Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol44.Issue1-3.Apr2000:
Agricultural Water Management 44 (2000) 317±335
Modelling ethoprophos and bentazone fate in a sandy
humic soil with primary pesticide fate model PRZM-2
M. Trevisana,*, G. Erreraa, G. Goerlitzb, B. Remyc, P. Sweeneyd
a
Istituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
b
Hoechst Schering AgrEvo GmbH, Werk Hochst, D-65926 Frankfurt am Main, Germany
c
MinisteÁre de l'Agriculture, de la PeÃche et de l'Alimentation, Direction ReÂgionale de l'Agriculture
et de la Foret ``Centre'', Service ReÂgional de la Protection des VegeÂtaux, Rue de Curambourg,
93-BP 210, 45403 Fleury les Aubrais, France
d
Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire RG42 6ET, UK
Abstract
Primary pesticide fate models are powerful tools for ranking pesticides within an environmental
contamination risk analysis context. The performance of primary pesticide root zone model PRZM2 to analyse ethoprophos and bentazone transport and dissipation is assessed. The evaluation was
performed within the framework of an European modelling validation exercise, which enabled us to
use a high quality data set and to adopt a standardised modelling protocol. Within this paper PRZM2 was evaluated by four different users using the Vredepeel data set. Simulations were carried out
with and without calibration of some parameters, as identi®ed by the independent model users.
Finally, a simulation with a consensus parameter set was performed.
The model did not accurately describe the behaviour of ethoprophos and bentazone. However the
model gave predictions of chemical concentration that were within an order-of-magnitude. It is
clear from this exercise that, given exactly the same data, different model users will parametrise a
model in very different ways which in turn will lead to very different model output. Control of
model input and guidance for the selection of inputs is therefore vital if a model is to be used to
predict the fate of pesticides in the environment. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: PRZM-2; Model validation; Model calibration; Ethoprophos; Bentazone
1. Introduction
Environmental risk assessment is a key component of the registration directive for
pesticides in Europe (Directive 91/414/EEC). Since field studies to evaluate environ*
Corresponding author.
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 8 - 0
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
mental fate and behaviour are very expensive and provide a site-specific evaluation of
environmental exposure, interest has developed in the use of models in predicting
environmental fate in a range of circumstances. This directive confirms the value of
models to predict pesticide fate and requires predicted environmental concentrations
(PEC's) for environmental compartments such as air, soil, and water (surface and
groundwater) and comparison with available toxicological data to calculate the toxicity
exposure ratio. Within models primary models should provide a standardised approach to
characterise pesticide fate and should permit rapid review of modelling submissions by
regulators and help to ensure consistent regulatory decision making. Specific models
should be selected based on acceptance by regulatory officials and the ability of the
models to accurately describe environmental processes for many typical pesticide
scenario. PRZM-2 falls in this category (Boesten et al., 1995).
The work aims to evaluate the prediction capability of the PRZM-2 model
(Mullins et al., 1992) using a data set describing results from field and laboratory
experiments performed in the Netherlands (Boesten and Van der Pas, 1996). The work
was carried out in the framework of a European modelling validation exercise supported
by the COST 66 Action `Pesticides in the soil environment' of DGXII-EU. The PRZM-2
model was considered both by FOCUS (FOrum for the Coordination of pesticide
fate models their USe) leaching and soil working groups, as a model useful to
calculate PEC's in soil and groundwater (Boesten et al., 1995, 1997). FIFRA considered
it as a primary model (FIFRA, 1994). Four different groups of users performed
different simulations without exchange of information. Input and output variability was
compared. It was possible to perform calibration of parameters after one uncalibrated
simulation during the exercise. Uncalibrated runs were performed by users using
only data set information and own knowledge, calibrated runs were performed using
data set output. Finally a simulation was done with an input file agreed after discussion
among the users and the providers of data set. Consensus simulation was therefore used
in attempt to eliminate individual error and allow for a fair assessment of model
capabilities.
2. Materials and methods
2.1. Model description
PRZM-2 is a management model, developed in US by EPA, which allows the user to
perform dynamic simulations of the fate of pesticides. It is a one-dimensional, field, daily
time scale model which can be used to estimate runoff, leaching and associated pesticide
loading. PRZM-2 uses an SCS curve technique to estimate runoff losses and the universal
soil loss equation to estimate erosion. The amount of infiltrating water is determined by
subtracting runoff, evapotranspiration and interception losses from rainfall. Subsequent
routing of soil water within the root zone is based upon a ``tipping bucket'' scheme that
requires the specification of field capacity and wilting point. Evapotranspiration losses
are divided between evaporation from crop interception, evaporation from the soil and
crop transpiration. Potential evaporation is calculated by the input of pan evaporation and
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
319
by using a pan factor. PRZM-2 does not simulate subsurface lateral flow, macropore flow,
by-pass flow or tile drainage.
