How do innovations move from the edges to the core of what an organization does? For maximum impact, innovations must cease to be innovative and become institutionalized and normalized.
This report summarizes studies conducted in a framework of TAP-AIS project implemented by FAO’s Research and Extension Unit, and funded by the European Union as a component of the European Union initiative on “Development Smart Innovation through Research in Agriculture” (DeSIRA).
The importance of extension services in helping smallholder farmers to address the many challenges of agricultural production cannot be over-emphasized. However, relatively few studies have been conducted that investigate how the capacities of agricultural extension agents can be built to more effectively assist smallholder farmers in managing climate risks and impacts. As climate change is a key threat to smallholder food production, addressing this issue is increasingly important.
In recent years, the agricultural industry has been experiencing an ever-increasing application of information and communication technologies globally. This new revolution has been touted to impact efficiency and productivity in the agricultural extension services within the agriculture sector. Notwithstanding this, empirical research need to be carried out amongst its users in the sector to ascertain these assertions.
This report provides an overview of the Tropical Agriculture Platform (TAP) since its inception in 2012, when it was officially launched by FAO at the first G20 Meeting of Agriculture Chief Scientists (MACS) in September 2012 in Mexico, until December 2018. The G20 Agriculture Deputies agreed on this stock taking exercise that started under the 2018 Argentinian G20 Presidency.
This exercise was done on the occasion of the G20 MACS meeting in April 2019 in Japan. Its purposes are the following:
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.
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.