The impulse towards a larger introduction of Information and Communication Technology (ICT) in the agricultural field is currently experiencing its momentum, as digitisation has large potentialities to provide benefits for both producers and consumers; on the other hand, pushing technological solutions into a rural context encounters several challenges.

The economic globalisation has opened new pathways for commerce and triggered a logistical revolution, which in turn has produced enormous technological innovations. In this context, the role of startups is becoming increasingly crucial since they are positioning themselves as innovation enablers among large and small companies. Between these innovations, IoT, Big Data Analytics and Blockchain can be used in various domains, among which the logistics of the whole wine supply chain.

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, https://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products.

This paper estimates the farm level impact of producer organizations’ (PO) membership in Slovakia and the effectiveness of support provided to POs under the EU Rural Development Programme (RDP). The study employ the  Propensity Score Matching and Difference in Differences econometric approach on a database of large Slovak commercial farms for 2006 and 2015. First, our results show that belonging to a PO improves the economic performance of farms in Slovakia.

The aim of the paper is to analyze the possibilities to improve the market capability of small farms in Latvia. Therefore the paper deals with the factors constraining development plans of small farm holders, the current possibilities for improving small farm market capability, the possible development strategies to choose, and proposals for the improvement of small farm market capability in Latvia.

According to transition science, system innovation requires experimentation and social learning to explore the potential of innovations for sustainable development. However, the transition science literature does not elaborate much on the learning processes involved. Senge's Field of Change provides a more detailed approach to the role of learning and action in innovation. We linked the Field of Change to transition management literature in order to explore social learning in an agricultural innovation experiment in the Netherlands called the ‘New Mixed Farm’.

Communities of Practice (CoPs) are a promising concept for transdisciplinary knowledge co-creation in sustainable agricultural development, but empirical evidence from the farmers’ viewpoint is scarce. This paper contributes to empirical insights on the knowledge creation in CoPs as valued by farmers. Using concepts from CoP theory (domain, community, and practice) and the value creation framework (VCF) developed by Wenger et al. (Promoting and assessing value creation in communities and networks: a conceptual framework.

Innovations are considered as a key driver for economic growth and increased competitiveness. Investments in agricultural research and development could generate not only economic and social effects but also environmental benefits. The study aims to analyse of agricultural innovation system in Bulgaria and the linkages with agricultural R&D to formulate conclusions and recommendation for future development. The paper presents some of the theoretical concepts of innovation systems and the role of R&D in the process.

In this paper the authors used a network perspective to study the micro level of agricultural innovation systems and investigate the different roles and functions that collaborating actors have to perform to spread their innovation both horizontally and vertically. Based on a literature review, we distinguish between three separate network functions: (1) learning and knowledge co-creation, (2) upscaling and institutional entrepreneurship and (3) outscaling and innovation brokerage.