38 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
1. Introduction
field measurements in different climates Keating et al., 1995; Probert et al., 1995, 1998; Asseng
Optimising economic returns in high N input et al., 1998a,b; Meinke et al., 1998 and have been
systems, while minimising loss of N to the environ- used in agronomic studies of farming system sustai-
ment, is one of the challenges for modern agricul- nability in the eastern states of Australia Keating
tural production Whitmore and Van Noordwijk, et al., 1995; Probert et al., 1995, 1998 and Western
1995. Field studies have been conducted to optim- Australia Asseng et al., 1998a.
ise N fertiliser in wheat production, but the results This paper aims 1 to evaluate the performance
are always season-specific e.g. Spiertz and Ellen, of the APSIM Nwheat model against a wide range
1978; Spiertz and Van de Haar, 1978; Ellen and of detailed field measurements in the temperate
Spiertz, 1980; Chaney, 1990; Darwinkel, 1998. maritime climate of the Netherlands, and 2 to
Crop models have been used to optimise manage- use the model in a simulation experiment to study
ment practices under variable environments Van interactions between N-fertiliser application and
Keulen and Seligman, 1987; Stapper and Harris, grain yield, grain protein and soil residual N.
1989; Keating et al., 1991; Meinke et al., 1993; Savin et al., 1995; Thornton et al., 1995; Asseng
et al., 1998a and application of a simulation
2. Material and methods
model may be useful in extrapolating results from such experiments. However, such simulation
2.1. APSIM Nwheat model models require rigorous testing against a wide
range of field experimental data before they can APSIM is a software tool that enables modules
be used with confidence Monteith, 1996; O’Leary or sub-models to be linked to simulate agricul-
and Connor, 1996. Groot and Verberne 1991 tural systems McCown et al., 1996. Examples of
have published a detailed data set, covering modules include crops, pastures, soil water, nitro-
different sites, seasons and N regimes in the gen and erosion. Four modules, i.e. wheat crop
Netherlands to validate simulation models for NWHEAT , soil water SOILWAT , soil N
nitrogen dynamics in crop and soil. Parts of these SOILN and crop residue RESIDUE are most
data have been used to validate models, with relevant to the simulation of wheat-based cropping
various success De Willigen, 1991. Whereas most systems. These modules have evolved from experi-
of these models dealt with the above-ground pro- ences in Australia with the CERES crop and soil
cesses more or less adequately, simulation of models Ritchie et al., 1985; Jones and Kiniry,
below-ground processes was more problematic. In 1986, and the PERFECT model Littleboy et al.,
particular, processes of N mineralisation and 1992, as modified by Probert et al. 1995, 1998.
immobilisation, which are related to soil microbio- The main differences of the APSIM Nwheat model
logical activities, were poorly simulated De to the CERES wheat model are summarised by
Willigen, 1991. Probert et al. 1995 and Asseng et al. 1998b.
The APSIM Agricultural Production Systems Documented model source code in hypertext
Simulator Nwheat model comprises crop growth, format can be obtained by writing to Dr B.A.
soil water, nitrogen and crop residue modules Keating CSIRO Division of Tropical Agriculture,
McCown et al., 1996 and enables simulation of Brisbane,
Australia, e-mail:
brian.keatingc- their interactions. The wheat crop, soil water and
netns.tcp.csiro.au or can be viewed at www.apsim- soil nitrogen modules Keating et al., 1995; Probert
help.tag.csiro.au. et al., 1995, 1998 have been derived from the
CERES models of Ritchie et al. 1985 and Jones 2.2. Experimental data
and Kiniry 1986, which have been widely tested and applied e.g. Ritchie et al., 1985; Otter-Nacke
The APSIM Nwheat model with modules et al., 1986; Savin et al., 1994, 1995; Thornton et al.,
NWHEAT, SOILWAT, SOILN and RESIDUE in 1995; Toure´ et al., 1995. Results of the APSIM
Nwheat model have been compared with various the APSIM framework was tested using field meas-
39 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
Table 1 Experimental sites used for simulation — observation comparisons Groot and Verberne, 1991
Location Latitude °N
Longitude °W Soil type
Rainfall mm The East
52.4 5.4
Silty loam 646
a PAGV
52.3 5.3
Silty loam 646
a The Bouwing
51.6 5.4
Silty clay loam 763
b a Average annual rainfall between 1974 and 1988.
b Average annual rainfall between 1954 and 1996.
urements from 18 treatments at three locations N treatments are listed in Table 2.
