Enhance and apply the IMPACT system of models to examine multiple and likely conflicting trends and related goals at the global and regional scales, and for selected countries, in the context of changes in population, income, technology and climate to 2050. This dataset is directly applicable for use as the baseline feed data for the global economic model IMPACT. It is adaptable for use with models with similar representation of the global agricultural and food system.

The International Center for Agricultural Research in the Dry Areas (ICARDA), the National Institute for Agronomic Research in Tunis (INRAT) and the Office for Livestock and Pasture (OEP) ) and the University of Georg-August in Goettingen, Germany, conducted a survey to provide more understanding about farmers' decision-making regarding the production and marketing of products. The survey focused in finding mechanisms by which farmers can effectively adopt agricultural technologies that can improve their economic status and well-being. The survey took place between October and December 2016.

The family farm dataset comes from a data collection conducted from July to October 2020 within two CGIAR Research Programs on Policies, Institutions, and Markets (PIM) and on Livestock (LIVESTOCK). The two CRP programs aimed to empower the CGIAR response to a sanitary crisis in analyzing the impacts of the COVID-19 pandemic. Here, we proposed to focus on the effects of the confinement on the agricultural productive systems with a particular attention to the gendered impacts in one country of the Mediterranean and Northern Africa region (MENA), Egypt. This dataset gathers the raw data collected among 210 respondents equally distributed between man and women respondents in two contrasted zones of Egypt, i.e. the old land along the Nile valley and the New land in the Western part of the Nile Delta. The raw data have been based on a semi-structure questionnaire (supplemental material). We collected sex-disaggregated data on the pandemic impacts and coping mechanisms in the crop and livestock domains (from production, to consumption to sales), on access to water and on personal lives. With increased dependence on digital interventions during the course of the pandemic, we also paid attention to phone ownership and access and preferred means for receiving digital information.

These data contain seven rounds of a longitudinal, socio-economic survey of 924 pastoral households from Marsabit County Kenya, collected between 2010 and 2020. Data collection was made possible, in part, by support provided by the generous funding of the UK Department for International Development, the Australian Department of Foreign Affairs and Trade and the Agriculture and Rural Development Sector of the European Union through DfID accountable grant agreement No: 202619-101, the UK Department for International Development through FSD Trust Grant SWD/Weather/43/2009, the United States Agency for International Development grant No: EDH-A-00-06-0003-00, the World Bank’s Trust Fund for Environmentally and Socially Sustainable Development Grant No: 7156906, the Consultative Group for International Agricultural Research (CGIAR) Research Programs on Climate Change, Agriculture and Food Security (CCAFS) and Dryland Systems, and the Standing Panel on Impact Assessment (SPIA) of the CGIAR.

Annual rainfall likely to be exceeded in 3 years out of 4, 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 in North and Southern Sudan. The map helps to characterize the potential and risks related to the natural resource base for agriculture in the different States of Sudan. For more information refer to linked report.

Survey data on impact of winter chickpea technology in Syria. The study was conducted in the 2005/06 season to collect information from farmers on the performance of winter-sown cultivars in comparison with the traditional spring plantings. The data were used to analyze the impact of winter chickpea technology and to identify constraints to its adoption, in order to guide future research at ICARDA. NOTE: The original dataset was stored in SPSS Statistics file format, but it was not accessible without proper licence.

LIVES is an initiative designed by the International Livestock Research Institute (ILRI), the International Water Management Institute (IWMI) and their national partners to build upon the success of the Canadian International Development Agency-funded project, Improving Productivity and Market Success of Smallholders in Ethiopia (IPMS). This dataset contains the household baseline Socio-economic survey.

The dataset contains pixel-based data of variables for Ethiopia and Kenya. Each data record (i.e. each row) is corresponding to one pixel of approximately 1 km2. The longitude and latitude coordinates of the pixel center (in decimal degree, GCS WGS 1984 datum) are the X and Y variables, respectively. Using these spatial coordinates of the pixels, the data represented in the file, or newly computed data from the data file, can be re-imported to a standard GIS software like ArcGIS (one raster layer is corresponding to one variable). For all variables, the value of -9999 indicates “no data”.

ICARDA has a regional mandate for chickpea within the Consultative Group of International Agricultural Research (CGIAR). ICARDA’s crop improvement programs develop nurseries for a wide range of agricultural systems and distribute them worldwide upon request. All nurseries are developed, prepared and dispatched from ICARDA. The international nursery trialing system is an integral part of the crop improvement programs of ICARDA and NARIs partners. It provides the cooperators with the opportunity to evaluate the genetically diverse germplasms generated through conventional and modern breading methodologies under their own agro-ecological conditions and socio-economic contexts. For this process to succeed, effective data collection and timely sharing are crucial.

Annual rainfall likely to be exceeded in 1 year out of 2, 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 in North and Southern Sudan. The map helps to characterize the potential and risks related to the natural resource base for agriculture in the different States of Sudan. For more information refer to linked report.

