Seasonal growth patterns of Arundo donax L. in the United States | IJAAR @slideshare

Int. J. Agron. Agri. R.
International Journal of Agronomy and Agricultural Research (IJAAR)
ISSN: 2223-7054 (Print) 2225-3610 (Online)
http://www.innspub.net
Vol. 16, No. 3, p. 1-11, 2020
RESEARCH PAPER OPEN ACCESS
Seasonal growth patterns of Arundo donax L. in the United
States
Ping Huang*, David I Bransby
Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, Alabama, USA
Article published on March 12, 2020
Key words: Giant reed, Cellulosic energy crop, Plant height and aboveground biomass yield,
Growth analysis and Gompertz function, Piecewise regression analysis, SAS PROC NLIN procedure
Abstract
Giant reed (Arundo donax L.) has been extensively evaluated as a dedicated energy crop for biomass and biofuel
production in southern Europe and the United States, with very favorable results. Current agronomic and biologic
research on giant reed focuses on management practices, development of new cultivars, and determining
differences among existing cultivars. Even though detailed information on the growth patterns of giant reed
would assist in development of improved management practices, this information is not available in the United
States. Therefore, the objective of this 2-year field study was to describe the seasonal growth patterns of giant
reed in Alabama, United States. Changes in both plant height and biomass yield of giant reed with time were well
described by a Gompertz function. The fastest growing period occurred at approximately 66 d after initiation of
regrowth (mid-May), when the absolute maximum growth rate was of 0.045 m d-1 and 0.516mg ha-1 d-1. After
mid-May, the rate of growth decreased until maturation at approximately 200 d after initiation of regrowth (mid-
to late September). The observed maximum average plant height and biomass yield were 5.28 m and 48.56mg ha-
1, respectively. Yield decreased following maturation up to 278 d after initiation (early to mid-December) of
growth in spring, partly as a result of leaf loss, and was relatively stable thereafter.
* Corresponding Author: Ping Huangpzh0001@auburn.edu
Huang and Bransby Page 1

Introduction
Giant reed (Arundo donax L.) is a perennial
rhizomatous C3 grass native to East Asia which is
grown in both grasslands and wetlands, and is
especially well adapted to Mediterranean
environments (Polunin and Huxley, 1987). Since
giant reed is sterile, it is propagated vegetatively,
either from stem cuttings or rhizome pieces, or by
means of micro-propagation. Due to its easy
adaptability to different environment conditions and
rapid growth with little or no fertilizer and pesticide
inputs, giant reed has been extensively evaluated as a
dedicated cellulosic energy crop for biomass and
biofuel production in southern Europe and the United
States, with very favorable results (Vecchiet et al.,
1996; Merlo et al., 1998; Hidalgo and Fernandez,
2000; Lewandowski et al., 2003; Odero et al., 2011;
Huang et al., 2014; Nocentini et al., 2018; Monti et
al., 2019). Most perennial grasses have poor yields
during the year of establishment, but giant reed is an
exception: a first-year yield of over 16mg ha-1 was
reported by Angelini et al. (2005) at a planting
density of 20,000 plants ha-1. Biomass yields are
typically 20-40mg ha-1 year-1 without any fertilization
after establishment (Angelini et al., 2005; Cosentino
et al., 2005; Angelini et al., 2009). Calorific value of
mature giant reed biomass is about 17 MJkg-1
(Angelini et al., 2005). The average energy input is
approximately 2% of the average energy output over a
12-year period (Angelini et al., 2009). Unlike most
other grasses, giant reed possesses a lignin content of
25%, which is similar to that of wood, and a cellulose
content of 42% and a hemicellulose content of 19%,
making it a desirable cellulosic energy crop for both
solid and liquid biofuels production (Faix et al., 1989;
Scordia et al., 2012; Lemons et al., 2015). Giant reed
can also help mitigate carbon dioxide (CO2) emissions
from fossil fuels because rhizomes sequester carbon
into the soil. The reported carbon (C) sequestration
by giant reed rhizomes was 40-50mg C ha-1 over an
11-year period (Huang, 2012), which is 6-8 times
higher than that by the roots of switchgrass (Panicum
virgatum L.) (Ma, 1999), a model cellulosic energy
crop selected by the United States Department of
Energy (Wright, 2007). Current agronomic and
biologic research on giant reed focuses on
management practices, development of new cultivars,
and determining differences among existing cultivars
(Nassi o Di Nasso et al., 2010; Nassi o Di Nasso et al.,
2011; Nassi Nassi o Di Nasso et al., 2013; Dragoni et
al., 2016). Even though detailed information on the
growth patterns of giant reed would assist in
development of improved management practices, this
information is not available in the United States.
Therefore, the objective of this study was to describe
the seasonal growth patterns of giant reed in
Alabama, United States.
Materials and methods Treatments
and experimental design
A small plot experiment was conducted at the E.V. Smith
Research Center, Plant Breeding Unit of the Alabama
Agricultural Experiment Station near Tallassee,
Alabama, United States. The soil test was performed by
Auburn University Soil Testing Laboratory (Auburn,
Alabama). The soil was a Wickham sandy loam (fine-
loam, mixed, semiactive, thermic Typic Hapludult),
containing 30mgkg-1 P, 67.5mgkg-1 K, 300.5mgkg-1 Ca,
65mgkg-1mg, and pH 6.5. The experimental site was
fallow prior to the planting of giant reed. In the spring of
1999, 1-m stem segments of a giant reed accession from
California were hand-placed end-to-end in furrows 75cm
apart and covered with 5-7.5cm of soil in prepared plots
that were 3m wide and 9m long. Plots were fertilized
with ammonium nitrate at a rate of 112kg N ha-1 in May
2000, but received no fertilizer in subsequent years.
Biomass was harvested annually each winter before this
study began, and rhizomes had completely filled in the
spaces between rows to form a solid stand.
Data collection
Giant reed emerged in mid- and early March in 2010
and 2011, respectively. Biomass was harvested by
hand from a 1-m2 quadrat within each of four plots at
approximate 30 d intervals from April 2010 to
February 2012, with all material from plots being
harvested in late February of each year. Cutting
height was 5cm above ground. More harvest date
information is presented in Table 2.