PRZM-2 is quite flexible in the way that pesticide application can be specified, to the
soil or to plant foliage; the model also allows for the specification of some types of tillage
operation and irrigation. Dissolved, adsorbed and vapour phase concentrations in the soil
are estimated by simultaneously considering the processes of pesticide uptake by plants,
surface runoff, erosion, decay, volatilisation, foliar wash-off, convection, dispersion and
sorption.
PRZM-2 uses the convection±dispersion equation to describe pesticide transport. Only
downward movement of water is simulated and no account is made of diffusive
movement due to soil water gradients. Major assumptions of the solute transport model
are that convection and dispersion are one-dimensional and that fluid properties are
independent of pesticide concentrations. The hydrodynamic dispersion coefficient is
defined as the sum of the coefficient of mechanical dispersion and molecular diffusion.
Pesticide degradation is described using first-order kinetics, without dependence on
temperature or moisture content and pesticide sorption is modelled as linear.
The model requires the input of a large number of parameters, some of which are
difficult to measure, as hydrodynamic dispersion coefficient, enthalpy of vapourisation,
depth to which evapotranspiration was extracted from the soil profile. This can lead to a
wide divergence of model results when different users use the same experimental data to
parametrise the model because the choice of parameter values will be open to
considerable subjectivity according to the experience and knowledge of the individual
user. Two categories are individuated: initial state of the system and extrapolating beyond
the information in order to better approximate personal view of the field situation. From
the literature (Fontaine et al., 1992; Del Re and Trevisan, 1993) we found that PRZM-2
has the following sensitive parameters: degradation constant, sorption coefficient (Kd),
Henry constant, thickness of compartments in the horizon, hydrodynamic dispersion
coefficient, bulk density and initial soil moisture content.
2.2. Data set description
The data set used was obtained in Vredepeel, The Netherlands, in a sandy soil and it is
described elsewhere (Boesten and Van der Pas, 1999). In this data set results are results on
movement and persistence in soil of bentazone, ethoprophos, and bromide.
The data set is well characterised but several problems arose when this data was used to
parametrise the PRZM-2 model. These were:
1. Lack of soil residue data. Only three sampling points were provided for movement of
pesticide in soil and also soil moisture. Ethoprophos was sampled for on seven
occasions, however this is the least mobile of the compounds applied.
2. Three types of measurements of soil hydraulic properties were carried out, water
retention and conductivity characteristics and saturated hydraulic conductivity. Field
capacities and wilting points for the soil were not measured and users should be
deriving from them.
3. Makkink evapotranspiration was provided rather than the required pan evaporation.
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 1
Meteorological input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Pan factor
Snowmelt factor (cm/8C above freezing)
Minimum depth of which evaporation is extracted (cm)
Average duration of rainfall produced by storm (h)
Pan factor ¯ag (zero daily pan evaporation
data read; one temperature data read)
Monthly daylight hours
January
February
March
April
May
June
July
August
September
October
November
December
0.7
0.4
20
0.85
0.46
10
1
0
10
5.4
0
1
0
10
1
0
10
1
0
1
1
8.6
10
11.8
13.8
15.4
16.3
15.9
14.5
12.7
10.7
9.1
8.1
9.6
9.7
12
14
15
16
16
15
13
11
9.5
9
8.6
10
12
14
15
16
16
15
13
11
9.1
8.1
2.3. Modelling parameters
Tables 1±5 show the main input parameters chosen by the users and these agreed after
deep laid discussion among users and the providers of the data set (consensus simulation).
It can be seen that, given exactly the same experimental data, different users chose
different values for the input parameters. For example, in Table 1, it can be seen that
different methods were chosen for calculating potential evapotranspiration; some allowed
the model to calculate this quantity using monthly daylight figures (and these figures
varied from user to user), others used a factor combined with the Makkink
evapotranspiration data. There were also differences in the depth to which evapotranspiration was extracted from the soil profile. Evapotranspiration is a key quantity used in
the estimation of soil water content, for the consensus simulations two runs were
performed: one using the Makkink evapotranspiration values and another that scaled
these according to the developmental stage of the crop.
Table 2 shows the values chosen by different users for the crop parameters. There
is again considerable disagreement between users. Perhaps the most important quantity
subject to variation is the maximum rooting depth as this determines the maximum depth
to which water can be extracted from the soil profile by evapotranspiration.
Pesticide application parameters and chemical values are shown in Table 3. The users
disagreed on the choice of the depth of application and of the pesticide application rate.
