NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.

The goal of the Farmland Mapping and Monitoring Program (FMMP) is to provide consistent and impartial data to decision makers for use in assessing nearly present status, reviewing trends, and planning for the future of California’s agricultural land resources. FMMP produces Important Farmland Maps, which are a hybrid of resource quality (soils) and land use information. Data is also released in statistical formats that are compiled within the biennial California Farmland Conversion Report.The first Important Farmland Maps, produced in 1984, covered 30.3 million acres (38 counties). Biennial farmland conversion data became available with the 1984-1986 Farmland Conversion Report. Data now spans more than 28 years (fourteen biennial mapping cycles) and has expanded to 49.1 million acres as modern soil surveys were completed by USDA. FMMP now maps agricultural and urban land use on nearly 98% of the state's privately held land. ​​​​​​​​California has some of the most productive farmland and diverse open spaces in the world. The Division of Land Resource Protection (DLRP) works with landowners, local governments, and researchers to conserve these important natural resources.​Source: https://www.conservation.ca.gov/dlrp/fmmp

The Kern County Department of Agriculture and Measurement Standards created this dataset to more precisely locate production agricultural field boundaries within the county.

© Inspectors created and maintained this database throughout the year as part of the permitting/supplementing process.

This layer is sourced from maps.co.kern.ca.us.

No description available

The Agriculture Capability mapping dataset is the digitized equivalent of the legacy Agriculture Capability Scanned Maps, which date from the 1960's to the 1990s. Agriculture Capability mapping is also known as 'Soil Capability for Agriculture' and 'Agricultural Capability' mapping. Agricultural Capability is an interpreted mapping product based on soil and climate information. In general, climate determines the range of crops possible in an area and the soils determine the type and relative level of management practices required. This is legacy data and changes in climate are not reflected. For more information about the classification system see: Land Capability Classification for Agriculture. Use caution utilizing these legacy maps as the classifications were based on common land management practices and typical crops of the 1960s-1990s era, and subsequent site specific land management practices (e.g. installation of drainage) may have modified the soil conditions since the mapping was completed. This Agriculture Capability legacy mapping is included in the Soil Information Finder Tool (SIFT) mapping application. The SIFT application provides more detailed climate data (e.g. Growing Degree Days, Frost Free Period (5 C), (1960-1990 climate normals). The SIFT 'Soil query tools' may be useful for identifying areas with specific 'growing conditions' of interest based on soils present (soil name), soil texture, drainage, coarse fragment content, slope, elevation, growing degree days and frost free period. Note: This Agriculture Capability Mapping dataset is based on soil mapping at 1:100,000, 1:50,000 or 1:20,000 scale, and is more detailed than the 1:250,000 scale Canada Land Inventory (CLI) Agricultural Capability mapping (available here).

The goal of the Farmland Mapping and Monitoring Program (FMMP) is to provide consistent and impartial data to decision makers for use in assessing nearly present status, reviewing trends, and planning for the future of California’s agricultural land resources. FMMP produces Important Farmland Maps, which are a hybrid of resource quality (soils) and land use information. Data is also released in statistical formats that are compiled within the biennial California Farmland Conversion Report.The first Important Farmland Maps, produced in 1984, covered 30.3 million acres (38 counties). Biennial farmland conversion data became available with the 1984-1986 Farmland Conversion Report. Data now spans more than 28 years (fourteen biennial mapping cycles) and has expanded to 49.1 million acres as modern soil surveys were completed by USDA. FMMP now maps agricultural and urban land use on nearly 98% of the state's privately held land. ​​​​​​​​California has some of the most productive farmland and diverse open spaces in the world. The Division of Land Resource Protection (DLRP) works with landowners, local governments, and researchers to conserve these important natural resources.​Source: https://www.conservation.ca.gov/dlrp/fmmp

This dataset may be a mix of two years and is updated as the data is released for each county. For example, one county may have data from 2014 while a neighboring county may have had a more recent release of 2016 data. For specific years, please check the service that specifies the year, i.e. California Important Farmland: 2016.Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

Descriptions Excel Application Tool for Statewide Agricultural Water Use Data 2016 - 2020 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2016 – 2020 statewide agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2023. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2016 - 2020 water years. It should also be mentioned that there are 3 other similar Excel applications that cover 1998 - 2005 and 2006 – 2010, & 2011 - 2015 agricultural water use data for the California Water plan Updates 2005/2009, 2013, and 2018 respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2016 – 2020 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised. Following are definitions of terminology and listing of 20 crop categories used in this Excel application. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres) 3- Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field. Please note that there are no double cropping acreages for 2017. Because on a normal year when Regional Offices (RO) receive data from Land IQ, they were able to provide double cropping acreages. Since the 2017 land use data was derived from average crop acres between water years 2016 and 2018,2019, & 2020, they lost spatial and temporal data necessary to calculate double cropping. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988). Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year. Crop category numbers and descriptions Crop Category Crop category description. 1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay) 2 Rice (rice, rice_wild, rice_flooded, rice-upland) 3 Cotton 4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early) 5 Corn 6 Dry beans 7 Safflower 8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane 9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual) 10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue) 11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc) 12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc) 13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon) 14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic) 15 Potatoes (potatoes, potatoes_sweet) 16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower) 17 Almond & pistachios 18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis) 19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba) 20 Vineyards (grape_table, grape_raisin, grape_wine)

Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).