In the second growing season, each harvest was
conducted on a different area within each plot from
the quadrats that had been harvested in the previous
season, to avoid any impact of cutting date in the
previous season on second season results. All
harvested plant material was weighed immediately
after harvesting to determine fresh weight in the field
by using a hanging scale. Five randomly identified
plants from each plot were used for determination of
plant height, which was measured from the base of
the stem to the collar of the highest leaf. Subsamples
taken from the harvested material from each plot at
each harvest date were dried at 60°C for 72 h for dry
matter determination.
Statistical analysis
Analysis of variance for biomass yield data was
performed using SAS v9.2 PROC GLIMMIX
procedure (SAS Institute, 2009, Cary, NC). This
analysis was conducted by year since harvest dates
were slightly different between years. Diagnostic plots
were obtained by using the option PLOTS=
STUDENTPANEL and were used to evaluate the
model assumptions. Harvest date was tested as a
fixed effect. The critical P-value of 0.05 was used as
cutoff for testing the fixed effect, and determination
of differences in least-squares means was based on
adjusted P-value obtained by using the option
ADJUST=SIMULATE in the LSMEANS statement.
Scatter diagrams of plant height and biomass yield
against time were drawn for each year to help
determine the most appropriate function to describe
the data. Generalized logistic (Eq. 1), Gompertz (Eq.
2) and logistic functions (Eq. 3) (Sit and Poulin-
Costello, 1994) were tested for describing changes in
plant height and biomass yield with time in the first
part of the year starting in March, because the scatter
diagrams suggested a sigmoid-shaped curve during
this phase of growth. In the latter part of the year
biomass yield decreased steadily with time, so this
suggested use of a linear function (Eq. 4).
The Generalized logistic function employed in the
analyses was:
= +
(1)
+ ( − ∗ )
where Yij is plant height or aboveground biomass
yield of j th experimental unit in the i th recording
time, A is the product of predicted maximum plant
height or biomass yield and D, B is the product of
relative growth rate and time when absolute growth
rate is maximum, C is the relative growth rate, D is
near which asymptote maximum growth occurs (D>1
near bottom, D<1 near top, D=1 normal), Xi is the i th
recording time, day after emergence, and eij is the
residual term for j th experimental unit at i th
recording time.
The Gompertz function employed in the analyses was:
= − ( − ∗ )
+ (2)
where Yij, Xi, B, and eij are as in Eq. 1, A is the
predicted maximum plant height or biomass yield,
and C is the relative growth rate at time when
absolute growth rate is maximum, and at which
growth has reached e-1*A.
The logistic function employed in the analyses was:
=
+ (3)
1+ ( − ∗ )
where Yij, Xi, A, and eij are as in Eq. 1, B is the product
of twice the relative growth rate and time when
absolute growth rate is maximum, and C is twice the
relative growth rate at time when absolute growth
rate is maximum.
The linear function employed in the analyses was:
=1+1 + (4)
Where Yij, Xi, and eij are as in Eq. 1, A1 is the intercept
or constant, and B1 is the slope of the regression line.
Initial starting parameter estimation and model
fitting of generalized logistic, Gompertz, and logistic
functions were performed using SAS v9.2 PROC
NLIN procedure, and linear function using SAS
PROC REG procedure. For biomass yield data, the
starting parameters were then used to fit the
piecewise regression model with the PROC NLIN
procedure in SAS. Specifically, the IF syntax was
included to determine when using different model to
fit the biomass yield data, and determination of the
breakpoint was based on the lease square values in
the iteration history (See SAS codes in
Supplementary Fig. S1).

