@article{Plantinga:335407,
      recid = {335407},
      author = {Plantinga, Andrew J.  and Alig, Ralph J. and Eichman,  Henry and Lewis, David J.},
      title = {Linking Land-Use Projections and Forest Fragmentation  Analysis},
      address = {2007-02},
      number = {1962-2023-792},
      series = {Research Paper PNW-RP-570},
      pages = {48},
      year = {2007},
      note = {A Technical Document Supporting the USDA Forest Service  Interim Update of the 2000 RPA Assessment.},
      abstract = {An econometric model of private land-use decisions is used  to project land use to 2030 for each county in the  continental United States.  On a national scale, forest  area is projected to increase overall between 0.1 and 0.2  percent per year between now and 2030.  However, forest  area is projected to decrease in a majority of regions,  including the key forestry regions of the South and the  Pacific Northwest Westside.  Urban area is projected to  increase by 68 million acres, and cropland, pasture,  rangeland, and Conservation Reserve Program land is  projected to decline in area.  Regional econometric models  are needed to better represent region-specific economic  relationships.  County-level models of forest fragmentation  indices are estimated for the Western United States.  The  core forest model is found to perform better than the model  of like adjacencies for forest land.  A spatially detailed  analysis of forest fragmentation in Polk County, Oregon,  reveals that forests become more fragmented even though  forest area increases.  By linking the land-use projection  and forest fragmentation models, we project increases in  the average county shares of core forest in 8 of the 11  Western States.  The average like adjacency measure  increases in six of the states.  The aggregate and  spatially detailed fragmentation methods are compared by  projecting the fragmentation indices to 2022 for Polk  County, Oregon.  Considerable differences in the results  were produced with the two methods, especially in the case  of the like adjacency metric.},
      url = {http://ageconsearch.umn.edu/record/335407},
      doi = {https://doi.org/10.22004/ag.econ.335407},
}