ArticlePDF Available

Abstract and Figures

A climate envelope approach was used to model the response of switchgrass, a model bioenergy species in the United States, to future climate change. The model was built using general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as the response variable. The model incorporated variables previously shown to be the main determinants of switchgrass yield, and utilized current and predicted 1 km climate data from WorldClim. The models were run with current WorldClim data and compared with results of predicted yield obtained using two climate change scenarios across three global change models for three time steps. Results did not predict an increase in maximum switchgrass yield but showed an overall shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the shift in high yields was concentrated in northern and north-eastern areas where there were increases in average growing season temperature, whereas for lowland cultivars the areas where yields were projected to increase were associated with increases in average early growing season precipitation. These results highlight the fact that the influences of climate change on switchgrass yield are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution of suitable areas for switchgrass production under climate change should be incorporated into planning of current and future biofuel production. Understanding how switchgrass yields will be affected by future changes in climate is important for achieving a sustainable biofuels economy.
This content is subject to copyright. Terms and conditions apply.
IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 7(2012) 045903 (6pp) doi:10.1088/1748-9326/7/4/045903
Response of switchgrass yield to future
climate change
Mirela G Tulbure1, Michael C Wimberly2and Vance N Owens3
1Australian Wetlands, Rivers and Landscapes Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, NSW 2052, Australia
2Geographic Information Science Centre of Excellence, South Dakota State University, Brookings,
SD 57007-3510, USA
3Department of Plant Science, South Dakota State University, Brookings, SD 57007-3510, USA
E-mail: Mirela.Tulbure@unsw.edu.au
Received 3 September 2012
Accepted for publication 16 October 2012
Published 6 November 2012
Online at stacks.iop.org/ERL/7/045903
Abstract
A climate envelope approach was used to model the response of switchgrass, a model
bioenergy species in the United States, to future climate change. The model was built using
general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as
the response variable. The model incorporated variables previously shown to be the main
determinants of switchgrass yield, and utilized current and predicted 1 km climate data from
WorldClim. The models were run with current WorldClim data and compared with results of
predicted yield obtained using two climate change scenarios across three global change
models for three time steps.
Results did not predict an increase in maximum switchgrass yield but showed an overall
shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the
shift in high yields was concentrated in northern and north-eastern areas where there were
increases in average growing season temperature, whereas for lowland cultivars the areas
where yields were projected to increase were associated with increases in average early
growing season precipitation.
These results highlight the fact that the influences of climate change on switchgrass yield
are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution
of suitable areas for switchgrass production under climate change should be incorporated into
planning of current and future biofuel production. Understanding how switchgrass yields will
be affected by future changes in climate is important for achieving a sustainable biofuels
economy.
Keywords: switchgrass, biofuels, climate change, spatial variability, climate envelope
1. Introduction
‘Second generation biofuels’ have long been proposed as a
sustainable alternative over crop feedstocks (Lynd et al 1991).
Although the potential negative indirect effects of biofuels
Content from this work may be used under the terms
of the Creative Commons Attribution-NonCommercial-
ShareAlike 3.0 licence. Any further distribution of this work must maintain
attribution to the author(s) and the title of the work, journal citation and DOI.
on biodiversity, water use, and indirect land use changes
have been documented (Cook et al 1991, Searchinger et al
2008, US Congress 2007), 80 billion litres of non-corn starch
based biofuels will be produced in the United States by 2022.
However, second generation biofuels that utilize perennial
native plants such as switchgrass (Panicum virgatum L.) are
thought to have a lower impact on biodiversity than crops
(Fletcher et al 2011) especially when considerations such as
reduced chemical inputs, increased heterogeneity in the fields
11748-9326/12/045903+06$33.00 c
2012 IOP Publishing Ltd Printed in the UK
Environ. Res. Lett. 7(2012) 045903 M G Tulbure et al
and delayed harvest until bird breeding has ceased are taken
into account. Switchgrass has been highlighted as the model
herbaceous feedstock in North America not only for its high
productivity and ability to tolerate a vast range of conditions
and to grow well on marginal environments, but also for
its environmental benefits such as carbon sequestration and
erosion control (McLaughlin and Walsh 1998).
Despite the potential for developing advanced biofuel
technologies, there is evidence that climate change might
alter the geography of suitable habitat for feedstock crops.
Climate envelope models (CEMs) are typically used to predict
how species ranges change given a future climate and are
commonly used in biogeography studies to predict changes
in species distribution. CEMs use the current distribution of
a species to infer its environmental requirements, and then
predict its future distribution based on these requirements
given changes in climate (Araujo et al 2005, Nunes et al
2007, Williams and Jackson 2007). For example, Barney
and DiTomaso (2010) used bioclimatic envelope models
and historical switchgrass presence/absence data to predict
that there will be an overall increase in suitable habitat for
switchgrass cultivation in the coming century under both
the CCCMA and HADCM3 climate models and A2 and B2
emission scenarios. The desired traits of an ideal biofuel
feedstock species (e.g., high biomass, ability to grow under
marginal conditions, drought, salinity and low-fertility soil
tolerance) are typically similar to traits of invasive plants
(Cousens 2008, Mack 2008). Thus, despite the attraction
of increased yields, there is also concern that feedstock
species, including switchgrass, have the potential of becoming
invasive under future climates (Barney and DiTomaso 2008,
Simberloff 2008).