This polygon shapefile contains areas of important farmland in Imperial County, California for 2010. Important Farmland Maps show the relationship between the quality of soils for agricultural production and the land's use for agricultural, urban, or other purposes. A biennial map update cycle and notation system employed by FMMP captures conversion to urban land while accommodating rotational cycles in agricultural use. The minimum land use mapping unit is 10 acres unless specified. Smaller units of land are incorporated into the surrounding map classifications. In order to most accurately represent the NRCS digital soil survey, soil units of one acre or larger are depicted in Important Farmland Maps. For environmental review purposes, the categories of Prime Farmland, Farmland of Statewide Importance, Unique Farmland, Farmland of Local Importance, and Grazing Land constitute 'agricultural land' (Public Resources Code Section 21060.1). The remaining categories are used for reporting changes in land use as required for FMMP's biennial farmland conversion report. This layer is part of the 2010 California Farmland Mapping and Montoring Project.

Geospatial data about Riverside County, CA Agricultural Preserves. Export to CAD, GIS, PDF, CSV and access via API.

For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology. Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region. The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Dataset Name Source Usage Gridded Soil Survey Geographic (gSSURGO) Database Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/ Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Important Farmland "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Statewide Crop Mapping (2019) "2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping The footprint is used as part of the mask for the CEC Cropland Index Model’s domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due to enhanced water supplies proposed under local groundwater agencies' Groundwater Sustainability Plans. -- LL (Land use management Low intensity): a scenario where prime farmland and groundwater recharge areas are not protected from urban sprawl. -- LH (Land use management High intensity): a scenario where almost all the state's priority habitats are preserved from urbanization or agricultural expansion.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial mapping products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. The imagery was classified using Random Forest (RF) Modeling to produce land cover maps with three main classifications - bare, vegetation, and shadows. An updated roads and trails map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate road and trail densities in the watershed. Separate metadata records for each product (Land_Cover_Maps_Scotts_Creek_Watershed_CA_2018_2020_2022_metadata.xml, and Roads_and_Trails_Map_Upper_Scotts_Creek_Watershed_CA _2022_metadata.xml) are provided on the ScienceBase page for each child item. Users should be aware of the inherent errors in remote sensing products.

The geospatial data presented here as ArcGIS layers denote landcover/landuse classifications to support field sampling efforts that occurred within the Cache Creek Settling Basin (CCSB) from 2010-2017. Manual photointerpretation of a National Agriculture Imagery Program (NAIP) dataset collected in 2012 was used to characterize landcover/landuse categories (hereafter habitat classes). Initially 9 categories were assigned based on vegetation structure (Vegtype1). These were then parsed into two levels of habitat classes that were chosen for their representativeness and use for statistical analyses of field sampling. At the coarsest level (Landcover 1), five habitat classes were assigned: Agriculture, Riparian, Floodplain, Open Water, and Road. At the more refined level (Landcover 2), ten habitat classes were nested within these five categories. Agriculture was not further refined within Landcover 2, as little consistency was expected between years as fields rotated between corn, pumpkin, tomatoes, and other row crops. Riparian habitat, marked by large canopy trees (such as Populus fremontii (cottonwood)) neighboring stream channels, also was not further refined. Floodplain habitat was separated into two categories: Mixed NonWoody (which included both Mowed and Barren habitats) and Mixed Woody. This separation of the floodplain habitat class (Landcover1) into Woody and NonWoody was performed with a 100 m2 moving window analysis in ArcGIS, where habitats were designated as either ≥50% shrub or tree cover (Woody) or <50%, and thus dominated by herbaceous vegetation cover (NonWoody). Open Water habitat was refined to consider both agricultural Canal (created) and Stream (natural) habitats. Road habitat was refined to separate Levee Roads (which included both the drivable portion and the apron on either side) and Interior roads, which were less managed. The map was tested for errors of omission and commission on the initial 9 categories during November 2014. Random points (n=100) were predetermined, and a total of 80 were selected for field verification. Type 1 (false positive) and Type 2 (false negative) errors were assessed. The survey indicated several corrections necessary in the final version of the map. 1) We noted the presence of woody species in “NonWoody” habitats, especially Baccharus salicilifolia (mulefat). Habitats were thus classified as “Woody” only with ≥50% presence of canopy species (e.g. tamarisk, black willow) 2) Riparian sites were over-characterized, and thus constrained back to “near stream channels only”. Walnut (Juglans spp) and willow stands alongside fields and irrigation canals were changed to Mixed Woody Floodplain. Fine tuning the final habitat distributions was thus based on field reconnaissance, scalar needs for classifying field data (sediment, water, bird, and fish collections), and validation of data categories using species observations from scientist field notes. Calibration was made using point data from the random survey and scientist field notes, to remove all sources of error and reach accuracy of 100%. The coverage “CCSB_Habitat_2012” is provided as an ARCGIS shapefile based on a suite of 7 interconnected ARCGIS files coded with the suffixes: cpg, dbf, sbn, sbx, shp, shx, and prj. Each file provides a component of the coverage (such as database or projection) and all files are necessary to open the “CCSB_Habitat_2012.shp” file with full functionality.

