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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 1451 - 1455 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.
Hierarchical responses of plant stoichiometry to nitrogen deposition and mowing in a temperate steppe
BACKGROUND AND AIMS: Stoichiometric relations drive powerful constraints on many ecosystem processes. However, our understanding of the hierarchical responses of plant C:N:P stoichiometry at different levels of biological organization to global change factors remains limited. METHODS: we examined the plant C:N:P stoichiometric responses to N deposition and mowing (hay making) at both species- and community-level by carrying out a 4-year field experiment in the temperate steppe of northern China.
Deforestation and the Environmental Kuznets Curve in Iran
Property rights, the agricultural price index, forest area, population, income and timber price are important factors in the deforestation process. The aim of this study was to test the impact of these factors on deforestation in Iran using an environmental Kuznets curve (EKC). The autoregressive distributed lag approach was also used to estimate the deforestation function. The existence of an inverted U-shaped EKC for deforestation in Iran was confirmed.