Hugues Boussard

Gilles Martel

Chloé Vasseur

Layers dependencies specifications in the APILand simulation approach: an application to the coupling of a farm model and a carabid model

  Through combinations of dynamic layers and spatial links, APILand permits to JAVA™ programmers to easily and quickly develop landscape simulators ENGINEERING ASPECT Layers dependencies specifications in the APILand simulation approach: an application to the coupling of a farm model and a carabid model Boussard H.*, Martel G.*, Vasseur C.* Corresponding author: hugues.boussard@rennes.inra.fr * INRA, UR 980 SAD Paysage, Rennes, France JAVA™ is a trademark of Sun Microsystems, Inc. in the United States and other countries. download APILand v0.4 : http://www.rennes.inra.fr/sad/outils_produits/outils_informatiques/APILand This work is partially financed by BIODIVAGRIM, Chloé Vasseur has a PhD grant partially funded by the "région Bretagne" and by the INRA institute « sharing » is used when externals models have the same representation of what is the Landscape 2. The « translation » is used when a layer comes up from another one. It can be : • a treatment (ex: vector to raster conversion), • an analysis (ex: heterogeneity), • a filtering, • …What is a spatial link ?3. The « inclusion » is used when the specification of a thematic layer is an extension of Emergency of a spatially structured metapopulation at the landscape level resulting from spatio-temporal connectivity

Modelling question: How to manage space ?

REASERCH ASPECT

Landscape model

conceptual modelling

Modelling question: How to assure communication between externals models ?

Which space representation ?

An explicit landscape model allows spatial structure emergence AND maximum independency of externals models to make the whole model more flexible


domain: sdv info

text:
Sabine Dippel

Armelle Prunier

Christine Leeb

Christine Leeb Why

Why observers should train clinical scoring

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Why observers should train clinical scoring Introduction

Epidemiological studies often involve clinical scoring by several observers. Extensive observer training improves observer agreement. However, this is still not common in international cooperations.

Sabine Dippel 1 , Armelle Prunier 1 , Christine Leeb 2

Results

-parameter with best agreement: "too thin" -good agreement but low prevalence: lameness, obesity (prevalence: 3 and 8 %) -explanation for poor agreement for skin problems and dirtiness: misunderstandings regarding parameter definition (e.g. inclusion of mud soiling). Archived at http://orgprints.org/18415

1

  INRA, UMR SENAH, Saint-Gilles, France, contact: s.dippel[a]web.de 2 BOKU -University of Natural Resources and Applied Life Sciences Vienna, Austria Objective illustrate consequences of suboptimal observer training Methods Scoring systems -body condition: 5-level scale -injuries: no. of lesions >3 cm -lameness: 3-level scale -dirtiness: 3-level scale -skin alterations: 3-level scale (adapted from Welfare Quality ®) Training & testing -training: 2 days, 2 farms, thereafter farm visits -testing: ≤ 50 gestating (indoor/ outdoor) and lactating sows (outdoor), 2 organic farms,

Conclusions 1 )Fig. 1 :

 11 Fig. 1: Pairwise PABAK for eight tested parameters.

Acknowledgements

We thank all observers for their participation, as well as the test farms for providing access to their animals. The presented data are part of CORE organic project no. 1904 "Prevention of selected diseases and parasites in organic pig herds -by means of a HACCP based management and surveillance programme (CorePig)"


domain: shs sdv

text:
Thomas Tari
email: thomas.tari@univ--paris--est.fr

Marc M Barbier
email: barbier@grignon.inra.fr

Philippe Breucker

Characterising dynamics of new sciences through project collaborations: a

come

Introduction

This communication proposes to show and discuss some concrete results in visualising and mapping project collaborations within dynamics of new sciences. Projects collaborations are considered as a relevant matter of enquiries, and their visualisation relies on the construction of data mining and database development and on the mobilisation of software and specific computer programming. This empirical and methodological project has been deployed on a specific area of research, namely 'bioenergies'.

We purposefully chose this type of quasi--domain of science and technology, which is politically and epistemologically driven by a quest for more sustainable energy sources. It reflects particularly well the so--called dynamics in 'mode 2' and the idea of emerging fronts of science and technology in relation to technological promise. Our aim here is to ground the study of project collaborations in specific methodological approach of databases, which are accounting for the existence of supports and grants for research and R&D projects. We claim that the study of this layer of information is underestimated in the literature about research evaluation, which is mainly targeting issues of sciences outcomes measurement and focusing on different types of authorship and citations analysis.

Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities, to build the analysis of facts on a convenient visualisation of structural relationships in--between collaborators of projects. The configuration of projects collaborations in dedicated databases is worthy to focus on, as they might reflect also an unexplored part of the dynamics of research and R&D activities.

Our intuition is that the aims, the perimeter, the contents of funded projects represent a relevant account of the under--going technological and scientific dynamics on the one hand, and a relevant account of the mobilisation and choices of scientific communities and science policy "makers" on the other hand. Those configurations rely firmly on spatial--based organisations, mixing European, national and regional scales in formal and informal clusters. Our perspective is thus to enrich the studies of sciences dynamics with the mobilisation of customised databases of research and R&D projects in order to study performative associations of laboratories, scientific teams or R&D firms.

In a first section, we propose some preliminary thoughts on the status of project collaborations to study research dynamics. Reviewing the literature on project collaborations we tried to position the interest of this matter of enquiry for the understanding of sciences and technology dynamics.

In a second section, we shall present quickly our empirical field of study - bioenergiesand present our data sets and the methodology used to build up a database for our research perspective. Therefore, we also present the software needed for the characterisation and visualisation of research collaborations through projects.

Then in a third section we propose our results in two steps. Firstly we deliver a structural characterisation of collaborations in successive research programmes with a classical approach of co--word analysis completed with indications of centrality of main laboratories. Taking into account the politics of research backgrounds in France during the last decade we analyse the dynamics of collaboration as being situated in regional clusters but also muddled by national science policy.

In order to go beyond this type of quite structural interpretation, the second step relies on the hypothesis that project collaborations could be considered independently from the funding programme, meaning that collaborations could be driven by laboratories strategies and interests in research dynamics that would precede or structure research programmes. Our methodology enables to visualise the dynamics of collaborations and we describe complex patterns of retraction, agglutination or emergence of clusters. We refine this visual understanding with a lexical analysis of project abstracts.

In conclusion we discuss the interest of our results for an ecology of research projects. We also propose to go further paying attention to the potential effects of collaboration dynamics: on collaborations themselves, on knowledge production and on technological development.

Theoretical backgrounds 1.Characterising science dynamics through project collaborations

Without ignoring the existence of a large array of scientific perspectives and information science about the measurement of science productions and science dynamics, we situate our empirical work in the branch of analysis and visualisation of social networks. This field as well as "science of indicators" are central for the evaluation and policy of science [START_REF] Callon | Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World[END_REF][START_REF] Law | Engineering and Sociology in a Military Aircraft Project: A Network Analysis of Technological Change[END_REF]. At present, the evolution of the analysis of scientific dynamics is largely attached to the question of characterising collaborative and cognitive dynamics of knowledge production [START_REF] Powell | Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences[END_REF] and to the emergence of multi or trans--disciplinary emerging fields of research (Lucio--Arias & Leydesdorff 2007) or paradigmatic field of research [START_REF] Chavalarias | Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study[END_REF]. Nanotechnology e.g. is an important field of enquiry for the study of new sciences and knowledge dynamics [START_REF] Bozeman | Understanding the emergence and deployment of "nano[END_REF]. Tracing and mapping knowledge in scientific database or in other electronic sources is representing a huge field of problems for many disciplines dealing with information. More locally, in relation to specific areas of research, mapping heterogeneous networks appears to help the understanding of social dynamic of research activities [START_REF] Cambrosio | Mapping Collaborative Work and Innovation in Biomedicine: A Computer-Assisted Analysis of Antibody Reagent Workshops[END_REF][START_REF] Cambrosio | Mapping the emergence and development of translational cancer research[END_REF][START_REF] Bourret | A New Clinical Collective for French Cancer Genetics: A Heterogeneous Mapping Analysis[END_REF][START_REF] Bonaccorsi | Search Regimes and the Industrial Dynamics of Science[END_REF].

