होम GCB Bioenergy Diverse lignocellulosic feedstocks can achieve high field-scale ethanol yields while providing...

Diverse lignocellulosic feedstocks can achieve high field-scale ethanol yields while providing flexibility for the biorefinery and landscape-level environmental benefits

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भाषा:
english
पत्रिका:
GCB Bioenergy
DOI:
10.1111/gcbb.12533
Date:
July, 2018
फ़ाइल:
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आप पुस्तक समीक्षा लिख सकते हैं और अपना अनुभव साझा कर सकते हैं. पढ़ूी हुई पुस्तकों के बारे में आपकी राय जानने में अन्य पाठकों को दिलचस्पी होगी. भले ही आपको किताब पसंद हो या न हो, अगर आप इसके बारे में ईमानदारी से और विस्तार से बताएँगे, तो लोग अपने लिए नई रुचिकर पुस्तकें खोज पाएँगे.
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Received: 12 February 2018

Revised: 18 May 2018

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Accepted: 4 June 2018

DOI: 10.1111/gcbb.12533

ORIGINAL RESEARCH

Diverse lignocellulosic feedstocks can achieve high field‐scale
ethanol yields while providing flexibility for the biorefinery and
landscape‐level environmental benefits
Yaoping Zhang1,* | Lawrence G. Oates1,2,*
1

Edward Pohlmann
Alan Higbee1

| Yury V. Bukhman

1

| Jose Serate1 | Dan Xie1
| Steven D. Karlen

1

|

| Megan K. Young1 |

| Dustin Eilert1 | Gregg R. Sanford1,2 | Jeff S. Piotrowski1 |

David Cavalier3 | John Ralph1,4 | Joshua J. Coon1,5,6 | Trey K. Sato1

|

7,8

Rebecca G. Ong
1

DOE‐Great Lakes Bioenergy Research Center, University of Wisconsin‐Madison, Madison, Wisconsin

2

Department of Agronomy, University of Wisconsin‐Madison, Madison, Wisconsin

3

DOE‐Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, Michigan

4

Department of Biochemistry, University of Wisconsin‐Madison, Madison, Wisconsin

5

Department of Chemistry, University of Wisconsin‐Madison, Madison, Wisconsin

6

Genome Center of Wisconsin, University of Wisconsin‐Madison, Madison, Wisconsin

7

Department of Chemical Engineering, Michigan Technological University, Houghton, Michigan

8

DOE‐Great Lakes Bioenergy Research Center, Michigan Technological University, Houghton, Michigan

Correspondence
Rebecca G. Ong, Department of Chemical
Engineering, Michigan Technological
University, Houghton, MI.
Email: rgong1@mtu.edu
Present addresses
Yury V. Bukhman, Morgridge Institute for
Research, Madison, Wisconsin.
and
Jeff S. Piotrowski, Yumanity Therapeutics,
Cambridge, Massachusetts.
Funding information
This material is based upon work
supported by the Great Lakes Bioenergy
Research Center, U.S. Department of
Energy, Office of Science, Office of
Biological and Environmental Research
under Award Numbers DE‐SC0018409
and DE‐FC02‐07ER64494; and DOE OBP
Office of Energy Efficiency and

Abstract
Increasing the diversity of lignocellulosic feedstocks accepted by a regional biorefiner; y has the potential to improve the environmental footprint of the facility; harvest,
storage, and transportation logistics; and biorefinery economics. However, feedstocks
can vary widely in terms of their biomass yields and quality characteristics (chemical
composition, moisture content, etc.). To investigate how the diversity of potential biofuel cropping systems and feedstock supply might affect process and field‐scale ethanol yields, we processed and experimentally quantified ethanol production from five
different herbaceous feedstocks: two annuals (corn stover and energy sorghum) and
three perennials (switchgrass, miscanthus, and mixed prairie). The feedstocks were
pretreated using ammonia fiber expansion (AFEX), hydrolyzed at high solid loading
(~17%–20% solids, depending on the feedstock), and fermented separately using
microbes engineered to utilize xylose: yeast (Saccharomyces cerevisiae Y128) or bacteria (Zymomonas mobilis 8b). The field‐scale ethanol yield from each feedstock was
dependent on biomass quality and cropping system productivity; however, biomass
yield had a greater influence on the ethanol yield for low‐productivity crops, while

*These authors contributed equally to this work.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the
original work is properly cited.
© 2018 The Authors. Global Change Biology Bioenergy Published by John Wiley & Sons Ltd
GCB Bioenergy. 2018;1–16.

wileyonlinelibrary.com/journal/gcbb

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Renewable Energy (DE‐AC05‐
76RL01830).

ET AL.

biomass quality was the main driver for ethanol yields from high‐yielding crops. The
process ethanol yield showed similar variability across years and feedstocks. A low
process yield for corn stover was determined to result from inhibition of xylose utilization by unusually elevated levels of hydroxycinnamates (p‐coumaric and ferulic
acids) in the untreated biomass and their acid and amide derivatives in the resulting
hydrolyzate. This finding highlights the need to better understand factors that influence process ethanol yield and biomass quality. Ultimately we provide evidence that
most feedstocks fall within a similar range of process ethanol yield, particularly for
the more resistant strain Z. mobilis 8b. This supports the claim that the refinery can
successfully diversify its feedstock supply, enabling many social and environmental
benefits that can accrue due to landscape diversification.
KEYWORDS
biorefinery, ethanol yield, feedstock diversity, fermentation, inhibitors, lignocellulosic hydrolyzate,
xylose utilization

1

| INTRODUCTION

Cellulosic biofuels generated from vegetative plant biomass
are part of a suite of carbon neutral solutions that can meet
the world's energy needs (Robertson et al., 2017). Lignocellulosic (second‐generation) bioethanol facilities have
been largely designed around a process model whereby
one, or at most two standardized feedstocks are grown and
harvested in nearby fields, then processed, and fermented
into ethanol or other bio‐based products (POET‐DSM,
2014; Raízen, 2014). This approach has perceived advantages with the use of consistent, standardized protocols and
equipment for crop production and harvest, biomass transport and preprocessing, and fuel production. It is also consistent with the manner in which first‐generation
bioethanol facilities currently operate the following: processing a single bioenergy crop specific to their geographical region. For example, ethanol companies in Brazil have
optimized to use sugarcane (Saccharum officinarum L.),
companies in the European Union use sugar beets (Beta
vulgaris L.) and wheat (Triticum aestivum L.), and companies in China and the United States have focused primarily
on corn grain (Bonin & Lal, 2012). However, for second‐
generation ethanol, it may not be feasible or economically
advisable to be overly selective in terms of accepted feedstocks, particularly in regions where cropping systems are
diversified. As lignocellulosic feedstocks are inherently
low‐density compared to the sugar‐ and starch‐based crops
used by first‐generation facilities, less material can be
loaded per vehicle, resulting in significantly higher transportation costs (Lin et al., 2016). Without some form of
biomass densification in the field, the feedstock collection
radius and the subsequent supply for the second‐generation
biorefinery are economically constrained compared to first‐
generation facilities.

Depending on the region, there are a large number of
potential lignocellulosic feedstocks available for use by a
biorefinery, including annual crops such as corn stover
(Zea mays L.) and sorghum (Sorghum bicolor (L.)
Moench); and perennial crops such as switchgrass (Panicum virgatum L.), miscanthus (Miscanthus × giganteus
Greef & Deuter ex Hodkinson & Renvoize), energycane
(Saccharum spp.), native grass polycultures, and coppiced
wood plantations. Additionally, many geographical regions
in the United States are capable of producing more than
one of these feedstocks at relatively high biomass yields
(Lee et al., 2018; Sanford et al., 2016). One criticism of
this approach is that feedstock diversity may lead to variability in conversion efficiencies and put an undue burden
on biorefineries, resulting in the need to change operating
conditions depending on the feedstock. One means to overcome this is through feedstock blending to ensure consistent feedstock properties (Hess, Wright, Kenney, & Searcy,
2009; Shi et al., 2012). However, this may not be necessary as long as the feedstocks are within the same species
classification (e.g., grass, hardwood, or softwood). In our
previous work, we evaluated the potential to use early successional plant biomass, an inherently unpredictable and
diverse set of plant species, as a bioenergy feedstock (Garlock, Bals, Jasrotia, Balan, & Dale, 2012). Based on our
findings, although there was a great deal of variability in
plant species composition, most of the samples either had
the same optimal pretreatment conditions or achieved high
yields under a standard set of processing conditions. Additionally, the different materials had consistent hydrolysis
yields as long as the feedstocks were mainly comprised of
grass species. In another study, we evaluated theoretical
ethanol yields from different feedstocks on a field basis
and found that ethanol yield was determined more by crop

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yield than by feedstock quality (chemical composition)
(Sanford et al., 2017). This finding supports the practicable
idea that biofuel refineries could process multiple different
regional feedstock types without experiencing significant
impacts on ethanol production. However, these conclusions
were based upon theoretical ethanol yields and not actual
fermentation experiments and did not account for specific
differences in chemical composition between feedstocks.
We have previously shown these can be highly variable
and have wide‐ranging impacts on microbial conversion of
cellulosic sugars to ethanol (Ong et al., 2016).
Here, we extend our previous work on ethanol yield
from diverse feedstocks by evaluating the fermentation performance of five different herbaceous bioenergy crops. We
evaluated two annuals: corn stover and energy sorghum;
and three perennials: switchgrass, miscanthus, and a
restored mixed prairie. All feedstocks were grown at the
same location (Arlington, WI) and harvested in 2014, pretreated using ammonia fiber expansion (AFEX) and hydrolyzed into fermentable sugars at high solid loadings
(~17%–20%, depending on the feedstock). Fermentation
studies using engineered ethanologenic microbes (Saccharomyces cerevisiae Y128 and Zymomonas mobilis 8b)
enabled calculation of process and field‐scale ethanol yields
for all feedstocks. Molecular studies identified significant
differences in the chemical compositions of the five feedstocks. This evaluation provides a measure of the relative
magnitude of expected feedstock and harvest year variability on process yields.

