The geospatial data product called the Cropland Data Layer (CDL) is hosted on CropScape (https://nassgeodata.gmu.edu/CropScape/). The CDL is a raster, geo-referenced, crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth.

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

The USDA National Agricultural Statistics Service (NASS) releases the annual Cropland Data Layer (CDL) via the NASS CropScape geospatial portal. The CDL product depicts agricultural land cover over the Continental United States at 30 meters resolution. CropScape includes historical data dating back to 1997 for some locales. The CDL is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground truth collected during the current growing season. The strength and emphasis of CropScape and the CDL is agricultural land cover. Please note that no farmer reported data are derivable from CropScape or the Cropland Data Layer.Please reference CropScape (https://nassgeodata.gmu.edu/CropScape/) or the official CDL website (https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php) for a list of the available states and years of data and the individual metadata files that contain the technical details. NASS developed both the CropScape and VegScape web services in cooperation with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. Metadata, Frequently Asked Questions (FAQs), and the most current year of data is available free for download at the official website: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.

This is the 2008 NASS USDA CDL dataset for Minnesota. The dataset was clipped and downloaded using the USDA NASS CropScape online web application: http://nassgeodata.gmu.edu/CropScape/

The Minnesota Department of Agriculture (MDA) did minimal processing on this dataset to make more useful for Minnesota-specific work. The lineage section describes these steps taken, but the full description of the Cropland Data Layer can be found in the NASS USDA Metadata.

Crop-specific land use data from the Cropland Data Layer for the Chicago Wilderness Region

This raster layer shows the complete U.S.D.A. Cropscape Data Layer. This layer shows crops distribution and general land usage across the continental United States.

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.

This is the 2017 NASS USDA CDL dataset for Minnesota. The dataset was clipped and downloaded using the USDA NASS CropScape online web application: http://nassgeodata.gmu.edu/CropScape/

The Minnesota Department of Agriculture (MDA) did minimal processing on this dataset to make more useful for Minnesota-specific work. The lineage section describes these steps taken, but the full description of the Cropland Data Layer can be found in the NASS USDA Metadata.

Cropland in New MexicoSource: USDA National Agricultural Statistics Service CropScape - Cropland Data Layer http://nassgeodata.gmu.edu/CropScape/Data Prepared by: Larry Heard, NMCDC, larryheard@gmail.com, (505)710-4062The geospatial data product called the Cropland Data Layer (CDL) is hosted on CropScape (http://nassgeodata.gmu.edu/CropScape/). The CDL is a raster, geo-referenced, crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. All historical CDL products are available for use and free for download through CropScape. For more information about the CDL Program please refer to the metadata for the particular state and year you are interested at the following webpage: (http://www.nass.usda.gov/research/Cropland/metadata/meta.htm).Classes were selected from the original cropland data layer that are common to New Mexico agriculture. Separate layers were created from those classes to be able to focus a select group of New Mexico crops.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

USA Cropland is a time enabled imagery layer of the USDA CropScape Cropland Data Layers dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.Why USA Cropland masks out NLCD land cover in its default templateUSDA CropScape Cropland Data Layers, by default as downloaded from USDA, fill in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 1/27/2021This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Pop or Orn Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Dbl Crop WinWht/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Misc Vegs & Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonag/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Dbl Crop WinWht/Corn226,Dbl Crop Oats/Corn227,Lettuce228,Dbl Crop Triticale/Corn229,Pumpkins230,Dbl Crop Lettuce/Durum Wht231,Dbl Crop Lettuce/Cantaloupe232,Dbl Crop Lettuce/Cotton233,Dbl Crop Lettuce/Barley234,Dbl Crop Durum Wht/Sorghum235,Dbl Crop Barley/Sorghum236,Dbl Crop WinWht/Sorghum237,Dbl Crop Barley/Corn238,Dbl Crop WinWht/Cotton239,Dbl Crop Soybeans/Cotton240,Dbl Crop Soybeans/Oats241,Dbl Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Dbl Crop Barley/Soybeans

