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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 1446 - 1450 of 9579High-resolution landcover classification using Random Forest
Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy.
Effects of grazing exclusion on soil carbon and nitrogen storage in semi-arid grassland in Inner Mongolia, China
The semi-arid grasslands in Inner Mongolia, China have been degraded by long-term grazing. A series of ecological restoration strategies have been implemented to improve grassland service. However, little is known about the effect of these ecological restoration practices on soil carbon and nitrogen storage.
new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data
A new locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) has been developed to meet requirements of land cover mapping over large areas using remote-sensing data. The LAGMA involves the grid-based supervised image classification using classes’ features estimated locally in classified pixels’ surrounding from spatially distributed reference data.
Environmental perception during rapid population growth and urbanization: a case study of Dhaka city
Dhaka city in Bangladesh has been passing through a hasty process of urbanization and population growth since the last few decades. Rapid growth of population, unplanned urbanization and industrialization in the periphery has generated pressure to the changes in land use pattern, which has also caused huge urban expansion. This expansion process is engulfing cultivated land, vegetation, wetlands and water bodies without considering their environmental impacts.
Spatial and temporal patterns of range expansion of white‐winged doves in the USA from 1979 to 2007
AIM: The geographical expansion of white‐winged doves (Zenaida asiatica) in North America has attracted the attention of biologists and sportsmen because of their recreational and aesthetic value; however, data on factors driving the spatial spread of this species are lacking. We examined spatial and temporal patterns of range expansion for white‐winged doves along the northern edge of their geographical range from 1979 to 2007 and used a dynamic occupancy model to estimate when and where doves would be found along an expansion gradient. LOCATION: Southern half of the USA.