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Optimization of bioenergy crop selection and placement based on a stream health indicator using an evolutionary

Matthew R Herman¹, A Pouyan Nejadhashemi¹, Fariborz Daneshvar¹

¹Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI 48824, USA.

Journal of Environmental Management

|July 16, 2016

Summary

Optimizing bioenergy landscapes can improve stream health. This study developed models to identify optimal crop placement, significantly increasing the stream health score in the Flint River Watershed.

Keywords:

Bioenergy Genetic algorithm Index of biological integrity Stream health

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Area of Science:

Environmental Science
Agricultural Science
Hydrology

Background:

Greenhouse gas emissions drive climate change, increasing reliance on renewable energy like biofuels.
Biofuel production can negatively impact water resources, necessitating sustainable landscape management.
Improving stream health is crucial alongside bioenergy development.

Purpose of the Study:

To introduce a novel strategy for optimizing bioenergy landscapes to enhance regional stream health.
To develop and link hydrological models for predicting stream health scores based on the Index of Biological Integrity.
To guide a genetic algorithm for designing watershed-scale bioenergy landscapes that maximize stream health.

Main Methods:

Coupling of the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neuro-Fuzzy Inference System to create stream health predictor models.
Utilizing a genetic algorithm guided by these models to optimize bioenergy crop placement.
Evaluating thirteen bioenergy management strategies based on farmer adaptability in the study area.

Main Results:

An optimal bioenergy crop placement was identified for the Flint River Watershed, significantly improving the stream health score from 48.19 to 50.93 (p < 0.01).
The most successful management strategies included miscanthus (27.07%), corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%).
The developed technique demonstrated a significant improvement in stream health through strategic bioenergy landscape design.

Conclusions:

The integrated modeling approach effectively optimizes bioenergy landscapes for improved stream health.
This methodology offers a transferable strategy for stakeholders to develop sustainable bioenergy landscapes in various regions.
Balancing bioenergy production with ecological considerations, specifically stream health, is achievable through advanced modeling and landscape design.