These data sets include frequently measured and two seasons, published by Groot and Verberne
1991. These data were used for a statistical shoot biomass, shoot N content, leaf area, grain
yield and its components, grain N, phenological analysis of model performance. An additional
qualitative test, involving the comparison of simu- development stages, changes in soil water, ground-
water table depth and soil N and are described in lated with measured data, was carried out with 1
grain yields from a long-term winter wheat detail by Groot and Verberne 1991.
To account for a continuous wet and dry depos- field experiment at Lovinkhoeve K.B. Zwart,
AB-DLO, pers. comm., 2 yield data from ition
of N
in the
Netherlands of
about 35–50 kg N ha−1 year−1 Neeteson and Hassink,
experiments by Spiertz and Ellen 1978 and Spiertz and Van de Haar 1978 and 3 grain
1997, a monthly N deposition of 3 kg N ha−1 of ammonium nitrate for sites at the Eest and PAGV
yields from the Gelderland region Crop Estimates, Statistics Netherlands, Voorburg. All grain yields
and 4 kg N ha−1 for the Bouwing was assumed for all model simulations.
refer to dry matter weight, and all grain protein contents are based on grain dry weight.
The soil characteristics are listed in Tables 3a and 3b. Simulations were initialised using the first
soil water and soil N measurements of each season 2.2.1. Field measurements after Groot and
Verberne 1991 Groot and Verberne, 1991. Hence, all the first
soil measurements were excluded from the statisti- Measured data sets from field experiments from
two Polder sites, a silty loam at the Eest and at cal performance test.
Estimated quantities of residues used to ini- PAGV near Swifterbant, and from a third site,
a silty clay loam at the Bouwing near Wageningen tialise the model are presented in Table 4.
SOILN module parameters for the effects of the Netherlands, were used for model validation.
A site description is given in Table 1. soil water content on the processes of mineralisa-
Table 2 N-fertiliser treatments N1, N2 and N3 after Groot and Verberne 1991
a Location
Year N1 kg N ha −1
N2 kg N ha −1 N3 kg N ha −1
The East 1983
60 120+40
1984 50+60
50+60+40 50+60+40
PAGV 1983
80 60+80
60+140+40 1984
80 80+60+40
80+120+40 The Bouwing
1983 60
120+40 1984
70 70+60+40
70+120+40 a Each number represents an application amount at a particular application time. The first application occurred in February, the
second application between tillering and the beginning of stem elongation, and the third application between flag leave stage and anthesis.
40 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
Table 3a Soil characteristics for the Eest, PAGV and the Bouwing
a Layer depth cm
The Eest PAGV
The Bouwing Silty loam
Silty loam Silty clay loam
LL m 3 m−3
DUL m 3 m−3
OC kg kg−1 OC kg kg−1
LL m 3 m−3
DUL m 3 m−3
OC kg kg−1 0–5
0.09 0.34
0.030 0.023
0.18 0.39
0.028 5–10
0.09 0.34
0.030 0.023
0.18 0.39
0.028 10–20
0.09 0.34
0.030 0.023
0.18 0.39
0.028 20–30
0.10 0.34
0.030 0.023
0.18 0.39
0.028 30–40
0.12 0.35
0.020 0.018
0.18 0.37
0.014 40–60
0.14 0.37
0.020 0.018
0.20 0.37
0.014 60–80
0.14 0.39
0.018 0.018
0.20 0.37
0.012 80–100
0.14 0.39
0.018 0.018
0.20 0.37
0.012 100–130
0.14 0.39
0.010 0.010
0.20 0.37
0.010 130–200
0.14 0.39
0.010 0.010
0.20 0.37
0.010 Total mm
264 757
392 746
a LL: lower limit for plant available soil water; DUL: drained upper limit; OC: soil organic carbon. Data after Groot and Verberne 1991. LL and DUL for PAGV are the same as for the Eest.
Table 3b
cients [after Ritchie et al. 1985] for Arminda are
Relative soil water conductivity K s
— proportion of water
given in Table 5.
