What is AGRIS?
AGRIS (International System for Agricultural Science and Technology) is a global public database providing access to bibliographic information on agricultural science and technology. The database is maintained by CIARD, and its content is provided by participating institutions from all around the globe that form the network of AGRIS centers (find out more here). One of the main objectives of AGRIS is to improve the access and exchange of information serving the information-related needs of developed and developing countries on a partnership basis.
AGRIS contains over 8 million bibliographic references on agricultural research and technology & links to related data resources on the Web, like DBPedia, World Bank, Nature, FAO Fisheries and FAO Country profiles.
More specifically
AGRIS is at the same time:
A collaborative network of more than 150 institutions from 65 countries, maintained by FAO of the UN, promoting free access to agricultural information.
A multilingual bibliographic database for agricultural science, fuelled by the AGRIS network, containing records largely enhanced with AGROVOC, FAO’s multilingual thesaurus covering all areas of interest to FAO, including food, nutrition, agriculture, fisheries, forestry, environment etc.
A mash-up Web application that links the AGRIS knowledge to related Web resources using the Linked Open Data methodology to provide as much information as possible about a topic within the agricultural domain.
Opening up & enriching information on agricultural research
AGRIS’ mission is to improve the accessibility of agricultural information available on the Web by:
- Maintaining and enhancing AGRIS, a bibliographic repository for repositories related to agricultural research.
- Promoting the exchange of common standards and methodologies for bibliographic information.
- Enriching the AGRIS knowledge by linking it to other relevant resources on the Web.
AGRIS is also part of the CIARD initiative, in which CGIAR, GFAR and FAO collaborate in order to create a community for efficient knowledge sharing in agricultural research and development.
AGRIS covers the wide range of subjects related to agriculture, including forestry, animal husbandry, aquatic sciences and fisheries, human nutrition, and extension. Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more. A growing number (around 20%) of bibliographical records have a corresponding full text document on the Web which can easily be retrieved by Google.
Members:
Resources
Displaying 3511 - 3515 of 9579Economic Explanation for Privatization of Forests and Forestland: Canada and the United States
SUMMARYThis paper analyses the differences between forestland tenure systems in Canada and the US. Evolution of the two systems is primarily explained by variation of scarcity and land productivity. In early colonial times, Canada's economy was tightly linked to the fur trade with Indian people, while New England's economy was based more on agriculture with more intensive land use.
Global versus local change effects on a large European river
Water temperature and discharge are fundamental to lotic ecosystem function, and both are strongly affected by climate. In large river catchments, however, climatic effects might be difficult to discern from background variability and other cumulative sources of anthropogenic change arising from local land and water management.
Shieling Areas: Historical Grazing Pressures and Landscape Responses in Northern Iceland
Historical domestic livestock grazing in sensitive landscapes has commonly been regarded as a major cause of land degradation in Iceland. Shieling areas, where milking livestock were taken to pasture for the summer, represented one element of grazing management and in this paper we consider the extent to which historical shieling-based grazing pressure contributed to land degradation.
Object-based change detection
Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time.
Land-cover classification of partly missing data using support vector machines
Land-cover classification based on multi-temporal satellite images for scenarios where parts of the data are missing due to, for example, clouds, snow or sensor failure has received little attention in the remote-sensing literature. The goal of this article is to introduce support vector machine (SVM) methods capable of handling missing data in land-cover classification.