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Simulation of biomass, carbon and nitrogenaccumulation in grass to link with a soil nitrogendynamics model
L. Wu* and M. B. McGechan
Soils Department, Scottish Agricultural College, Edinburgh, UK
Abstract
In order to represent nitrogen and carbon cycling in the
soil±plant±atmosphere continuum, a previously devel-
oped weather-driven grass growth model has been
adapted to become the crop growth component of the
soil nitrogen dynamics model SOILNSOILN. This provides a
means of simulating nitrogen uptake by the grass crop,
an important component of the overall nitrogen bal-
ance in grassland.
Grass growth is represented by a photosynthesis
equation adjusted to take account of respiration as well
as constraints due to lack of water and nitrogen in the
soil. Water shortage is represented by linked simula-
tions with the soil water and heat model SOILSOI L, and
nitrogen shortage by links with the SOILNSOILN model.
Accumulated biomass and the nitrogen component of
biomass are allocated to leaf, stem and root pools, and
¯ows from live biomass pools to those representing
above- and below-ground senescent material are also
represented. The model is tested by comparing simu-
lated cut grass yields and nitrogen contents of cut
material with measured data at a test site. Soil nitrogen
processes in the model are tested by comparing simu-
lated and measured nitrate in drain¯ows. Agreement is
reasonable, indicating that the combined model gives
a realistic representation of carbon and nitrogen pro-
cesses in grassland.
The use of the combined model in a predictive
manner has been demonstrated in a comparison of
nitrogen balances with a number of alternative slurry
and mineral nitrogen fertilizer application scenarios.
Introduction
It is increasingly evident that agricultural research and
policy have transferred their goals from means to
provide the plant with suf®cient nitrogen to maximize
output towards means to prevent pollution from nitro-
gen inputs while maintaining adequate output. Simu-
lation models can be bene®cial in understanding
interactions among the processes involved in the
cycling of nitrogen and carbon in the soil±grass±atmo-
sphere system, and to assist in making decisions on
optimal nitrogen inputs and grass management.
Many simulation models representing nitrogen turn-
over in the soil±crop system are in existence and are
being used. The SOILNSOILN model, developed by the Swedish
University of Agricultural Sciences, focuses on nitrogen
¯ows in agricultural and forest soils. It includes all the
major processes determining the inputs, transformations
and outputs of nitrogen in soils, including uptake of
nitrogen for arable (cereal) crops or forest trees. The
main functions and processes are described in papers by
the authors and users of the model (Johnsson et al.,
1987; Eckersten and Jansson, 1991; Eckersten, 1993; Wu
and McGechan, 1998). Parameter selection and testing
for grassland and arable crop land have been carried out
by Wu et al. (1998). The model has been applied to
investigate nitrogen, carbon and water cycling in agro-
ecosystems (BergstroÈm and Jarvis, 1991; BergstroÈm
et al., 1991; Eckersten and Jansson, 1991; Eckersten,
1994; BlombaÈck et al., 1995; Eckersten et al., 1995).
Much of the interest in soil nitrogen cycling in
Scotland relates to grassland rather than arable crops.
Also, ruminant animal manure and slurry, important
components of the soil nitrogen balance, are products of
grassland farming and tend to be spread on grassland
rather than arable soils. It is therefore appropriate to
extend the range of growth submodels associated with
the SOILNSOILN model to include grass crops, and eventually
also grass±clover mixed crops.
One approach to creating a grass growth submodel
for SOI LNSOILN is to make adjustments to the parameters of
*Present address: Department of Agrometeorology, China
Agricultural University, Beijing.
Correspondence to: Dr M. B. McGechan, Environmental
Division, SAC, West Mains Road, Edinburgh EH9 3JG, UK.
Received 28 April 1997; revised 23 January 1998
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249 233
the cereal growth subroutine to make it appear like
grass, as already attempted by BlombaÈck and Eckersten
(1997). However, an alternative grass growth model
applicable to both grass and grass±clover crops has been
developed at the Scottish Agricultural College (SAC) by
Topp and Doyle (1996). This has already been used in
stand-alone mode, and also adapted to become a crop
growth submodel for a whole-system modelling study
of conservation of forage crops as hay or silage as
described by McGechan and Cooper (1995). A similar
adaptation of the model to become a grass growth
submodel of SOILNSOILN appeared feasible and desirable for
the current application.
This paper describes adaptation of the Topp and Doyle
(1996) grass growth model to link with SOILNSOILN, param-
eter selection and testing of the combined model against
measured crop and nitrate leaching data. An application
of the modi®ed model to test effects of slurry and
fertilizer management options is also described.
A grass growth model to link with theSOILN model
In the earlier versions of the SOILNSOILN model, growth and
hence uptake of nitrogen by the growing crop was
represented by a logistic curve with parameters speci®ed
by the user (Johnsson et al., 1987). In more recent
versions, an alternative option is provided with a weath-
er-dependent plant growth submodel, for a cereal crop
(Eckersten and Jansson, 1991) or forest trees, operating
interactively with the soil nitrogen routines in the main
model. This plant growth submodel simulating biomass
production and nitrogen uptake linking with transfor-
mation of nitrogen and carbon cycling in the soil is an
important feature in the current SOI LNSOILN model which
distinguishes it from other soil nitrogen models as
reviewed by Wu and McGechan (1998). The quantity of
available nitrogen in the soil simulated by SOILNSOILN is an
input variable to the plant growth submodel, and in turn
the nitrogen uptake estimated by the submodel is
considered as one component in the nitrogen balance in
the soil by the main routines of SOI LNSOILN. The accuracy of the
plant growth estimation affects the accuracy of the whole
model and the ¯ows of nitrogen and carbon to different
pools.
The cereal crop growth model for SOILNSOILN consists of
two submodels representing biomass production (in-
cluding allocation to plant components) and plant
uptake of nitrogen. For grass growth, equations repre-
senting biomass production were replaced by equiva-
lent equations for grass based on the model of Topp and
Doyle (1996), and equations representing biomass
allocation and nitrogen uptake were also adapted to
represent a grass crop.