Understanding how switchgrass yields will respond to
projected future climate change is of fundamental importance
for planning, implementation, and operational management of
future biofuel production systems. A recent study by Tulbure
et al (2012) showed that switchgrass yields are sensitive
to climatic variability in space and time, with different
cytotypes exhibiting different responses. Utilizing switchgrass
yield data in bioclimatic envelope models provides improved
insights into how future changes in climate will affect
switchgrass productivity compared to models based on
presence–absence data. Therefore the aim of our research
was to apply our modelling approach to project how future
changes in climate will influence changes in switchgrass
yields.
2. Methods
2.1. Yield data used and data analysis
Switchgrass yield data collected from 1167 observations at
45 field trials across the native range of switchgrass were
used. Ten variables previously shown to influence switchgrass
yields were used as explanatory variables (Tulbure et al 2012).
Explanatory variables included early and late growing season
precipitation, average growing season temperature, cultivar,
germplasm origin, month of harvest, soil texture (per
cent sand and clay), stand age, and amount of nitrogen
fertilizer applied. Given that the relationship between several
explanatory variables and the dependent variable showed
non-linear trends, we used general additive models (GAMs)
to model switchgrass as a non-linear function of explanatory
variables. GAMs allow for non-linear relationships between
the dependent and explanatory variables thus revealing
structure in the data that might otherwise be missed with
linear models (Faraway 2004, Hastie and Tibshirani 1990).
For a description of the sources of switchgrass yield data,
their location and explanatory variables please refer to
(Tulbure et al 2012). The models previously developed were
refitted using the WorldClim dataset. We used the mgcv
package (Wood 2006,2010) in the statistical software R (R
Development Core Team 2012). The degree of smoothness
for each covariate in the model was estimated as part of
model fitting using generalized cross validation for parameter
selection in mgcv (Wood 2006). In order to limit the amount
of smoothing and avoid overfitting the models we set the
kparameter to 4, thereby allowing only three degrees of
freedom for each covariate in the model and producing
ecologically defensible results (Wood 2006).
2.2. Climate data
We used interpolated climate surfaces of high resolution
monthly maximum (tmax), minimum (tmin ), and mean
temperatures (tmean), and monthly precipitation (prec). For
both current and future conditions we used WorldClim
data (www.worldclim.org/) of approximately 1 km spatial
resolution (Hijmans et al 2005). Data were created by
interpolation using a thin-plate smoothing spline of observed
climate at weather stations, with latitude, longitude, and
elevation as independent variables (Hijmans et al 2005).
Current conditions are interpolations of observed data,
representative of 1950–2000. Downscaled projected future
climate included three global climate models (GCM) based
on the International Panel on Climate Change 3rd assessment
data (Randall et al 2007). The three GCMs included
the Canadian Centre for Climate Modelling and Analysis
(CCCMA), the Hadley Centre Coupled Model (HADCM3),
and the Commonwealth Scientific and Industrial Research
Organization (CSIRO). Coupled with each GCM, there were
two emission scenarios (A2 and B2) based on differences
in demographic, technological and economic advances
(Nakicenovic et al 2000) and three time frames (2020, 2050
and 2080), yielding a total of eighteen combinations (Hijmans
et al 2005). The A2 scenario family describes a heterogeneous
world and assumes a high increase in population up to 15
billion in 2100, a CO2emission rate of 30 Gt Cy1, and a
CO2concentration of 850 ppm by 2100. In contrast, the
B2 scenario family emphasizes local solutions to economic
and environmental sustainability and assumes an increase in
population of up to 10 billion in 2100, a CO2emission rate of
12 Gt Cy1and a total CO2concentration of 600 ppm by
2100.
For modelling the response of switchgrass yields to
climate predictions, we used the predict.gam function, which
2
Environ. Res. Lett. 7(2012) 045903 M G Tulbure et al
Figure 1. Mean predicted switchgrass yield (Mg ha1)for upland cytotypes for the (a) baseline period and under the A2 scenario for (b)
2020, (c) 2050, and (d) 2080 and under the B2 scenario for (e) 2020, (f) 2050, and (g) 2080. Areas shown in grey fell outside the ranges of
precipitation and temperature conditions used to parameterize our GAM models. The analysis was conducted at 1 km resolution of the
WorldClim data.
uses a fitted gam object (the result of our GAM modelling)
and produces predictions with a new set of values for the
model covariates (R Development Core Team 2012). Here,
the only functions and the only variables allowed to vary over
time were the climate variables (April–May precipitation,
June–September precipitation, and average growing season
temperature), whereas the other variables were set to mean
values or the mode. We worked at the spatial resolution of
the climate data sets and summarized switchgrass yield under
current climate and predicted yields per scenario (A2 and B2)
averaged over the three global climate models per time step.
3. Results
Predicted switchgrass yields are shown in figures 1and 2
for upland and lowland cytotypes, respectively. For both
upland and lowland cytotypes, the maximum switchgrass
yields under climate change were projected to stay relatively
similar to current values with a maximum of 15 Mg ha1for
upland and 42 Mg ha1for lowland cytotypes. The spatial
distribution of high yields shifted for both cytotypes towards
northern areas of the switchgrass range, but the patterns were
heterogeneous and cytotype specific (figures 1and 2).
For upland cytotypes, the highest yields under current
climate conditions occurred in Iowa, Wisconsin, and
Pennsylvania. Predicted yields under both A2 and B2
scenarios showed similar patterns for the three time steps
(figures 1(b) and (e), (c) and (f), and (d) and (g)) with yields
generally decreasing over time within the current range of
switchgrass. A shift in the locations of the highest predicted
yields towards the north and the northeast also occurred
(figures 1(b)–(g)).
