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 Principal AgroParisTech

Welcome to ECOSYS

UMR ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes

Poster 2 : MarkTheobald_AmmoniaEmissionModel

Poster 2
Mark R. Theobald1, David Makowski2, Carole Bedos3, Julie Ramanantenasoa3, Sophie Génermont3

AN AMMONIA EMISSION MODEL FOR FERTILISER APPLICATIONS SUITABLE FOR USE IN CLIMATE CHANGE SCENARIOS

The field-application of organic and mineral fertilisers is a large source of ammonia (NH3) emissions in Europe. In addition to management factors (e.g. fertiliser application rates), these emissions are strongly dependent on soil properties and climatic conditions. Including this dependence in the NH3 emission data used in chemical transport models (CTMs, such as the EMEP Unified Model) would improve the spatial and temporal distributions of the emissions. This is particularly important for climate change simulations since changes in air temperatures and precipitation patterns could have a large influence on the temporal and spatial distribution of NH3 emissions from fertilisers. In this work, meta-models have been developed for three fertiliser types (slurry, farm yard manure; FYM and the mineral fertiliser urea ammonium nitrate; UAN) using emission estimates from a modified version of the process-based model Volt’Air1,2 for a large range of European soil and climate conditions. These simple meta-models, which have a much shorter run-time than Volt’Air, are suitable for inclusion into the emission routines of CTMs using spatial soil data and the CTM meteorological data in order to better represent the spatial and temporal distributions of NH3 emissions.

Download documents