2
2.1

| MATERIALS AND METHODS
| Hydrolyzate production

Hydrolyzates were produced from five different AFEX‐
pretreated feedstocks using enzymatic hydrolysis methods
as described previously for AFEX‐pretreated corn stover
and switchgrass (Serate et al., 2015). Hydrolyzates (~1 L)
were generated in a 3 L Applikon ez‐control bioreactor system (Applikon Biotechnology, Foster City, CA, USA).
Because of the higher glucan and xylan conversions for
corn stover, final glucose concentrations were normalized
across feedstocks by increasing the solids and enzyme
loadings for the other four feedstocks. Solids were loaded
at 6% glucan loading for corn stover and 7% glucan loading for the other four feedstocks (~17%–20% solids loading, depending on the feedstock). CTec2 and HTec2
(Novozymes, Franklinton, NC, USA) were used for hydrolysis, at loadings of 32 mg CTec2 protein per g glucan and
9 mg HTec2 protein per g glucan for corn stover, and
48 mg CTec2 protein per g glucan and 13.5 mg HTec2
protein per g glucan for the other four feedstocks, the same
as described previously (Serate et al., 2015). The same

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solids and enzyme loadings were used for miscanthus, sorghum, and mixed prairie as for switchgrass. Due to the difference in feedstock buffering capacity, different amounts
of undiluted HCl were used to neutralize each feedstock
prior to hydrolysis: 5.6 ml for corn stover, 6.0 ml for
switchgrass and sorghum, 6.5 ml for mixed prairie, and
5 ml for miscanthus. The hydrolysis was carried out at
50°C for 5 days, and the final pH for all hydrolyzates was
between 5.0 and 5.5. After the solids were removed by
centrifugation at 8,200 x g and 4°C for 10–12 hr, the
supernatant was filter‐sterilized sequentially through 0.5‐µm
GVS Maine Glass Prefilters (Thermo Fisher Scientific Inc.
Waltham, MA, USA) and 0.2 µm 1 L Filter Units (Nalge
Nunc International Corporation, Rochester, NY, USA), and
stored at 4°C.

2.2 | Microbial fermentation of lignocellulosic
feedstocks
Engineered xylose‐utilizing S. cerevisiae Y128 (Parreiras
et al., 2014) and Z. mobilis 8b (obtained from the American Type Culture Collection, PTA‐6976) were used for
comparative fermentations. Cultures for inoculation were
prepared as described previously (Serate et al., 2015). Fermentations were conducted in 500 ml bioreactors (BIOSTAT Qplus system; Sartorius Stedim Biotech, Bohemia,
NY, USA) containing 250 ml of hydrolyzate. Prior to
fermentation, the hydrolyzates were adjusted to pH 5
(S. cerevisiae) or 5.8 (Z. mobilis) using 10 N NaOH or
undiluted HCl and filtered through a 0.2‐μm filter to
remove precipitates and to ensure sterility. The seed culture
was centrifuged at 14,000 g for 3 min after which the
supernatant was discarded and the cell pellets were resuspended into 10 ml of hydrolyzate from the presparged vessels. The starter was then inoculated into each vessel to
give an initial OD600 (optical density at 600 nm) of 0.5 in
the bioreactor. Fermentations were conducted at 30°C with
continuous stirring (300 rpm) and sparging (150 ml/min;
100% N2). During the fermentation, pH was controlled at
5.0 for Y128 and 5.8 for Z. mobilis 8b, and samples were
periodically removed from the bioreactors for an OD600
measurement to monitor cell growth and for HPLC‐RID
analysis of the concentration of glucose, xylose, and the
end products as described previously (Serate et al., 2015).
To obtain a more accurate final ethanol yield, evaporated
ethanol from an off‐gas line was trapped in ice water and
quantified using HPLC. Growth and substrate uptake rates
were calculated as previously described (Sato et al., 2016).
Process ethanol yields, expressed as the percentage of maximal theoretical ethanol yield (0.51 g ethanol/g sugar) produced from the total glucose, and xylose present in each
hydrolyzate, were calculated from the initial sugar and final
ethanol concentrations for each experiment.

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To evaluate the inhibition of fermentation by ferulate
and p‐coumarate compounds, additional compounds were
supplemented in corn stover (Y2012) hydrolyzate to match
the concentrations in corn stover (Y2014) hydrolyzate:
ferulic acid (0.027 mM), feruloyl amide (3.262 mM), p‐
coumaric acid (0.858 mM), and p‐coumaroyl amide
(4.441 mM). Feruloyl and coumaroyl amides were synthesized in the laboratory using previously described methods
(Keating et al., 2014).

2.3

| Chemical genomics

Chemical genomic analysis of the hydrolyzates was performed as described previously, using a collection of
~4,000 yeast deletion mutants (Piotrowski et al., 2017),
and ~3,500 Z. mobilis transposon insertion mutants in 1578
ORFs (Skerker et al., 2013). Cultures (200 µl) of the
pooled collection of S. cerevisiae deletion mutants or
Z. mobilis transposon insertion mutants were cultured
anaerobically in the different biomass hydrolyzates, rich
media (YPD for yeast: 10 g/L yeast extract, 20 g/L peptone, and 20 g/L dextrose; rich ZRMG for Z. mobilis:
20 g/L glucose, 10 g/L yeast extract, and 2 g/L KH2PO4
(Skotnicki, Tribe, & Rogers, 1980) or synthetic hydrolyzate
(SynH) mimic of AFEX‐pretreated corn stover hydrolyzate
(Keating et al., 2014) without (SynHv2.6) or with inhibitory compounds (SynHv2.7) in triplicate for 48 hr at 30°C.
Genomic DNA was extracted from the cells using PureLink™ Pro 96 Genomic DNA Kit (Thermo Fisher Scientific
Inc.), and mutant‐specific molecular barcodes were amplified using specially designed multiplex primers as
described previously (Piotrowski, Simpkins, & Li, 2015).
The barcodes were sequenced using an Illumina HiSeq2500
in rapid run mode (Illumina, Inc, San Diego, CA, USA).
Barcode counts were obtained using barseq counter software (Simpkins et al., 2018). Further analysis was performed using R and Bioconductor (Huber et al., 2015; The
R Foundation, 2017). The count matrix was filtered to
remove failed samples and mutants that did not grow sufficiently well in at least three samples, that is, samples with
median counts <10 and mutants whose counts did not
exceed a set threshold (70 in S. cerevisiae and 20 in
Z. mobilis) in at least three samples. Filtering removed
approximately 10% of low‐count mutants in each dataset.
Gene‐level counts used in some analyses were computed
by adding up counts from multiple mutants with the same
gene deletion. The counts were further processed using
Bioconductor limma workflow for RNA‐seq (Law, Alhamdoosh, Su, Smyth, & Ritchie, 2016). Specifically, the
counts were log‐transformed and normalized by the EdgeR
TMM method (Robinson, McCarthy, & Smyth, 2010) and
observation weights were computed using voom function
in limma. Multidimensional scaling (MDS) was performed

ET AL.

using plotMDS function in limma. For purposes of hierarchical clustering, we used gene‐level data, which was further filtered to only retain high‐variance genes. We kept
genes with standard deviations > 1 in S. cerevisiae and
>0.5 in Z. mobilis dataset. Log‐count values for each gene
were standardized. Average linkage clustering was performed using hclust function in R with distances computed
using Pearson correlation for genes and Spearman correlation for samples (Girke, 2018).

2.4 | 24‐Well assays for yeast growth and
xylose consumption
Single colonies of Y330 (a version of Y128 containing a
deletion mutation in FLO8 for reduced flocculation) yeast
were inoculated into 5 ml YPD media and grown aerobically at 30°C. After 16–18 hr of growth, cultures were
diluted to OD600 = 0.3 and recultured until they reached
logarithmic growth phase (OD600 = 0.6–0.8). Cells were
harvested, pelleted at 3,000 x g, and washed with sterile
water. Cell pellets were resuspended in SynH containing
20 g/L xylose and 60 g/L sorbitol (SynHXS) in lieu of glucose to maintain osmolarity and then inoculated in 1.5 ml
SynHXS to a final OD600 = 0.1 in sterile 24‐well plates
(Greiner Cellstar). Cell growth was monitored by OD600
measurements in a multimode plate reader (Tecan; Männedorf, Switzerland) every 10 min for 48 hr, at 30°C with
continual shaking. Relative growth and xylose consumption
were determined by dividing the total cell growth or
consumed xylose in the presence of phenolic compounds
(ferulic acid, feruloyl amide, p‐coumaric acid, and/or
p‐coumaroyl amide) by the total cell growth or xylose
consumed in the absence of phenolic compounds.

