Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol44.Issue1-3.Apr2000:
Agricultural Water Management 44 (2000) 247±262
Sources of error in model predictions of pesticide
leaching: a case study using the MACRO model
N.J. Jarvisa,*, C.D. Brownb, E. Granitzac
a
Department of Soil Sciences, SLU, Box 7014, 750 07 Uppsala, Sweden
Soil Survey and Land Research Centre, Cran®eld University, Silsoe, Bedford MK45 4DT, UK
c
Hoechst Schering AgrEvo GmbH, Hoechst Works, D-65926 Frankfurt, Germany
b
Abstract
Uncalibrated predictions of soil water balance, water content, non-reactive solute transport
(bromide) and pesticide leaching (bentazone) made by three users of a comprehensive mechanistic
model (MACRO) are compared to measured data obtained for a sandy soil at Vredepeel in the
Netherlands. The objective was to assess the signi®cance of different sources of error for making
predictions of pesticide leaching. Objective statistical indices were used to compare the simulations
made by different users and to evaluate overall model performance. All three users predicted very
similar water balances. Soil water contents were in good agreement with the measurements, with
the simulation based on measured hydraulic functions giving somewhat better predictions than
those based on automatic estimation procedures (pedo-transfer functions). Bromide movement was
also satisfactorily predicted by all three users despite an inability to reproduce the strong retention
near the soil surface caused by ®nger ¯ow. Bentazone dissipation in the ®eld was severely
underpredicted by all three users based on laboratory measurements of degradation. This error
overshadowed the effects of differences in parameterisation between users. # 2000 Elsevier
Science B.V. All rights reserved.
Keywords: Model; Pesticide; Leaching; MACRO; Predictive errors
1. Introduction
Reliable information on pesticide fate and mobility can be obtained from field or
lysimeter experiments. However, such experiments are time-consuming, site-specific and
expensive. For these reasons, simulation models are now being increasingly used in
pesticide registration programs as potentially effective and inexpensive screening tools
*
Corresponding author. Fax: 46-18-672795.
E-mail address: nicholas.jarvis@mv.slu.se (N.J. Jarvis).
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 9 4 - 3
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
(Russell et al., 1994). With this increasing use of simulation models, it is vital that
confidence can be placed in model outputs. In a review of the literature, Jarvis et al.
(1995) concluded that most applications to date have involved some degree of calibration
to improve model performance so that the predictive accuracy of pesticide leaching
models is still unclear.
There are two main sources of error in simulation model outputs: model error and
parameter error (Loague and Green, 1991). Model errors result from incorrect or undue
simplification of process descriptions in the model and neglect of significant processes
(Russell et al., 1994). Clearly, some degree of model error is inevitable, since by
definition models are simplifications of reality. However, in principle, model errors
should be minimised when mechanistic process descriptions are used (Wauchope, 1992)
and in detailed models which include as many relevant processes as possible. Parameter
error is the use of inappropriate parameter values. These errors arise either because the
required data is not available, because they are interpreted in different ways by different
users, or because the measurements are themselves subject to error or for some reason do
not adequately reflect the prevailing field conditions. This may be potentially serious for
comprehensive data-demanding simulation models and for those parameters for which
the relevant model outcome (e.g. leaching) is especially sensitive. Parameter error is
particularly critical where models are used predictively, since methods for parameter
estimation (e.g. default values, pedo-transfer functions) may introduce additional sources
of error. In a model `ring test', Brown et al. (1996) demonstrated that errors in
parameterising pesticide leaching models resulted in significant variations in model
predictions, even among `expert-users'. These potential errors are magnified for users of
the more complex simulation models, who may be specialists in some aspects of the
subject (e.g. pesticide chemistry) but relatively inexperienced in others (e.g. soil physics).
In this paper, we investigate the relative significance of different sources of error for
one of the more detailed mechanistic models available (MACRO, version 4.0) using
independent predictions made by three users of water balance, non-reactive solute
transport (bromide) and pesticide leaching in a sandy soil (Vredepeel, Netherlands).
2. Materials and methods
2.1. Site details and measurements
Simulations are presented of experiments carried out in a sandy soil (Vredepeel) in
Netherlands under winter wheat. In this paper, simulation results are compared to
contents of soil water, bromide and bentazone measured in the soil profile by coring on
three occasions during a 14-month experimental period. For site descriptions and detailed
information concerning the measurements, the reader is referred to Boesten and van der
Pas (2000).
2.2. Description of the model
MACRO is a dual-porosity model which accounts for rapid preferential flow in soil by
dividing the pore system into two flow domains, macropores and micropores, with the
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
boundary between the domains defined by a soil water pressure head close to saturation
and its associated water content and hydraulic conductivity. Water flow in micropores is
calculated by the Richards' equation while simple gravity flow is assumed in the
macropores. Solute transport in micropores is given by the convection±dispersion
equation (CDE), with pesticide assumed to obey first-order kinetics for degradation and
linear instantaneous, reversible, sorption. In the case of the sandy soil at Vredepeel, the
hydraulic conductivity of the micropore region is sufficiently large to prevent the
macropores acting as a preferential flow region so that the model effectively reduces to
the classical Richards/CDE approach. For a full description of the model, the reader is
referred to Jarvis (1994) and Saxena et al. (1994).
2.3. Modelling strategy
For the simulations presented in this paper, no model calibration against the
measurements was allowed. Apart from this, each model user was given the freedom to
parameterise the model in any way considered appropriate. In practice, this meant that the
users could derive parameter values either from the available measurements, from default
values in the model, from previous experience, or from automatic estimation procedures
(pedo-transfer functions). Table 1 summarises the methods chosen by the three users for
different parameter groups in the model.
In this study, model performance is assessed objectively by comparing the degree of
agreement of the model with the measurements using statistical indices (Loague and
Green, 1991). Four such indices are used, the modelling efficiency EF, the nonnormalised root mean square error NRMSE, the coefficient of shape CD and the
coefficient of residual mass CRM (Vanclooster et al., 2000). The NRMSE is defined as
s
P
Pi ÿ Oi 2
:
(1)
NRMSE
n
The ideal value of CD is unity. The ideal values of NRMSE and CRM are zero. If CRM
takes large positive or negative values, then the mass of the substance is strongly
underestimated and overestimated, respectively. In the case of pesticide residues, this
implies errors in predicting degradation.
