The map shows the total annual water withdrawal. Water withdrawals are downscaled to to a five arc-minute grid. Water is considered scarce when the withdrawals exceed 40% of the renewable resource. According to statistics compiled by FAO (FAOSTAT), several countries in North Africa, the Middle East and Central Asia withdraw more water than their total renewable resources. Domestic water withdrawals are downscaled by applying the per capita domestic water use to population of each pixel. Industrial water withdrawals were downscaled by using the industrial water use per unit GDP and applying downscaled information on GDP. Water consumption is assumed to be 30% of domestic use and 10% of industrial use. Finally, agricultural water consumption is assumed to be the crop water deficit in irrigated areas generated in the AEZ analysis and water used for livestock consumption, applied to a global spatial data set of livestock distribution prepared by FAO. Source of the map: GAEZ 2009 and AQUASTAT; downscaling simulations by authors.

The FGGD severe environmental constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Pixels with no severe environmental constraints contain a value of zero. Each other pixel contains a cumulative class value that shows which environmental constraint is binding in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Toxicities (SQ.6) Low pH leads to acidity related toxicities, e.g., aluminum, iron, manganese toxicities, and to various deficiencies, e.g., of phosphorus and molybdenum. Calcareous soils exhibit generally micronutrient deficiencies, for instance of iron, manganese, and zinc and in some cases toxicity of molybdenum. Gypsum strongly limits available soil moisture. Tolerance of crops to calcium carbonate and gypsum varies widely (FAO, 1990; Sys, 1993). Low pH and high calcium carbonate and gypsum are mutually exclusive. Acidity related toxicities such as aluminum toxicities and micro-nutrient deficiencies are accounted for respectively in SQ1, nutrient availability, and in SQ2, nutrient retention capacity. This soil quality SQ6 is therefore only including calcium carbonate and gypsum related toxicities. The most limiting of the combination of excess calcium carbonate and gypsum in the soil, and occurrence of petrocalcic and petrogypsic soil phases is selected for the quantification of SQ6. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Excess salts (SQ5) Accumulation of salts may cause salinity. Excess of free salts referred to as soil salinity is measured as Electric Conductivity (EC in dS/m) or as saturation of the exchange complex with sodium ions, which is referred to as sodicity or sodium alkalinity and is measured as Exchangeable Sodium Percentage (ESP). Salinity affects crops through inhibiting the uptake of water. Moderate salinity affects growth and reduces yields; high salinity levels may kill the crop. Sodicity causes sodium toxicity and affects soil structure leading to massive or coarse columnar structure with low permeability. Apart from soil salinity and sodicity, conditions indicated by saline (salic) and sodic soil phases may affect crop growth and yields. In case of simultaneous occurrence of saline (salic) and sodic soils the limitations are combined. The most limiting of the combined soil salinity and/or sodicity conditions and occurrence of saline (salic) and/or sodic soil phase is selected. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

Annual temperature sum for days with Ta >5 °C (degree-days) at about 9 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 Annual temperature sum for days with Ta >5 °C (degree-days) dataset is part of the GAEZ v4 Agro-climatic Resources - Thermal Regime sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