Differences among users in application rate for ethoprophos and bentazone may be
explained in the use of the concentration founded one day after treatment in the field
instead the value measured during spraying. One user in application rate of ethoprophos
Table 2
Crop input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Number of different crop
2
3
2
2
2
15/04/1990
01/10/1990
10/10/1990
0.35
150
100
Residue
2.1
50
Wheat
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
06/12/1990
31/07/1991
14/08/1991
0.008
100
85
Fallow
0
80
22/11/1990
10/07/1991
14/08/1991
0.1
140
100
Residue
1.4
80
03/12/1990
31/07/1991
14/08/1991
0.15
100
90
Residue
0
80
06/12/1990
25/07/1991
14/08/1991
0.1
60
80
Residue
0
80
06/12/1990
31/07/1991
14/08/1991
0.15
40
100
Fallow
0
80
Yellow mustard
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
29/09/1991
29/11/1991
29/11/1991
0.2
45
90
Fallow
0
75
19/09/1991
29/11/1991
29/11/1991
0.5
70
100
Cropping
0.53
30
25/09/1991
29/11/1991
29/11/1991
0.15
40
70
Fallow
0
40
15/09/1991
28/11/1991
29/11/1991
0.1
30
80
Residue
0
35
29/09/1991
28/11/1991
29/11/1991
0.20
30
100
Fallow
0
40
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Sugarbeet
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 3
Management input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Application date
Depth of application (cm)
Total application of bentazone (kg/ha)
Total application of ethoprophos (kg/ha)
Total depth of soil core (cm)
Plant uptake factor
Method of characteristics
Diffusion coef®cient for pest in air
(cm2/day)
Henry's law constant ethoprophos
Henry's law constant bentazone
Enthalpy of vapourisation ethoprophos
(kcal/mol)
Enthalpy of vapourisation bentazone
(kcal/mol)
22/11/1990
4
0.8
3.35
105
0
No
0
22/11/1990
0
0.8
3.35
200
0
No
0.43
23/11/1990
0
0.73
3
100
1
No
4300
23/11/1990
0
0.73
3
60
0.8
No
4350
23/11/1990
0
0.63
1.33
105
1
No
4300
0
0
0
0
0
0
6.00 10ÿ6 4.20 10ÿ6 2.00 10ÿ6
2.00 10ÿ10 1.00 10ÿ7 1.00 10ÿ7
22.5
20
22.5
0
0
21.7
20
21.7
Table 4
Numerical discretisation of the soil pro®le used in calibrated and consensus simulations
User
Horizon
1
2
3
1
Horizon thickness (cm)
Compartment thickness (cm)
7.5
0.1
7.5
0.1
2
Horizon thickness (cm)
Compartment thickness (cm)
50
2.5
50
2.5
100
2.5
3
Horizon thickness (cm)
Compartment thickness (cm)
4
4
3.5
3.5
25
2.5
17.5
2.5
50
2.5
100
4
Horizon thickness (cm)
Compartment thickness (cm)
4
4
3.5
3.5
22.5
7.5
7.5
7.5
22.5
7.5
60
Consensus
Horizon thickness (cm)
Compartment thickness (cm)
4
0.5
3.5
0.5
22.5
1.5
22.5
1.5
52.5
7.5
105
22.5
1.25
4
5
Total core (cm)
22.5
2.5
45
5
105
200
took into account the experimental large volatilisation losses that were showed for this
compound in data set in the first week.
Table 4 shows the numerical discretisation of the soil profile. The core depth
(from 60 to 200 cm) varied between users; the data set included information on the
soil core to a depth of 2 m, however the presence of a fluctuating water table, which
PRZM is not designed to simulate, probably accounts for the different core depths
chosen.
The number and the thickness of horizons and the thickness of horizon compartments selected by different users were also different. Subdivisions are important
because numerical dispersion can arise from using too small or too large a com-
323
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 5
Decay rate (per day) in dissolved (DWRATE) and adsorbed (DSRATE) phase and partition coef®cient (KD) (ml/
g) of bentazone (B) and ethoprophos (E) for each horizon used in calibrated and consensus simulations
Horizon
Parameter
User 1
User 2
User 3
User 4
Consensus
1
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160
4.5
0.004
0.012
0.004
0.012
4.29
0.11
0.04
0.009
0.003
0.006
1.8
0.1
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
2
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160a
4.5a
0.002
0.002
0.002
0.002
0.206
0.005
0.04
0.009
0.003
0.006
1.8
0.1
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
3
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160a
4.5a
0.002
0.002
0.002
0.002
0.225
0.006
0.04
0.009
0.003
0.006
1.8
0.10
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
4
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0011
0.0000
0.0011
0.0000
160a
4.5a
0.02
0.0045
0.0025
0.003
0.74
0.04
0.0008
0.0011
0.0008
0.0011
0.17
0.04
0.0022
0.0002
0.0022
0.0002
1.11
0.02
5
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0011
0.0000
0.0011
0.0000
160a
4.5a
0.01
0.002
0.002
0.0002
0.08
0.004
0.0008
0.0011
0.0008
0.0011
0.17
0.04
0.0015
9.2 10ÿ5
0.0015
9.2 10ÿ5
0.21
0.01
a
As Koc value.
partment size. The choice of horizon thickness can be made from the soil data provided
with the data set, although there was considerable variation between users for these
values. Compartment size within these horizons however, is a matter of personal choice
based upon experience and this may account for the wide range of compartment sizes
(Table 4).