Biomass submodel
To be equivalent to the cereal growth submodel in
SOI LNSOILN, the grass growth submodel required an equation
similar to the original expression to calculate daily gross
photosynthesis, considered to be proportional to light
intercepted by the canopy and products of the response
functions of air temperature, nitrogen and soil water. As
the basis of their grass growth model, Topp and Doyle
(1996) used an equation for daily gross photosynthesis,
taking account of respiration but without nitrogen and
water constraints, as developed by Johnson and
Thornley (1984), and combined this with equations
representing stress due to shortages of nitrogen and soil
water. For the current study, the Topp and Doyle
(1996) model was linked with SOILNSOI LN to calculate daily
photosynthesis for grass, but in this case the nitrogen
and water constraints are related to soil nitrogen
availability in the SOILNSOILN model and to soil water
availability in the SOILSOIL model.
Equations representing the canopy gross photosyn-
thesis rate Pj, net photosynthesis rate Pn, and leaf
photosynthetic rate at saturating light levels Pmax, are
presented by Topp and Doyle (1996). Variables in these
equations are listed in Table 1 and parameters in
Table 2, with their symbols and suggested values given
by Topp and Doyle (1996), together with the equivalent
symbols used in the description of the cereal growth
submodel for SOILNSOILN (Eckersten and Jansson, 1991). For
implementation of the grass growth model with SOILNSOILN,
some variable and parameter units needed to be
converted so that the photosynthesis equations would
work in the manner of the SOILNSOILN cereal growth model
using intercepted shortwave radiation (estimated from
global radiation) to indicate biomass dry matter accu-
mulation, whereas the Topp and Doyle (1996) equations
work with photosynthetically active radiation (PAR,
assumed to be half global radiation) to indicate CO2
absorption (44/32 ´ biomass accumulation). The equa-
tion for the effect of stress on net photosynthesis /g
(expressed as a fraction in the range 0±1) is of the same
form as in the Topp and Doyle (1996) model. However,
it now incorporates the effects of the nitrogen content of
the leaves n1 as for a cereal crop in the SOILNSOILN model, and
also available soil water W derived from the SOILSOI L model:
/g � b1
�����������W
Wmax
r� b2
��������������������������nl ÿ nlMin
nlMax ÿ nlMin
r� �2
: �1�
Allocation of accumulated biomass to plantcomponents
Daily net photosynthesis representing grass growth
must be divided (`partitioned'), then each component
added into its respective live biomass pool for root, leaf
234 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
or stem. There are also daily losses by senescence from
each of these pools to above- or below-ground litter
pools. Strictly, during the reproductive stage, a fourth
live pool might be included to represent grass seed
(equivalent to the grain pool in the cereal growth
submodel of the standard SOILNSOILN), but in this study
a simpli®cation is made by including seeds in the
stem pool. The partition equations adopted were a
compromise between those assumed by Topp and Doyle
(1996) and those in the original SOILNSOILN model. There
was general agreement between the two models that
photosynthesis allocation to root should have the
Table 1 Variables used in grass crop growth submodel of S O I L NS O I L N .
Symbol
Topp and Doyle (1996) S O I L NS O I L N papers Unit Description
Biomass accumulation
/g ± Effect of stress on net photosynthesis
H h Effective daylength
I0 MJ ha±1 d±1 (PAR) Photosynthetically active radiation (PAR)
Ii MJ ha±1 d±1 (global) Shortwave radiation intercepted by canopy
(from global radiation)
L Ali ha ha±1 Leaf area index (LAI)
Pj kg CO2 J±1 (PAR) Canopy gross rate of photosynthesis
W¢t g DM J±1 (global)
Pmax kg CO2 ha±1 d±1 Leaf photosynthesis at saturation light levels
Pn kg CO2 J±1 (PAR) Canopy net rate of photosynthesis
T T °C Mean daily temperature
W mm Available soil water
Wmax mm Available soil water at ®eld capacity
Biomass allocation and senescence
k Proportion of daily gain in above-ground
biomass allocated to leaves
bi m2 g±1 DM Speci®c leaf area (ratio of leaf area to
above-ground biomass)
DD g DM m±2 Dead biomass
DL Wl g DM m±2 Accumulated growth of leaf biomass
Wr g DM m±2 Accumulated growth of root biomass
DS Ws g DM m±2 Accumulated growth of stem biomass
W¢l g DM m±2 d±1 Daily change in leaf biomass
W¢r g DM m±2 d±1 Daily change in root biomass
W¢s g DM m±2 d±1 Daily change in stem biomass
Fp®l g DM m±2 d±1 Biomass accumulation partitioned to leaves
Fp®r g DM m±2 d±1 Biomass accumulation partitioned to roots
Fp®s g DM m±2 d±1 Biomass accumulation partitioned to stems
Fl®Li g DM m±2 d±1 Senescent ¯ow of biomass from leaves to litter
Fr®Li g DM m±2 d±1 Senescent ¯ow of biomass from roots to litter
Fs®Li g DM m±2 d±1 Senescent ¯ow of biomass from stems to litter
Nitrogen submodel
Dix ± Index indicating stage of grass development
nl g N g±1 DM Nitrogen concentration in leaf biomass
npMax g N g±1 DM Maximum nitrogen concentration in plant in
relation to stage of development and
soil nitrogen concentration
npm g N g±1 DM Maximum nitrogen concentration in
plant at emergence
N Nsoil g N m±2 Available nitrogen in soil
XNd g DM m±2 d±1 Daily uptake demand for nitrogen by plant
Modelling C, N and biomass accumulation in grass 235
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
Table 2 Parameter values used in grass crop growth submodel of S O I L NS O I L N .
