Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol65.Issue2.Aug2000:

Agricultural Systems 65 (2000) 73±97
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Modelling the growth and utilisation of kikuyu
grass (Pennisetum clandestinum) under grazing.
1. Model de®nition and parameterisation
M. Herrero a,b,*, R.H. Fawcett a, V. Silveira a, J. Busque a,
A. BernueÂs a, J.B. Dent c
a

Institute of Ecology and Resource Management, University of Edinburgh, West Mains Road,
Edinburgh EH9 3JG, UK
b
International Livestock Research Institute, PO Box 30709, Nairobi, Kenya
c
Royal Agricultural College, Cirencester, Gloucestershire GL7 6JS, UK
Received 20 August 1999; received in revised form 8 October 1999; accepted 1 May 2000

Abstract
This paper describes a Tropical Pasture Simulator. This is a simple mechanistic model
representing the growth of vegetative tropical pastures under rotational or continuous grazing

with responses to N fertiliser, temperature and irradiance that can be used as a tool to study
management options for the development of sustainable grazing systems. The model was
derived as an adaptation of the `Hurley pasture models' which were originally designed to
simulate the growth of ryegrass. The model incorporates the processes of light interception
and photosynthesis, leaf area expansion; growth, ageing and senescence of plant tissues,
recycling of substrates from senescing tissues, nitrogen uptake, mineralisation of soil organic
N, N leaching and grazing. The paper discusses the main structure of the model, its components and the main adaptations required to simulate the growth of tropical pastures. The
model was parameterised for kikuyu grass (Pennisetum clandestinum) growing in dairy systems in highland regions of Costa Rica. This paper also discusses the sensitivity of parameter
values and the development of minimum parameter datasets for time and cost-e€ective
implementation of the model. # 2000 Elsevier Science Ltd. All rights reserved.
Keywords: Grazing; Pennisetum clandestinum; Grazing management; Modelling; Costa Rica

* Corresponding author. Tel.: +44-131-5354384; fax: +44-131-6672601.
E-mail address: mario.herrero@ed.ac.uk (M. Herrero).
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00028-7

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M. Herrero et al. / Agricultural Systems 65 (2000) 73±97

1. Introduction
In pastoral ruminant production systems, grassland management is one of the key
determinants of their success (Humphreys, 1991; Clark and Jans, 1995). Together,
with environmental variables, it determines the quantity and quality of herbage
available and the physical structure of the sward, factors which will subsequently
determine pasture intake and animal performance. However, sustainable grazing
management should ensure both the ecological stability of the grazing system and
therefore requires knowledge about the processes controlling growth and utilisation
of pasture species.
The complexity of grazing-based animal production systems justi®es the development of whole farm decision support systems (DSS) based on the integration of
simulation, optimisation modelling and participatory approaches such as those
outlined by Herrero et al. (1996, 1997). The objective of this paper is to describe the
development of the Tropical Pasture Simulator (TPS), one of a range of components
of such integrated DSS. The TPS is a simple mechanistic model representing the
growth of vegetative pasture under rotational or continuous grazing with responses
to N fertiliser, temperature and irradiance that can be used as a tool to study management options for the development of sustainable grazing systems. Minimum
datasets were de®ned and parameterisation of the model was done with reference to
kikuyu grass (Pennisetum clandestinum) growth and utilisation in dairy systems of
the highlands of Costa Rica. Validation and analyses of management practices were

studied with the model, and results are presented in a companion paper (Herrero et
al., 2000).

