This study examined the effects of water availability and genotype on the decomposition rate of switchgrass litter. Six genotypes of switchgrass representing different ploidy types and geographic origins were exposed to three different rainfall treatments (extreme drought, mean rainfall, extreme wet) over four months using the litterbag method. The results showed that decomposition rate generally increased with more rainfall availability but this effect depended on date. About a third of the variation in decomposition rate was explained by changes in maximum temperature from July to November. No significant effects of genotype or ploidy were found, suggesting that differences in litter chemistry among these genotypes were not large enough to impact decomposition rates. Overall climate factors like rainfall and temperature had a stronger influence on decomposition than litter chemistry traits

1. Water Availability Effects on the Decomposition of
Six Genotypes of Panicum virgatum
Megan Archer, Dr. Christine Hawkes
The University of Texas at Austin
Abstract
Decomposition of plant-derived litter is a critical ecological process essential for nutrient
cycling, particularly because it returns carbon fixed by plants during photosynthesis back to the
atmosphere. Litter chemistry strongly regulates decomposition and soil organic matter dynamics
at local scales, but decomposition is predominantly controlled at a global scale by climate
rainfall and temperature. Any changes in how litter chemistry interacts with climate could lead to
a change in soil carbon balance, with more or less carbon stored vs. released to the atmosphere
via microbial decomposition and respiration. To examine the potential interaction of litter
chemistry and rainfall, which is expected to change in the future. We focused on six genotypes of
Panicum virgatum, using a common garden approach. Mass loss rate generally increased with
more rainfall, but this depended on date. About a third of the observed variation in mass loss rate
was due to changes in ambient maximum temperature from July to November. We found no
effects of litter genotype or ploidy, suggesting that litter chemistry is not as important as rainfall,
or that the genotypes were too similar in litter chemistry to detect differences. For practical
applications, such as understanding the carbon cycle impact of P. virgatum biofuels production
under altered climate, traits other than decomposition must be considered.
Keywords: Decomposition, litter, climate change, terrestrial carbon levels,
genotype, switchgrass (Panicum virgatum), ANOVA, regression

2. Introduction
Decomposition of organic matter is a natural process essential for nutrient cycling (Davies et al.,
2013) and plant-derived litter is a fundamental process in terrestrial carbon cycling (Graca et al.,
2005). The rate of decomposition is predominantly controlled at a global scale by climate rainfall
and temperature, which are likely to face critical changes as climate change continues to become
more extreme (Davies et al., 2013). As temperatures increase, it is likely we would see an
increase in decomposition as long as moisture was not limited. However, if drought was more
prevalent we might see less decomposition, and therefore an increase in the carbon storage of
grasslands (Hartman et al., 2011). Changes in plant biodiversity can also alter the decomposition
process, because leaf litter varies tremendously in chemical composition - i.e. some types of litter
are nutrient-rich, whereas others are nutrient-poor or contain high concentrations of organic
compounds such as lignin that are resistant to degradation (Gessner et al., 2010). Such
differences may also be important within species, as different genotypes can vary substantially in
litter traits. For example, genotypic variation can cause differences in litter decomposition as
genotypes can produce tissues that vary in leaf toughness, nutrient concentration, lignin
concentration, or susceptibility to leaf-modifying arthropods (Genung et al., 2013). Plant
monocultures consist of many different plant genotypes whose litter also decomposes in mixtures
in the field, and possible effects of intraspecific variation in plant litter on decomposition
processes are only starting to be assessed (Schweitzer et al., 2014). For example, Schweitzer et
al. (2014) found that changes in decomposition rates in genotype litter mixes were nearly as large
as differences observed at the plant species level, with a varying 16% mass loss at the end of
their experiment.

3. There is a triangular relationship between climate, leaf litter chemistry, and leaf litter
decomposition. At a global climate scale, climate is the best predictor for decomposition
constants (k-values) of litter. There are three main levels of litter decomposition control that
operate in the following order: climate > litter chemistry > soil organisms. However, although
climate has a direct effect on litter decomposition due to the effects of temperature and moisture,
it has an indirect effect through the climatic impact on litter chemistry as a result of its control on
soil formation and nutrient cycling (Aerts, 1997). As a response to the variation in climate, plant
population genetics structure is changing, and has changed in the past. These adaptive responses
of plants to climate change are intimately coupled with soil communities and soil function
(Fischer et al., 2013), affecting decomposition.
Here, I focused on switchgrass (Panicum virgatum) decomposition because it has multiple
genotypes with different litter chemistry found across a wide climate range. Switchgrass is an
abundant, warm season perennial grass adapted to various habitats across North America and can
grow on marginal lands with high dry biomass yields under low fertility conditions (Kim et al.,
2011). Switchgrass also has the ability to increase soil quality and sequester carbon (Hartman et
al., 2011). All of these qualities make it a promising biofuel energy crop (Kim et al, 2011). There
are many characteristics of switchgrass that affect biomass yield and composition, some of which
include: genotype (lowland vs. upland), ecotype (southern vs. northern), harvest time,
precipitation, and other environmental and cultivation conditions (Kim et al., 2011). Across its
native geographic range, switchgrass, lowland ecotypes are vigorous, tall, thick-stemmed,
adapted to wet conditions, and are predominantly tetraploid; upland ecotypes are short,
rhizomatous, thin-stemmed, adapted to drier conditions, and are typically hexaploid or octoploid
(Lemus et al., 2002).

4. In this study, I addressed how the decomposition rate of switchgrass genotypes was affected by
changing rainfall. I hypothesized that the overall rate of decomposition would be directly
proportional to water availability, and thus negatively affected by drought. An upper limit on the
positive effect of water could also occur under anaerobic conditions, but this was not expected in
the current experimental conditions. I also expected that plants with higher tissue quality would
decompose more regardless of water availability. To test these ideas, I exposed litter from six
genotypes of switchgrass to three levels of rainfall (extreme wet, mean rainfall, and extreme
drought) over four months. I selected three lowland (southern ecotypes) and three upland
(northern ecotypes) genotypes. The lowland genotypes should decompose more slowly because
of differences in tissue quality and morphological characteristics and morphological
characteristics.
Methods
Study Site and Experimental Design
This experiment leveraged an existing rainfall experiment at the Lady Bird Johnson Wildflower
Center where natural rainfall is excluded from 24 5 x 5 m plots arranged into four blocks using a
rainout shelter. Each block is divided into six plots, with rainfall treatments applied at the plot
level with irrigation sprinklers. For this study, I used three rainfall treatments - low rainfall
(extreme drought, 326 mm yr-1), mean rainfall (850 mm yr-1), and high rainfall (extreme wet,
1331 mm yr-1). Twelve sets of six genotypes of Panicum virgatum were placed randomly into the
areas of the four experimental plots containing the rainfall treatments of interest. Decomposition
rates of the six genotypes were determined using the litterbag method. The full experimental

5. design was 4 blocks X 6 genotypes (litter) X 3 rainfall treatments X 4 collection dates, for a total
of 288 bags (48 bags per genotype).
Plant Material
This study included six different Panicum virgatum genotypes representing different climate
origins, ploidy types, and a range of leaf characteristics (Aspinwall et al., 2013). As stated by
Aspinwall, the term ‘genotype’ signifies that these individuals originated from vegetative
propagation of a single plant and are representatives of the gene pool present in their locale. Use
of these genotypes will allow us to consider broader patterns of genetic variation in relation to
climate, rather than within-population variation (Aspinwall et al., 2013). The genotypes used
included the following: WBC, NAS, AP13, WWF, ENC, and Summer (Table 1). WBC, ENC
and WWF genotypes were propagated from wild collections. NAS is an upland genotype from
northern Texas that has been used for land reclamation in the dry west. The AP13 genotype is an
extension of cv Alamo, a lowland ecotype. AP13, Summer and WBC were tetraploids; NAS,
ENC and WWF were octoploids. (Aspinwall et al., 2013)