Symbol
Topp and
Doyle (1996)
S O I L NS O I L N papers
Value Unit Description Source²
Biomass accumulation
a
e
0á01
3.41
kg CO2 MJ±1 (PAR)
g DM MJ±1 (global)
Photochemical ef®ciency (growth
per unit of radiation)
under optimal conditions
b1 PWNSE(1) 0á366 ± Constant in stress equation
(water stress term)
b2 PWNSE(2) 0á664 ± Constant in stress equation
(nitrogen stress term)
e PMAX20(2) 0á35 ± Rate of decline of maximum leaf
photosynthesis
with increased leaf area index
1
H 0á95 ± Parameter in leaf photosynthesis equation
kg 0á5 ± Light extinction coef®cient 1
mg 0á1 ± Leaf transmission coef®cient 1
P0max PMAX20(1) 43á2 kg CO2 ha±1(leaf) h±1 Maximum hourly rate of leaf
photosynthesis
1
T0 0 °C Daily temperature at which growth ceases 3
TRef 20 °C Lower threshold daily temperature
for optimum photosynthesis
1
Cu 0á08 d±1 Fraction of mineral N available
for plant uptake per day
11
Y 0á83 ± Respiration growth conversion ef®ciency 1
Biomass allocation and senescence
Ag als 0.0258 m2 g±1 Speci®c leaf area 5
bi0 0á06 ± Coef®cients for leaf area
development as a function of above
8
bil 0á008 ± ground biomass
q br 0á1 ± Constant fraction of daily total
growth allocated to roots
4
dAge 200 d Winter Maximum leaf life
d 75 Summer 10
cL ml 0á023 Vegetative, winter³ Fraction of
leaf biomass senescing (lost to litter)
1
0á0146 d±1 Reproductive, summer³
0á0311 Vegetative, summer³
mr1 0á03 d±1 Fraction of daily root growth
senescing (lost to litter)
7
mr2 0á03 d±1 Fraction of root biomass
senescing (lost to litter)
7
cS ms 0á0259 d±1 Fraction of stem biomass
senescing (lost to litter)
6
Nitrogen submodel
h 0á768 ± Coef®cient in nitrogen concentration
equation, Equation 7 (new)
nlMin 0á005 g N g±1 DM Leaf nitrogen concentration at
which minimum growth occurs
2
nlMax 0á05 g N g±1 DM Leaf nitrogen concentration at
which maximum growth occurs
2
236 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
highest priority and that allocation to leaf should take
precedence over that to stem.
A new variable Wr, not used by Topp and Doyle
(1996), was introduced for the root biomass. For the
proportion of daily biomass accumulation partitioned to
root q, the constant value assumed by Topp and Doyle
(1996) was retained, rather than the complex power law
equation in the cereal growth submodel of SOI LNSOILN (one of
several alternative equation forms provided with SOILNSOILN
to give ¯exibility for different crops). No experimental
data for grass could be found to justify any of the
complex equation forms. An exponential expression was
used to represent decline in root density with root depth.
The constant value assumed by Topp and Doyle
(1996) for k, the proportion of the daily gain in above-
ground biomass partitioned to leaves, was replaced by
an equation from the SOILNSOILN model so this parameter
would vary according to the stage of plant development.
This assumes that the ratio bi between leaf area index
and above-ground biomass declines with increase in
plant size according to a logarithmic relationship:
bi � Ali
Wl �Ws
� bi0 ÿ bil ln�Wl �Ws� �bi � bil�; �2�
k � W 0l
W 0l �W 0
s
� �bi ÿ bil�=als: �3�
Values of the parameters bi0 and bil were chosen to give
the required trend, similar to a simple model of leaf/
stem ratio variation in grass described by Wu et al.
(1997) based on data from literature sources such as
Wilman et al. (1976; 1994).
A root senescence equation was also required similar
to the existing leaf and stem senescence equations,
which was assumed to be of the same form as for the
cereal growth submodel in the standard SOILNSOILN model
(Eckersten, 1993):
Fr!Li � mr1Fp!r �mr2Wr : �4�
The chosen values of mr1 and mr2 give a rate of root
senescence similar to that measured by Forbes et al.
(1997) at a temperature of 10°C, a typical mean annual
soil temperature. Temperature dependence of the root
senescence rate found in these experiments could not
readily be incorporated in the model, but since the
experiments had been conducted in the laboratory over
a much wider temperature range than that found in the
®eld this was considered to be unnecessary. The stem
senescence equation was retained from the Topp and
Doyle (1996) model, but leaf senescence was assumed
to consist of two parts ± a daily part as in the Topp and
Doyle (1996) model, and an old leaf part which occurs
when leaves exceed a certain age, as in the cereal
growth submodel of SOILNSOI LN (Eckersten, 1993).
Nitrogen submodel
Since the Topp and Doyle (1996) grass growth model
had no nitrogen uptake and allocation submodel
(considering only soil nitrogen in the stress equation),
the nitrogen submodel for this study was based on that
described by Eckersten and Jansson (1991) for a cereal
crop, but with some modi®cations. The main factors
controlling the nitrogen uptake mechanism are shown
diagrammatically in Figure 1. Two components in the
process ± potential demand and the size of the soil
mineral nitrogen pool ± are so important that inade-
quate representation in the model will undermine the
validity of representation of other components in the
soil nitrogen cycling process.
The main modi®cation to the cereal crop growth
routine in the standard SOILNSOILN model required to
represent a perennial grass species concerns the poten-
tial demand for nitrogen uptake by the plant. As for the
cereal crop, this demand is assumed to be determined
by the maximum nitrogen concentration in the plant.
Table 2 (contd.)
nup 0á026 g N m±3 Upper threshold plant nitrogen
concentration above which growth
is unconstrained by lack of nitrogen
in the soil (new)
12, 13
Nc0 0á08 g N m±2 Minimum nitrate and ammonium
concentration in the root layer at
which crop growth ceases
9
²Source: 1, Topp and Doyle (1996); 2, Bolton and Brown (1980); 3, Johnson et al. (1983); 4, Jones and Lazenby (1988)
5, Davidson and Robson (1986); 6, Sheehy et al. (1980); 7, Thornley and Verberne (1989); 8, based on leaf/stem ratio experiments;
9, based on McCaskill and Blair (1990); 10, H. Eckersten (personal communication); 11, Eckersten et al. (1995); 12, Adams et al.
(1966); 13, Overman and Evers (1992).
³The reproductive stage is assumed to be the period 15 March to the ®rst cut each year, and winter is de®ned as the period from
the third cut until the beginning of the reproductive stage.
Modelling C, N and biomass accumulation in grass 237
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
However, for the cereal crop these maximum concen-
trations were assumed to be constant for each compo-
nent (leaf, stem and root), whereas for grass there is
strong evidence that they vary substantially according
to two factors, namely the soil nitrogen status and the
stage of crop development. Variable maximum concen-
trations for individual plant components could not be
derived directly from experimental data, whereas it was
possible to estimate such a maximum nitrogen concen-
tration for a whole plant and then allocate it propor-
tionally to different parts.
