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
Adoption of temperate agroforestry practices generally remains limited despite considerable advances in basic science. This study builds on temperate agroforestry adoption research by empirically testing a statistical model of interest in native fruit and nut tree riparian buffers using technology and agroforestry adoption theory. Data were collected in three watersheds in Virginia's ridge and valley region and used to test hypothesized predictors of interest in planting these buffers. Confirmatory factor analysis was used to verify independence of underlying latent measures. Multiple linear regression was used to model interest using the Universal Theory of Acceptance and Use of Technology (UTAUT). A second model that added agroforestry-specific predictors from Pattanayak et al. (Agrofor Syst 57:173-186, 2003) to UTAUT was tested and compared with the first. The first model was robust (Adj R (2) = 0.49) but was improved by adding agroforestry specific predictors (Adj R (2) = 0.57). Model generalizability was confirmed using double cross validation and normality indices. Social influence, risk expectancy, planting experience, performance expectancy, parcel size, and the interaction of gender and risk were significant in the final model. In addition, socioeconomic variables were used to characterize landowners according to their level of interest. Respondents with greater interest were newer owners that have higher incomes and are less active in farming. The result implies that future agroforesters may in large part consist of owners that have recently acquired land and manage their property more extensively with higher discretionary income and multiple objectives in mind.
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