2.5 | Statistical analysis of hydrolyzate
composition and ethanol yields
Statistical analysis of the hydrolyzate composition was conducted in R‐Studio®, version 1.0.143 (Boston, MA, USA).
A linear model of each chemical component was developed
based on the feedstock, control method, and their interaction, and evaluated using Tukey's HSD test based on 95%
confidence intervals (Agricolae package, version 1.2‐1) (De
Mendiburu, 2009). When a reported value was below the
limit of quantitation (LOQ), the value was recalculated as
LOQ/√2 (Croghan & Egeghy, 2003). These recalculated
values were used to determine the mean and standard deviation for each sample, and statistical differences between
samples. The principal component analysis of hydrolyzate
composition was conducted on hydrolyzate batches that
had complete suites of data (sugars, aromatic inhibitors,
amino acids, and elements). Static and interactive plots
were constructed using the ggbiplot (v. 0.55) (Vu, 2011),

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ggplot2 (v. 2.2.1) (Wickham, 2009), and plotly packages
(v. 4.7.1) (Plotly Technologies Inc, 2015).
Average ethanol yields per hectare for each feedstock
were calculated from the average dry matter yields per hectare (Mg biomass per ha) from the GLBRC biomass cropping system experimental (BSCE) plots and average
ethanol yields (L per Mg untreated feedstock) from this
study. Corn stover was harvested from other plots, also in
Arlington, WI; however, yield data were not collected at
harvest, and so the BSCE corn stover yields were used as
an estimate. The standard deviations of ethanol yield per
hectare were calculated using statistical propagation of
uncertainty. Confidence and prediction intervals (95%)
were calculated in R (The R Foundation, 2017) based on
the linear regression (lm function) of ethanol yields per
hectare as a function of the biomass yield per hectare
across all feedstocks. The confidence and prediction intervals for the regression equations were calculated and plotted using the ggplot2 package (v.2.2.1) (Wickham, 2009).
Additional methods are included in the supplemental
information.

3

| RESULTS

3.1 | Impaired xylose fermentation in corn
stover hydrolyzate fermentations contributed
to lower process ethanol yield compared to
other feedstock hydrolyzates
For the 2014 feedstocks, the process ethanol yields from Z.
mobilis fermentations were relatively similar, ranging from
81% to 84% of the theoretical ethanol yield based on glucose and xylose concentrations in the hydrolyzates (Figure 1a,c and Supporting Information Table S3). This falls
within the range of our previously reported values for corn
stover and switchgrass (75%–86%) (Figure 1a,c). In contrast, the process ethanol yields from the yeast fermentations showed much greater variability, both for different
feedstocks within the same year (2014:58%–75%) and for
the same feedstock between years (corn stover: 58%–90%,
and switchgrass: 85%–97%) (Figure 1b and Supporting
Information Table S3). The lower process yields for corn
stover and mixed prairie hydrolyzates (2014 harvest year)
seem to be at least partially related to lower extents and/or
efficiencies of xylose utilization. For Z. mobilis fermentation of the 2014 hydrolyzates, corn stover had a significantly slower rate of xylose consumption (1.5 mM xylose
per OD600 per hour) compared to the other feedstocks
(2.4–3.0 mM xylose per OD600 per hour), (Supporting
Information Figure S1f–j and Table S3). With the exception of 2012 switchgrass, which did not support either
yeast growth or fermentation, the yeast fermentation process ethanol yields were lowest for the 2014 corn stover

F I G U R E 1 Experimentally determined process ethanol yields are
less variable for Z. mobilis 8b (a) compared to S. cerevisiae Y128 (b)
and are positively correlated with xylose consumption for both
Z. mobilis 8b (c) and S. cerevisiae Y128 (d). Anaerobic bioreactor
fermentations with hydrolyzates generated from the indicated AFEX‐
pretreated biomass were performed using Z. mobilis 8b (a, c) and
S. cerevisiae Y128 (b, d) engineered strains. The gray bar (a, b)
represents the range in process yields across all feedstocks. Process
ethanol yields represent the ethanol produced based on the amount
theoretically possible from complete conversion of the glucose and
xylose in each hydrolyzate (see Methods). Data for other years and
strains of corn stover and switchgrass were published previously (Ong
et al., 2016). Xylose consumption is the proportion of xylose consumed
in terms of the total xylose available in the hydrolyzate. Average
process ethanol yields were calculated from three or four independent
biological replicates. To minimize confusion, error bars are not
included in this figure; however, standard deviations for the data are
included in the supplemental information (Supporting Information
Figure S2, Table S3). P0448R and 36H56 are different varieties of corn

(58%) and the 2014 mixed prairie (67%) hydrolyzates
across all feedstocks from all years (Figure 1b, Supporting
Information Table S3). Xylose consumption was linearly

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correlated with process ethanol yields for yeast fermentations (Figure 1d), and the 2014 corn stover and mixed
prairie had the lowest xylose consumption of all the
feedstocks evaluated (Figure 1d; Supporting Information
Figure S1a,e; and Table S3). Together, these results indicate that yeast xylose fermentation was impaired in the
2014 corn stover and mixed prairie, and bacterial fermentation was also impaired in the 2014 corn stover hydrolyzate. This was one likely reason for lower process
ethanol yields from these feedstocks. Notably, although
the mixed prairie contained a mixture of herbaceous
dicots (forbs) and grasses (Supporting Information
Table S4), it was not significantly lower quality, and
achieved similar ethanol yields compared to the other
feedstocks investigated, particularly when Z. mobilis was
used as the ethanologen.

3.2 | Yeast and bacterial fitness were
significantly altered in corn stover hydrolyzate
The impaired xylose utilization by both yeast and bacteria
suggests that the quality of the 2014 corn stover hydrolyzate, in terms of its fermentability, may be lower compared
to the four other feedstocks. Previously, we found that the
Y128 yeast strain fermented xylose from hydrolyzates generated from AFEX‐pretreated corn stover harvested in

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multiple years (Figure 1d) (Ong et al., 2016; Parreiras
et al., 2014; Serate et al., 2015), suggesting that this effect
is specific to 2014 corn stover. Chemical genomic profiles
were compiled for all five hydrolyzates to evaluate their
relative fitness for microbial growth. Each showed a distinctive chemical genomic profile, with corn stover
hydrolyzates clustered together in both the S. cerevisiae
and Z. mobilis profiles (Supporting Information Figure S3).
There was also a greater range in the relative fitness of S.
cerevisiae and Z. mobilis mutants cultured in corn stover
hydrolyzates compared to the other four hydrolyzates (Figure 2a,b). This indicates that, in general, any single gene
deletion will have a greater negative or positive effect on a
mutant's fitness in the corn stover hydrolyzate compared to
the other four. This unique effect of the corn stover hydrolyzate on S. cerevisiae and Z. mobilis was also visualized
using multidimensional scaling (MDS) plots generated from
the chemical genomic data (Figure 2c,d). These show that
the corn stover hydrolyzates segregated strongly from the
other feedstocks by first and second dimension for
S. cerevisiae and by first dimension for Z. mobilis,
Together, these results support our fermentation results
that the hydrolyzate from 2014 corn stover was a significantly different environment for microbial growth compared to the hydrolyzates generated from the four other
feedstocks.

F I G U R E 2 Yeast and bacteria mutant fitness are more strongly affected in corn stover hydrolyzates compared to other 2014 feedstocks.
Boxplots of mutant fitness ordered by length of the interquartile region reveal that corn stover hydrolyzates segregate with the greatest
interquartile range for both S. cerevisiae (a) and Z. mobilis (b) mutants. Corn stover also segregates within multidimensional scaling (MDS) plots
on fitness of both S. cerevisiae (c) and Z. mobilis (d) mutants. All experiments were cultured anaerobically. Dimensions in the MDS plot are
based on the leading log2(FC). CPM = counts per million

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F I G U R E 3 Principal component analysis of hydrolyzate composition shows each feedstock fully segregates. Miscanthus and switchgrass
hydrolyzates have more similar compositions compared to the other feedstocks (a) and are only differentiated by components 3 and 4 (b)

3.3 | The 2014 corn stover and hydrolyzates
contained elevated levels of hydroxycinnamic
acids and their derivatives
The fermentation and chemical genomic studies indicated
that the 2014 corn stover was of lower quality relative to
the other feedstocks harvested that year, which may have
been due to compositional differences in the hydrolyzates
that affected xylose fermentation. Principal component
analysis of these data showed that all five feedstocks had
distinctive compositional signatures (Figure 3). However,
the 2014 corn stover hydrolyzate was the most different of
the five, as this segregated clearly through the first principal component (31% of the variance in the data). The second principal component (27% of variance) further
segregated sorghum and mixed prairie, and the third and
fourth principal components (12% and 11% of the variance,
respectively) fully differentiated the miscanthus and switchgrass hydrolyzates, which appeared to be the most similar
of the five based on their analyzed compositions.
A number of compounds contributed toward the segregation of corn stover from the other feedstocks, including four
hydroxycinnamates that were present at comparatively high
concentrations in the 2014 corn stover hydrolyzate compared to the others: ferulic acid, feruloyl amide, p‐coumaric
acid, and p‐coumaroyl amide (Supporting Information
Table S5, Files S1 and S2). The concentrations of the acids
and amides in the untreated feedstocks and hydrolyzates
were strongly correlated (Figure 4), which indicates that
untreated feedstocks that have higher concentrations of
hydroxycinnamates were more likely to release higher
concentrations of hydroxycinnamates during enzymatic
hydrolysis. For all hydrolyzates, a higher and consistent proportion of the amide form was present compared with the
acid form: feruloyl amide (97%–99%) and p‐coumaroyl
amide (79%–88%). Untreated corn stover tended to have
higher levels of hydroxycinnamates and correspondingly

higher levels of the derivatives in the hydrolyzates compared to switchgrass. However, of all the feedstocks including other years of corn stover, the 2014 corn stover had the
highest concentrations of ferulic and p‐coumaric acid in the
untreated biomass, and likewise the hydrolyzate contained
the highest concentrations of ferulic and p‐coumaric acids
and amides (Figure 4). In particular, the ferulic acid levels
in the untreated 2014 corn stover were more than 40%
higher than the other feedstocks. These results indicate that,
in terms of the sample set, the 2014 corn stover was unique
in terms of the biomass and hydrolyzate hydroxycinnamate
contents.