For lowland cytotypes the area of highest yields currently
includes most of Iowa, north-western Missouri along with
parts of north-eastern Kansas, Illinois, Indiana, Kentucky,
Tennessee and West Virginia (figure 2(a)). In 2020 the areas of
highest yield shifted northward (figures 2(b) and (e)). In 2050
higher yields shifted northwest into much of South Dakota and
3
Environ. Res. Lett. 7(2012) 045903 M G Tulbure et al
Figure 2. Mean predicted switchgrass yield (Mg ha1)for lowland cytotypes for the (a) baseline period and under the A2 scenario for (b)
2020, (c) 2050, and (d) 2080 and under the B2 scenario for (e) 2020, (f) 2050, and (g) 2080. Areas shown in grey fell outside the ranges of
precipitation and temperature conditions used to parameterize our GAM models. The analysis was conducted at 1 km resolution of the
WorldClim data.
southern North Dakota and Nebraska (figures 2(c) and (f)).
In 2080 for the A2 scenario the highest switchgrass yields
were projected to occur in most of North Dakota, Eastern
South Dakota, south eastern Nebraska, Iowa and southern
Minnesota (figure 2(d)) and the pattern was fairly similar to
the B2 scenario for the areas where we could predict the yield
based on our models (figure 2(g)).
For both current and projected climate conditions, areas
shown in grey (e.g., Texas, Louisiana) fell at the extremes or
outside of the ranges of precipitation and temperature data
used to parameterize our GAM models. We masked these
areas in the results because of our low confidence about our
ability to extrapolate yield estimates outside the range of
the parameterization dataset (figures 1and 2). The models
thus only included pixels with early and late growing season
precipitation between 50 and 150 mm of and with an average
growing season temperature between 15 and 25 C. This
masking highlighted the areas that lay outside the range of our
parameterization dataset under the different climate change
scenarios.
4. Discussion
Understanding how switchgrass yields will be affected
by future changes in climate is important for achieving
a sustainable biofuel economy (Glaser and Glick 2012).
Here we provide results of how switchgrass yields are
predicted to change under different climate change scenarios
at the highest available spatial resolution using 1 km
WorldClim global climate data and employing CEMs. CEMs
have been previously used to predict distribution of birds
in the UK (Araujo et al 2005), butterflies in Australia
(Thomas et al 2004) and vascular plants in the Americas
(Hijmans and Graham 2006) under different climate change
scenarios. CEMs are typically based on presence–absence
data. However, in the context of biofuels where productivity is
important, models can be improved by using biomass data as
a measure of performance rather than using presence/absence
data which is only a rough proxy for potential productivity.
Our analysis thus improves on the work of Barney and
DiTomaso (2010) by using switchgrass yield data as opposed
to presence/absence.
4
Environ. Res. Lett. 7(2012) 045903 M G Tulbure et al
For both cytotypes the spatial distribution of maximum
switchgrass yield is predicted to shift considerably. For upland
cytotypes, the shift in distribution is more towards northern
and north-eastern areas following an increase in average
growing season temperature, whereas for lowland cultivars the
areas where yields are projected to increase follow an increase
in average early growing season (April–May) precipitation,
which is the highest for 2050 (figures 2(c) and (f)). It should
be noted that for areas where field trials are located (black
dots in figures 1and 2) which fell outside or were at the
extremes of the precipitation and temperature conditions
used to parameterize our GAM models, we cannot claim
that yields will not change in the future, but rather that
based on our current data and models we cannot make an
informed prediction. In the future, it would be valuable to
conduct switchgrass field trials in more extreme climates that
reflect the future predictions projected under climate change
scenarios.
It should also be noted that CEMs are empirical
models therefore do not necessarily describe cause and
effect relationships between the explanatory variables and
the dependent variable (Kearney and Porter 2004) and thus
may not reflect how a different set of conditions in the
future might affect the variable of interest (e.g., higher
temperature combined with higher precipitation that extend
beyond the values observed at present). An alternative to
CEMs is mechanistic models (MM), which use physiological
requirements of species to parameterize the models and
have the potential to give more realistic predictions under
future climates that have no current analogue, but do not
account for non-climatic influences (e.g., biotic interactions,
dispersal limitations) (Pearson et al 2006). The use of MMs
to model switchgrass yields is expected to increase as more
switchgrass physiological data become available. As both
MM and CEMs have pros and cons, a comparison of results
produced by both approaches should be conducted to assess
their performance and ultimately integrate the two modelling
approaches (Midgley and Thuiller 2005, Thuiller et al 2005).
This study helps improve our understanding of how
switchgrass yield will be affected by future changes in
climate. The study by Barney and DiTomaso (2010) predicted
an overall increase in suitable habitat for switchgrass
cultivation in the coming century under both the CCCMA and
HADCM3 climate models and A2 and B2 emission scenarios,
whereas our findings project that the influences of climate
change on yield will be spatially heterogeneous and will
vary depending on cytotype. The continued development of
bioclimatic envelope models using data on switchgrass yields
can provide improved insights into how future changes in
climate will affect switchgrass biomass yields. Given that
switchgrass for biofuel production will likely be planted on
marginal cropland, future work should map marginal cropland
and integrate the maps thus created with the switchgrass yield
maps provided here to assist planners with information on
where it is more feasible to plant switchgrass. Projections of
the geographic distribution of suitable areas for switchgrass
production under climate change should be incorporated into
planning of current and future locations for biofuel cultivation
and therefore help maximize production while minimizing
negative effects on biodiversity and losses of perennial land
cover.
Acknowledgments
This research was supported by the US Department of
Energy through the Sun Grant Initiative’s Regional Biomass
Feedstock Partnership. We thank two anonymous reviewers
for comments which improved the manuscript.
