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 685 Results
Feb 27, 2025
De Pauw, Eddy; Atassi, Layal; Tulaymat, Mohammad Fawaz; Nseir, B., 2019, "Climate Productivity Index (Crop Group I, Rainfed)", https://hdl.handle.net/20.500.11766.1/FK2/MKHBVB, MELDATA, V3
Data for characterization of Central Asia climatic conditions. Climate Productivity Index (Crop Group I, Rainfed) was calculated by using interpolated raster from climatic stations using CLIMAP tool developed at ICARDA.
Feb 27, 2025
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, V3
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 18, 2025
Atassi, Layal; Al-Shamaa, Khaled; Biradar, Chandrashekhar, 2018, "Al_Khattem Potential Hot-spots Of Red Palm Weevil (RPW) Risk Based On Trap-data 2015", https://hdl.handle.net/20.500.11766.1/FK2/GX5B7F, MELDATA, V5
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...
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2024:12:18 10:29:16
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Comma Separated Values - 344 B - MD5: f791f50a1df8ea39b94f567bd00b886a
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