The Global Land Cover-SHARE (GLC-SHARE) is a new land cover database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.

It provides a set of major thematic land cover layers resulting by a combination of “best available” high resolution national, regional and/or sub-national land cover databases with the weighted average land cover information derived from large-scale available datasets. The database is produced with a resolution of 30 arc second (1km). The approach implemented is based on the utilization of the Land Cover Classification System (LCCS) and SEEA (System of Environmental-Economic Accounting) legend systems for the harmonization of the various global, regional and national land cover legends.

The major benefit of the GLC-SHARE product is its capacity to preserve the existing and available high resolution land cover information at the regional and country level obtained by spatial and multi-temporal source data, integrating them with the best synthesis of global datasets.

Preliminary validation campaign was performed using 1000 random points statistically distributed over each land cover classes.

The database is distributed in the following eleven layers, in raster format (GeoTIFF ), whose pixel values represent the percentage of density coverage in each pixel of the land cover type. The dominant layer, representing the value of the dominant land cover type, is also available along with a legend in LYR ESRI format. Finally, information on each layer's source is retrievable in sources layer, by joining the raster values with an Excel table.

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FAO Major Fishing Areas for Statistical Purposes are arbitrary areas, the boundaries of which were determined in consultation with international fishery agencies on various considerations, including (i) the boundary of natural regions and the natural divisions of oceans and seas; (ii) the boundaries of adjacent statistical fisheries bodies already established in inter-governmental conventions and treaties; (iii) existing national practices; (iv) national boundaries; (v) the longitude and latitude grid system; (vi) the distribution of the aquatic fauna; and (vii) the distribution of the resources and the environmental conditions within an area.

The FAOSTAT domain “Pre- and Post-Production” (PPP) includes the greenhouse gas (GHG) emissions, and related activity data, generated from pre- and post-agriculture production stages of the agri-food systems. Data are computed following the Tier 1 methods of the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). The domain includes methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2) and CO2 equivalent (CO2eq) emissions from the above activities as well as the aggregate fluorinated gases (F-gases) emissions. Estimates are available by country, with global coverage for the period 1990–2020. The database is updated annually.The FAOSTAT domain disseminates information estimates of CH4, N2O, CO2 emissions, F-gases, their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg), and the underlying activity data. CO2eq emissions are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014). Data are available for most countries and territories, for standard FAOSTAT regional aggregations, and for Annex I and non-Annex I country groups.

This is a database workflow for SIMPLE Databate and Model for Base Year 2017. This database is for the non-gridded version of SIMPLE and is defined for 153 regions. It takes crop production data from FAOSTAT via API and allows users to change database ass

The 2015 Global Forest Resources Assessment (FRA2015) continues the tradition of seeking to describe the world’s forests – a tradition that began in 1948. The world’s forest 2010 map is a raster product with a pixel size of 250 meters by 250 meters. It was prepared with the following geospatial layers : - Forest cover data from the Vegetation Continuous Fields product (VCF) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, on-board the Terra and Aqua satellites (Earth Observation System, NASA) with a 250 m spatial resolution (Hansen et al., 2003). - Water data from the Shuttle Radar Topography Mission (SRTM, NASA) Water Body Data at 250 m spatial resolution in combination with the MODIS global water mask (Carroll et al., 2009). - Elevation data from the SRTM at 1 km resolution, down-sampled to the 10 million scale. - Country boundaries and coastlines from the Global Administrative Unit Layer (GAUL, 2008) of the FAO. - Global ecological zones (FAO, 2012)

References: - Hansen, M., R. DeFries, J.R. Townshend, M. Carroll, C. Dimiceli, and R. Sohlberg, 2003. Vegetation Continuous Fields MOD44B, 2001 Percent Tree Cover, Collection 3, University of Maryland, College Park, Maryland, 2001. - Carroll, M., Townshend, J., DiMiceli, C., Noojipady, P., Sohlberg, R. 2009. A New Global Raster Water Mask at 250 Meter Resolution. International Journal of Digital Earth. ( volume 2 number 4) - FAO, 2012 Global ecological zones for FAO forest reporting: 2010 Update. Forest resources Assessment Working Paper 179, Rome, 2012.

The FAOSTAT Temperature change on land domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Simplified AEZ classification (33 classes) at about 1 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100.

The Simplified AEZ classification dataset is part of the GAEZ v4 Theme 1 Land and Water Resources, Agro-Ecological Zones sub-theme.

The agro-ecological zones classification provides a characterization of bio-physical resources relevant to agricultural production systems. AEZ definitions and map classes follow a rigorous methodology and an explicit set of principles. The inventory combines spatial layers of thermal and moisture regimes with broad categories of soil/terrain qualities. It also indicates locations of areas with irrigated soils and shows land with severely limiting bio-physical constraints including very cold and very dry (desert) areas as well as areas with very steep terrain or very poor soil/terrain conditions.

For further details, please refer to the GAEZ v4 Model Documentation.