Whether networks of University--Government--Industry relations [START_REF] Etzkowitz | Innovation in Innovation: The Triple Helix of University-Industry-Government Relations[END_REF]) constitute an infrastructure or the results of collaboration is still a research question, in order to understand the characterisation of sciences dynamics in relation to innovation processes. There are many institutional facts --but also empirical evidences in many case study researches -showing that innovation processes are situated here in--between. Some empirical works and theoretical considerations within the mode1/mode2 model suggest that multi--disciplinary, multi--institutional collaborations are means in the production of scientific knowledge in society [START_REF] Gibbons | The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies[END_REF]. But this perspective has been challenged [START_REF] Hessels | Re-thinking new knowledge production: A literature review and a research agenda[END_REF]. [START_REF] Bonaccorsi | Search Regimes and the Industrial Dynamics of Science[END_REF] is for instance quite critical towards the mode 2 description. He rather suggests that new sciences "are reductionist sciences that address new complex phenomena by breaking the boundary between natural and artificial" (ibid. p. 296) and that there is "consistent dynamic properties of the search process that transcend specific disciplines and that can be identified and to a certain extent, operationalised". He grounds his empirical work in the parsing of new words and their statistical properties within scientific database, one would suggest that project collaborations database represents an other relevant source of information.

Research and R&D projects are seen as episodic financed facilities that support the search and innovation process. Establishing the existence of a kind of "infrastructure" by the means of project collaborations sounds like an invitation to characterise them as a specific layer of information. As already mentioned above, this is also accompanied by the fact that information concerning research programme and science policy is more and more accessible in structured databases.

All STS and SPS researchers rely for their own proof regime on the existence and the reliability of the accountability of scientific productions. Recently, [START_REF] Zitt | Challenges for scientometric indicators: data demining, knowledgeflow measurements and diversity issues[END_REF] have indicated three challenges for scientometrics and for the relations between those who study science and technology production and those who create knowledge and techniques: the quality of accountability of knowledge dynamics within various source of data, the necessity to characterise dynamics as well as evaluating positions and the common problem of diversity. Our understanding of these challenges -and of their consequent methodological and ethical requirements -is that there is a need to encourage characterisation of knowledge and technological dynamics for a common understanding, and to do it beyond the necessity of evaluating the performance of normal science and technological creativity. This trend is certainly reinforced by an on--going critical self--evaluation of the reliability of science indicators [START_REF] Barré | Sense and nonsense of ST productivity indicators[END_REF][START_REF] Freeman | Developing science, technology and innovation indicators: What we can learn from the past[END_REF]. In this trend, characterising and mapping collaboration appears to become a compulsory instrument both for scholars and decision makers, but also for researchers [START_REF] Heimeriks | Mapping communication and collaboration in heterogeneous research networks[END_REF].

Thus, the nature of collaborative relationships is to be questioned in accordance to the reality of practices of "making science and technology". We would like to raise the contention that the production of knowledge in society has issued a cognitive bias about the understanding of science and technological regime. As a consequence --or perhaps due to --the focus on co-authorship and citation measurement has blurred our understanding of collaborations in science and technology, since publication of articles and patents is certainly an indicator of knowledge dynamic but not necessarily the only dimension. Our communication is pulsed by this claim and we would like then to focus on collaborations at the level of research project, not considering those collaborations in a pure communication theory perspective but as collaborations existing within a social world of science and technology.

Project collaborations: elements for a preliminary review

The importance of the article of [START_REF] Katz | What is research collaboration?[END_REF] for our perspective is obvious and the research agenda proposed by [START_REF] Beaver | Reflections on Scientific Collaboration (and its study): Past, Present, and Future[END_REF] has also fostered our interest to consider scientific and technological collaborations as a central matter of enquiry. Katz and Martin explicitly grounded their analysis of empirical findings in scientometrics of scientific publications, though their review and definition of motives for collaboration has established the reasons to collaborate in the multiple dimensions of "being a productive researcher". Their approach made explicit the different levels of collaborations (inter and intra organisational forms from individuals to nation), but they rather stay within the stream of measuring collaboration through authorships relation (see ibid, pp 6--14) and suggest keeping very sceptical towards the cost and advantage of a research policy based on collaborations.

Beaver's empirical work on motives of collaboration (Beaver, 2001: 372--375) is based on an account of the past evolving conditions of collaborations in the face of "teamwork" research policy. He acknowledged a series of motives (ibid; p.373) that clearly do not put the publication of articles as unique and central. The heterogeneity of those motives particularly fits well with the conditions in which the practices of Internet and the self--accountability of research works through web pages are representing a growing enhancer of collaborations, far over institutional or geographical proximity. The multiple sources of scientific knowledge accounts (articles, patents, grants database, funding programme) certainly represent a growing field of enquiry for informetrics (Bar--Ilan 2008) but could also issue in a restrictive approach of science dynamics through the measurement of scientific publications.

Within this recent evolution, [START_REF] Bozeman | A churn model of scientific knowledge value: Internet researchers as a knowledge value collective[END_REF] issued a very contending claim towards a culture of counting outputs, when they have called that research evaluation should take into account the social configurations through which new scientific and technical knowledge is produced. Following their anchorage in a tradition focusing on laboratory life or laboratory profile [START_REF] Latour | Give me a laboratory and I will raise the world[END_REF][START_REF] Larédo | Laboratory Activity Profiles: An Exploratory Approach[END_REF] and R&D programme they have claimed that "many scientists do no conceptualize their work in terms of funding source or the project account and, instead, view project as chiefly a bureaucratic artifice", ibid, p.771). Their conceptual model (the churn model) reflects their strategic approach of interactions between researchers1 and thus they distinguish between "projects that exists in the mind of bureaucrats" and "knowledge communities that exist as human interactions with information", ibid, p.392. At the stage of development of our work on projects collaborations, we do not intend to challenge this model but rather suggest that --even though such a divide between the realm of bureaucrats and Knowledge Value Collective would exist -- the strategic use of project collaborations becomes a matter of enquiry. This may particularly be the case if one follows the fact that R&D or Research programme policy is particularly open to researchers and scientific experts (because of ex--ante evaluation with peer--reviewing of proposals) or to lobbying pressure of visible or invisible colleges. All this is more or less related to science dynamics and to researchers at work, not only to bureaucrats, and very often to boundary organisations [START_REF] Guston | Boundary Organizations in Environmental Policy and Science: An Introduction[END_REF]. Thus, in order to consider this dimension of boundary work at the frontier of funding agency or national programme committees, it seems relevant to characterise project collaborations as a heuristic.

Mapping project collaborations: data demining and methods

The 1973 oil crisis put forward the coupling bio--energy as a solution to the end of an energy paradigm relying on fossil fuels, or at least as a way to improve national autonomy, therefore including substantial economic and geopolitical consequences. Brazil launched in 1975 the Proalcool program, which consists in the large--scale transformation from sugar cane to ethanol for individual cars, while the United States was building pilot plants for corn--and maize-based ethanol production. If ethanol for fuel was known and used since the end of the 19 th century, for example in the Ford Model T until the prohibition, the term and therefore the object "biofuel" appears as an energy production medium, in a liquid form, from diverse biomass in the 1970's. The oil prices fall--down in the mid--1980s froze all research programs in the United States, but the "peak oil crisis" led to envisage a profitable biofuel production.

Why focus on R&D programmes and projects? Bioenergies as a new science deals with a lot of issues. If the quest for energy independence always constituted the chief goal of the ethanol development in Americas, the European promotion of biodiesel mainly depended on agricultural and environmental contexts. The 1992 MacSharry reforms of the Common Agricultural Policy (CAP) incited the production of non food crops on fallows. Since then, agricultural lobbies and producers are fervent supporters of bioenergies. At the turn of the century, climate change and sustainability challenges led the European Union to seeing in biofuels an ideal technological fix, quickly applicable, which could compensate greenhouse gases emissions, thanks to the absorption of carbon dioxide from the air by biomass during the growth of dedicated plants. The incentive EU Directive 2003/30/EC "On the Promotion of the Use of Biofuels or other Renewable Fuels for Transport" proposed a reference target of 5.75 % biofuel blend by 2010; the "EU Strategy for Biofuels" from the European Commission brought this purpose to 10% by 2020. Until 2006, national plans abounded, and a eulogistic media buzz grew up in a regime of techno--economic promise.