Table 1
Methods used to parameterise the modela
Parameter group
Soil
A
User 1
User 2
User 3
a
Pesticide
B
H
H
H
C
D
A
H
H
H
B
Crop
C
D
A
H
Solute transport
B
C
D
H
H
H
H
H
A
B
C
D
H
H
H
A: direct measurements/®eld observations; B: pedo-transfer functions in MACRO_DB (Jarvis et al., 1997);
C: default values in the model; D: experience/general knowledge.
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The modelling results obtained by the different users were compared to the
measurements using the stepwise test procedure adopted for the COST modelling
exercise (Vanclooster et al., 2000). In this procedure, the water balance and soil
hydrology are compared first, followed by non-reactive solute transport, heat flow and
soil temperatures, and finally, pesticide movement and persistence.
3. Results and discussion
3.1. Soil water balance
The calculated soil water balance depends strongly on evapotranspiration which, in
turn, depends on the crop parameterisation chosen by the user. For most model
applications, little information is available concerning crop development, and Vredepeel
is no exception in this respect. All three users set emergence and harvest dates to known
values. Also, apart from the parameters discussed below, all crop parameters were set to
default values in MACRO 4.0 by all three users. User 1 set the maximum root depth to
0.6 m despite the reported root depth of 0.4 m (Boesten and van der Pas, 2000). From past
experience in using the model, this user concluded that the effective root depth is usually
somewhat deeper than that observed. The maximum leaf area index was also reduced
from the default value in the model of 5 to 3, based on comments in Boesten and van der
Pas (2000) that wheat growth during the experiment was poor due to drought stress. The
critical water tension for uptake was reduced from the default value of 10 to 2 m, a typical
value for sandy soils. User 2 set all crop parameters, except emergence and harvest dates,
to default values in MACRO 4.0. Thus, in contrast to user 1, the maximum root depth was
assumed 1 m, the maximum leaf area index was 5.0, and the critical tension for water
uptake was 10 m. User 3 derived crop parameters from the estimation routines in the
MACRO_DB database version of the model (Jarvis et al., 1997). The maximum root
depth was 0.5 m, the maximum leaf area index 4.4 and the critical tension for water
uptake 1.37 m.
Table 2 shows that accumulated actual evapotranspiration was similar for all three
users (448±468 mm), giving a very similar water balance, despite the documented
differences in crop parameterisation particularly between user 2 and users 1 and 3. The
reason for this is that potential evapotranspiration (PET) was calculated in different ways:
users 1 and 3 took the meteorological data supplied and calculated PET by the Penman±
Table 2
Predicted soil water balances
Water balance
component (mm)
User 1
(Penman±Monteith)
User 2
(Makkink)
User 3
(Penman±Monteith)
Precipitation
Evapotranspiration
Groundwater recharge
Surface run-off
Change in storage
768
468
323
0
ÿ23
768
463
339
0
ÿ34
768
448
277
0
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
251
Monteith equation internally in the model, while user 2 supplied the model with the
Makkink estimates of PET given in Boesten and van der Pas (2000). It can be noted that
MACRO allows the user both options. PET calculated by the Penman±Monteith equation
was larger than the Makkink estimates, so that the ratio between actual transpiration and
potential transpiration was smaller for users 1 and 3 than for user 2. Supplementary
calculations showed that if user 2 had calculated PET with the Penman±Monteith
equation, then actual evapotranspiration would have been ca. 12% larger than that
estimated by user 1, and the net recharge to groundwater reduced by a corresponding
amount.
3.2. Soil hydraulic properties
User 1 derived the soil hydraulic parameter values from the reported measurements
using the RETC program (Yates et al., 1992), fitting the data simultaneously to the
Brooks and Corey (1964) water retention function and the Mualem (1976) model of
unsaturated hydraulic conductivity. In contrast, users 2 and 3 made use of the pedotransfer functions available in the auxiliary shell program MACRO_DB (Jarvis et al.,
1997) to predict the hydraulic properties from soil texture, bulk density and organic
carbon content. On the whole, these pedo-transfer functions succeeded quite well in
reproducing the parameter values derived from least-squares fitting to the measured water
retention and hydraulic conductivity functions. However, in one important respect, they
failed to match the data. The air-entry pressures were overestimated by the pedo-transfer
functions, and the pore size distribution index strongly underestimated. The reason for
this is that the pedo-transfer functions are based only on total sand, silt and clay contents,
and cannot therefore discriminate between soils dominated by fine, medium or coarse
sand fractions. In sandy soils, the distribution and relative abundance of these sand
fractions has very important implications for the shape of the hydraulic functions near
saturation. It is clear from the measured water retention data (Boesten and van der Pas,
2000) that Vredepeel is dominated by the finer sand fraction and is also quite narrowly
graded (especially in the subsoil), giving small values of air-entry pressure (ÿ39 cm in
the topsoil and ÿ45 cm in the subsoil), and large values of the pore size distribution index
(0.85 and 1.96, respectively). In contrast, the pedo-transfer functions gave air-entry
pressures varying from ÿ8 to ÿ10 cm and pore size distribution indices of ca. 0.2±0.3.
These values may be considered to represent an `average' sand, in effect, one that is
characterised by a broader pore size distribution.
All three users assumed similar values for the limiting water content for root water
uptake (`extractable' water content, Fig. 1). All three users also assumed similar values
for the saturated water content (Fig. 1). The small differences result from differences in
defining how the bulk density varied with depth in the soil profile. However, Fig. 1 shows
that the pedo-transfer functions adopted by users 2 and 3 result in significantly larger
water contents at intermediate pressure heads (e.g. ÿ100 cm in Fig. 1) compared to the
values calculated by user 1 based on the measurements, especially in the subsoil. This is
largely due to the underestimate in pore size distribution index discussed above. Fig. 2
shows that the pedo-transfer functions (users 2 and 3) gave reasonable estimates of
saturated hydraulic conductivity based on calculated values of effective porosity
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 1. Soil water contents estimated at saturation (ys), ÿ100 cm pressure head (y100), and at the lower limit of
root water extraction (ye).
(saturated water content minus water content at a pressure head of ÿ50 cm). In contrast,
due to the errors in estimating the air-entry pressure and pore size distribution index, the
unsaturated conductivity at ÿ50 cm was underestimated by up to two orders of
magnitude.
3.3. Water ¯ow and water contents
All three users predicted similar flow patterns at Vredepeel, with the two main
recharge periods characterised by typical flow rates of ca. 0.1±0.4 mm hÿ1 (see Fig. 3).
Fig. 2. Hydraulic conductivities estimated at saturation (Ks) and at ÿ50 cm pressure head (K50).
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
253
Fig. 3. Soil water ¯ow predicted at 27 cm depth in the pro®le.
This is to be expected because recharge is governed by the overall soil water balance,
which was similar for all three users, rather than soil hydraulic properties. However, given
similar water flow rates, the errors in hydraulic properties generated by the pedo-transfer
functions should result in larger profile water contents. The simulations confirm that users
2 and 3 do indeed predict water contents which are ca. 0.04±0.05 m3 mÿ3 larger in the
upper part of the soil profile during recharge periods (see Fig. 4a,c).
It is not easy to say for certain which user produced the best simulations of soil
water balance, because the data available for comparison consists of only three
`snapshots' of measured water content profiles. The statistics of the goodness-of-fit of
model predictions shown in Table 3 show that all three users produced acceptable
simulations of soil water content with positive values of EF and NRMSE less
than 0.06 m3 mÿ3. However, the simulation based on the measured data (user 1) does
give the largest overall EF and also appears subjectively to better capture the shape of
the measured profiles, in particular the clear difference in water contents between
topsoil and subsoil horizons (Fig. 4a±c). However, the values of coefficient of shape do
not always confirm this impression (Table 3), and on one sampling occasion (Table 3,
Fig. 4a), the NRMSE are larger than those obtained through the use of the pedo-transfer
functions (users 2 and 3), which are generally of the order of 0.05 m3 mÿ3. The three
users made similar (and accurate) predictions of water contents on the one summer
sampling occasion (Table 3), presumably because the `extractable' water content
largely controls the minimum water contents attained during extended dry periods.
This parameter was set to almost identical values by the three users (Fig. 1). It can be
noted here that deep in the profile, water contents are controlled more by the water
table position than by the hydraulic properties and surface boundary conditions. Although
it is not shown here, user 2 generally predicted a deeper water table than both users 1 and
3 and thus, a drier lower subsoil. This is reflected in the slightly poorer EF obtained
by user 2.
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 4. Comparison of measured and predicted soil water contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
255
Table 3
Statistics of goodness-of-®t for predictions of soil water content
Sampling occasion
EFa
NRMSEb
CSc
CRMd
User 1
1
2
3
All
ÿ1.999
0.948
0.578
0.771
0.050
0.018
0.035
0.034
0.481
0.791
0.432
0.756
ÿ0.207
0.005
ÿ0.034
ÿ0.070
User 2
1
2
3
All
ÿ0.255
0.563
ÿ0.651
0.397
0.033
0.052
0.069
0.055
5.623
1.874
9.038
1.084
ÿ0.027
ÿ0.234
0.099
ÿ0.049
User 3
1
2
3
All
ÿ0.427
0.615
ÿ0.241
0.498
0.035
0.049
0.060
0.050
7.269
2.494
5.045
1.732
ÿ0.017
0.03
0.142
0.059
0.555
0.047
1.039
ÿ0.02
Overall
a
Model ef®ciency.
Non-normalised root mean square error (m3 mÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
3.4. Bromide transport
There were no significant differences in parameterisation of non-reactive solute
transport between the three users (only the diffusion coefficient was given different
values, but this is of minor significance for transport in a sand soil). Since water contents
are larger, and recharge rates are similar (almost identical water balance), the pore water
velocity controlling the mean convective solute displacement predicted by users 2 and 3
should be smaller (rough calculations suggest by perhaps up to 20%). This conclusion is
confirmed by the comparison of predicted bromide depth profiles (Fig. 5a±c). The
difference in predicted bromide transport is particularly noticeable for the profile on 27
August 1991 (Fig. 5b) which represents the system state at the end of the first recharge
period.
A statistical comparison with the measured data (Table 4) shows that the use of pedotransfer functions has resulted in apparently slightly better predictions of bromide
transport, despite poorer predictions of soil water content. This is particularly the case on
27 August 1991 (Fig. 5b), when the retardation of bromide transport is captured by users
2 and 3 but not by user 1. This is probably fortuitous: the much slower bromide transport
observed in the field experiment compared to the model predictions is probably due to
`immobile water' and by-pass `finger flow' caused by topsoil water repellency. This is an
interesting illustration of the fact that `model error' (MACRO cannot predict transport
retardation due to preferential flow in sands) can sometimes fortuitously cancel out
`parameter error' (underestimated pore water velocity) resulting in quite reasonable
model predictions. Indeed, the overall EF values shown in Table 4 are positive for all
three users indicating an acceptable model performance.
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Fig. 5. Comparison of measured and predicted bromide contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
257
Table 4
Statistics of goodness-of-®t for predictions of bromide content
Sampling occasion
EFa
NRMSEb
CSc
CRMd
User 1
1
2
3
All
0.272
ÿ1.950
ÿ2.257
0.102
3.511
3.231
1.596
2.874
1.906
0.484
0.502
1.303
ÿ0.459
ÿ0.460
ÿ0.603
ÿ0.482
User 2
1
2
3
All
0.468
ÿ1.694
ÿ2.588
0.221
3.001
3.087
1.675
2.677
1.085
0.525
0.992
0.789
ÿ0.233
ÿ0.020
ÿ0.702
ÿ0.200
User 3
1
2
3
All
0.434
ÿ0.261
ÿ2.036
0.458
3.096
2.112
1.541
2.233
1.924
1.193
0.306
1.062
ÿ0.162
0.035
ÿ0.456
ÿ0.109
0.260
2.609
0.951
ÿ0.264
Overall
a
Model ef®ciency.
Non-normalised root mean square error (mg dmÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
3.5. Soil temperatures
Soil temperature strongly influences pesticide biodegradation so that the prediction of
temperature should be an important part of any pesticide leaching model. Fig. 6 shows
that the soil temperatures predicted by the three users at 1 m depth are within ca. 38C at
all times. This is also the case for other depths in the profile (not shown), and is not
particularly surprising since the soil thermal properties in MACRO are defined internally
Fig. 6. Soil temperatures predicted at 1 m depth.
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 7. Sorption constants for bentazone estimated by the three users.
in the program so that the user cannot influence predictions of temperature to any great
degree. There are only two parameters influencing temperature which the user can vary,
the annual average air temperature and the annual amplitude in air temperature on a
monthly basis. These parameters only influence the bottom boundary condition in the
model.