Data publication: 2021-05-01

Contact points:

Metadata Contact: GAEZ@fao.org

Resource Contact: Fischer Gunther

Resource Contact: UNFAO - NSL Geospatial Unit

Resource constraints:

GAEZ v4 Disclaimer at https://gaez.fao.org/pages/disclaimer

Online resources:

GAEZ v4 Model Documentation

GAEZ v4 Data Portal

SLD

Data for download: Simplified AEZ classification (33 classes) for the time period 1981-2010 using climate data source CRUTS32 based on historical data

Data for download: Simplified AEZ classification (33 classes) for the time period 2041-2070 using climate data source ENSEMBLE based on RCP8.5

Data for download: Simplified AEZ classification (33 classes) for the time period 2071-2100 using climate data source ENSEMBLE based on RCP8.5

The main sources of information for the species distribution are the habitat description and geographic range contained in the published FAO Catalogues of Species (more details at http://www.fao.org/fishery/fishfinder ). Terms used in the descriptive context of the FAO Catalogues were converted in standard depth, geographic and ecological regions and inserted into a Geographic Information System.

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FAO Texture of upper soil horizon of the Soil Typological Unit (STU)

The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalaspectsGAUL2015.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalaspectsGAUL2015.pdf and G2015_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. 5 territories have been updated respect to the previous release. Moreover, the coastline of American countries or other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf). GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see ReleaseNoteGAUL2015.pdf). GAUL 2015 is the eighth release of the GAUL Set.

The main sources of information for the species distribution are the habitat description and geographic range contained in the published FAO Catalogues of Species (more details at http://www.fao.org/fishery/fishfinder ). Terms used in the descriptive context of the FAO Catalogues were converted in standard depth, geographic and ecological regions and inserted into a Geographic Information System.

The main sources of information for the species distribution are the habitat description and geographic range contained in the published FAO Catalogues of Species (more details at http://www.fao.org/fishery/fishfinder ). Terms used in the descriptive context of the FAO Catalogues were converted in standard depth, geographic and ecological regions and inserted into a Geographic Information System.

Prevalence of Undernourishment (% of Population) by country for 2013. This is a filtered layer based on the "Undernourished population by country, 2000-2010 time series" layer.

The dataset on fish catches in the European waters by FAO statistical areas was created in 2015 by Cogea for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation of EUROSTAT's fish catches datasets fish_ca_atl 27, fish_ca_atl 34, fish_ca_atl 37, fish_ca_atl271, fish_ca_atl272, fish_ca_atl34_h and fish_ca_atl37_h. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). EUROSTAT data have been related to FAO's georeferenced fishing areas (polygons) for statisticl purposes (FAO, 2020. FAO Statistical Areas for Fishery Purposes. In: FAO Fisheries and Aquaculture Department). Tonnes live weight is provided for each fish species caught (3-alpha code and english or scientific name if the english one is not available), by EUMOFA's larger aggregations such as EUMOFA's Commodity Groups and Main Commercial Species (see 'Species_Eumofa_ASFIS_2023' table), by year of reference and country (code and name). The dataset is updated yearly and it covers a time series from 1950 to 2021, where available. Compared with the previous version this new one's schema have been updated.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Crop production values in this dataset are given per ha for each technology aggregated by categories - crops/food/non-food - with no information on individual crops.

Unit of measure: Production per ha for each technology: mt/ha

This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005.

More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

Data publication: 2019-10-09

Citation:

Citation: International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V3.

Contact points:

Metadata Contact: Koo, Jawoo International Food Policy Research Institute (IFPRI)

Resource Contact: You, Liangzhi International Food Policy Research Institute (IFPRI)

Resource Contact: Harvard Dataverse

Data lineage:

Differences compared to SPAM 2010 V1r0 (Uploaded December 2018): - No more rounding errors of 0.1 ha or mt, ie areas and production in each pixel satisfy conditions: R=A-I and R=H+L+S. - CSV files do not have 'strange' entries for Yemen - admin names with "," and ";" were corrected. - Value of production only has one set of entries for Sudan - in previous version it had 2. - Missing values for maize in Nigeria/Osun (fisp1=NI31) now included.

Resource constraints:

IFPRI DATAVERSE TERMS OF USE This work is licensed under a Creative Commons Attribution 4.0 International License.

Online resources:

Map SPAM Data Center

ReadMe_V1r1_GeoTiff.txt

Download from Harvard Dataverse

Download - MapSpam Dimensions:Technology and Crops (CVS)

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The main sources of information for the species distribution are the habitat description and geographic range contained in the published FAO Catalogues of Species (more details at http://www.fao.org/fishery/fishfinder ). Terms used in the descriptive context of the FAO Catalogues were converted in standard depth, geographic and ecological regions and inserted into a Geographic Information System.

New Zealand Food Production and Nutrition Data Stack (NZFPNDS) contains 19 spatial layers informing on annual nutrition indicators for energy, macronutrients, minerals, and vitamins. NZFPNDS has been modelled employing data from the Food and Agriculture Organisation of the United Nations, AgriBase® (a product of AsureQuality) and Land Cover Database version 5.0. Produced for analyses including health and well-being aspects. Coverage of the New Zealand mainland, its near-shore Islands, and Chatham Islands is included for all layers. Results are temporally relevant from 2018 to 2022 and resolution of all grids is one kilometre.

For information on codes used, please see the FAO nutrient conversion tables at https://openknowledge.fao.org/server/api/core/bitstreams/d3dd48cd-b157-4741-9a7d-bfd91d17fbf4/content.