Description of the AgPasture and SoilN modules in APSIM
AgPasture has the facility to model multiple
plant species. Typically the module is
parameterised for grass-legume mixtures
(using ryegrass (Lolium perenne L.) and
white clover (Trifolium repens L.) as
representatives of these functional groups)
and this formulation has been thoroughly
validated against long-term, multi-site
grassland measurements from a diverse
range of soil types and climatic zones in
New Zealand (Li et al. 2011). The model
projects daily plant potential growth from
intercepted solar radiation, temperature,
atmospheric CO2 and the N concentration of
plant green tissue. Plant water and nutrient
limitation reduce plant growth from
potential to actual (Fig. S1). Plant water
demand is calculated using the PenmanMonteith approach and a water limitation
effect (fW) is derived using the ratio of
actual plant water uptake to water demand.
Plant N demand and N-deficiency effects
are quantified using the concept of a critical
N concentration (Lemaire & Salette 1984;
Ghannoum et al. 2006) which assumes each
plant species has a maximum (Nmax),
minimum (Nmin) and critical (Ncrit) shoot N
concentration during its growth and
development. In the pasture model, the
maximum N uptake by new plant growth is
calculated using the Nmax of different plant
organs, while the N demand for new plant
growth is calculated using Ncrit. A soil N
growth limitation factor for pasture growth
(fNs) is quantified using the ratio of plant
available N in soil to plant N demand. The
symbiotic N fixation (SNF) of legumes is
estimated based on fNs using the approach
described in Thornley et al. (1995),
Schwinning and Parsons (1996a) and
Johnson et al. (2003). An explicit
partitioning of plant available N among the
plant species is done each day according to
their N demands from the soil, and the fNs
of each species is updated separately.
Plant species have different strategies to
manage N limitation (Vos et al. 2005;
Lemaire et al. 2008). They may reduce C
uptake, and therefore leaf area expansion, in
order to maintain a high leaf N
concentration (“C uptake reduction”) or may
dilute leaf N concentration to allow maximal
C intake (“leaf N dilution”) or a
combination of both strategies in different
proportions (Vos et al. 2005; Lemaire et al.
2008; Li et al. 2009). A dilution coefficient
(ρ) is used to describe the difference among
species in the pasture model, i.e., to relate
actual daily plant growth to its potential
without N limitation by a factor (fNs)ρ ( Fig.
S1). The coefficient ρ can vary between 0
and 1, with ρ = 1 indicating a complete “C
uptake reduction” strategy, while a ρ of 0
indicating a complete adoption of the
opposite “leaf N dilution” strategy. In the
pasture model, we assumed a value of ρ =
0.5 for grasses, i.e., grasses respond to N
limitation through both leaf N dilution and C
uptake reduction but a ρ = 1.0 for legumes
reflecting their capacity to maintain a high N
concentration when soil N is low through
SNF and a reduction in C uptake, as shown
by Darvey et al. (1999). The dilution of
plant N concentration in turn reduces plant
radiation use efficiency (RUE) by a plant N
nutrition factor defined as
fNc = (Nactual - Nmin)/( Ncrit - Nmin)
The effects of elevated CO2 on plant growth
are modelled as described in Cullen et al.
(2009) by modifying plant functions for
photosynthesis (fCp), N demand (fCn) and
stomatal conductance (fCs) (Fig. S1) That is,
plant potential growth is determined not
only by intercepted solar radiation and air
temperature but also by atmospheric CO2
concentration (through fCp) and the N
concentration of plant green tissue (through
fNc). Plant water demand is affected by
elevated CO2 through reduced stomatal
conductance (fCs). Water limitation (via fW)
reduces plant growth and associated N
demand. The CO2-induced reduction of
plant N demand (fCn) reduces plant Ncrit,
while plant Nmax and Nmin are unchanged
1
(Hartwig et al. 2000). The reduction in plant
Ncrit means a reduction in the N required to
produce a unit of dry matter, resulting in an
increase in plant photosynthetic N use
efficiency (PNUE).
The SoilN module models soil N and C
dynamics (Probert et al. 1998). In SoilN, soil
organic matter (SOM) is divided into two
pools: a biomass pool representing the more
labile soil microbial biomass and microbial
products, and a humus pool comprising the
rest of the SOM. A fraction of the humus
pool is specified as inert, which is
conceptually the same as the ‘passive’ SOM
pool in the CENTURY model (Parton et al.
1988); the rest of the pool is considered to
be recalcitrant SOM. Plant shoot and root
senescence deposits fresh organic matter
into either the fresh organic matter pool on
the soil surface (modelled by the SurfaceOM
module) or the fresh organic matter pool
(FOM) in the soil. Decomposition of the
fresh organic matter results in evolution of
CO2 to the atmosphere and transfers of
carbon and nitrogen to the biomass and
humus pools of SOM, thereby entering the
processes of SOM transformation and
turnover (Probert et al. 1998). Plant litter
quality is specified through lignin, fibrous
and carbohydrate fractions which have
different decomposition rates. Changes in
plant litter quality affect all SOM processes.