Although differences in the soil properties selected can be seen between the users, the
discrepancies are not large enough to have caused a significant difference to model
output.
Table 5 shows the decay rates and sorption coefficients. PRZM-2 requires only the
input of linear sorption coefficients and a first-order decay rate, nonetheless there
was variability between users in the choice of these parameters which are vital to
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
the prediction of leachate concentration. Note that one user entered a Koc value
which the model then uses to derive a Kd value based upon the percentage carbon in the
horizon.
2.4. Comparison
Evaluation of performance of model are carried out using graphical method and
statistical indices. The indices used are chosen to evaluate the overall fit (model
efficiency, EF), the prediction of total soil residues (coefficient of residual mass, CRM)
(Vanclooster et al., 1999) and the prediction of the distribution of residues in soil (mean
depth ratio, MDR) (Walker et al., 1995). It should be noted that these indices must be
used carefully and the way people have to interpret them must not adapted to their interest
and purpose. However they do provide a useful method of comparing the degree of fit of
model outputs produced by different users to measured data. For the purposes of
graphical presentation confidence at the 95% level limits were calculated.
Indices of model performance were calculated for each user for both the calibrated and
uncalibrated runs and also for all of the simulations grouped together (global indices). We
have included the consensus simulation in the graphical data to indicate probably the best
performance of PRZM-2 model.
3. Results and discussion
Soil moisture profiles were sufficiently in agreement with observed data after
calibration of field capacity and wilting point values. In Fig. 1 the experimental data and
PRZM-2 simulation for the first layer (0±30 cm) are reported. Discrepancies could be due
to the error in the parametrisation of field capacities and wilting point values and in the
lack of the observed pan evaporation data. The model controls water flow using only
these parameters.
Solute transport was poorly predicted by all PRZM-2 simulations, as showed in
Fig. 2. The experimental high content of bromide in the top soil layer was poorly
predicted by all simulations. The high content of bromide in the winter wheat plants
(Boesten and Van der Pas, 1996) and the turnover of plant litter may have played a key
role in the release of bromide during the study, leading to a steady content of solute in the
top soil layer.
The model output did not match the measured data for bentazone well. In general the
model tended to over-estimate the concentration of bentazone in the 30±60 cm zone
which may indicate that the model underestimated the mobility of this chemical for the
Vredepeel site. Figure 3 shows that the consensus simulation lay outside the global 95%
confidence limits of experimental data for this chemical at some depths. Table 6 shows
that the model efficiency for all the simulations is poor for this chemical and the model
only adequately described its distribution of residues along the profile, as indicated by
MDR index near at 1 for user 2 and 3.
The observed ethoprophos concentration profile was also not well described by the
model, however here the consensus simulation gave a better fit to the data, especially for
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 1. Observed vs. predicted soil moisture content in top soil layer. Mean and 95% con®dence interval of observed data are shown. Model simulation with the
consensus parameters are also shown.
325
326
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 2. Observed vs. predicted bromide concentration in top soil layer. Mean and 95% con®dence interval of observed data are shown. Model simulation with the
consensus parameters are also shown.
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 3. Observed vs. predicted bentazone concentration pro®les. Mean and 95% con®dence interval of observed data are shown. Model simulation with the consensus
parameters are also shown.
327
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 3. (Continued ).
328
329
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 6
Statistical model performance indicators for simulated ethoprophos and bentazone pro®lesa
Index
Bentazone profile
Data
Not
calibrated
Ethoprophos profile
Calibrated
Data
Not
calibrated
Poorc
1.371
135
135
Poor
1.184
15
15
Poor
2.289
15
15
0.362
1.481
Calibrated
Global
EFb
CMRb
108
108
User 1
EF
MDRb
12
12
Poor
0.436
User 2
EF
MDR
12
12
Poor
1.082
User 3
EF
MDR
12
12
Poor
1.039
Poor
1.114
15
15
0.204
2.854
0.262
2.690
User 4
EF
MDR
12
12
Poor
0.500
Poor
0.982
15
15
Poor
2.054
Poor
2.054
Consensus
EF
MDR
12
12
Poor
1.150
Poor
1.120
15
15
0.771
1.282
0.758
1.280
Poor
0.514
Poor
2.094
a
The number of data points used to calculate the indicator are reported. User 2 has not performed calibrated
simulations.
b
EF Ð model ef®ciency; CRM Ð coef®cient of residual mass; MDR Ð mean depth ratio.
c
Poor Ð when the value of index becomes negative, the ®t is unacceptably poor.
the first sampling point (Fig. 4). This simulation used a reduced application rate to
account for the volatilisation losses observed with ethoprophos, some users did not
include this loss and this may be the reason for the improved prediction obtained by
consensus. Users 2 and 3 did get a reasonable value for the model efficiency, but a poor
one for the MDR index, indicating a poor prediction of distribution of residues along the
profile (Table 6).