County-Level Pesticide Use Estimates - 1993

The NAWQA Pesticide National Synthesis Project, which began in 1992, is a national-scale assessment of the occurrence and behavior of pesticides in streams and ground water of the United States and the potential for pesticides to adversely affect drinking-water supplies or aquatic ecosystems.

The tables, maps, and graphs provided by Pesticide National Synthesis Project provide estimates of agricultural pesticide use in the conterminous United States for numerous pesticides. The tables report agricultural pesticide use at the county level and are based on farm surveys of pesticide use and estimates of harvested crop acres. The maps show agricultural pesticide use on a finer scale and are created by allocating the county-level estimates to agricultural land within each county. A graph accompanies each map and shows annual national use by major crop for the mapped pesticide for each year.

These pesticide-use estimates are suitable for evaluating national and regional patterns and trends of annual pesticide use. The reliability of estimates, however, generally decreases with scale and these estimates and maps are not intended for detailed evaluations, such as comparing within or between specific individual counties.

About The Pesticide National Synthesis Project

Estimated Annual Agricultural Pesticide Use

How the estimates and maps are made

For all States except California, proprietary farm survey pesticide-use data are aggregated and reported at the multi-county Crop Reporting District (CRD) level. Harvested-crop acreage data by county from the U.S. Department of Agriculture Census of Agriculture are used to calculate the median pesticide-by- crop use rates for each crop in each CRD. These rates are applied to the harvested acreage of each crop in a county to obtain pesticide-use estimates at a county level. Estimates for California are obtained from annual Department of Pesticide Regulation Pesticide Use Reports (California Department of Pesticide Regulation). Methods for generating county-level pesticide-use estimates are described in Estimation of Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 1992–2009 (Thelin and Stone, 2013) and Estimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 2008-12 (Baker and Stone, 2015).

Maps are created by allocating county-level use estimates to agricultural land within each county based on land classifications defined in the National Land Cover Database 2011 (NLCD11) (Jin and others, 2013; NLCD 2011 Data Download). The NLCD11 is used for the entire period of record because at a national level agricultural land use has not changed much during that time frame, and by using a single snapshot in time, changes in pesticide use are not obscured by changes in land use. NLCD11 Planted/Cultivated categories 81 (Pasture/Hay) and 82 (Cultivated Crops) were combined to differentiate agricultural land from non-agricultural land. The NLCD11 was then generalized to 1 square kilometer cell size and the percentage of agricultural land for each cell was calculated. The proportion of county agricultural land included in each 1 square kilometer cell was multiplied by the total county use for each pesticide to calculate the proportional amount of use allocated to each cell. To display pesticide use on the annual maps for each compound, the range of all of the cell values nationwide for the entire period are divided into quartiles and a color-coded map is generated for each year based on these quartiles. The quartile classes are converted to pounds per square mile.

Estimates of Pesticide Use – EPest-low and EPest-high methods

For all States except California, two different methods, EPest-low and EPest- high, are used to estimate a range of pesticide use. Both EPest-low and EPest-high methods incorporate proprietary surveyed rates for Crop Reporting Districts (CRDs), but EPest-low and EPest-high estimates differ in how they treat situations when a CRD was surveyed and pesticide use was not reported for a particular crop present in the CRD. In these situations, EPest-low assumes zero use in the CRD for that pesticide-by- crop combination. EPest-high, however, treats the unreported use for that pesticide-by- crop combination in the CRD as missing data. In this case, pesticide-by- crop use rates from neighboring CRDs or CRDs within the same region are used to estimate the pesticide-by- crop EPest-high rate for the CRD.

