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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 6846 - 6850 of 9579Safeguarding water resources by making the land greener: knowledge management through WOCAT.
A Land Market Cycle in the Netherlands
This paper develops a disequilibriummodel of land prices in the Netherlands. It shows thatthe behaviour of traded quantities and prices of Dutchland have some resemblance with a disequilibrium landmarket model developed by Søgaard. An errorcorrection model based on Søgaard’s model generatessignificant results with GDP and the real interest rate asexplanatory variables, but regrettably farm income norgovernment demand for land generate significantresults.
Ecological agriculture in light of conception realization of sustainable development
Agricultural production is based on environmental natural resources, that is soil, water, air and the whole landscape with its biodiversity. The using with resources of soil should therefore be balanced which would combine the tasks of farmhold utilizing from both economical, social and environmental protection point of views. Principles these be realized in special means by ecological agriculture as sustainable system of food production.
Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover
We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models.