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 Watershed Aspect
TIFF Image - 40.8 MB - MD5: fdd656b024ec0624b25f06606342387f
Sep 30, 2019 - Balkh Watershed Aspect
JPEG Image - 8.0 MB - MD5: 54180000d159065ac120692d47a711ec
Sep 30, 2019
Atassi, Layal; Biradar, Chandrashekhar, 2019, "Balkh Watershed Slope", https://hdl.handle.net/20.500.11766.1/FK2/EOXOIZ, MELDATA, V1
Balkh slope was generated from digital terrain models (DTM) with 5 m resolution derived from DigitalGlobe imagery assets. The layer covers Balkh watershed. This layer is intended to be used in Afghanistan Electronic Atlas, as one of the surface characterization layers in Afghanis...
Sep 30, 2019 - Balkh Watershed Slope
TIFF Image - 40.3 MB - MD5: 4b8874906e9d5063e771b5fb5322f21a
Sep 30, 2019 - Balkh Watershed Slope
JPEG Image - 4.7 MB - MD5: 01358a2bc5d6bd72f419b20adf128d45
Sep 30, 2019
Atassi, Layal; Biradar, Chandrashekhar, 2019, "Takhwsh Watershed Aspect", https://hdl.handle.net/20.500.11766.1/FK2/HM04SV, MELDATA, V1
Takhwsh aspect 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 Afg...
Sep 30, 2019 - Takhwsh Watershed Aspect
JPEG Image - 5.8 MB - MD5: f21fd01e21ccac52d9aae94620e61762
Sep 30, 2019 - Takhwsh Watershed Aspect
TIFF Image - 197.5 KB - MD5: 24c2b4e5a743c25dabdedd82ad0f8ff5
Sep 30, 2019
Atassi, Layal; Biradar, Chandrashekhar, 2019, "Takhwsh Watershed Slope", https://hdl.handle.net/20.500.11766.1/FK2/EPF3W1, MELDATA, V1
Takhwsh slope 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 Afgh...
Sep 30, 2019 - Takhwsh Watershed Slope
JPEG Image - 6.1 MB - MD5: 5ce21c0d94198c5d3d3dedf86288b024
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