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
Increasing tree planting on farms is beneficial to increase the supply of forest functions including provision or raw materials, sequestration of carbon and wildlife habitat. Tree planting decisions by farmers are governed by the knowledge base of farm households, and other factors if the farmers are rational decision-makers under their given resource endowments. A recent surge of tree planting in several states in northern India, though inadequate, coincided with efforts by national and state forest agencies to promote tree planting. This paper reports on the results of a survey into tree plantations of 176 randomly selected households distributed in 34 villages in Western Uttar Pradesh based on demographic data and other factors potentially associated with tree planting decisions. The data were used to develop a binary logistic regression model, to identify which factors influence tree planting decisions. The factors found to correlate positively and significantly with tree planting include size of landholding, overall annual income, area of irrigated land and prior experience with tree planting. It is hypothesized that the lack of significance of other factors including family size and education level was probably due to the relatively homogeneous sample. The result can be used to devise policies to promote tree planting among famers in Uttar Pradesh and possibly other states and other countries.
- Kyoto_Univ (JP)
- ICFRE_Indian_Council_Forest_Res_&_Educ (IN)
- Hemwati_Nandan_Bahuguna_Garhwal_Univ (IN)
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