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Community Organizations AGRIS
AGRIS
AGRIS
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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 AGROVOCFAO’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 CGIARGFAR 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.

 

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Resources

Displaying 3851 - 3855 of 9579

assessment of the effectiveness of a random forest classifier for land-cover classification

Journal Articles & Books
декабря, 2012
Spain

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

Journal Articles & Books
декабря, 2012
China

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)

Journal Articles & Books
декабря, 2012
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.

Spatial analysis and mapping of malaria risk in an endemic area, south of Iran: A GIS based decision making for planning of control

Journal Articles & Books
декабря, 2012
Iran

Bashagard district is one of the important malaria endemic areas in southern Iran. From this region a total of 16,199 indigenous cases have been reported in recent years. The aim of this study was to determine the situation of the disease and provide the risk map for the area. ArcGIS9.2 was used for mapping spatial distribution of malaria incidence. Hot spots were obtained using evidence-based weighting method for transmission risk.

Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions

Journal Articles & Books
декабря, 2012
China

That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification.