The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For a complete list of categories and definitions, see the National Soil Survey Handbook.All areas are prime farmlandFarmland of local importanceFarmland of statewide importanceFarmland of statewide importance, if drainedFarmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigatedFarmland of statewide importance, if irrigated and drainedFarmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigated and reclaimed of excess salts and sodiumFarmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if warm enoughFarmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing seasonFarmland of unique importanceNot prime farmlandPrime farmland if drainedPrime farmland if drained and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigatedPrime farmland if irrigated and drainedPrime farmland if irrigated and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigated and reclaimed of excess salts and sodiumPrime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Prime farmland if protected from flooding or not frequently flooded during the growing seasonPrime farmland if subsoiled, completely removing the root inhibiting soil layerDataset SummaryPhenomenon Mapped: FarmlandUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class (farmlndcl).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Global maps of agricultural expansion potential at a 300 m resolution

This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.

Abstract:

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Data description:

We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.

Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.

Repository content:

Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository.

Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).

File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”

• "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.

• "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.

• "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.

Raster metadata:

Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Notes on use:

Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.

Value of farmland and buildings per acre, for Canada and the provinces at July 1 (in dollars).

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

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

1. "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)

2. "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

Accurate and precise measurements of global cropland extent are needed for monitoring the sustainability of agriculture at all scales. Recent advancement in remote sensing and land cover mapping methods have greatly increased the ability to estimate cropland area distribution and trends. Here the FAO presents a map of cropland agreement produced by consolidating information at pixel level from six high-resolutions maps for circa 2020. The following six high resolution layers were used: ESRI 10 meter LU/LC, FROM-GLC, GLAD, GLC-FCS30, Globeland30 and Worldcover.

Two bands are included in the dataset:

1. Simple agreement (values between 1 and 6)
2. Detailed agreement (values between 1 and 63)

The map, developed in the Google Earth Engine platform, combines the 6 land cover/cropland layers to show their cropland agreement on pixel level at a spatial resolution of 30 meters. The simple agreement has pixel values that range from 1 (only 1 dataset classifies as cropland) to 6 (all datasets agree on presence of cropland). Pixels with a value of 0 indicate pixels where all datasets agree on absence of cropland. The second band includes a detailed agreement, showing which combination of the 6 datasets classify a pixel as cropland. The overview table (DetailedAgreement_LookupTable.xlsx) shows what the pixel values of this detailed agreement (from 1 to 63) correspond to.

The dataset has been uploaded in 16 tiles, in the preview below and in the file "ACroplandAgreement_30m_Tiles.png" the extent of each tile can be found.

For more information on FAO statistics on land cover and land use:

FAO. 2022. Land use statistics and indicators. Global, regional and country trends, 2000–2020. FAOSTAT Analytical Brief, no. 48. Rome. https://doi.org/10.4060/cc0963en

FAO. 2021. Land cover statistics. Global, regional and country trends, 2000–2019. FAOSTAT Analytical Brief Series No. 37. Rome.

[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980. Sources: State Department of Agriculture; Hawaii Statewide GIS Program, Office of Planning. Note: August, 2018 - Corrected one incorrect record, removed coded value attribute domain.For more information on data sources and methodologies used, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/alum.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

