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”.
Dans de nombreux pays, les décideurs ont besoin d'informations pertinentes sur les systèmes d'innovation agricole (SIA) pour guider la formulation des stratégies et des instruments politiques de soutien à l'innovation.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
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.
This paper contends that the exclusion of millions of poor from agricultural development gains is inexorably linked to the innovation system features that have evolved over time. An oft repeated lament of the Government of India about the inadequacy of reforms in agricultural research and extension, is used to explore the structure and institutions of agricultural innovation. Three main components of the agricultural innovation system, are the agricultural research and extension actors, the farming communities, and policy making agencies.
Malaria afflicts many people in the developing world, and due to its direct and indirect costs it has widespread impacts on growth and development. The global impact of malaria on human health, productivity, and general well-being is profound. Human activity, including agriculture, has been recognized as one of the reasons for the increased intensity of malaria around the world, because it supports the breeding of mosquitoes that carry the malaria parasite.
Good governance of community fish refuge-rice field fishery (CFR-RFF) systems, which are a vital source of nutritious aquatic foods, is integral to the food and nutrition security of rural households in Cambodia. Intentional integration of nutrition and gender activities into CFR management has the potential to further bolster these outcomes. Using qualitative and quantitative data, we aimed to document the impacts of the nutrition and gender activities conducted alongside CFR management activities.
Le projet RIVAGE veut favoriser l’adoption de pratiques alternatives pour gérer les impacts de la pollution diffuse dans le bassin versant de la rivière Pérou en Guadeloupe. Son objectif est de produire et partager les connaissances sur les processus, les impacts et les pratiques innovantes avec les acteurs du territoire. Pour faciliter la prise en compte des résultats, le projet a créé une « école-acteurs ». L’école-acteurs est un espace d’échanges autour des thématiques liées à la pollution diffuse agricole.
The COVID-19 pandemic and accompanying responses to mitigate this global health crisis have resulted in substantial disruptions to demand, production, distribution and labor in fisheries, aquaculture and food systems. These disruptions have severely impacted women processors and traders, who play a critical role in the fisheries and aquaculture sectors and associated food systems in sub-Saharan Africa. And yet, COVID related data and responses have tended to be gender-blind or overly representative of men’s experiences and needs in the sector.
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.