In relation to the soil nitrogen concentration effect,
Gordon and Burton (1956) concluded from experi-
ments that increasing the mineral nitrogen application
rate increased the protein content; conversely, increas-
ing the cutting interval decreased the protein content.
Results of other studies also support this and the general
conclusion that the maximum nitrogen concentration
in the plant increases with the available nitrogen
content N of the soil. For the current study, an
exponential function is assumed for the maximum
nitrogen concentration at emergence npm:
npm � nup 1ÿ eÿ N
Nc0
� ��n � Nc0�; �5�
from which the daily nitrogen uptake by the plant is
estimated:
XNd � npm�W 0r �W 0
l �W 0s �; �6�
where Nc0 is de®ned as the limiting content of nitrate
plus ammonium in the soil root layer (expressed as
g N m±2) below which growth ceases, and nup is the
upper threshold plant nitrogen concentration above
which growth is unconstrained by lack of nitrogen in
the soil. An average value of 254 g kg±1 dry matter
(DM) was chosen for nup based on literature sources
(Adams et al., 1966; Overman and Evers, 1992). Max-
imum plant nitrogen concentration increases with soil
nitrogen concentration, becoming close to its maximum
value when the soil concentration reaches ®ve times Nc0
(Figure 2).
The trend that the nitrogen concentration in the
plant decreases as grass development progresses is also
supported by a number of publications. Green et al.
(1971) presented curves representing the decline in
Figure 1 Block diagram of nitrogen uptake
process in the grass plant.
Figure 2 Relationship between maximum grass
plant nitrogen concentration (at emergence) and soil
nitrogen content.
238 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
crude protein content (and other grass quality param-
eters) with increase in grass maturity, ®tted to mea-
sured values for a number of grass species and varieties.
This trend is used extensively in planning the optimum
maturity at which grass should be cut, and has been
incorporated into the whole-system forage conservation
model reported by McGechan (1990) and McGechan
and Cooper (1995) which includes a grass growth
submodel based on the work of Topp and Doyle (1996).
For the current study, an exponential function is used
to represent this trend:
npMax � npm � eÿh�Dixÿ1� �1 � Dix � 4�; �7�where npMax is the maximum nitrogen concentration in
the plant related to its stage of development and the
nitrogen concentration in the soil. Dix is an index
indicating the stage of grass development, which is
calculated from accumulated temperature (degree
days), ignoring dependence of stage of development
on daylength. At commencement of growth in spring
Dix is set to 1 and at maturity to 4 with linear
interpolation in between, assuming 1180°C d of accu-
mulated temperature over this growth period (Moore
et al., 1991), with the value for the coef®cient h selected
from literature sources. Curves plotted in Figure 3 show
that differences in maximum nitrogen concentration
npMax for different values of npm are large at the
initiation stage, but they become less as the crop
develops, especially during the reproductive stage.
In both annual and perennial species, large variations
are to be expected in the allocation of the principal
nutrient elements such as nitrogen according to the
stage of growth. Williams (1955) observed that phos-
phate and nitrogen are apparently moved tactically from
organ to organ as vegetative growth and ¯owering
proceeds in oats over a 20-week growing season, which
seems to be a generally acceptable model of allocation
processes in annual species. For the current study with
perennial ryegrass, the variation in allocation of nitro-
gen to leaf, stem and root with crop development was
adjusted on the basis of the data shown in Figure 4,
adapted from Figure 5.4 in Jeffrey (1988).
The routine for allocation of the daily total nitrogen
uptake to root, stem and leaf for a cereal crop in the
standard SOILNSOI LN model assumes that roots receive
nitrogen ®rst up to the maximum concentration,
followed by stem and ®nally by leaf. For grass a similar
procedure was applied, except that instead of assuming
a ®xed maximum concentration for each component,
the maximum concentration for the whole plant was
®rst estimated from Equations 5 and 7, and then this
nitrogen was allocated to each component according to
the stage of development in the ratios shown in
Figure 4. The nitrogen content of the leaves n1 is
required by the stress function (Equation 1).
Simulation procedure and experimentalsite
Link with soil water and heat simulations
Since most of the soil nitrogen transformation processes
represented in the SOILNSOILN model are very dependent on
both temperature and soil water content, each simula-
tion using SOILNSOILN must be carried out in conjunction
with the soil water and heat model SOI LSOIL (Jansson,
1996). A simulation with SOILSOIL, which must be carried
out prior to a simulation with SOILNSOI LN, requires input
data representing weather parameters including tem-
perature, radiation (or sunshine hours), windspeed and
precipitation. Selection of other input parameters for
SOI LSOIL (mainly soil hydraulic parameters), and testing the
Figure 3 Relationships between maximum
nitrogen concentration in grass plant and stage
of development, for different values of the
maximum nitrogen concentration at emergence
npm: б 1á6; - - - - - 1á8; ± - - ± 2á0; ± ± ± 2á2;
± - ± - 2á4; ÐÐ2á5. (Stage of development index
Dix � 1 at emergence and 4 at maturity).
Modelling C, N and biomass accumulation in grass 239
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
SOILSOIL model at the site used in this study, have been
reported by McGechan et al. (1997). A standard set of
output parameters from a SOILSOIL simulation (which
relate to a soil pro®le divided up into a number of
horizontal layers) become input parameters for SOILNSOILN.
These are water ¯ow between each pair of adjacent
layers, water ¯ow into the surface layer, surface runoff
(overland ¯ow), water ¯ow out of each layer to ®eld
drain back®ll (if ®eld drains are present), deep perco-
lation ¯ow from lowest layer in pro®le, water content
(by volume) of each layer, soil temperature in each
layer, air temperature, solar radiation and rate of
evapotranspiration.
In relation to grass growth, soil water contents
provided by simulations with the SOILSOIL model are
required by the stress function (Equation 1), where W
is the sum of `available water' (i.e. relative to the water
content at the wilting point), and Wmax is the sum of
available water at ®eld capacity, for the soil layers of the
root zone. Field capacity and wilting point are assumed
to be at tensions of 5 kPa and 1500 kPa, respectively,
estimated from the soil water release (tension against
water content) curve for each layer, as speci®ed in the
parameter values for simulations with the SOILSOIL model.
