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Optimizing resource use efficiencies in the foodenergywater nexus for sustainable agriculture from conceptual model to decision support system



Details

  • Journal Title:
    Current Opinion in Environmental Sustainability
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Increased natural and anthropogenic stresses have threatened the Earth's ability to meet growing human demands of food, energy and water (FEW) in a sustainable way. Although much progress has been made in the provision of individual component of FEW, it remains unknown whether there is an optimized strategy to balance the FEW nexus as a whole, reduce air and water pollution, and mitigate climate change on national and global scales. Increasing FEW conflicts in the agroecosystems make it an urgent need to improve our understanding and quantification of how to balance resource investment and enhance resource use efficiencies in the FEW nexus. Therefore, we propose an integrated modeling system of the FEW nexus by coupling an ecosystem model, an economic model, and a regional climate model, aiming to mimic the interactions and feedbacks within the ecosystem–human–climate systems. The trade-offs between FEW benefit and economic cost in excess resource usage, environmental degradation, and climate consequences will be quantitatively assessed, which will serve as sustainability indicators for agricultural systems (including crop production, livestock and aquaculture). We anticipate that the development and implementation of such an integrated modeling platform across world's regions could build capabilities in understanding the agriculture-centered FEW nexus and guiding policy and land management decision making for a sustainable future.
  • Source:
    Current Opinion in Environmental Sustainability, 33: 104-113
  • DOI:
  • Document Type:
  • Funding:
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    CHORUS
  • Download URL:
  • File Type:
    Filetype[PDF - 1.12 MB]
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:a5097412168ca064749bf29d9307744736d6d3a69d2319646d2bf8973b9e192e
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