State-based restrictions on pesticide use were not incorporated into EPest- high or EPest-low estimates. However, EPest-low estimates are more likely to reflect these restrictions than EPest-high estimates. Users of the maps and data should consult the methods presented in Thelin and Stone (2013) and Baker and Stone (2015) to understand the details of how both estimates were determined. Maps are provided for both EPest-low and EPest-high estimates.

Use estimates for California are obtained from annual California Department of Pesticide Regulation pesticide use reports. Because these reports provide county-level use estimates, they are incorporated into the data without further processing and low and high rates are the same for counties in California. California county data are appended after the estimation process is completed for the rest of the Nation.

Pesticide Use-Trend Graphs by Crop

Graphs showing annual use by crop for each pesticide are created by summing the national pesticide use for each compound, for each crop or combination of crops. Combined crops are Pasture and Hay (cropland for pasture, fallow and idle cropland, pastureland, and other hay); Alfalfa; Orchards and grapes (stone fruit trees, citrus, nut trees, apples, pears, and grapevines); Vegetables and fruit (all vegetables and non-orchard fruit, including beans, peas, greens, berries, and melons); and Other (sorghum, non-wheat grains, tobacco, peanuts, sugarcane, sugarbeets, and other miscellaneous crops). The relations of graphed crops and combinations of crops to individual Epest Crop Names are shown in the following table. State-by crop estimates are available in tabular format.

Applications and limitations

Pesticide-use estimates from this study are suitable for making national, regional, and watershed estimates of annual pesticide use; however, the reliability of these estimates generally decreases with scale. For example, detailed interpretation of where and how much use occurs within a county is not appropriate. Although county-level estimates were used to create the maps and are provided in the dataset, it is important to understand that surveyed pesticide-by- crop use was not available for all CRDs and, therefore, extrapolation methods were used to estimate pesticide use for some counties. Moreover, surveyed pesticide-by- crop use may not reflect all agricultural use on all crops grown. In addition, State-based restrictions on pesticide use were not incorporated into EPest-high or EPest-low estimates. EPest-low estimates are more likely to reflect these restrictions than EPest- high estimates. With these caveats in mind, including other details discussed in Thelin and Stone (2013) and Baker and Stone (2015), the maps, graphs, and associated county-level use data are critical information for water- quality models and provide a comprehensive graphical overview of the geographic distribution and trends in agricultural pesticide use in the conterminous United States.

References

• Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008-12: U.S. Geological Survey Data Series 907, 9 p., http://dx.doi.org/10.3133/ds907.
• California Department of Pesticide Regulation, Variously dated, Pesticide use reporting: California Department of Pesticide Regulation pesticide use reporting (PUR) database, at http://www.cdpr.ca.gov/docs/pur/purmain.htm.
• Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159-175.
• Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013-5009, 54 p.
• U.S. Department of Agriculture, 2014, State and county data, Geographic area series parts 1–50, AC–12–A–1—AC–12–A–50: U.S. Department of Agriculture 2012 Census of Agriculture, v. 1, at http://www.agcensus.usda.gov/Publications/2012

References and Resources:

• 1993 County-Level Pesticide Use Estimates
• Estimated Annual Agricultural Pesticide Use - County Level

SoilWeb applications can be used to access and explore USDA-NCSS detailed soil survey maps and data (SSURGO) for most of the United States, as well as maps and data outside of Web Soil Survey. Developed by the University of California. Available interface apps:

SoilWeb SoilWeb Earth SEE: Soil Series Extent Explorer Soil Properties Soil Agricultural Groundwater Banking Index (SAGBI) Resources in this dataset:Resource Title: Website Pointer for SoilWeb Apps. File Name: Web Page, url: https://casoilresource.lawr.ucdavis.edu/soilweb-apps/ SoilWeb products that can be used to access USDA-NCSS detailed soil survey data (SSURGO) for most of the United States.

This polygon shapefile contains areas of important farmland in Ventura County, California for 2010. Important Farmland Maps show the relationship between the quality of soils for agricultural production and the land's use for agricultural, urban, or other purposes. A biennial map update cycle and notation system employed by FMMP captures conversion to urban land while accommodating rotational cycles in agricultural use. The minimum land use mapping unit is 10 acres unless specified. Smaller units of land are incorporated into the surrounding map classifications. In order to most accurately represent the NRCS digital soil survey, soil units of one acre or larger are depicted in Important Farmland Maps. For environmental review purposes, the categories of Prime Farmland, Farmland of Statewide Importance, Unique Farmland, Farmland of Local Importance, and Grazing Land constitute 'agricultural land' (Public Resources Code Section 21060.1). The remaining categories are used for reporting changes in land use as required for FMMP's biennial farmland conversion report. This layer is part of the 2010 California Farmland Mapping and Montoring Project.