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 Croplands

This repository contains data from “Global 10 arc-seconds land suitability maps for projecting future agricultural expansion” study.Abstract:Conversion of nature into agricultural land is a major driver of environmental change globally. However, the spatial resolution of current global land change models is relatively coarse and does not capture fine-grained patterns relevant for quantifying local impacts. To fill this gap, we created global suitability maps for conversion into agricultural land at a resolution of 10 arc-seconds (~300 meters at the equator). To that end, we developed Artificial Neural Network models relating locations of conversions into agricultural land, as recorded from 2003 to 2013, to various explanatory variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with a mean Area Under the Curve (AUC) value of 0.95. Hindcasting from 1992 to 2002 revealed a similar performance (AUC = 0.94), indicating that our models are robust when applied to a different temporal context. Our high-resolution land suitability maps can be used to project future expansion of agricultural land worldwide, which is an asset for global environmental assessment studies.Data description:We provide here raster maps of suitability for agricultural expansion into three categories of agriculture - (i) homogeneous cropland, (ii) mosaic cropland with >50% crops, and (iii) mosaic cropland with >50% vegetation. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - homogeneous cropland, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the homogeneous croplands, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaic with >50% crops, and mosaic with <50% crops, respectively.Global suitability maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_suitability_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - homogeneous cropland, 30 - mosaic cropland with >50% crops, and 40 - mosaic cropland with >50% vegetation). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for more coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, mean, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.Repository content:Main folder:- “Agri_suitability_mosaic_10.tif” is the global raster map for homogeneous cropland category at the spatial resolution of 10 arc-seconds.- “Agri_suitability_mosaic_30.tif” is the global raster map for mosaic cropland with >50% crops category at the spatial resolution of 10 arc-seconds.- “Agri_suitability_mosaic_40.tif” is the global raster map for mosaic cropland with >50% vegetation category at the spatial resolution of 10 arc-seconds.- "readme.txt" is the text file with the basic description and the metadata for the repository.Aggregated_layers folder:This folder contains three subfolders for files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below), with 9 files in each folder.File names for the aggregated maps of global land suitability contain the following information: “Agri_suitability_aggregated_RESL_TYPE_CATG.tif”- "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.- "TYPE" is the type of aggregated values. Value is either "min", "avg", or "max", corresponding to the minimum, mean, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.- "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is homogeneous cropland, category 30 is mosaic cropland with >50% crops, and category 40 is mosaic cropland with >50% vegetation.Raster metadata:Driver: GTiffProjection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows six condensed maps illustrating the occurrence of important characteristics of farms. The two maps at the top show the distribution of part-time farms and occupied farms. Each of these two maps is accompanied by a pie chart showing percentage distribution of both classifications of farm operations for Canada by province. A third map shows the percentage of occupied farm lands that are occupied by owners. This map is accompanied by a chart showing the percentage of farmland, nationally and provincially, that is operated by an owner or manager. The fourth map shows the percentage of occupied farms reporting the availability of electricity and is accompanied by a chart showing percentages for Canada and each province. The fifth map shows the percentage of occupied farms reporting the usage of tractors. This map is also accompanied by a chart which shows the percentage of farms reporting tractors for Canada and each province. The sixth map, on the bottom right portion of this plate, shows the value of farm products sold per farm. These maps are based on data which was available as of the 1958 publication date of this atlas map.

The article analyses the relation of market prices in the agricultural land market and selected pedological characteristics of traded lands. During the period of 2009–2018 in 12 districts of Slovakia more than 153,000 plots with different pedo-ecological and geographic conditions have been analysed. Based on soil types, texture composition, steepness, gravel content, and depth, corresponding price levels were derived, and soil price maps were developed. The highest valued soils are of chernozem type (EUR 1.64 m−2), loamy soils (EUR 0.86 m−2), soils on flat land (EUR 1.09 m−2), slightly gravelly soils (EUR 1.02 m−2), and deep soils (EUR 1.10 m−2). The land price is evidently highly correlated with its qualitative parameters. Using GIS technologies, the entire territory of Slovakia has been categorized by this means and a so-called basic map of agricultural soil market prices in Slovakia has been created.

