USDA Crop Explorer: Global Crop Condition and Commodity Production Analysis from the USDA/Production Estimates and Crop Assessment Division (PECAD)Entry ID: USDA0557
Abstract: The Production Estimates and Crop Assessment Division (PECAD) of USDA's Foreign
Agricultural Service (FAS) is responsible for global crop condition assessments
and estimates of area, yield, and production for grains, oilseeds, and cotton.
The primary mission of PECAD is to produce the most objective and accurate
assessment of the global agricultural production outlook and ... the conditions
affecting food security in the world. Regional analysts use a Geographic
Information System (GIS) to collect market intelligence, and forecast reliable
global production numbers for grains, oil seeds and cotton.
The FAS has a global network of attach's that provide on-the-ground reports of
observed crop and contextual information. Also, the FAS regional analysts
travel extensively in the countries they cover to more fully develop the
context and constraints within which their assessments are made. Other
contextual information such as official governmental reports, trade and news
sources play a significant role in interpreting how these factors will affect
price and policies and other econometric analyses. PECAD's final production
estimate, produced by the 10th day of each month and cleared by the World
Agricultural Outlook Board, is based on an all source convergence of evidence
methodology. The final production estimates are used in a variety of ways:
- Official USDA statistics
- Principle Federal Economic Indicators
- Crop conditions and early warning alerts
- Agricultural monitoring and food security
- Foreign aid assessments for food import needs
- Disaster monitoring and relief efforts related to food aid
- Commercial market trends and analysis
- Trade policy and exporter assistance
PECAD relies on remote sensing data from satellite sensors (Figure 2) as an
important source of data for its GIS. Selected data sets provide daily,
weekly, and targeted coverage with resolutions ranging from 1km to less than
1m. The data is stored on a terabyte server accessible to each analyst
workstation. The most common data formats used operationally with ArcGIS at
PECAD are TIFF and compressed MrSID images.
The Crop Condition Data Retrieval and Evaluation database (CADRE) is the main
decision support tool used in the GIS by PECAD analysts. CADRE is a global
grid-based, geospatial database that stores daily, monthly, and decadal
(10-day) data. The sources for this data are the Air Force Weather Agency
(AFWA) and the World Meteorological Organization (WMO). PECAD takes this
source data and models precipitation, temperature, soil moisture and crop
stages. To measure vegetative vigor, PECAD calculates vegetation index numbers
(VINs) from satellite derived data. The data is imported into 1/8 degree mesh
grid cells (Figure 3) and can be categorized by:
Time-series data sets:
- Daily WMO station data (precipitation, min and max temperatures)
- Daily agrometeorological data derived from station and
satellite data include precipitation; min and max temperatures; snow depth;
solar and longwave radiation; potential and actual evapo-transpiration.
- Biweekly and decadal vegetation index (VINs) derived from Local Area Coverage
(LAC), approx 1.1-km pixels and Global Area Coverage (GAC), approx 8-km pixels
from the NOAA-AVHRR satellite series.
Normal baseline data sets:
- Normal precipitation and temperature values for WMO stations (from WMO and
- Normal precipitation, temperature, potential evaporation, and elevation
imported into1/8 mesh grid cells from UNEP/GRID-Geneve
- Soil-water holding capacity based upon the United Nations Food and
Agriculture Organization's (FAO) Digital Soil Map of the World at 1:5M scale
- Biweekly VIN normals or averages for the GAC data set.
Crop Information and Models:
- Crop type and average start of season
- Average yield and area planted
- Percent crop production within a country
- Two-layer soil moisture algorithm
- Crop calendars (based on growing-degree days)
- Crop stress or alarm models for corn and wheat (based on soil moisture and
- Crop water production functions to estimate relative
yield reductions (yield reduction models)
- Crop stage models by Ritchie
CADRE extraction routines:
- Automated maps and graphs generated every 10 days for diaplay
- Interactive ARCVIEW 3.2 scripts for displaying station and grid cell data
- Interactive CADRE X-tract program for displaying graphs
The Crop Explorer web application features near-real-time global crop condition
information based on the satellite imagery and weather data processed by PECAD.
