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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 4096 - 4100 of 9579Application of an evidential belief function model in landslide susceptibility mapping
The objective of this paper is to exploit the potential application of an evidential belief function model to landslide susceptibility mapping at Kuala Lumpur city and surrounding areas using geographic information system (GIS). At first, a landslide inventory map was prepared using aerial photographs, high resolution satellite images and field survey. A total 220 landslides were mapped and an inventory map was prepared. Then the landslide inventory was randomly split into a testing dataset 70% (153 landslides) and remaining 30% (67 landslides) data was used for validation purpose.
Can “fragile states” decide to reduce their deforestation? The inappropriate use of the theory of incentives with respect to the REDD mechanism
The originality of the REDD proposal is its incentives-based mechanism designed to reward the governments of developing countries for their performance in reducing deforestation as measured against a baseline. This mechanism is founded on the hypothesis that developing countries ‘pay’ an opportunity cost to conserve their forests and would prefer other choices and convert their wooden lands to other uses. The basic idea is, therefore, to pay rents to these countries to compensate for the anticipated foregone revenues.
Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data
The incorporation of a red edge channel in multi-spectral satellite sensors has potential for improving land-use classification, as the related electromagnetic spectrum is specifically sensitive to vegetation chlorophyll content. RapidEye is the first high-resolution multi-spectral satellite system that operationally provides a red edge channel. The objective of this study is to test the potential of the RapidEye red edge channel for improving the classification of land use, investigated at a study site west of Berlin.
Unsupervised and supervised classification of hyperspectral imaging data using projection pursuit and Markov random field segmentation
This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation.
Coupling landscape water storage and supplemental irrigation to increase productivity and improve environmental stewardship in the US Midwest
Agriculture must expand production for a growing population while simultaneously reducing its environmental impacts. These goals need not be in tension with one another. Here we outline a vision for improving both the productivity and environmental performance of agriculture in the US Corn Belt. Mean annual precipitation has increased throughout the region over the past 50 years, consistent with climate models that attribute it to a warming troposphere.