposing the use of audience measurement tracers strictly necessary for the functioning and current administration of the website in the cookie management window accessible via the link located in the footer of the site.

Technical cookies

Name of the cookie

Purpose

Shelf life

CAS and PHP session cookies

Login credentials, session security

Session

Tarteaucitron

Saving your cookie consent choices

12 months

Audience measurement cookies (AT Internet)

Name of the cookie

Purpose

Shelf life

atid

Trace the visitor's route in order to establish visit statistics.

13 months

atuserid

Store the anonymous ID of the visitor who starts the first time he visits the site

13 months

atidvisitor

Identify the numbers (unique identifiers of a site) seen by the visitor and store the visitor's identifiers.

13 months

About the AT Internet audience measurement tool :

AT Internet's audience measurement tool Analytics is deployed on this site in order to obtain information on visitors' navigation and to improve its use.

The French data protection authority (CNIL) has granted an exemption to AT Internet's Web Analytics cookie. This tool is thus exempt from the collection of the Internet user's consent with regard to the deposit of analytics cookies. However, you can refuse the deposit of these cookies via the cookie management panel.

Good to know:

  • The data collected are not cross-checked with other processing operations
  • The deposited cookie is only used to produce anonymous statistics
  • The cookie does not allow the user's navigation on other sites to be tracked.

Third party cookies to improve the interactivity of the site

This site relies on certain services provided by third parties which allow :

  • to offer interactive content;
  • improve usability and facilitate the sharing of content on social networks;
  • view videos and animated presentations directly on our website;
  • protect form entries from robots;
  • monitor the performance of the site.

These third parties will collect and use your browsing data for their own purposes.

How to accept or reject cookies

When you start browsing an eZpublish site, the appearance of the "cookies" banner allows you to accept or refuse all the cookies we use. This banner will be displayed as long as you have not made a choice, even if you are browsing on another page of the site.

You can change your choices at any time by clicking on the "Cookie Management" link.

You can manage these cookies in your browser. Here are the procedures to follow: Firefox; Chrome; Explorer; Safari; Opera

For more information about the cookies we use, you can contact INRAE's Data Protection Officer by email at cil-dpo@inrae.fr or by post at :

INRAE

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

Last update: May 2021

Menu Logo Principal Institut Agro Montpellier LEPSE membre de Montpellier Université d'Excellence Labex AGRO Institut Carnot DigitAg

Home page

Bouidghaghen Jugurta

Bouidghaghen Jugurta
PhD researcher with ARVALIS and UMR LEPSE

Topic: "Simulation of maize yields on a European scale by combining phenomena, genomic prediction and modelling".

Abstract :

Genomic prediction is effective in simulating the average performance of varieties from genotyping information, but does not predict well the variation in yield for each variety according to local environmental conditions (genotype x environment interactions). This situation could change thanks to two sets of recent advances. (i) The rapid development of sensor networks and environmental grids makes it possible to characterise the environment in any agricultural plot, and thus to use yield simulation models. (ii) Phenomics allows the characterisation of hundreds of varieties, in particular the response of key physiological traits to environmental conditions, which allows simulation models to be fed with genotype-dependent information.

The aim of the thesis is to predict the behaviour and simulate the yields of maize varieties, each characterised by allelic combinations; in hundreds of locations in France and Europe, characterised by environmental conditions measured or deduced from environmental grids. To do this, it will combine (i) the measurement of genotype-dependent physiological traits by phenomena, (ii) the genomic prediction of these traits, (iii) the environmental characterisation of plots, and (iv) a crop model parameterised on the basis of the phenomena data The main areas of application for ARVALIS are the enrichment of the annual characterisation of maize varieties in post-registration, thanks to an in silico evaluation of their performance and stability in current or future scenarios. In addition, this approach would make it possible to identify varieties with complementary interactions that would be recommended in clusters for better stability.

Management :

UMR LEPSE – INRAE : François Tardieu (thesis director) and Claude Welcker (co-supervisor)

ARVALIS – Institut du Végétal : Matthieu Bogard and Delphine Hourcade (R&D engineers)