En este trabajo se analiza la configuración del Sistema de Innovación del Sector Agroalimentario Mexicano (SNIA). La información generada en un taller con especialistas nacionales se sistematizó para construir una matriz que contiene el listado de actores relevantes en el SNIA y sus relaciones e intensidad de las mismas. Para clasificar la información, se aplicaron escalas ordinales y se calcularon los indicadores de redes denominados densidad y centralidad de grado, utilizando el software Ucinet©; también se efectuó un análisis gráfico de redes, utilizando el software Net Draw©
Se analizan los efectos de las interacciones, directas e indirectas, entre agricultores y otros actores relevantes en el intercambio de información y conocimiento para la innovación agrícola. Los datos se obtuvieron al preguntar a 120 agricultores «¿de quién aprende y/o a quién recurre para obtener información o conocimiento de cuestiones técnicas y productivas en torno a su unidad de producción?». Se emplean indicadores del análisis de redes sociales para proponer lineamientos que permitan catalizar la innovación agrícola.
La innovación, producción y comercialización de un producto resultan de la interacción de una diversidad de actores. Así, el modelo de extensión hub del programa gubernamental MasAgro busca ser un espacio en el que agricultores, extensionistas, proveedores de insumos, instituciones gubernamentales y de enseñanza e investigación, entre otros, interactúen, con el fin de promover bienestar individual y colectivo a través de la innovación.
Well-designed and supported innovation niches may facilitate transitions towards sustainable agricultural futures, which may follow different approaches and paradigms such as agroecology, local place-based food systems, vertical farming, bioeconomy, urban agriculture, and smart farming or digital farming.
On-farm agricultural innovation through incorporation of new technologies and practices requires access to resources such as knowledge, financial resources, training, and even emotional support, all of which require the support of different actors such as peers, advisors, and researchers. The literature has explored the support networks that farmers use and the overall importance ranking of different support actors, but it has not looked in detail at how these networks may differ for different farmers.
While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous connectivity, artificial intelligence, digital twins, and blockchain among others), social science researchers have recently started investigating different aspects of digital agriculture in relation to farm production systems, value chains and food systems. This has led to a burgeoning but scattered social science body of literature.
This editorial paper brings together different streams of research providing novel perspectives on co-design and co-innovation in agriculture, including methods, tools and organizations.
Recently, increasing attention has been paid to intermediaries, actors connecting multiple other actors, in transition processes. Research has highlighted that intermediary actors (e.g. innovation funders, energy agencies, NGOs, membership organisations, or internet discussion forums) operate in many levels to advance transitions. The authors argue that intermediation, and the need for it, varies during the course of transition. Yet, little explicit insight exists on intermediation in different transition phases.
Invasive species such as Ambrosia (an annual weed) pose a biosecurity risk whose management depends on the knowledge, attitudes and practices of many stakeholders. It can therefore be considered a complex policy and risk governance problem. Complex policy problems are characterised by high uncertainty, multiple dimensions, interactions across different spatial and policy levels, and the involvement of a multitude of actors and organisations. This paper provides a conceptual framework for analysing the multi-level and multi-actor dimensions of Ambrosia management.
Agriculture 4.0 is comprised of different already operational or developing technologies such as robotics, nanotechnology, synthetic protein, cellular agriculture, gene editing technology, artificial intelligence, blockchain, and machine learning, which may have pervasive effects on future agriculture and food systems and major transformative potential. These technologies underpin concepts such as vertical farming and food systems, digital agriculture, bioeconomy, circular agriculture, and aquaponics.