However bioenergies also had to face some serious controversies as since 2007 biofuels have been accused to starve the developing world. This global dilemma "Food vs. Fuel" was the main one and highlighted the land use change to non food crops in detriment of the food supply. A large number of sub--controversies erupted and the following list is not exhaustive. Life Cycle Analysis on biofuel production are contradicting each other, biomass absorptions of greenhouse gases may not be sufficient to compensate the emissions according to the Nobel Prize Winner Paul Crutzen because of the N 2 O [START_REF] Crutzen | N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels[END_REF]), the energetic efficiency and economic profitability are being discussed, deforestation is condemned, the work conditions of sugar cane hand harvesters in Brazil are denounced... Which plants should one use, with genetically modified organisms (GMO) or not? Which production processes have to be supported: local, international, biorefinery? For which products: ethanol, biodiesel, straight vegetable oil that is prohibited in some countries when used as a private fuel, in which blend? Which spectrum of bio--based products (food, feed, materials, chemicals) and energy (fuels, power, heat) should be targeted? Is there any tangible difference between technological "generations" of biofuel? Are petroleum substitutes the right path, instead of moving out of the car civilisation? So many questions we would like to see how researchers and research managers deal with in France. In such a context, did bioenergies emerge as a new science, what dynamics pulsed its development? On a national scale, classical scientometrics studies based on publications are not numerous enough to be relevant [START_REF] Tari | A la recherche du biocarburant, entre promesses et controverses. Etude de la forme projet dans les dynamiques de recherche sur les bioénergies en France[END_REF]); as bioenergies is highly trans--disciplinary there are not easy to gather even if some dedicated journal were born, Bioenergy e.g.. We then decided to focus on research projects and the collaborations within.

Unlike articles, books, conference proceedings or patents, research projects have not yet been conformed to a standard and collected into a global database. Although most funding institutions -especially national agencies -have created their dedicated structures, the latter are partially opened to public access and relate to their subsidies only. The constitution of mutual research project databases has been promoted by the European Research Area NETwork (ERA--NET) scheme, in order to develop networking and opening of national and regional research programmes. The importance of such a construction in scientific work, as research tool, has been a subject of inquiry for science studies [START_REF] Hine | Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work[END_REF]), especially focusing on the "practices of accessibility" [START_REF] Hine | The politics and practice of accessibility in systematics[END_REF]) that enable collaterally the virtual social sciences to study them. Concerning the bioenergies field, we took great advantage of the BioMatNet project base2 that covers a scope from ECLAIR, AIR, FAIR programmes to the FP6. Created within the FP6 EPOBIO project, it is, alas, not updated. Once again however, as in Cordis, Framework Programmes' projects are the only one inventoried. In the United States, a trans--institutional structure, Grants.gov SM3 provides an overview on all R&D programmes through grants, still does not scale down to the project unit.

We question research dynamics through a large scope of sources related to research programs. Within those programs, the R&D project is our unit. The variety of project ecologies and organisational features lead us to conduct systematic data mining, data extraction and to constitute an adequate and robust information structure through the creation of a heterogeneous and relational database. Our methodology, also based on the grounded theory, has a certain similarity to the one described in [START_REF] Hellström | RD management through network mapping: using the Internet to identify strategic network actors in cooperative research networks[END_REF], especially dealing with the construction of a relational database thanks to a "snowball effect" mode of data collection. Thus we conducted via the Web a manual survey of more than 200 research projects, or 130 laboratories, 180 institutions including corporations and 260 researchers more or less involved in bioenergy research in France. Project properties, specifications concerning researchers, laboratories and institutions, as well as traditional scientometrical indicators but also geolocation data have been informed as precisely as possible.

We collected material from several French national programmes: the energy related projects in AGRICE (AGRIculture pour la Chimie et l 'Energie, 1994'Energie, --2007), the whole PNRB (Programme National de Recherche sur les Bioénergies, 2005--2007) and BIOENERGIES (2008--). The first one was lead by the funding agency ADEME (Agence de l'Environnement et de la Maîtrise de l'Energie), the third one by the ANR (Agence Nationale de la Recherche), while the second was a mix funded by the ANR and directed by ADEME. We also took into account some regional programmes, mainly oriented through the regional cluster structure of « pôles de compétitivité » and the French participants in European framework programmes through BioMatNet.

First, we completed the programme and project table from funding agencies websites: project's name, acronym, first year, duration, funders, cost, subsidies, abstract, manager, research theme, url… Then, we entered each participant in the researcher table, with his self-declared affiliations, laboratories and institutions, in a data demining perspective (Zitt & Bassecoulard 2008, p.51). Online, we checked the personal, lab and institution pages to fill in diverse fields: id, contact, profession, geolocation and compared them to ISI Web of Knowledge, CAB Abstracts and Thomson Factiva press data for biofuels is a controversial subject 4 . If a researcher belongs to one lab and is paid by one institution, laboratories are often patronised by several institutions. Participation in projects is traced through links in the collaboration table; we wished our data to be accurate enough to join only individuals and projects, but, as represented in figure 1 by dotted arrows, we often had to content ourselves with relationships between laboratories, and in a few cases institutions when the involvement of big corporations is scarcely mentioned.

The relations between chosen entities are specified in order to formulate requests based on our research hypothesis. We developed a semi--automatic language translation tool between our database, requests and various existing visualisation software. The first step consists in extracting with traditional relational database requests a list of nodes, edges, their associated 4 Those who have once dealt with such crossovers would understand how time consuming it is. weight and other properties later used to create typologies. Our tool can convert this .csv list into .graphml, .xml and .net files, current standards used in network mapping visualisation tools. The use of specific tools to index database and visualise networks in the context of characterising emerging domain of collaborations within Research and R&D projects represents a step forward to avoid an evaluative perspective with scientometrics. It also enables to open the field of design of methodology and visualising solution to enhance new ways of tacking with relational data. This attitude towards methodological equipments for the visualisation of co-words clusters and mapping shares many ideas of shifting the use of tool from a scientific context to a science policy context [START_REF] Noyons | Bibliometric mapping of science in a policy context[END_REF]. We think that this turn should also apply to the technical construction of facts within the scientific stance.

Many tools have been used; we hereby list the only ones providing visualisations in this paper. They are all open source software that have been developed in an academic context; we have interoperated their use within the platform CorTexT5 of IFRIS:

• Adequate and heterogeneous database construction: MySQL

A Project--based Insight into French Bioenergies Research

When Miettinen (1998) described the "construction of a research object and agenda" in the community exploring cellulose--degrading enzymes (mainly the Finnish one), a main research theme in bioenergies, he centred his analysis on the concept of "application objects" born from the innovation network co--constructed. Quoting [START_REF] Latour | Ethnography of a "High-Tech" Case, About Aramis[END_REF], he insisted on the idea that such "an object exists first in the form of a project. If a strong network of actors can be constructed, it turns into an institution". Research project is therefore the first step forward leading to the co--construction of a research object and a scientific community. Moreover, bioenergy, then unnamed, appears to be a pertinent new science to investigate this way.

Exploring to understand programmes' structure

The below maps (figure 2 and 3) represent the network of laboratories' collaboration through projects, respectively in the national programmes AGRICE and PNRB thanks to the Visone "software for the analysis and visualization of social networks".

The analysis unit is the laboratory. Node size designates the number of projects in which a lab is involved, while the edge width counts project collaborations between two institutions. The node colour depicts a five--classes typology relating to the public or private degree of an institution. Off--white stands for French Public Scientific and Technical Research Establishment (EPST), lighter grey for Industry--Oriented Public Establishment (EPIC) and the medium one (e.g. GIE ARVALIS ONIDOL) for occupational collective structure. The two shades of dark grey and black indicate big corporations and small and medium enterprises. AGRICE structure is scattered in several clusters; one major however constituted itself around some agricultural professional organisations like GIE ARVALIS ONIDOL, BENP, CETIOM and SOFIPROTEOL for cereals and oleaginous, CGB for sugar beet: a "first generation" world of ethanol and biodiesel production in close relationship with IFP (the national oil research centre) labs. Close to the latter, the oil company TOTAL FINA ELF, but also car manufacturers collaborated within some projects while the agroindustrial and green chemistry research firm ARD developed a regional cluster (Picardy and Champagne--Ardennes) around it.