References
Araujo M B, Pearson R G, Thuiller W and Erhard M 2005
Validation of species-climate impact models under climate
change Glob. Change Biol. 11 1504–13
Barney J N and DiTomaso J M 2008 Nonnative species and
bioenergy: are we cultivating the next invader? Bioscience
58 64–70
Barney J N and DiTomaso J M 2010 Bioclimatic predictions of
habitat suitability for the biofuel switchgrass in North America
under current and future climate scenarios Biomass Bioenergy
34 124–33
Cook J H, Beyea J and Keeler K H 1991 Potential impacts of
biomass production in the United-States on biological diversity
Annu. Rev. Energy Environ. 16 401–31
Cousens R 2008 Risk assessment of potential biofuel species: an
application for trait-based models for predicting weediness?
Weed Sci. 56 873–82
Faraway J J 2004 Linear Models with R (Boca Raton, FL: CRC
Press)
Fletcher R J, Acevedo M A, Reichert B E, Pias K E and
Kitchens W M 2011 Social network models predict movement
and connectivity in ecological landscapes Proc. Natl Acad. Sci.
USA 108 19282–7
Glaser A and Glick P 2012 Growing Risk: Addressing the Invasive
Potential of Bioenergy Feedstocks (Washington, DC: National
Wildlife Federation)
Hastie T and Tibshirani R J 1990 General Additive Models
(London: Chapman & Hall)
Hijmans R J, Cameron S E, Parra J L, Jones P G and Jarvis A 2005
Very high resolution interpolated climate surfaces for global
land areas Int. J. Climatol. 25 1965–78
Hijmans R J and Graham C H 2006 The ability of climate envelope
models to predict the effect of climate change on species
distributions Glob. Change Biol. 12 2272–81
Kearney M and Porter W P 2004 Mapping the fundamental niche:
physiology, climate, and the distribution of a nocturnal lizard
Ecology 85 3119–31
Lynd L R, Cushman J H, Nichols R J and Wyman C E 1991 Fuel
ethanol from cellulosic biomass Science 251 1318–23
Mack R N 2008 Evaluating the credits and debits of a proposed
biofuel species: giant reed (Arundo donax)Weed Sci. 56 883–8
McLaughlin S B and Walsh M E 1998 Evaluating environmental
consequences of producing herbaceous crops for bioenergy
Biomass Bioenergy 14 317–24
Midgley G F and Thuiller W 2005 Global environmental change
and the uncertain fate of biodiversity New Phytol. 167 638–41
Nakicenovic N, Alcamo J, Davis G, Vries B, Fenhann J and
Gaffin S 2000 Special Report on Emissions Scenarios: a
Special Report of Working Group III of the Intergovernmental
Panel on Climate Change (Cambridge: Cambridge University
Press) p 599
Nunes M F C, Galetti M, Marsden S, Pereira R S and
Peterson A T 2007 Are large-scale distributional shifts of the
blue-winged macaw (Primolius maracana) related to climate
change? J. Biogeogr. 34 816–27
5
Environ. Res. Lett. 7(2012) 045903 M G Tulbure et al
Pearson R G, Thuiller W, Araujo M B, Martinez-Meyer E,
Brotons L, McClean C, Miles L, Segurado P, Dawson T P and
Lees D C 2006 Model-based uncertainty in species range
prediction J. Biogeogr. 33 1704–11
Randall D A et al 2007 Climate models and their evaluation Climate
Change 2007 ed S Solomon, D Qin, M Manning, Z Chen,
M Marquis, K B Averyt, M Tignor and
H L Miller (Cambridge: Cambrige University Press)
R Development Core Team 2012 R: a language and environment for
statistical computing (Vienna: R Foundation for Statistical
Computing)
Searchinger T, Heimlich R, Houghton R A, Dong F X, Elobeid A,
Fabiosa J, Tokgoz S, Hayes D and Yu T H 2008 Use of US
croplands for biofuels increases greenhouse gases through
emissions from land-use change Science 319 1238–40
Simberloff D 2008 Invasion biologists and the biofuels boom:
Cassandras or colleagues? Weed Sci. 56 867–72
Thomas C D et al 2004 Extinction risk from climate change Nature
427 145–8
Thuiller W, Richardson D M, Pysek P, Midgley G F,
Hughes G O and Rouget M 2005 Niche-based modelling as a
tool for predicting the risk of alien plant invasions at a global
scale Glob. Change Biol. 11 2234–50
Tulbure M G, Wimberly M C, Boe A and Owens V N 2012
Climatic and genetic controls of yields of switchgrass, a model
bioenergy species Agric. Ecosyst. Environ. 146 121–9
US Congress 2007 Energy and Security Act of 2007
Williams J W and Jackson S T 2007 Novel climates, no-analog
communities, and ecological surprises Front. Ecol. Environ.
5475–82
Wood S N 2006 Generalized Additive Models: An Introduction with
R(Boca Raton, FL: Taylor & Francis)
Wood S N 2010 MGCV: Mixed GAM Computation Vehicle with
GCV/AIC/REML Smoothness Estimation R package version
1.7–13
6
... In general, the southern United States was predicted to have the largest decrease while the Great Plains were expected to experience large increases in biomass production by 2080s on the ecotype level (Behrman et al., 2013). An overall shift in areas of high switchgrass productivity was predicted for both upland and lowland ecotypes (Tulbure, Wimberly & Owens, 2012b). In general, regions with increases in future temperature and precipitation were expected to have increased biomass production. ...
