Global agriculture data

The dataset contains a set of financial indicators describing national expenditures on agricultural research and development (R&D). The indicators are derived primarily from responses to the financial resources section of the ASTI questionnaire, complemented with selected external macroeconomic indicators required to ensure comparability across countries and over time.The dataset includes both directly reported financial aggregates and derived indicators, reflecting different dimensions of agricultural R&D investment. Reported expenditure data are collected in current local currency units (LCU) and refer to total institutional expenditures, as provided by national respondents. Coverage of the private for-profit sector remains limited and is therefore excluded from the dataset. To support temporal analysis and international comparability, expenditure indicators are also expressed in constant local currency units, using country-specific GDP deflators sourced from FAOSTAT. In addition, selected indicators relate agricultural R&D expenditures to key structural variables, such as agricultural value added, allowing for meaningful cross-country comparisons of research intensity and resource allocation.

Exchange rates represent the nominal value of a national currency expressed in terms of the value of another currency. The nominal value of a national currency in a specific month or year is defined as Local Currency Units (LCU). Standard Local Currency Units (SLC) are the values of an old national currency expressed in terms of the most recent national currency against the US dollar. Annual exchange rates data are expressed as SLC and LCU against the US dollar. Monthly exchange rates data are expressed as LCU against the US dollar.

The FAOSTAT Employment indicators domain focuses on indicators related to employment in agrifood systems and rural areas. The update is performed yearly, using data from the International Labour Organization (ILO) database that contains a rich set of indicators from a wide range of topics related to labour statistics. The indicators published in FAOSTAT are derived from the labour force statistics (LFS) and rural and urban labour markets (RURURB) databases of the ILOSTAT database.

Arable land (hectares per person) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.

The ASTI Researchers Dataset provides indicators on human resources engaged in agricultural research and development (R&D) at the national level. Data are compiled from the human resources section of the ASTI questionnaire and presented in full-time equivalent (FTE) researchers to reflect the effective research effort devoted to agricultural R&D, using information on time allocation. The dataset disseminates two indicators: total agricultural researchers (FTE) and agricultural researchers (FTE) per 100 000 agricultural workers, the latter calculated using employment in agriculture data sourced from FAOSTAT to support cross-country comparability of research intensity.

The annual Gross Biomass Water Productivity expresses the quantity of output (total biomass production) in relation to the total volume of water consumed in the year (actual evapotranspiration). By relating biomass production to total evapotranspiration (sum of soil evaporation, canopy transpiration and interception), this indicator provides insights on the impact of vegetation development on consumptive water use and thus on water balance in a given domain. When the focus is on monitoring performance of irrigated agriculture in relation to water consumption, it is more appropriate to use transpiration alone as a denominator, as a measure of water beneficially consumed by the plant. This latter indicator, for which we use the term \"net water productivity\", provides useful information on how effectively vegetation (and particularly crops) uses water to develop its biomass (and thus yield). The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ is the ratio of kg of dry matter per cubic meter of water transpired by vegetation in one hectare Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of gross biomass water productivity (GBWP) is as follows: GBWP = TBP/ETIa Where TBP is annual Total Biomass Production in kg/ha and ETIa is annual Actual EvapoTranspiration and Interception in m³/ha. The following data is used for calculating it: - Annual TBP - Annual ETIa **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage Gross biomass water productivity (Africa and Near East - Annual - 250m)

The food and agricultural trade indicators dataset is based on trade, production and gross domestic product (GDP) data. Agri-food trade data are collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics (IMTS) Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. The source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take total cross-border trade flows into account. The trade database includes the following variables: export quantity, export value, import quantity, and import value. It includes all food and agricultural products imported/exported annually by all countries in the world. Production data details are available at https://www.fao.org/faostat/en/#data/QCL and GDP data details are available at https://www.fao.org/faostat/en/#data/MK