3.6. Bentazone leaching
Figs. 7 and 8 show sorption distribution coefficients and degradation rate coefficients
assumed by the three users. The parameter values chosen by the users should be very
similar since they are largely based on the same set of laboratory measurements. This is
Fig. 8. First-order degradation rate coef®cients for bentazone estimated by the three users.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
259
true in the case of sorption parameters, but some significant differences are apparent in
the parameterisation of degradation. This is partly because the laboratory degradation
measured in the topsoil at 158C did not exactly follow the model assumption of first-order
kinetics, but instead showed a two-phase pattern. Up to 157 days, bentazone degraded at a
half-life of ca. 35 days, while from 157 to 450 days a half-life of 70 days was more
appropriate. Users 1 and 2 selected an average value for the whole period, while user 3
fitted the rate constant to the initial, faster, degradation phase, making the reasonable
assumption that the slower phase resulted from the commonly observed die-off of soil
microbial populations in laboratory tests (Fig. 8). The temperature response function for
degradation represents another significant difference in parameterisation between the
users. User 1 sets the exponent in this function to 0.172 based on the measurements made
at two temperatures in the laboratory, while users 2 and 3 chose the default value in the
model (0.08), since a value of 0.172 results in an unreasonably large activation energy.
Clearly, only two sets of measurements are insufficient to reliably parameterise the
model. The net effects of these differences in parameterisation is that user 3 predicts the
fastest degradation at the reference temperature of 108C, while users 1 and 3 would
predict similar rates at 208C, slightly more than twice those of user 2 (the differences at
cold temperatures are not so significant, because rates are low anyway).
Fig. 9a±c shows that user 2 does indeed predict somewhat slower dissipation of
bentazone than users 1 and 3, particularly in the summer period during the experiment.
The statistical comparison with the measured data (Table 5) shows that all three MACRO
users considerably underestimated the observed dissipation of bentazone, with large
positive values for the CRM, and negative values of EF. One explanation may be that the
model underestimated leaching of bentazone below 1.1 m depth. It is difficult to test this
hypothesis without flux measurements (e.g. lysimeters, drainage outflows), although the
model showed no tendency to underpredict the rate of bromide leaching. Therefore, we
consider that a more likely reason for the discrepancy is that bentazone degradation was
underestimated either because the microbial activity in the laboratory incubation
experiments did not reflect that of undisturbed populations in the field soil or because
there are significant dissipation pathways and mechanisms for bentazone in the field
which are not observed in the laboratory and are not included in the model. In this
respect, much shorter laboratory aerobic half-lives than those used in this study are
Table 5
Statistics of goodness-of-®t for predictions of bentazone content
EFa
NRMSEb
CSc
CRMd
User 1
User 2
User 3
ÿ1.02
ÿ2.81
ÿ0.38
21.36
29.33
17.66
0.60
0.46
0.58
0.79
1.31
0.81
Overall
ÿ1.05
21.75
0.51
1.15
a
Model ef®ciency.
Non-normalised root mean square error (mg mÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
260
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 9. Comparison of measured and predicted bentazone contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
261
normally reported for bentazone in freshly collected field soils (on average 14 days at
208C equivalent to ca. 31 days at 108C, assuming 0.08 for the exponent in the temperature
response function), while field half-lives for bentazone as short as 12 days have also been
reported (Tomlin, 1997). Using MACRO, Jarvis et al. (1994) also underpredicted
bentazone dissipation observed in lysimeter experiments. This was tentatively attributed
to photolysis (Nilles and Zabik, 1975).
4. Conclusions
The three users introduced significant differences in parameterisation of the
degradation submodel in MACRO due to (i) difficulty in estimating the reference firstorder rate coefficient for degradation from laboratory incubation tests exhibiting biphasic
degradation, (ii) uncertainty concerning the reliability of the laboratory degradation
measurements made at two different temperatures leading to differences in parameterisation of the temperature response function. Since leaching predictions are strongly
dependent on degradation, it is recommended that for registration purposes, strict
protocol guidelines with respect to interpretation of laboratory incubation tests should be
developed and followed. The model users also adopted different methods to parameterise
soil hydraulic properties. The use of pedo-transfer functions resulted in root mean square
errors in predicted soil water contents of the order of 0.06 m3 mÿ3, which were only
slightly larger than the values obtained when fitting to actual measured data. Of course,
whenever real data are available, it is recommended that these are used. However, this
study shows that the use of such pedo-transfer functions may be sufficiently accurate for
registration purposes.
Nevertheless, the main source of error was common to all three users, namely that
larger dissipation rates were observed in the field compared to those predicted from the
laboratory incubation measurements. This may have resulted from the exclusion of
significant processes, particularly the presumed existence of additional abiotic dissipation
pathways for bentazone (photolysis) or because the laboratory was not representative of
field conditions either due to sampling disturbance, soil handling, the dosing method used
or population die-off. Another process which is not included in the model is preferential
finger flow. This was shown to influence non-reactive bromide transport, but its effect on
bentazone transport could not be assessed due to the dominating influence of errors in
degradation.
Acknowledgements
This study was carried out within the framework of the mathematical modelling
working group of COST Action 66 `Pesticide fate in the soil environment' organised by
DGXII of the EU. The authors are grateful to Jos Boesten (Winand Staring Centre,
Netherlands) for supplying the dataset `Vredepeel', especially for the user-friendly way
in which it was compiled. The authors also thank Allan Walker (HRI, UK) for the use of
the program STATIND for calculating statistical indices of model performance.
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Jarvis, N.J., BergstroÈm, L.F., Brown, C.D., 1995. Pesticide leaching models and their use for management
purposes. In: Roberts, T.R., Kearney, P.C. (Eds.), Environmental Behaviour of Agrochemicals. Wiley, NY,
pp. 185±220.
Jarvis, N.J., Hollis, J.M., Nicholls, P.H., Mayr, T., Evans, S.P., 1997. MACRO_DB: a decision-support tool for
assessing pesticide fate and mobility in soils. Environ. Modelling Software 12, 251±265.
Loague, K.M., Green, R.E., 1991. Statistical and graphical methods for evaluating solute transport models:
overview and application. J. Contam. Hydrol. 7, 51±73.
Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water
Resources Res. 12, 513±522.
Nilles, G.P., Zabik, M.J., 1975. Photochemistry of bioactive compounds. Multiphase photodegradation and mass
spectral analysis of Basagran. J. Agric. Food Chem. 23, 410±415.
Russell, M.H., Layton, R.J., Tillotson, P.M., 1994. The use of pesticide leaching models in a regulatory setting:
an industrial perspective. J. Environ. Sci. Health A 29, 1105±1116.
