The advent of big data, which incorporates geo- informatics, remote sensing, and the large volumes of data being generated by technological advances in genomics, will revolutionize the way we work in the future. The use of this information to increase research efficiencies and decision-making, from the farm level to the policy level, is beginning to take place. Using big data in an effective manner will be a key element in addressing the challenges facing research programs and dry areas as a whole. We intend to capitalize on big data to the benefit of our breeding programs, thereby ensuring a continuous supply of improved varieties to smallholder farmers. We will also build digital platforms to generate maps of crop productivity and water consumption in near real time, which can be used for water accounting and agro-ecosystem assessment. In order to make full use of big data and ICT, we will partner with other CGIAR centers, CGIAR Research Programs (CRPs), ARIs, and the public and private sectors. ICARDA’s geo-informatics research focuses on knowledge-based prioritization of agricultural landscapes for improved interventions, implementation, and impacts through the use of multi-sensor, multi-scale observations of agro-ecosystem productivity, resource use efficiency, land potential, and associated drivers to assist addressing issues related to food and nutritional security, natural resource management, and resilience. We will develop advanced analytics (machine learning, artificial intelligence) for research, development, and outreach in collaboration with research programs, partners, collaborators, and citizen science. We will support the work in the SRPs by working on quantification of yield gaps and land potential for better targeting developmental interventions towards bridging the yield gaps in dry areas.
ICARDA Strategic Plan 2017 - 2026: https://dx.doi.org/20.500.11766/8237
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

81 to 90 of 112 Results
Feb 10, 2023
Kosimov, Sherzod, 2020, "Spatial data from Aral Sea region on canals, roads, drainage, crop fields and wells", https://hdl.handle.net/20.500.11766.1/FK2/ICTJMI, MELDATA, V4
The dataset is composed by six shape files containing spatial data of the Aral Sea region including canals, crop fields, drainage, road lines, and wells
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2007", https://hdl.handle.net/20.500.11766.1/FK2/MNQGI1, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2007. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2012", https://hdl.handle.net/20.500.11766.1/FK2/XIITWH, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2012. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Jani, Sara; Bonaiuti, Enrico; Wery, Jacques, 2019, "ICARDA ISI Journal Articles 2018 dataset", https://hdl.handle.net/20.500.11766.1/C4OABW, MELDATA, V4
The data contains the list of journal articles published in ISI Journals by the International Center for Agricultural Research in the Dry Areas (ICARDA) Scientists during the year 2018 with the quartiles analysis to study the journal article relevance to each research topic based...
Feb 10, 2023
Biradar, Chandrashekhar, 2022, "Central Asia and NW China annual mean precipitation 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/8HWJXZ, MELDATA, V7
Central Asia and North-West China (Xingjiang province) annual mean precipitation in 2020 according to IPCC near-term climate change scenario A2
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2005", https://hdl.handle.net/20.500.11766.1/FK2/5MLBY8, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2005. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2010", https://hdl.handle.net/20.500.11766.1/FK2/2FNWXD, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2010. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2014", https://hdl.handle.net/20.500.11766.1/FK2/CH39IP, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2014. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2004", https://hdl.handle.net/20.500.11766.1/FK2/YJU7Y7, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2004. The map is part of a series on crop distribution from 2004 to 2015.
Feb 10, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2021, "Crop Type Map of Fergana, 2009", https://hdl.handle.net/20.500.11766.1/FK2/FA63XD, MELDATA, V3
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2009. The map is part of a series on crop distribution from 2004 to 2015.
Add Data

Sign up or log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.