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

61 to 70 of 112 Results
Mar 24, 2025
De Pauw, Eddy; Atassi, Layal; Tulaymat, Mohammad Fawaz; Nseir, B., 2019, "Climate Productivity Index (Crop Group III, Rainfed)", https://hdl.handle.net/20.500.11766.1/FK2/H9JU6P, MELDATA, V5
Data for characterization of Central Asia climatic conditions. Climate Productivity Index (Crop Group III, Rainfed) was calculated by using interpolated raster from climatic stations using CLIMAP tool developed at ICARDA.
Mar 24, 2025
Atassi, Layal; Al-Shamaa, Khaled; Biradar, Chandrashekhar, 2018, "Al_Khattem Potential Hot-spots Of Red Palm Weevil (RPW) Risk Based On Trap-data 2014", https://hdl.handle.net/20.500.11766.1/FK2/V7YTW3, MELDATA, V8
The layer was part of Enhancing date palm integrated pest management and agricultural extension and technology transfer systems in Abu Dhabi project, the layer was generated from survey information on date palm in Abu Dhabi obtained from Abu Dhabi Farmers Services Center. The tra...
Mar 24, 2025
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, V7
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...
Mar 24, 2025
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, V6
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...
Mar 24, 2025
Biradar, Chandrashekhar, 2015, "Characterization of crop fallows for agricultural intensification, length of crop fallows duration between two crops in one year in Eastern Gangetic plains from 2000 to 2014", https://hdl.handle.net/20.500.11766.1/FYTDWG, MELDATA, V9
This dataset contains characterization of the crop fallows using remote sensing for agricultural intensification and diversification in Eastern Gangetic plains from 2000 to 2014. Time series satellite data (MODIS and Landsat) were used to map the length of crop fallows in days (d...
Mar 24, 2025
Atassi, Layal; Al-Shamaa, Khaled; Biradar, Chandrashekhar, 2018, "Rahba Potential Hot-spots Of Red Palm Weevil (RPW) Risk Based On Trap-data 2014", https://hdl.handle.net/20.500.11766.1/FK2/SFF71X, MELDATA, V9
The layer was part of enhancing date palm integrated pest management and agricultural extension and technology transfer systems in Abu Dhabi project, the layer was generated from survey information on date palm in Abu Dhabi obtained from Abu Dhabi Farmers Services Center. The tra...
Mar 24, 2025
Biradar, Chandrashekhar, 2015, "Vegetation dynamics in central Asia: Enhanced Vegetation Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/F4DCR8, MELDATA, V5
Enhanced Vegetation Index (EVI) derived from modis time-series satellite data at 8 days interval from 2000-2014
Mar 24, 2025
Biradar, Chandrashekhar, 2015, "Central Asia and NW China relative change in annual mean precipitation 2020s_A1b", https://hdl.handle.net/20.500.11766.1/FK2/184AQQ, MELDATA, V4
Central Asia and North-West China (Xingjiang province) relative change in annual mean precipitation in 2020 according to IPCC near-term climate change scenario A1b
Mar 24, 2025
Biradar, Chandrashekhar, 2015, "Central Asia and NW China annual average of monthly maximum temperature 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/L5PTRP, MELDATA, V5
Central Asia and North-West China (Xingjiang province) annual average of monthly maximum temperature in 2020 according to IPCC near-term climate change scenario A2
Mar 24, 2025
Biradar, Chandrashekhar, 2015, "Central Asia and NW China absolute change of annual mean precipitation 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/AYX737, MELDATA, V5
Central Asia and North-West China (Xingjiang province) absolute change of annual mean precipitation in 2020 according to IPCC near-term climate change scenario A2
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.