The web mapping application uses Cold Fusion, Java, ArcIMS, SQL Server, and
ArcSDE to manage and store the geospatial data. ArcSDE relationships are set
up between PECAD "regions" and the various feature classes used by the maps
(e.g. rivers, administrative boundaries, etc.). The Crop Explorer ArcIMS
MapService is built using these same ArcSDE features. During a map generation
request, the grid-cell layer feature class is joined to the appropriate
attribute data such as precipitation, soil moisture, or temperature. Java is
used to build the necessary AXL code to make the request to ArcIMS. Cold Fusion
manages the map display in the web browser.
Thematic maps of major crop growing regions depict vegetative vigor,
precipitation, temperature, and soil moisture. Time-series charts depict
growing season data for specific agro-meteorological zones. Regional crop
calendars and crop area maps are also available for selected regions. U.S.
producers, traders, researchers, and the public can use Crop Explorer to
visualize this information with a web browser.
Regional droughts or excessively wet conditions can be easily identified by the
amount of ground-surface "greenness" as depicted by the Normalized Difference
Vegetation Index (NDVI), a measure of vegetative vigor derived from AVHRR
satellite imagery. In addition, daily satellite image composites originating
from the NASA MODIS Rapid Response System are now directly linked to selected
agricultural regions within Crop Explorer.
Data Center: Foreign Agricultural Service (FAS)
Dissemination Media: Online
Web Mapping and Metadata: http://www.pecad.fas.usda.gov/cropexplorer
Country Reports: http://www.pecad.fas.usda.gov/
Data Set Citation
Dataset Originator/Creator: USDA FAS
Dataset Title: Global Crop Condition and Commodity Production Analysis from the USDA/Production Estimates and Crop Assessment Division (PECAD)
Dataset Release Date: 2001
Dataset Release Place: Washington, D.C.
Dataset Publisher: USDA/FASOnline Resource: http://www.pecad.fas.usda.gov/cropexplorer/
Start Date: 1981-01-01
Latitude Resolution: 1/8 mesh grids
Longitude Resolution: 1/8 mesh grids
Temporal Resolution: daily, monthly, decadal
AGRICULTURE > AGRICULTURAL PLANT SCIENCE > CROP/PLANT YIELDS
AGRICULTURE > SOILS > SOIL MOISTURE/WATER CONTENT
AGRICULTURE > SOILS
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE
ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT
BIOSPHERE > VEGETATION > VEGETATION COVER
BIOSPHERE > VEGETATION > VEGETATION INDEX > NDVI
HUMAN DIMENSIONS > ECONOMIC RESOURCES > AGRICULTURE PRODUCTION
TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER FEATURES > LAKES/RESERVOIRS > RESERVOIRS
LAND SURFACE > SURFACE THERMAL PROPERTIES > LAND SURFACE TEMPERATURE
LAND SURFACE > LAND USE/LAND COVER > LAND PRODUCTIVITY
LAND SURFACE > LAND USE/LAND COVER > LAND RESOURCES
LAND SURFACE > SOILS > SOIL MOISTURE/WATER CONTENT
SPECTRAL/ENGINEERING > VISIBLE WAVELENGTHS > VISIBLE IMAGERY
CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > PRECIPITATION INDICES > STANDARDIZED PRECIPITATION INDEX > WASP
CLIMATE INDICATORS > LAND SURFACE/AGRICULTURE INDICATORS > SATELLITE SOIL MOISTURE INDEX
ISO Topic Category
Quality Users are cautioned that this in Not Official USDA Data. Official
USDA production, supply, and distribution data are determined after
analyzing all available sources of data, including those provided on
Data Set Progress
Distribution Media: online WWW
Role: DIF AUTHOR
Email: bob.baldwin at usda.gov
USDA Foreign Agricultural Service CMP-PECAD, MS 1045-S 1400 Independence Avenue, SW
Province or State: DC
Postal Code: 20250
Ahn C.-H. and R. Tateishi. 1994. Development of a Global 30-minute grid
Potential Evapotranspiration Data Set. Journal of the Japan Soc. Photogrammetry
and Remote Sensing, 33(2): 12-21.
Allen, R. G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop
evapotranspiration; Guidelines for computing crop water requirements, FAO
Irrigation and Drainage Paper 56, pp. ... 27-65.
Bethel, G. and B. Doorn, 1998. USDA Remote Sensing Technical and Systems
Support for Operational Worldwide Agricultural Analysis, 1st International
Conference: Geospatial Information in Agricultural and Forestry, Disneyýs
Coronado Springs Resort, Lake Buena Vista, Florida, USA, 1-3 June 1998
Boatwright, G.O. and V.S. Whitefield. 1986. Early Warning and Crop Condition
Assessment Research. IEEE Transactions on Geosciences and Remote Sensing. Vol
GE-24, No 1., January.