We only sketch here a general frame as we are going to study more precisely project collaboration through temporal dynamics. The agricultural world has disappeared as PNRB is built on a grape--like distribution frame, for clusters are linked to a technological and industrial centre through obligatory passage points. The top--western one e.g. is clearly a wood--resource network, which communicates with the bioenergy community through FCBA, a wood technological institute. Industry--oriented public establishments like CEA (LPTM, LITENN, LB3M), IFP, CIRAD (BIOMASSE ENERGIE) stand in a central position: it is typical of a national project management ideology that privileges public--private partnership, in order to guarantee both public good and effectiveness through profitability.

Analysing betweenness centrality reveals the obligatory passage points best than degree centrality; underneath (figure 4) are to be found the main actors that play a 'broker' role in the previous networks (BC>3,5% in PNRB or AGRICE).

Three schemes appear: six out of seven key actors in AGRICE that are not any longer central in PNRB are resource--oriented, persisting lead institutions act in the oil and combustion world (six out of six) while the five emerging in their new inbetween role labs are specialised in Project collaborations between laboratories enabled us to sketch inherent logics in programmes' structures, logics that are strong. This is not stunning since the call for a so--named bioenergies research action, focusing on second generation biofuels, was performed into a 2005 programme (PNRB) in the newly born research--funding French leading agency ANR. Other structural logics can be unveiled. In project ecologies, local and regional networks play a major role, but if [START_REF] Grabher | Learning in Projects, Remembering in Networks? Communality, Sociality, and Connectivity in Project Ecologies[END_REF] proposes to think knowledge spaces topologically, it is specifically in order to overcome the strong/weak--tie dichotomy that ascripts strong ties and social coherence to the local level and sparse networks to the non--local realm. The following map (figure 5) is an attempt to mingle the scales as it counts the national projects in which at least one lab is involved per region, and at a given time compares the number of still running or newly selected one. We entered our geolocated data in the OECD regional statistics display (http://stats.oecd.org/OECDregionalstatistics) since it provides a both spatial and time dynamic representation.

We also represented the geolocated networks of project collaborations thanks to AMMAP (maps are not shown here) and could interpret the embeddedness of research places and relations in territories. AGRICE network was the archetypal star--network around Paris, although some links do not trace an effective partnership within Ile de France region but denote the numerous headquarters of big corporations in the area. It draws an interesting triangle of regions: Ile de France -Nord Pas de Calais -Champagne--Ardenne enclosing Picardy, which matches the traditional first generation biofuels territory since 1986 and the first R&D platforms construction resulting from a strong commitment of agricultural cooperatives, and which gathers some biotechnology--specialised universities that developed vivid links with local industry [START_REF] Cassier | Les Contrats de recherche entre l'université et l'industrie: l'émergence d'une nouvelle forme d'organisation industrielle[END_REF]. This sub--network progressively disappeared from national funding scope, on the one hand thanks to regional programmes development in which local universities have been involved and on the other hand via the trans--regional Industries and Agroresources (IAR) cluster creation, a structure dedicated to bioenergies and green chemistry and which became his own projects funder. On the top right window we can see that Southeast regions (selected on the left geographical map) follow a similar pattern over the period 2005--2008, as major new actors in bioenergies research. Paradoxically, this regional trend does not reflect strong regional logics in the field. Like IAR, the new "pôles de compétitivité" clusters prevail and their creation explains the shift. Unlike IAR, those one are not dedicated to the biofuel world; therefore researchers still have to seek support through national calls for projects even if they get a regional cluster label that will favour their acceptance 6 . This label helped us to identify the involved clusters and their associated research themes that are: Mer (sea) and Capenergies (GHG saving energies) in Provence--Alpes--Côtes d'Azur, Tenerrdis (technologies, new and renewable energies) and Axelera (chemistry and environment) in Rhône--Alpes, Agrimip Innovation (agroindustry) in Midi--Pyrénées and Xylofutur (wood) in Aquitaine. The mapping of collaboration delivered the evidence that research localisation follows resources production, as it is planned to produce second--generation biofuels from lignocellulose out of wood and dedicated plants, and third generation ones from microalgae or waste.

It is deceitful to ignore the opacity of such cluster structures as pôles de compétitivité, which have been created as a spatial planning policy tool but shifted from an equity logic to an effectiveness one [START_REF] Duranton | Les pôles de compétitivité : Que peut-on en attendre ?[END_REF] and "pôles" became not only competitive but also in competition one with another. Even if some projects are half--publicly--funded, we cannot easily find data. This is when some demining work has to be done: how can one measure such opacities? Thanks to our database requests, we can e.g. easily compare the composition of national programmes' scientific committees and their involvement in selected projects. In the Programme National de Recherche sur les Bioénergies (PNRB), eight out of seventeen members of the Evaluation Committee (EC) have been funded through a research project they evaluated. If there was no public information on AGRICE I and II scientific committees, in 2005, as the new programme has been launched the same year the ANR was created, the EC appeared to be a self-funding structure (cf. figure 6), i.e. an award for the successive co--construction of a 'hype object', the biofuel, and a related research agenda, as described in [START_REF] Miettinen | Object Construction and Networks in Research Work: The Case of Research on Cellulose-Degrading Enzymes[END_REF]. The flow of collaborations in project exemplifies this coproduction. Progressively, the programme opened to others scientists and communities. How can we depict these research communities?

Static network structures do not allow us to question the cohesiveness of a bioenergies scientific community. [START_REF] Grabher | Learning in Projects, Remembering in Networks? Communality, Sociality, and Connectivity in Project Ecologies[END_REF] suggested a project--related typology through social and communicative logics: "whereas communality signifies lasting and intense ties, sociality signifies intense and yet ephemeral relations and connectivity indicates transient and weak networks". These stylised features, also analysed with 'substance' (respectively narration or knowledge or information) and 'governance' (trust or swift trust or peer recognition) categories, are primarily defined by time dynamics. We therefore need to rely on the embedded structure of projects collaborations, their evolution, this focusing on to the flows of collaborations. 6 This analysis is not only extracted from the lecture of mapping but reflects what have been found in the 20 interviews done with the actors.

The project collaboration as analysis unit of dynamics in new sciences

The following hypothesis has to be considered as a relevant heuristic to tackle with the complexity of motives and conditions of project collaborations: research entities (such as laboratory, research unit, department or even board of research institute or university) are developing strategic collaborations through projects in order to frame a loosely coupled organisational form (with social, cognitive and technical interests) with the view to foster their own development, independently of the policy of programmes.

Taking a set of projects as a population of reciprocal collaborations evolving through time offers then a way to look at science dynamics from this point of view. Moreover, when these dynamics correspond to the affirmation of the emergence of a new science or to the existence of technological promises, the characterisation of project collaborations in this context -and possibly their effects on science production -is becoming a good matter of enquiry.

We used SoNIA, the Social Network Image Animator 7 [START_REF] Bender-Demoll | The Art and Science of Dynamic Network Visualization[END_REF][START_REF] Moody | Visualizing Network Dynamics[END_REF] to visualise our data and study the patterns in project collaborations, all programmes aggregated, through years. Colours stand for thematic axes, according to an ANR typology evolving continuously that we stabilised and adapted for a coherent trans--programmes investigation.