The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. For detailed FAQ please visit CropScape and Cropland Data Layers - FAQs. To explore details about the classification accuracies and utility of the data, see state-level omission and commission errors by crop type and year. The asset date is aligned with the calendar year of harvest. For most crops the planted and harvest year are the same. Some exceptions: winter wheat is unique, as it is planted in the prior year. A hay crop like alfalfa could have been planted years prior. For winter wheat the data also have a class called "Double Crop Winter Wheat/Soybeans". Some mid-latitude areas of the US have conditions such that a second crop (usually soybeans) can be planted immediately after the harvest of winter wheat and itself still be harvested within the same year. So for mapping winter wheat areas use both classes (use both values 24 and 26). While the CDL date is aligned with year of harvest, the map itself is more representative of what was planted. In other words, a small percentage of fields on a given year will not be harvested. Some non-agricultural categories are duplicate due to two very different epochs in methodology. The non-ag codes 63-65 and 81-88 are holdovers from the older methodology and will only appear in CDLs from 2007 and earlier. The non-ag codes from 111-195 are from the current methodology which uses the USGS NLCD as non-ag training and will only appear in CDLs 2007 and newer. 2007 was a transition year so there may be both sets of categories in the 2007 national product but will not appear within the same state. Note: The 2024 CDL only has the data band. The cultivated and confidence bands are yet to be released by the provider.

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.

This is the 2020 NASS USDA CDL dataset for Minnesota. The dataset was clipped and downloaded using the USDA NASS CropScape online web application: http://nassgeodata.gmu.edu/CropScape/

The Minnesota Department of Agriculture (MDA) did minimal processing on this dataset to make more useful for Minnesota-specific work. The lineage section describes these steps taken, but the full description of the Cropland Data Layer can be found in the NASS USDA Metadata.

Crop area for crops represented in the USGS pesticide dataset, in hectares, from 1997-2017. 87 crops are included. This file is for all states in the contiguous US, other than California.

Origin: USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL): https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.phpData Access: https://nassgeodata.gmu.edu/CropScape/The Crop Frequency Layers identify crop specific planting frequency and are based on land cover information derived from every year of available CDL data beginning with the 2008 CDL, the first year of full Continental U.S. coverage. The Cultivated Layer and Crop Frequency Data Layers with accompanying metadata detailing the methodology are available for download at /Research_and_Science/Cropland/Release/.From the CDL Metadata:How has the methodology used to create the CDL changed over the program's history?The classification process used to create older CDLs (prior to 2006) was based on a maximum likelihood classifier approach using in-house software. The pre-2006 CDL's relied primarily on satellite imagery from the Landsat TM/ETM satellites which had a 16-day revisit. The in-house software limited the use of only two scenes per classification area. The only source of ground truth was the NASS June Area Survey (JAS). The JAS data is collected by field enumerators so it is quite accurate but is limited in coverage due to the cost and time constraints of such a massive annual field survey. It was also very labor intensive to digitize and label all of the collected JAS field data for use in the classification process. Non-agricultural land cover was based on image analyst interpretations.Starting in 2006, NASS began utilizing a new satellite sensor, new commercial off-the-shelf software, more extensive training/validation data. The in-house software was phased out in favor of a commercial software suite, which includes Erdas Imagine, ESRI ArcGIS, and Rulequest See5. This improved processing efficiency and, more importantly, allowed for unlimited satellite imagery and ancillary dataset inputs. The new source of agricultural training and validation data became the USDA Farm Service Agency (FSA) Common Land Unit (CLU) Program data which was much more extensive in coverage than the JAS and was in a GIS-ready format. NASS also began using the most current USGS National Land Cover Dataset (NLCD) dataset to train over the non-agricultural domain. The new classification method uses a decision tree classifier.NASS continues to strive for CDL processing improvements, including our handling of the FSA CLU pre-processing and the searching out and inclusion of additional agricultural training and validation data from other State, Federal, and private industry sources. New satellite sensors are incorporated as they become available. Currently, the CDL Program uses the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 A and B sensors. Imagery is downloaded daily throughout the growing season with the objective of obtaining at least one cloud-free usable image every two weeks throughout the growing season.Please refer to (FAQ Section 4, Question 4) on this FAQs webpage to learn more about how the handling of grass and pasture related categories has evolved over the history of the CDL Program.Extensive metadata records are available by state and year at the following webpage: (/Research_and_Science/Cropland/metadata/meta.php).