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. Other sections: Spatial Data Organization: Direct_Spatial_Reference_Method: Vector Point_and_Vector_Object_Information: SDTS_Terms_Description: SDTS_Point_and_Vector_Object_Type: G-polygon Point_and_Vector_Object_Count: Varies by Year Spatial_Reference_Information: Horizontal_Coordinate_System_Definition: Planar: Map_Projection: Map_Projection_Name:Albers Conical Equal Area Albers_Conical_Equal_Area: Albers Conical Equal Area Standard_Parallel: 34.000000 Standard_Parallel: 40.500000 Longitude_of_Central_Meridian: -120.000000 Latitude_of_Projection_Origin: 0.000000 False_Easting: 0.000000 False_Northing: -4000000.000000 Planar_Coordinate_Information: Planar_Coordinate_Encoding_Method: coordinate pair Coordinate_Representation: Abscissa_Resolution: 0.000256Ordinate_Resolution: 0.000256 Planar_Distance_Units: meters Geodetic_Model: Horizontal_Datum_Name: North American Datum of 1927 Ellipsoid_Name: Clarke 1866 Semi-major_Axis: 6378206.400000 Denominator_of_Flattening_Ratio: 294.978698 Vertical_Coordinate_System_Definition: Altitude_System_Definition: Altitude_Resolution: 0.000010 Altitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinates Entity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Label: Important Farmland Categories Entity_Definition: Technical ratings of the soils and current land use information are combined to determine the appropriate map category. Definition_Source: Farmland Mapping and Monitoring Program Attribute: Attribute_Label: OBJECTID Attribute_Definition: Internal feature number. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated. Attribute: Attribute_Label: Shape Attribute_Definition: Feature geometry. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Coordinates defining the features. Attribute: Attribute_Label: POLYGON_TY Attribute_Definition: Identifies the mapping categories used by the Farmland Mapping and Monitoring Program. Attribute_Definition_Source: Definitions were developed by the USDA-NRCS as part of their nationwide Land Inventory and Monitoring (LIM) system. Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: Prime Farmland (P) Enumerated_Domain_Value_Definition: Irrigated land with the best combination of physical and chemical features able to sustain long term production of agricultural crops. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for production of irrigated crops at some time during the four years prior to the mapping date. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Farmland of Statewide Importance (S) Enumerated_Domain_Value_Definition: Irrigated land similar to Prime Farmland that has a good combination of physical and chemical characteristics for the production of agricultural crops. This land has minor shortcomings, such as greater slopes or less ability to store soil moisture than Prime Farmland. Land must have been used for production of irrigated crops at some time during the four years prior to the mapping date. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Unique Farmland (U) Enumerated_Domain_Value_Definition: 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. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Farmland of Local Importance (L) Enumerated_Domain_Value_Definition: Lands that do not qualify for the Prime, Statewide, or Unique designation but are considered Existing Agricultural Lands, or Potential Agricultural Lands, in the Agricultural Land Element of the County General Plan. Timberlands are excluded. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Grazing Land (G) Enumerated_Domain_Value_Definition: Land on which the existing vegetation is suited to the grazing of livestock. This category is used only in California and was developed in cooperation with the California Cattlemen's Association, University of California Cooperative Extension, and other groups interested in the extent of grazing activities. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Urban and Built-Up Land (D) Enumerated_Domain_Value_Definition: Urban and Built-Up land is occupied by structures with a building density of at least 1 unit to 1.5 acres, or approximately 6 structures to a 10-acre parcel. Common examples include residential, industrial, commercial, institutional facilities, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, and water control structures. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Other Land (X) Enumerated_Domain_Value_Definition: Land which does not meet the criteria of any other category. Typical uses include low density rural development, heavily forested land, mined land, or government land with restrictions on use. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Water (W) Enumerated_Domain_Value_Definition: Water areas with an extent of at least 40 acres. Domain Value Attribute: Enumerated_Domain: Enumerated_Domain_Value: Area not mapped (Z) Enumerated_Domain_Value_Definition: Area which falls outside of the NRCS soil survey. Not mapped by the FMMP. Domain Value Attribute: Attribute: Attribute_Label: POLYGON_AC Attribute_Definition: The acreage of the polygon feature. Attribute_Definition_Source: Computer calculated. Attribute: Attribute_Label: COUNTY_NAM Attribute_Definition: County name identified by a three letter abbreviation. Attribute_Definition_Source: County abbreviations are in the text file located at the following url: ftp://consrv.ca.gov/pub/dlrp/FMMP/county_index.txt Attribute_Domain_Values: Unrepresentable_Domain: Coordinates defining the features. Attribute: Attribute_Label: UPD_YEAR Attribute_Definition: The year the data was captured. Attribute_Definition_Source: FMMP updates their data biennially. Attribute: Attribute_Label: SHAPE_LENG Attribute_Definition: Perimeter of the polygon feature in meters. Attribute_Definition_Source: Computer calculated. Attribute_Domain_Values: Unrepresentable_Domain: Positive real numbers that are automatically generated. Attribute: Attribute_Label: SHAPE_AREA Attribute_Definition: Area of feature in meters squared. Attribute_Definition_Source: Computer calculated. Attribute_Domain_Values: Unrepresentable_Domain: Positive real numbers that are automatically generated.