Selection of the best fitting function was done based
on the mean square error (MSE) rather than the
residual sum of squares (RSS), since the MSE also
takes into account the number of parameters in the
models.
Following determination of the best fitting function,
the full model, which has different parameters set for
each year, was compared with the reduced model,
which has common parameters for both years, via a
sum of square reduction F test (Eq. 5) (Draper and
Smith, 1966) (See SAS codes in Supplementary Fig.
S2), in order to determine whether the growth pattern
of plant height or biomass yield in the two years can
be expressed via a single set of parameters or not. The
t test was also conducted to compare the estimated
coefficients of the best-fitting full models for plant
height and biomass yield data between the two years
(See SAS codes in Supplementary Fig. S3).
The sum of square reduction F test employed in the
analyses was:
= ( − )/ ~ ( , )(5)
where RSSR is the residual sum of square
for reduced model, RSSF is the residual sum of square
for full model, D is the difference in residual degrees
of freedom between reduced (RdfR) and full (RdfF)
models, MSEF is the mean square error for full model,
and Fα (D, RdfF) is the tabulated value of the F
distribution with D and RdfF degrees of freedom for
the selected α.
Results and discussion
Precipitation and temperature data are presented in Table 1
and Fig. 1, respectively. Growing season rainfall (March to
October) in both years was slightly below the average
growing season rainfall from 1990 to 2009. Mean minimum
air temperature in mid-winter was -8.8℃ and mean
maximum air temperature in mid-summer was 37.5℃.
Table 1. Monthly and growing season (March to October) precipitation 2009-2011.
Precipitation (mm)
Year Month Growing
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec. season
2009 154 276
2010 152 76 124 32 176 56 128 122 46 31 52 59 716
2011 57 100 139 49 56 57 204 16 124 25 670
Average
119 120 169 106 85 109 126 103 91 85 115 146 875
(1990-2009)
Fig. 1. Monthly maximum and minimum air
temperatures from November 2009 to October 2011.
Recognizing that experimental plots received no fertilizer
and that giant reed is a C3 species, growth rates and
yields recorded for giant reed in this study were
remarkably high: maximum yield was 47.11mg ha-1 and
48.56mg ha-1 in September of 2010 and 2011,
respectively. In contrast, yields of unfertilized ‘Alamo’
switch grass during the same period and at the same
location were 11.55 and 6.80mg ha-1 in 2010 and
2011, respectively (Huang et al., 2014). Biomass yield
for consecutive harvests differed in the first three
months of each year, but this difference was reduced
or eliminated as maturity was reached and as yield
declined in the latter part of the year (Table 2).
A Gompertz function provided the best fit for changes
in plant height with time in the two years, probably
because the data were distinctly asymmetrical. No
difference was detected for the estimated coefficients
of the fitted Gompertz function between the two years
(Table 3). Result from the sum of square reduction
test also suggests that the growth curve of plant
height in the two years can be expressed via a single

