The United Nations predicts that we need to increase food production globally by 70 percent to feed 9.6 billion people by 2050. But at the same time, given the climate crisis, we need to significantly reduce the use of energy, water, and land needed to produce food and lower its carbon footprint. In other words, we must figure out how to produce and distribute more food using fewer resources and emissions. We must learn to do farming better with less.
Food security is a critical challenge – the World Bank includes it among the eight global challenges to be addressed at scale in 2024. Climate shocks, economic instability and geopolitics have significantly impacted crop yields and food supply chains.
The OECD InDeF team developed a portfolio approach to innovation. A portfolio approach takes a balcony view on innovation which helps organizations align innovation processes, resources and performance with organizational objectives and enables them to track innovation with a view to scaling. Coached by the OECD team, Enabel colleagues in Benin, Morocco and Palestine piloted this portfolio approach by reviewing their current innovation supporting activities and investments against a set of key criteria.
Assessing or understanding the agriculture innovation system (AIS) is an essential step to better understand the needs, new skills and functions needed by the actors and the system. To accelerate the uptake of innovation and progress towards eradicating poverty, there is an urgent need for well-coordinated, demand-driven, and market-oriented information, knowledge, technologies and services.
The Digital Innovation Strategy (DIS) of the Regional Office for Africa (RAF) of FAO has been prepared to respond to critical challenges facing inclusive and sustainable agrifood system transformation in sub-Saharan Africa. It is enshrined in the new Strategic framework 2022–2030 that aims to accelerate the "transformation to more efficient, inclusive, resilient and sustainable agri-food systems for better production, better nutrition, a better environment and a better life, leaving no one behind".
Geographic information system (GIS) data is often used to map socio-economic data with a spatial component. This data, which is obtained from multiple open-source databases, complements official statistics and generates additional spatial inputs to statistical and econometric analyses. IFAD uses impact assessments using data from face-to-face interviews in order to determine the impact of their projects on strategic goal and objectives. However, the COVID-19 pandemic meant these interviews could no longer take place.
Digitalization and internet use are transforming every aspect of our lives. Digital technologies are profoundly changing how we grow food, pack it, transport it and even shop for food. Digitalization and use of digital data, applications, and platforms are opening new possibilities for developing and restructuring the agrifood system. Digital agriculture is turning to digitalizing agrifood, rural economy, and rural societies. This report introduces the FAO Digital Village Initiative, which aims to facilitate through knowledge and information.
A huge increase in investment for innovation in sustainable agri-food systems (SAS) will be critical for meeting the objectives of the UN Sustainable Development Goals and the Paris Climate Agreement.
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).
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.