So far, numerous studies have exhibited Silicon Valley and other thriving innovation ecosystems by distinguishing special characteristics in which their survival rely on sustaining activities that convert them to specific regions. These regions provide ready-made grounds for networking to be innovative. Meantime, it is struggling for innovations to be transformed into measurable economic results if players encounter a weak network of collaborative relationships in the ecosystem.
This study identifies systemic problems in the New Zealand Agricultural Innovation System (AIS) in relation to the AIS capacity to enact a co-innovation approach, in which all relevant actors in the agricultural sector contribute to combined technological, social and institutional change. Systemic problems are factors that negatively influence the direction and speed of co-innovation and impede the development and functioning of innovation systems. The contribution in the paper is twofold.
Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions.
In this paper the developments in agricultural research and education in the Netherlands will be presented in a historic context and the recent evolutions in agriculture-based research and knowledge systems are evaluated. It is concluded that societal needs, scientific discoveries, and public and private funding are the driving forces behind change. However, most important for the quality and vigour of knowledge centres is the ability to adapt to change
An analysis of the impact of simulation modelling in three diverse crop-livestock improvement projects in Agricultural Research for Development (AR4D) reveals benefits across a range of aspects including identification of objectives, design and implementation of experimental programs, effectiveness of participatory research with smallholder farmers, implementation of system change and scaling-out of results. In planning change, farmers must consider complex interactions within both biophysical and socioeconomic aspects of their crop and animal production activities.
There is a broad consensus that farmers are not simply recipients of promoted techniques: rather, they are also an important source of agricultural innovations. They invent farm tools and equipment, develop new crop varieties, and add value to externally promoted technologies. When scouting, documenting and promoting such farmer-generated innovations, the thorny issue of intellectual property rights (IPRs) often emerges.
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.
Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail.
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.
This study aims to analyse three case studies of smallholder dairy farming in Pakistan. The study involved two stages. The first stage involved a scoping study which used a purposive sampling method to identify and sample fresh, unpackaged milk and informal and formal chains in both districts. Twenty-seven producers, eleven small, eight medium and five large Dhodhis, twenty-two retailers, two formal processors and eleven consumers were interviewed personally by the first author, using four different questionnaires.