Data for characterization of Central Asia climatic conditions. Climate Productivity Index (Crop Group IV, Rainfed) was calculated by using interpolated raster from climatic stations using CLIMAP tool developed at ICARDA.

A characterisation and baseline survey of smallholder farmers undertaken at the start of the Livestock CRP Vietnam Priority Country project.

The study is part of project “Designing effective extension service delivery systems for enhancing wider adoption of agricultural technologies”. The project aims to understand how extension services influence smallholder farmers’ decisions to adopt a new improved wheat variety, but also offer guidance to the Ethiopian government on alternative extension approaches and capacity gaps of development agents. The dataset contains the result of a survey conducted to assess the baseline conditions of the experiment, targeting 1663 Ethiopian farmers and categorizing them into model and non-model farmers. NOTE: The original dataset was stored in SPSS Statistics file format.

This is 600K SNP data from 121 animals of five ecotypes of Sudan thin-tail Desert sheep. Additionally, genotypes from four breeds of Chinese fat-tail sheep were included in the study for comparative purposes. The data was used to investigate genome diversity, structure and dynamics of five ecotypes of Sudan thin-tail Desert sheep.

Datasets of all measured parameters for ET-based irrigation scheduling in Fergana Valley, Uzbekistan. Series of data is on winter wheat and cotton crops. Data collection occurred in 2015.

In this dataset, the main barley aspects are collected in order to monitor its trend in production and trade in Tunisia. The data are not collected in the field but downloaded from other web sources: FAOSTAT (http://www.fao.org/faostat/en/#data/QC) and Statistiques Tunisie (http://www.ins.tn/en/themes/agriculture). The period considered is from the year 1996 to 2014.

SRTM v4.1 is based on the finished-grade 2006 SRTM v2 release by NASA that was post-processed and published in 2008 by CGIAR-CSI (Consortium for Spatial Information). The SRTM v4.1 data set offers 3 arc-second (approximately 90 meters) spatial resolution and covers about 80% of Earth’s landmass, between 60° North and 56° South. SRTM v4.1 is divided onto 5° x 5° of latitude and longitude tiles in “geographic” projection, shown here.

The original SRTM v2 release contained voids (areas not or not well observed by the SRTM radar), mostly occurring in topographically steep terrain. The overcome this problem, CGIAR-CSI focused on filling the voids (holes) using various interpolation techniques, such as Kriging, moving window averaging, and importantly, the use of auxiliary elevation data sets (DEMs from other sources, e.g., national DEMs). CGIAR-CSI DEM v4.1 data comes at 5 deg x 5 deg tiles, and has a typical file size of 23 MB for one tile, which comprises two kinds of information; the DEM file and a mask file. The mask file is a binary file which identifies areas within the DEM that have been interpolated. The SRTM v4.1 datasets are available in ArcInfo ASCII and GeoTIFF (.tif) formats.

Geodetic information: The SRTM V4.1 DEMs are vertically referenced to the EGM96 geoid and horizontally referenced to the WGS84 (World Geodetic System 1984).

Further notes: This data set contains artefacts, e.g., pits or steps, over parts of the Himalayas, the Andes and other mountainous regions. Artefacts in SRTM v4.1 tend to occur over void-filled areas. The SRTM DEM represents bare ground elevations only where vegetation cover and buildings are absent. Over most areas, the DEM elevations reside between the bare ground (terrain) and top of canopies (surface), so are technically a mixture of a terrain and surface model.

Data access to the v4.1 data set: A detailed description is found at http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1 and access is possible via the data search page on http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp.

References:

Reuter H.I, A. Nelson, A. Jarvis, 2007, An evaluation of void filling interpolation methods for SRTM data, International Journal of Geographic Information Science, 21:9, 983-1008. Available on http://srtm.csi.cgiar.org/download/Reuteretal2007.pdf

This dataset compiles aggregated variables describing both global livestock sector and macroeconomic attributes of 217 countries globally for 2016. The data is compiled to help with context-specific and typology analyses of the importance of livestock within each of the emerging patterns of developing and developed countries. It further helps with analysis aiming at better understanding whether different structural characteristics of livestock sectors (including livestock production and importation by type) exist for (and influence) different levels of economic development of countries. This dataset was collected from different sources including FAOSTAT, World Banks’ World Development Indicators (WDI), and OCDE.

This is a SNP's dataset to investigate genome-wide signals in relation with resistance of sheep in Tunisia to gastrointestinal nematodes. Out of a phenotypic database comprising 320 animals, 96 where selected as being phenotypically resistant, vulnerable or potentially resistant and their ADN was SNPs genotyped.

The file contains single nucleotide polymorphisms (SNP) data generated in Sudanese desert sheep to investigate genetic diversity and structure. The research took place in Sudan between 2017 and 2019. The data is stored in a zip folder containing 3 files: SudanDesertSheepFinalReport1.txt, SudanDesertSheepFinalReport2.txt and SudanDesertSheepFinalReport3.txt.