Saxena, R.K., Jarvis, N.J., BergstroÈm, L., 1994. Interpreting non-steady state tracer breakthrough experiments in
sand and clay soils using a dual-porosity model. J. Hydrol. 162, 279±298.
Tomlin, C.D.S., 1997. The Pesticide Manual, 11th ed. British Crop Protection Council, Farnham, Surrey, UK.
Vanclooster, M., Boesten, J.J.T.I., Trevisan, M., Brown, C.D., Capri, E., Vacek, O., Eklo, O.M., GottesbuÈren, B.,
Gouy, V., van der Linden, A.M.A., 2000. A European test of pesticide-leaching models: methodology and
major recommendations. Agric. Water Mgmt. 44, 1±19.
Wauchope, R.D., 1992. Environmental risk assessment of pesticides: improving simulation model credibility.
Weed Technol. 6, 753±759.
Yates, S.R., van Genuchten, M.T., Warrick, A.W., Leij, F.J., 1992. Analysis of measured, predicted, and
estimated hydraulic conductivity using the RETC computer program. Soil Sci. Soc. Am. J. 56, 347±354.
Sources of error in model predictions of pesticide
leaching: a case study using the MACRO model
N.J. Jarvisa,*, C.D. Brownb, E. Granitzac
a
Department of Soil Sciences, SLU, Box 7014, 750 07 Uppsala, Sweden
Soil Survey and Land Research Centre, Cran®eld University, Silsoe, Bedford MK45 4DT, UK
c
Hoechst Schering AgrEvo GmbH, Hoechst Works, D-65926 Frankfurt, Germany
b
Abstract
Uncalibrated predictions of soil water balance, water content, non-reactive solute transport
(bromide) and pesticide leaching (bentazone) made by three users of a comprehensive mechanistic
model (MACRO) are compared to measured data obtained for a sandy soil at Vredepeel in the
Netherlands. The objective was to assess the signi®cance of different sources of error for making
predictions of pesticide leaching. Objective statistical indices were used to compare the simulations
made by different users and to evaluate overall model performance. All three users predicted very
similar water balances. Soil water contents were in good agreement with the measurements, with
the simulation based on measured hydraulic functions giving somewhat better predictions than
those based on automatic estimation procedures (pedo-transfer functions). Bromide movement was
also satisfactorily predicted by all three users despite an inability to reproduce the strong retention
near the soil surface caused by ®nger ¯ow. Bentazone dissipation in the ®eld was severely
underpredicted by all three users based on laboratory measurements of degradation. This error
overshadowed the effects of differences in parameterisation between users. # 2000 Elsevier
Science B.V. All rights reserved.
Keywords: Model; Pesticide; Leaching; MACRO; Predictive errors
1. Introduction
Reliable information on pesticide fate and mobility can be obtained from field or
lysimeter experiments. However, such experiments are time-consuming, site-specific and
expensive. For these reasons, simulation models are now being increasingly used in
pesticide registration programs as potentially effective and inexpensive screening tools
*
Corresponding author. Fax: 46-18-672795.
E-mail address: nicholas.jarvis@mv.slu.se (N.J. Jarvis).
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 9 4 - 3
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
(Russell et al., 1994). With this increasing use of simulation models, it is vital that
confidence can be placed in model outputs. In a review of the literature, Jarvis et al.
(1995) concluded that most applications to date have involved some degree of calibration
to improve model performance so that the predictive accuracy of pesticide leaching
models is still unclear.
There are two main sources of error in simulation model outputs: model error and
parameter error (Loague and Green, 1991). Model errors result from incorrect or undue
simplification of process descriptions in the model and neglect of significant processes
(Russell et al., 1994). Clearly, some degree of model error is inevitable, since by
definition models are simplifications of reality. However, in principle, model errors
should be minimised when mechanistic process descriptions are used (Wauchope, 1992)
and in detailed models which include as many relevant processes as possible. Parameter
error is the use of inappropriate parameter values. These errors arise either because the
required data is not available, because they are interpreted in different ways by different
users, or because the measurements are themselves subject to error or for some reason do
not adequately reflect the prevailing field conditions. This may be potentially serious for
comprehensive data-demanding simulation models and for those parameters for which
the relevant model outcome (e.g. leaching) is especially sensitive. Parameter error is
particularly critical where models are used predictively, since methods for parameter
estimation (e.g. default values, pedo-transfer functions) may introduce additional sources
of error. In a model `ring test', Brown et al. (1996) demonstrated that errors in
parameterising pesticide leaching models resulted in significant variations in model
predictions, even among `expert-users'. These potential errors are magnified for users of
the more complex simulation models, who may be specialists in some aspects of the
subject (e.g. pesticide chemistry) but relatively inexperienced in others (e.g. soil physics).
In this paper, we investigate the relative significance of different sources of error for
one of the more detailed mechanistic models available (MACRO, version 4.0) using
independent predictions made by three users of water balance, non-reactive solute
transport (bromide) and pesticide leaching in a sandy soil (Vredepeel, Netherlands).
2. Materials and methods
2.1. Site details and measurements
Simulations are presented of experiments carried out in a sandy soil (Vredepeel) in
Netherlands under winter wheat. In this paper, simulation results are compared to
contents of soil water, bromide and bentazone measured in the soil profile by coring on
three occasions during a 14-month experimental period. For site descriptions and detailed
information concerning the measurements, the reader is referred to Boesten and van der
Pas (2000).
2.2. Description of the model
MACRO is a dual-porosity model which accounts for rapid preferential flow in soil by
dividing the pore system into two flow domains, macropores and micropores, with the
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
boundary between the domains defined by a soil water pressure head close to saturation
and its associated water content and hydraulic conductivity. Water flow in micropores is
calculated by the Richards' equation while simple gravity flow is assumed in the
macropores. Solute transport in micropores is given by the convection±dispersion
equation (CDE), with pesticide assumed to obey first-order kinetics for degradation and
linear instantaneous, reversible, sorption. In the case of the sandy soil at Vredepeel, the
hydraulic conductivity of the micropore region is sufficiently large to prevent the
macropores acting as a preferential flow region so that the model effectively reduces to
the classical Richards/CDE approach. For a full description of the model, the reader is
referred to Jarvis (1994) and Saxena et al. (1994).
2.3. Modelling strategy
For the simulations presented in this paper, no model calibration against the
measurements was allowed. Apart from this, each model user was given the freedom to
parameterise the model in any way considered appropriate. In practice, this meant that the
users could derive parameter values either from the available measurements, from default
values in the model, from previous experience, or from automatic estimation procedures
(pedo-transfer functions). Table 1 summarises the methods chosen by the three users for
different parameter groups in the model.
