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Start Over Author "Bechini L" Topic [sde]environmental sciences
3 results on '"Bechini L"'

1. Liste des pilotes et des obstacles qui régissent la gestion des sols par les agriculteurs, y compris les aspects de coûts

Author

Pronk, A., Bijttebier, J., Ten Berge, H., Ruysschaert, G., Hijbeek, R., Rijk, B., Werner, Michel, Raschke, I., Steinmann, H.H., Zylowska, K., Schlatter, N., Guzmán, G., Syp, Alina Teresa, Bechini, L., Turpin, N., Guiffant, Nadia, Perret, Eric, Mauhé, Nicolas, Toqué, Claire, Zavattaro, L., Costamagna, C., Grignani, C., Lehninen, T., Baumgarten, A., Spiegel, H., Portero, A., Van Walleghem, T., Pedrera, A., Laguna, A., Vanderlinden, K., Giráldez, V., Verhagen, A., Research Institute for Agricultural, Fisheries and Food (ILVO), Wageningen University and Research [Wageningen] (WUR), UNIVERSITAT GOTTINGEN DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Mutations des activités des espaces et des formes d'organisation dans les territoires ruraux (UMR METAFORT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Européen (appel d'offres international), irstea, Institut National de la Recherche Agronomique (INRA)-AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), and AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
Soil management, EUROPE, Farmers, [SDE]Environmental Sciences, Soils, European countries
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM-QT2-ADAPTATION; This report consists of two components: (i) an overview of drivers and barriers for the adoption of so-called ‘Best Management Practices’ (BMPs) in soil management, as seen through the eyes of farmers (the extensive Chapter 2); and (ii) an inventory of cost associated with the implementation of certain BMPs at the farm (the brief Chapter 3). The overview of drivers and barriers presented in Chapter 2 is based on a survey held among 10,000 farmers in different farm types across all CATCH-C partner countries, 2520 of whom responded. The inventory of costs to implement BMPs is based on empirical information collected by the research team in the project partner countries, through various channels. The BMPs studied in the farmer survey include options for crop rotation, tillage, nutrient management, crop residue management, water management, and grassland management. The survey was carried out in 24 major ‘farm type x agri-environmental zone’ (FTZ) units across eight partner countries, three per country. An FTZ unit is defined by the combination of an agri-environmental zone (with climate, slope, and soil texture as keys) with a farm type (arable-cereal, arable-specialised, dairy, mixed, etc.). The criteria to select FTZs for the farm surveys were 1) representation of a large agricultural area, 2) large economic value of the FTZ and/or 3) occurrence of soil degradation problems. In most agri-environmental zones, one specific farm type was studied, or sometimes two. Our FTZ units were also called ‘major farm types’ in other project documents. To identify drivers and barriers for adopting Best Management Practices (BMPs), we applied a behavioural approach, based on the Theory of Planned Behaviour (Ajzen, 1988; Ajzen, 1991), to identify the main barriers and drivers of farmers towards adoption of sustainable management practices. The theory and details of the results obtained were extensively reported in Deliverable D4.422 of the CATCH-C project (Bijttebier et al., 2014).

Published
2015

2. Farmers review of Best Management Practices: drivers and barriers as seen by adopters and non-adopters

Author

Bijttebier, J., Ruysschaert, G., Hijbeek, R., Rijk, B., Werner, Michel, Raschke, I., Steinmann, H.H., Zylowska, K., Pronk, A., Schlatter, N., Guzmán, G., Syp, Alina Teresa, Bechini, L., Turpin, N., Guiffant, Nadia, Perret, Eric, Mauhé, Nicolas, Toqué, Claire, Zavattaro, L., Costamagna, C., Grignani, C., Lehninen, T., Baumgarten, A., Spiegel, H., Portero, A., Van Walleghem, T., Pedrera, A., Laguna, A., Vanderlinden, K., Giráldez, V., Research Institute for Agricultural, Fisheries and Food (ILVO), Wageningen University and Research [Wageningen] (WUR), Georg-August-University = Georg-August-Universität Göttingen, Mutations des activités des espaces et des formes d'organisation dans les territoires ruraux (UMR METAFORT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Européen (appel d'offres international), irstea, UNIVERSITY OF GOTTINGEN DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
[SDE]Environmental Sciences
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM-QT2-ADAPTATION; During the past decennia, best management practices (BMPs) have been developed in order to maintain or restore soil health which is essential to the sustainability and resilience of the farm. Adoption rates of these practices vary among different countries and even among different regions within a country. Adoption rates depend on the specific context of a region or a country, consisting of biophysical, economic, social but also regulatory and institutional conditions. Hence, the objective of this study was to investigate farmers’ barriers towards adoption of BMPs across Europe. To identify drivers and barriers for adopting BMPs, we applied a behavioral approach, based on the theory of planned behavior. This approach has been proven successful and offers a repeatable methodology which is very valuable for performing attitudinal research in an wide European context. According to this theory, the greater the intention to behave, the more likely one is to actually perform the behavior. The intention of a farmer to implement a certain BMP is determined by individual beliefs on a set of outcomes (expected effects) associated with the practice, on a set of referents who think the farmer should perform the behavior, and on a set of control factors that might facilitate or obstruct the behavior. All these beliefs influence a farmers’ intention to adopt a certain BMP, and are acting as cognitive drivers or barriers which encourage or discourage the farmer to adopt a specific BMP. This report describes the main drivers and barriers on BMPs perceived by the farmers in 24 Farm Type Zones (FTZ) spread over 8 European countries (Poland, The Netherlands, Spain, Italy, Belgium, France, Austria, Germany). These FTZs are regions within a country and are characterized by land use, farm specialization and by slope, soil texture and climate. The criteria to select FTZs for the study were 1) representation of a large agricultural area, 2) large economic value of the FTZ and/or 3) occurrence of soil degradation problems. The BMPs studied in each FTZ were determined based on expert judgment for their potential contribution to improve soil sustainability in that specific FTZ. About 20 different BMPs were studied across all countries.

