WDFW combined Sport/ Commercial/ Treaty salmon harvest data.

An extract from the departments Wildlife Resources System (WRS) bear hunting and snaring harvest data.

According to Indiana Statute, IC 6-1.1-4-25(b)(13), counties are required to submit parcel data in a GIS file format to the Indiana Office of Technology. In addition to the parcel data, the GIO works with the Counties to voluntarily obtain these additional datasets (addresses, centerlines, and numerous government boundaries).To learn more about the Indiana Geographic Information Office's annual data sharing efforts, please visit https://www.in.gov/gis/data-sharing/.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of France, Western Europe. The time period coverage is from 596 to -56 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This data set is a compilation of image data from several sources including:BHL title-levelBHL item-levelBHL page-levelBHL namesBHL Flickr harvestsThis dataset was created for an event held on October 23, 2023 entitled Transforming Biodiversity Heritage Library Images - Data Modeling with OpenRefine. Combined, the 4 files comprise approximately 65MB zipped, and 1.4GB expanded. Each unzipped file is roughly 350MB. This data is sourced from the Biodiversity Heritage Library Flickr Collection.Data Dictionary: BHL Flickr Images to Wiki Structured Data CommonsRelease Date: 23-10-2023Frequency: InfrequentbureauCode: 452:11Access Level: publicRights: https://rightsstatements.org/page/NoC-US/1.0/