6. Table 1. Ploidy, geographic origin, and historical climate data for the nine Panicumvirgatum genotypes
included in this study. Modified from Aspinwall et al. (2013).
Variable† NAS WBC AP13 WWF ENC
Ploidy 8x 4x 4x 8x 8x
Lat. (°N) 33.1 30.1 28.3 28.1 26.9
Long. (°W) 96.1 98.0 98.1 97.4 98.1
MAP 1110 855 850 903 646
MAT 17.2 20.3 21.2 21.2 22.3
*Climate data (1971-2000) is from the National Oceanic and Atmospheric Administration (NOAA) weather
station closest to the genotype’s geographic origin. †MAP, mean annual precipitation (mm); MAT, mean annual
temperature (°C);
Carbon (C) and nitrogen (N) levels for six
switchgrass genotypes are presented in Figure 1
(Hawkes, unpublished data). There is a gradient in
leaf litter (C:N) among the four genotypes
measured that were included in this study (Summer,
AP13, NAS and WBC). We do not have C:N data
on the other two genotypes, but expect them to fall
within this range given the large latitudinal, size,
and ecological differences among the genotype.
Figure 1. Carbon (C) and Nitrogen (N) levels for
switgrass (Panicum virgatum) genotypes.

7. Litter Bags
Decomposition within terrestrial ecosystems is commonly studied using the litterbag method,
which consists of enclosing plant material of a known mass and chemical composition within a
screened container. Although this method may underestimate actual decomposition, it is assumed
that the results of litterbag studies will reflect trends characteristics of unconfined decomposing
litter, and as such follows for comparisons of sites and experimental manipulations (Wieder and
Lang, 1982).
Switchgrass leaf litter collected in 2011 was cut into approximately 8-cm long pieces and then
dried for at least 24 hours. Litter was then weighed to the nearest gram and placed into
previously weighed fiberglass window screen mesh bags consisting of three soldered sides and
one folded side, and 10 x 10 cm external dimensions. Mesh bag and litter were then weighed
together, labeled using metal tags and aluminum wire, and welded shut. A total of 48 litterbags
were created for each genotype, resulting in 288 bags: this was enough to have 4 collection dates
(4 replicates) for each genotype in the experiment.
For each replicate, one litterbag per genotype was chosen randomly and tied to a flag in a net-
like fashion using approximately 12 cm long pieces of fluorescent yellow string to allow for easy
recovery. Each flag was tagged using a permanent marker and placed into a rainfall plot - 4 flags
x 6 genotypes x 3 rainfall treatments/plots x 4 blocks. Litterbags were placed in the field rain
treatments on the morning of July 7th. After placement, each bag was covered with a thin layer
of soil. Collection dates were 2.5 months (September 12th), 3 months (October 3rd), 3.5 months
(November 3rd), and 4 months (November 19th). After collection, adhered debris was removed
from the outside of the litterbags before being placed into plastic sandwich bags to be transported