2. Model description
2.1. General characteristics
The objective of the model is to predict growth, instantaneous herbage mass and
sward structure of a vegetative pasture sward under rotational or continuous grazing with responses to N fertiliser applications, temperature and irradiance. The
model was built in SB-ModelMaker (Zeton Tech, Nottingham, UK) and runs at
daily timesteps.
The main processes represented include light interception and photosynthesis, leaf
area expansion; growth, ageing and senescence of plant tissues, recycling of substrates from senescing tissues, nitrogen uptake, mineralisation of soil organic N, N
leaching and grazing.
The sward is divided into leaves, stems and roots of di€erent age categories to
simulate the ageing processes leading to senescence, accumulation and turnover of
dead material (Johnson and Thornley, 1985). The division of the above-ground
dry matter into leaves, stems and dead material is necessary for an adequate
description of the e€ect of grazing management practices on sward structure, which
is an important factor controlling intake of grazing ruminants and to simulate diet

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75

selection (Stobbs, 1973; Chacon and Stobbs, 1976; Hodgson, 1985; Ungar and NoyMeir, 1988). Sward height and bulk density are also incorporated from empirical
relations obtained from experimental observations.
The two main substrates controlling plant growth are modelled. Carbon (C),
synthesised from photosynthesis by the canopy leaf area index (LAI) and nitrogen
(N), obtained from the soil by the roots from mineralisation of organic N and N
fertiliser. Recycling of nutrients from dead material also contribute to the C and
N pools. Substrates are partitioned towards new growth of the above- or belowground plant parts or are lost by maintenance and growth respiration in the case of
C or by leaching in the case of N.
The basic structure of the grass growth model was derived from the ryegrass
models of Johnson and Thornley (1983, 1985) and Thornley and Veberne (1989;
often referred to as the `Hurley pasture models'). These were chosen as the starting
point because:
1. They were mechanistic, and therefore, the description of the physiological
processes represented could be used to simulate the growth of, not only kikuyu
grass, but of a range of pasture species. This was an important objective for
future work in other agroecological zones (increased ¯exibility).
2. They were driven by environmental and management variables, hence, the

model could be calibrated for agro-ecoregions and production systems with
di€erent characteristics (increased adaptability and portability).
3. They were robust and ¯exible models which had been validated.
4. The user-de®ned management parameters and variables were simple and easily
obtainable at farm level. They represented the variables that farmers currently
use to make grazing management decisions. For example, stocking rates (animal numbers), bodyweight of animals (sizes), rate of N fertiliser application,
paddock rotation lengths (rest period and grazing days), and land area available for grazing.
Several steps were followed for the construction of the model. The ®rst step consisted of writing the Johnson and Thornley (1985) model with parameters for ryegrass in SB-ModelMaker. Comparison of outputs against the original source code
showed good agreement (Herrero and Thornley, unpublished). The second step was
to adopt some changes of the model of Thornley and Veberne (1989), which was an
expansion of the Johnson and Thornley (1983, 1985) models. These were: (1) aboveground biomass was separated into leaves, stems and dead material for a better
de®nition of the plant/animal interface (Herrero et al., 1998); (2) photosynthesis was
described by a non-rectangular hyperbola to obtain biologically meaningful parameters and a better ®t of the photosynthesis data (Johnson and Thornley, 1984;
Hanson et al., 1988; Johnson et al., 1989); (3) the functions describing the partitioning of nutrients between roots and shoots were also changed. The teleonomic
functions described by Thornley and Veberne were used to allow the maintenance of
a substrate-dependent equilibrium between shoot and root maximum growth; and
(4) grazing was incorporated using the Johnson and Parsons (1985) model based on

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M. Herrero et al. / Agricultural Systems 65 (2000) 73±97

LAI and implemented in Thornley and Veberne's model. This was further adapted
to account for larger sward structure di€erences of tropical pastures (Stobbs, 1975).
The next step consisted of a sensitivity analysis of model outputs to changes in
parameter values. This step was considered very important for the de®nition of a
minimum dataset of parameters for an ecient data gathering process and parameterisation of the model to kikuyu grass in the highlands of Costa Rica. The model
was not sensitive to changes in some parameters. Values for these were not changed,
even if they did not re¯ect the real values for the species modelled. The adaptation
and parameterisation of the model to kikuyu grass started after these basic steps had
been followed. The last stages consisted of model validation and an analysis of
grazing management practices (Herrero et al., 2000).
The following sections describe brie¯y the model and a summary of the variables
used is presented in Appendix A. Readers are referred to Johnson and Thornley
(1983, 1985) and Thornley and Veberne (1989) for a complete description of the
model. A similar de®nition as the Hurley models has been used, unless otherwise
stated.
2.2. Sward components
The sward was divided into three components: leaf laminae (WL1ÿ4), sheaths and
stems/stolons (Wsh1ÿ4), and roots (Wr1ÿ4) (all in kg structure/m2). All components

