This report explores the role of rural networks in enhancing innovation processes, questioning the features of the agricultural/rural networks could enhance farmers’ ability to co-innovate in cooperation with other actors. The prospect of this investigation is also to provide interesting and significant experiences that constitute examples for the ‘European Innovation Partnership’ by increasing farmers’ capacities to create, test, implement and evaluate innovations in cooperation with other rural actors.
The report synthesises the research conducted under the PRO AKIS project for the topic "Designing, implementing and maintaining agricultural/rural networks to enhance farmers’ ability to innovate in cooperation with other rural actors".
The present case study investigated a policy-induced agricultural innovation network in Brandenburg.
This report has the aim of contributing to the PRO AKIS overall goal of exploring and identifying the possibilities, conditions and requirements of rural networks to enhance the farmers’ ability to create, test, implement and evaluate innovation in cooperation with other actors.In particular, the report presents two cases: the Small Fruit Cluster (SFC) and the Drosophila Suzukii Monitoring (DSM) network. The SFC is a nationwide, multi-actor network composed of several actors, interacting in the small fruit sector in Portugal.
The aim of this document is to produce a state-of-the-art of the academic literature in order to identify theories and concepts available for: a) describing the structure, the dynamics and the functioning of agricultural advisory services; b) understanding how these services are embedded into national Agricultural Knowledge and Innovation Systems (AKIS), and into various agricultural and rural policies across the European Union (EU) countries; c) providing some conceptual elements to support the methodology for an inventory of agricultural advisory services in EU 27 countries (WP3 of the PR
Linking farmers to markets is widely viewed as a milestone towards promoting economic growth and poverty reduction. However, market and institutional imperfections along the supply chain thwart perfect vertical and spatial price transmission and prevent farmers and market actors from getting access to information, identifying business opportunities and allocating their resources efficiently. This acts as a barrier to market-led rural development and poverty reduction.
Tanzania has tremendous potential to support a thriving agribusiness sector. Agriculture is diverse and extensive, employing more than 80 percent of the population, and contributing about 28 percent of Gross Domestic Product, or GDP and 30 percent of export earnings. A wide range of agricultural commodities are produced in Tanzania, including fiber (sisal, cotton), beverages (coffee, tea), sugar, grains (a diverse range of cereals and legumes), horticulture (temperate and tropical fruits, vegetables and flowers) and edible oils.
The report introduces 30 young innovators, 21 featured with full stories, and nine other "innovators to watch". They come from countries including Barbados, Botswana, Cameroon, Côte d'Ivoire, Kenya, Nigeria, Uganda, Jamaica, Senegal, Tanzania. The publication presents a multidimensional picture of the emerging field of ICT entrepreneurship in agriculture in developing countries. It describes challenges but also successes already achieved. It contains advice for aspiring agtech entrepreneurs as well as recommendations from youth on how to support their ventures.
This paper comparatively analyzes the structure of agricultural policy development networks that connect organizations working on agricultural development, climate change and food security in fourteen smallholder farming communities across East Africa, West Africa and South Asia.
The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.