above DUL flowing to the next deeper layer with a time step of 1 day and relative macro flow water conductivity K
m —
proportion of water in macro pores flowing to the next deeper
2.2.2. Grain yields from a long-term winter wheat
layer with a time step of 1 day for the Eest, PAGV and the
experiment at Lovinkhoeve
Bouwing
Simulation results were compared with grain yields from a long-term winter wheat experiment
Layer depth The Eest, PAGV,
The Eest, The
cm the Bouwing
PAGV Bouwing
at Lovinkhoeve near the Eest site, same soil type as at the Eest with three N treatments 0, 38 and
K m
− K
s −
K s
−
188 kg N ha−1 between 1974 and 1987 K.B. Zwart, AB-DLO, pers. comm.. The wheat variety
0–5 1
0.5 0.5
5–10 1
0.5 0.5
Arminda was sown in most years, except in 1974
10–20 1
0.5 0.5
when Clement, and 1975, 1977 when Lely was
20–30 1
0.5 0.5
sown. Since Arminda, Clement and Lely are rela-
30–40 1
0.5 0.5
tively similar in phenology and yield parameters,
40–60 1
0.4 0.5
the genetic coefficients of Arminda were used for
60–80 1
0.3 0.5
80–100 1
0.1 0.5
all the simulations. In every fourth year, no yield
100–130 1
0.1 0.3
data were available from the long-term experiment
130–200 0.6
0.001 0.001
due to a specific rotation K.B. Zwart, AB-DLO, pers. comm..
For two years, in 1976 and 1977, additional yield measurements from N-fertiliser experiments
tion and nitrification, and the potential decomposi- tion rates for soil organic matter 0.00015 day−1
at the same location by Spiertz and Ellen 1978 and Spiertz and Van de Haar 1978 were included
and microbial biomass 0.0081 day−1 were used as suggested by Probert et al. 1998 for clay soils
in the comparison. Each year was re-initialised with the soil water and sowing conditions from
in Queensland, Australia. The sown variety was Arminda. Genetic coeffi-
the Eest in 1982 Groot and Verberne, 1991;
41 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
Table 4 Initial plant residues for 17 October 1982 and 1983, for the Eest, PAGV and the Bouwing
Location The Eest
PAGV The Bouwing
1982, 1983 1982
1983 1982, 1983
Above ground Amount t ha−1
4.0 4.0
2.0 4.0
C:N ratio 50
50 50
50 Relative potential decomposition rate d−1
0.05 0.05
0.05 0.05
Root residues Amount t ha−1
1.5 1.5
1.0 1.5
C:N ratio 50
50 50
50 Residue type
Potatoes Sugarbeets
Sugarbeets Potatoes
Table 5 Wheat genotype coefficients for cv. Arminda
Arminda Coefficient
Explanation 4.0
p1v Sensitivity to vernalisation [1 lowest–5 highest]
4.0 p1d
Sensitivity to photoperiod [1 lowest–5 highest] 640
p5 Thermal time base 0°C from beginning of grain filling to maturity °Cd
32 Grno
Coefficient of kernel number per stem weight at the beginning of grain filling [kernels g stem−1] 2.5
Fillrate Potential kernel growth rate [mg kernel−1 day−1]
3.0 stwt
Potential final dry weight of a single stem, excluding grain g stem−1 100
phint Phyllochron interval
Table 4; Section 2.2.1. A low mineral N content 4 kg N ha−1 of ammonium nitrate was included
Neeteson and Hassink, 1997. Since none of the at sowing 5 kg N ha−1 was assumed for each
year, and a monthly N deposition of 3 kg N ha−1 initial conditions or proportions of soil types were
known for the wheat growing area in the of ammonium nitrate was included Neeteson and
Hassink, 1997. Gelderland region, initial soil and sowing condi-
tions from the Bouwing 1982 Groot and Verberne, These comparisons between simulated and
observed yields allowed another qualitative model 1991; Table 4; Section 2.2.1 were used as the
nearest approximation. This allowed an additional test of N, season and growth interactions.
However, caution should be paid to the interpreta- qualitative model test of season–yield interactions
over a long number of seasons, but as mentioned tion of model performance with estimated initial
conditions, since initial conditions could have been in Section 2.2.2, test results will be limited by the
estimation of initial conditions, but also by very different in some years and might not reflect
the true model performance. different earlier farming practices.
2.3. Weather data 2.2.3. Grain yields from the Gelderland region
A simulation
was carried
out with
200 kg N ha−1 using Wageningen weather data Daily data for solar radiation, maximum and
minimum temperature
and rainfall
from from 1975 to 1996. Simulation results were com-
pared with grain yields from winter wheat yields Swifterbant from 1974 to 1988 near the Eest and
PAGV and Wageningen from 1975 to 1996 near of the Gelderland region between 1975 and 1996
Crop Estimates,
Statistics Netherlands,
the Bouwing were made available by the Department
of Meteorology
of Wageningen
Voorburg. A
monthly N
deposition of
42 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
Agricultural University. Fig. 1 presents summar- 2.5. Simulation experiments
ised average
monthly weather
data for
A simulation experiment was carried out to Wageningen.