This procedure using measured hydraulic parameters for
a particular soil is an advance on the very simple water
balance procedure in the Topp and Doyle (1996) model.
Soil nitrogen processes
The nitrogen content of the plant estimated by Equa-
tions 5 and 7 is dependent on the available nitrogen
(nitrate plus ammonium) in the soil root zone simu-
lated by the soil process routines of SOILNSOI LN. Selection of
parameter values for these soil process routines (in-
cluding a listing of parameter values), as well as testing
of SOILNSOI LN at grassland and arable sites, are reported by
Wu et al. (1998). This selection was based on informa-
tion about soil nitrogen transformation rates and other
parameter values found in literature sources reviewed
by Wu and McGechan (1998). Equations for the effect
of soil temperature (a Q10 expression) and water
content on soil biological processes are also discussed
in detail by Wu and McGechan (1998).
Management of experimental site
The experimental site was located at the Crichton Royal
Farm, Dumfries, in south-west Scotland, a dairy farming
area with above-average annual rainfall. The soil type
was a silty clay loam of Stirling/Duffus/Pow/Carbrook
Association, as classi®ed by Bown and Shipley (1982).
Two isolated plots, each 0á5 ha in area, were ®tted with
equipment to record drain¯ow quantities and solute
concentrations. The grass crop on the plots was managed
for silage-making with two or three cuts for a dairy herd,
receiving applications of mineral nitrogen fertilizer and
slurry as listed in Table 3. The available nitrogen content
of slurry in®ltrating the soil was estimated by adjusting
the measured composition at the time of application to
allow for ammonia volatilization occurring before in®l-
tration, following guidelines reported by Dyson (1992).
The third silage cut was not taken in some drier years
owing to demands for grazing grass on the farm. In
simulations, three cuts were assumed on the same dates
as the real cuts, with the third cut (where it had not been
taken in practice) at an estimated likely time to represent
grass offtake by grazing.
Simulations with SOILNSOILN with the grass growthsubmodel
Simulations with a one-day timestep to represent soil
water, nitrogen cycling and grass growth were carried
out for the period from January 1992 to August 1995,
Figure 4 Proportion of nitrogen uptake allocated to
each plant component at different stages of grass plant
development. Leaf; ±r± Stem; - - n - - Root.
(Stage of development index Dix � 1 at emergence
and 4 at maturity).
240 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
starting 2 years ahead of the collection of experimental
data (with estimated cutting dates for those 2 years) to
reduce the effect of errors in the assumed initial soil
nitrogen and water contents. Initial values of soil
nitrogen pools in Table 4 were estimated from an
analysis of organic matter in samples taken from the
experimental site. Values of grass tissue biomass in leaf,
stem and root, both at the beginning of simulation and
in the stubble after cutting, were based on Topp and
Doyle (1996) and BlombaÈck et al. (1995), as listed in
Table 4. The assumption was made that all roots would
remain alive after each of the ®rst two cuts, but after the
third cut a proportion would become dead plant
material transferring to the soil litter pool.
Model testing and validation
Approach
Models require to be developed and parameterized
using one set of data, then tested or `validated' using an
independent set of data. With complex interactive
models such as those used in this study an appropriate
approach is to select model parameters for individual
processes on the basis of laboratory or other small-scale
experiments, and use ®eld-measured data only for
validation of the whole model, perhaps applying `®ne
tuning' to a few parameter values only at this stage. For
this study, parameters of the grass growth submodel
were either theoretically based or based on small-scale
growth experiments, while parameters of the soil
nitrogen routines were selected from laboratory mea-
surements of processes such as organic matter decom-
position. Field measurements of cut grass yields and
leached nitrate could then be used for validating the
combined model with the chosen parameters.
Further adjustment to parameters
The only `®ne tuning' found to be necessary concerned
leaf senescence, where the value of cL suggested for
vegetative growth by Topp and Doyle (1996), combined
with a maximum leaf life of 75 d commonly assumed
for cereal crops (Eckersten and Jansson, 1991), caused
grass plants to die out completely during the winter. In
fact, Topp and Doyle (1996) had restarted plant growth,
with new initial values for the leaf, stem and root
components, on 1 January each year, but the aim in
this study was to avoid such a discontinuity and
produce continuous simulations throughout the winter.
This was achieved by choosing alternative values of cL
and the limit on leaf life for the winter period dAge
(Table 2), but for the summer retaining the original
values including different values of cL for the vegetative
and reproductive periods of growth.
Plotted output from simulations
Values of variables simulated by the model over the
2 years when measured data were available are plotted
against time in Figure 5. Variables include biomass
accumulation in leaf, stem and root pools, the water,
nitrogen and combined stress functions, and the pools
of soil nitrogen readily available to the crop. This
illustrates the effects of mineral fertilizer and slurry
applications on the soil nitrogen pools (including
Table 3 Nitrogen applications in mineral fertilizer and slurry to experimental plots.
Mineral fertilizer Slurry application
Date (g N m±2) Organic nitrogen NH4-N
14 February 1994 5á91 5á53
21 March 1994 8á02
24 May 1994 8á75
25 May 1994 2á23 3á03
11 July 1994 7á79
21 November 1994 2á93 5á01
21 March 1995 8á19
25 May 1995 7á52
31 May 1995 1á80 3á68
7 July 1995 0á89 1á31
10 July 1995 7á5
31 January 1996 12á20 12á2
16 March 1996 9á9
27 May 1996 1á38 1á82
2 July 1996 5á4
Modelling C, N and biomass accumulation in grass 241
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
conversion of ammonium to nitrate), how the size of
the soil nitrogen pools and crop uptake in¯uence the
nitrogen stress function, and the effect of stress on crop
growth. Nitrogen stress, which occurs over almost the
whole active growth period, is usually more severe than
soil water stress. The underlying pattern is weekly
growth peaking during a period centred on the summer
solstice when daylength and clear sky radiation are at
their maximum, but this pattern is broken periodically
by dips caused by stress, mainly nitrogen stress.
