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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1 to 10 of 94 Results
Nov 29, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China annual average of monthly maximum temperature 2020s_A1bV1", https://hdl.handle.net/20.500.11766.1/FK2/YW2EPD, MELDATA, V1
Central Asia and North-Western China annual average of monthly maximum temperature in 2020 according to IPCC near-term climate change scenario A1b
Nov 29, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of annual minimum temperature 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/LRM1Z7, MELDATA, V1
Central Asia and North-Western China absolute change of annual minimum temperature in 2020 according to IPCC near-term climate change scenario A2
Nov 29, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of the annual minimum temperature 2020s_A1bV1", https://hdl.handle.net/20.500.11766.1/FK2/OPNQUD, MELDATA, V1
Central Asia and North-Western China absolute change of annual minimum temperature in 2020 according to IPCC near-term climate change scenario A1b
Nov 29, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of the annual maximum temperature 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/H4RWHJ, MELDATA, V1
Central Asia and North-Western China absolute change of annual maximum temperature in 2020 according to IPCC near-term climate change scenario A2
Nov 6, 2022
Biradar, Chandrashekhar, 2022, "Ground truth data from the Ferghana valley for remote sensing analysis and validation", https://hdl.handle.net/20.500.11766.1/FK2/NSXDEA, MELDATA, V2
Ground truth data from the Ferghana vallet for land use and land cover mapping, crop productivity and land degradation assessment
Nov 2, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of annual maximum temperature 2020s_A1bV1", https://hdl.handle.net/20.500.11766.1/FK2/C0HLKT, MELDATA, V1
Central Asia and North-Western China absolute change of annual maximum temperature in 2020 according to IPCC near-term climate change scenario A1b
Oct 3, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of mean annual precipitation 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/AYX737, MELDATA, V1
Central Asia and North-Western China Absolute Change of mean annual precipitation in 2020 according to IPCC near-term climate change scenario A2
Oct 3, 2022
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of mean annual precipitation 2020s_A1bV1", https://hdl.handle.net/20.500.11766.1/FK2/AHVZDB, MELDATA, V1
Central Asia and North-Western China Absolute Change of mean annual precipitation in 2020 according to IPCC near-term climate change scenario A1b
Aug 31, 2022
Biradar, Chandrashekhar, 2022, "Vegetation dynamics in central Asia: Land Surface Water Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/PDAP3M, MELDATA, V1
Land Surface Water Index (LSWI) derived from MODIS time-series satellite data at 8 days interval from 2000-2014
Aug 23, 2022
Biradar, Chandrashekhar, 2022, "Vegetation dynamics in central Asia: Normalized Differential Vegetation Index 2000-2014", https://hdl.handle.net/20.500.11766.1/FK2/UQ39C8, MELDATA, V1
Normalized Differential Vegetation Index (NDVI) from derived MODIS time series data from 2000-2014
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