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: https://www.ghostery.com/fr/products/

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: http://www.youronlinechoices.com/fr/controler-ses-cookies/, 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

Realytics
Google Analytics
Spoteffects
Optimizely

Targeted advertising cookies

DoubleClick
Mediarithmics

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 cil-dpo@inra.fr or by post at:

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

Dernière mise à jour : Mai 2018

Menu logo faccejpi Déroulé - logo Faccejpi

FACCEJPI

Zone de texte éditable et éditée et rééditée

MODCARBOSTRESS

Improving models and plant phenotyping pipelines for a smart agriculture under abiotic stress combination and elevated CO2

Climate change accelerates the need for a smarter, more efficient, more secure agriculture. Because climate change is predicted to increase spatial and temporal variability, crop models able to predict the best local allele/phene combinations within a species, in addition to the best management systems (such as, for instance, species choice, rotations, sowing dates…) will be of great value for farmers and breeders worldwide. However, current crop models have large uncertainties in particular under drought and high temperatures that often occur in combination and while their occurrences are likely to increase in several regions of the world. Accounting for the impact of elevated atmospheric CO2 in the picture will add another level of difficulty with possible positive or negative infleunces depending on complex interactions We thus raise the double hypothesis that important reasons for crop model uncertainties are (i) Lack of accurate dataset under combined stresses hampering proper parameterisation. (ii) Inappropriate modelling hypotheses. Because CO2 control in experimental facilities is the exception rather than the rule, our project will aim at delivering to simple, low cost, principles and solutions for manipulating combined stresses, including elevated CO2, in experimental set-ups. Crop models can be broadly split into 2 distinct categories depending on whether growth is essentially source or sink limited. However, drought and CO2 are likely to shift growth limitation from source to sink while elevated temperature could shift growth limitation towards the source. A possibility is thus that both types of models find their limits under stress combinations. Our project will thus assess models of these two types in front of stress combinations. We will both improve model parameterisation thanks to the experiments performed in the frame of this project and evaluate model performance using field data obtained from other consortia (in particular FACE experiments). A final outcome of the project will be to propose model improvements and to run them against climate model projections for Europe. Two crop species, bread wheat (Triticum aestivum L.) and oilseed rape (Brassica napus L.), will used but the project intends to revisit crop model rationales in a species independent manner. In both species, a set of genotypes contrasted for stress sensitivity and for which field data are available will be selected.

Coordinator: Bertrand Muller, INRA Montpellier, France

Consortium:

  • Fabio Fiorani, Forschungszentrum Juelich GmbH, Germany
  • Carl-Otto Ottosen, Aarhus University, Denmark
  • Eva Rosenqvist, Copenhagen University, Denmark
  • Bernard Genty, CNRS/CEA, France
  • Xinyou Yin, Wageningen University and Research Centre, The Netherlands
  • John Doonan, Aberystwyth University, United Kingdom
  • Pierre Martre, INRA Montpellier, France

Requested funding: 1137k€