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


Shelf life

CAS and PHP session cookies

Login credentials, session security



Saving your cookie consent choices

12 months

Audience measurement cookies (AT Internet)

Name of the cookie


Shelf life


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

13 months


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

13 months


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 or by post at :


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

Last update: May 2021

Menu Logo Principal logo SOERE PRO Logo CIRAD Logo AnaEE Logo IRD Logo Ouagadougou university

Home page

Using Data Mining to Predict Soil Quality after Application of Biosolids in Agriculture

Jérôme Cortet (1), Dragi Kocev (2), Caroline Ducobu (1), Sašo Džeroski (2), Marko Debeljak (2), and Christophe Schwartz (1)

Cortet & al., 2011
Cortet & al., Journal: J Environ Qual., (2011), 40(6):1972-82

The amount of biosolids recycled in agriculture has steadily increased during the last decades. However, few models are available to predict the accompanying risks, mainly due to the presence of trace element and organic contaminants, and benefits for soil fertility of their application. This paper deals with using data mining to assess the benefits and risks of biosolids application in agriculture.

Main results

The analyzed data come from a 10-yr field experiment in northeast France (La Bouzule experimental farm) focusing on the effects of biosolid application and mineral fertilization on soil fertility and contamination (figure 1).

fig.1 Cortet 2011

Figure 1 : Biosolid characteristics, trace elements and organic pollutants content. Mean of four values for each parameter (one for each spreading during the study). Values in italics are close to or above the threshold values. † LSS, liquid sewage sludge; LDSS, lightly dehydrated sewage sludge; LDCSS, lightly dehydrated composted sewage sludge; LDCSSO, lightly dehydrated composted sludge added with organic pollutants; LDCSSM, lightly dehydrated composted sludge added with metals; MPS, mixed paper sludge; CA, coal ashes; HWA, household waste ashes. ‡ French Council decision of 8 Jan. 1998 (Voynet et al., 1998). § PCB, polychlorinated biphenyl.

Biosolids were applied at agriculturally recommended rates. Biosolids had a significant effect on soil fertility, causing in particular a persistent increase in plant-available phosphorus (P) relative to plots receiving mineral fertilizer. However, soil fertility at seeding and crop management method had greater effects than biosolid application on soil fertility at harvest, especially soil nitrogen (N) content. Levels of trace elements and organic contaminants in soils remained below legal threshold values. Levels of extractable metals correlated more strongly than total metal levels with other factors. Levels of organic contaminants, particularly polycyclic aromatic hydrocarbons, were linked to total metal levels in biosolids and treated soil (figure 2).

fig.2 Cortet 2011

Figure 2: Regression trees predicting values for total polycyclic aromatic hydrocarbons (PAH) and polychlorinated biphenyl (PCB) (mg kg−1 dry matter [DM]) at harvest time (Scenario 4). MI = minimum instances in a leaf (i.e., minimum number of samples from which the predicted value is calculated); MD = maximum tree depth (i.e., number of hierarchical levels of the tree); “(before)” = content in soil at sowing time; “w” = content in biosolids; “tot” = total metal content (mg kg−1 DM); “f” = content in fertilizers. Trees should be read like “what-if” questions from the root of each tree. The closer the attribute is to the root of the tree, the more it is statistically infl uent for the considered predicted attribute.

This study confirmed that biosolid application at rates recommended for agriculture is a safe option for increasing soil fertility. However, the quality of the biosolids selected has to be taken into account. The results also indicate the power of data mining in examining links between parameters in complex data sets.


1. Nancy Université, UMR INPL/INRA 1120, Laboratoire Sols et Environnement, 2 av. de la Forêt de Haye, 54505 Vandoeuvre-lès- Nancy, France;

2. Jozef Stefan Institute, Dep. Of Knowledge Technologies, Jamova 39, 1000 Ljubljana, Slovenia. Assigned to Associate Editor César Plaza.