The fit of the model output to the aeric mass of ethoprophos data showed similar
features (Fig. 5).
The model fitted the data for ethoprophos better than for bentazone and this was
especially true for the consensus simulations. The reason for this may be the greater
mobility of bentazone. In order to produce a good model fit to the data, a model would
have to accurately predict the movement of soil water; more so for a mobile than for a
more strongly adsorbed chemical such as ethoprophos. It is therefore not surprising that
the model did not predict the bentazone concentration well, but did so for ethoprophos,
which is less mobile. Whether the model would have produced better predictions with an
accurate description of water balance is a matter of conjecture.
The model gave predictions of chemical concentration that were within an order-ofmagnitude in all cases. The movement of chemicals through soil is a complex process and
no model can reproduce exactly what happens in the field. So, although the model did not
give a good fit to the data according to statistical criteria, it still gave a reasonable
prediction of the magnitude of concentrations that were observed in the field when
ethoprophos and bentazone were applied to the Vredepeel site.
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 4. Observed vs. predicted ethoprophos concentration pro®les. Mean and 95% con®dence interval of observed data are shown. Model simulation with the consensus
parameters are also shown.
331
Fig. 4. (Continued ).
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 4. (Continued ).
332
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 5. Observed vs. predicted areic mass of ethoprophos as a function of time. Mean and 95% con®dence interval of observed data are shown. Model simulation with
the consensus parameters are also shown.
333
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
4. Conclusion
The PRZM-2 model was evaluated to predict pesticide transport in soil using the
Vredepeel data set. Simulations were carried out by several groups, with and without
calibration of some parameters, and finally a consensus simulation was performed.
The model did not accurately describe the behaviour of ethoprophos and bentazone.
However the model predictions of the total pesticide residues were generally within an
order-of-magnitude of those measured on the site. It was difficult to parametrise the
model to get a good fit to the water balance observed on the site, probably because key
parameters of the model, as field capacity and wilting point, were derived from retention
curve, and that increase the degree of results uncertainty.
Volatilisation routine does not simulate ethoprophos losses during first week as well as
data set shows; it is a matter of conjecture why this happen.
The volatilisation routine is affected by chemical properties and soil temperature and
wind speed. The users adopted Henry constant values in agreement with indication of
data set provider, but in Vredepeel conditions soil temperature is always less than 158C
after treatment and wind speed is slowly (300±400 cm/s); for this reason may be that
model not forecast the observed pesticide volatilisation.
The main question in application of pesticide fate model is whether the model
parameters can be derived from the data provided. This exercise shows that, given exactly
the same data, different model users will parametrise a model in very different ways. That
will lead to very different model output. The reason for this user-dependent variability
was the interpretation of the experimental data. Although the data set was extensive and
well described many parameters were derived from experimental data and their user
interpretation could be different. Control of model input and guidance for the selection of
inputs is therefore vital if a model is to be used to predict the fate of pesticides in the
environment, as has been indicated by Brown et al. (1996).
References
Boesten, J.J.T.I., Businelli, M., Delmas, A., Edwards, V., Helweg, A., Jones, R., Klein, M., Kloskowski, R.,
Layton, R., Marcher, S., SchaÈfer, H., Smeets, L., Styczen, M., Travis, K., Walker, A., Yon, D., 1995.
Leaching models and EU registration. Final report, Regulatory Modelling Work Group of FOCUS, Forum
for the Coordination of Pesticide Fate Models and their Use, 123 pp.
Boesten, J.J.T., Helweg, A., Businelli, M., Bergstrom, L., Schaefer, H., Delmas, A., Kloskowski, R., Walker, A.,
Travis, K., Smeets, L., Jones, R., Vanderbroeck, V., Van Der Linden, A., Broerse, S., Klein, M., Layton, R.,
Jacobsen, O.S., Yon, D., 1997. Soil persistence models and EU registration. Final report, Soil Modelling
Work group of FOCUS, Forum for the Coordination of Pesticide Fate Models and their Use, 74 pp.
Boesten, J.T.T.I., Van der Pas, L.J.T., 1996. Movement of water, bromide ion and pesticides ethoprophos and
bentazone measured in a sandy soil in Vredepeel (The Netherlands). Wageningen DLO-Staring Centrum.
report 122, 86 pp.
Boesten, J.T.T.I., Van der Pas, L.J.T., 1999. Movement of water, bromide and pesticides ethoprophos and
bentazone measured in a Dutch sandy soil: the Vredepeel data set. Agric. Water Mgmt. 44, 21±42.