In this study, model performance is assessed objectively by comparing the degree of
agreement of the model with the measurements using statistical indices (Loague and
Green, 1991). Four such indices are used, the modelling efficiency EF, the nonnormalised root mean square error NRMSE, the coefficient of shape CD and the
coefficient of residual mass CRM (Vanclooster et al., 2000). The NRMSE is defined as
s
P
Pi ÿ Oi 2
:
(1)
NRMSE
n
The ideal value of CD is unity. The ideal values of NRMSE and CRM are zero. If CRM
takes large positive or negative values, then the mass of the substance is strongly
underestimated and overestimated, respectively. In the case of pesticide residues, this
implies errors in predicting degradation.
Table 1
Methods used to parameterise the modela
Parameter group
Soil
A
User 1
User 2
User 3
a
Pesticide
B
H
H
H
C
D
A
H
H
H
B
Crop
C
D
A
H
Solute transport
B
C
D
H
H
H
H
H
A
B
C
D
H
H
H
A: direct measurements/®eld observations; B: pedo-transfer functions in MACRO_DB (Jarvis et al., 1997);
C: default values in the model; D: experience/general knowledge.
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
The modelling results obtained by the different users were compared to the
measurements using the stepwise test procedure adopted for the COST modelling
exercise (Vanclooster et al., 2000). In this procedure, the water balance and soil
hydrology are compared first, followed by non-reactive solute transport, heat flow and
soil temperatures, and finally, pesticide movement and persistence.
3. Results and discussion
3.1. Soil water balance
The calculated soil water balance depends strongly on evapotranspiration which, in
turn, depends on the crop parameterisation chosen by the user. For most model
applications, little information is available concerning crop development, and Vredepeel
is no exception in this respect. All three users set emergence and harvest dates to known
values. Also, apart from the parameters discussed below, all crop parameters were set to
default values in MACRO 4.0 by all three users. User 1 set the maximum root depth to
0.6 m despite the reported root depth of 0.4 m (Boesten and van der Pas, 2000). From past
experience in using the model, this user concluded that the effective root depth is usually
somewhat deeper than that observed. The maximum leaf area index was also reduced
from the default value in the model of 5 to 3, based on comments in Boesten and van der
Pas (2000) that wheat growth during the experiment was poor due to drought stress. The
critical water tension for uptake was reduced from the default value of 10 to 2 m, a typical
value for sandy soils. User 2 set all crop parameters, except emergence and harvest dates,
to default values in MACRO 4.0. Thus, in contrast to user 1, the maximum root depth was
assumed 1 m, the maximum leaf area index was 5.0, and the critical tension for water
uptake was 10 m. User 3 derived crop parameters from the estimation routines in the
MACRO_DB database version of the model (Jarvis et al., 1997). The maximum root
depth was 0.5 m, the maximum leaf area index 4.4 and the critical tension for water
uptake 1.37 m.
Table 2 shows that accumulated actual evapotranspiration was similar for all three
users (448±468 mm), giving a very similar water balance, despite the documented
differences in crop parameterisation particularly between user 2 and users 1 and 3. The
reason for this is that potential evapotranspiration (PET) was calculated in different ways:
users 1 and 3 took the meteorological data supplied and calculated PET by the Penman±
Table 2
Predicted soil water balances
Water balance
component (mm)
User 1
(Penman±Monteith)
User 2
(Makkink)
User 3
(Penman±Monteith)
Precipitation
Evapotranspiration
Groundwater recharge
Surface run-off
Change in storage
768
468
323
0
ÿ23
768
463
339
0
ÿ34
768
448
277
0
43
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
251
Monteith equation internally in the model, while user 2 supplied the model with the
Makkink estimates of PET given in Boesten and van der Pas (2000). It can be noted that
MACRO allows the user both options. PET calculated by the Penman±Monteith equation
was larger than the Makkink estimates, so that the ratio between actual transpiration and
potential transpiration was smaller for users 1 and 3 than for user 2. Supplementary
calculations showed that if user 2 had calculated PET with the Penman±Monteith
equation, then actual evapotranspiration would have been ca. 12% larger than that
estimated by user 1, and the net recharge to groundwater reduced by a corresponding
amount.
3.2. Soil hydraulic properties
User 1 derived the soil hydraulic parameter values from the reported measurements
using the RETC program (Yates et al., 1992), fitting the data simultaneously to the
Brooks and Corey (1964) water retention function and the Mualem (1976) model of
unsaturated hydraulic conductivity. In contrast, users 2 and 3 made use of the pedotransfer functions available in the auxiliary shell program MACRO_DB (Jarvis et al.,
1997) to predict the hydraulic properties from soil texture, bulk density and organic
carbon content. On the whole, these pedo-transfer functions succeeded quite well in
reproducing the parameter values derived from least-squares fitting to the measured water
retention and hydraulic conductivity functions. However, in one important respect, they
failed to match the data. The air-entry pressures were overestimated by the pedo-transfer
functions, and the pore size distribution index strongly underestimated. The reason for
this is that the pedo-transfer functions are based only on total sand, silt and clay contents,
and cannot therefore discriminate between soils dominated by fine, medium or coarse
sand fractions. In sandy soils, the distribution and relative abundance of these sand
fractions has very important implications for the shape of the hydraulic functions near
saturation. It is clear from the measured water retention data (Boesten and van der Pas,
2000) that Vredepeel is dominated by the finer sand fraction and is also quite narrowly
graded (especially in the subsoil), giving small values of air-entry pressure (ÿ39 cm in
the topsoil and ÿ45 cm in the subsoil), and large values of the pore size distribution index
(0.85 and 1.96, respectively). In contrast, the pedo-transfer functions gave air-entry
pressures varying from ÿ8 to ÿ10 cm and pore size distribution indices of ca. 0.2±0.3.
These values may be considered to represent an `average' sand, in effect, one that is
characterised by a broader pore size distribution.
All three users assumed similar values for the limiting water content for root water
uptake (`extractable' water content, Fig. 1). All three users also assumed similar values
for the saturated water content (Fig. 1). The small differences result from differences in
defining how the bulk density varied with depth in the soil profile. However, Fig. 1 shows
that the pedo-transfer functions adopted by users 2 and 3 result in significantly larger
water contents at intermediate pressure heads (e.g. ÿ100 cm in Fig. 1) compared to the
values calculated by user 1 based on the measurements, especially in the subsoil. This is
largely due to the underestimate in pore size distribution index discussed above. Fig. 2
shows that the pedo-transfer functions (users 2 and 3) gave reasonable estimates of
saturated hydraulic conductivity based on calculated values of effective porosity
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 1. Soil water contents estimated at saturation (ys), ÿ100 cm pressure head (y100), and at the lower limit of
root water extraction (ye).
