The concept of an innovation system is used to understand how innovation contributes to economic growth. However, innovation systems do not evolve evenly in different parts of the world. This paper contributes to the ongoing debate on the emergence of innovation systems in the context of developing countries. It uses the Rwandan case, where agriculture is a dominant socio-economic sector with high innovation potential. It explores how stakeholder interactions and policies contribute to the emergence of an agriculture innovation system in Rwanda.
The study was conducted in Thakurgaon sadar Upazila to determine farmers’ perception of the extent and factors of ICTs effectiveness in transferring farming information. A total of 250 people who were already been taken services from different ICT center was selected as sample respondents following a random sampling technique. Primary data were collected using a predesigned interview schedule.
Technology and innovation are important in addressing complex problems in the agricultural sector in many developing communities. However, ways and mechanisms to integrate them in the agricultural sector are still a challenge due to the lack of clear pathways and trajectories. Value chains are seen as a strong policy instrument to increase profitability in the agricultural sector; there is also debate around whether value chains can be a potential option to organize technology and innovation trajectories in agriculture.
Participation of actors is essential for achievement of the United Nation’s (UN) Sustainable Development Goals (SDGs). With respect to sustainable agriculture the UN has introduced a collaborative framework for food systems transformation encompassing: 1) food system champions identification; 2) food systems assessment; 3) multi-stakeholder dialogue and action facilitation; and, 4) strengthen institutional capacity for food systems governance. The last two actions are the focus of this thesis.
The Progress towards Sustainable Agriculture initiative (PROSA) is a framework that seeks to complement ongoing efforts on the Sustainable Development Goals (SDGs), and particularly indicator 2.4.1, to support country-level assessments using data already available at the national level. Making agriculture more sustainable – productive, environmentally friendly, resilient and profitable is fundamental, as agriculture remains the main source of livelihood for the majority of the world’s poor and hungry.
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”.
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.
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.
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.
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.