3.4 | Elevated hydroxycinnamates in 2014
corn stover hydrolyzate impair xylose
fermentation by yeast
Specific amounts of ferulic and p‐coumaric acids, and
chemically synthesized feruloyl and p‐coumaroyl amides,
were added to noninhibitory 2012 corn stover hydrolyzate,
elevating their concentrations equal to that found in the
inhibitory 2014 corn stover hydrolyzate. The subsequent
anaerobic yeast fermentations with 2012 corn stover hydrolyzate with and without supplementation of the phenolic
compounds are shown in Figure 5b,c. In the unmodified
2012 corn stover hydrolyzate, the Y128 yeast strain fermented xylose at a rate similar to that observed previously
(Ong et al., 2016). In 2012 corn stover hydrolyzate containing additional phenolic acids and amides, xylose fermentation by the Y128 strain was substantially reduced,
similar to what was seen with 2014 corn stover hydrolyzate
(Figure 5a).
When Y330 yeast (a less flocculant version of Y128)
was cultured in SynHXS media (a synthetic hydrolyzate
with only xylose as a carbon source) and supplemented
with the individual phenolic inhibitors (0 to 13.8 mM),
inhibition of xylose consumption was observed to be

8

|

mostly additive, with all four compounds contributing to
the inhibition (Figure 6). The ferulates were more inhibitory than p‐coumarates, particularly at low concentrations.

ZHANG

ET AL.

Ferulic acid was the most inhibitory compound tested, with
~20% inhibition of xylose consumption at the lowest
concentration tested (1.9 mM) (Figure 6). Although less
inhibitory than the acids, the amides were present in the
hydrolyzates at concentrations shown to inhibit xylose
consumption (7.6 and 4.8 mM, for p‐coumaroyl amide and
feruloyl amide, respectively, vs. 1.3 and 0.05 mM for
p‐coumaric acid and ferulic acid, respectively) (Figure 6).
The acid amide mix at 13.8 mM was significantly inhibitory toward xylose utilization, achieving only ~20% of the
xylose consumption observed in the synthetic media. This
is similar to the relative difference in % xylose consumption between the 2012 and 2014 corn stover, with the 2014
corn stover xylose consumption only 25% of the 2012
corn stover xylose consumption. Combined, these results
indicate that the uncommonly elevated ferulic acid and
p‐coumaric acid levels in 2014 corn stover resulted in
higher concentrations of hydroxycinnamates in the
hydrolyzates, which negatively affected yeast xylose
utilization and ethanol production.

3.5 | Ethanol yields per hectare are largely
determined by biomass productivity rather
than biomass quality
F I G U R E 4 Elevated hydroxycinnamates in the 2014 corn stover
hydrolyzate trace back to the untreated feedstock. The indicated
phenolic compounds were quantified in hydrolyzates from AFEX‐
pretreated biomass hydrolyzates and the corresponding untreated
biomass. Values reported are averages and standard deviations from
technical triplicates. Hydrolyzate values for feedstocks harvested in
2010, 2012, and 2013 were reported previously (Ong et al., 2016).
Data for corn stover (36H56 and P0448R) and switchgrass (Shawnee
and Cave‐In‐Rock (CIR)) included two different varieties. The data
points for the 2014 corn stover are labeled as a red‐outlined star

Field‐scale ethanol yields (L/ha) were calculated based on
the amount of dry biomass harvested per hectare and the
experimental data from yeast and Z. mobilis fermentations
(Figure 7 and Table 1). Ethanol yields for yeast fermentations ranged from 740 to 3,618 liters of ethanol per hectare, with the exception of drought‐stressed 2012
switchgrass, which was too inhibitory to sustain yeast
growth. Ethanol yields for Z. mobilis fermentations were
slightly higher than those obtained for S. cerevisiae and
ranged from 802 to 4,073 L ethanol/ha. Ethanol yield per

F I G U R E 5 Elevated hydroxycinnamates impair anaerobic xylose fermentation by yeast. The Y128 yeast strain was cultured anaerobically in
bioreactors containing hydrolyzate from 2014 corn stover (a), and 2012 corn stover without (b) or with the addition of ferulic and p‐coumaric
acids and feruloyl and coumaroyl amides to equal the final concentrations in 2014 corn stover hydrolyzate (c). Graphs display average cell
densities (OD600, teal circles), and extracellular glucose (purple circles), xylose (red diamonds), and ethanol (orange diamonds) with standard
deviations from three independent biological replicates

ZHANG

|

ET AL.

hectare from 2014 corn stover was higher than the median
(1,642 L/ha) for Z. mobilis fermentations and at the median
(1,603 L/ha) for S. cerevisiae fermentations, and were
within the range expected based on linear regression of the
data (Figure 7). With the exception of drought‐year (2012)
switchgrass, which was completely unfermentable for reasons previously identified (Ong et al., 2016), field ethanol
yields were highly correlated with biomass yields based on
experimental data for both S. cerevisiae (R2 = 0.95) and
Z. mobilis (R2 = 0.95) fermentations (Figure 7).

F I G U R E 6 Hydroxycinnamates inhibit yeast xylose
consumption. A nonflocculant derivative of the Y128 strain was
cultured anaerobically in 24‐well plates containing synthetic SynHXS
media (see Methods) and the indicated concentration of phenolic
compounds. Each compound or compound mix was added at 1.9, 3.8,
7.5, and 13.8 mM. For the acid amide mix, the ratios for each
compound were identical to the ratio of concentrations in 2014 corn
stover hydrolyzate (0.4% ferulic acid, 9.8% p‐coumaric acid, 34.8%
feruloyl amide, and 55.1% p‐coumaroyl amide). Xylose consumption
was determined by the differences in initial and final extracellular
xylose concentrations for each condition relative to SynHXS without
the addition of phenolic compounds

9

The ethanol yield (L/ha) resulting from an increase in
biomass productivity of 1 Mg/ha for each feedstock was
compared to the calculated increase in biomass ethanol
yield (L/Mg of dry biomass) that would be necessary to
achieve this same field‐scale ethanol yield without increasing biomass productivity. The calculated biomass ethanol
yield increase plotted against the actual biomass yield
showed that the two parameters were highly correlated via
an inverse power law function (Figure 8). This result indicates that biomass quality and yield improvements have
different effects on field‐scale ethanol yields, depending on
whether the biomass has low or high productivity. At low
biomass productivities, biomass yield increases have a
greater impact on field‐scale ethanol yields compared with
improvements in biomass quality or conversion efficiencies, whereas the opposite is true at high biomass productivities. For example, increasing the biomass yield of the
2014 mixed prairie from 3.7 to 4.7 Mg/ha would increase
the ethanol yield (L/ha) by an amount that is comparable
to increasing the biomass ethanol yield by 54 or 65 L/Mg
(depending on the microorganism). In contrast, miscanthus,
which already has a high productivity of 14.4 Mg/ha,
would only require an increase in biomass ethanol yield of
18–20 L/Mg to equal a biomass yield increase of 1 Mg/ha.

4

| DISCUSSION

Recently, there has been interest in generating renewable
biofuels in an economical, as well as an environmentally
and socially sustainable manner (Robertson et al., 2017).
Industrial biofuel processing facilities have generally
focused on using one type of feedstock that is of high quality and is also highly productive. However, there are a
number of potential bioenergy feedstocks that can achieve

F I G U R E 7 Experimentally determined ethanol yields per hectare correlate with biomass yield. (a) S. cerevisiae linear regression with 2012
SG excluded from the regression analysis, and (b) Z. mobilis linear regression

10

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ZHANG

ET AL.

T A B L E 1 Summary of ethanol yield data for S. cerevisiae Y128 and Z. mobilis 8b fermentations of five feedstocks. Data are
averages ± standard deviation. Ethanol yield standard deviations were calculated using propagation of error. Data on feedstocks from years other
than 2014 are from (Ong et al., 2016). Dry matter yields for the actual corn stover plots were unavailable and instead were estimated based on
the values for the GLBRC BCSE plots. Both were planted at the same site in Arlington, Wisconsin
Saccharomyces cerevisiae
Feedstock

Harvest
year

Dry matter yield
(Mg/ha)

Zymomonas mobilis

Ethanol yield (L/Mg
untreated feedstock)

Ethanol yield
(L/ha)

Ethanol yield (L/Mg
untreated feedstock)

Ethanol yield
(L/ha)