The concept of High Nature Value (HNV) farmland ties together biodiversity to the continuation of farming on certain types of land and the maintenance of specific farming systems. The general goal of the data set is to enhance the European map of HNV farmland 2000 that shows the estimated distribution and presence likelihood of HNV farmland across the whole European territory. The HNV farmland map aims to gain a better insight into the distribution and extent of farmland that holds a special biodiversity value, and to develop a more effective tool for carrying out further analyses on spatial and time trends. A first version of the European HNV map developed by JRC/EEA in 2008 was based on CORINE land cover 2000 and biodiversity-related data sets (Paracchini, M. L.; Petersen, J.-E.; Hoogeveen, Y.; Bamps, C.; Burfield, I. and van Swaay, C., 2008. High Nature Value Farmland in Europe. An estimate of the distribution patterns on the basis of land cover and biodiversity data. JRC Scientific and Technical Reports. European Communities, Luxembourg). A further update of HNV farmland in Europe was carried out in 2012 to update the HNV farmland dataset based on the CLC data 2006 and to include countries previously not part of the European HNV farmland assessment. The main focus of the 2017 exercise was to update the HNV farmland dataset based on the CLC 2012 accounting layer and to recalculate HNV 2000 based on the CLC 2000 accounting layer in order to maintain coherence for the calculation of a time series and changes between HNV 2000 and HNV 2012.

The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For more information of farmland classification see the National Soil Survey Handbook. Dataset SummaryPhenomenon Mapped: FarmlandGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class(farmlndcl). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Data Dictionary"All areas are prime farmland" 1;"Farmland of local importance" 2;"Farmland of statewide importance" 3;"Farmland of statewide importance, if drained" 4;"Farmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing season" 5;"Farmland of statewide importance, if irrigated" 6;"Farmland of statewide importance, if irrigated and drained" 7;"Farmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing season" 8;"Farmland of statewide importance, if irrigated and reclaimed of excess salts and sodium" 9;"Farmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60" 10;"Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing season" 11;"Farmland of statewide importance, if warm enough" 12;"Farmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing season" 13;"Farmland of unique importance" 14;"Not prime farmland" 15;"Prime farmland if drained" 16;"Prime farmland if drained and either protected from flooding or not frequently flooded during the growing season" 17;"Prime farmland if irrigated" 18;"Prime farmland if irrigated and drained" 19;"Prime farmland if irrigated and either protected from flooding or not frequently flooded during the growing season" 20;"Prime farmland if irrigated and reclaimed of excess salts and sodium" 21;"Prime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60" 22;"Prime farmland if protected from flooding or not frequently flooded during the growing season" 23;"Prime farmland if subsoiled, completely removing the root inhibiting soil layer" 24;"Farmland of local importance, if irrigated" 25" Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2013.

The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I develop the first high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimated conservation costs, especially at the expensive tail of the distribution. This might have led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges, and can assist with the targeting of public ecosystem service investments. # Data for: High-resolution land value maps reveal underestimation of conservation costs in the United States https://doi.org/10.5061/dryad.np5hqbzq9 ## Description of the data and file structure For methods & data, see Nolte (2020) PNAS: https://www.pnas.org/doi/10.1073/pnas.2012865117 ### Files and variables All dollar estimates are in USD per hectare, deflated to Jan 2017. Raster values are logged (natural log). #### File: places_fmv_all.tif Description: Raster of estimated land values in 2010, based on sales of vacant and non-vacant properties. Unit: ln($2017 / hectare) #### File: places_fmv_vacant.tif Description: Raster of estimated land values in 2010, based on sales of vacant properties. Unit: ln($2017 / hectare) #### File: validation_goco.csv Description: Easement prices (actual, $2017 / hectare) vs. estimated land value (places_fmv_vacant, $2017 / hectare). #### File: validation_public_acquisitions.csv Description: Prices (spending_per_ha, $2017 / hectare) of publicly funded land acquisitions for conservation vs. all proxies shown in Fig. 2 of Nolte (2020) PNAS. The density of training data and the spatial distribution of prediction error are important indicators of data quality. See Methods & Materials and SI Appendix in Nolte (2020) PNAS. See Methods & Materials in Nolte (2020) PNAS

description: Environmental Modeling dataset current as of unknown. Six suitable maps (natural values, working forests, farmland, residential, commercial, industrial) for an eleven county region..; abstract: Environmental Modeling dataset current as of unknown. Six suitable maps (natural values, working forests, farmland, residential, commercial, industrial) for an eleven county region..