study the effect of rate and timing of N-fertiliser applications on predicted grain yield, grain protein
and soil residual N for a silty loam at the Eest, 2.4. Quantification of model performance
using weather data from the nearest weather sta- tion at Swifterbant, 1974–1988. Six N-fertiliser
Model performance was quantified for data treatments A, B, C, D, E and F were applied at
from the Eest, PAGV and the Bouwing Groot different growth stages described by a decimal
and Verberne, 1991. Since there is no single means code DC Zadoks et al., 1974 as shown in
to quantify the performance of a model, a range Table 6.
of performance indicators was calculated. To assess The initial, crop residue and sowing conditions
model performance, a correlation analysis was for this simulation experiment were identical to
carried out, and the coefficient of determination the Eest 19821983 simulations Groot and
for the 1 to 1 or y=x line [r 2 1:1, which is NOT
Verberne, 1991; Table 4; Section 2.2.1, except for for the fitted regression line] was computed, which
the initial soil mineral N profile. Simulations were measures the true deviation of the estimates from
initialised before each simulation with the relative observations. The slope m is presented to quan-
low soil N profile from PAGV in 1982 Groot and tify a possible over- or underestimation, calculated
Verberne, 1991; Section 2.2.1 to avoid any addi- from a best-fit regression line forced through the
tional N effects from residual soil mineral N of a origin. Note that a traditional regression analysis
previous year. was carried out only to show the slope, but not to
A second simulation experiment was carried out produce a coefficient of determination for a fitted
to study the effect of zero 0 and maximum max line, which is not relevant to a model validation
N-fertiliser applications Table 6 on predicted Mitchell, 1997. Another indicator of model per-
grain yield, grain protein and soil residual N. formance, which also represents the true deviation
Simulations were done for a silty loam at the Eest of the estimates from observations, the root mean
using weather data from the nearest weather sta- square deviation RMSD, i.e. the root of the
mean squared error of prediction MSEP after
Table 6
Wallach and Goffinet 1989, was computed to
Simulation experiment
treatments with
different N
provide a measure of the absolute magnitude of
applications a
the error.
Treatment N application kg N ha−1 at growth stage:
February DC23
DC31 DC39
A 140–N
min B
140–N min
45 45
C 140–N
min 45
45 20
D 140–N
min 45
45 40
E 140–N
min 45
45 60
F 140–N
min 45
45 80
Max 220–N
min 65
65 60
a Treatments A–F received a N application based on Fig. 1. Average monthly solar radiation ———, maximum
140 kg N ha−1 minus soil mineral N Nmin of the soil profile 0–100 cm on 17 February of each year. Treatment maximum
– – – – and minimum · · · · · · temperature and rainfall entire bars based on 31 years of weather data from
max N received a N application based on 220 kg N ha−1 minus soil mineral N of the soil profile 0–100 cm on 17 February
Wageningen, the Netherlands. The dark lower parts of the bars show the minimum recorded amount of monthly rainfall.
of each year.
43 S. Asseng et al. European Journal of Agronomy 12 2000 37–54
Table 7 Summary of the APSIM Nwheat model performance at the Eest, at PAGV, and the Bouwing in the Netherlands
Model attribute Number of paired data points
Observed range r
2 1:1a m
b RMSD
c Grain
d yield t ha−1 63
0.4–8.3 0.90
0.96 0.8
Kernels d m−2
27 12726–27916
0.55 0.95
2604 Kernel
d weight mg 27
9–37 0.79
1.04 3.9
Anthesis date day 6
14 0.76
0.98 3.7
Biomass t ha−1 129
0.03–20 0.97
1.02 1.2
LAI m m−2 126
0–5.5 0.65
1.28 1.2
Crop N kg ha−1 128
2–276 0.82
1.03 28.5
Grain d N kg ha−1
63 10–204
0.88 0.98
22.4 Grain protein
18 7.1–15.6
0.59 1.00
1.6 Soil mineral N kg ha−1
546 0–78
0.46 0.75
9 a r2 1:1=r2 for the 1 to 1 line y=x.
b Slope of linear regression forced through the origin. c Root mean square deviation.
d Including pre-maturity harvests.
tion at Swifterbant, 1974–1988 and a silty clay been summarised in Table 7. The observed grain
yields ranged from 4.5 t ha−1 or when including loam at the Bouwing, using weather data from the
nearest weather station at Wageningen, 1955–1996. pre-maturity harvests from as low as 0.4 t ha−1
to 8.3 t ha−1 Fig. 2. The yield simulations cap- tured the response to season and N-fertiliser effects
with a coefficient of determination of r2 1:1 of
3. Results