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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Resource allocation for efficient environmental management.

Michael A McCarthy1, Colin J Thompson, Cindy Hauser

  • 1School of Botany, The University of Melbourne, Parkville, Vic. 3010, Australia. mamcca@unimelb.edu.au

Ecology Letters
|August 20, 2010
PubMed
Summary
This summary is machine-generated.

Environmental managers can now optimize resource allocation with a unified theory that incorporates uncertainty. This new framework addresses diverse challenges, from biodiversity conservation to disease surveillance, balancing risk and reward.

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Last Updated: Jun 10, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

  • Environmental Management
  • Conservation Science
  • Risk Analysis

Background:

  • Environmental managers face complex resource allocation decisions.
  • Previous research addressed specific issues but lacked a unified theory.
  • Uncertainty is a critical factor in environmental management.

Purpose of the Study:

  • To present a unified theory for optimal environmental management.
  • To incorporate uncertainty into resource allocation models.
  • To synthesize and extend previous approaches.

Main Methods:

  • Developed a general theory for optimal environmental management.
  • Integrated risk-benefit analysis, similar to financial investment decisions.
  • Applied the theory to diverse case studies.

Main Results:

  • The theory provides a unified framework for various environmental management problems.
  • Demonstrated applicability to biodiversity conservation, disease surveillance (avian influenza H5N1), and biodiversity surveys (Hemiptera).
  • The approach effectively balances trade-offs between risk and reward.

Conclusions:

  • A unified theory for optimal environmental management under uncertainty is now available.
  • This theory offers a robust framework for diverse resource allocation challenges.
  • The approach enhances decision-making by explicitly considering risk and reward.