The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Factors that determine use of breeding services by smallholder dairy farmers in Central Kenya
This study examined the determinants of smallholder dairy farmers' use of breeding services in Nyandarua and Kiambu districts, Central Kenya. Data was collected through semi-structured interviews with 140 randomly selected respondents. The breeding services considered were artificial insemination (AI), natural bull service, or a combination of AI and bull services. A multinomial logit econometric model was used fitting AI as the base category. There was a negative relationship between higher levels of education, herd size, and location and the use of bull service. However, education, herd size, and credit were positively related to the combined option. The results indicate that uptake of AI services after the liberalization of the sector is influenced by other factors besides cost-related factors. Factors such as accessibility to breeding services and product markets had influence on the farmer decision to choose among the available breeding services. The effectiveness of the breeding services in terms of successful conception also plays a big role in the choice. A need for concerted efforts to increase farmer's knowledge base on utilization and effectiveness of available breeding services is imperative. Furthermore, smallholder dairy farming could be made more sustainable and economically viable by implementing initiatives geared towards enhancing access to breeding services that would guarantee access to quality genetic material.
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