Published
2014

3. A component-based framework for simulating agricultural production and externalities

Author

Ariel Serrano, Laura Carlini, Ephrem Habyarimana, Meine van Noordwijk, Frits K. van Evert, Arezki Zerourou, Simone Bregaglio, Luca Criscuolo, Enrico Ceotto, Pablo Cruz, Pierre Martin, Frank Ewert, Grégoire Vincent, E.C. Meuter, G. Russell, Myriam Adam, Andrea Di Guardo, J. P. Theau, Francesca Salinari, Roberto Confalonieri, Nora Mugueta, Gianni Bellocchi, Diego Fiorani, Vittorio Rossi, Daniel Wallach, Peter A. Leffelaar, Ioannis N. Athanasiadis, Michel Duru, Olivier Therond, Andrea Emilio Rizzoli, Marie Hélène Charron-Moirez, Roelof Oomen, Christian Gary, Christian Dupraz, Marcello Donatelli, Erik Braudeau, Marco Acutis, Matteo Balderacchi, Luca Bechini, Jacques-Eric Bergez, K. Kansou, Luisa M. Manici, Domenico Ditto, A. Sorce, Alexandra Stella Rosenmund, P. Trevisiol, Guillaume Martin, Jacques Wery, Marc Corbeels, Claire Jouany, Hatem Belhouchette, A. Gentile, M. Botta, Rob Knapen, Florian Celette, Marco Trevisan, Eric Casellas, Giovanni Lanza Filippi, Rachmat Mulia, Agriculture Research Council, European Commission's Joint Research Centre, University of Edinburgh, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Università della Svizzera italiana = University of Italian Switzerland (USI)-Scuola universitaria professionale della Svizzera italiana [Manno] (SUPSI), Department of Crop Science, Università degli Studi di Milano [Milano] (UNIMI), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Fonctionnement et conduite des Systèmes de culture Tropicaux et Méditerranéens, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Catholic University of the Sacred Heart, Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Agricultural Research Council (CRA), Institut de Recherche pour le Développement (IRD [Réunion]), Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), European Commission, Wageningen University and Research Centre (WUR), Université Montpellier 2 - Sciences et Techniques (UM2), World Agroforestry Centre, Institut de Recherche pour le Développement (IRD), Floor M. Brouwer (Editor), Martin K. van Ittersum (Editor), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Brouwer, FM, van Ittersum, M, Donatelli, M, Russell, G, Rizzoli, A, Acutis, M, Adam, M, Athanasiadis, I, Balderacchi, M, Bechini, L, Belhouchette, H, Bellocchi, G, Bergez, J, Botta, M, Braudeau, E, Bregaglio, S, Carlini, L, Casellas, E, Celette, F, Ceotto, E, Charron Moirez, M, Confalonieri, R, Corbeels, M, Criscuolo, L, Cruz, P, DI GUARDO, A, Ditto, D, Dupraz, C, Duru, M, Fiorani, D, Gentile, A, Ewert, F, Gary, C, Habyarimana, E, Jouany, C, Kansou, K, Knapen, R, Filippi, G, Leffelaar, P, Manici, L, Martin, G, Martin, P, Meuter, E, Mugueta, N, Mulia, R, van Noordwijk, M, Oomen, R, Rosenmund, A, Rossi, V, Salinari, F, Serrano, A, Sorce, A, Vincent, G, Theau, J, Thérond, O, Trevisan, M, Trevisiol, P, van Evert, F, Wallach, D, Wery, J, and Zerourou, A

Subjects
0106 biological sciences, Research groups, [SDV]Life Sciences [q-bio], 01 natural sciences, [SHS]Humanities and Social Sciences, Component (UML), Production (economics), Environmental impact assessment, [INFO]Computer Science [cs], Agricultural productivity, [MATH]Mathematics [math], 2. Zero hunger, business.industry, Environmental resource management, 04 agricultural and veterinary sciences, 15. Life on land, Environmental economics, quantitative evaluation, Geography, Agricultural and Biological Sciences (all), [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Arable land, business, Cropping, Externality, 010606 plant biology & botany
Abstract

International audience; Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. The EU Sixth Framework Integrated Project SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.

Published
2010

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