La presente investigación analiza la influencia de las relaciones sociales existentes al interior de un grupo de 22 productores de rambután (Nephelium lappaceum) del Soconusco, Chiapas en 2014. Se analizaron las acciones conjuntas relacionadas con la mejora de la comercialización de su producto. Se empleó la escala de construcción de vínculos relacionales para el trabajo colectivo integrada por los niveles de identificación, aportación, colaboración, cooperación y asociación.
Agricultural communication to mitigate climate change enables information dissemination of both scientific knowledge (SCK) and indigenous knowledge (IDK) for practical farming. This research analyzed knowledge utilization and conducted community-based participatory communication to propose a practical agricultural communication framework for climate mitigation. Based on a qualitative method of data collection in Phichit province, the key findings showed that SCK and IDK can be mutually utilized to enhance the good relationship among the people and for the people with nature.
Agricultural production systems are a composite of philosophy, adoptability, and careful analysis of risks and rewards. The two dominant typologies include conventional and organics, while biotechnology (GM) and Integrated Pest Management (IPM) represent situational modifiers. We conducted a systematic review to weigh the economic merits—as well as intangibles through an economic lens—of each standalone system and system plus modifier, where applicable. Overall, 17,485 articles were found between ScienceDirect and Google Scholar, with 213 initially screened based on putative relevance.
Agricultural production is a crucial and fundamental aspect of a stable society in China that depends heavily on the climate situation. With the desire to achieve future sustainable development, China’s government is taking actions to adapt to climate change and to ensure food self-sufficiency.
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one.
Rather than merely supporting R&D and strengthening innovation systems, the focus of innovation policy is currently shifting towards addressing societal challenges by transforming socio-economic systems. A particular trend within the emerging era of transformative innovation policy is the pursuit of challenge-based innovation missions, such as achieving a 50 % circular economy by 2030. By formulating clear and ambitious societal goals, policy makers are aiming to steer the directionality and adoption of innovation.
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.
Establishing food security remains a global challenge; it is thus a specific objective of the United Nations Sustainable Development Goals for 2030. Successfully delivering productive and sustainable agricultural systems worldwide will form the foundations for overcoming this challenge. Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment. The practical realization, deployment, and adoption of smart agricultural systems remain distant due to a confluence of technological, social, and economic factors.
The Guidance Note on Operationalization provides a brief recap of the conceptual underpinnings and principles of the TAP Common Framework as well as a more detailed guide to operationalization of the proposed dual pathways approach. It offers also a strategy for monitoring and evaluation as well as a toolbox of select tools that may be useful at the different stages of the CD for AIS cycle.
The Conceptual Background provides an in-depth analysis of the conceptual underpinnings and principles of the TAP Common Framework. It is also available in French and Spanish.