This material was presented duting the conference: Big Data, a multiscale solution for a sustainable agriculture in Copenhagen Denmark in 2017 and brings an overview of the technological innovations of the French agricultural sector.
Agricultural machinery manufacturers historically referred to the intermediate players for selling, maintenance, customer service and/or training of equipment appear to interact with farmers and end-users. Intermediate players have therefore faced the burden to master the technology, in constant evolution, and the associated training needs at the interface between sophisticated equipment and the end-user and its sociological characteristics (age, education, background, etc.).
The research programme URBAL (Urban-driven Innovations for Sustainable Food Systems) (2018–2020), funded by Agropolis Fondation (France), Fondation Daniel & Nina Carasso (France/Spain), and Fundazione Cariplo (Italy), and coordinated by CIRAD (France) and the Laurier Center for Sustainable Food Systems at Wilfrid Laurier University (Canada), seeks to build and test a participatory methodology to identify and map the impact pathways of urban-driven innovations on all the dimensions of food systems sustainability.
The aim of this article is to show the relevance of the sociology of market agencements (an offshoot of actor-network theory) for studying the creation of alternative agri-food networks. The authors start with their finding that most research into alternative agri-food networks takes a strictly informative, cursory look at the conditions under which these networks are gradually created. They then explain how the sociology of market agencements analyzes the construction of innovative markets and how it can be used in agri-food studies.
According to the literature on regime transition, niches are sources of innovation that may lead to the transformation of the dominant regime, if processes at other level of the system – the landscape and the mainstream regime - are supportive. A focus on actors involved in the transition process and the analysis of their specific role in knowledge networks can help assessing the robustness of a specific niche and its growth potential. Knowledge systems, and in particular the dynamics of local and expert knowledge, have in fact a key role in innovation models.
The European small ruminants (i.e. sheep and goats) farming sector (ESRS) provides economic, social and environmental benefits to society, but is also one of the most vulnerable livestock sectors in Europe. This sector has diverse livestock species, breeds, production systems and products, which makes difficult to have a clear vision of its challenges through using conventional analyses. A multi-stakeholder and multi-step approach, including 90 surveys, was used to identify and assess the main challenges for the sustainability of the ESRS to prioritize actions.
The French Ministry of Agriculture has called for agro-ecological transitions that reconcile farming and the environment. In this review, we examine the transformations of farmers and AKIS (Agriculture Knowledge Innovation System) actors’ work during agro-ecological transitions, and argue that the content, organization, and aim of farmers’ work are influenced by agricultural training, agricultural development, and discussions between peers, research, and regulations. Our main findings concern those transformations.
Description du sujet. Une approche système basée sur la co-conception et l’évaluation expérimentale in situ de prototypes de systèmes de culture (SDC) a été mise en œuvre dans le projet INRA « GeDuNem » pour une gestion durable des nématodes à galles (NG) dans les systèmes maraîchers sous abris.
L’une des avancées les plus importantes dans le domaine de l’observation de la terre est la découverte des indices spectraux, ils ont notamment prouvé leur efficacité dans la caractérisation des surfaces agricoles, mais ils sont généralement définis de manière empirique. Cette étude basée sur l’intelligence artificielle et le traitement du signal, propose une méthode pour trouver un indice optimal. Et porte sur l’analyse d’images issues d’une caméra multi-spectrale, utilisée dans un contexte agricole pour l’acquisition en champ proche de végétation.
Mobiliser les approches issues de l’Intelligence Artificielle (IA) en Santé Animale (SA) permet d’aborder des problèmes de forte complexité logique ou algorithmique tels que rencontrés en épidémiologie quantitative et prédictive, en médecine de précision, ou dans l’étude des relations hôtes × pathogènes.