The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports broken down by agricultural and nonagricultural commodities. The USDA endpoint in the Census data API provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Agricultural Producer Prices (APP) are prices received by farmers for their produce at the farm gate; i.e. at the point where the commodity leaves the farm. APP do not cover the costs after the farm gate; e.g. transportation cost from the farm gate to the nearest market or first point of sale, warehousing costs, processing costs and market charges (if any) for selling the produce. FAOSTAT gives free access to food and agriculture data for over 245 countries and territories and covers all FAO regional groupings from 1961 to the most recent year available. Annual APP data are provided from 1991 to the previous year for over 160 countries and about 200 commodities, and monthly APP data are provided from January 2010 to December of the previous year for over 60 countries and about 200 commodities. APP are used, along with production data, to estimate value of production for a country, by commodity and in total, as well as Producer Price Indexes, which measure price inflation. APP also enable analysts to analyse price transmission and volatility.

PPI: Prev Month=100: Agricultural Production: CF: Kostroma Region data was reported at 99.510 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 102.010 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: CF: Kostroma Region data is updated monthly, averaging 100.850 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 123.800 Prev Mth=100 in Feb 2023 and a record low of 83.700 Prev Mth=100 in Jun 2015. PPI: Prev Month=100: Agricultural Production: CF: Kostroma Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.

Agricultural land resources – a global suitability evaluation (v3.0)

Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature.

Determining Agricultural Suitability

Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.

Agricultural Suitability

The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km2 at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.

Food, feed, fiber and first-generation bioenergy crops


    Barley
    Potato
    Sugarbeet


    Cassava
    Rapeseed
    Sugarcane


    Groundnut
    Rice
    Sunflower


    Maize
    Rye
    Summer wheat


    Millet
    Sorghum
    Winter wheat


    Oilpalm
    Soybean

Second-generation bioenergy crops

    Jatropha
    Reed canary grass


    Miscanthus
    Eucalyptus


    Switchgrass
    Willow

Growing Season Adaptation

The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.

Most Suitable Crop

The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.

Further information

Detailled information are available in the following publications:

Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: 10.1371/journal.pone.0107522

Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: 10.1111/gcbb.12697

Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: 10.1038/s41597-022-01632-8

Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: 10.5194/hess-22-1119-201

Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41– 65, doi: 10.1002/2015MS000526.

Improvements in v3.0

Compared to the previous version (v2.0), this version (v3.0) uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.

Contact

Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department of Geography, LMU München (www.geografie.uni-muenchen.de)

The International Production Assessment Division (IPAD) is part of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS), an agency within the US Department of Agriculture (USDA). FAS-IPAD uses satellite imagery and remote sensing data to assist in its agricultural estimates of global crop conditions. The division provides monthly estimates of area, yield and production for 17 distinct commodities in over 160 countries around the world, including post-disaster assessments. GADAS is a powerful visualization tool based on an ArcGIS platform that enables FAS-IPAD analysts, and other users, to rapidly assess real-time crop conditions using a wide variety of data layers from a multitude of sources.GADAS integrates a vast array of highly detailed data streams to include daily precipitation data, vegetation index, crop masks, land cover data, irrigation and water data, elevation and infrastructure, political data, and much more. In addition, FAS-IPAD has partnered with the Pacific Disaster Center (PDC) in Hawaii to incorporate real-time data streams into GADAS for worldwide monitoring, tracking, and pre- and post-disaster agricultural assessments resulting from hurricanes, typhoons, tsunamis, floods, droughts, earthquakes and volcanic eruptions.You may want to begin exploring GADAS for the many things it can be used for, such as:Global agricultural monitoring and commodity forecastingComparative climatic and satellite-derived vegetation analysisEnvironmental change detection studies and analysisDrought monitoringNatural disaster assessment and analysisTracking current and historical disaster eventsHighlighting regional risk posed by natural disastersSpatial modeling of potential disaster impactsDelineation of major land-use categories worldwideRegional planning and climate-resilience studiesProgram or project-specific data archive and data miningWe welcome your feedback on how GADAS has worked or is working for you, and are enthusiastic about expanding the data layers, utilization, and future development of this very powerful GIS tool. Please contact us at OGA.IPAD@fas.usda.gov to provide your valued comments…we look forward to hearing from you!Here’s a screenshot centered over the northern Atlantic Ocean:

PPI: Prev Month=100: Agricultural Production: FE: Magadan Region data was reported at 99.050 Prev Mth=100 in Feb 2025. This records an increase from the previous number of 92.870 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: FE: Magadan Region data is updated monthly, averaging 100.435 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 117.050 Prev Mth=100 in Sep 2017 and a record low of 88.520 Prev Mth=100 in Dec 2017. PPI: Prev Month=100: Agricultural Production: FE: Magadan Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: CF: Kursk Region data was reported at 100.620 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 101.480 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: CF: Kursk Region data is updated monthly, averaging 100.565 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 144.000 Prev Mth=100 in May 1999 and a record low of 89.900 Prev Mth=100 in Jun 1999. PPI: Prev Month=100: Agricultural Production: CF: Kursk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: CF: Orel Region data was reported at 101.270 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 101.280 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: CF: Orel Region data is updated monthly, averaging 100.775 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 120.950 Prev Mth=100 in Aug 2010 and a record low of 90.000 Prev Mth=100 in Aug 2013. PPI: Prev Month=100: Agricultural Production: CF: Orel Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