1,600 ± 52

230 ± 4

1511 ± 47

Corn Stover
36H56

2010

6.58 ± 0.45

243 ± 6

Corn Stover
36H56

2012

3.14 ± 0.61

241 ± 19

756 ± 71

262 ± 2

822 ± 72

Corn Stover
P0448R

2012

3.14 ± 0.61

236 ± 7

740 ± 65

255 ± 5

802 ± 70

Corn Stover
P0448R

2013

6.08 ± 0.40

312 ± 2

1,897 ± 56

288 ± 6

1752 ± 54

Corn Stover
P0448R

2014

6.90 ± 0.36

233 ± 9

1,605 ± 47

327 ± 22

2,255 ± 85

Switchgrass

2010

5.07 ± 0.51

223 ± 3

1,128 ± 51

246 ± 7

1,249 ± 58

Switchgrass

2012

7.16 ± 1.13

1.1 ± 0.1

214 ± 5

1532 ± 109

Switchgrass

2013

8.61 ± 0.59

202 ± 3

1,737 ± 54

245 ± 3

2,114 ± 65

8±1

Switchgrass

2014

8.17 ± 0.67

215 ± 12

1,757 ± 79

241 ± 2

1968 ± 73

Sorghum

2014

11.11 ± 1.55

239 ± 15

2,656 ± 182

256 ± 2

2,843 ± 177

Miscanthus

2014

14.36 ± 3.30

252 ± 2

3,618 ± 373

284 ± 2

4,073 ± 419

Mixed prairie

2014

3.72 ± 1.02

200 ± 10

747 ± 93

243 ± 20

high biomass yields in the eastern United States (Lee et al.,
2018; Sanford et al., 2016). If a lignocellulosic biorefinery
could accommodate multiple feedstocks, there would be
many potential benefits for both the biorefinery and the
surrounding landscape. Logistical benefits include reductions in the area required for biomass procurement with
resulting reductions in transportation costs and emissions
(Maung et al., 2013) and the possibility of staging harvest
timing to reduce biomass storage requirements (Rentizelas,
Tolis, & Tatsiopoulos, 2009). Having the capability to process a variety of materials also provides resiliency against
abnormally low biomass yields, allowing the refinery to
switch feedstocks if some are in short supply. Environmental benefits from implementing landscape feedstock diversity can include soil carbon sequestration, climate
stabilization, water quality (Oates, Duncan, Gelfand, et al.,
2016; Robertson et al., 2008; Robertson, Hamilton, Grosso,
& Parton, 2011), as well as reduced N2O emissions (Oates,
Duncan, Gelfand, et al., 2016), and nitrate losses (Donner
& Kucharik, 2008; Duran, Duncan, Oates, Kucharik, &
Jackson, 2016) that occur with a reduction in fertilizer
requirements and greater nutrient use efficiency (Ruan,
Bhardwaj, Hamilton, & Robertson, 2016). Greater crop
diversity in the landscape, particularly in the form of an
increased proportion of perennials, provides increased wildlife and insect habitat and accrues associated biodiversity

905 ± 116

benefits such as pest suppression and pollination services
(Bennett & Isaacs, 2014; Werling et al., 2014; Werling,
Meehan, Robertson, Gratton, & Landis, 2011) and plant‐associated soil microbes (Oates, Duncan, Sanford, Liang, &
Jackson, 2016). Adding perennial crops can also increase
belowground C inputs and the attendant reduction in C
debt (Robertson et al., 2017). These benefits may improve
long‐term yield stability and allow the use of marginal
lands to increase yield potential (Gelfand et al., 2013).
Perennials can also be strategically added to sensitive or
low‐productivity regions within the landscape, such as marginal field sites, riparian buffers, and in‐field erosion strips
(Bonner, Muth, Koch, & Karlen, 2014; Gopalakrishnan,
Cristina Negri, & Salas, 2012; Ha & Wu, 2015; Ssegane,
Negri, Quinn, & Urgun‐Demirtas, 2015). This incorporation of perennials into the landscape would avoid competition with food production while maintaining economic
productivity and providing most of the ecosystem services
that would be gained by completely replacing annual crops
with perennials (Asbjornsen et al., 2014; Lautala et al.,
2015). Feedstock diversity can also improve harvest logistics and reduce overall labor and equipment requirements
using the same equipment to harvest multiple feedstocks at
different times during the growing season. By moving from
a single feedstock to three feedstocks with different harvest
windows, the refinery could save an expected 25% of the

ZHANG

ET AL.

F I G U R E 8 Improvements in biomass productivity are more
important for increasing field‐scale ethanol yields at low biomass
productivity, whereas biomass quality improvements have a greater
impact at high biomass productivity. The graphs show the increase in
biomass ethanol yield required to match the field‐scale ethanol yield
resulting from a 1 Mg/ha increase in productivity for each feedstock
for S. cerevisiae (a) and Z. mobilis (b) fermentations. Power law
regressions were fit to the data points. The S. cerevisiae figure does
not include 2012 switchgrass, which was unfermentable

total logistics costs (Correll, Suzuki, & Martens, 2014). A
broad range of cropping systems would also provide
greater economic opportunities for rural communities and
may increase food security through better integration of lignocellulosic biomass and animal feed production (Dale
et al., 2014).
To evaluate whether there are any major differences in
fermentation efficiency and ethanol yields from key herbaceous lignocellulosic feedstocks, we investigated the fermentability of five feedstocks that had been processed
using AFEX pretreatment and high solid enzymatic hydrolysis. These feedstocks were all harvested at the same
location in 2014 and included both annual crops and residues (corn stover and energy sorghum) and perennial
mono‐ and polyculture feedstocks (switchgrass, miscanthus,
and mixed prairie). Each feedstock was separately fermented using a yeast, S. cerevisiae Y128, or bacteria,
Z. mobilis 8b, both of which have been engineered to utilize xylose. Additional information on the cropping systems
used in these experiments can be found in a number of
papers that evaluate crop yields (Sanford et al., 2016), biomass characteristics and predicted ethanol yields (Sanford
et al., 2017), potential farm‐level economic returns (Skevas,
Swinton, Tanner, Sanford, & Thelen, 2016), and environmental impacts of the cropping systems including nitrous
oxide emissions during establishment (Oates, Duncan, Gelfand, et al., 2016), and differences in soil microbial communities (Oates, Duncan, Sanford, et al., 2016).

|

11

Our findings ultimately depended on the ethanol yield
metric that was used to compare the feedstocks. Process
ethanol yields represent the efficiency of the microbes to
convert sugars to ethanol during fermentation. The process
ethanol yields are affected by the resiliency of the microorganism, initial sugar concentrations in the hydrolyzate, and
any inhibitors that may also be present. These latter two
parameters are in turn dictated by the interaction between
the untreated feedstock composition and the pretreatment
method used to increase access to the sugars. In contrast,
field‐scale ethanol yield represents the ethanol production
potential per land area, often in L/ha. This metric encompasses both biomass yield and feedstock quality in terms of
the ability to achieve a high process ethanol yield. We have
previously reported that field‐scale ethanol yield was more
dependent on biomass productivity (Mg/ha) than biomass
quality (digestibility) for a variety of feedstocks (Sanford
et al., 2017). However, this finding was based on constant
defined process ethanol yields (i.e., metabolic yields) for
each feedstock (0.931 and 0.897 g ethanol per g sugar consumed for corn stover, and all perennials, respectively).
These field‐scale ethanol yields therefore included experimental variability due to enzymatic hydrolysis but not fermentation. However, our experimental fermentation data
also revealed that biomass yield is a key driver of field‐
scale ethanol yield, as indicated by the strong linear
correlation between the two values across feedstocks. The
relative influence of biomass quality (ability to generate
high ethanol yield from a given feedstock) and biomass
productivity also varied in a consistent manner across all of
the feedstocks. When the total biomass produced is low,
increasing biomass productivity, as opposed to increasing
the biomass ethanol yield (L/Mg), has a larger effect on
the field‐scale ethanol yield. As biomass productivity
increases the opposite holds true; each incremental increase
in productivity has less of an impact compared to potential
improvements in biomass ethanol yields (L/Mg). From a
grower's or breeder's perspective, although both productivity and quality influence the results and should ideally be
improved simultaneously, in the event this is not possible,
it would be more efficient to focus on strategies to improve
the ethanol yield for specific crops. For crops that are
already highly productive, such as miscanthus, instead of
targeting improvements in crop yield, it may instead be
more efficient to increase the ethanol yield by improving
biomass quality and/or conversion efficiency. In contrast,
for feedstocks that are low yielding, it may be more efficient to increase crop yield rather than targeting improvements in biomass quality or conversion. That said, biomass
yield is still critically important for the farmer and perennial crops are inherently risky, with extremely high breakeven yields to achieve the same economics compared to
corn (20–100 Mg/ha) (Skevas et al., 2016).

12

|

Although field‐scale ethanol yield is important for growers and breeders of lignocellulosic crops, process ethanol
yield is more important for the biorefinery as it is a key
factor affecting the minimum ethanol selling price (MESP),
the main metric of biorefinery economic sustainability
(Vicari et al., 2012). From our results, the range and variability in the process ethanol yield were highly dependent
on the microbe used for fermentation. S. cerevisiae fermentations tended to have lower process ethanol yields and
greater variability (58%–75%) compared to Z. mobilis
(81%–84%) (Figure 1). In our previous study, we found
that for these particular strains, Z. mobilis is significantly
more resistant to inhibitors and generates higher process
ethanol yields compared to S. cerevisiae Y128 (Ong et al.,
2016). Process yields also showed similar levels of variability whether compared between feedstocks or between
years. Evaluation of the 2014 feedstocks showed that corn
stover from that year had significant inhibition of xylose
consumption that resulted in process yield losses for that
feedstock. In another study, process ethanol yields from
glucose and xylose contributed ~25% of the uncertainty in
MESP calculations (Vicari et al., 2012). Efficient microbial
utilization of both glucose and xylose will be important to
achieve high process ethanol yields and low MESP. There
are a number of ways to increase biomass ethanol yield
including: increasing the biomass polymeric sugar content,
increasing the sugar released during enzymatic hydrolysis,
or increasing the process ethanol yields during fermentation. These changes can be attained either through biomass
or process modifications. Identifying biomass characteristics that contribute to inefficient conversion is critical to
designing feedstocks or modifying processes to overcome
these limitations and achieve high yields. We were able to
utilize a tiered approach to confirm the uniqueness of the
2014 corn stover compared to the other hydrolyzates and
identify the inhibitors responsible for the poor xylose utilization. During this analysis, we discovered that untreated
corn stover harvested in 2014 contained higher than average concentrations of the hydroxycinnamates, ferulic acid,
and p‐coumaric acid. During AFEX pretreatment, these
were largely converted to their amide forms (feruloyl amide
and p‐coumaroyl amide). Further experiments revealed that
the ferulates and the acid forms of the hydroxycinnamates
were more inhibitory of S. cerevisiae xylose utilization
compared to the p‐coumarate and amide forms, respectively. This may indicate that although the AFEX‐
pretreated biomass generated amides that inhibited xylose
fermentation, it may be less inhibition than would be
caused by an acidic pretreatment that leaves these compounds predominantly in the acid form (Chundawat et al.,
2010). A previous study found a similar effect on yeast
xylose utilization for the different hydroxycinnamate
derivatives, with ferulic acid having the strongest negative