State and local governments are increasingly interested in understanding the role forests and harvested wood products play in regional carbon sinks and storage, their potential contributions to state-level greenhouse gas (GHG) reductions, and the interactions between GHG reduction goals and potential economic opportunities. We used empirically driven process-based forest carbon dynamics and harvested wood product models in a systems-based approach to project the carbon impacts of various forest management and wood utilization activities in Maryland and Pennsylvania from 2007–2100. To quantify state-wide forest carbon dynamics, we integrated forest inventory data, harvest and management activity data, and remotely-sensed metrics of landuse change and natural forest disturbances within a participatory modeling approach. We accounted for net GHG emissions across (1) forest ecosystems (2) harvested wood products, (3) substitution benefits from wood product utilization, and (4) leakage associated with reduced instate harvesting activities. Based on state agency partner input, a total of 15 management scenarios were modeled for Maryland and 13 for Pennsylvania, along with two climate change scenarios and two bioenergy scenarios for each state. Our findings show that both strategic forest management and wood utilization can provide substantial climate mitigation potential relative to business-as-usual practices, increasing the forest C sink by 29% in Maryland and 38% in Pennsylvania by 2030 without disrupting timber supplies. Key climate-smart forest management activities include maintaining and increasing forest extent, fostering forest resiliency and natural regeneration, encouraging sustainable harvest practices, balancing timber supply and wood utilization with tree growth, and preparing for future climate impacts. This study adds to a growing body of work that quantifies the relationships between forest growth, forest disturbance, and harvested wood products. Methods Primary data inputs to CBM-CFS3 included a detailed forest inventory, growth-yield relations to estimate forest productivity, and estimates of harvest yields and intensity, land-use change, and natural disturbances. Inventory data are categorized by a series of forest classifiers defining relevant characteristics such as spatially referenced boundaries, ownership, forest type, site productivity, or reserve status. Allometric equations are used to predict tree volume-to-biomass relationships (Boudewyn et al., 2007). For this study, forest inventory, growth-yield curves, and harvest data were estimated from the USDA Forest Service Forest Inventory and Analysis (FIA) program, which we accessed through the FIA DataMart (USDA Forest Service, 2019) using the rFIA package (Stanke et al., 2020) in the R programming environment (R Core Team, 2020), which enables data exploration and user-defined spatio-temporal queries and estimation of the FIA database (FIADB). Methodologies derived from Bechtold & Patterson, 2005 and Pugh et al., 2018 were used to estimate each state’s forest inventory by a predetermined list of classifiers. Natural disturbance history was estimated from both the FIADB and LANDFIRE (USGS, 2016) datasets to better constrain initial belowground and soil carbon parameters during what the modeling framework refers to as the model spin-up period (Kurz et al., 2009). Estimates of merchantable volume and corresponding biomass from FIADB were used to calibrate the model’s allometric volume-to-biomass assumptions to match forest type groups and growth conditions in Maryland and Pennsylvania. Harvest removals were estimated as average annual removal of merchantable timber in cubic feet between 2007 and 2019, converted to metric tons of carbon using methodologies and specific gravities reported by Smith et al., 2006. To assign a harvest type and intensity to each record of volumetric removal, stand age at the time of removal was calculated by taking the mid-point average between time t1 and t2 (Bechtold & Patterson, 2005) where t1 is the year the unharvested stand was measured and t2 is the repeat interval year measurement post-harvest. In collaboration with state partners, harvest type and intensity were determined heuristically for each forest type based upon state-level management documentation, peer-reviewed literature, and expert input. A complete list of harvest types and intensities prescribed to each forest type group as a product of stand age can be found in Table S3 of the manuscript. Longer-term averages from 2007-2019 were used to estimate annual area targets for all land-use change (LUC) and natural disturbance events including wind, fire, disease, and insects. Annual LUC average rates by ownership and forest type group were derived by overlaying a geospatial forestland ownership dataset (Sass et al., 2020), the Protected Areas Database of the U.S. (PAD-US), a national geodatabase of protected areas (USGS, 2018), and the National Land Cover Database (NLCD), a remotely-sensed data product used to characterize land cover and land cover change (Wickham et al., 2021). Wind disturbance events were calculated using the LANDFIRE Historic Disturbance dataset (USGS, 2016), a remotely-sensed data product provided by the USGS that estimates annual disturbance events. Annual averages for wildfire disturbances were derived from the LANDFIRE Historic Disturbance dataset (USGS, 2016) and validated through annual reports from the National Interagency Fire Center (NIFC). Annual prescribed fire acres were estimated from reports provided by the Maryland DNR Forest Service and Pennsylvania DCNR Bureau of Forestry and scaled to represent treatments on forestlands only. Annual acreages of insect and disease disturbance were derived from the National Insect & Disease Detection Survey (USDA Forest Service, 2020), a spatial data product produced by USDA that collects and reports data on forest insects, diseases, and other disturbances. For more information on all input and activity data see Supplementary Materials 1.2. A complete list of BAU parameters can be found in Table S2. State-specific trade and commodity data from Resource Planning Act (RPA) assessments (USDA Forest Service, 2021), US Commodity Flow Surveys (US Department of Transportation et al., 2020), US International Trade Commission export data (US International Trade Commission, 2021), and published peer-reviewed data (Howard & Liang, 2019) when available, or US averages from the same sources, were used to adapt and parameterize both HWP models. FAOSTAT data (FAO, 2021) were utilized to determine the commodity distributions of exported roundwood. Softwood products were parameterized and modeled separately from hardwood products, as the two wood types differ in exports and commodities produced as well as their associated product half-lives and displacement (Dymond, 2012; Howard et al., 2017). Published data were used to calculate softwood- and hardwood-specific half-lives for Maryland and Pennsylvania sawn wood and veneer products, while we relied on literature estimates for other products (Skog, 2008; J. E. Smith et al., 2006b). To calculate substitution benefits, we coupled region-specific data (USDA Forest Service, 2021), US consumption rates (Howard et al., 2017), product weights (C. Smyth et al., 2017), and LCA data (Bala et al., 2010; Dylewski & Adamczyk, 2013; Hubbard et al., 2020; Meil & Bushi, 2013; Puettmann, 2020a, 2020b; Puettmann et al., 2016; Puettmann & Salazar, 2018, 2019), following methods developed by Smyth et al. (2017). Landfill CO2 and CH4 emissions rely on PICC defaults for methane generation (k) and landfill half-lives for wet, temperate climates (Pingoud et al., 2006). See Supplementary Materials 1.3 for more details on substitution and leakage calculation methods.