Russia PPI: OKVED2: Prev Month=100: Agricultural Production data was reported at 101.250 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 101.530 Prev Mth=100 for Jan 2025. Russia PPI: OKVED2: Prev Month=100: Agricultural Production data is updated monthly, averaging 100.075 Prev Mth=100 from Jan 2016 (Median) to Feb 2025, with 110 observations. The data reached an all-time high of 103.810 Prev Mth=100 in Dec 2020 and a record low of 97.480 Prev Mth=100 in May 2017. Russia PPI: OKVED2: Prev Month=100: Agricultural Production data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: CF: Tver Region data was reported at 99.230 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 99.890 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: CF: Tver Region data is updated monthly, averaging 100.490 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 120.300 Prev Mth=100 in Sep 1998 and a record low of 89.640 Prev Mth=100 in Jan 2020. PPI: Prev Month=100: Agricultural Production: CF: Tver Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: NW: Novgorod Region data was reported at 100.320 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 104.880 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: NW: Novgorod Region data is updated monthly, averaging 100.935 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 118.190 Prev Mth=100 in Nov 2004 and a record low of 91.200 Prev Mth=100 in Jun 2009. PPI: Prev Month=100: Agricultural Production: NW: Novgorod Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: FE: Khabarovsk Territory data was reported at 100.540 Prev Mth=100 in Feb 2025. This records an increase from the previous number of 92.630 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: FE: Khabarovsk Territory data is updated monthly, averaging 100.790 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 145.300 Prev Mth=100 in Feb 2001 and a record low of 80.700 Prev Mth=100 in Jul 2001. PPI: Prev Month=100: Agricultural Production: FE: Khabarovsk Territory data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: NC: Republic of Dagestan data was reported at 101.290 Prev Mth=100 in Feb 2025. This records an increase from the previous number of 99.790 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: NC: Republic of Dagestan data is updated monthly, averaging 100.415 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 126.900 Prev Mth=100 in Jan 1999 and a record low of 94.880 Prev Mth=100 in Jun 2006. PPI: Prev Month=100: Agricultural Production: NC: Republic of Dagestan data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: NC: Stavropol Territory data was reported at 101.670 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 102.400 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: NC: Stavropol Territory data is updated monthly, averaging 100.705 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 121.300 Prev Mth=100 in Feb 1999 and a record low of 91.210 Prev Mth=100 in Dec 2008. PPI: Prev Month=100: Agricultural Production: NC: Stavropol Territory data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: SF: Rostov Region data was reported at 100.860 Prev Mth=100 in Feb 2025. This records a decrease from the previous number of 101.920 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: SF: Rostov Region data is updated monthly, averaging 100.560 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 117.100 Prev Mth=100 in Mar 1999 and a record low of 93.570 Prev Mth=100 in Nov 2008. PPI: Prev Month=100: Agricultural Production: SF: Rostov Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

PPI: Prev Month=100: Agricultural Production: CF: Tambov Region data was reported at 102.340 Prev Mth=100 in Feb 2025. This records an increase from the previous number of 101.060 Prev Mth=100 for Jan 2025. PPI: Prev Month=100: Agricultural Production: CF: Tambov Region data is updated monthly, averaging 100.365 Prev Mth=100 from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 114.800 Prev Mth=100 in Feb 1999 and a record low of 92.700 Prev Mth=100 in Aug 1998. PPI: Prev Month=100: Agricultural Production: CF: Tambov Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IC006: Producer Price Index: Previous Month=100: by Region: Agricultural Production.

China Producer Price Index: Agricultural Input: Chemical Fertilizer data was reported at 107.766 Prev Year=100 in Nov 2018. This records a decrease from the previous number of 108.148 Prev Year=100 for Oct 2018. China Producer Price Index: Agricultural Input: Chemical Fertilizer data is updated monthly, averaging 101.912 Prev Year=100 from Jan 2006 (Median) to Nov 2018, with 155 observations. The data reached an all-time high of 142.800 Prev Year=100 in Aug 2008 and a record low of 82.800 Prev Year=100 in Sep 2009. China Producer Price Index: Agricultural Input: Chemical Fertilizer data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Production Price Index: Input: Monthly.

Indonesia Monthly Average Wage: Female: Agriculture, Forestry and Fishing data was reported at 901,916.000 IDR in 2018. This records an increase from the previous number of 843,605.000 IDR for 2017. Indonesia Monthly Average Wage: Female: Agriculture, Forestry and Fishing data is updated yearly, averaging 814,253.000 IDR from Aug 2015 (Median) to 2018, with 4 observations. The data reached an all-time high of 901,916.000 IDR in 2018 and a record low of 652,520.000 IDR in 2015. Indonesia Monthly Average Wage: Female: Agriculture, Forestry and Fishing data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBB002: Monthly Average Wage: by Industry.

China Producer Price Index: Agricultural Input: Farm Tool data was reported at 100.145 Prev Mth=100 in Mar 2020. This records an increase from the previous number of 100.001 Prev Mth=100 for Feb 2020. China Producer Price Index: Agricultural Input: Farm Tool data is updated monthly, averaging 100.200 Prev Mth=100 from Jan 2006 (Median) to Mar 2020, with 171 observations. The data reached an all-time high of 102.400 Prev Mth=100 in Mar 2008 and a record low of 99.500 Prev Mth=100 in Dec 2008. China Producer Price Index: Agricultural Input: Farm Tool data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Production Price Index: Input: Previous Month=100.