8. back to the lab. Once transported, bags were immediately placed into a refrigerator to prevent
further decomposition. Litter was then carefully removed from the interior of the litter bags and
placed onto pre-weighed aluminum foil, weighed, labeled/numbered, and placed into an oven at
65 – 70 °C (Gingerich and Anderson, 2011).
After 2 – 3 days, the leaf litter was removed from the oven and any excess, non-litter material
was carefully removed using gloves. After being reweighed, the leaf litter was rewrapped and
placed in the heater for another 2-3 days until a constant mass was, after which a final dry weight
was recorded reached (Gingerich and Anderson, 2011). In order to determine the amount of
decomposition, the following was recorded from the samples: collection date, genotype, rainfall
treatment, initial litter weight (g), final litter weight (g), and mass loss (g). We calculated mass
loss rate (g day-1) as a standardized means of decomposition across the collection dates.
Statistical Analysis
To examine how mass loss rate was affected by the experimental factors, we used a univariate
ANOVA with ploidy, genotype nested in ploidy, rainfall treatment, date, and interactions of the
non-nested factors. Block was also included as a random factor. Posthoc Ryan-Einot-Gabriel-
Welsch F tests were used for significant main effects (P < 0.05). When interaction terms were
significant, the data were broken down by each factor and analyzed with one-way ANOVA.
In addition, to examine ambient weather conditions not captured by the ANOVA, we used
multiple stepwise regression with mass loss rate as the dependent variable and the independent
variables consisting of the following: rain applied since placement on July 7th; and the mean,
maximum, and minimum temperatures since date of placement. Temperatures were taken from

9. the Lady Bird Johnson Wildflower Center weather station located adjacent to the experimental
plots. Rain data were based on the experimental treatments used for this study. All statistics were
done in IBM SPSS v.21.
Results
Mass loss rate was affected by rainfall treatment (P= 0.032), date (P=0.001), and the interaction
of rainfall treatment with date (P= 0.002). In general, decomposition was faster under wetter
conditions and this pattern was strongest at the third harvest in October (Figure 2). However,
overall the decomposition rate was fastest at the fourth harvest in November. There was also
some spatial variation across blocks, but no effect of ploidy or genotype. Ambient maximum
temperature also affected decomposition, explaining 36% of the variation in mass loss rate with
increasing decomposition as the season progressed and temperatures cooled (R2 = 0.356, P
<0.001; Figure 3). (Appendix)
Figure 2. Mass loss
rate (mean ± 1 SE) under
three rainfall treatment
levels (low, mean, high)
across 4 collection dates.
Figure 3. Mass loss rate
under max temperature
values (61.1, 84.6, 87.6,
0
0.005
0.01
0.015
0.02
0.025
0.03
Low Mean High
AvgMassLossRate(gday)
Rainfall Treatment
7/7 - 9/12
9/12 - 10/3
10/3 - 11/3
11/3 - 11/19

10. 98.5) across 4 collection dates.
Discussion
Plant productivity depends largely on the recycling of nutrients brought about by litter
decomposition. Two of the most important factors influencing plant decomposition are
temperature and water availability (Aerts, 1997), but recent studies have shown that
microenvironment, litter chemical composition, and the decomposer community also influence
leaf litter decay (Gartner et al., 2004). Results of this experiment support climatic control of
decomposition. Mass loss rate was 1.7x greater on average, and up to 3x greater in high vs. low
rainfall treatments. In addition, the rate of mass loss changed across dates, with the greatest mass
loss rate occurring at the fourth collection date, likely reflecting the change in temperature over
time. There was not enough evidence to show a difference in decomposition among genotypes.
Similar to our findings, Wieder et al. (2009), observed ~20% less decomposition when
precipitation was reduced in wet tropical forest. Increased water typically results in more
0.00000000000
0.00400000000
0.00800000000
55.0 60.0 65.0 70.0 75.0 80.0 85.0 90.0 95.0 100.0
MassLossRate(gday-1)
Temperature (F)