On 8 November 2013, Typhoon Haiyan made landfall in the Philippines causing widespread devastation, tremendous loss of life and catastrophic impact on infrastructure and natural resources. As a result of the devastating damage to agriculture and fisheries sectors and the severe impact on lives and livelihoods FAO has declared Level 3 Emergency Response. In response to the call for needs assessment and coordinating humanitarian assistance in agriculture and fisheries sectors the FAO, Land and Water Division Geospatial Unit collected a number of datasets from various sources including FAO, WFP, UNITAR-UNOSAT, EC-JRC, USDA, USGS as well as updated reports from national agencies as well as the National Disaster Risk Reduction and Management council. Severity zones were mapped using data from the EC-JRC updates based on the Public Storm Warning Signals (PSWC) in four main levels: S1-very high for PSWC #1, S2-high for PSWC #2, S3-medium for PSWC #3 and S4-medium-low for PSWC#4. The severity area mask was calibrated using remote sensing data combined with the data reported on November 13, 2013 by the National Disaster Risk Reduction and Management Council of Philippines. The severity intensity rate was classified proportionally with the affected population figures. The data on crop production, harvested area, yield for major crops were collected from FAO, Global Spatial Database of Agricultural Land-Use Statistics AgroMaps ( http://kids.fao.org/agromaps ), FAOSTAT ( http://faostat.fao.org/) and Bureau of Agricultural Statistics of Philippines (http://countrystat.bas.gov.ph ). Data on arable land and permanent crops were derived from the FAO Global Land Cover Share database (Beta-version) and data on the area by district were derived by the FAO Global Administrative Units Layer, GAUL version 2013-12 (www.fao.org/geonetwork). In addition data on crop calendars to identify crop growing stage were collected from the FAO-IIASA Global Agro-Ecological Zones Data portal, GAEZ (www.fao.org/nr/gaez) , FAO Crop Calendar from Data@Fao.org (https://data.apps.fao.org/) and FAO Agricultural Market Information System, AMIS (http://www.amis-outlook.org/) .Philippines rice crop calendar was provided by the Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI). The assessment was done by first georefencing, harmonizing and creating a central database in the UTM WGS 84 reference system. Areas of crop growth stages were analyzed according to their stage at the time of the event. Major season and secondary season for rice were considered for analysis at the time of the event. The areas of standing rice at the moment of the event were mapped using the district level crop calendars for the major season. Also the planted rice areas for the second season were mapped. The affected area was then calculated considering both these areas which were considered as area loss, calibrated by the severity intensity rate class. The extent (ha) and fraction (%) of the potential affected crop areas was calculated by intersecting the tropical cyclone severity areas with the administrative layers, arable land and permanent crops, annual harvested area, yield and production for year 2012 (which was used as a proxy to assess the planted area). Major crops which were affected include Rice Paddy, Coconut and Sugar Cane. The maps show the impacts of the tropical cyclone on these crops. The outputs were classified in 5 classes of affected areas: less the 10%, 10-25%, 25-50%, 50-75%, and >75%. The tabular information provides information on the estimated affected area for Rice Paddy (extent in ha and share in percentage) by Typhoon “YOLANDA” (HAIYAN) severity Level and by District (updated on 15 November 2013). The affected planted areas for each severity zone were calculated as a fraction of the planted area versus by severity zone by administrative unit area. In addition, the analysis was done for major livestock, pig, poultry and cattle, affected population. Sources: Agromaps, FAOSTAT, CountrySTAT Philippines, GAEZ, AMIS, GLC-SHARE, FAO NRL Geospatial Unit datasets, Data@FAO.ORG, EC-JRC, ESA GlobCover, UNITAR, UNOSAT, WFP, National Disaster Risk Reduction and Management Council, Republic of Philippines, Philippines rice crop calendar, Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI) sponsored by the DA-National Rice Program via the Rice Self Sufficiency Program (RSSP) and the Global Rice Science Partnership (GRiSP), the CGIAR Research Program on Rice.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.

This dataset forms part of a global livestock mapping project by the Food and Agriculture Organization's Animal Production and Health Division (FAO-AGA). The methodology and sources of data are fully described in a document entitled: "The Gridded Livestock of the World FAO (2007)". In summary, for each country the most recent available sub-national livestock census data and corresponding administrative boundaries have been collected. These are then converted into densities, excluding land unsuitable for livestock (either monogastric or ruminant), to provide the 'observed' data. The data are then disaggregated based on statistical relations with some environmental variables in similar agro-ecological zones to produce the 'predicted' distribution. The predicted data are further manipulated to match national census totals for the year 2000 and the year 2005 according to the FAOSTAT database. The project includes: a global network of data providers on livestock and sub-national boundaries; an Oracle database in which these data are managed and processed; a system for predicting livestock distributions based on environmental data and an interactive web application, the Global Livestock Production and Health Atlas (GLiPHA - http://www.fao.org/ag/aga/glipha/index.jsp), through which data are viewed and disseminated. The files are in a raster GRID format, with an ArcGis layer file and an ArcView legend file. Pixel values represent actual densities (per square kilometre). Projection details are given in the metadata. The map should ideally be viewed with the overlay of national boundaries, water bodies and unsuitable land. All of these supplementary data are available in accompanying zip files. These data have been produced by FAO's Animal Production and Health Division in collaboration with ERGO and the TALA research group, University of Oxford, UK.