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Rethinking the Vision for Environmental Research in US Agriculture

Author

Listed:
  • Miranowski, John
  • Robertson, G. P.
  • Broome, J. C.
  • Chornesky, E. A.
  • Frankenberger, J. R.
  • Johnson, P.
  • Lipson, M.
  • Owens, E. D.
  • Pimmentel, D.
  • Thrupp, L. A.

Abstract

Environmental research in agriculture is today largely reactive, focused on problems at small scales and conducted within narrow disciplinary boundaries. This approach has worked to abate a number of environmental problems created by agriculture, but it has not provided effective solutions for many of the most recalcitrant ones. Furthermore, the approach fails to position agriculture to deliver new environmental benefits that the public and policymakers increasingly demand. A new vision is needed for environmental research in agriculture--one that is anticipatory; promotes long-term, systems-level research at multiple scales; better incorporates important interactions between the biophysical and social sciences; and provides for the proper evaluation of deployed solutions. Achieving this vision will require major changes in funding strategies, in institutional reward structures, and in policies that presently inhibit collaborations across disciplinary and institutional boundaries. It is, nevertheless, time to act.

Suggested Citation

  • Miranowski, John & Robertson, G. P. & Broome, J. C. & Chornesky, E. A. & Frankenberger, J. R. & Johnson, P. & Lipson, M. & Owens, E. D. & Pimmentel, D. & Thrupp, L. A., 2004. "Rethinking the Vision for Environmental Research in US Agriculture," Staff General Research Papers Archive 12374, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12374
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    Cited by:

    1. Salim Almaliki & Nasim Monjezi, 2021. "Using new computer based techniques to optimise energy consumption in agricultural land levelling," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 67(4), pages 149-163.
    2. Shuyao Li & Wenfu Wu & Yujia Wang & Na Zhang & Fanhui Sun & Feng Jiang & Xiaoshuai Wei, 2023. "Production Data Management of Smart Farming Based on Shili Theory," Agriculture, MDPI, vol. 13(4), pages 1-26, March.

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