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

The following describes standards for assigning Important Farmland Classes to soil survey map units of Massachusetts soil survey areas.

Criteria for the designation “Prime Farmland” per Code of Federal Regulations (CFR)

The prime farmland class is assigned to soil map units, the major component/s relative value data[1] for which, meet prime farmland criteria per 7CFR657.5 as edited to exclude soil properties and climate not relevant to Massachusetts, and to quantify adequate available water holding capacity as follows:

available water capacity of 3.5 in (8.9 cm) or more[2] within a depth of 40 in (1 m) or the depth to an impermeable layer if less than 40 in (1 m) and,pH between 4.5 and 8.4 in all horizons within a depth of 40 in (1 m) and,water table, if present, not shallower than 15 in (38 cm) during May through October and,infrequent (less often than once in 2 years) or no flooding during May through October and,the product of Kw (erodibility factor, whole soil) of the mineral soil surface and percent slope is less than 2.0[3]; and,permeability rate of at least 0.06 in (0.15 cm) per hour in the upper 20 in (50 cm); and,upper 6 in (15 cm) of the soil surface contains less than 10 percent rock fragments by volume coarser than 3 in (7.6 cm) diameter; and,not more than 0.1 percent of the soil surface is covered by stones 10 in (25cm) to 24 in (60cm) diameter, and/or boulders >24 in (60 cm) diameter, and.less than 2 percent bedrock exposure.

Qualifiers for data application to Massachusetts soil survey map unit prime farmland criteria per CFR:

Entire pH data range is applied to the pH criterion. All soil survey map unit components that otherwise meet prime farmland criteria have mineral horizon pH ranges w/in the CFR criterion. Tillage and accepted agricultural practices negate the pH limitation where attribute relative value is less than 4.5. Map units having a predominance of soils of coarse-loamy or coarse-silty particle size class overlying densic contact on 0 to 8% slopes with available water capacity data values <3.5 in (8.1 cm), and that meet remaining criteria per CFR are designated prime farmland. Although attribute data indicates the available water holding capacity minimum of 3.5 in (8.1 cm) is not met, these soils maintain a reservoir of moisture that supports plant growth due to reduced gravitational water loss and meets criteria per CFR of adequate moisture supply for the crops commonly grown. This qualifier is applicable to soil map components with moderately coarse to medium textured mantles overlying lodgment till.Where the product of K and slope percent is 2 or less for the lower part of a 3 to 8 percent map unit slope phase range but exceeds 2 for the upper part of the slope range, and remaining criteria per CFR are met, the map unit is designated prime farmland. Map units that meet all prime farmland criteria per CFR except the relative value data representing the predominant components reflects available water capacity of less than 3.5 in (8.9 cm) through the upper 40 in (1 m) but has sufficient available water capacity in the upper profile, are designated prime farmland. This qualifier is applicable to soil survey map unit components having moderately coarse to medium textured mantles overlying coarse textured deposits.Complexes and Associations - Soil map units with more than 50 percent components that meet any of the above scenarios are designated prime.

Criteria for the designation “Farmland of Statewide Importance"

Soil map units, the predominant composition of which does not meet criteria for prime farmland and have all the following characteristics…available water capacity of 2.0 in (5.1 cm) or more[4] within a depth of 40 in (1 m); and,pH between 4.5 and 8.4 in all horizons within a depth of 40 in (1 m) and,water table, if present, not shallower than 15 in (38 cm) during May through October; and,infrequent (less often than once in 2 years) or no flooding during May through October; and,the product of Kw (erodibility factor, whole soil) of the mineral soil surface and percent slope is less than 4.2[5]; and,permeability rate of at least 0.06 in (0.15 cm) per hour in the upper 20 in (50 cm); and,upper 6 in (15 cm) with less than 35 percent rock fragments by volume coarser than 3 in (7.6 cm); and,not more than 3 percent of the soil surface is covered by stones 10 in (25 cm) to 24 in (60 cm) diameter and, not more than 0.1 percent of the surface is covered by boulders >24 in (60 cm) diameter, andless than 2 percent bedrock exposures.

