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 Its content includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.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 3961 - 3965 of 9579Exploring the Potential of Object Based Image Analysis for Mapping Urban Land Cover
The paper investigates the performance and potential of object based image analysis technique (OBIA) for land cover information extraction from high resolution satellite datasets. Efficiency of the technique has been assessed in different urban land cover situations using merged CARTOSAT-1 and IRS-P6 LISS-IV image subsets of Dehradun, India. Multi-scale iterative Nearest Neighbour classification has been applied. Classification results have been quantitatively evaluated.
Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years
Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments.
Regional spatial pattern of deep soil water content and its influencing factors
Plant root systems can utilize soil water to depths of 10 m or more. Spatial pattern data of deep soil water content (SWC) at the regional scale are scarce due to the labour and time constraints of field measurements. We measured gravimetric deep SWC (DSWC) at depths of 200, 300, 400, 500, 600, 800 and 1000 cm at 382 sites across the Loess Plateau, China. The coefficient of variation was high for soil water content (SWC) in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%).
comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region
This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification.
Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests
The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm.