Networks and partnerships are commonly-used tools to foster knowledge sharing between actors and organisations in the Agricultural Knowledge and Innovation System (AKIS), but in Europe the policy emphasis on including users, such as farmers and foresters, is relatively recent. This paper assesses user involvement in a diverse set of European Union (EU)-funded and non-EU (formal and informal) multi-actor partnerships. This research used a common methodology to review several forms of multi-actor partnerships involving users and other actors.
The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities.
FAO Eritrea, in partnership with the Ministry of Agriculture is implementing the national component of a global project entitled “Developing capacity in Agriculture Innovation System project: Scaling up the Tropical Agriculture Platform Framework”.
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.
This paper addresses how co-producing knowledge can assist local farmers in reshaping their territories into sustainable farming systems. We describe the emergence and consolidation of an agroforestry system in an Eastern Amazon forest frontier, unpacking the co-production of a new farming system over recent decades. Instead of assuming pre-defined categories (e.g., traditional/technical, local/external), the analysis focuses on interactions among knowledge holders and how multiple knowledge sources are intercalated.
The future of inclusive forestry in Nepal depends on forestry professionals who can recognise patriarchal roots of gender injustice as they operate in the ideologies and apparatus of forest governance, and who can resist those injustices through their work. This paper uses the notion of knowledge practices to explore the recognition of injustice amongst Nepal’s community forestry professionals, and the relationship between recognition and resistance, highlighting the inherently political nature of all knowledge practices.
L’érosion hydrique est une préoccupation agronomique, économique et environnementale importante en milieu semi-aride méditerranéen. Le choix de la méthode pour l’estimer dépend des données disponibles et des conditions de la zone d’étude. Cet article vise à quantifier et spatialiser l’érosion hydrique en utilisant l’équation universelle des pertes en sol (Universal Soil Loss Equation [USLE]) dans ses versions modifiée (Modified − MUSLE) et révisée (Revised − RUSLE) et couplées à un Système d’information géographique (SIG) : MUSLE-SIG et RUSLE-SIG.
Where CGIAR breeding programs rely on the private sector for the multiplication and distribution of improved cultivars, persistent challenges have dampened their impact on varietal adoption and turnover rates. Part of the problem is that research and practice in CGIAR and among its national breeding program partners tend to treat the private sector as a vehicle for seed delivery, rather than as commercial businesses facing a range of unique constraints and threats.
L’objectif de cet article est d’explorer les défis et les contraintes de l’adoption des technologies de l’agriculture de précision, en se plaçant du point de vue des entreprises opérant dans le secteur des nouvelles technologies agricoles. L’étude s’appuie sur une approche qualitative moyennant une analyse de contenu issue de huit entretiens semi-directifs.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.