Experiential learning is prevalent in secondary and university agricultural education programs. An examination of the agricultural education literature showed many inquiries into experiential learning practice but little insight into experiential learning theory. This philosophical manuscript sought to synthesize and summarize what is known about experiential learning theory. The literature characterizes experiential learning as a process or by the context in which it occurs.
The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.
The Farmer Field School (FFS) approach has been very successful and witnessed a strong expansion in many areas beyond crop production. Notwithstanding this success, the adoption of FFS in national extension often remains problematic and FFS activities have often been implemented in the margin of national institutions with strong reliance on donor funding. The creation of an enabling environment for institutional support is essential for expanding the effort, improving quality, and strengthening impact and continuity of the FFSs.
Several posters have been created on the occasion of the 5th TAP Partners Assembly (Laos, 20-22 September 2017) to show recent activities and achievements in the eight pilot countries of the CDAIS project.
This presentation for the Third Global Conference on Agricultural Research for Development (GCARD3,Johannesburg, South Africa, 5-8 April 2016) illustrates the topic of competitiveness in Africa smallholders system, focusing on the Integrated Agricultural Research for Development (IAR4D) and Agricultural Innovation Systems (AIS) concepts and on the role of the innovation platforms.
This paper outlines key areas of intervention that are identified as the core of FAO's strategy on strengthening Agricultural Innovation Systems (AIS) across multiple areas of work (e.g. research and extension, agroecology, biotechnology, green jobs, resourcing etc.) for achieving sustainable rural development.
Dans le besoin urgent de lutter contre le changement climatique, une priorité essentielle est de renforcer la capacité de ces groupes et communautés les plus vulnérables, et déjà fortement affectés, à améliorer leur capacité à adapter leurs systèmes de subsistance.
Qu’en est-il des « activités non agricoles » ? Peuvent-elles être pensées au-delà d’une perspective de survie ? De la transformation des récoltes à la commercialisation d’artisanat culturel, en passant par le transport routier, la location de téléphone portable ou le conseil en technologies de l’information, les activités non agricoles occupent un éventail très large. Leur utilité est de plus en plus reconnue.
El presente documento recoge una buena cantidad de insumos que informan sobre los procesos que están dirigiendo el desarrollo tecnológico de la cadena, se describen las temáticas y las tecnologías que cada actor o actores así como la agenda que están impulsando. Además, se identifican las áreas líderes hacia donde se está enfatizando y/o orientando la promoción de la innovación, temas comunes, posibilidades de cooperación y vacios temáticos.
Este artículo tiene por propósito comparar las redes de compras públicas para la agricultura campesina y familiar en los programas de alimentación escolar de los municipios de Granada (Antioquia-Colombia) y São Lourenço do Sul (Rio Grande do Sul-Brasil) en los años de 2016 y 2017. Para tal fin, se construyó un abordaje teórico-metodológico desde la perspectiva de las redes de política pública, articulado a dos metodologías, el Análisis de Redes Sociales y la comparación de Sistemas de Máxima Diferencia.