Simulation of biomass, carbon and nitrogen accumulation in grass to link with a soil nitrogen dynamics model

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


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


7

cS ms 0á0259 d±1 Fraction of stem biomass


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



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.



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.



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: Ð± 1á6; - - - - - 1á8; ± - - ± 2á0; ± ± ± 2á2;

± - ± - 2á4; ÐÐ2á5. (Stage of development index

Dix � 1 at emergence and 4 at maturity).



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).



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



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).



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).



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



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.



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.



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



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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le8

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

nce

123á2

113á3

42á2

238á7

719á7

11á0

13á6

324á3

60á0

20á0

00á1

0)

0á9

80á0

015á1

30á0

10á0

914á3

70á0

4

223á2

113á3

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538á7

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031á0

40á9

50á0

2)

2á1

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7)

0á6

67á0

81á0

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88á7

7)

1á0

7

323á2

113á3

42á2

238á7

719á7

11á0

13á6

324á3

60á0

20á0

00á1

0)

0á9

80á0

015á1

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10á0

914á3

70á0

4

423á2

113á3

42á2

238á7

718á3

22á5

33á5

924á4

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0

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153á3

62á2

278á7

944á7

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658á9

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9)

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2á6

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5)

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0á0

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815á0

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270á6

038á6

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13á5

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1)

0á0

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318á6

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319á0

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0



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Simulation of biomass, carbon and nitrogen accumulation in grass to link with a soil nitrogen dynamics model