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 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

1 to 10 of 112 Results
Feb 7, 2024
De Pauw, Eddy; Atassi, Layal, 2019, "Annual Rainfall Likely To Be Exceeded In 9 Years Out Of 10", https://hdl.handle.net/20.500.11766.1/FK2/GLYCLI, MELDATA, V2
Annual rainfall likely to be exceeded in 9 years out of 10, in millimeters, at 30 arcsecond resolution, was prepared for the IFAD-ICARDA Project "Poverty Assessment in Sudan". Map prepared as part of three reports that detail the results of a poverty assessment and mapping projec...
Feb 7, 2024
De Pauw, Eddy; Atassi, Layal, 2019, "Annual Rainfall Likely To Be Exceeded In 4 Years Out Of 5", https://hdl.handle.net/20.500.11766.1/FK2/LGJ9Q1, MELDATA, V2
Annual rainfall likely to be exceeded in 4 years out of 5, in millimeters, at 30 arcsecond resolution, was prepared for the IFAD-ICARDA Project "Poverty Assessment in Sudan". Map prepared as part of three reports that detail the results of a poverty assessment and mapping project...
Feb 7, 2024
De Pauw, Eddy; Atassi, Layal, 2019, "Annual Rainfall Likely To Be Exceeded In 3 Years Out Of 4", https://hdl.handle.net/20.500.11766.1/FK2/2WOQVJ, MELDATA, V3
Annual rainfall likely to be exceeded in 3 years out of 4, in millimeters, at 30 arcsecond resolution, was prepared for the IFAD-ICARDA Project "Poverty Assessment in Sudan". Map prepared as part of three reports that detail the results of a poverty assessment and mapping project...
Feb 7, 2024
De Pauw, Eddy; Atassi, Layal, 2019, "Annual Rainfall Likely To Be Exceeded In 2 Years Out Of 3", https://hdl.handle.net/20.500.11766.1/FK2/X3BWKV, MELDATA, V3
Annual rainfall likely to be exceeded in 2 years out of 3, in millimeters, at 30 arcsecond resolution, was prepared for the IFAD-ICARDA Project "Poverty Assessment in Sudan". Map prepared as part of three reports that detail the results of a poverty assessment and mapping project...
Feb 7, 2024
De Pauw, Eddy; Atassi, Layal, 2019, "Annual Rainfall Likely To Be Exceeded In 1 Year Out Of 2", https://hdl.handle.net/20.500.11766.1/FK2/85RZD9, MELDATA, V3
Annual rainfall likely to be exceeded in 1 year out of 2, in millimeters, at 30 arcsecond resolution, was prepared for the IFAD-ICARDA Project "Poverty Assessment in Sudan". Map prepared as part of three reports that detail the results of a poverty assessment and mapping project...
Nov 7, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Major Land Use (key crops), 2010", https://hdl.handle.net/20.500.11766.1/FEMSKO, MELDATA, V6
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 2010 to 2014. Major crop types like cotton or rice were classified as separately, wh...
Nov 7, 2023
Biradar, Chandrashekhar, 2015, "Vegetation dynamics in central Asia: Normalized Differential Vegetation Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/UQ39C8, MELDATA, V5
Normalized Differential Vegetation Index (NDVI) from derived MODIS time series data from 2000-2014
Nov 7, 2023
Feger, Sebastian; Pertiwi, Cininta; Bonaiuti, Enrico, 2021, "Study I - Research Data Management Questionnaire Responses", https://hdl.handle.net/20.500.11766.1/FK2/REFFD7, MELDATA, V2
The dataset contains the responses to a questionnaire on Research Data Management (RDM) practices collected at ICARDA/CGIAR level. The survey was conducted in 2020/2021.
Nov 7, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Major Land Use (key crops), 2014", https://hdl.handle.net/20.500.11766.1/WMCR1B, MELDATA, V5
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 2010 to 2014. Major crop types like cotton or rice were classified as separately, wh...
Nov 7, 2023
Biradar, Chandrashekhar, 2015, "Vegetation dynamics in central Asia: Enhanced Vegetation Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/F4DCR8, MELDATA, V4
Enhanced Vegetation Index (EVI) derived from modis time-series satellite data at 8 days interval from 2000-2014
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.