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Browsing by Author "Conner, J. Richard"

Now showing 1 - 2 of 2
  • Regional cost share necessary for rancher participation in brush control
    Lee, Andrew C.; Conner, J. Richard; Mjelde, James W.; Richardson, James W.; Stuth, Jerry (Bozeman, MT: Western Agricultural Economics Association, 2001)
    This paper uses biophysical and economic simulation models to assess the proposed large-scale brush-control programs in four regions of the Edwards Plateau area in Texas. The objective of the brush control is to increase off-site water yields. For representative ranches in three of the four regions, brush control decreases the net present value. For these three regions to break even on brush control costs, there would need to be cost sharing of 7 to 31% of the total costs. Consequently, the state of Texas would have to supply a significant investment for large-scale brush-control programs to be feasible.
  • Use of seasonal climate forecasts in rangeland-based livestock operations in West Texas
    Jochec, Kristi G.; Mjelde, James W.; Lee, Andrew C.; Conner, J. Richard (Boston, MA: American Meterological Society, 2001)
    This paper discusses the potential value of seasonal climate forecasts for ranchers in West Texas, based on the assessment of a focus group and ecological-economic modeling. The conclusion of the focus group was that the forecasts could potentially increase their ranch productivity by providing information to use in stocking, brush control, and deer herd management decisions. However, because there was also concern that "value-added" forage forecasts could be misused, the authors suggest that the concept of "value-added" should be reassessed by the climate-forecasting community. The authors estimate the value of seasonal forage forecasts in guiding stocking rate choices. The results confirmed their hypothesis that numerous economic factors , including restocking and destocking prices, influence the value of the forecasts. Based on the economic model and the results from the focus group, the authors conclude that multiyear modeling is needed to better assess the potential of climate forecasts.