were represented using four compartments for each component to describe the ageing processes of the sward (1=new to 4=senescent) and to appropriately describe
tissue turnover within the sward. These are presented in Appendices A and B.
Therefore, total live leaf (WL), stem/stolon (Wsh) and root (Wr) structural masses
(kg structure/m2) are, respectively:
X
W Li
…1†
WL ˆ
i

X
Wshi
Wsh ˆ

…2†

i

Wr ˆ

X
Wri

…3†

i

The last compartment (i=4) comprises senescing material, and it is assumed that
only 0.5 of each of these compartments is alive.
The above-ground (Wag) and total sward live structural weight (Wg) then become,
respectively (kg structure/m2):
Wag ˆ WL ‡ Wsh

…4†

Wg ˆ WL ‡ Wsh ‡ Wr

…5†

The total crop weight is described by its structural and storage weights, with

the storage compartment representing the fractional substrate pools within the

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77

plant parts. Although there are di€erences in the substrate concentrations of
above-ground plant parts of kikuyu grass, their distribution between roots
and above-ground plant parts is considered homogeneous, therefore:
X
X
Wagt ˆ fsh Ws ‡
W Li ‡
Wshi
…6†
i

i

X

Wr i
Wrt ˆ fr Ws ‡

…7†

i

where Wagt and Wrt are the total above- and below-ground dry weights (kg DM/m2),
respectively, and fsh and fr are the above-ground and root structural fractions.
2.3. LAI, light interception and photosynthesis
The main input to the C cycle comes from photosynthesis as a product of light
interception by the canopy LAI. Each structural compartment WL1ÿ4 contribute to
the LAI (L1ÿ4) as follows:
X
Li
…8†
LAI ˆ
i

where the contribution of each leaf compartment is represented by:

dL1
dt
dL2
dt
dL3
dt
dL4
dt

ˆ SLAfL Gsh ÿ 2
sh L1 ÿ garea1

…9†

ˆ 2
sh L1 ÿ
sh L2 ÿ garea2

…10†

ˆ
sh L2 ÿ
sh L3 ÿ garea3

…11†

ˆ
sh L3 ÿ
sh L4 ÿ garea4

…12†

In Eq. (9), SLA represents the speci®c leaf area (m2 leaf/kg structure). This
parameter is dependent on the carbon concentration (C), hence on irradiance and
photosynthesis (Ludlow and Wilson, 1971; Jones, 1992).
sh is the rate of leaf
appearance per day and garea, is the fraction of the LAI removed by grazing in each
leaf compartment.
The original Hurley model (Johnson and Thornley, 1983) used a rectangular
hyperbola to represent single leaf photosynthesis; however, it was replaced by a nonrectangular hyperbola (Johnson and Thornley, 1984) in the subsequent models
(Johnson and Thornley, 1985; Thornley and Veberne, 1989) because it provided
better descriptions of photosynthesis data and it had biologically interpretable
parameters (Johnson et al., 1989; Jones, 1992; Hanson et al., 1994). It is described by:

0:5 i
1h
I ‡ Pmax ÿ …I ‡ Pmax †2 ÿ4IPmax
…13†
Pg ˆ
2

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M. Herrero et al. / Agricultural Systems 65 (2000) 73±97

where Pg is single leaf photosynthesis (mg CO2/m2/s).  is the light utilisation eciency of leaves (mg CO2/J). Pmax is the asymptote value of Pg at saturated irradiance, which is temperature-dependent.  is a dimensionless parameter (0