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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 3126 - 3130 of 9579Land appropriation, surplus people and a battle over visions of agrarian futures in Africa
The debate about ‘land grabs’ by foreign agents should not obscure the role of national governments or the accelerating process of appropriation of land by national agents. Much of the appropriated land is under forms of ‘customary’ tenure. In arguing that a fundamental problem is the denial of property in land to Africans, I lay out the colonial and post-colonial reproduction of ‘customary’ tenure as not equivalent to property rights, the documentation of mounting competition and conflict centring on land, and the more recent threats by national and international agents.
Classifying a high resolution image of an urban area using super-object information
In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification.
Messy hectares: questions about the epistemology of land grabbing data
Recent research on land deals reports gigantic quantities of hectares seized, with relatively little regard for the solidity of the evidence or for considerations of scale other than area. This commentary questions the usefulness of aggregating data of uneven quality and transforming it into ‘facts’. Making claims on the basis of problematic evidence does not serve agrarian and human rights activists well, since it may undercut their legitimacy and make it difficult for them to identify their adversaries.
Clustering based on eigenspace transformation – CBEST for efficient classification
Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors.
Retrospective analysis of landscape dynamics using normalized spectral entropy
Forest dynamics, such as deforestation, reforestation and afforestation, could be caused by the natural and human-induced disturbances. Traditional methods rarely quantify the history of such disturbances along a continuous temporal trajectory; yet, such disturbance history often determines system course and evolution. We adopted a retrospective perspective to study system history by implementing a newly proposed entropy index.