... Relative stable biomass production between current and future climate conditions for each cultivar is consistent with previous projections that switchgrass biomass under climate change will be similar to current values for both lowland and upland ecotypes. However, the spatial distribution of maximum switchgrass biomass was predicted to shift considerably by Tulbure, Wimberly, and Owens (2012b). Further, previous studies focused largely on overall spatial predictions across large regions of USA with different GCMs and on the ecotype level (Tulbure, Wimberly, Boe, et al., 2012a;Behrman et al., 2013). ...
Article
Full-text available
Previous studies have shown that switchgrass has a wide range of genetic variation and that productivity is linked to local adaptation to the location of origin for many cultivars. In this meta‐analysis, we compiled and analyzed 900 observations associated with 41 field trials for four switchgrass cultivars (two lowlands, Alamo and Kanlow, and two uplands, Cave‐In‐Rock and Shelter). This extensive dataset and machine learning was used to identify the most influential variables impacting switchgrass productivity, to search for evidence of local adaptation to each cultivar's location of origin, and to predict change in productivity under future climate for each cultivar. In general, variables associated with climate and management are more important predictors of productivity relative to soil variables. Three climatic variables, annual mean temperature, annual precipitation, and precipitation in the wettest month, are identified as key environmental variables for productivity of all cultivars. Productivity under future climate (2041‐2060) is predicted to stay stable for all cultivars relative to the prediction under current climate (1986‐2005) across all trial locations and over a 20‐yr simulation period. However, the productivity of each cultivar varies from location to location and from year to year, although productivity varies more between locations than between years. Additionally, we observe shifts in the most productive cultivar at the local field scale depending on the combination of management practice and climates. The shape of the relationship between productivity and the annual mean temperature relative to the cultivar's location of origin is bell‐shape curve for Kanlow, Cave‐in‐Rock, and Shelter, indicative of local adaptation. Identifying influential environmental variables, and their relationships to productivity with respect to cultivar's location of origin helps predicting productivity on the local field scale, and will help with the biofuel production planning through the selection of suitable cultivars for different locations under climate changes. This article is protected by copyright. All rights reserved.
... The effect of climate change on the productivity of switchgrass upland cultivars used at the sites was predicted to be small. This is consistent with findings of a recent simulation study with the ALMANAC model (Kim et al., 2020) and climate envelope models for the Midwest (Tulbure, Wimberly, & Owens, 2012), though another study in Michigan found greater impacts on yields mainly associated with increased risk of water stress (Liu & Basso, 2017). In our simulations, the reductions in plant growth and soil C inputs (4%-6%) together with the increases in SOC decomposition (2%-11%) were predicted to decrease the rate of SOC gain by 70% below current rates under the most extreme climate change scenario ( Figure 5). ...
... Both of these are related to insufficient heat units available for nutrient relocation and senescence in the fall (Casler & Vogel, 2014;Parrish & Fike, 2005). Yet, with the milder winter temperatures and longer growing seasons projected ( Figures S4.1 and S4.2), areas in the USGLR could increasingly become suitable environments for lowland ecotypes in the coming decades, as shown in a previous modeling study (Tulbure et al., 2012). We must point out, however, that here genotype adaptation was simulated rather coarsely, that is, by increasing thermal time requirement for maturity by 30%, without considering other aspects of genotype adaptation such as response to daylength, winter survival, and nutrient relocation. ...
Article
Full-text available
The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (SALUS) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha‐1 yr‐1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2‐1.3 Mg C ha‐1 yr‐1). Principal component analysis revealed that the predicted inter‐site variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Though climate change impacts on switchgrass plant growth were predicted to be small (4‐6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha‐1 yr‐1 under projected climate. On average, this was 0.3 Mg C ha‐1 yr‐1 more C sequestered than the no‐adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.
... As the future global climatic change has forced modification in agricultural methods and applications, microalgal cultivation systems for biofuels production is also influenced by the prevailing environmental conditions. Therefore understanding the impact of biofuels production on climatic changes is vital in order to achieve a sustainable biofuel economy (Tulbure et al., 2012). Therefore this chapter is mainly focused on various biofuels production through conversion of algal biomasses. ...
Chapter
Algal biofuel production is one of the current renewable alternative and green energy solutions addressing the present global energy needs. Algal biofuels have been considered as the alternative clean energy carrier due to their environmentally friendly nature. In order to fulfill the energy demand, algal biomass could be biotransformed into several biofuels such as bioethanol, biobutanol, biogas, biohydrogen, and biodiesel using an integrated biorefinery approach. However, there are a number of challenges that hinder production, development, and application of microalgal biomass technology. These challenges range from development of low-cost microalgal production systems, effective microalgal growth systems, efficient and energy-saving microalgal harvesting methods, efficient microalgal extraction techniques, and microalgal conversion processes that are eco-friendly and cost-effective. This chapter explores several microalgal technologies, scopes, challenges, case studies, and opportunities.
... As the future global climatic change has forced modification in agricultural methods and applications, microalgal cultivation systems for biofuels production is also influenced by the prevailing environmental conditions. Therefore understanding the impact of biofuels production on climatic changes is vital in order to achieve a sustainable biofuel economy (Tulbure et al., 2012). Therefore this chapter is mainly focused on various biofuels production through conversion of algal biomasses. ...