Cochrane, M.A. 1981. Soil Moisture and Agromet Models, Technical Report, USAF
Air Weather Service (MAC), USAFETAC, Scott AFB, Illinois, TN-81/001, March
Ek, M., and L. Mahrt, 1991. OSU 1-D PBL Model User's Guide. A one-dimensional
planetary boundary layer model with interactive soil layers and plant canopy.
Version 1.0.4. March 1991. Dept of Atmospheric Sciences, Oregon State
FAO. 1996. The Digitized Soil Map of the World Including Derived Soil
Properties, CD-ROM, Food and Agriculture Organization, Rome.
Hoke, J.E., J.L. Hayes, and L.G. Renninger, 1981. Map Projections and Grid
Systems for Meteorological Applications, AFGWTC/TN-79/003 (AD-A100324), Air
Force Weather Agency, Offutt AFB, Nebraska.
Hollinger, J., 1989: DMSP Special Sensor Microwave /Imager Calibration/
Validation Report, Volume I, Naval Research Laboratory, Washington D.C.
Idso, S. 1981. A set of equations for full spectrum and 8 to 14?m and 10.5 to
12.5?m thermal radiation from cloudless skies. Water Resources Research,
Kiess, R.B. and W.M. Cox , 1988. The AFWGWC Automated Real-Time Cloud Analysis
Model, Technical Report AFGWTC/TN-88/001 (AD-B121615), Air Force Weather
Agency, Offutt AFB, Nebraska.
Kopp, T.J. 1995. The Air Force Global Weather Central Temperature Model,
Technical Report AFGWTC/TN-95/004, Air Force Weather Agency, Offutt AFB,
Leemans, R. and W. Cramer. 1991. "The IIASA database for mean monthly values of
temperature, precipitation and cloudiness on a global terrestrial grid".
Research Report RR-91-18. November 1991. International Institute of Applied
Systems Analyses, Laxenburg, pp. 61.
Luces, S.A., J.D. Matens, and S. J. Hall. 1986. The AFGWC Snow Analysis Model,
Technical Report AFGWTC/TN-86/001 (AD-A176202), Air Force Weather Agency,
Offutt AFB, Nebraska.
Mahrt, L. and M. Ek. 1984. The influence of atmospheric stability on potential
evaporation, J. Climate and Appl. Meteor., 23:222-234.
Richtie, J.T., U.Singh, D.C. Godwin, and W.T. Bowen. 1998. Cereal growth,
development, and yield. G.Y. Tsuiji, et al, (eds), Understanding Options in
for Agricultural Production, Luwer Academic Publishers, Great Britian, 79-98.
Robine, K. 1998. Global Agrometeorological Database Exploitation Using Avenue
Scripts and Sybase SQL Server. 18th Annual International ESRI User Conference,
San Diego, CA, 26-30 July, 1998.
Sinclair, T. R., S. Kitani, K. Hinson, J. Bruniard, and T. Horie. 1991.
Soybean Flowering Date: Linear and Logistic Models Based on Temperature and
Photoperiod. Crop Science, Vol 31, May-June, pp.786-790.
Shapiro, R. 1987. A Simple Model for the Calculation of the Flux of Direct and
Diffuse Solar Radiation Through the Atmosphere, AFGL-TR-87-0200, Air Force
Geophysics Lab, Hanscom AFB, MA.
Row III, W.R., and D. Hastings. 1997. TerrainBase Global Terrain Model,
National Geophysical Data Center and World Data Center-A for Solid Earth
Geophysics, Boulder, Colorado U.S.A.
Tingley, W. 1988. Crop Condition Data Retrieval and Evaluation (CADRE) DBMS
Dictionary. Lockheed Engineering and Sciences Company, Inc. Unpublished.
Watchmann, R. 1975. Expansion of the atmospheric temperature moisture
profiles in empirical orthogonal functions for remote sensing applications.
Digest of preprints, Topical Mtg on Rem. Sens. of the Atm., Optical Society of
America, Anaheim, CA.
Extended Metadata Properties
(Click to view more)
Creation and Review Dates
Last DIF Revision Date: 2016-01-27