set of parameters (Table 3) (Fig. 2). Therefore, plant = 5.0918 ∗
− (1.4675−0.0238∗ )
0< ≤202 ;
2
=
height data from the two years were pooled for model 0.9762 ∗∗∗
fit. The fitted model is as follows: * p < 0.05, ** p < 0.01, and *** p < 0.001, model fit.
Table 2. Aboveground biomass yield of giant reed at different harvest times in 2010 and 2011 growing seasons.
Harvest date
Day after Biomass yield
Harvest date
Day after
Biomass yield (Mg ha-1)
emergence (Mg ha-1) emergence
04/15/2010 36 3.81±1.71d 04/15/2011 44 4.09±0.99e
05/14/2010 65 21.10±1.71c 05/19/2011 78 24.98±0.99d
06/14/2010 96 31.07±1.71b 06/15/2011 105 32.01±0.99c
07/15/2010 127 40.78±3.44ab 07/18/2011 138 41.94±0.99b
08/16/2010 159 44.25±5.94ab 08/15/2011 166 46.80±2.93ab
09/15/2010 189 47.11±5.94ab 09/20/2011 202 48.56±0.99a
10/20/2010 224 42.48±5.84ab 10/21/2011 233 43.62±4.41abc
11/15/2010 250 41.95±1.71a 12/14/2011 287 38.62±2.93abc
12/03/2010 268 38.19±3.44ab 01/25/2012 329 32.74±2.93bcd
01/21/2011 317 34.60±3.44ab
02/18/2011 345 32.10±3.44ab
abcde means within each column with different superscripts differ significantly (p<0.05).
± numbers after means represent standard errors.
growth then started to decrease over time until
maturity approximately 200d after emergence (mid-
to late September).
Fig. 2. Changes in plant height of giant reed with time
fitted with a Gompertz function in 2010 and 2011
(pooled). Vertical bars represent standard errors.
According to this model, an absolute maximum
growth rate of 0.045m d-1 or 0.315m week-1
Fig. 3. Predicted plant height and growth rate of
occurred 62 d after initiation of growth (mid-May)
giant reed with time in 2010 and 2011 (pooled).
and at a plant height of 1.89m (Fig. 3). The rate of
Table 3. Estimated coefficients and standard errors for plant height response to day after emergence fitted with
a Gompertz function for giant reed in 2010 and 2011 in Alabama, USA.
Estimated coefficients
p-value for
p-value for sum
Model Year
A B C
square reduction
model fit F test
Full
2010 5.1536±0.1817 a 1.5166±0.1761 a 0.0266±0.0033 a
<0.001
2011 5.0338±0.2202 a 1.5032±0.1826 a 0.0222±0.0030 a 0.1238
Reduced 2010 & 2011 5.0918±0.1830 1.4675±0.1571 0.0238±0.0028 <0.001
Estimated coefficients within each column between the two years with different superscripts differ significantly (p<0.05).
± numbers after estimated coefficients represent standard errors.