ZHANG

ET AL.

effect (Tang et al., 2015), though the mechanism of inhibition in yeast has yet to be determined. In E. coli, feruloyl
amide has been shown to impair xylose metabolism by
inhibiting glutamine PRPP amidotransferase (PurF), the
first step in de novo purine biosynthesis (Pisithkul, Jacobson, O'Brien, Stevenson, & Amador‐Noguez, 2015). This
may also be the mechanism for the inhibition of Z. mobilis
xylose utilization in the 2014 corn stover hydrolyzate.
It is unclear as to the reason for the elevated hydroxycinnamate levels in the 2014 corn stover compared to the other
years investigated for the same hybrid (2012 and 2013).
Maize pest herbivory has been shown to cause an increase in
hydroxycinnamate content (Santiago et al., 2017), and it may
be that pest infestation of the maize grown in 2014 was
higher compared with other years, although we have no data
on pest infestation to support or disprove this hypothesis.
Another possibility is that environmental conditions, such as
temperature and precipitation, affected the quality of the corn
biomass during that growing season. This is similar to what
we found for switchgrass grown and harvested during 2012,
a major drought year in the Midwest. The switchgrass from
2012 built up high concentrations of soluble sugars due to
drought stress that were converted by AFEX pretreatment to
pyrazines and imidazoles, which completely blocked yeast,
but not Z. mobilis, growth, and glucose fermentation (Ong
et al., 2016).
Together, these results suggest that cellulosic biofuel producers can assess for high concentrations of hydroxycinnamates or soluble sugars in their untreated biomass and
subsequently make decisions on how to ferment their feedstock. Although the inability of yeast to ferment xylose from
2014 corn stover had minimal impact on ethanol yield per
hectare, this limitation still affected the process ethanol yield.
Thus, additional measures can be made to maximize ethanol
production. Based on our results, it may be desirable to
improve biomass quality by reducing the biomass hydroxycinnamate content, which can be accomplished by repressing expression of certain BAHD acyltransferase genes (de
Souza et al., 2018; Molinari, Pellny, Freeman, Shewry, &
Mitchell, 2013). This would be expected to have a positive
effect on fermentation performance by reducing the concentration of hydroxycinnamates in the biomass and resulting
hydrolyzate, but may come at the expense of feedstock viability. Higher hydroxycinnamate contents have been shown
to increase plant resistance to herbivory and pathogen infestation, and reductions in these levels can lead to losses in
crop yields (Barros‐Rios, Santiago, Jung, & Malvar, 2015;
Buanafina & Fescemyer, 2012; Reem et al., 2016; Santiago,
Barros‐Rios, Alvarez, & Malvar, 2016). Alternatively,
bioethanol producers could utilize Z. mobilis or other tolerant microbial ethanologens, or reengineer yeast with greater
tolerance to hydroxycinnamates and other lignocellulose
derived inhibitors, such as has been done with E. coli

ZHANG

(Sariaslani, 2007). This would likely require minimal modifications to processing but may require more significant
research investment to identify key genes related to
improved resistance and engineer tolerant strains. Alternatively, cellulosic plant operators may opt to perform additional hydrolyzate conditioning to remove high
concentrations of ferulic acid or other phenolic inhibitors
prior to fermentation (Tomek et al., 2015). One option is to
use an ammonia extraction step that is able to remove the
majority of hydroxycinnamates (da Costa Sousa, Foston,
et al., 2016; Mittal et al., 2017). The benefit of this approach
is twofold. First, the resulting biomass is significantly more
digestible (da Costa Sousa, Jin, et al., 2016), and second, the
hydroxycinnamates could be sold or biologically upgraded
into value‐added products (Linger et al., 2014).

5

|

ET AL.

| CONCLUSIONS

We have provided evidence supporting the idea that multiple
plant types with a range in feedstock quality can be used
without a major impact on field‐scale ethanol yields. Instead
of being overly concerned about biomass quality, as long as
biomass yields are low, feedstock producers can focus on
increasing productivity. In contrast, for feedstocks that are
already high‐yielding, it may be more efficient to focus on
improving feedstock quality, which can have a major impact
on the biorefinery by increasing process ethanol yields and
lowering the MESP. In our study, with a few notable exceptions, most feedstocks showed very similar process ethanol
yields across multiple harvest years. Although most of the
feedstocks were grasses, they all have very different characteristics (morphology, chemical composition, etc.), so the
similarity in their yields is encouraging. It seems likely that
bioethanol producers can be somewhat feedstock agnostic in
terms of the materials they are able to take in and process.
Together, this data provide evidence that the biofuel industry
could successfully produce and process multiple sources of
lignocellulosic feedstocks, which could achieve key social,
economic, and environmental goals by increasing supply
while offering numerous ecosystem services. As an added
benefit, the industry can accomplish these goals while avoiding the use of food/feed crops such as corn grain, without
significant impacts on net bioethanol production.
ACKNOWLEDGEMENTS
We thank Randy Jackson, Ken Keegstra, Robert Landick,
Eva Ziegelhoffer, Donna Bates, Chrislyn Particka, and Sarynna Lopez‐Meza for scientific input. We also thank Pete
Donald for running the AFEX pretreatments, Jeff Skerker
for providing Z. mobilis barcoded transposon library,
Novozymes for providing CTec2 and HTec2 enzymes used
for hydrolyzate production, GLBRC Metabolomics Facility

13

for HPLC‐RID analysis, Jason Russell for additional metabolomics support, Scott Bottoms for technical support, and
the University of Wisconsin Biotechnology Center DNA
Sequencing Facility for bar code sequencing. AFEX is a
trademark of MBI, International (Lansing, MI).
CONFLICT OF INTEREST
The authors declare that they have no competing interests.
AUTHOR CONTRIBUTIONS
YZ, DC, DE, LGO, JSP, JJC, JR, RGO, GRS, and TKS
designed the project and experiments. YZ, YB, MKY, AH,
LGO, TKS, and RGO wrote the manuscript with input
from all authors. DE, GRS, YZ, JS, DX, EP, JP, MKY,
AH, and SDK performed experiments. YB and RGO performed computational data analysis.
ORCID
Lawrence G. Oates
http://orcid.org/0000-0003-48297600
DanXie
http://orcid.org/0000-0002-7335-3726
Alan Higbee
http://orcid.org/0000-0001-9379-8362
Trey K.Sato
http://orcid.org/0000-0001-6592-9337
Rebecca G. Ong
http://orcid.org/0000-0001-5020-646X

REFERENCES
Asbjornsen, H., Hernandez‐Santana, V., Liebman, M., Bayala, J.,
Chen, J., Helmers, M., … Schulte, L. A. (2014). Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem
services. Renewable Agriculture and Food Systems, 29, 101–125.
https://doi.org/10.1017/S1742170512000385
Barros‐Rios, J., Santiago, R., Jung, H.‐J.‐G., & Malvar, R. A. (2015).
Covalent cross‐linking of cell‐wall polysaccharides through esterified diferulates as a maize resistance mechanism against corn borers. Journal of Agricultural and Food Chemistry, 63, 2206–2214.
https://doi.org/10.1021/jf505341d
Bennett, A. B., & Isaacs, R. (2014). Landscape composition influences pollinators and pollination services in perennial biofuel
plantings. Agriculture, Ecosystems & Environment, 193, 1–8.
https://doi.org/10.1016/j.agee.2014.04.016
Bonin, C., & Lal, R. (2012). Agronomic and ecological implications
of biofuels. In L. S. Donald (Ed.), Advances in agronomy (pp. 1–
50). San Diego: CA, Academic Press.
Bonner, I. J., Muth, D. J. Jr, Koch, J. B., & Karlen, D. L. (2014).
Modeled impacts of cover crops and vegetative barriers on corn
stover availability and soil quality. BioEnergy Research, 7, 576–
589. https://doi.org/10.1007/s12155-014-9423-y
Buanafina, M. M. O., & Fescemyer, H. W. (2012). Modification of
esterified cell wall phenolics increases vulnerability of tall fescue
to herbivory by the fall armyworm. Planta, 236, 513–523.
https://doi.org/10.1007/s00425-012-1625-y

