Sustainability

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GLBRC's Sustainability Research Area

Sustainability

Focusing on one attribute comes at a high price.

At the GLBRC, sustainability researchers are exploring complex issues in agricultural and industrial systems. Research focuses on understanding the attributes and mechanisms responsible for the environmental sustainability of biofuel production systems, such as environmental impacts — many of which may be positive — and socioeconomic factors including incentives and policy options

Learn about the Center's research approach

Sustainability Leadership

Sustainability Leader

A crop and soil scientist and ecosystem ecologist, Robertson focuses much of his research on the role that agriculture plays in greenhouse gas dynamics, and he is internationally known for his expertise in this area. Robertson has been the director...

Sustainability Co-Leader

Jackson’s program focuses on structure and function of managed, semi-natural and natural grassland ecosystems. Research in Jackson’s grassland ecology lab spans many levels of ecological organization, from grass identification at the DNA level to landscape diversity effects on alternative biofuels...

Project Overview

A device used for measuring plant utilization of solar radiation sits in front of plots of switchgrass, corn and poplar growing in the Great Lake Bioenergy Research Center's fields at the Arlington Agricultural Research Station in Arlington, WI.GLBRC Sustainability research ranges from the microbial community level to regional modeling, and researchers conduct fieldwork at different project sites to reflect this diversity of scale. Small plots at Kellogg Biological Station in Michigan and the Arlington Agricultural Research Station in Wisconsin provide locations for measurement-intensive experiments, while investigators work in larger scale-up fields to collect data on carbon balances and biogeochemical processes. Finally, researchers pursue ecosystem-level biodiversity questions across landscapes, including marginal lands, in central Michigan and Wisconsin.

Specific sustainability projects include:

  • Novel biofuel production systems
  • Microbial-plant interactions for improved biofuel production
  • Biogeochemical responses
  • Biodiversity responses
  • Economic responses
  • Modeling, design and testing of drop-in fuels
  • Process synthesis and technoeconomic evaluation for biomass-to-fuels technologies.

 

Sustainability Publications

Biomass potential of switchgrass and miscanthus on the USA’s marginal lands

V. Bandaru; César Izaurralde; K. Zhao

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2015

not yet available

Integrating price feedbacks into a Great Lakes model of biomass production and environmental impact assessment

A. Egbendewe-Mondzozo; Scott M. Swinton; S Kung; W. M. Post; J.C. Binfield; W Thompson

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2015

Using subregional models of crop production choices in central Wisconsin and southwest Michigan, we predict biomass production, land use, and environmental impacts with details that are unavailable from national scale models. When biomass prices are raised exogenously, we find that the subregional models overestimate the supply, the land use, and the beneficial environmental aspects of perennial biomass crops. Multi-market price feedbacks tied to realistic policy parameters predict high threshold absolute prices for biomass to enter production and less environmental benefice from perennial biomass crops production. Also, regional specialization of biomass production in areas with lower food crop yields is observed.

Mitigation of greenhouse gases in agricultural ecosystems

Ilya Gelfand; Philip Robertson

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2015

not yet available

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Xuesong Zhang; Roberto C. Izaurralde; David H. Manowitz; Ritvik Sahajpal; Tristram O. West; Allison M. Thomson; Min Xu; Kaiguang Zhao; Stephen D. LeDuc; Jimmy R. Williams

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2015

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.

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