Other studies have shown that growth of many
annuals, such as corn and wheat are better described
by a logistical curve than by a Gompertz curve
(Katsadonis et al., 1997; Karadavt et al., 2008),
indicating that the data were distinctly symmetrical.
Unlike these annual crops, but in agreement with the
findings of another study on giant reed and
miscanthus conducted in central Italy (Nassi o Di
Nasso et al., 2011), a Gompertz function provided the
best description of changes in biomass yield of giant
reed with time until maturity in this study, and
thereafter a linear function was used to describe the
subsequent decline in yield. Again, no difference was
detected for the estimated coefficients of the fitted
Gompertz function between the two years (Table 4).
Result from the sum of square reduction test also
suggests that the growth pattern of biomass yield in
the two years can be expressed via a single set of
parameters (Table 4) (Fig. 4). Therefore,
aboveground biomass yield data from the two years
were pooled for model fit. The fitted model is as
follows:
= {47.8698 ∗ − (1.9240−0.0293∗ ) 0 < ≤ 200 ; 2 68.3698 − 0.1069 ∗ 200 < ≤ 350 .
= 0.9731 ∗∗∗
* p < 0.05, ** p < 0.01, and *** p < 0.001, model fit.
Table 4. Estimated coefficients and standard errors for aboveground biomass yield response to day after
emergence fitted with a Gompertz function (pre-maturity) and a linear function (post-maturity) for giant reed in
2010 and 2011 in Alabama, USA.
Estimated coefficients p-value p-value for
Model Year
Gompertz function Linear function
for
sum square
reduction
A B C A1 B1 model fit
F test
Full
2010 47.4623±3.3666a 1.8156±0.4162a 0.0291±0.0071a 63.1714±8.9462a -0.0901±0.0315a
<0.001
2011 48.2687±4.8814a 2.0833±0.5394a 0.0300±0.0087a 72.2847±8.5066a -0.1195±0.0318a
0.8741
2010 &
Reduced 47.8698±2.7425 1.9240±0.3201 0.0293±0.0053 68.3698±5.9512 -0.1069±0.0215 <0.001
2011
Estimated coefficients within each column between the two years with different superscripts differ significantly (p<0.05).
± numbers after estimated coefficients represent standard errors.
Fig. 4. Changes in aboveground biomass yield of
giant reed with time fitted with a Gompertz function
(pre-maturity) and a linear function (post-maturity)
in 2010 and 2011 (pooled). Vertical bars represent
standard errors.
According to this model, inflection point was
predicted on day 66 (mid-May) after initiation of
growth, when biomass yield was 17.78mg ha-1 (Fig. 5).
At this inflection point, relative growth rate for
biomass yield was 0.0293mg ha-1 d-1, which is very
close to the findings by Nassi o Di Nasso et al. (2011)
in central Italy, and absolute maximum growth rate
was 0.516mg ha-1 d-1 or 3.61mg week-1. The rate of
growth subsequently decreased over time until
maturation at approximately 200 d after emergence
(mid- to late September) when a maximum yield of
46.94mg ha-1 was reached. After this point yield
decreased steadily at a rate of 0.1069mg ha-1 d-1, or
0.75mg ha-1week-1, probably due mainly to leaf loss
(Fig. 6) and possibly translocation of nutrients from
shoots to rhizomes.

Fig. 5. Predicted aboveground biomass yield and
growth rate of giant reed with time in 2010 and 2011
(pooled).
(a)
(b)
Fig. 6. Pictures of giant reed showing leaf loss in late
season: (a) taken on October 8th, 2010; and (b) taken
on November 18th, 2010.
The asymmetrical nature of the growth curve of giant
reed reflects extremely rapid growth following
emergence, and attainment of the maximum growth
rate within a third (66 d) of the time it takes to reach
maximum yield (200 d). This pattern is probably due
to existence of an extensive permanent root system,
and stored energy and nutrients in the very large
rhizomes of giant reed, which facilitate rapid growth
in the early part of the season. Therefore, while
results from this study indicate that maximum yield is
attained in mid- to late September, annual harvesting
at this time might reduce long-term yields. Results
from other studies support this view by
demonstrating that yield of giant reed is sensitive to
time of harvest (Huang, 2012; Nassi o Di Nasso et al.,
2010; Dragoni et al., 2016). Consequently, unless
additional research indicates otherwise, harvesting
giant reed after it reaches dormancy (November or
December) will likely ensure the highest sustainable
yields, even though this will result in approximately 6
to 9% reduction in short-term yield when compared
to harvesting in mid- to late September.
Conclusions
The overall objective of this 2-year field study was to
describe the seasonal growth patterns of giant reed in
the United States, with the hope to provide
information that would assist in development of
improved field management practices for giant reed.
Results demonstrated that the growth pattern of giant
reed in Alabama, United States is distinctly
asymmetrical, and a Gompertz function provided the
best fit for changes in plant height and aboveground
biomass yield with time till maturation in the two
years. Maximum growth rate is achieved
approximately 60 d after emergence (mid-May), and
maximum yield is attained approximately 200 d after
emergence (mid- to late September). Yield decreased
linearly following maturation up to 278 d after
initiation (early to mid-December) of growth in
spring, partly as a result of leaf loss, and was
relatively stable thereafter. Harvesting giant reed
after initiation of dormancy in November or
December will probably be the best strategy to ensure
sustainable long-term yields, even though this will
result in a 6-9% reduction in short term yield
compared to harvesting in mid- to late September.

Acknowledgments
The authors express their appreciation to Ms. Suan
Sladden and the team at the E.V. Smith Plant
Breeding Unit of Auburn University for their help
with field work. The authors would also like to thank
the reviewers for the insightful comments that helped
to improve the paper.
Conflicts of interest
The authors declare that there is no conflict of
interest regarding the publication of this article.
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Seasonal growth patterns of Arundo donax L. in the United States | IJAAR @slideshare

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