SDE Raster Dataset
Tags
UK-DMC 2, agriculture, CropScape, cropland, farming, 001, Continent > North America > United States of America > North Dakota, imageryBaseMapsEarthCover, 010, crop estimates, North Dakota, environment, 007, Landsat, crop cover, DEIMOS-1, Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands, ND, land cover, Earth Science > Land Surface > Land Use/Land Cover > Land Cover, 2014
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2014 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
USDA, National Agricultural Statistics Service
The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) file formats then we suggest using the CropScape website <http://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <http://www.esri.com/>.
NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. The data is available free for download through CropScape at <http://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <http://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.
USDA, National Agricultural Statistics Service
2014 growing season
Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (HQ_RDD_GIB@nass.usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>.
None
The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) file formats then we suggest using the CropScape website <http://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <http://www.esri.com/>.
North Dakota
The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database 2011 (NLCD 2011). More information about the FSA CLU Program can be found at <http://www.fsa.usda.gov/>. More information about the NLCD can be found at <http://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.
The entire state is covered by the Cropland Data Layer.
If the following table does not display properly, then please visit this internet site <http://www.nass.usda.gov/research/Cropland/metadata/meta.htm> to view the original metadata file. >USDA, National Agricultural Statistics Service, 2014 North Dakota Cropland Data Layer >STATEWIDE AGRICULTURAL ACCURACY REPORT > >Crop-specific covers only *Correct Accuracy Error Kappa >------------------------- ------- -------- ------ ----- >OVERALL ACCURACY** 9,201,412 83.2% 16.8% 0.799 > > >Cover Attribute *Correct Producer's Omission User's Commission Cond'l >Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa >---- ---- ------ -------- ----- ----- -------- ----- ----- >Corn 1 1102620 92.50% 7.50% 0.917 95.24% 4.76% 0.947 >Sorghum 4 1679 21.46% 78.54% 0.214 54.23% 45.77% 0.542 >Soybeans 5 2508400 95.16% 4.84% 0.938 95.07% 4.93% 0.937 >Sunflower 6 281632 87.52% 12.48% 0.872 92.45% 7.55% 0.922 >Barley 21 113738 47.55% 52.45% 0.469 78.73% 21.27% 0.783 >Durum Wheat 22 345546 69.03% 30.97% 0.678 74.30% 25.70% 0.732 >Spring Wheat 23 2833330 92.20% 7.80% 0.893 87.85% 12.15% 0.836 >Winter Wheat 24 190260 64.52% 35.48% 0.638 83.68% 16.32% 0.833 >Other Small Grains 25 0 0.00% 100.00% 0.000 0.00% 100.00% 0.000 >Dbl Crop WinWht/Soybeans 26 1098 26.01% 73.99% 0.260 41.97% 58.03% 0.420 >Rye 27 3064 31.20% 68.80% 0.312 82.88% 17.12% 0.829 >Oats 28 21993 25.86% 74.14% 0.256 57.90% 42.10% 0.576 >Millet 29 3218 22.16% 77.84% 0.221 53.75% 46.25% 0.537 >Canola 31 551447 94.55% 5.45% 0.943 97.34% 2.66% 0.972 >Flaxseed 32 85423 71.79% 28.21% 0.715 84.94% 15.06% 0.848 >Safflower 33 4031 58.61% 41.39% 0.586 73.75% 26.25% 0.737 >Mustard 35 1168 50.94% 49.06% 0.509 87.49% 12.51% 0.875 >Alfalfa 36 148218 52.38% 47.62% 0.515 67.52% 32.48% 0.667 >Other Hay/Non Alfalfa 37 454079 52.92% 47.08% 0.505 79.43% 20.57% 0.778 >Buckwheat 39 1810 43.76% 56.24% 0.438 89.07% 10.93% 0.891 >Sugarbeets 41 94447 96.80% 3.20% 0.968 97.86% 2.14% 0.978 >Dry Beans 42 208750 83.47% 16.53% 0.831 88.37% 11.63% 0.881 >Potatoes 43 30290 79.23% 20.77% 0.792 91.94% 8.06% 0.919 >Other Crops 44 0 n/a n/a n/a 0.00% 100.00% 0.000 >Lentils 52 39269 78.23% 21.77% 0.782 88.96% 11.04% 0.889 >Peas 53 128085 83.45% 16.55% 0.832 87.22% 12.78% 0.871 >Clover/Wildflowers 58 122 8.99% 91.01% 0.090 38.01% 61.99% 0.380 >Sod/Grass Seed 59 0 0.00% 100.00% 0.000 0.00% 100.00% 0.000 >Fallow/Idle Cropland 61 47441 33.63% 66.37% 0.333 73.04% 26.96% 0.727 >Triticale 205 83 11.91% 88.09% 0.119 28.92% 71.08% 0.289 >Dbl Crop WinWht/Corn 225 0 0.00% 100.00% 0.000 0.00% 100.00% 0.000 >Dbl Crop Corn/Soybeans 241 0 n/a n/a n/a 0.00% 100.00% 0.000 >Radishes 246 171 100.00% 0.00% 1.000 90.48% 9.52% 0.905 > >*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix. >**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255). >FSA-sampled grass and pasture, and all NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy. The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2011). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <http://www.mrlc.gov/>.