11. decomposition, unless anoxia slows microbial activity and nutrient mineralization (Schurr 2011).
To detect these effects, it is important to account for temperature as well (Crohn, 2003),
otherwise strong temperature effects on decomposition rates may obscure other patterns.
Although effects of genetic variation in plants on ecosystem-level processes are beginning to be
recognized, we found no evidence of differences in decomposition among six genotypes or two
ploidy levels. In other systems, reductions in leaf litter quality among genotypes decelerated
decomposition by up to 40% (Schweitzer et al., 2005). It is possible that in the current study the
genotypes did not vary sufficiently in aspects of litter chemistry important for decomposition,
such as lignin content (Garner et al., 2004). In addition, four months may have been too short a
time period to detect differences among genotypes, particularly if the litters varied in recalcitrant
compounds (Fischer et al., 2013). Other facts may have influenced the results in ways that might
obscure biological patterns. For example, small animal and people disturbances may have
uncovered some litterbags, positioning of litter bags may have made them more or less exposed
to the rain treatments, and some litterbags may have been contaminated with soil particles that
altered the mass loss calculations. Additional studies would be required to confirm the lack of
genotype effects on switchgrass decomposition.
Switchgrass has been chosen as the herbaceous model for the Biofuels Feedstock Development
Program (BFDP) based on several factors: (i) broad species adaptation, (ii) relatively high
biomass yields, (iii) high tolerance to marginal conditions, such as low fertility and drought, (iv)
the ease and simplicity of seed processing and handling (Casler et al., 2011). Because of these
factors, it is essential to provide further insight into the functionality of switchgrass, especially as

12. the world’s climate continues to change, in order to better understand the future viability of and
uses for switchgrass. This study is helpful in understanding the basic link between genotype,
rainfalll, and decomposition of switchgrass, and could be incorporated into future experiments
pertaining to terrestrial C storage.
Results from this study may also be useful in considering climate change impacts on
decomposition of other C4 grasses. Switchgrass is widespread, from Canada to Mexico and from
the Rocky Mountains to the Atlantic Ocean (Casler et al., 2011), where it experiences a wide
variety of environmental conditions. Therefore, switchgrass could be used as a model plant for
studies on the effects of changing climates on North American grasslands. Ultimately, the effects
of global climate change on soil C storage will depend on its effects on decomposition
(Trofymow et al., 2002). Increased temperature could accelterate the release of C if moisture is
sufficient, which would lead to a potential decrease in C stored in soils. In contrast, an increase
in soil C levels is largely a result of restricted decomposition rates, which could result from
drought effects. Thus, understanding decomposition response to climate change is necessary for
predicting ecosystem C storage in the future.

13. Appendix
Table 2. Univariate ANOVA results. Fixed effects: ploidy, Genotype nested in Ploidy, Rain, Date. Random effect:
Block. Model is a full factorial of fixed effects, but no block interactions were included. Statistically significant
independent variables include Date (<0.001) and the interaciton between Rainfall Treatment*Date (p < 0.002).
Source df Mean Square F P
Ploidy 1 4.50E-05 0.808 0.369
Genotype(Ploidy) 4 1.00E-05 0.18 0.949
Rainfall Treatment 2 0 3.474 0.032
Block 3 0 3.002 0.031
Date 3 0.004 75.184 <0.001
Ploidy*RainfallTreatment 2 5.52E-07 0.01 0.990
Ploidy*Date 3 3.18E-05 0.57 0.635
RainfallTreatment*Date 6 0 3.641 0.002
Ploidy*RainfallTreatment*Date 6 4.65E-07 0.008 1
Table 3. Multiple Stepwise Regression. Dependent variable: masslossrate.Independent variables: RainApplied,
MeanTemp, MaxTemp, MinTemp. Only MaxTemp was included in the model explaining 36% of the variation in
mass loss rate.
Model R R2 Adjusted R2
SE of the
Estimate
1* 0.598 0.358 0.356 0.008092116
*Predictors: (Constant), MaxTemp
ANOVA** df Mean Square F P
Regression 1 0.01 159.31 <0.001
Residual 286 0
**Dependent Variable: MassLossRate; Predictors: (Constant), MaxTemp
Coefficients***
Unstandardized
Coefficients
Standardized
Coefficients
B SE Beta t P
(Constant) 0.041 0.003 14.163 <0.001
MaxTemp 0 0 -0.598 -12.622 <0.001
***Dependent Variable: MassLossRate

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