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In response to this need, Matt Reeves, a Research Ecologist with the Rocky Mountain Research Station, worked in partnership with private industry to develop a new data service freely available to all stakeholders and managers.This service uses a forage projection system that utilizes machine learning to process near real-time climate and remote sensing data to estimate the magnitude and timing of annual production across all rangelands in the Northern and Intermountain Regions of the U.S. Forest Service. This automated projection system operates between March and July of the growing season and is updated weekly. This timeframe allows for a new estimate of the total annual yield to be made in concert with an estimate of the timing of the peak of the growing season. These data are complementary to the GrassCast offered at: http://grasscast.agsci.colostate.edu/, which focuses on the Great Plains, using long-term weather forecasts.Rangeland productivity in pounds per acre: This represents the projected yield of all shrubs, grasses, and forbs, corresponding to the 1-hour time lag fuel category (less than 1/4 inch diameter fuel).Rangeland productivity, percent change from average: Percent change between this year's projected yield and the average yield from 2000-2015.Rangeland productivity, percent change from average (herbaceous): The same as above, but for herbaceous vegetation only (annual grasses and forbs).More information: https://www.fuelcast.net/", "mapName": "WO_OSC_CurrentRangelandProductivity_01", "description": "Rangeland managers and livestock producers need timely and consistent tools that produce information to inform grazing strategies, risk management, and allotment management plans. In addition, National Forests are now in various stages of Forest Plan revisions which require assessments of current rangeland conditions and past vegetation performance in a clear, unbiased manner. On-the-ground monitoring is extremely expensive and difficult to employ consistently due to limited resources, limited trained staff, and shifting priorities. In response to this need, Matt Reeves, a Research Ecologist with the Rocky Mountain Research Station, worked in partnership with private industry to develop a new data service freely available to all stakeholders and managers.This service uses a forage projection system that utilizes machine learning to process near real-time climate and remote sensing data to estimate the magnitude and timing of annual production across all rangelands in the Northern and Intermountain Regions of the U.S. Forest Service. This automated projection system operates between March and July of the growing season and is updated weekly. This timeframe allows for a new estimate of the total annual yield to be made in concert with an estimate of the timing of the peak of the growing season. These data are complementary to the GrassCast offered at: http://grasscast.agsci.colostate.edu/, which focuses on the Great Plains, using long-term weather forecasts.Rangeland productivity in pounds per acre: This represents the projected yield of all shrubs, grasses, and forbs, corresponding to the 1-hour time lag fuel category (less than 1/4 inch diameter fuel).Rangeland productivity, percent change from average: Percent change between this year's projected yield and the average yield from 2000-2015.Rangeland productivity, percent change from average (herbaceous): The same as above, but for herbaceous vegetation only (annual grasses and forbs).More information: https://www.fuelcast.net/", "copyrightText": "The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. 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In addition, National Forests are now in various stages of Forest Plan revisions which require assessments of current rangeland conditions and past vegetation performance in a clear, unbiased manner. On-the-ground monitoring is extremely expensive and difficult to employ consistently due to limited resources, limited trained staff, and shifting priorities. In response to this need, Matt Reeves, a Research Ecologist with the Rocky Mountain Research Station, worked in partnership with private industry to develop a new data service freely available to all stakeholders and managers.\nThis service uses a forage projection system that utilizes machine learning to process near real-time climate and remote sensing data to estimate the magnitude and timing of annual production across all rangelands in the Northern and Intermountain Regions of the U.S. Forest Service. This automated projection system operates between March and July of the growing season and is updated weekly. This timeframe allows for a new estimate of the total annual yield to be made in concert with an estimate of the timing of the peak of the growing season. These data are complementary to the GrassCast offered at: http://grasscast.agsci.colostate.edu/, which focuses on the Great Plains, using long-term weather forecasts.\n\nRangeland productivity in pounds per acre: This represents the projected yield of all shrubs, grasses, and forbs, corresponding to the 1-hour time lag fuel category (less than 1/4 inch diameter fuel).\n\nRangeland productivity, percent change from average: Percent change between this year's projected yield and the average yield from 2000-2015.\n\nRangeland productivity, percent change from average (herbaceous): The same as above, but for herbaceous vegetation only (annual grasses and forbs).\n\nMore information: https://www.fuelcast.net/", "Subject": "Seasonal rangeland productivity estimates, based on most recent data.", "Category": "", "Version": "2.9.0", "AntialiasingMode": "None", "TextAntialiasingMode": "Force", "Keywords": "USDA Forest Service,USFS,Rocky Mountain Research Station,RMRS,Office of Sustainability and Climate,OSC,rangelands,rangeland productivity,climate,drought,forage" }, "capabilities": "Map,Query,Data", "supportedQueryFormats": "JSON, geoJSON, PBF", "exportTilesAllowed": false, "referenceScale": 0.0, "supportsDatumTransformation": true, "archivingInfo": {"supportsHistoricMoment": false}, "supportsClipping": true, "supportsSpatialFilter": true, "supportsTimeRelation": true, "supportsQueryDataElements": true, "mapUnits": {"uwkid": 9001}, "maxRecordCount": 2000, "maxImageHeight": 4096, "maxImageWidth": 4096, "supportedExtensions": "" }