Harvested biomass
A comparison between simulated and measured results
was made for cut biomass dry matter, nitrogen content
and nitrogen offtake in cut biomass dry matter, as listed
in Table 5. Simulated biomass dry matter is in close
agreement with the measured values for the third cut in
1994, both cuts in 1995, and the second cut in 1996.
There is a signi®cant discrepancy for each of the ®rst
two cuts in 1994 and for the ®rst cut in 1996, but if the
biomass dry matter for the ®rst two 1994 cuts is
summed the discrepancy is much reduced. This suggests
some inaccuracy in the timing rather than the total
quantity of modelled biomass accumulation. The dis-
crepancy for the sum of the seven cuts taken over
3 years is about 5%, a good result by standards
commonly found when modelling biological processes.
The simulated results underestimate the nitrogen con-
centration in harvested biomass, but by varying
degrees. When expressed as nitrogen offtakes, simu-
lated values are also consistently lower than measured
values, with the exception of the ®rst cut in 1996.
The maximum soil water de®cit (relative to ®eld
capacity, assumed to be at 5 kPa soil water tension)
simulated by the SOI LSOIL model during the timespan for
which experimental data were available, was 14 mm in
1995 and 10 mm in 1996. This did not represent a
severe water shortage, so the performance of the model
under drought conditions, including operation of the
soil water constraint function, could not be tested fully.
The decision to graze the grass on the experimental
plots rather than take a third silage cut in 1995 and
1996 was made because of a shortage of grass over the
Table 4 Initial conditions of grass and soil pools assumed in simulations (soil layers 1±4 each 0á1 m thick).
Parameter Value Description Source²
LEAFW 135 Initial value of leaf biomass (g DM m±2) 1
STEMW 45á0 Initial value of stem biomass (g DM m±2) 1
ROOTW 25á0 Initial value of root biomass (g DM m±2) 3
LEAFN 6á32 Initial value of nitrogen in the leaves (g N m±2) 2
STEMN 0á624 Initial value of nitrogen in the stems (g N m±2) 2
ROOTN 0á40 Initial value of nitrogen in the roots (g N m±2) 2
NLIT(1) 1á490 Total nitrogen in litter in each soil layer (g N m±2) 3
NLIT(2) 1á560
NLIT(3) 0á710
NLIT(4) 0á710
CL(1) 149á0 Litter carbon in each soil layer (g C m±2) 3
CL(2) 155á9
CL(3) 71á3
CL(4) 71á3
NH(1) 482á5 Humus nitrogen in the layer (g N m±2) 3
NH(2) 504á2
NH(3) 230á5
NH(4) 230á5
NO3(1) 0á128 Nitrate nitrogen in the layer (g N m±2) 4
NO3(2) 0á128
NO3(3) 0á128
NO3(4) 0á872
NH4(1) 1á149 Ammonium nitrogen in the layer (g N m±2) 4
NH4(2) 1á149
NH4(3) 1á149
NH4(4) 1á040 _
²Source: 1, Topp and Doyle (1996); 2, based on Wilman et al. (1994); 3, based on experimental data; 4, BlombaÈck et al. (1995).
242 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
whole farm during dry spells of short duration, rather
than due to severe drought conditions on the plots.
Nitrate leaching
Uptake of nitrogen by the plant represents a large
component of the soil nitrogen balance, particularly in
months when the crop is growing vigorously. Leached
nitrate as measured in the experimental plots is a useful
indication of residual nitrate remaining in the soil and
hence of whether crop uptake of nitrogen is being
realistically modelled.
A comparison of accumulation of simulated and
measured nitrate leached over the period 23 October
1994 to 16 January 1996 is shown in Figure 6.
Simulation results and measurements indicate a similar
variation in the quantity of nitrate leached in different
months of the year, with peaks around the times when
external inputs (both as mineral fertilizer and as slurry)
were applied. However, there was a tendency for
leaching to be underestimated in the simulations, with
a more marked discrepancy in some particular months.
These differences between measurements and simula-
tions were thought to be caused by inaccuracies
in simulated drainage ¯ow from the SOILSOIL model
(Figure 7), as discussed in detail by Wu et al. (1998),
rather than poor representation by the combined SOILNSOILN
and grass growth model. Bearing in mind the require-
Figure 5 Simulated dynamics of soil avail-
able nitrogen pools with applications of min-
eral fertilizer (F) and slurry (S) in g N m±2 (a),
nitrogen, water and combined stress functions
(b), and growth rates of plant components (c).
Modelling C, N and biomass accumulation in grass 243
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
ment for more accurate drain¯ow representation, the
overall cumulative discrepancy between simulated and
measured leached nitrate at the end of the study period
of about 15% was not considered to be a serious error
for the residual component when modelling such a
complex interactive system.
Application of model
Nitrogen balance
The magnitudes of fourteen components of the annual
and seasonal nitrogen balances, including inputs, out-
puts and changes in plant and soil pools, determined by
simulation with the combined grass growth and soil
nitrogen model over the period of the experimental
data, are summarized in Table 6. The dynamics of grass
nitrogen uptake and leached nitrate are shown in
Figure 8.
The highest proportion of applied nitrogen is recycled
to harvested grass biomass in the leaf and stem
components removed by cutting, the desired result
from the environmental protection standpoint. Grass
can grow almost throughout the year under the climatic
conditions at Dumfries, indicating that nitrate can be
absorbed from the soil continuously to a limited degree,
but the peak of harvested nitrogen occurs during the
summer, when temperatures are highest and soil water
supply is moderate.
By contrast, leached nitrate and denitri®cation are
smaller components of the nitrogen balance. This is
partly because there is no nitrogen application as
mineral fertilizer during the winter when grass main-
tains a small canopy and photosynthesis is low as it is
limited by low temperatures, but if slurry applications
had also been avoided during this period these losses
would have been reduced further as illustrated later in
this paper. With the exception of the humus pool which
continuously increases signi®cantly as a result of slurry
application, changes in pool sizes in each period are
small and roughly counterbalanced by changes in the
opposite direction in subsequent periods.