(saturated water content minus water content at a pressure head of ÿ50 cm). In contrast,
due to the errors in estimating the air-entry pressure and pore size distribution index, the
unsaturated conductivity at ÿ50 cm was underestimated by up to two orders of
magnitude.
3.3. Water ¯ow and water contents
All three users predicted similar flow patterns at Vredepeel, with the two main
recharge periods characterised by typical flow rates of ca. 0.1±0.4 mm hÿ1 (see Fig. 3).
Fig. 2. Hydraulic conductivities estimated at saturation (Ks) and at ÿ50 cm pressure head (K50).
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
253
Fig. 3. Soil water ¯ow predicted at 27 cm depth in the pro®le.
This is to be expected because recharge is governed by the overall soil water balance,
which was similar for all three users, rather than soil hydraulic properties. However, given
similar water flow rates, the errors in hydraulic properties generated by the pedo-transfer
functions should result in larger profile water contents. The simulations confirm that users
2 and 3 do indeed predict water contents which are ca. 0.04±0.05 m3 mÿ3 larger in the
upper part of the soil profile during recharge periods (see Fig. 4a,c).
It is not easy to say for certain which user produced the best simulations of soil
water balance, because the data available for comparison consists of only three
`snapshots' of measured water content profiles. The statistics of the goodness-of-fit of
model predictions shown in Table 3 show that all three users produced acceptable
simulations of soil water content with positive values of EF and NRMSE less
than 0.06 m3 mÿ3. However, the simulation based on the measured data (user 1) does
give the largest overall EF and also appears subjectively to better capture the shape of
the measured profiles, in particular the clear difference in water contents between
topsoil and subsoil horizons (Fig. 4a±c). However, the values of coefficient of shape do
not always confirm this impression (Table 3), and on one sampling occasion (Table 3,
Fig. 4a), the NRMSE are larger than those obtained through the use of the pedo-transfer
functions (users 2 and 3), which are generally of the order of 0.05 m3 mÿ3. The three
users made similar (and accurate) predictions of water contents on the one summer
sampling occasion (Table 3), presumably because the `extractable' water content
largely controls the minimum water contents attained during extended dry periods.
This parameter was set to almost identical values by the three users (Fig. 1). It can be
noted here that deep in the profile, water contents are controlled more by the water
table position than by the hydraulic properties and surface boundary conditions. Although
it is not shown here, user 2 generally predicted a deeper water table than both users 1 and
3 and thus, a drier lower subsoil. This is reflected in the slightly poorer EF obtained
by user 2.
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 4. Comparison of measured and predicted soil water contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
255
Table 3
Statistics of goodness-of-®t for predictions of soil water content
Sampling occasion
EFa
NRMSEb
CSc
CRMd
User 1
1
2
3
All
ÿ1.999
0.948
0.578
0.771
0.050
0.018
0.035
0.034
0.481
0.791
0.432
0.756
ÿ0.207
0.005
ÿ0.034
ÿ0.070
User 2
1
2
3
All
ÿ0.255
0.563
ÿ0.651
0.397
0.033
0.052
0.069
0.055
5.623
1.874
9.038
1.084
ÿ0.027
ÿ0.234
0.099
ÿ0.049
User 3
1
2
3
All
ÿ0.427
0.615
ÿ0.241
0.498
0.035
0.049
0.060
0.050
7.269
2.494
5.045
1.732
ÿ0.017
0.03
0.142
0.059
0.555
0.047
1.039
ÿ0.02
Overall
a
Model ef®ciency.
Non-normalised root mean square error (m3 mÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
3.4. Bromide transport
There were no significant differences in parameterisation of non-reactive solute
transport between the three users (only the diffusion coefficient was given different
values, but this is of minor significance for transport in a sand soil). Since water contents
are larger, and recharge rates are similar (almost identical water balance), the pore water
velocity controlling the mean convective solute displacement predicted by users 2 and 3
should be smaller (rough calculations suggest by perhaps up to 20%). This conclusion is
confirmed by the comparison of predicted bromide depth profiles (Fig. 5a±c). The
difference in predicted bromide transport is particularly noticeable for the profile on 27
August 1991 (Fig. 5b) which represents the system state at the end of the first recharge
period.
A statistical comparison with the measured data (Table 4) shows that the use of pedotransfer functions has resulted in apparently slightly better predictions of bromide
transport, despite poorer predictions of soil water content. This is particularly the case on
27 August 1991 (Fig. 5b), when the retardation of bromide transport is captured by users
2 and 3 but not by user 1. This is probably fortuitous: the much slower bromide transport
observed in the field experiment compared to the model predictions is probably due to
`immobile water' and by-pass `finger flow' caused by topsoil water repellency. This is an
interesting illustration of the fact that `model error' (MACRO cannot predict transport
retardation due to preferential flow in sands) can sometimes fortuitously cancel out
`parameter error' (underestimated pore water velocity) resulting in quite reasonable
model predictions. Indeed, the overall EF values shown in Table 4 are positive for all
three users indicating an acceptable model performance.
256
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 5. Comparison of measured and predicted bromide contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
257
Table 4
Statistics of goodness-of-®t for predictions of bromide content
Sampling occasion
EFa
NRMSEb
CSc
CRMd
User 1
1
2
3
All
0.272
ÿ1.950
ÿ2.257
0.102
3.511
3.231
1.596
2.874
1.906
0.484
0.502
1.303
ÿ0.459
ÿ0.460
ÿ0.603
ÿ0.482
User 2
1
2
3
All
0.468
ÿ1.694
ÿ2.588
0.221
3.001
3.087
1.675
2.677
1.085
0.525
0.992
0.789
ÿ0.233
ÿ0.020
ÿ0.702
ÿ0.200
User 3
1
2
3
All
0.434
ÿ0.261
ÿ2.036
0.458
3.096
2.112
1.541
2.233
1.924
1.193
0.306
1.062
ÿ0.162
0.035
ÿ0.456
ÿ0.109
0.260
2.609
0.951
ÿ0.264
Overall
a
Model ef®ciency.