After a start with separated resource--based projects (green) and engine and fuel combustion research (yellow) the AGRICE programme has evolved towards other directions. Firstly a socio--economical and --environmental project (pink) emerged in relation to one of the resource projects, the cereal institute ARVALIS being central. Then in 1996, new comers and new thematic axes surfaced, with a big cluster of thermochemistry (red) research that showed no link at all with the previous project while new bio--resource projects have aggregated with previous ones. Schematically thermochemistry is one way of producing oil via BtL, Biomass to Liquid process through combustion and gasification followed by a liquefaction (Fischer--Tropsch) while another, biochemistry, deals with enzymes that separate fermentable cellulose from lignin, or microalgae that under stress conditions produce oil. 1997 displayed the appearance of ICARE, a combustion research lab that, being at the same time in the pink cluster and in the red one, played thus a role in joining technological research on thermochemistry and an evaluation oriented research gathering all the agroresources: CGB, SOFIPROTEOL, VALORGA Int. etc. In 1998 (see figure 7--1) the dynamic of the first phase of AGRICE is clearly revealed, (cf. previously the betweenness centrality in figure 4): GIE ARVALIS is still major in centrality; but IFP is landing through a set of biochemistry projects (blue). Exogenous sources of information -French participants in framework programmes censed in our database and interviews -allow us to precise that ICARE and ARVALIS are having a key position in the European landscape and possibly are also central aggregates for this reason. Until 2001, new comers do not reshape the landscape and research themes are staying apart; some actors only interfacing; the economical--sociological-environmental theme is creating an evaluative stance of the effect projects and major institutional actors implicated produce. A new wave clearly arose in 2002 around the oil national research institute IFP and biochemistry oriented projects. Our interpretation is that for the 1 st generation of biofuels -direct fermentation of sugars or oil pressurisation from traditional agroresources -research had sufficiently achieved good outcomes in terms of industrial research (adaptation of engine and results about combustion), then it seems that the promise emerging in this year is to figure out a 2 nd generation biofuel initiative. One notices a real structuring of the biochemistry theme (blue) and thermochemistry one (red) in a kind of butterfly wings feature that is centered on IFP.

In 2005 joined in a whole new bunch of projects; we cannot totally forget that this year is the starting year of the PNRB programme. There is one and only obligatory point of passage: IFP (see figure 7--2). A paradox is emerging: biochemistry, centred on enzymatic hydrolysis in relation to ligno--cellulosis, reveals no link with the resource cluster. It gives the impression that there is no research front that would associate bioresource and biochemistry. This is surprising since resource--oriented researches now only concern the wood sector, which tends to indicate the driven force of the 2 nd generation promise. We also note that major companies and industrial actors are staying close to thermochemistry researches when they are involved in the evaluation theme. We think boards of managers or directorates of big organisations are involved in such projects, exactly for this central position, in--between technological clusters. It remains to be studied whether or not this position is transferred to the inner organisation of those big institutions (Total, CEA, Air Liquide…) in terms of transdisciplinary research in the inside. New comers (cf. figure 7--3) through new collaboration networks are then very much concerned with so--called forthcoming 3 rd generation projects on microalgae (CEA LB3M is central), waste valorisation (INRA LBE) and still bioprocesses (CNRS BIP, LISBP at INSA Toulouse). This new combination of laboratories not much linked to agro--resource production represents a new potential in favour of researches focused on bioprocess linked to thermochemistry. It also introduces a clear difference compared to the coalition of major fossil oil companies and car industries that has been largely driven, until recently, by a substitution and no innovation strategy.

We do not endorse a generation based analysis as close attention to the field and definitions of these co--called generations vary extremely with actors -a quali--quantitative approach mixed with interviews is essential, but we indentified several search regimes (as defined by [START_REF] Bonaccorsi | Search Regimes and the Industrial Dynamics of Science[END_REF]) based on diverse actors, centralities and themes. In fact the 'generation' speech is here seen as a performative involvement, therefore real, without taking into account the actual development of biofuels and market configuration.

This research axes typology can be refined with a more precise, yet dynamic, discourse analysis focused on research projects abstracts. We purposefully used the BELUGA tool [START_REF] Turenne | BELUGA : un outil pour l'analyse dynamique des connaissances de la littérature scientifique d'un domaine. Première application au cas des maladies à prions[END_REF]) that enabled a textual analysis of motives and their evolution through time, cf. figure 8 below. Of course discourse motives are all new in 1994 and it is only from 1997 that we really can analyse the indices trend. We entered projects until 2008 so the period 2008--2012 neither is to be studied as it only indicates still running projects. New research themes or at least new ways of presenting them emerged in 2000--2001, 2004--2005 and 2008, confirming our previous description in terms of social network of collaborations within projects.

Abstracts in R&D projects are interesting media as they are a performative discourse that convinced funding agencies and resulted in research collaborations. It is not surprising that new narrative infrastructures [START_REF] Deuten | Narrative Infrastructure in Product Creation Processes[END_REF] precede new programmes: the invention of a speech in terms of generations, whether it actually deals with researches and results or not, is a major tool to structure new programmes and therefore new sciences, before thinking of new patents or new journals for dedicated publications. Such a project collaborations analysis is then essential to understand how researchers collectively construct their own science, not only through science policy media; this is a research question we intend to investigate further on.

AGRICE I AGRICE II PNRB BIOENERGIES

Conclusion

In this paper we have only addressed the matters of characterising the dynamics of collaborations and we have tried to open a perspective enriching the 'ecology of projects' perspective developed by [START_REF] Grabher | Learning in Projects, Remembering in Networks? Communality, Sociality, and Connectivity in Project Ecologies[END_REF]. When one enriched the follow up of project collaborations with a preliminary fastidious work of de--mining the information on principal collaborators (PC) dataset, we have shown that many social dynamics become accessible for the characterisation of research dynamics. We have proposed a characterisation of the implication of PC in the programme scientific board. We have also shown with a geolocation tool that project collaborations could be mapped and we have advocated for changes in the localisation of regional clusters in relation to a 3 rd generation of bioenergies. We also have tried to push a hypothesis, which might be taken as counter--intuitive: we assess that project collaborations could be explored as if there were independent from the attachment of collaborators to programme for financial resources--based strategies only. This is a heuristic hypothesis that we want to keep in mind for further empirical work.

We have developed the idea that project collaborations can be analysed thanks to the type of ties that are at work according to the duration, the profile and the intensity of those relations. In this communication we have basically paid attention to the visualisation of the structure and the dynamic of those ties, with a quick look of the centrality of laboratories involved in project collaborations. The visualisation of project collaborations enables to shed light on the dynamics of the technological promises of 2 nd and then 3 rd generation of bioenergies. We have established the localisation and the enforcement of new coalition of principal collaborators linking technological research on bioprocess and on thermochemistry. The structure of ties and their thematic composition shows a potential innovative turn in the coalitions between major companies of fossil oil and car industries, which have been largely driven until recently by a substitution strategy. Further development will be needed to characterise the communality, the sociality or the connectivity of projects collaborations profiles of bioenergies in France. This type of empirical work requests to go further with the characterisation of projects collaboration paying attention to the potential effects of collaboration dynamics. Firstly we have to measure the effect of collaborations in a period on collaborations in another period. As we noticed there is a promising methodology of characterising the trend of association of specific item sets (authors, keywords or indexed textual features) with ratios (see also the work realised with Key-Words Lab on the nanotechnology domain). Secondly there is a need to measure the effect of collaborations through projects on publication. Of course, basic or advanced statistics could be requested to measure the effect of project collaborations on ranking or co--citation indexes or also on patents. But we would be very curious to keep on paying attention to qualitative effect such as changes in the profile of ties in between periods, or such as changes in lexical composition of the repertoire of laboratories and principal investigators. Because we think that, there, in the performative narrative infrastructure of research projects and through R&D collaborations, is a good place to look at new sciences emerging and dynamics shifting, for it is there that actors construct them.

Figure 1

 1 Figure 1 Schematic representation of the heterogeneous biofuel projects' relational database structure

Figure 2

 2 Figure 2 Network of project collaborations between laboratories or institutions within the national programme AGRICE(1994--2007)

Figure 4 Figure 3

 43 Figure 4 Betweenness Centrality of Project Participants to two almost successive Programmes (AGRICE then PNRB) Betweenness centrality AGRICE Betweenness centrality in PNRB

Figure 5

 5 Figure 5 Repartition and evolution of the number of running and newly selected ANR bioenergies projects per region

Figure 6

 6 Figure 6 Involvement of ANR Evaluation Committee members in selected projects

  Figure 7 Networks of projects collaborations in the bioenergies field in France in 1998 (right), 2005 and 2008 (below). Visualisation from SoNIA software. Networks become dynamic in a video that can be seen at: http://sens.inra--ifris.org/Project-collaborations--dynamics.html

Figure 8

 8 Figure 8 A visualisation of discourse changes in project abstracts through time, BELUGA tool. BELUGA first performs a terminological extraction with lemmatised forms, then through class extraction algorithms, is able to produce indices of knowledge production dynamics. The square dots stand for the Change Index (IC) and triangle ones for the Novelty Index (IN). If N t characterises the number of new descriptors between t--1 and t, T t the global number of descriptors at time t, IC t and IN t are then defined such as:

  Semantic analysis based on abstracts: BELUGA[START_REF] Turenne | BELUGA : un outil pour l'analyse dynamique des connaissances de la littérature scientifique d'un domaine. Première application au cas des maladies à prions[END_REF] Languages for development: FLASH, GRAPHML, NET, PHP, SQL, XML...