14

|

Chundawat, S. P., Vismeh, R., Sharma, L. N., Humpula, J. F., da
Costa Sousa, L., Chambliss, C. K., … Dale, B. E. (2010). Multifaceted characterization of cell wall decomposition products
formed during ammonia fiber expansion (AFEX) and dilute acid
based pretreatments. Bioresource Technology, 101, 8429–8438.
https://doi.org/10.1016/j.biortech.2010.06.027
Correll, D., Suzuki, Y., & Martens, B. J. (2014). Logistical supply
chain design for bioeconomy applications. Biomass and Bioenergy, 66, 60–69. https://doi.org/10.1016/j.biombioe.2014.03.036
Croghan, C., & Egeghy, P. P. (2003). Methods of dealing with values
below the limit of detection using SAS (pp. 22–24). St. Petersburg,
FL: At Southern SAS User Group.
da Costa Sousa, L., Foston, M., Bokade, V., Azarpira, A., Lu, F.,
Ragauskas, A. J., … Balan, V. (2016). Isolation and characterization of new lignin streams derived from extractive ammonia (EA)
pretreatment. Green Chemistry, 18(15), 4205–4215. https://doi.
org/10.1039/C6GC00298F
da Costa Sousa, L., Jin, M., Chundawat, S. P. S., Bokade, V., Tang,
X., Azarpira, A., … Balan, V. (2016). Next‐generation ammonia
pretreatment enhances cellulosic biofuel production. Energy &
Environmental Science, 9, 1215–1223.
Dale, B. E., Anderson, J. E., Brown, R. C., Csonka, S., Dale, V. H.,
Herwick, G., … Wang, M. Q. (2014). Take a closer look: Biofuels can support environmental, economic and social goals. Environmental Science & Technology, 48, 7200–7203. https://doi.org/
10.1021/es5025433
de Souza, W. R., Martins, P. K., Freeman, J., Pellny, T. K., Michaelson,
L. V., Sampaio, B. L., … Molinari, H. B. C. (2018). Suppression of
a single BAHD gene in Setaria viridis causes large, stable decreases
in cell wall feruloylation and increases biomass digestibility. New
Phytologist, 218, 81–93 https://doi.org/10.1111/nph.14970.
DeMendiburu, F. (2009). Una herramienta de analisis estadistico
para la investigacion agricola. Lima, Peru: Universidad Nacional
de Ingenieria (UNI‐PERU).
Donner, S. D., & Kucharik, C. J. (2008). Corn‐based ethanol production compromises goal of reducing nitrogen export by the Mississippi River. Proceedings of the National Academy of Sciences of
the United States of America, 105, 4513–4518. https://doi.org/10.
1073/pnas.0708300105
Duran, B. E. L., Duncan, D. S., Oates, L. G., Kucharik, C. J., & Jackson, R. D. (2016). Nitrogen fertilization effects on productivity
and nitrogen loss in three grass‐based perennial bioenergy cropping systems. PLoS One, 11, e0151919. https://doi.org/10.1371/
journal.pone.0151919
Garlock, R. J., Bals, B., Jasrotia, P., Balan, V., & Dale, B. E. (2012).
Influence of variable species composition on the saccharification
of AFEX™ pretreated biomass from unmanaged fields in comparison to corn stover. Biomass and Bioenergy, 37, 49–59. https://
doi.org/10.1016/j.biombioe.2011.12.036
Gelfand, I., Sahajpal, R., Zhang, X., Izaurralde, R. C., Gross, K. L.,
& Robertson, G. P. (2013). Sustainable bioenergy production from
marginal lands in the US Midwest. Nature, 493, 514–517.
https://doi.org/10.1038/nature11811
Girke, T. (2018). Hierarchical Clustering (HC). R & Bioconductor
Manual. Accessed January 26 2018. Retrieved from http://manu
als.bioinformatics.ucr.edu/home/R_BioCondManual#TOC‐Hierarchi
cal‐Clustering‐HC‐
Gopalakrishnan, G., Cristina Negri, M., & Salas, W. (2012). Modeling biogeochemical impacts of bioenergy buffers with perennial

ZHANG

ET AL.

grasses for a row‐crop field in Illinois. GCB Bioenergy, 4, 739–
750. https://doi.org/10.1111/j.1757-1707.2011.01145.x
Ha, M., & Wu, M. (2015). Simulating and evaluating best management practices for integrated landscape management scenarios in
biofuel feedstock production. Biofuels, Bioproducts and Biorefining, 9, 709–721. https://doi.org/10.1002/bbb.1579
Hess, J. R., Wright, C. T., Kenney, K. L., & Searcy, E. M. (2009).
Uniform‐format solid feedstock supply system: A commodity‐
scale design to produce an infrastructure‐compatible bulk solid
from lignocellulosic biomass – Executive summary. Idaho
National. Laboratory, (INL), INL/EXT‐09–15423.
Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M.,
Carvalho, B. S., … Morgan, M. (2015). Orchestrating high‐
throughput genomic analysis with Bioconductor. Nature Methods,
12, 115. https://doi.org/10.1038/nmeth.3252
Keating, D. H., Zhang, Y. P., Ong, I. M., McIlwain, S., Morales, E.
H., Grass, J. A., … Landick, R. (2014). Aromatic inhibitors
derived from ammonia‐pretreated lignocellulose hinder bacterial
ethanologenesis by activating regulatory circuits controlling inhibitor efflux and detoxification. Frontiers in Microbiology, 5, 402.
https://doi.org/10.3389/fmicb.2014.00402
Lautala, P. T., Hilliard, M. R., Webb, E., Busch, I., Richard Hess, J.,
Roni, M. S., … Laitinen, T. (2015). Opportunities and challenges
in the design and analysis of biomass supply chains. Environmental Management, 56, 1397–1415. https://doi.org/10.1007/s00267015-0565-2
Law, C. W., Alhamdoosh, M., Su, S., Smyth, G. K., & Ritchie, M.
E. (2016). RNA‐seq analysis is easy as 1‐2‐3 with limma, Glimma
and edgeR. F1000Research, 5, 1408.
Lee, D. K., Aberle, E., Anderson, E. K., Anderson, W., Baldwin, B.
S., Baltensperger, D., … Owens, V. (2018). Biomass production
of herbaceous energy crops in the United States: Field trial results
and yield potential maps from the multiyear regional feedstock
partnership. GCB Bioenergy, https://doi.org/10.1111/gcbb.12493
Lin, T., Rodriguez, L., Davis, S., Khanna, M., Shastri, Y., Grift, T.,
… Ting, K. C. (2016). Biomass feedstock preprocessing and long‐
distance transportation logistics. Global Change Biology Bioenergy, 8, 160–170. https://doi.org/10.1111/gcbb.12241
Linger, J. G., Vardon, D. R., Guarnieri, M. T., Karp, E. M., Hunsinger, G. B., Franden, M. A., … Beckham, G. T. (2014). Lignin
valorization through integrated biological funneling and chemical
catalysis. Proceedings of the National Academy of Sciences of the
United States of America, 111, 12013–12018. https://doi.org/
10.1073/pnas.1410657111
Maung, T. A., Gustafson, C. R., Saxowsky, D. M., Nowatzki, J.,
Miljkovic, T., & Ripplinger, D. (2013). The logistics of supplying
single vs. multi‐crop cellulosic feedstocks to a biorefinery in
southeast North Dakota. Applied Energy, 109, 229–238. https://d
oi.org/10.1016/j.apenergy.2013.04.003
Mittal, A., Katahira, R., Donohoe, B. S., Pattathil, S., Kandemkavil,
S., Reed, M. L., … Beckham, G. T. (2017). Ammonia pretreatment of corn stover enables facile lignin extraction. ACS Sustainable Chemistry & Engineering, 5, 2544–2561. https://doi.org/10.
1021/acssuschemeng.6b02892
Molinari, H. B. C., Pellny, T. K., Freeman, J., Shewry, P. R., &
Mitchell, R. A. C. (2013). Grass cell wall feruloylation: distribution of bound ferulate and candidate gene expression in Brachypodium distachyon. Frontiers in Plant Science, 4, 50. https://doi.
org/10.3389/fpls.2013.00050

ZHANG

ET AL.

