Digitalisation is widely regarded as having the potential to provide productivity and sustainability gains for the agricultural sector. However, there are likely to be broader implications arising from the digitalisation of agricultural innovation systems. Agricultural knowledge and advice networks are important components of agricultural innovation systems that have the potential to be digitally disrupted.
The European small ruminants (i.e. sheep and goats) farming sector (ESRS) provides economic, social and environmental benefits to society, but is also one of the most vulnerable livestock sectors in Europe. This sector has diverse livestock species, breeds, production systems and products, which makes difficult to have a clear vision of its challenges through using conventional analyses. A multi-stakeholder and multi-step approach, including 90 surveys, was used to identify and assess the main challenges for the sustainability of the ESRS to prioritize actions.
This paper considers how farmers engage with, utilise and share knowledge through a focus on the Catchment Sensitive Farming (CSF) initiative in the UK. In exploring the importance of social contexts and social relations to these practices, the paper brings together understandings of knowledge with those from the literature on good farming to consider how different knowledges gain credibility, salience and legitimacy in different contexts.
Despite typically beingregarded as ‘low-tech,’ the Food Manufacturing and Technology Sectoris increasingly turning to open innovation practices involving collaboration with universities in order to innovate. Given the broad range of activities undertaken by this sector and thefact that it utilises analytical, synthetic, and symbolic knowledge for innovation, it makes an interesting case study on the factors that influence the formation of University-Industry links.
Participatory agricultural extension programmes aimed at encouraging knowledge transfer and the adoption of new technology and innovation at the farm level are a novel approach to advisory service provision. In order to drive sustainable agricultural production systems that address farm-level economic and environmental objectives, the College of Agriculture, Food and Rural Enterprise (CAFRE) in November 2015, developed a new participatory extension programme for farmers in Northern Ireland, the Business Development Groups (BDGs).
Small farms in Northern Europe are found alongside some of the largest - and in some cases, most industrialised - farms in the whole of Europe.
Les petites exploitations agricoles du nord de l’Europe côtoient certaines des exploitations les plus grandes – et dans certains cas, les plus industrialisées – de toute l’Europe.
Cross bred cow adoption is an important and potent policy variable precipitating subsistence household entry into emerging bulk markets. This paper focuses on the design of policies that create and sustain milk-market expansion among a sample of households in the Ethiopian highlands. In this context it is desirable to measure a household's `proximity' to market in terms of the level of deficiency of an essential input. This problem is compounded by four factors.
Many smallholder farmers in developing countries grow multiple crop species on their farms, maintaining de facto crop diversity. Rarely do agricultural development strategies consider this crop diversity as an entry point for fostering agricultural innovation. This paper presents a case study, from an agricultural research-for-development project in northern Ghana, which examines the relationship between crop diversity and self-consumption of food crops, and cash income from crops sold by smallholder farmers in the target areas.
This study uses 344 women and men survey respondents involved in conservation agriculture (CA) and small-scale irrigation schemes (SSIS) as data sources for examining the effect of gendered constraints for adopting climate-smart agriculture amongst women in three areas in Ethiopia. Qualitative and quantitative data collections were applied using survey, in-depth interviews and focus group discussions. Quantitative data were analyzed using descriptive statistics, Pearson's chi-square test and binary logistic regression using statistical software for the social sciences (SPSS) version 24.