Chapter
Global energy demand is expected to increase by 48% in the next 20 years owing to the precipitous increase in the global population. Currently, 80% of the energy demand is met by fossil fuels. However, rapidly depleting fossil fuel reserves coupled with the negative environmental impacts from its combustion has prompted significant interest in sustainable biofuels. This will aid in the transition toward a carbon-neutral bio-economy. Several feedstocks have been identified as possible substrates for biofuel production. Agricultural residues have shown significant potential since they are environmentally benign, abundant, and low cost. Nevertheless due to its structural complexity, an appropriate pretreatment is required to enhance enzymatic and microbial conversion. Currently, first-generation biofuels such as bioethanol do not require intensive pretreatments; however, the major drawback is the utilization of food crops, thus contributing to the food versus fuel debate. Additionally, greenhouse gas emissions associated with first-generation biofuels are another obstacle. Second-generation biofuels such as bioethanol, biohydrogen, and biomethane appear to be most promising owing to its bioconversion from waste material. A major bottleneck in this process is the requirement of costly pretreatments and subsequent effluent treatment. Third-generation biofuels such as bioethanol from microalgae also show potential since process optimization could significantly enhance yields. Fourth-generation biofuels aim to utilize genetically optimized feedstocks that are designed to enhance capture of carbon dioxide; however, carbon capture and sequestration technology has limited the commercialization of this process. Integrated biorefineries have the potential to produce several generations of biofuels in one process, thereby completing valorizing the feedstock and enhancing the life cycle and techno-economic assessment of the bioprocess.
... As the future global climatic change has forced modification in agricultural methods and applications, microalgal cultivation systems for biofuels production is also influenced by the prevailing environmental conditions. Therefore understanding the impact of biofuels production on climatic changes is vital in order to achieve a sustainable biofuel economy (Tulbure et al., 2012). Therefore this chapter is mainly focused on various biofuels production through conversion of algal biomasses. ...
Chapter
Recent advancements in the technology, sustainability, and applications of biodiesel from advanced and sustainable sources have been revolutionized. New and innovative thrusts of research priorities and approach in biodiesel development especially from wastes and nonedible feedstocks have given rise to the new concept of advanced and sustainable biodiesel fuels. These sources and alternatives to the conventional crop-based sources have to be friendly to the environment and generated essentially from waste materials by way of either recycling or reprocessing of feedstock viciously as energy. Advanced biodiesel fuels have become useful in bridging the alternative energy sources gap as they are from different feedstocks with variations such as from waste plastics, waste cooking oils, from microalgae, waste residue of oil processing, glycerol after transesterification, alcohols and including biomass materials. An important research thrust is the hybridization of biodiesel feedstocks to produce new products with new properties. This chapter reviews biodiesel feedstocks, development of first-, second-, and third-generation biodiesel, the new concept of advanced and sustainable biodiesel fuels, and feedstock hybridization. Additionally, case studies, biodiesel production technologies and processes, and novel bioprocessing technologies employing metabolic engineering and biotechnology are explored. Further expose was presented in research and development needs on properties, novel approaches, production processes, and relevant outcomes, while also addressing future development and prospects in the new and gray untapped areas.
... Introduction Perennial switchgrass (SG: Panicum virgatum L) and gamagrass (GG: Tripsacum dactyloides L) are two important bioenergy crops that are common alternative energy sources for sustainable replacement of fossil fuels [1][2][3]. Added together with other cellulosic biofuel crops, these dedicated energy crops will contribute to more than 30% of biofuel plant biomass in the coming decades [2,4]. N fertilizers generally increase bioenergy crop yields [5,6], but many studies report highly varied magnitudes and signals of soil C and N contents in response to N fertilization [7][8][9][10]. ...
Article
Full-text available
Nitrogen (N) fertilization affects bioenergy crop growth and productivity and consequently carbon (C) and N contents in soil, it however remains unclear whether N fertilization and crop type individually or interactively influence soil organic carbon (SOC) and total N (TN). In a three-year long fertilization experiment in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) croplands in Middle Tennessee USA, soil samples (0–15cm) were collected in plots with no N input (NN), low N input (LN: 84 kg N ha⁻¹ yr⁻¹ in urea) and high N input (HN: 168 kg N ha⁻¹ yr⁻¹ in urea). Besides SOC and TN, the aboveground plant biomass was also quantified. In addition to a summary of published root morphology data based on a separated mesocosm experiment, the root leachable dissolved organic matter (DOM) of both crops was also measured using archived samples. Results showed no significant interaction of N fertilization and crop type on SOC, TN or plant aboveground biomass (ABG). Relative to NN, HN (not LN) significantly increased SOC and TN in both crops. Though SG showed a 15–68% significantly higher ABG than GG, GG showed a 9.3–12% significantly higher SOC and TN than SG. The positive linear relationships of SOC or TN with ABG were identified for SG. However, GG showed structurally more complex and less readily decomposed root DOM, a larger root volume, total root length and surface area than SG. Collectively, these suggested that intensive N fertilization could increase C and N stocks in bioenergy cropland soils but these effects may be more likely mediated by the aboveground biomass in SG and root chemistry and morphology in GG. Future studies are expected to examine the root characteristics in different bioenergy croplands under the field fertilization experiment.
... Unlike the first-generation feedstocks (e.g. corn and sugarcane), cellulosic biofuel has not been fully utilized at a commercial scale due to high feedstock cost resulting from the uncertainty of biomass production [12,15,18]. The reliance of biorefineries on local areas of feedstock production is affected by year to year variability in local climate conditions [44,45]. Moreover, transportation costs for supplying sufficient amounts of biomass feedstock to a biorefinery facility are still high, which is a major obstacle to creating an economically sustainable biofuel production system [9,52]. ...