Brown, C.D., Baer, U., Gunther, P., Trevisan, M., Walker, A., 1996. Ring test with the models LEACHP, PRZM2 and VARLEACH: variability between model users in prediction of pesticide leaching using a standard data
set. Pestic. Sci. 47, 249±258.
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Del Re, A.A.M., Trevisan, M., 1993. Testing models of the unsaturated zone. In: Del Re, A.A.M., Capri, E.,
Evans, S.P., Natali, P., Trevisan, M. (Eds.), Proceedings of Ninth Symposium Pesticide Chemistry,
Degradation and Mobility of Xenobiotics, Piacenza, 11±13 October 1993. Edizioni Biagini, Lucca, pp. 5±31.
FIFRA Exposure Modelling Work Group, 1994. Primary, secondary and screening models for pesticide
registration. National Agric. Chemicals Assoc., Washington, DC, p. 64.
Fontaine, D.D., Havens, P.L., Blau, G.E., Tillotson, P.M., 1992. The role of sensitivity analysis in groundwater
risk modelling for pesticides. Weed Technol. 6, 716±724.
Mullins, J.A., Carsel, R.F., Scarbrough, J.E., Ivery, A.M., 1992. PRZM-2, a model for predicting pesticide fate in
the crop root and unsaturated soil zones. Users manual for release 2.0. Environmental Research Laboratory
Of®ce of Research and Development, US Environmental Protection Agency, Athens, GA 30605-2720.
Vanclooster, M., Boesten, J.T.T.I., Trevisan, M., Brown, C.D., Capri, E., Eklo, O.M., Gottesburen, B., Gouy, V.,
Van der Linden, A.M.A., 1999. A european test of pesticide-leaching models: methodology and major
recommendations. Agric. Water Mgmt. 44, 1±19.
Walker, A., Calvet, R., Del Re, A.A.M., Pestemer, W., Hollis, J., 1995. Evaluation and improvement of
mathematical models of pesticide mobility in soils and assessment of their potential to predict contamination
of water systems. Mitt. Biol. Bundesanstalt f. Land- und Forstwirtschaft Berlin-Dahlem 307, 1±115.
Modelling ethoprophos and bentazone fate in a sandy
humic soil with primary pesticide fate model PRZM-2
M. Trevisana,*, G. Erreraa, G. Goerlitzb, B. Remyc, P. Sweeneyd
a
Istituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
b
Hoechst Schering AgrEvo GmbH, Werk Hochst, D-65926 Frankfurt am Main, Germany
c
MinisteÁre de l'Agriculture, de la PeÃche et de l'Alimentation, Direction ReÂgionale de l'Agriculture
et de la Foret ``Centre'', Service ReÂgional de la Protection des VegeÂtaux, Rue de Curambourg,
93-BP 210, 45403 Fleury les Aubrais, France
d
Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire RG42 6ET, UK
Abstract
Primary pesticide fate models are powerful tools for ranking pesticides within an environmental
contamination risk analysis context. The performance of primary pesticide root zone model PRZM2 to analyse ethoprophos and bentazone transport and dissipation is assessed. The evaluation was
performed within the framework of an European modelling validation exercise, which enabled us to
use a high quality data set and to adopt a standardised modelling protocol. Within this paper PRZM2 was evaluated by four different users using the Vredepeel data set. Simulations were carried out
with and without calibration of some parameters, as identi®ed by the independent model users.
Finally, a simulation with a consensus parameter set was performed.
The model did not accurately describe the behaviour of ethoprophos and bentazone. However the
model gave predictions of chemical concentration that were within an order-of-magnitude. It is
clear from this exercise that, given exactly the same data, different model users will parametrise a
model in very different ways which in turn will lead to very different model output. Control of
model input and guidance for the selection of inputs is therefore vital if a model is to be used to
predict the fate of pesticides in the environment. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: PRZM-2; Model validation; Model calibration; Ethoprophos; Bentazone
1. Introduction
Environmental risk assessment is a key component of the registration directive for
pesticides in Europe (Directive 91/414/EEC). Since field studies to evaluate environ*
Corresponding author.
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 8 - 0
318
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
mental fate and behaviour are very expensive and provide a site-specific evaluation of
environmental exposure, interest has developed in the use of models in predicting
environmental fate in a range of circumstances. This directive confirms the value of
models to predict pesticide fate and requires predicted environmental concentrations
(PEC's) for environmental compartments such as air, soil, and water (surface and
groundwater) and comparison with available toxicological data to calculate the toxicity
exposure ratio. Within models primary models should provide a standardised approach to
characterise pesticide fate and should permit rapid review of modelling submissions by
regulators and help to ensure consistent regulatory decision making. Specific models
should be selected based on acceptance by regulatory officials and the ability of the
models to accurately describe environmental processes for many typical pesticide
scenario. PRZM-2 falls in this category (Boesten et al., 1995).