Effects of nitrogen application and manage-ment on leaching
Dates and quantity of nitrogen applied both as slurry
and as mineral fertilizer affect the proportion recycled
into harvested plant material relative to that lost as
leached nitrate and by other routes. This was investi-
gated in simulations with the model over 10 years of
historic weather data for the Dumfries site. Eight
alternative scenarios regarding quantities and timing
of applications are listed in Table 7, with inputs of 7á5g N m±2 (75 kg N ha±1) in mineral fertilizer on threeT
ab
le5
Com
par
ison
of
sim
ula
ted
and
mea
sure
dhar
vest
bio
mas
s.
Cu
tb
iom
ass
(th
a)
1)
Nit
rogen
co
ncen
trati
on
(gN
kg
)1
DM
)N
itro
gen
incu
tb
iom
ass
(kg
ha
)1)
Harv
est
date
Sim
ula
ted
Measu
red
Rela
tiv
e
err
or
(%)
Sim
ula
ted
Measu
red
Rela
tiv
e
err
or
(%)
Sim
ula
ted
Measu
red
Rela
tiv
e
err
or
(%)
19
May
1994²
4á2
23á0
40á6
15á6
28á3
³)
44á9
65á9
84á9
)22á4
5Ju
ly1994
3á5
04á4
9)
22á0
20á5
20á8
)1á2
71á9
93á6
)23á2
27
Au
gu
st1994
3á2
03á1
51á6
20á7
28á3
)26á8
66á3
89á9
)26á2
20
May
1995
2á8
93á1
4)
8á0
15á9
22á7
³)
29á9
46á0
71á2
)35á4
1Ju
ly1995
4á4
14á4
3)
0á5
16á6
23á4
³)
29á2
73á1
103á7
)29á5
27
Au
gu
st1995§
4á4
820á6
76á5
23
May
1996
4á8
53á4
142á8
24á1
25á9
)6á9
117á0
88á1
32á8
15
July
1996
3á0
43á2
1)
5á3
20á7
21á3
)2á8
62á9
68á4
)8á0
²Th
est
art
date
of
the
gro
wth
peri
od
was
est
imate
dfr
om
the
foll
ow
ing
year.
³E
stim
ate
dfr
om
cru
de
pro
tein
con
ten
ts.
§Th
ecu
tdate
was
est
imate
dfr
om
the
pre
vio
us
year.
Not
cut
Not
cut
Not
cut
244 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
possible dates plus inputs of 13á3 g N m±2 in slurry on
four possible dates.
Annual nitrogen balances from simulations with each
of the eight scenarios are listed in Table 8. Results show
large variations in the quantity of nitrogen recycled to
the crop and the quantity and timing of nitrate leached,
with higher crop yields and more nitrate leached with
higher nitrogen applications, but little variation in losses
by denitri®cation between scenarios. Leaching losses are
much higher with four slurry applications (Scenarios 5±
8) than one (Scenarios 1±4), with a tenfold increase in
loss from a fourfold increase in input for Scenarios 5±8
compared with Scenario 2. With a single application of
slurry, leaching is highest when it is applied in October,
lowest when it is applied in March, and intermediate
with December or February applications.
Balances over parts of the season between silage cuts
(not shown in Table 8) indicate a particularly low level
of harvested nitrogen at the third cut with Scenario 6.
This can be attributed to inadequate nitrogen supply
Figure 6 Simulated (ÐÐ) and measured (m) cumulative leached nitrate.
Figure 7 Simulated (ÐÐ) and measured (m) cumulative water ¯ows to drainage tiles.
Modelling C, N and biomass accumulation in grass 245
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
Tab
le6
Annual
and
seas
onal
nitro
gen
bal
ance
sove
rper
iod
of
exper
imen
ts(g
Nm
±2).
Nit
rogen
ap
pli
cati
on
sN
itro
gen
rem
ov
ed
Ch
an
ges
inp
oo
lso
ver
peri
od
Inte
rval
Fert
iliz
er
Man
ure
Atm
os-
ph
eri
c
dep
o-
siti
on
Su
b-
tota
l
In
harv
est
ed
bio
mass
Leach
-
ing
Den
itri
-
®cati
on
Su
b-
tota
l
Pla
nt
(liv
e,
un
cu
t)
Un
dis
-
solv
ed
fert
iliz
er
Pla
nt
resi
-
du
es
Lit
ter
Faeces
Hu
mu
sN
H4
NO
3
Su
b-
tota
lB
ala
nce
28
Au
gu
st1993
±31
Marc
h1994
8á0
215á7
71á4
225á2
10á0
00á9
70á9
71á9
45á3
31á5
8±1á1
60á6
90á7
45á3
63á5
87á2
123á3
3±0á0
6
1A
pri
l1994
±19
May
1994
0á0
00á0
00á2
50á2
57á9
00á1
40á2
98á3
3±6á3
5±1á5
82á5
84á7
9±0á8
72á4
9±3á6
7±6á7
3±9á3
41á2
6
20
May
1994
±5
July
1994
8á7
55á2
60á2
214á2
38á3
70á0
70á8
39á2
7±0á1
80á0
1±1á1
1±0á3
91á2
84á0
80á5
90á5
84á8
60á1
0
6Ju
ly1994
±27
Au
gu
st1994
7á7
90á0
00á3
38á1
27á8
00á2
31á3
69á3
90á1
0±0á0
10á2
8±2á8
8±1á1
73á3
8±0á4
5±0á5
1±1á2
6±0á0
1
28
Au
gu
st1994
±31
Marc
h1995
8á1
97á9
41á4
917á6
20á0
02á0
10á9
52á9
63á6
61á6
1±2á1
41á2
1±1á3
54á7
7±0á3
98á6
816á0
5±1á3
9
1A
pri
l1994
±31
Marc
h1995
24á7
313á2
02á2
940á2
224á0
72á4
53á4
329á9
5±2á7
70á0
3±0á3
92á7
3±2á1
114á7
2±3á9
22á0
210á3
1±0á0
4
1A
pri
l1995
±20
May
1995
0á0
00á0
00á2
10á2
15á3
50á0
50á4
45á8
4±3á3
8±1á6
11á6
21á8
80á8
91á4
1±0á1
8±6á9
6±6á3
30á7
0
21
May
1995
±1
July
1995
7á5
25á4
80á2
213á2
28á0
90á1
31á0
19á2
3±0á8
40á0
2±0á0
3±2á5
31á0
73á1
70á9
40á8
22á6
21á3
7
2Ju
ly1995
±27
Au
gu
st1995
7á5
02á2
00á2
89á9
811á4
70á0
24á9
516á4
40á3
7±0á0
22á2
0±13á6
3±0á6
65á6
4±0á5
2±0á0
7±6á6
90á2
3
28
Au
gu
st1995
±30
Marc
h1996
0á0
024á4
01á3
425á7
40á0
01á7
51á1
52á9
05á7
80á0
0±3á5
07á5
3±4á0
23á5
58á5
67á4
425á3
4±2á5
0
1A
pri
l1995
±30
Marc
h1996
15á0
232á0
82á0
549á1
524á9
11á9
57á5
534á4
11á9
3±1á6
10á2
9±6á7
5±2á7
213á7
78á8
01á2
314á9
4±0á2
0
Note
:ro
ws
inh
eavy
pri
nt
are
an
nu
al
tota
ls.
