GLBRC's Sustainability Research Area


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

Scientific Director, Sustainability Lead

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 Lead

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

Molecular cloning of the tomato Hairless gene implicates actin dynamics in trichome-mediated defense and mechanical properties of stem tissue

Jin-Ho Kang; ; Starla Zemelis-Durfee; Jameel M. Al-Haddad; ; Frank W. Telewski; Federica Brandizzi; Gregg A. Howe

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Trichomes are epidermal structures that provide a first line of defense against arthropod herbivores. The recessive hairless (hl) mutation in tomato (Solanum lycopersicum L.) causes severe distortion of trichomes on all aerial tissues, impairs the accumulation of sesquiterpene and polyphenolic compounds in glandular trichomes, and compromises resistance to the specialist herbivore Manduca sexta Here, we demonstrate that the tomato Hl gene encodes a subunit (SRA1) of the highly conserved WAVE regulatory complex that controls nucleation of actin filaments in a wide range of eukaryotic cells. The tomato SRA1 gene spans a 42-kb region containing both Solyc11g013280 and Solyc11g013290 The hl mutation corresponds to a complex 3-kb deletion that removes the last exon of the gene. Expression of a wild-type SRA1 cDNA in the hl mutant background restored normal trichome development, accumulation of glandular trichome-derived metabolites, and resistance to insect herbivory. These findings establish a role for SRA1 in the development of tomato trichomes and also implicate the actin-cytoskeleton network in cytosolic control of specialized metabolism for plant defense. We also show that the brittleness of hl mutant stems is associated with altered mechanical and cell morphological properties of stem tissue, and demonstrate that this defect is directly linked to the mutation in SRA1.

Nitrogen fertilization effects on productivity and nitrogen loss in three grass-based perennial bioenergy cropping systems

Brianna E.L. Duran; David S. Duncan; Lawrence G. Oates; Christopher J. Kucharik; Randall D. Jackson

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Nitrogen (N) fertilization can greatly improve plant productivity but needs to be carefully managed to avoid harmful environmental impacts. Nutrient management guidelines aimed at reducing harmful forms of N loss such as nitrous oxide (N2O) emissions and nitrate (NO3-) leaching have been tailored for many cropping systems. The developing bioenergy industry is likely to make use of novel cropping systems, such as polycultures of perennial species, for which we have limited nutrient management experience. We studied how a switchgrass (Panicum virgatum) monoculture, a 5-species native grass mixture and an 18-species restored prairie responded to annual fertilizer applications of 56 kg N ha-1 in a field-scale agronomic trial in south-central Wisconsin over a 2-year period. We observed greater fertilizer-induced N2O emissions and sub-rooting zone NO3- concentrations in the switchgrass monoculture than in either polyculture. Fertilization increased aboveground net primary productivity in the polycultures, but not in the switchgrass monoculture. Switchgrass was generally more productive, while the two polycultures did not differ from each other in productivity or N loss. Our results highlight differences between polycultures and a switchgrass monoculture in responding to N fertilization.

Predicting plant attractiveness to pollinators with passive crowdsourcing

Christie A. Bahlai; Douglas A. Landis

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Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.

Quantitative analysis of lignin monomers by a thioacidolysis method tailored for higher-throughput analysis

Anne E. Harman-Ware; Cliff Foster; Renee M. Happs; Crissa Doeppke; Kristoffer Meunier; Jackson Gehan; Fengxia Yue; Fachuang Lu; Mark Davis

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We report on a quantitative thioacidolysis method that is accessible with general laboratory equipment and uses a non-chlorinated organic solvent and is tailored for higher-throughput analysis. The method utilizes lignin arylglycerol monomer standards for calibration, requires 1-2 mg of biomass per assay and has been quantified using fast-GC techniques including a Low Thermal Mass Modular Accelerated Column Heater (LTM MACH). Cumbersome steps, including standard purification, sample concentrating and drying have been eliminated to help aid in consecutive day-to-day analyses needed to sustain a high sample throughput for large screening experiments without the loss of quantitation accuracy. The method reported in this manuscript has been validated against a commonly used thioacidolysis method and across two different research sites with three common biomass varieties to represent hardwoods, softwoods, and grasses.

Reconstructing the backbone of the Saccharomycotina yeast phylogeny using genome-scale data

Xing-Xing Shen; Xiaofan Zhou; Jacek Kominek; Cletus P. Kurtzman; Chris T. Hittinger; Antonis Rokas

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Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multilocus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing nine of the 11 known major lineages and 10 nonyeast fungal outgroups to generate a 1233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence) and two data matrices (amino acids or the first two codon positions) yielded identical and highly supported relationships between the nine major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, eight of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and the species Ascoidea rubescens These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at