No direct CO2 effects are explicitly added to
the soil processes.
References
Cullen BR, Johnson IR, Eckard RJ, Lodge GM,
Walker RG, Rawnsley RP, Mccaskill MR (2009)
Climate change effects on pasture systems in southeastern Australia. Crop and Pasture Science, 60, 933942.
Darvey PA, Parsons AJ, Atkinson L, Wadge K, Long
SP (1999) Does photosynthetic acclimation to
elevated CO2 increase photosynthetic nitrogen-use
efficiency? A study of three native UK grassland
species in open-top chambers. Functional Ecology,
13 (Suppl.1), 21-28.
Ghannoum O, Searson MJ, Conroy JP (2006)
Nutrient and water demand of plants under global
change. In: Agroecosystems in Changing Climate
(eds Newton PCD, Carran RA, Edwards GR, Niklaus
PA), pp 53-84. Taylor & Francis, Boca Raton.
Hartwig UA, Lüscher A, Daepp M, Blum H,
Soussana J-F, Nösberger J (2000) Due to symbiotic
N2 fixation, five years of elevated atmospheric pCO2
had no effect on the N concentration of plant litter in
fertile, mixed grassland. Plant and Soil, 224, 43-50.
Johnson IR, Lodge M, White RE (2003) The
sustainable grazing systems pasture model:
description, philosophy and adaptation to the SGS
national experiment. Australian Journal of
Experimental Agriculture, 43, 711-728.
Lemaire G, Salette J (1984) Relation entre
dynamique de croissance et dynamique de
prelevement d’azote pour un peuplement de
graminees fourageres I. Etude de l‘effet du milieu.
Agronomie, 4, 423-430.
Lemaire G, van Oosteron E, Jeuffroy M-H, Gastal F,
Massignam A (2008) Crop species present different
qualitative types of response to N deficiency during
their vegetative growth. Field Crops Research, 105,
253-265.
Li FY, Jamieson PD, Johnstone PR, Pearson AJ
(2009) Mechanisms of nitrogen limitation affecting
maize growth: a comparison of different modelling
hypotheses. Crop & Pasture Science, 60, 738–
752.
Li FY, Snow VO, Holzworth D (2011) Modelling the
seasonal and geographical pattern of pasture
production in New Zealand. New Zealand Journal of
Agricultural Research, 54, 331-352.
Parton WJ, Stewart JWB, Cole CV (1988) Dynamics
of C, N, P, and S in grassland soils: A model.
Biogeochemistry, 5, 109-131.
Probert ME, Dimes JP, Keating BA, Dalal RC,
Strong WM (1998) APSIM's water and nitrogen
modules and simulation of the dynamics of water and
nitrogen in fallow systems. Agricultural Systems, 56,
1-28.
Schwinning S, Parsons AJ (1996a) Analysis of the
coexistence mechanisms for grasses and legumes in
grazing systems. Journal of Ecology, 84, 799-813.
Thornley JHM, Bergelson J, Parsons AJ (1995)
Complex dynamics in a carbon-nitrogen model of
grass-legume pasture. Annals of Botany, 75, 79-94.
Vos J, van der Putten CEL, Birch CJ (2005) Effects
of nitrogen supply on leaf appearance, leaf growth,
leaf nitrogen economy and photosynthetic capacity in
maize (Zea mays L.). Field Crops Research, 93, 6473.
2
Gross photosynthesis
GP = ψ × Pm × PAR × fT × fNc × fCp
Potential Growth
GG = GP – α×GP – β× B ×fT× fNc
Growth with water-deficit effect
Gw = GG × fW  fCs
Net growth with N-deficit effect
Gwn = Gw × (fNs)ρ
where fNs = Nupt/Ndem
Ndem =∑ Gw × Ncrit × fCn
fNc = (Nact - Nmin)/( Ncrit× fCn - Nmin)
Net herbage accumulation
Plant dry matter partitioning and tissue
turnover, also affected by fT & fW
Parameters:
α:
coefficient for growth respiration
β:
coefficient for maintenance
respiration
ψ:
canopy scaling factor
PAR: photosynthetically active radiation
Pm:
maximum CO2 assimilation rate
fCp:
fCn:
fCs:
CO2 enhancing photosynthesis
CO2 reducing plant N demand
CO2 reducing plant stomatal
conductance
fT:
fW:
temperature factors
(different in different processes)
water growth limiting factor
fNc:
fNs:
ρ:
Nupt :
Ndem:
Nact:
Ncrit:
Nmin:
plant N concentration factor
soil N growth limiting factor
plant N dilution coefficient
plant N uptake
plant N demand
actual N concentration
critical N concentration
minimum N concentration
Fig. S1 Plant growth process during a daily time step in the pasture model, showing the
effects of CO2 being added using plant response functions to enhance photosynthesis (fCp),
reduce N demand (fCn) and reduce stomatal conductance (fCs).
3