Article
Full-text available
This study presents a two-phase simulation-based framework for finding the optimal locations of biomass storage facilities that is a very critical link on the biomass supply chain, which can help to solve biorefinery concerns (e.g. steady supply, uniform feedstock properties, stable feedstock costs, and low transportation cost). The proposed framework consists of two simulation phases: (1) crop yield estimation using a process-based model such as Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) and (2) biomass transportation cost estimation using agent-based simulation (ABS) such as AnyLogic® with geographic information system (GIS). The OptQuest® in AnyLogic is used as an optimization engine to find the best locations of biomass storage facilities based on evaluation results given by the two-phase simulation framework. In addition, network partitioning and integer linear programming techniques are used to mitigate computation demand of the optimization problem. Since the proposed hybrid simulation approach utilizes realistic biofuel feedstock production and considers dynamics of supply chain activities, it is able to provide reliable locations of biomass storage facilities for operational excellence of a biomass supply chain.
... Bioenergy crops are important alternative technology for sustainable replacement of fossil fuels 1,2 . A significant portion (over 30%) of biofuel plant biomass will come from dedicated energy crops such as the perennial switchgrass (Panicum virgatum) and gamagrass (Tripsacum dactyloides L) 3,4 . ...
Article
Full-text available
The effects of intensive nitrogen (N) fertilizations on spatial distributions of soil microbes in bioenergy croplands remain unknown. To quantify N fertilization effect on spatial heterogeneity of soil microbial biomass carbon (MBC) and N (MBN), we sampled top mineral horizon soils (0-15 cm) using a spatially explicit design within two 15-m2 plots under three fertilization treatments in two bioenergy croplands in a three-year long fertilization experiment in Middle Tennessee, USA. The three fertilization treatments were no N input (NN), low N input (LN: 84 kg N ha-1 in urea) and high N input (HN: 168 kg N ha-1 in urea). The two crops were switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.). Results showed that N fertilizations little altered central tendencies of microbial variables but relative to LN, HN significantly increased MBC and MBC:MBN (GG only). HN possessed the greatest within-plot variances except for MBN (GG only). Spatial patterns were generally evident under HN and LN plots and much less so under NN plots. Substantially contrasting spatial variations were also identified between croplands (GG > SG) and among variables (MBN, MBC:MBN > MBC). This study demonstrated that spatial heterogeneity is elevated in microbial biomass of fertilized soils likely by uneven fertilizer application in bioenergy crops.
Article
Soil moisture, pH, dissolved organic carbon and nitrogen (DOC, DON) are important soil biogeochemical properties in switchgrass (SG) and gamagrass (GG) croplands. Yet their spatiotemporal patterns under nitrogen (N) fertilization have not been studied. The objective of this study is to investigate the main and interactive effects of N fertilization and bioenergy crop type on central tendencies and spatial heterogeneity of soil moisture, pH, DOC and DON. Based on a 3‐year long fertilization experiment in Middle Tennessee, USA, 288 samples of top horizon soils (0–15 cm) under three fertilization treatments in SG and GG croplands were collected. The fertilization treatments were no N input (NN), low N input (LN: 84 kg N ha−1 in urea) and high N input (HN: 168 kg N ha−1 in urea). Soil moisture, pH, DOC and DON were quantified. And their within‐plot variations and spatial distributions were achieved via descriptive and geostatistical methods. Relative to NN, LN significantly increased DOC content in SG cropland. LN also elevated within‐plot spatial heterogeneity of soil moisture, pH, DOC and DON in both croplands though GG showed more evident spatial heterogeneity than SG. Despite the pronounced patterns described above, great plot to plot variations were also revealed in each treatment. This study informs the generally low sensitivity of spatiotemporal responses in soil biogeochemical features to fertilizer amendments in bioenergy croplands. However, the significantly positive responses of DOC under low fertilizer input informed the best practice of optimizing agricultural nutrient amendment.
Article
Full-text available
Switchgrass is a promising bioenergy feedstock, but industrial-scale production may lead to negative environmental effects. This study considers one such potential consequence: the life cycle monetized damages to human health from air pollution. We estimate increases in mortality from long-term exposure to fine particulate matter (PM2.5), which is emitted directly ("primary PM2.5") and forms in the atmosphere ("secondary PM2.5") from precursors of nitrogen oxides (NOx), sulfur oxides (SOx), ammonia (NH3), and volatile organic compounds (VOCs). Changes in atmospheric concentrations of PM2.5 (primary + secondary) from on-site production and supporting supply chain activities are considered at 2694 locations (counties in the Central and Eastern US), for two biomass yields (9 and 20 Mg ha⁻¹), three nitrogen fertilizer rates (50, 100, and 150 kg ha⁻¹), and two nitrogen fertilizer types (urea and urea ammonium nitrate). Results indicate that on-site processes dominate life-cycle emissions of NH3, NOx, primary PM2.5, and VOCs, whereas SOx is primarily emitted in upstream supply chain processes. Total air quality impacts of switchgrass production, which are dominated by NH3 emissions from fertilizer application, range widely depending on location, from 2 to 553 $ Mg⁻¹ (mean: 45) of dry switchgrass at a biomass yield of 20 Mg ha⁻¹ and fertilizer application of 100 kg ha⁻¹ N applied as urea. Switching to urea ammonium nitrate solution lowers damages to 2 to 329 $ Mg⁻¹ (mean: 28). This work points to human health damage from air pollution as a potentially large social cost from switchgrass production and suggests means of mitigating that impact via strategic geographical deployment and management. Furthermore, by distinguishing the origin of atmospheric emissions, this paper advances the current emerging literature on ecosystem services and disservices from agricultural and bioenergy systems.