This dataset contains data and code from the manuscript:Heintzman, L.J., McIntyre, N.E., Langendoen, E.J., & Read, Q.D. (2024). Cultivation and dynamic cropping processes impart land-cover heterogeneity within agroecosystems: a metrics-based case study in the Yazoo-Mississippi Delta (USA). Landscape Ecology 39, 29 (2024). https://doi.org/10.1007/s10980-024-01797-0There are 14 rasters of land use and land cover data for the study region, in .tif format with associated auxiliary files, two shape files with county boundaries and study area extent, a CSV file with summary information derived from the rasters, and a Jupyter notebook containing Python code.The rasters included here represent an intermediate data product. Original unprocessed rasters from NASS CropScape are not included here, nor is the code to process them.List of filesMS_Delta_maps.zipMSDeltaCounties_UTMZone15N.shp: Depiction of the 19 counties (labeled) that intersect the Mississippi Alluvial Plain in western Mississippi.MS_Delta_MAP_UTMZone15N.shp: Depiction of the study area extent.mf8h_20082021.zipmf8h_XXXX.tif: Yearly, reclassified and majority filtered LULC data used to build comboall1.csv - derived from USDA NASS CropScape. There are 14 .tif files total for years 2008-2021. Each .tif file includes auxiliary files with the same file name and the following extensions: .tfw, .tif.aux.xml, .tif.ovr., .tif.vat.cpg., .tif.vat.dbf.comboall1.csv: Combined dataset of LULC information for all 14 years in study period.analysis.ipynb_.txt: Jupyter Notebook used to analyze comboall1.csv. Convert to .ipynb format to open with Jupyter.This research was conducted under USDA Agricultural Research Service, National Program 211 (Water Availability and Watershed Management).

This spreadsheet contains an explanation of what the columns mean in all of the datasets. The titles of tabs correspond to the shortened filenames - each file has one tab.

Key relating crop groups from the USGS pesticide dataset to land cover classes in the USDA Cropland Data Layer (only for California)

Arthropod density observations and measures of crop production in California citrus

GENERAL INFORMATION

Title of Dataset: Arthropod density observations and measures of crop production in California Citrus

Author/Principal Investigator Information Name: Mia Lippey ORCID:0000-0002-8292-3444 Institution: University of California, Davis Address: 1 Shields Ave, Davis, CA 95616 Email: mklippey@ucdavis.edu

Date of data collection: 2006 - 2019

Geographic location of data collection: San Joaquin Valley, California

Information about funding sources that supported the collection of the data: USDA-FACT

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: none; data were previously restricted, but all sensitive information has been redacted

Was data derived from another source? If yes, list source(s): Citrusformatics database (privately hosted)

Recommended citation for this dataset: pending

DATA & FILE OVERVIEW

Are there multiple ...

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.

This is the 2018 NASS USDA CDL dataset for Minnesota. The dataset was clipped and downloaded using the USDA NASS CropScape online web application: http://nassgeodata.gmu.edu/CropScape/

The Minnesota Department of Agriculture (MDA) did minimal processing on this dataset to make more useful for Minnesota-specific work. The lineage section describes these steps taken, but the full description of the Cropland Data Layer can be found in the NASS USDA Metadata.