The work aims to evaluate the prediction capability of the PRZM-2 model
(Mullins et al., 1992) using a data set describing results from field and laboratory
experiments performed in the Netherlands (Boesten and Van der Pas, 1996). The work
was carried out in the framework of a European modelling validation exercise supported
by the COST 66 Action `Pesticides in the soil environment' of DGXII-EU. The PRZM-2
model was considered both by FOCUS (FOrum for the Coordination of pesticide
fate models their USe) leaching and soil working groups, as a model useful to
calculate PEC's in soil and groundwater (Boesten et al., 1995, 1997). FIFRA considered
it as a primary model (FIFRA, 1994). Four different groups of users performed
different simulations without exchange of information. Input and output variability was
compared. It was possible to perform calibration of parameters after one uncalibrated
simulation during the exercise. Uncalibrated runs were performed by users using
only data set information and own knowledge, calibrated runs were performed using
data set output. Finally a simulation was done with an input file agreed after discussion
among the users and the providers of data set. Consensus simulation was therefore used
in attempt to eliminate individual error and allow for a fair assessment of model
capabilities.
2. Materials and methods
2.1. Model description
PRZM-2 is a management model, developed in US by EPA, which allows the user to
perform dynamic simulations of the fate of pesticides. It is a one-dimensional, field, daily
time scale model which can be used to estimate runoff, leaching and associated pesticide
loading. PRZM-2 uses an SCS curve technique to estimate runoff losses and the universal
soil loss equation to estimate erosion. The amount of infiltrating water is determined by
subtracting runoff, evapotranspiration and interception losses from rainfall. Subsequent
routing of soil water within the root zone is based upon a ``tipping bucket'' scheme that
requires the specification of field capacity and wilting point. Evapotranspiration losses
are divided between evaporation from crop interception, evaporation from the soil and
crop transpiration. Potential evaporation is calculated by the input of pan evaporation and
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
319
by using a pan factor. PRZM-2 does not simulate subsurface lateral flow, macropore flow,
by-pass flow or tile drainage.
PRZM-2 is quite flexible in the way that pesticide application can be specified, to the
soil or to plant foliage; the model also allows for the specification of some types of tillage
operation and irrigation. Dissolved, adsorbed and vapour phase concentrations in the soil
are estimated by simultaneously considering the processes of pesticide uptake by plants,
surface runoff, erosion, decay, volatilisation, foliar wash-off, convection, dispersion and
sorption.
PRZM-2 uses the convection±dispersion equation to describe pesticide transport. Only
downward movement of water is simulated and no account is made of diffusive
movement due to soil water gradients. Major assumptions of the solute transport model
are that convection and dispersion are one-dimensional and that fluid properties are
independent of pesticide concentrations. The hydrodynamic dispersion coefficient is
defined as the sum of the coefficient of mechanical dispersion and molecular diffusion.
Pesticide degradation is described using first-order kinetics, without dependence on
temperature or moisture content and pesticide sorption is modelled as linear.
The model requires the input of a large number of parameters, some of which are
difficult to measure, as hydrodynamic dispersion coefficient, enthalpy of vapourisation,
depth to which evapotranspiration was extracted from the soil profile. This can lead to a
wide divergence of model results when different users use the same experimental data to
parametrise the model because the choice of parameter values will be open to
considerable subjectivity according to the experience and knowledge of the individual
user. Two categories are individuated: initial state of the system and extrapolating beyond
the information in order to better approximate personal view of the field situation. From
the literature (Fontaine et al., 1992; Del Re and Trevisan, 1993) we found that PRZM-2
has the following sensitive parameters: degradation constant, sorption coefficient (Kd),
Henry constant, thickness of compartments in the horizon, hydrodynamic dispersion
coefficient, bulk density and initial soil moisture content.
2.2. Data set description
The data set used was obtained in Vredepeel, The Netherlands, in a sandy soil and it is
described elsewhere (Boesten and Van der Pas, 1999). In this data set results are results on
movement and persistence in soil of bentazone, ethoprophos, and bromide.
The data set is well characterised but several problems arose when this data was used to
parametrise the PRZM-2 model. These were:
1. Lack of soil residue data. Only three sampling points were provided for movement of
pesticide in soil and also soil moisture. Ethoprophos was sampled for on seven
occasions, however this is the least mobile of the compounds applied.
2. Three types of measurements of soil hydraulic properties were carried out, water
retention and conductivity characteristics and saturated hydraulic conductivity. Field
capacities and wilting points for the soil were not measured and users should be
deriving from them.
