Find research datasets worth reusing

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network

<p><strong>Data Description</strong>:</p> <p>The USDA Long-Term Agroecosystem Research (LTAR) Network coordinates agricultural research across 18 research sites in the conterminous United States (CONUS). However, it is unclear how well these sites represent the totality of agricultural working lands within the CONUS. Therefore, we performed a quantitative analysis of the 18 sites, based on 15 climatic and edaphic characteristics, to produce maps of representativeness and constituency across the CONUS. Representativeness shows how well the combination of environmental drivers at each CONUS location was represented by the LTAR sites&rsquo; environments, while constituency shows which LTAR site was the closest match for each location.</p> <p>Files in collection (22):</p> <p>Collection contains 11 geospatial rasters and 11 PNGs visualizing them.</p> <p>TIF files:</p> <p>├── conus_ltar_constituency_workinglands.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 1]<br> ├── conus_ltar_v5.pc2.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 2]<br> ├── conus_ltar_v5.pc3.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 3]<br> ├── conus_ltar_v5.pc4.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 4]<br> ├── conus_ltar_v5.pc5.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 5]<br> ├── conus_ltar_v5.pc6.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 6]<br> ├── conus_ltar_v5.pc7.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.tif&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.tif&nbsp; &nbsp;[Representativeness of combined LTAR + NEON + LTER networks]</p> <p>PNG files:</p> <p>├── conus_ltar_constituency_workinglands.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 1]<br> ├── conus_ltar_v5.pc2.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 2]<br> ├── conus_ltar_v5.pc3.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 3]<br> ├── conus_ltar_v5.pc4.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 4]<br> ├── conus_ltar_v5.pc5.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 5]<br> ├── conus_ltar_v5.pc6.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 6]<br> ├── conus_ltar_v5.pc7.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.png&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.png&nbsp; &nbsp;[Representativeness of combined LTAR + NEON + LTER networks]</p> <p><strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution, while the geospatial visualizations are provided in PNG format.</p> <p><strong>Geospatial projection</strong>:&nbsp;</p> <pre><code class="language-bash">GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p><strong>Category labels for Constituency data</strong>:</p> <table> <caption>&nbsp;</caption> <thead> <tr> <th scope="col">Cat</th> <th scope="col">LTAR Siite</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Archbold-University of Florida</td> </tr> <tr> <td>2</td> <td>Central Mississippi River Basin</td> </tr> <tr> <td>3</td> <td>Central Plains Experimental Range</td> </tr> <tr> <td>4</td> <td>Eastern Corn Belt</td> </tr> <tr> <td>5</td> <td>Great Basin</td> </tr> <tr> <td>6</td> <td>Gulf Atlantic Coastal Plain</td> </tr> <tr> <td>7</td> <td>Jornada Experimental Range</td> </tr> <tr> <td>8</td> <td>Kellogg Biological Station</td> </tr> <tr> <td>9</td> <td>Lower Chesapeake Bay</td> </tr> <tr> <td>10</td> <td>Lower Mississippi River Basin</td> </tr> <tr> <td>11</td> <td>Northern Plains</td> </tr> <tr> <td>12</td> <td>Platte River High Plains Aquifer</td> </tr> <tr> <td>13</td> <td>R.J. Cook Agronomy Farm</td> </tr> <tr> <td>14</td> <td>Southern Plains</td> </tr> <tr> <td>15</td> <td>Texas Gulf</td> </tr> <tr> <td>16</td> <td>Upper Chesapeake Bay</td> </tr> <tr> <td>17</td> <td>Upper Mississippi River Basin</td> </tr> <tr> <td>18</td> <td>Walnut Gulch Experimental Watershed</td> </tr> </tbody> </table> <p><strong>Paper describing data and methods</strong>:</p> <p>Kumar, J., Coffin, A. W., Baffaut, C., Ponce-Campos, G. E., Witthaus, L., &amp; Hargrove, W. W. (2023). Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks. In Environmental Management. Springer Science and Business Media LLC. https://doi.org/10.1007/s00267-023-01834-9</p> <p>&nbsp;</p>