As part of the FAO Agriculture Capital Stock (ACS) database, the Statistics Division of FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). The database is further supplemented by OECD Structural Analysis database (OECD STAN) and, in a few cases, data from country’s statistics websites. If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official submissions by Member Countries. With a view of producing internationally comparable net capital stock estimates, the Perpetual Inventory Method (PIM) with a constant geometric depreciation rate is employed to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required.An attempt is made to rely as much as possible on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, econometric techniques to impute missing observations are employed. Depending on the pattern of data missingness for the countries, different imputation methods are applied (from among the ARIMAX, PANEL regression, and OLS approaches) for the data series from 1995 to 2022. The values of Agriculture Capital Stock related indicators for 2023, including Agriculture Investment Ratio, Agriculture Orientation Index, Net Capital Stock, Gross Fixed Capital Formation and Consumption of Fixed Capital, are estimated using the Holt-Winters (HW) method (Cipra et al., 1995). The HW method is an exponential smoothing method for forecasting the annual values of economic variables. In this context, the HW method relies on existing (historical) values of the Agriculture Capital Stock. The predicted value is an extrapolation of the historical values to the specified target date, which extends the timeline without considering seasonality in the annual series.All data series in the database are provided both in national currencies and in US dollars as well as in current prices and constant prices with base year 2015.

The FAOSTAT domain on Emissions from Pre- and post- agricultural production 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 and are 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 10^6 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.

Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016.

Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. For the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.

The FAOSTAT Temperature change on land domain disseminates statistics of mean surface temperature change by country, with annual updates. 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).

This sub-domain contains data on agriculture producer prices and the producer price index. Agriculture producer prices are prices received by farmers for primary crops, live animals and livestock primary products as collected at the point of initial sale (prices paid at the farm gate). Annual data are provided from 1991 (while mothly data start in January 2010) for 180 countries and 212 products. The producer price index is the index of agricultural producer prices that measures the average annual change over time in the selling prices received by farmers (prices at the farm gate or at the first point of sale). The three categories of producer price index available in FAOSTAT comprise: single-item price index, commodity group index and the agriculture producer price index.

Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.

Credit to agriculture measures the amount of loans and advances given by the banking sector to farmers, rural households, agricultural cooperatives, or any other agri-related businesses. Each country’s central bank compiles its credit stocks by economic activities as part of its monetary and financial statistics publications via annual or quarterly reports online. Data collection has been carried out mostly through reports found on the central bank websites. Data are collected and compiled according to the International Standard Industrial Classification of All Economic Activities (ISIC Rev. 4: https://unstats.un.org/unsd/classifications/Econ/isic) with a special concentration on the credit to agriculture, forestry and fishing sector separately from the ones to manufacturing and services.

Agricultural land refers to the land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.

Arable land (in hectares) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.

The database contains data on pulp and paper production, pulp and paper production capacity, information about utilization of recovered paper, and short-term capacity forecasts for major pulp and paper producing countries. The main types of pulp included in this database are wood pulp (mechanical, thermo-mechanical, semi-chemical pulp, and chemical pulp), pulp of other fibre, and dissolving wood pulp. The main types of paper included in this database are graphic papers (newsprint, printing and writing paper) and other paper and paperboard. The latter is further subdivided into detailed categories of packaging paper and paperboard, household and sanitary paper, and other paper and paperboard not elsewhere specified. The database also provides information about consumption of recovered paper for paper and paperboard making domestically. Refer to this document for detailed definitions: https://www.fao.org/3/cc7461t/cc7461t.pdf#page=11

The Landsat-derived Global Rainfed and Irrigated-Cropland Product Level 3 2020 (LGRIP30_L3) Version 2 data provides high-resolution, 30 meter (m) cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP L3 V2 maps agricultural croplands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas across the globe. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2019 through 2021 time period to create a nominal 2020 product. Each LGRIP30 L3 V2 30 m resolution GeoTIFF file contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation without any artificial watering), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also included. Currently, LGRIP30 V2 products only contain data for the conterminous United States (CONUS). LGRIP30 V2 data covering the rest of the globe will be added at a later date.