Qualifiers for data application to Massachusetts Farmland of Statewide Importance Criteria

Where the product of K and slope percent is 4.2 or less for the lower part of an 8 to 15 percent map unit slope phase range but exceeds 4.2 for the upper part of the slope range, and remaining criteria are met, the map unit is designated farmland of statewide importance. Complexes and Associations - Soil map units with more than 50 percent components that meet the above criteria are designated farmland of statewide importance.

Important Farmland Soil Map Unit Designation Overriding Scenarios

Application of anomalous or non-representative data elements to important farmland criteria may result in inaccurate class placement. The consideration of the characteristics of the soil survey map unit as a whole as assessed by Massachusetts NRCS staff overrides point specific data.

K factors and available water capacity data for the same nominal component may vary among soil survey areas resulting in different data-derived farmland classes. The characteristics of the predominant condition based on acreage extent will be applied state-wide for prime farmland and farmland of state-wide importance designations.

The following address specific scenarios where calculations based on attribute data may inaccurately place a map unit in prime farmland or farmland of statewide Importance classes. Soil map units having any of the following characteristics are precluded from important farmland designations:A major component that is shallow to lithic contact: complex slopes, surface stones and boulders associated with these map units, and very shallow components within these landscapes are significant limitations to agriculture.Slope phase range that includes 20 percent or more. Per recommendation from MA NRCS ecological sciences staff, 20 percent slope or greater is limiting for equipment operations.Hydric soil composition greater than or equal to 50 percent.Quartzipsamment composition greater than or equal to 50 percent: droughty, inherently low fertility. A major component of urban land and/or major component classified to level above series i.e. Udorthents.Map unit complexes associated with the undulating, rolling, irregular slopes of the Cape Cod terminal moraines.

Soil map units having any of the following characteristics are precluded from the designation, Prime Farmland:

Composition of soil components in the sandy-skeletal particle size class greater than or equal to 50 percent.Slope phase range that exceeds 8 percent.[6]

Unique Farmland

Soil survey map units designated as Unique Farmland, are those suitable for, and have an established history of cranberry production. The Unique Farmland designation is excluded from soil survey areas with few or no lands with cranberry production.

[1] Relative value refers to the value assigned to specific data elements in the National Soils Information System. Application of anomalous or non-representative values to important farmland criteria may result in inaccurate class placement. The consideration of the characteristics of the soil map unit as a whole overrides point specific data as determined by Massachusetts NRCS staff.

[2]Available water capacity needs determined from “Conservation Irrigation Guide for Massachusetts, 1981”

[3]Slope range values applied to this criterion exclude the lowest whole number in the range to separate overlap with the adjacent lower slope phase as follows: 0-3, 4-8, 9-15.

[4]Available water capacity needs determined from Conservation Irrigation Guide for Massachusetts, 1981

[5]Product of K and slope criterion based on historical precedent, MA Soil Conservation Service document, “Additional Farmland of State or Local Importance”,1/17/1986. Slope range values applied to this criterion exclude the lowest whole number in the range to separate overlap with the adjacent lower slope phase as follows: 0-3, 4-8, 9-15.

[6]Based on data, some map units meet Prime Farmland criteria on the lower part of the 8-15 percent slope range. About a dozen map units with available water capacity >3.5 inches and Kw of .1, .2, .15, or .17 were noted, all of which have loamy surface textures and parent material like other map units with higher Kw factors. The decision to exclude slopes greater than 8 percent from Prime Farmland is based on the preponderance of attribute data for similar soils.

Contained within the 4th Edition (1974) of the Atlas of Canada is a set of two maps. The first map shows the total value of farm sales for 1960 to 1961 by census division for all of Canada. The second map shows Ontario and Quebec in greater detail (at a scale of 1:5 000 000). The second map shows areas in which soil and climatic conditions are judged suitable for agriculture in North-West Canada.