Reports estimated industrial timber harvest production level from New York’s forests, the consumption level of New York’s primary wood processors, and the flow of harvested timber products to/from New York. Data is available for the pilot year, 1999, and from 2001 to the most currently available data year.

This dataset contains a web application (written in Flask, Python) that allows green roofs (an other locations) to collect harvesting data that is automatically added to an SQLite database. In its presented form, six green roofs of Barcelona are included, but this can be customised as wished. An empty database file is provided along with the SQLite schema to generate the tables. The Python code is also included in order to allow the application to be offered through a server. The application allows harvesting events to be registered, where data needs to be recorded in kg. The menu is offered in Catalan, but can be customised for other languages. The list of available products is dynamic and can be expanded and edited. The list of uses is also expandable and editable. Moreover, each harvest can be edited after its creation.

2274 Global import shipment records of Harvest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

China Industrial Production: Harvest Machine data was reported at 59,819.000 Unit in Oct 2015. This records a decrease from the previous number of 70,294.000 Unit for Sep 2015. China Industrial Production: Harvest Machine data is updated monthly, averaging 73,912.500 Unit from Jan 2008 (Median) to Oct 2015, with 90 observations. The data reached an all-time high of 151,819.000 Unit in Oct 2012 and a record low of 14,172.000 Unit in Feb 2008. China Industrial Production: Harvest Machine data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BA: Industrial Production.

17871 Global export shipment records of Harvest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Find the number of bears harvested in Massachusetts over the past two decades.

This code is an improvement of the r-code found in [2]. The most important improvements are the incorporation of encounter rate variance into the population estimate and the ability to use stratified designs. Additional refinements are documented by comments in the -r-code. The r-package mrds [24] is required. (R)

This data breaks down estimated hunters as well as antlered, antlerless and total harvest numbers by:

• wildlife management unit (WMU)
• calendar year

Harvest and active hunter numbers are estimates based on replies received from a sample of resident hunters and are therefore subject to statistical error.

Additional technical and statistical notes can be found in the data dictionary.

Literature database created from the review (published in Horticulturae 2024) conducted on "Management Information Systems for Tree Fruit" with adoption of a multivocal approach, a combined systematic and grey literature review)

There are three datasets associated with the manuscript ‘Experience matters: Commercial fishing can reduce biomass of invasive bigheaded carps’. The first dataset represent harvest from the invasive carp harvest program in Kentucky and Tennessee from 2009-2021 only in Lake Barkley and Kentucky Lake where the majority of harvest occurs. The second data are length data from a subset of those fish harvested in 2018-2021. Kentucky Department of Fish and Wildlife Resources and Tennessee Wildlife Resource Agency conduct observations onboard commercial vessels. During these observations total lengths are collected from a subset of that capture which we have used to develop a length-based stock assessment. The last dataset are the results from a survey delivered to fishers participating in the invasive carp harvest program in Kentucky and Tennessee.

Depicts the area planned and accomplished acres treated as a part of the timber harvest program of work, funded through the budget allocation process and reported through the FACTS database. Activities are self-reported by Forest Service Units. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This data breaks down estimated hunter and harvest numbers by:

• wildlife management unit (WMU)
• calendar year

Harvest and active hunter numbers are estimates based on replies received from a sample of hunters and are therefore subject to statistical error.

Additional technical and statistical notes can be found in the data dictionary.

Harvest Moon Farm and Orchard is a family-owned farm that has been in operation for many years, offering a wide range of products and services to its customers. From farm-fresh produce to pasture-raised meats and local dairy, Harvest Moon is a one-stop-shop for all your needs. The farm also has a store on site where customers can purchase a variety of goods, including meats, dairy, and baked goods.