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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 4346 - 4350 of 9579Assessing strategic water availability using remote sensing, GIS and a spatial water budget model: case study of the Upper Ing Basin, Thailand
This paper assesses strategic water availability and use under different development pathways on a basin scale using remote sensing (RS), geographical information systems (GIS) and a spatial water budget model (SWBM). The SWBM was applied to the Upper Ing Basin in northern Thailand to investigate the spatial and temporal variations in the location of streams and water yields from different parts of the basin. The base simulation was carried out for the years 1998–2007 using a DEM and actual land-use data at 100-m resolution.
predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya
This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km² (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors.
Super-resolution mapping using Hopfield Neural Network with panchromatic imagery
Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used.
potential use of high-resolution Landsat satellite data for detecting land-cover change in the Greater Horn of Africa
To assess the potential of high-resolution satellite data for land-cover monitoring in the Greater Horn of Africa, we used a regular sampling grid of 170 sites (each measuring 20 km × 20 km) located at the confluence of the latitudes and meridians across the study area. For each of these sites, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite data were acquired for the years 1990 and 2000. A dot grid visual interpretation was used to assess land-cover change between the two dates in each of the sites.
Identification of hydrologic and geochemical pathways using high frequency sampling, REE aqueous sampling and soil characterization at Koiliaris Critical Zone Observatory, Crete
Koiliaris River watershed is a Critical Zone Observatory that represents severely degraded soils due to intensive agricultural activities and biophysical factors. It has typical Mediterranean soils under the imminent threat of desertification which is expected to intensify due to projected climate change. High frequency hydro-chemical monitoring with targeted sampling for Rare Earth Elements (REE) analysis of different water bodies and geochemical characterization of soils were used for the identification of hydrologic and geochemical pathways.