Digital technologies such as sensors, drones, satellites and blockchain are seen as a promising developments for value chain transparency. These technologies are often envisioned to enable more detailed, objective, and complete information collection, and to create secure, transparent and democratic ways of information sharing. The development of such digitally-enabled modes of information collection and analysis leads to what we term the rise of ‘hyper-transparency’.

The integration of male and female smallholders in high-end value chains (e.g. those for tree crops like cocoa, oil palm, avocado, and mango), has been promoted throughout the global South as a strategy for poverty alleviation, economic growth, employment generation, gender equality, and improved wellbeing. More critical literature, however, questions the inclusiveness of farmers’ value chain engagement. Despite rapid mainstreaming of inclusiveness in policy discourse, remarkably little literature sheds light on the operationalization of the concept. This paper addresses this gap.

Knowledge on indigenous chicken production exists but its potential is not yet fully exploited. Although the actors could be known, it is not clear where value is lost or gained, neither is it clear which of the actor gains or losses most, nor the challenges they face. Moreover, if some of the actors are exploited and therefore, realize glaring losses, the entire value chain will be affected and this will affect not only the actors who earn a direct living from the chain, but the entire nation for loss of gainful employment and revenue.

Dairy foods provide a significant portion of the recommended daily nutrition for much of the US population. Improving the availability of safe and nutritious dairy products and decreasing the environmental impact of the dairy community continue to be high priorities for both industry and the public sector. In recognition of these shared priorities, scientists and other specialists from the USDA, National Dairy Council, industry, academia, and nongovernmental organizations participated in the “Elevating Dairy Research and Extension Through Partnership” meeting on June 19, 2018.

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.

The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system's capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management.

Agricultural Innovation Systems (AIS) approach, arise as tool for better understanding dynamics and complexity of agricultural innovation. The objective of this article is present a framework for AIS capability development, taking as a scientific reference, emerging economy countries experiences. A multi-dimensional methodology of literature review and content analysis is implemented, supported in bibliometric and data mining techniques.