Title :
Agriculture Data for All - Integrated Tools for Agriculture Data Integration, Analytics, and Sharing
Author :
Nabrzyski, Jarek ; Cheng Liu ; Vardeman, Charles ; Gesing, Sandra ; Budhatoki, Milan
Author_Institution :
Center for Res. Comput., Univ. of Notre Dame, Notre Dame, IN, USA
fDate :
June 27 2014-July 2 2014
Abstract :
Agriculture produces an abundance of data in both public and private domains. Such data include, but are not limited to: national soil databases, long-term data on carbon balance across different climate zones and vegetative land covers, digital elevation models, regional and national inventories, remote sensing data, geophysical data, socio-economic and many other data sets. These agriculture - related records are interesting not only to the agriculture sector. Ecology, environment, business, policy, various sciences, etc. can use this data for their discoveries. They can investigate the impact of land-management approaches, such as fertilization, grazing, irrigation, and more. Agriculture data analysis can help understand the problems and lead policy makers to implementing risk mitigation and restoration strategies.
Keywords :
agricultural engineering; data analysis; data integration; digital elevation models; land cover; remote sensing; socio-economic effects; agriculture data analysis; agriculture sector; agriculture-related records; carbon balance; climate zones; digital elevation models; fertilization; geophysical data; grazing; irrigation; land-management approach; long-term data; national inventories; national soil databases; private domains; public domains; regional inventories; remote sensing data; restoration strategies; risk mitigation; risk mitigation strategies; socioeconomic data sets; vegetative land covers; Agriculture; Computational modeling; Data integration; Data models; Geospatial analysis; Meteorology; Semantics; data semantic integration; geospatial analysis;
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
DOI :
10.1109/BigData.Congress.2014.117