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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 3851 - 3855 of 9579Does large-sized cities' urbanisation predominantly degrade environmental resources in China? Relationships between urbanisation and resources in the Changjiang Delta Region
Outward expansion of urban lands in the developing nations is often associated with a substantial loss of environmental resources such as forests, wetlands, freshwater and cash crop fields. Yet, determining how different aspects of urbanisation – such as city population size and spread pattern of built-up lands – contribute to the cumulative loss of resources remains controversial. In this study, data sets were constructed describing changes to land cover across 65,200 grid cells at 1 km² spatial resolution for China's Changjiang Delta Region over the past 60 years.
Wind farm land suitability indexing using multi-criteria analysis
Wind farm siting can be considered as Multi Criteria Decision Making (MCDM) problem that consists of set of alternative locations and set of selection criteria. This study applied multi-criteria decision making approach using Analytical Hierarchy Process with Ordered Weigh Averaging AHP-OWA aggregation function to derive wind farm land suitability index and classification under Geographical Information System (GIS) environment. Linguistic quantifier’s version of AHP-OWA aggregation function was used to classify lands based on their suitability for wind farm installation.
assessment of the effectiveness of a random forest classifier for land-cover classification
Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance.
trend of land-use sustainability around the Changbai Mountain Biosphere Reserve in northeastern China: 1977–2007
Extensive land-use and land-cover change, triggered by rapid development of tourism and the expansion of townships, has occurred in the area surrounding the Changbai Mountain Biosphere Reserve (CMBR) in northeast China, a reservoir for distinctive ecosystems and biological diversity. The objective of this study was to examine the land-use changes surrounding the reserve in the context of forest and nature reserve management with the aid of maps from Landsat MSS imagery of 1977 and Landsat TM imagery of 1991 and 2007.
Land degradation during the Bronze Age in Hexi Corridor (Gansu, China)
Pollen and charcoal analysis, with high resolution AMS ¹⁴C dating, on two sediment sections in the Hexi Corridor track the process of settlement development and abandonment during the Bronze Age. The evidence shows that agricultural activity during the Bronze Age caused an increase in farmland and a decrease in the abundance of Artemisia grassland in the Hexi Corridor. Land degradation is probably the main cause for decreased agricultural activity and settlement abandonment. Agriculture- induced soil fertility loss and land salinization contributed to the process of land degradation.