	• Visualisation of social networks: ViSoNe (Brandes 1999; Baur 2008), SoNIA
	(Bender--deMoll & McFarland 2006; Moody et al. 2005)
	• Cartography based on geolocation: AMMAP, OECD Regional Statistics Tool (Jern
	2009)
	•

This strategic approach is also present in[START_REF] Lee | The Impact of Research Collaboration on Scientific Productivity[END_REF].

http://www.biomatnet.org

http://grants.gov

http://www.cortext.fr


Isabelle Piot-Lepetit

On the usefulness of the directional distance function in analyzing environmental policy on manure management

In many European countries, water quality problems associated with the use of synthetic fertilizers and the disposal of animal waste have become a major environmental policy issue.

Nitrates in drinking water supplies and eutrophication of inland and coastal waters are of particular concem. Increasing concentrations of nitrates in groundwaterthe primary source of drinking water in many regions -have been observed, notably in France. Agriculture is not the only source of nitrates in ground and surface waters but it is one of those that give most cause for concern. There is widespread interest in implementing policies that will be more effective in protecting water quality without causing undue economic harm to agricultural producers.

The two farming practices that most concern policymakers are the use of large amounts of feftilizet for crop growth and the disposal of livestock manure. Both materials are sources of nitrogen, which is transformed into nitrate once in the soil. Nitrate that is not used by plants or transformed back into atmospheric nitrogen leaches through the soil or runs off into water supplies. Intensive livestock production is an important source of pollution, due to an insufficient area of land available to farmers for spreading manure. This is particularly relevant for pig production. The direct impact of pig production on the environment is in some areas really severe. Along with the expansion of production, there have also been significant structural changes in the pig sector. Pig farming has become more intensive, with fewer farms producing a larger number of pigs, often with very little land, and more specialized, with feed obtained from off-farm sources. Developments in production technologies have allowed significant productivity gains, particularly for large-scale producers. Pig farming has become more regionally concentrated. A major factor encouraging the development and uptake of productivity-enhancing technologies has been the intense competition in the meat market and the long-run decline in real prices received by farmers, which in turn is driven by productivity improvements.

In response to high nitrate levels in water supplies, the European Union passed its Nitrate Directive in 1991. Its objective is to limit the amount of nitrogen remaining in the soil as a residual after uptake by crops. The Directive limits the spreading of organic nitrogen per farm to 170 kilograms per hectare in nitrate vulnerable zones. Each European member state has organized the implementation of this Directive, defining a set of constraints relevant for their own country on the use of nitrogen fertilizer, the numbers of livestock, and the storage and disposal of manure. In France, this implementation has been in effect since 1993.

The purpose of this paper is to model the manure management policy implemented in the European Union, and more specifically the limit imposed on the spreading of organic nitrogen. A theoretical model is defined in such a way that a number of specificities concerning livestock production can be introduced. Manure, the by-product of livestock production, is not in fact directly a polluting output. It can be used on farms as a fertilizer for productive land and only actually becomes a pollutant when it is disposed of in the environment without taking account of the uptake capacity of the soil or crops. Up to a certain level therefore, it can be used as an input in the crop production process. Beyond this level, it is considered as an undesirable output that has to be disposed of. The European regulation on the maximum amount of organic nitrogen that can be spread per farm is applicable to the by- product of livestock production, i.e. directly to the manure itself, rather than to the actual polluting output, which is the nitrogen surplus. This particular featureo which is introduced into the theoretical framework, can be used to assess the impact of this regulatory measure on the production choices made by livestock farmers.

As a result of the specialization of pig production, farms have very liffle of their own land available for disposing of their undesirable output. They have to look for land outside their own farm perimeter, but within a restricted geographical area limited by the cost of transporting the manure. The theoretical framework is used to investigate how the land can be shared out optimally between the non-productive purpose of spreading manure in a manner compliant with the environmental regulation and the productive function of providing crops.

In the second part of this paper, we define an empirical model derived from the previous theoretical model, using the directional distance function. This function allows a relatively flexible representation of the production technology and environmental regulation. It also provides a framework for deriving shadow prices of pollutant, of productive and non productive use of land and of the constraint on organic manure involved by the European environmental regulation. The directional distance function also enables us to take any inefficiency present into account and therefore to measure any indirect benefits resulting from the environmental regulation.

The final part of this paper provides an illustration of the analysis framework, developed from a sample of farms in Brittany, France, some specializing in the intensive production of pigs in 1996 and others not. The illustration gives a value to the non-productive function of land, in other words to the disposal of nitrogen excess through spreading. This paper provides an analysis of the impact the European environmental regulation on manure management has on the production choices made by livestock farmers, and also an assessment of certain indirect costs andlor benefits borne by producers.

Theoretical model

This section presents a theoretical model incorporating undesirable output in agricultural production and more specifically considering the regulation concerning manure management in the European Union.

We consider the pig sector and assume that farms produce two consumption goods: livestock (A) and crops (C) using two distinct farming technologies. As a frrst step, we assume that there is two separate production function. We don not consider the link between crop and livestock production processes. All crops are assumed to be sold on the crops market. These two production functions are .fn(Ln,k) and fr(Lr,fr.), respectively, with Lu the land area allocated to the disposal of manure from rearing activities and L" the land area allocated to crop production. At this step, we assume that the total land available is Z = Ln + L, and L" cT . Ln is the land area where manure can not be spread for best agronomic practices, like lands located near rivers, drinking sources or in sensible areas. Lu and Ln are variable input while Z is fîxed in the short run. The main difference in the production function is the given technology and the sector specific capital ko and fr" . We assume that these production functions are differentiable and concave. ô J Simultaneously with production of marketable goods, we assume that an unmarketable good is produced by each farm. As a first step, we focus our analysis on undesirable output or bad. However, this model can be easily extended to unmarketable desirable goods as landscape.

Thus, we assume the production of a bad output b causing environmental deterioration: b = anf n(Ln,kn) * a"f"(L",k") with au anda" non negative coefficients.

The environmentally undesirable output related to pig and crops production is nitrogen surplus, i.e.o manure that cannot be spread on farm's land due to the crop production process chosen by the farmer related to the good agricultural practices involved.

In environmental economics, a first best optimal solution can be derived by setting up a social planner's utility maximization problem. The bad output causing a nuisance is considered as an argument of the social planner's utility function as it has an impact on the society's well- being. This utility function is U(A,C,b). We assume this function to be differentiable, strictly quasi-concave, increasing in desirable outputs and decreasing in undesirable output (U, U">0 and U, < 0 ).

The optimality conditions of this economy are derived from the following program:

maxU(A,C,b) s/t fu(Ln,k) = A (1) f"(L",k") = C b = anf n(Ln,kn) + arf"(L*k") T=Lu+Lu L" 3T
For the maximization problem of the social planner, we have the following Lagrangian: l, =U(A,C,b)+ )rffnQn,kn)-A)+ ÇU"(Lc,kà-C) (2\ + 4(ba nf u(L u, k u) -a, "f, (L* k")) + lo(L -L n + Lu) + .Lr(L" -L) and the first conditions are

(3) (4) (s) (6) (7) ôtlôA-(Jt-4=0 ôllôC -(Jc -L=0b
ôl.lôb=(Ju+4=0 a(f ôLA = hfl^ -lroufi" -2+ = o ôlf ôLc = Lf:" -xr""ff" -2s = o From (3), ( 5) and (6), we have: 4 h=IJn+an(Ju=h and from ( 4), ( 5) and ( 7), we have:

(8) U" = )"a" + 4 f; )U" + d,"Uu = ),s f:" Equations ( 7) and ( 8) are the optimality conditions that guarantee an efficient input use in livestock and crop farming processes. In particular, the environmental deterioration is optimal up to the point where the marginal utility value of the marketable goods plus the marginal disutility value of the unmarketable good equals the private cost of production of the desirable goods.