Article
Full-text available
Most prior studies have found that substituting biofuels for gasoline will reduce greenhouse gases because biofuels sequester carbon through the growth of the feedstock. These analyses have failed to count the carbon emissions that occur as farmers worldwide respond to higher prices and convert forest and grassland to new cropland to replace the grain (or cropland) diverted to biofuels. By using a worldwide agricultural model to estimate emissions from land-use change, we found that corn-based ethanol, instead of producing a 20% savings, nearly doubles greenhouse emissions over 30 years and increases greenhouse gases for 167 years. Biofuels from switchgrass, if grown on U.S. corn lands, increase emissions by 50%. This result raises concerns about large biofuel mandates and highlights the value of using waste products.
Article
Full-text available
Aim To evaluate whether observed geographical shifts in the distribution of the blue-winged macaw (Primolius maracana) are related to ongoing processes of global climate change. This species is vulnerable to extinction and has shown striking range retractions in recent decades, withdrawing broadly from southern portions of its historical distribution. Its range reduction has generally been attributed to the effects of habitat loss; however, as this species has also disappeared from large forested areas, consideration of other factors that may act in concert is merited.
Book
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.
Article
Predicting the probability of successful establishment of plant species by matching climatic variables has considerable potential for incorporation in early warning systems for the management of biological invasions. We select South Africa as a model source area of invasions worldwide because it is an important exporter of plant species to other parts of the world because of the huge international demand for indigenous flora from this biodiversity hotspot. We first mapped the five ecoregions that occur both in South Africa and other parts of the world, but the very coarse definition of the ecoregions led to unreliable results in terms of predicting invasible areas. We then determined the bioclimatic features of South Africa's major terrestrial biomes and projected the potential distribution of analogous areas throughout the world. This approach is much more powerful, but depends strongly on how particular biomes are defined in donor countries. Finally, we developed bioclimatic niche models for 96 plant taxa (species and subspecies) endemic to South Africa and invasive elsewhere, and projected these globally after successfully evaluating model projections specifically for three well-known invasive species (Carpobrotus edulis, Senecio glastifolius, Vellereophyton dealbatum) in different target areas. Cumulative probabilities of climatic suitability show that high-risk regions are spatially limited globally but that these closely match hotspots of plant biodiversity. These probabilities are significantly correlated with the number of recorded invasive species from South Africa in natural areas, emphasizing the pivotal role of climate in defining invasion potential. Accounting for potential transfer vectors (trade and tourism) significantly adds to the explanatory power of climate suitability as an index of invasibility. The close match that we found between the climatic component of the ecological habitat suitability and the current pattern of occurrence of South Africa alien species in other parts of the world is encouraging. If species' distribution data in the donor country are available, climatic niche modelling offers a powerful tool for efficient and unbiased first-step screening. Given that eradication of an established invasive species is extremely difficult and expensive, areas identified as potential new sites should be monitored and quarantine measures should be adopted.
Article
Dedicated biofuel crops, while providing economic and other benefits, may adversely impact biodiversity directly via land use conversion, or indirectly via creation of novel invasive species. To mitigate negative impacts bioclimatic envelope models (BEM) can be used to estimate the potential distribution and suitable habitat based on the climate and distribution in the native range. We used CLIMEX to evaluate the regions of North America suitable for agronomic production, as well as regions potentially susceptible to an invasion of switchgrass (Panicum virgatum) under both current and future climate scenarios. Model results show that >8.7 millionkm2 of North America has suitable to very favorable habitat, most of which occurs east of the Rocky Mountains. The non-native range of western North America is largely unsuitable to switchgrass as a crop or potential weed unless irrigation or permanent water is available. Under both the CGCM2 and HadCM3 climate models and A2 and B2 emissions scenarios, an overall increase in suitable habitat is predicted over the coming century, although the western US remains unsuitable. Our results suggest that much of North America is suitable for switchgrass cultivation, although this is likely to shift north in the coming century. Our results also agree with field collections of switchgrass outside its native range, which indicate that switchgrass is unlikely to establish unless it has access to water throughout the year (e.g., along a stream). Thus, it is the potential invasion of switchgrass into riparian habitats in the West that requires further investigation.
Conference Paper
The environmental costs and benefits of producing bioenergy crops can be measured both in kterms of the relative effects on soil, water, and wildlife habitat quality of replacing alternate cropping systems with the designated bioenergy system, and in terms of the quality and amount of energy that is produced per unit of energy expended. While many forms of herbaceous and woody energy crops will likely contribute to future biofuels systems, The Dept. of Energy`s Biofuels Feedstock Development Program (BFDP), has chosen to focus its primary herbaceous crops research emphasis on a perennial grass species, switchgrass (Panicum virgatum), as a bioenergy candidate. This choice was based on its high yields, high nutrient use efficiency, and wide geographic distribution, and also on its poistive environmental attributes. The latter include its positive effects on soil quality and stabiity, its cover value for wildlife, and the lower inputs of enerty, water, and agrochemicals required per unit of energy produced. A comparison of the energy budgets for corn, which is the primary current source of bioethanol, and switchgrass reveals that the efficiency of energy production for a perennial grass system can exceed that for an energy intensive annual row crop by as much as 15 times. In additions reductions in CO{sub 2} emission, tied to the energetic efficiency of producing transportation fuels, are very efficient with grasses. Calculated carbon sequestration rates may exceed those of annual crops by as much as 20--30 times, due in part to carbon storage in the soil. These differences have major implications for both the rate and efficiency with which fossil energy sources can be replaced with cleaner burning biofuels.