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
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81 to 90 of 112 Results
Jan 13, 2026
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, V6
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
Jan 13, 2026
Biradar, Chandrashekhar, 2015, "Vegetation dynamics in central Asia: Land Surface Water Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/PDAP3M, MELDATA, V7
Land Surface Water Index (LSWI) derived from MODIS time-series satellite data at 8 days interval from 2000-2014
Jan 13, 2026
Biradar, Chandrashekhar, 2015, "Characterization of crop fallows for agricultural intensification, Mapping start date of the crop fallows in Eastern Gangetic plains from 2000 to 2014", https://hdl.handle.net/20.500.11766.1/7QRGVG, MELDATA, V6
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...
Jan 13, 2026
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Rice Yield Map (Pixel Based), 2012", https://hdl.handle.net/20.500.11766.1/FK2/ZYHJSH, MELDATA, V7
Yield map (Pixel based) for rice paddy in Fergana valley, Uzbekistan in 2012. The map is part of a series on crop distribution from 2004 to 2015.
Jan 13, 2026
De Pauw, Eddy; Atassi, Layal; Tulaymat, Mohammad Fawaz; Nseir, B., 2019, "Climate Productivity Index (Crop Group IV, Rainfed)", https://hdl.handle.net/20.500.11766.1/FK2/IQ480N, MELDATA, V9
Data for characterization of Central Asia climatic conditions. Climate Productivity Index (Crop Group IV, Rainfed) was calculated by using interpolated raster from climatic stations using CLIMAP tool developed at ICARDA.
Jan 13, 2026
Zhang, Geli; Biradar, Chandrashekhar; Xiao, Xiangming; Zhou, Yuting; Qin, Yuanwei; Zhang, Yao; Liu, Fang; Thomas, Richard; Ding, Mingjun; Dong, Jinwei, 2017, "Spatial distribution of grassland and sparsely vegetated land in Central Asia from 2000 to 2014", https://hdl.handle.net/20.500.11766.1/WOGD57, MELDATA, V6
Spatial distribution of grassland and sparsely vegetated land in Central Asia from 2000 to 2014. Raster data was done for Exacerbated grassland degradation and desertification in Central Asia during 2000–2014 study. The data was generated by using standard deviation of Enhanced V...
Jan 13, 2026
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, V14
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...
Jan 13, 2026
Biradar, Chandrashekhar; Atassi, Layal; Oweis, Theib; Haddad, Mira, 2017, "Agricultural water productivity for rainfed areas in 2006", https://hdl.handle.net/20.500.11766.1/NLMS9P, MELDATA, V8
The dataset contains one of the layers produced for “Supporting Coordination and Cooperation in Water Management in the Euphrates and Tigris Area CPET” project. The project aims to assess the status of water use in agriculture in the Euphrates-Tigress basin, determine and map the...
Jan 13, 2026
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Rice Yield Map (Pixel Based), 2010", https://hdl.handle.net/20.500.11766.1/FK2/V1XZTD, MELDATA, V7
Yield map for rice in Fergana valley, Uzbekistan in 2010. The map is part of a series on crop distribution from 2004 to 2015.
Jan 13, 2026
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Wheat Yield Map (Pixel Based), 2010", https://hdl.handle.net/20.500.11766.1/FK2/1FDAFV, MELDATA, V10
Yield map for wheat in Fergana valley, Uzbekistan in 2010. The map is part of a series on crop distribution from 2004 to 2015.
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