246 L. Wu and M. B. McGechan
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
over the period between the ®rst and third cuts when no
mineral fertilizer is applied, despite the large nitrogen
input over the previous winter. This suggests that
mineral fertilizer needs to be applied during the current
growth period in order to obtain an adequate yield.
Discussion
The precision with which carbon and nitrogen cycling,
as biological and physical processes in the soil±grass±
atmosphere continuum, can be represented by a soil
nitrogen dynamics model such as SOILNSOILN depends on an
accurate description of the main constituent processes.
The most important process is the growth of the crop,
since extraction of nitrogen by this route is the largest
¯ow in the system. Development, parameterization and
testing of a growth submodel for grass in this study, as an
alternative to the previously developed submodel for
cereal crops, represents a major step towards a model
representation of soil nitrogen cycling processes in
grassland soils receiving slurry as well as mineral
fertilizer inputs. The predicted cut biomass yields and
nitrogen contents of cut grass were in reasonable
agreement with measured values by the standards
commonly found when modelling complex biological
processes, and also considering the uncertainty about
equation forms and parameter values for some of the
processes. This work has indicated areas where model
representation of growth processes might be improved if
more detailed measurements were available, including
allocation of biomass to plant components, senescence
processes, and the effects of soil water constraints.
The other important nitrogen ¯ows, but of smaller
magnitude than crop uptake, are losses of nitrate by
leaching and by denitri®cation, and build-up of soil
humus from applications of organic material in slurry.
An accurate description of these processes depends on
the description of water processes in the SOI LSOIL model,
especially ¯ows through ®eld drains which transport
leached solutes. This study has shown how ®eld
measurements of leached nitrate can be used to check
that the combined crop growth and soil nitrogen model
is operating in a reasonable manner, despite uncertain-
ty about some parameter values.
Figure 8 Simulated dynamics of grass nitrogen uptake (ÐÐ) and nitrate leaching (- - - -). (Leached nitrate ®gures multiplied by 10.)
Table 7 Dates of slurry and fertilizer applications.
Slurry application Mineral fertilizer application
Scenario 15 October 15 December 1 February 15 March 22 March 24 May 11 July
1 X X X X
2 X X X X
3 X X X X
4 X X X X
5 X X X X X X X
6 X X X X
7 X X X X X
8 X X X X X X
Modelling C, N and biomass accumulation in grass 247
Ó 1998 Blackwell Science Ltd. Grass and Forage Science, 53, 233±249
The value of the parameterized model in predictive
mode has also been demonstrated. This further illus-
trates that a very high proportion of applied nitrogen can
be recycled to harvested grass biomass owing to the long
growth period of the grass crop, and that nitrate leaching
is very dependent on the timing and quantity of slurry
and fertilizer application. The proportion of nitrogen
recycled is maximized and environment-polluting losses
of nitrate are minimized by carrying out a single slurry
application in spring, while mineral fertilizer applica-
tions should be targeted to each relevant growth period
in order to achieve the desired yield.
This study represents the link between two important
components (the soil and the crop) in a closed-loop
representation of nitrogen cycling round a grassland
and ruminant livestock system.
Acknowledgments
This research was supported by funds from the Scottish
Of®ce Agriculture, Environment and Fisheries Depart-
ment, and also from the European Union under the
project `Optimal use of animal slurry for input reduc-
tion and protection of the environment in sustainable
agricultural systems'.
The authors express sincere thanks to: Professor P.-E.
Jansson and Dr H. Eckersten of the Department of Soil
Sciences, The Swedish University of Agricultural Sci-
ences, Uppsala, for permission to use the source code of
the SOILNSOILN and SOILSOI L models; Dr C. F. E. Topp of SAC
Auchincruive for access to her source code in BASIC for
the photosynthesis equations; John Bax of SAC
Crichton Royal Farm for providing measured crop data
and information about fertilizer and slurry applications;
and Dr P. S. Hooda of SAC Auchincruive for supplying
the drain¯ow and nitrate leaching data.
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Mea
nan
nual
nitro
gen
bal
ance
sove
r10
year
s(g
Nm
)2).
Nit
rogen
ap
pli
cati
on
sN
itro
gen
rem
ov
ed
Ch
an
ges
inp
oo
lso
ver
peri
od
Scen
ari
oF
ert
iliz
er
Slu
rry
Dep
o-
siti
on
Su
b-
tota
lH
arv
est
Leach
ing
Den
itri
-
®cati
on
Su
b-
tota
lP
lan
t
Un
dis
-
solv
ed
fert
iliz
er
Resi
du
eL
itte
rF
aeces
Hu
mu
sN
H4
NO
3
Su
b-
tota
lB
ala
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123á2
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42á2
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60á0
20á0
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0á9
80á0
015á1
30á0
10á0
914á3
70á0
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223á2
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323á2
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60á0
20á0
00á1
0)
0á9
80á0
015á1
30á0
10á0
914á3
70á0
4
423á2
113á3
42á2
238á7
718á3
22á5
33á5
924á4
50á0
80á0
00á1
1)
0á8
60á0
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248 L. Wu and M. B. McGechan
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Recommended