Consequently, each technology (livestock and crops) should be optimally implemented when the following equation hold: Uu+auUu _4J;

(J" + a"(Ju Xo f:" Assume that competitive farms are producing both products at market prices pn and pç, respectively. Farms are assumed to determine the level of desirable goods on the basis of an profit maximisation objective function. In the specific case of pig production, there exists an environmental standard conceming the spreading of organic manure from rearing activities.

The regulation states the maximum amount of organic manure spread such as:

(10) anfu3boln where bt =l7}kglha of organic manure spread on fields. Thus, we have: where rn and rc are land rent and qo the shadow price or Lagrangian multipliers related to the organic manure constraint. The optimality conditions imply:

(13) p^"fl^-r.q* Andn.fl^-Qule=0 and ( 14) prf:" -rc =0 The above optimal conditions imply that regulation setting a constraint bo onthe spreading of organic manure from rearing activities correspond to a specific relation where the level of the restriction and the shadow price related to organic manure. If the negative environmental impact of these activities is not introduced, then the usual measures of economic efficiency are biased. The shadow price Qe provides an indication on how producers value the constraints on organic manure induced by the regulation on organic manure.

Furthermore, the above model provides some additional information concerning the value of agricultural land in two different functions. The rent value ro characterises the value of land in its unproductive function that is the disposal of manure while the rent value r. is the value of land in its productive function.

Empirical model

In this section, we provide an empirical model for the previous theoretical framework. We based our analysis on developments from Fâre et al. (2001[START_REF] Fâre | Characteristics of a Polluting Technology : Theory and Practice[END_REF] and Fâre and Grosskopf (2004). These authors provide a generalisation of the Shephard output distance function that allows one to consider non-proportional changes in outputs. The directional output distance function allows one output to be expanded while another is contracted. This property is particularly suitable when we study polluting outputs since most of the regulation concerning these undesirable goods is interested in pollution reduction. The directional output distance function is also a measure of efficiency. Since it simultaneously accounts for pollution reduction and good improvements, it is a combined environmental and technical efficiency measures.

Let y = (!p...,!u). R( and b = (br,...,bs) . Rj be vectors of goods and bad outputs, respectively.Let x=(x,,...,xr)e,Rf be a vector of inputs. The reference technology can be described as an output possibilities set P(x) that for a given vector of inputs denotes all technically feasible output vectors. This output set is assumed to be convex and compact with P(0) = {O,O}. f"rttrerïnore, inputs and good outputs are assumed to be freely disposable and bad outputs only weakly disposable. Livestock good and organic manure good are assumed to be null-joint. This means that livestock cannot be produced without organic manure. The directional output distance function is defined on P(x) as:

(17) D,7x,y,b;g) =^u*{Ê :(y + p.gr,b -f .S)e P(x)} s, e R{ ,& € Rf

The function in (17) inherits its properties from P(x).If D"(x,y,b;gr,-gr) =0, the farm is efficient in the (gr,-g) direction. Feasible but inefficient farms will take a value greater that zero, reflecting the additional good output and reduction in pollution that this particular productive unit could achieve if it were on the best practice frontier. The higher the value the more inefficient is the productive unit.

This approach can be used to derive shadow prices based on the duality between the directional output distance function and the revenue function. Following [START_REF] Fâre | Characteristics of a Polluting Technology : Theory and Practice[END_REF], let Do(x,y,b;1,-1) = 0 be the selected output distance function. This function completely characterises the technology. Let p = (pp..., pu) . R( and q = (qv...,er) . Rf represent absolute prices of good and bad outputs, respectively. In our specific case, p is desirable good prices (livestock and crops) while 4 is the price of undesirable outputs (pollution). The revenue function can be specify as:

(l 8) R(x,p,q) = max{py -qb: D"(x,t,à;1,-l) > 0} Chambers et al. (1998) have shown that the lagrangian multiplier for the above problem is )" = pB y -8go = p.l -q.l for g -(1,-1) . Therefore, we can write the revenue function as:

( I 9) R(x, p,q) = ^u*{py -qb + (p.1 + q.1)Ô"@, y,b;|,-l)\ Equation ( 19) is an unconstrained maximisation problem. From the first-order conditions, we get:

(20) (p.l+ q.\Y ,0,(x,y,b;1,-I) = -p (p.l+ q.l)Y urù,@,y,b;7,-I) = q and (21) _ p _Y rD_"(x,y,b;1,-l) q Y uD"(x,y,b;|,-l) Furthermore, if we assume that one price is knowno for example, one of the market prices of the desirable outputs, then we may compute the absolute price for the undesirable output as argued in Fâre et al. (2005). Here, we assume that p, the absolute price for the first output is known and is equalled to its shadow price (pr=qr). Then, we may compute the price of the bad output ô as: The dual values for each equation can be evaluated at the optimum of these linear programs.

Thus, if we assume that p, the absolute price for crops output is known and is equalled to its shadow price (p" = Qc). we have:

(24) Qr = -Pc( v uD,7L n, L", k A, kc o c o A,b;1,1,-l) v c Ô,(L A, Lc, k A, kc, c, A,b;r,|,-r) with qu the shadow price of the polluting good, i.e;, the nitrogen surplus.

Based on the same reasoning, it is possible to derive the price of land in its two distinct function: the productive one and the disposal one.

'ikni

V D,(L A, Lc, k A, k",C, A,b;r,1,-1) lc = -Pc v c D,(L A, Lc, k A, kc,c, A,b;1,1,-1)
In order to evaluate the shadow price of the constraint on organic manure, we have to develop the link between nitrogen surplus and organic manure as follows:

(26) b = aufn(Lpkn) + a"f"(L",k") and anfn(Ln,k) 36nLu

In the case of nitrate management, the equation Q6) can be rewritten as: with Norg the level of organic manure produced by rearing activities on farm, Nmin the level of mineral fertilizers used in crops production,Nexp the level of nitrogen exported by crops I during their growth process and Lr*, the land acreage on which organic manure can be spread. When, we introduce the above equations, we can derive the shadow prices for the nitrogen surplus and for the constraint on the spreading of organic manure.

Data

This analyical framework is illustrated on a sample of farm data from Brittany pig farms that participate in the Farm Accountancy Data Network in 1996. The selective sample consists on 188 farms lhat are specialised in pig production and have a nitrogen surplus. Some of them do not respect the constraint imposed by the European regulation on organic manure.

The directional output distance function is estimated on the following input and output variables. The good outputs are total livestock products and the total crops products in euros.

The bad output is nitrogen surplus. It is an estimation based on the nitrogen balance of each farm. Inputs are an aggregate in euros describing the capital structure of the farm with capital, machinery and buildings, labour in annual worker units, arable area in hectare and an aggregate in euros of other variable inputs. These variables describe the technology of this productive sector and are used to derive the shadow prices of the polluting good, of the restrictive constraint on organic manure spreading and of the productive and non-productive land use.

Results and Discussion

Forthcoming

  pe.fnrnln -Q u(FoLnd oTn) trc = pcfc -r"L"

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Catherine Cetre-Sossah

B Mathieu

M L Setier-Rio

Colette Grillet

Thierry Baldet

J C Delecolle

Emmanuel Albina

Development and evaluation of a real-time quantitative PCR assay for Culicoides imicola, one the main vectors of Bluetongue (BT) and African Horse Sickness

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

MATERIALS AND METHODS

Field collections

The sites at risk selected for the entomosurveillance of C. imicola ss into mainland France are spread at 50 km intervals along the French Mediterranean coast (Baldet et al., 2004) with one collection/night/site/month from April to November 2004. Specimens of the genus Culicoides were identified at species level and the absence of C. imicola ss was confirmed based microscopically upon wing pattern (Delécolle et al., 1985) .Then, 99 original trap catches, coming from this entomosurveillance network, were reconstituted with specimens of all insects families, and specimens belonging to different species of Culicoides. One additional light trap catch was an original sample with already one specimen of C. imicola ss. Either 1 or 5 specimens of Corsican C. imicola ss were further added randomly in some of the 99 sampled trap catches To assess the capacity of the real-time PCR assay to quantify C. imicola ss, 12 individual trap catches were reconstituted with 10, 50, 100 or 1,000 C. imicola ss as previously described (3 trap catches for each condition).

