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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61 to 70 of 112 Results
Nov 7, 2023
Biradar, Chandrashekhar; Loew, Fabian; Fliemann, Elisabeth, 2016, "Land Cover Map of Fergana 2013", https://hdl.handle.net/20.500.11766.1/TYRWGS, MELDATA, V6
Land use map shows the spatial distribution of dominant crop types (at the per-parcel level) in the major part of the Fergana Valley in 2012. The map is part of a series on crop distribution from 2010 to 2014. Major crop types like cotton or rice were classified as separately, wh...
Aug 3, 2023
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, V2
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...
May 23, 2023
Kosimov, Sherzod, 2023, "Soil sampling points in Fergana valley and Sogd", https://hdl.handle.net/20.500.11766.1/FK2/AEFLE7, MELDATA, V1
Soil sampling points in Fergana valley and Sogd, CRP WUE activities 2015
Feb 11, 2023
Biradar, Chandrashekhar; Atassi, Layal; Oweis, Theib; Haddad, Mira, 2019, "Agricultural water productivity for rainfed areas in 2006", https://hdl.handle.net/20.500.11766.1/NLMS9P, MELDATA, V4
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...
Feb 11, 2023
Biradar, Chandrashekhar; Atassi, Layal; Oweis, Theib; Haddad, Mira, 2019, "Agricultural water productivity for irrigated areas in 2008", https://hdl.handle.net/20.500.11766.1/CUZ56W, MELDATA, V5
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...
Feb 11, 2023
Biradar, Chandrashekhar; Atassi, Layal; Oweis, Theib; Haddad, Mira, 2019, "Agricultural water productivity for rainfed areas in 2008", https://hdl.handle.net/20.500.11766.1/L9USGL, MELDATA, V5
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...
Feb 11, 2023
Biradar, Chandrashekhar; Atassi, Layal; Oweis, Theib; Haddad, Mira, 2019, "Agricultural water productivity for irrigated areas in 2006", https://hdl.handle.net/20.500.11766.1/LDJRMX, MELDATA, V6
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...
Feb 11, 2023
Biradar, Chandrashekhar, 2022, "Central Asia and NW China annual average of monthly maximum temperature 2020s_A1b", https://hdl.handle.net/20.500.11766.1/FK2/YW2EPD, MELDATA, V5
Central Asia and North-West China (Xingjiang province) relative change in annual mean precipitation in 2020 according to IPCC near-term climate change scenario A1b
Feb 11, 2023
Biradar, Chandrashekhar, 2022, "Central Asia and NW China absolute change of annual maximum temperature 2020s_A1b", https://hdl.handle.net/20.500.11766.1/FK2/C0HLKT, MELDATA, V5
Central Asia and North-West China (Xingjiang province) absolute change of annual maximum temperature in 2020 according to IPCC near-term climate change scenario A1b
Feb 11, 2023
Biradar, Chandrashekhar, 2022, "Central Asia and NW China annual average of monthly minimum temperature 2020s_A2", https://hdl.handle.net/20.500.11766.1/FK2/9GIFQ5, MELDATA, V4
Central Asia and North-West China (Xingjiang province) annual average of monthly minimum temperature in 2020 according to IPCC near-term climate change scenario A2
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