The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics (IMTS) Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. The source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity, and import value. It includes all food and agricultural products imported/exported annually by all the countries in the world.

The annual Total Biomass Production expresses the total amount of dry matter produced over the year. It is calculated by dekad and summarized as annual total. Each pixel represents the annual amount of dry matter in kg per hectare. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/ha Map code: L1-TBP-A Scale Factor: 1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** Annual TBP is the sum of the TBP by dekad in the year of reference (TBP by dekad is the average daily value in a dekad multiplied by number of days in each dekad). TBP is calculated by applying a unit conversion factor to net primary production (NPP) that converts carbon grams per m² into dry matter (DM) per hectare. In the previous version of the database, a fixed shoot/root ratio of 0.65 was applied to distribute DM into below ground and above ground production. For Version 2, also based on the findings of the Quality assessment, we decided to release the total biomass (above and below ground) instead, and leave users the possibility to apply a shoot/root ratio that better suits their analysis. **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

The Suite of Gender Indicators presents a set of indicators relevant to agrifood systems that are disaggregated by sex, with the aim of highlighting gender differences across agrifood systems. The initial set of indicators is based on those used in FAO’s 2023 Status of Women in Agrifood Systems (SWAFS) report and on the recommendations of an expert consultation held in December 2023, and covers key dimensions including economic participation, governance, agency, social norms, assets and services, education, health, and nutrition. Indicator selection was informed by expert judgment and by the availability of data with sufficient geographic and temporal coverage to enable meaningful comparisons across regions and over time. While most indicators are produced and published by FAO and other international organizations, the objective of this domain is to provide a one-stop shop for sex-disaggregated data on agrifood systems.

Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.

The FAOSTAT Population module contains time series data on population, by sex and urban/rural. The series consist of both estimates and projections for different periods as available from the original sources, namely: 1. Population data refers to the World Population Prospects: The 2024 Revision from the UN Population Division. 2. Urban/rural population data refers to the World Urbanization Prospects: The 2018 Revision from the UN Population Division.

The seasonal Gross Biomass Water Productivity expresses the quantity of output (total biomass production) in relation to the total volume of water consumed (actual EvapoTranspiration) during the growing cycle of the vegetation. By relating biomass production to total EvapoTranspiration (sum of soil evaporation, canopy transpiration and interception), this indicator provides insights on the impact of vegetation development on consumptive water use and thus on water balance in a given domain. When the focus is on monitoring performance of irrigated agriculture in relation to water consumption, it is more appropriate to use transpiration alone as a denominator, as a measure of water beneficially consumed by the plant. This latter indicator, for which we use the term \"net water productivity\", provides useful information on how effectively vegetation (and particularly crops) uses water to develop its biomass (and thus yield). The data is provided from January 2010 to 2020. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.001 Map code : L2-GBWP-S Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of gross biomass water productivity (GBWP) is as follows: GBWP = TBP/ETIa Where TBP is seasonal Total Biomass Production in kg/ha and ETIa is the Actual EvapoTranspiration and Interception in m³/ha cumulated during the course of the growing season, as defined by the phenology information. The following data is used for calculating it: - Seasonal TBP - Dekadal ETIa - Phenology (start of season and end of season) Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

This dataset provides annual statistics on crop and livestock production across countries and territories. For crops, data are provided on area harvested, production quantities and yields. For livestock, data are provided on animal stocks, numbers of producing and slaughtered animals, and the production of meat, milk, eggs and other livestock products. The dataset also includes information on production quantities for selected processed products, such as vegetable oils, sugar, fibres, beer, wine and major dairy products.

The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. Indices for meat production are computed based on data on production from indigenous animals.