Extraction of genomic DNA and construction of C. imicola ss ITS-1 plasmid

After grinding up to 50 mg of insects in a mortar and pestle containing liquid nitrogen, the extraction of DNA was performed with the DNeasy Tissue kit (Qiagen, USA). The purified ITS1 C. imicola ss specific PCR product was cloned into PCR-Blunt vector (Zero Blunt PCR Cloning Kit, Invitrogen, USA). Aliquots of this plasmid, stored at -80°C were used for the establishment of a reproducible standard curve run in each plate.

C. imicola ss ITS-1 quantitative PCR amplification

Amplification was performed using the Mx3000P System with a SYBR Green I detection method in a total volume of 20 ml consisting of 2x PCR reaction buffer (Stratagene, France), 300 nM of each primer (PanCulR-5'-TGCGGTCTTCATCGAACCCAT-3'/Cul-imicola-5'-TTACAGTGGCTTCGGCAAG-3') under the following cycling conditions: an initial denaturation stage at 94°C for 5 min; then 40 cycles at [94°C, 1 min; 62°C, 1 min; 72°C, 30 sec] and a final dissociation phase. The plasmid PCR-Blunt-C. imicola ss ITS-1 construct was used in serial dilutions as a standard curve. Results were expressed in Ct values and the Ct values were determined at a preset threshold.

RESULTS AND DISCUSSION

Linearity of the real time PCR method

The standard curve was linear over the entire quantification range and resulted in an error of less than 1.0 with a linear regression value r 2 of 0.999, indicating a strong correlation between the ranges used from 10 -2 to 10 -8 ng (Figure 1)

CONCLUSION

When correlating the Ct values obtained from the real-time PCR with the microscopy method, all 39 samples found positive by microscopy had Ct values between 22.5 and 30.5 while the 54 samples found negative by microscopy value were higher than 30.5. As mentioned in Table 2, 39 out of 41 samples were positive by both the real-time PCR screening method and the microscopy whereas 54 samples were negative by both methods giving a concordance rate of 93% between microscopy and ITS-1 real-time PCR. The sensitivity and specificity of the real-time PCR were 95% and 92% respectively Since the samples caught for BT vector surveillance are made of Culicoides genus insects and of many other insect families, the analytical sensitivity of the real-time PCR (e.g. the minimal number of C imicola detectable) was evaluated with the aim not only to detect at least one single specimen of C. imicola ss but also to quantify the number of C. imicola ss within a pool of other insects The assays repeated in triplicates are highly reproducible with SD values very low (for 10 specimens of C. imicola ss, mean of Ct value: 24.13, SD: 0.83; for 50 specimens, mean of Ct value: 20.75, SD: 0.055; for 100 specimens, mean of Ct value: 19.8, SD: 1.58; for 1000 specimens, mean of Ct value: 16.79, SD value: 0.117

Sensitivity and specificity

The quantitative real-time PCR method was evaluated by testing DNA extracted from 100 light trap catches collected in Southern France, considered as gold standard where no specimens of C. imicola ss were detected except for one trap catch with one single specimen of C. imicola ss. Either 1 or 5 specimens of C. imicola ss were randomly added in 40 of these 99 samples. The distribution of the Ct values of negative versus positive samples in Figure 2 allowed the determination of a cut-off value at 30.5 (Figure 2) These 100 samples were included in the ROC analysis (Figure 3), the area under curve was 0.9752 (between 0.7634 and 0.9721 with a 95% confidence interval, CI). The cut-off point to which sensitivity and specificity were optimal was for a Ct value of 30.5. The sensitivity of the test for this cut-off was 95% (95% CI: 0.8824-1.0) and the specificity was 92% (95% CI: 0.8241-0.9759).

The current microscopy method for identifying the Culicoides imicola Kieffer, 1913 species can be time and labour intensive. There is a need for the development of a rapid and quantitative tool to quantify the biting midges Culicoides imicola ss in light-trap catches. A reproducible and sensitive real-time polymerase chain reaction method that targets the internal transcribed spacer (ITS

Conventional cytochrome oxidase specific and C. imicola ss specific PCR amplification

To control the quality of DNA extracted from the trap catches, a conventional PCR based on cytochrome oxidase was performed COX-F-5'-CAggTAAAATTAAAATATAAACTTCTGG-3'/COX-R-5'-GGAGGATTTGGAAATTGA TTAGT-3') (Simon et al., 1994) In addition, a conventional PCR specific of C. imicola ss ITS-1 was included to assess the performances of the real-time PCR assay PanCul-R/Cul-imicola for the C. imicola ss PCR (Cêtre-Sossah et al., 2004) Data analysis A receiver operating characteristic (ROC) analysis was performed to assess the performances of the C. imicola ss real time PCR test that could be considered as a diagnostic and surveillance tool. In our study, the true identification of Culicoides was performed by a microscopy examination and was considered as our gold standard. All statistical analyses were carried out with intercooled Stata 8 software. Sensitivity and specificity were estimated by using "diagt" stata command. Ninety-five percent confidence intervals (CI 95%) were provided assuming a binomial distribution Figure 1: Linear regression of the plasmid PCR-Blunt-ITS-1 standard ranging from 1 fg to 10 pg was determined using fit points analysis method. The standard curve showed a regression coefficient r2=0.999, mean squared error, efficacy of 96.5% and slope =-3.408. Each point of the curve represents the average value (SEM) of three independent qPCR reactions. Figure 1

  -1) of ribosomal DNA of C. imicola ss species was developed. This real-time PCR assay was first performed on 10-fold serial dilutions of purified plasmid DNA containing specific C. imicola ss ITS-1. It was then possible to construct standard curves with a high correlation coefficient (r 2 = 0.99) in the range of 10 -2 to 10 -8 ng of purified DNA. The performances of this PCR were evaluated in comparison with morphological determination on Culicoides trapped along the Mediterranean coastal mainland France. ROC statistical analysis was carried out using morphology as gold standard and the area under the ROC curve had a satisfactory value of 0.9752. The results indicated that this real-time PCR assay holds promise for monitoring C. imicola ss population in both surveillance and research programmes because of its good specificity (92%) and sensitivity (95%).

  Linear regression of the number of C. imicola ss specimens with the Ct values obtained from the real-time PCR method. The standard curve showed a regression coefficient r=0.98, mean squared error with the equation: y=-1.5663 ln(x) +27.21 (without the values of the mean for 1 specimen and with 5 specimens). The average values (SEM) and SD from three independent qPCR reactions are shown *. curve = 0.9752 For a cut-off value of 30.5, se=95%, sp=92%

Figure 2 :Figure 3 :

 23 Figure 2: Distribution of the Ct value of 100 light trap catches samples from a French BT free area collected in 2004

Table 1 :

 1 represents cycle numbers in y-axis and ng quantities in x-axis Distribution of the results of the C. imicola ITS-1 screening real-time PCR versus morphological determination for one hundred light traps samples containing either 1 or 5 C. imicola within thousands of others insects

			Morphology
			Positive	Negative	Total
	Real time	Positive	39	5	44
	PCR	Negative	2	54	56
		Total	41	59	100
				Morphology
			Positive	Negative	Total
	Conventional	Positive	40	2	42
	PCR	Negative	1	57	58
		Total	41	59	100

Table 2 :

 2 Distribution of the results of the C. imicola ITS-1 conventional PCR versus morphological determination for one hundred light traps samples containing either 1 or 5 C. imicola within thousands of others insects

ACKNOWLEDGMENTS

The choice of the morphological determination as a reference test is justified even if both PCR methods (conventional and real-time) are concordant and gave similar levels of sensitivity (respectively 97.5% and 95%) and specificity (respectively 96% and 92%) with equivalent concordance rate (0.97 versus 0.93). The difference in Ct values in samples containing 1 specimen or samples containing 5 specimens of C. imicola ss was not possible. A corresponding Ct value of 30.5 was chosen and therefore gave a relatively high sensitivity of 95% and a specificity of 92%. Larvae identification by this molecular tool has already been successfully performed (data not shown) and will help for ecology based research. Large scale studies should therefore be undertaken using real-time quantitative PCR to identify breeding sites in the farming environment which are implicated in the spread of the vectors and the disease.

Part of the project was funded by the French Ministry of Agriculture -DGAl and the EPIZONE network of excellence (contract 016236).

Published paper: Res.Vet. Sci. 2008Sci. , doi:10.1016Sci. /j.rvsc.2007.12.001. .12.001.