The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2011). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <http://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 and DMC-UK 2 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.
OVERVIEW: The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.' SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check the 'Process Description' section of the specific state and year metadata file to verify the methodology used. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships. GROUND TRUTH: As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground truth data from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground truth data provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <http://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data. INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. The DEIMOS-1 and UK-DMC 2 imagery was resampled to 30 meters using cubic convolution, rigorous transformation to match the traditional Landsat spatial resolution. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL. ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD 2011 (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report. PUBLIC RELEASE: The USDA, NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. The data is available free for download through CropScape <http://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <http://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed download instructions. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.
spatial and attribute information used in the spectral signature training and validation of non-agricultural land cover
The NLCD 2011 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2011 Imperviousness and Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2011 can be found at <http://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs. Preferred NLCD2006 citation: "Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2012. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."
ground condition
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <http://www.fsa.usda.gov/>.
ground condition, updated annually
spatial and attribute information used in land cover spectral signature analysis
The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <http://ned.usgs.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
ground condition
Raw data used in land cover spectral signature analysis
The Landsat 8 OLI/TIRS data are free for download through the following website <http://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <http://eros.usgs.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.
ground condition
Raw data used in land cover spectral signature analysis
The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <http://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
ground condition
Raw data used in land cover spectral signature analysis
The UK-DMC 2 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about UK-DMC 2 data can be obtained at <http://www.dmcii.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The UK-DMC 2 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
ground condition
Please visit the official website <http://www.nass.usda.gov/research/Cropland/SARS1a.htm> for distribution details. The Cropland Data Layer is available free for download at <http://nassgeodata.gmu.edu/CropScape/> and <http://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
The CDL is available online and free for download from the CropScape website <http://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <http://datagateway.nrcs.usda.gov/>. IMPORTANT NOTE: When downloading the CDL using the NRCS Geospatial Data Gateway all available years of CDL production for the requested state are included in a single compressed file. >Instructions for downloading from the NRCS Geospatial Data Gateway: > >Start by clicking on 'Get Data' > >Select a state from the dropdown menu > >Select any county and then click 'Submit Selected Counties' > >Choose 'Land Use Land Cover' and select 'Cropland Data Layer by State' and 'Continue' > >Choose 'FTP' and then 'Continue' > >Fill out the required user information and then you are given the option to download the data for free.