The FAOSTAT Macro Indicators database provides a selection of country-level macro indicators relating to total economy (Gross Domestic Product, Gross Fixed Capital Formation); agriculture activity; agriculture, forestry and fishing activity; total manufacturing activity; manufacturing of food products and beverages activity; manufacturing activity of tobacco products; and manufacturing activity of food, beverage and tobacco products.It releases time series for a selection of National Accounts variables, including gross domestic product, gross fixed capital formation, industry-level value added and gross output. The database also proposes additional indicators such as gross domestic product per capita, year-on-year growth rates and measures of industry contribution to gross domestic product. All data relating to Gross Domestic Product, Gross Fixed Capital Formation, agriculture, forestry and fishing activity, and to total manufacturing activity originates from the United Nations Statistics Division (UNSD) National Accounts Estimates of Main Aggregates database, which consists of a complete and consistent set of time series of the main National Accounts aggregates of all UN Members States and other territories in the world for which National Accounts information is available. The UNSD database's content is based on the countries' official National Accounts data reported to UNSD through the annual National Accounts Questionnaire, supplemented with data estimates for any years and countries with incomplete or inconsistent information (See http://unstats.un.org/unsd/snaama/Introduction.asp). Data series relating to the sub-industry Agriculture activity are obtained from the UNSD national accounts Official Country Data databases while series on the Manufacturing activity of food and beverages products, manufacturing activity of tobacco products and manufacturing activity of food, beverages and tobacco products originates from the United Nations Industrial Development Organization (UNIDO) INDSTAT2 database. In order to ensure that sub-industry series are consistent in levels with National Accounts based series, which is needed to support comparability across industries (agriculture vs. agro-industry and sub-industries), UNIDO originating series are rescaled on UNSD National Accounts Estimates of Main Aggregates data series (See Section 17.5 for a more detailed description of the data processing steps).

The FAOSTAT domain on Emissions from Crops provides estimates of emissions associated with crop processes, namely 1) crop residues, 2) burning of crop residues, and 3) rice cultivation and the application of nitrogen (N) fertilizers, including mineral and chemical fertilizers, to soils. Estimates are computed at Tier 1 following the 2006 IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 2006).

Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.

The annual Net Biomass Water Productivity expresses the quantity of output (total biomass production) in relation to the volume of water beneficially consumed (by canopy transpiration) in the year, and thus net of soil evaporation. Contrary to gross water productivity, net water productivity is particularly useful in monitoring how effectively vegetation (and, more importantly, crops) uses water to develop biomass (and thus yield). The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ is the ratio of kg of dry matter per cubic meter of water transpired by vegetation in one hectare Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of Net Biomass Water Productivity (NBWP) is as follows: NBWP = TBP/Ta Where TBP is annual total biomass production in kgDM/ha and Ta is annual actual transpiration in m³/ha. Only areas with annual T of 100 mm or higher have been included in the computation. The following data is used for calculating it: - Annual TBP - Annual Ta **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Gridded Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3G_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. This data product is mosaicked from the L3 tiled SM (ECO_L3T_SM) product, is projected to a globally snapped 0.0006° grid, and has a spatial resolution of 70 m. The ECO_L3G_SM Version 2 data product contains three layers distributed in an HDF5 file including SM, cloud mask, and water masks. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Missing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the ‘cloud mask’ layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide). * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). *During the time period of May 15th, 2025, through July 1st, 2025, ECOSTRESS data was noisier than expected. Cycling the payload resolved the issue, but researchers should use all levels of ECOSTRESS data acquired during this time period with caution. * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

The food and nutrient apparent consumption statistics were computed from Household Consumption and Expenditure Survey (HCES) data. HCES is an umbrella term for household-level surveys developed to inform economic policy, such as Household Budget Surveys, Household Income Expenditure Surveys, and Living Standard Measurement Surveys. These surveys collect information on household characteristics (e.g., region and urban-rural), household members characteristics (e.g., sex, age, education, food and non-food expenditures, etc.), and food quantities consumed and/or acquired during a reference period. Despite their limitations HCES have been used for producing food and nutrient apparent consumption estimates at national and subnational levels for the whole population.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. Known Issues * See Section 3.0 of the GFSAD 1 km User Guide.

A food balance sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per capita food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Known issues, including constraints and limitations, are provided on page 18 of the ATBD.