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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Sep 30, 2019 - Balkh Contour
Unknown - 145 B - MD5: c742bee3d4edfc2948a2ad08de1790a5
Sep 30, 2019 - Balkh Contour
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Sep 30, 2019 - Balkh Contour
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JPEG Image - 4.3 MB - MD5: 4e20d32f89c73252fbffd987bf63e6e7
Sep 30, 2019
Atassi, Layal; Biradar, Chandrashekhar, 2019, "Takhwsh Contour", https://hdl.handle.net/20.500.11766.1/FK2/0SQYTU, MELDATA, V1
Takhwsh contour was generated from digital terrain models (DTM) with 5 m resolution derived from DigitalGlobe imagery assets. The layer covers Takhwsh watershed. This layer is intended to be used in Afghanistan Electronic Atlas, as one of the surface characterization layers in Af...
Sep 30, 2019 - Takhwsh Contour
Unknown - 5 B - MD5: ae3b3df9970b49b6523e608759bc957d
Sep 30, 2019 - Takhwsh Contour
Unknown - 693 B - MD5: 4da6eaf999947a64bbf1f1b4d8a7d7aa
Sep 30, 2019 - Takhwsh Contour
Unknown - 145 B - MD5: c742bee3d4edfc2948a2ad08de1790a5
Sep 30, 2019 - Takhwsh Contour
Unknown - 132 B - MD5: e764cefef3197dc971b6864aa36f5417
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