Non-normalised root mean square error (mg dmÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
3.5. Soil temperatures
Soil temperature strongly influences pesticide biodegradation so that the prediction of
temperature should be an important part of any pesticide leaching model. Fig. 6 shows
that the soil temperatures predicted by the three users at 1 m depth are within ca. 38C at
all times. This is also the case for other depths in the profile (not shown), and is not
particularly surprising since the soil thermal properties in MACRO are defined internally
Fig. 6. Soil temperatures predicted at 1 m depth.
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N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 7. Sorption constants for bentazone estimated by the three users.
in the program so that the user cannot influence predictions of temperature to any great
degree. There are only two parameters influencing temperature which the user can vary,
the annual average air temperature and the annual amplitude in air temperature on a
monthly basis. These parameters only influence the bottom boundary condition in the
model.
3.6. Bentazone leaching
Figs. 7 and 8 show sorption distribution coefficients and degradation rate coefficients
assumed by the three users. The parameter values chosen by the users should be very
similar since they are largely based on the same set of laboratory measurements. This is
Fig. 8. First-order degradation rate coef®cients for bentazone estimated by the three users.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
259
true in the case of sorption parameters, but some significant differences are apparent in
the parameterisation of degradation. This is partly because the laboratory degradation
measured in the topsoil at 158C did not exactly follow the model assumption of first-order
kinetics, but instead showed a two-phase pattern. Up to 157 days, bentazone degraded at a
half-life of ca. 35 days, while from 157 to 450 days a half-life of 70 days was more
appropriate. Users 1 and 2 selected an average value for the whole period, while user 3
fitted the rate constant to the initial, faster, degradation phase, making the reasonable
assumption that the slower phase resulted from the commonly observed die-off of soil
microbial populations in laboratory tests (Fig. 8). The temperature response function for
degradation represents another significant difference in parameterisation between the
users. User 1 sets the exponent in this function to 0.172 based on the measurements made
at two temperatures in the laboratory, while users 2 and 3 chose the default value in the
model (0.08), since a value of 0.172 results in an unreasonably large activation energy.
Clearly, only two sets of measurements are insufficient to reliably parameterise the
model. The net effects of these differences in parameterisation is that user 3 predicts the
fastest degradation at the reference temperature of 108C, while users 1 and 3 would
predict similar rates at 208C, slightly more than twice those of user 2 (the differences at
cold temperatures are not so significant, because rates are low anyway).
Fig. 9a±c shows that user 2 does indeed predict somewhat slower dissipation of
bentazone than users 1 and 3, particularly in the summer period during the experiment.
The statistical comparison with the measured data (Table 5) shows that all three MACRO
users considerably underestimated the observed dissipation of bentazone, with large
positive values for the CRM, and negative values of EF. One explanation may be that the
model underestimated leaching of bentazone below 1.1 m depth. It is difficult to test this
hypothesis without flux measurements (e.g. lysimeters, drainage outflows), although the
model showed no tendency to underpredict the rate of bromide leaching. Therefore, we
consider that a more likely reason for the discrepancy is that bentazone degradation was
underestimated either because the microbial activity in the laboratory incubation
experiments did not reflect that of undisturbed populations in the field soil or because
there are significant dissipation pathways and mechanisms for bentazone in the field
which are not observed in the laboratory and are not included in the model. In this
respect, much shorter laboratory aerobic half-lives than those used in this study are
Table 5
Statistics of goodness-of-®t for predictions of bentazone content
EFa
NRMSEb
CSc
CRMd
User 1
User 2
User 3
ÿ1.02
ÿ2.81
ÿ0.38
21.36
29.33
17.66
0.60
0.46
0.58
0.79
1.31
0.81
Overall
ÿ1.05
21.75
0.51
1.15
a
Model ef®ciency.
Non-normalised root mean square error (mg mÿ3).
c
Coef®cient of shape.
d
Coef®cient of residual mass.
b
260
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
Fig. 9. Comparison of measured and predicted bentazone contents on the three sampling occasions.
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
261
normally reported for bentazone in freshly collected field soils (on average 14 days at
208C equivalent to ca. 31 days at 108C, assuming 0.08 for the exponent in the temperature
response function), while field half-lives for bentazone as short as 12 days have also been
reported (Tomlin, 1997). Using MACRO, Jarvis et al. (1994) also underpredicted
bentazone dissipation observed in lysimeter experiments. This was tentatively attributed
to photolysis (Nilles and Zabik, 1975).
4. Conclusions
The three users introduced significant differences in parameterisation of the
degradation submodel in MACRO due to (i) difficulty in estimating the reference firstorder rate coefficient for degradation from laboratory incubation tests exhibiting biphasic
degradation, (ii) uncertainty concerning the reliability of the laboratory degradation
measurements made at two different temperatures leading to differences in parameterisation of the temperature response function. Since leaching predictions are strongly
dependent on degradation, it is recommended that for registration purposes, strict
protocol guidelines with respect to interpretation of laboratory incubation tests should be
developed and followed. The model users also adopted different methods to parameterise
soil hydraulic properties. The use of pedo-transfer functions resulted in root mean square
errors in predicted soil water contents of the order of 0.06 m3 mÿ3, which were only
slightly larger than the values obtained when fitting to actual measured data. Of course,
whenever real data are available, it is recommended that these are used. However, this
study shows that the use of such pedo-transfer functions may be sufficiently accurate for
registration purposes.
Nevertheless, the main source of error was common to all three users, namely that
larger dissipation rates were observed in the field compared to those predicted from the
laboratory incubation measurements. This may have resulted from the exclusion of
significant processes, particularly the presumed existence of additional abiotic dissipation
pathways for bentazone (photolysis) or because the laboratory was not representative of
field conditions either due to sampling disturbance, soil handling, the dosing method used
or population die-off. Another process which is not included in the model is preferential
finger flow. This was shown to influence non-reactive bromide transport, but its effect on
bentazone transport could not be assessed due to the dominating influence of errors in
degradation.
Acknowledgements
This study was carried out within the framework of the mathematical modelling
working group of COST Action 66 `Pesticide fate in the soil environment' organised by
DGXII of the EU. The authors are grateful to Jos Boesten (Winand Staring Centre,
Netherlands) for supplying the dataset `Vredepeel', especially for the user-friendly way
in which it was compiled. The authors also thank Allan Walker (HRI, UK) for the use of
the program STATIND for calculating statistical indices of model performance.
262
N.J. Jarvis et al. / Agricultural Water Management 44 (2000) 247±262
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