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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 9579analysis of forest land use, forest land cover and change at policy-relevant scales
Quantifying the amount of forest and change in the amount of forest are key to ensure that appropriate management practices and policies are in place to maintain the array of ecosystem services provided by forests. There are a range of analytical techniques and data available to estimate these forest parameters, however, not all ‘forest’ is the same and various components of change have been presented. Forest as defined by use and forest as defined by cover are different, although it is common for scientists and policy makers to infer one from the other.
Conservation genetics and breeding system of Penstemon debilis (Plantaginaceae), a rare beardtongue endemic to oil shale talus in western Colorado, USA
Rare species of plants are especially vulnerable to extinction when populations are few, have small numbers of individuals, and are fragmented. Such conditions lead to a reduction in gene flow and genetic diversity, and encourage inbreeding depression. We conducted a study of the reproductive biology and population genetics of Penstemon debilis (Plantaginaceae), a Federally Threatened species endemic to a small region of oil shale extraction in western Colorado, USA. Most of the habitat area is privately owned and undergoing natural gas extraction activities.
Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery
Image segmentation is a basic and important procedure in object-based classification of remote-sensing data. This study presents an approach to multi-scale optimal segmentation (OS), given that single-scale segmentation may not be the most suitable approach to map a variety of land-cover types characterized by various spatial structures; it objectively measures the appropriate segmentation scale for each object at various scales and projects them onto a single layer. A 1.8 m spatial resolution Worldview-2 image was used to perform successive multi-scale segmentations.
Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China
The effects of land use policy and socioeconomic changes on urban landscape dynamics have been increasingly investigated around the world, but our knowledge of the underlying processes of these effects is still inadequate for sustainably managing urban ecosystems.
High-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.