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
If the following table does not display properly, then please visit the following website to view the original metadata file <http://www.nass.usda.gov/research/Cropland/metadata/meta.htm>. > ***NOTE: The 1997-2013 CDLs were recoded and re-released in January 2014 to better represent pasture and grass-related categories. A new > category named Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous > (code 171), and Pasture/Hay (code 181). This was done to eliminate confusion among these similar land cover types which were not always > classified definitionally consistent from state to state or year to year and frequently had poor classification accuracies. This follows > the recoding of the entire CDL archive in January 2012 to better align the historical CDLs with the current product. For a detailed list > of the category name and code changes, please visit the Frequently Asked Questions (FAQ's) section at <http://www.nass.usda.gov/research/Cropland/sarsfaqs2>. > > > Data Dictionary: USDA, National Agricultural Statistics Service, 2014 Cropland Data Layer > > Source: USDA, National Agricultural Statistics Service > > The following is a cross reference list of the categorization codes and land covers. > Note that not all land cover categories listed below will appear in an individual state. > > Raster > Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0 > > Categorization Code Land Cover > "0" Background > > Raster > Attribute Domain Values and Definitions: CROPS 1-20 > > Categorization Code Land Cover > "1" Corn > "2" Cotton > "3" Rice > "4" Sorghum > "5" Soybeans > "6" Sunflower > "10" Peanuts > "11" Tobacco > "12" Sweet Corn > "13" Pop or Orn Corn > "14" Mint > > Raster > Attribute Domain Values and Definitions: GRAINS,HAY,SEEDS 21-40 > > Categorization Code Land Cover > "21" Barley > "22" Durum Wheat > "23" Spring Wheat > "24" Winter Wheat > "25" Other Small Grains > "26" Dbl Crop WinWht/Soybeans > "27" Rye > "28" Oats > "29" Millet > "30" Speltz > "31" Canola > "32" Flaxseed > "33" Safflower > "34" Rape Seed > "35" Mustard > "36" Alfalfa > "37" Other Hay/Non Alfalfa > "38" Camelina > "39" Buckwheat > > Raster > Attribute Domain Values and Definitions: CROPS 41-60 > > Categorization Code Land Cover > "41" Sugarbeets > "42" Dry Beans > "43" Potatoes > "44" Other Crops > "45" Sugarcane > "46" Sweet Potatoes > "47" Misc Vegs & Fruits > "48" Watermelons > "49" Onions > "50" Cucumbers > "51" Chick Peas > "52" Lentils > "53" Peas > "54" Tomatoes > "55" Caneberries > "56" Hops > "57" Herbs > "58" Clover/Wildflowers > "59" Sod/Grass Seed > "60" Switchgrass > > Raster > Attribute Domain Values and Definitions: NON-CROP 61-65 > > Categorization Code Land Cover > "61" Fallow/Idle Cropland > "63" Forest > "64" Shrubland > "65" Barren > > Raster > Attribute Domain Values and Definitions: CROPS 66-80 > > Categorization Code Land Cover > "66" Cherries > "67" Peaches > "68" Apples > "69" Grapes > "70" Christmas Trees > "71" Other Tree Crops > "72" Citrus > "74" Pecans > "75" Almonds > "76" Walnuts > "77" Pears > > Raster > Attribute Domain Values and Definitions: OTHER 81-109 > > Categorization Code Land Cover > "81" Clouds/No Data > "82" Developed > "83" Water > "87" Wetlands > "88" Nonag/Undefined > "92" Aquaculture > > Raster > Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195 > > Categorization Code Land Cover > "111" Open Water > "112" Perennial Ice/Snow > "121" Developed/Open Space > "122" Developed/Low Intensity > "123" Developed/Med Intensity > "124" Developed/High Intensity > "131" Barren > "141" Deciduous Forest > "142" Evergreen Forest > "143" Mixed Forest > "152" Shrubland > "176" Grassland/Pasture > "190" Woody Wetlands > "195" Herbaceous Wetlands > > Raster > Attribute Domain Values and Definitions: CROPS 195-255 > > Categorization Code Land Cover > "204" Pistachios > "205" Triticale > "206" Carrots > "207" Asparagus > "208" Garlic > "209" Cantaloupes > "210" Prunes > "211" Olives > "212" Oranges > "213" Honeydew Melons > "214" Broccoli > "216" Peppers > "217" Pomegranates > "218" Nectarines > "219" Greens > "220" Plums > "221" Strawberries > "222" Squash > "223" Apricots > "224" Vetch > "225" Dbl Crop WinWht/Corn > "226" Dbl Crop Oats/Corn > "227" Lettuce > "229" Pumpkins > "230" Dbl Crop Lettuce/Durum Wht > "231" Dbl Crop Lettuce/Cantaloupe > "232" Dbl Crop Lettuce/Cotton > "233" Dbl Crop Lettuce/Barley > "234" Dbl Crop Durum Wht/Sorghum > "235" Dbl Crop Barley/Sorghum > "236" Dbl Crop WinWht/Sorghum > "237" Dbl Crop Barley/Corn > "238" Dbl Crop WinWht/Cotton > "239" Dbl Crop Soybeans/Cotton > "240" Dbl Crop Soybeans/Oats > "241" Dbl Crop Corn/Soybeans > "242" Blueberries > "243" Cabbage > "244" Cauliflower > "245" Celery > "246" Radishes > "247" Turnips > "248" Eggplants > "249" Gourds > "250" Cranberries > "254" Dbl Crop Barley/Soybeans
No restrictions on the distribution or use of the metadata file
No restrictions on the distribution or use of the metadata file