This article conceptualizes the diffusion of user innovations from a service ecosystem perspective. With the focus on sustainable innovations, the service ecosystem is evaluated, along with other systemic innovation concepts, as a possible theoretical basis for explaining the first adoption and diffusion of user innovations.

This article focus on studying brand experience under the background of IOT through data selecting and analysis , try to make a service design plan according to the design-driven branding innovation. The study take a local fruit brand as study object named “Taozhiyuan” , not only focus on logo or package but try to establish a co-design platform which all the stakeholders and take part in . This platform is based on the system supported by the Wuxi PeachWell IOT Technology Co. Ltd

Continually increasing food demand from a still–growing human population and the need for environmentally–friendly strategies for sustainable agricultural development require innovation and further enhancement of cropping systems’ factor productivity. The system of rice intensification (SRI) has been proposed as a suitable strategy to improve rice yields with reduced input requirements, most notably water and seed, while enhancing soil and water quality because agrochemical applications can be cut back.

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.

ICT-driven digital tools to support smallholder farmers are arguably inevitable for agricultural development, and they are gradually evolving with promising outlook. Yet, the development and delivery of these tools to target users are often fraught with non-trivial, and sometimes unanticipated, contextual realities that can make or mar their adoption and sustainability. This article unfolds the experiential learnings from a digital innovation project focusing on surveillance and control of a major banana disease in East Africa which is being piloted in Rwanda.

This note is a preview on the agricultural innovation systems (AIS) assessment methdology which is being tested in the nine countries of the European Union-funded TAP-AIS DeSIRA project. It presents the rationale, the steps, ethe expected outputs and outcomes.

This practitioner’s guide, a companion volume to The Innovation Paradox picks up where the previous report left off. It aims to help policy makers in developing countries better formulate innovation policies. It does so by providing a rigorous typology of innovation policy instruments, including evidence of impact—and more importantly, the critical conditions in terms of institutional capabilities to successfully implement these policy instruments in developing countries.

This guide is the second in a series of documents designed to support agencies implementing participatory agroenterprise development program operating within defined geographical areas.

Relying entirely on survey information and personal exchanges with over 70 scientists from within the CGIAR network, this working paper attempts to achieve a better understanding of the scope of social learning related efforts undertaken in CGIAR and main issues of relevance to more current efforts, such as that planned by the CGIAR program on Climate Change Agriculture and Food Security (CCAFS). A wide range of methods was identified, where groups of people learn in order to jointly arrive at solutions to pressing food security problems.

The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.