Oates, L. G., Duncan, D. S., Gelfand, I., Millar, N., Robertson, G. P.,
& Jackson, R. D. (2016). Nitrous oxide emissions during establishment of eight alternative cellulosic bioenergy cropping systems
in the North Central United States. Global Change Biology Bioenergy, 8, 539–549.
Oates, L. G., Duncan, D. S., Sanford, G. R., Liang, C., & Jackson, R.
D. (2016). Bioenergy cropping systems that incorporate native
grasses stimulate growth of plant‐associated soil microbes in the
absence of nitrogen fertilization. Agriculture, Ecosystems & Environment, 233, 396–403.
Ong, R. G., Higbee, A., Bottoms, S., Dickinson, Q., Xie, D., Smith,
S. A., … Zhang, Y. (2016). Inhibition of microbial biofuel production in drought‐stressed switchgrass hydrolysate. Biotechnology
for Biofuels, 9, 237. https://doi.org/10.1186/s13068-016-0657-0
Parreiras, L. S., Breuer, R. J., Avanasi Narasimhan, R., Higbee, A. J.,
La Reau, A., Tremaine, M., … Sato, T. K. (2014). Engineering
and two‐stage evolution of a lignocellulosic hydrolysate‐tolerant
Saccharomyces cerevisiae strain for anaerobic fermentation of
xylose from AFEX pretreated corn stover. PLoS One, 9, e107499.
https://doi.org/10.1371/journal.pone.0107499
Piotrowski, J. S., Li, S. C., Deshpande, R., Simpkins, S. W., Nelson,
J., Yashiroda, Y., … Boone, C. (2017). Functional annotation of
chemical libraries across diverse biological processes. Nature
Chemical Biology, 13, 982. https://doi.org/10.1038/nchembio.2436
Piotrowski, J. S., Simpkins, S. W., Li, S. C., Deshpande, R., McIlwain,
S. J., Ong, I. M., … Andersen, R. J. (2015). Chemical genomic profiling via barcode sequencing to predict compound mode of action.
In J. E. Hempel, C. H. Williams, & C. C. Hong (Eds.), Chemical
biology (pp. 299–318). New York, NY: Springer.
Pisithkul, T., Jacobson, T. B., O'Brien, T. J., Stevenson, D. M., &
Amador‐Noguez, D. (2015). Phenolic amides are potent inhibitors
of de novo nucleotide biosynthesis. Applied and Environmental
Microbiology, 81, 5761–5772.
Plotly Technologies Inc (2015). Collaborative data science. Retrieved
from https://plot.ly
POET‐DSM (2014). Biomass program overview. Retrieved 20
November 2017 from http://www.poetdsm.com/resources/docs/
POET‐DSM%20Biomass%20Program%20Overview.pdf
Raízen. (2014). Renewable energy technology: Second generation
ethanol. Retrieved from https://www.raizen.com.br/en/energy‐future/renewable‐energy‐technology/second‐generation‐ethanol November, 20 2017
Reem, N. T., Pogorelko, G., Lionetti, V., Chambers, L., Held, M. A.,
Bellincampi, D., & Zabotina, O. A. (2016). Decreased polysaccharide feruloylation compromises plant cell wall integrity and
increases susceptibility to necrotrophic fungal pathogens. Frontiers
in Plant Science, 7, 630. https://doi.org/10.3389/fpls.2016.00630
Rentizelas, A. A., Tolis, A. J., & Tatsiopoulos, I. P. (2009). Logistics
issues of biomass: The storage problem and the multi‐biomass
supply chain. Renewable and Sustainable Energy Reviews, 13,
887–894. https://doi.org/10.1016/j.rser.2008.01.003
Robertson, G. P., Dale, V. H., Doering, O. C., Hamburg, S. P.,
Melillo, J. M., Wander, M. M., … Wilhelm, W. W. (2008). Sustainable biofuels redux. Science, 322, 49–50.
Robertson, G. P., Hamilton, S. K., Barham, B. L., Dale, B. E., Izaurralde, R. C., Jackson, R. D., … Tiedje, J. M. (2017). Cellulosic
biofuel contributions to a sustainable energy future: Choices and
outcomes. Science, 356, eaal2324. https://doi.org/10.1126/scie
nce.aal2324

|

15

Robertson, G. P., Hamilton, S. K., Del Grosso, S. J., & Parton, W. J.
(2011). The biogeochemistry of bioenergy landscapes: Carbon,
nitrogen, and water considerations. Ecological Applications, 21,
1055–1067. https://doi.org/10.1890/09-0456.1
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A
Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140. https://doi.
org/10.1093/bioinformatics/btp616
Ruan, L., Bhardwaj, A. K., Hamilton, S. K., & Robertson, G. P.
(2016). Nitrogen fertilization challenges the climate benefit of cellulosic biofuels. Environmental Research Letters, 11, 064007.
https://doi.org/10.1088/1748-9326/11/6/064007
Sanford, G. R., Oates, L. G., Jasrotia, P., Thelen, K. D., Robertson,
G. P., & Jackson, R. D. (2016). Comparative productivity of alternative cellulosic bioenergy cropping systems in the North Central
USA. Agriculture, Ecosystems & Environment, 216, 344–355.
https://doi.org/10.1016/j.agee.2015.10.018
Sanford, G. R., Oates, L. G., Roley, S. S., Duncan, D. S., Jackson, R.
D., Robertson, G. P., & Thelen, K. D. (2017). Biomass production
a stronger driver of cellulosic ethanol yield than biomass quality.
Agronomy Journal, 109, 1911–1922. https://doi.org/10.2134/agron
j2016.08.0454
Santiago, R., Barros‐Rios, J., Alvarez, A., & Malvar, R. A. (2016).
Agronomic performance of maize populations divergently selected
for diferulate cross‐linkage. The Journal of Agricultural Science,
154, 1270–1279. https://doi.org/10.1017/S0021859615001161
Santiago, R., Cao, A., Butrón, A., López‐Malvar, A., Rodríguez, V.
M., Sandoya, G. V., & Malvar, R. A. (2017). Defensive changes
in maize leaves induced by feeding of Mediterranean corn borer
larvae. BMC Plant Biology, 17, 44. https://doi.org/10.1186/
s12870-017-0991-9
Sariaslani, F. S. (2007). Development of a combined biological and
chemical process for production of industrial aromatics from
renewable resources. Annual Review of Microbiology, 61, 51–69.
https://doi.org/10.1146/annurev.micro.61.080706.093248
Sato, T. K., Tremaine, M., Parreiras, L. S., Hebert, A. S., Myers, K.
S., Higbee, A. J., … Landick, R. (2016). Directed evolution
reveals unexpected epistatic interactions that alter metabolic regulation and enable anaerobic xylose use by Saccharomyces cerevisiae. PLoS Genetics, 12, e1006372.
Serate, J., Xie, D., Pohlmann, E., Donald, C., Shabani, M., Hinchman,
L., … Zhang, Y. (2015). Controlling microbial contamination during hydrolysis of AFEX‐pretreated corn stover and switchgrass:
Effects on hydrolysate composition, microbial response and fermentation. Biotechnology for Biofuels, 8, 1–17. https://doi.org/10.
1186/s13068-015-0356-2
Shi, J., Thompson, V. S., Yancey, N. A., Stavila, V., Simmons, B.
A., & Singh, S. (2012). Impact of mixed feedstocks and feedstock
densification on ionic liquid pretreatment efficiency. Biofuels, 4,
63–72. https://doi.org/10.4155/bfs.12.82
Simpkins, S. W., Nelson, J., Deshpande, R., Li, S. C., Piotrowski, J.
S., Wilson, E. H., … Myers, C. L. (2018). Predicting bioprocess
targets of chemical compounds through integration of chemicalgenetic and genetic interaction networks. bioRxiv. https://doi.org/
10.1101/111252.
Skerker, J. M., Leon, D., Price, M. N., Mar, J. S., Tarjan, D. R., Wetmore, K. M., … Arkin, A. P. (2013). Dissecting a complex chemical
stress: Chemogenomic profiling of plant hydrolysates. Molecular
Systems Biology, 9, 674. https://doi.org/10.1038/msb.2013.30

16

|

Skevas, T., Swinton, S. M., Tanner, S., Sanford, G., & Thelen, K. D.
(2016). Investment risk in bioenergy crops. GCB Bioenergy, 8,
1162–1177. https://doi.org/10.1111/gcbb.12320
Skotnicki, M. L., Tribe, D. E., & Rogers, P. L. (1980). R‐plasmid
transfer in Zymomonas mobilis. Applied and Environmental
Microbiology, 40, 7–12.
Ssegane, H., Negri, M. C., Quinn, J., & Urgun‐Demirtas, M. (2015).
Multifunctional landscapes: Site characterization and field‐scale
design to incorporate biomass production into an agricultural system. Biomass and Bioenergy, 80, 179–190. https://doi.org/10.
1016/j.biombioe.2015.04.012
Tang, X., da Costa, S. L., Jin, M., Chundawat, S., Chambliss, C.,
Lau, M. W., … Balan, V. (2015). Designer synthetic media
for studying microbial‐catalyzed biofuel production. Biotechnology for Biofuels, 8, 1–17. https://doi.org/10.1186/s13068-0140179-6
The R Foundation (2017). R: The R project for statistical computing.
Retrieved from www.r‐project.org November 20, 2017
Tomek, K. J., Saldarriaga, C. R. C., Velasquez, F. P. C., Liu, T.,
Hodge, D. B., & Whitehead, T. A. (2015). Removal and upgrading of lignocellulosic fermentation inhibitors by in situ biocatalysis
and
liquid‐liquid
extraction.
Biotechnology
and
Bioengineering, 112, 627–632. https://doi.org/10.1002/bit.25473
Vicari, K., Tallam, S., Shatova, T., Joo, K., Scarlata, C. J., Humbird,
D., … Beckham, G. T. (2012). Uncertainty in techno‐economic
estimates of cellulosic ethanol production due to experimental
measurement uncertainty. Biotechnology for Biofuels, 5, 23.
https://doi.org/10.1186/1754-6834-5-23
Vu, V. Q. (2011). ggbiplot package. Retrieved from https://github.
com/vqv/ggbiplot

ZHANG

ET AL.

Werling, B. P., Dickson, T. L., Isaacs, R., Gaines, H., Gratton, C.,
Gross, K. l.,… Landis, D. A. (2014). Perennial grasslands enhance
biodiversity and multiple ecosystem services in bioenergy landscapes. Proceedings of the National Academy of Sciences of the
United States of America, 111, 1652–1657. https://doi.org/10.
1073/pnas.1309492111
Werling, B. P., Meehan, T. D., Robertson, B. A., Gratton, C., & Landis, D. A. (2011). Biocontrol potential varies with changes in biofuel–crop plant communities and landscape perenniality. GCB
Bioenergy, 3, 347–359. https://doi.org/10.1111/j.1757-1707.2011.
01092.x
Wickham, H. (2009). ggplot2: Elegant graphics for data analysis.
New York, NY: Springer.

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How to cite this article: Zhang Y, Oates LG, Serate
J, et al. Diverse lignocellulosic feedstocks can
achieve high field‐scale ethanol yields while
providing flexibility for the biorefinery and
landscape‐level environmental benefits. GCB
Bioenergy. 2018;00:1–16.
https://doi.org/10.1111/gcbb.12533