3. Makkink evapotranspiration was provided rather than the required pan evaporation.
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 1
Meteorological input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Pan factor
Snowmelt factor (cm/8C above freezing)
Minimum depth of which evaporation is extracted (cm)
Average duration of rainfall produced by storm (h)
Pan factor ¯ag (zero daily pan evaporation
data read; one temperature data read)
Monthly daylight hours
January
February
March
April
May
June
July
August
September
October
November
December
0.7
0.4
20
0.85
0.46
10
1
0
10
5.4
0
1
0
10
1
0
10
1
0
1
1
8.6
10
11.8
13.8
15.4
16.3
15.9
14.5
12.7
10.7
9.1
8.1
9.6
9.7
12
14
15
16
16
15
13
11
9.5
9
8.6
10
12
14
15
16
16
15
13
11
9.1
8.1
2.3. Modelling parameters
Tables 1±5 show the main input parameters chosen by the users and these agreed after
deep laid discussion among users and the providers of the data set (consensus simulation).
It can be seen that, given exactly the same experimental data, different users chose
different values for the input parameters. For example, in Table 1, it can be seen that
different methods were chosen for calculating potential evapotranspiration; some allowed
the model to calculate this quantity using monthly daylight figures (and these figures
varied from user to user), others used a factor combined with the Makkink
evapotranspiration data. There were also differences in the depth to which evapotranspiration was extracted from the soil profile. Evapotranspiration is a key quantity used in
the estimation of soil water content, for the consensus simulations two runs were
performed: one using the Makkink evapotranspiration values and another that scaled
these according to the developmental stage of the crop.
Table 2 shows the values chosen by different users for the crop parameters. There
is again considerable disagreement between users. Perhaps the most important quantity
subject to variation is the maximum rooting depth as this determines the maximum depth
to which water can be extracted from the soil profile by evapotranspiration.
Pesticide application parameters and chemical values are shown in Table 3. The users
disagreed on the choice of the depth of application and of the pesticide application rate.
Differences among users in application rate for ethoprophos and bentazone may be
explained in the use of the concentration founded one day after treatment in the field
instead the value measured during spraying. One user in application rate of ethoprophos
Table 2
Crop input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Number of different crop
2
3
2
2
2
15/04/1990
01/10/1990
10/10/1990
0.35
150
100
Residue
2.1
50
Wheat
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
06/12/1990
31/07/1991
14/08/1991
0.008
100
85
Fallow
0
80
22/11/1990
10/07/1991
14/08/1991
0.1
140
100
Residue
1.4
80
03/12/1990
31/07/1991
14/08/1991
0.15
100
90
Residue
0
80
06/12/1990
25/07/1991
14/08/1991
0.1
60
80
Residue
0
80
06/12/1990
31/07/1991
14/08/1991
0.15
40
100
Fallow
0
80
Yellow mustard
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
29/09/1991
29/11/1991
29/11/1991
0.2
45
90
Fallow
0
75
19/09/1991
29/11/1991
29/11/1991
0.5
70
100
Cropping
0.53
30
25/09/1991
29/11/1991
29/11/1991
0.15
40
70
Fallow
0
40
15/09/1991
28/11/1991
29/11/1991
0.1
30
80
Residue
0
35
29/09/1991
28/11/1991
29/11/1991
0.20
30
100
Fallow
0
40
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Sugarbeet
Emergence date
Maturation date
Harvest date
Maximum interception storage (cm)
Maximum rooting depth (cm)
Maximum areal coverage of the canopy (%)
Surface condition after harvest
Maximum dry weight at full canopy (kg/m2)
Maximum canopy height at maturation (cm)
321
322
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 3
Management input parameters for the PRZM-2 model
PRZM-2 parameters
User 1
User 2
User 3
User 4
Consensus
Application date
Depth of application (cm)
Total application of bentazone (kg/ha)
Total application of ethoprophos (kg/ha)
Total depth of soil core (cm)
Plant uptake factor
Method of characteristics
Diffusion coef®cient for pest in air
(cm2/day)
Henry's law constant ethoprophos
Henry's law constant bentazone
Enthalpy of vapourisation ethoprophos
(kcal/mol)
Enthalpy of vapourisation bentazone
(kcal/mol)
22/11/1990
4
0.8
3.35
105
0
No
0
22/11/1990
0
0.8
3.35
200
0
No
0.43
23/11/1990
0
0.73
3
100
1
No
4300
23/11/1990
0
0.73
3
60
0.8
No
4350
23/11/1990
0
0.63
1.33
105
1
No
4300
0
0
0
0
0
0
6.00 10ÿ6 4.20 10ÿ6 2.00 10ÿ6
2.00 10ÿ10 1.00 10ÿ7 1.00 10ÿ7
22.5
20
22.5
0
0
21.7
20
21.7
Table 4
Numerical discretisation of the soil pro®le used in calibrated and consensus simulations
User
Horizon
1
2
3
1
Horizon thickness (cm)
Compartment thickness (cm)
7.5
0.1
7.5
0.1
2
Horizon thickness (cm)
Compartment thickness (cm)
50
2.5
50
2.5
100
2.5
3
Horizon thickness (cm)
Compartment thickness (cm)
4
4
3.5
3.5
25
2.5
17.5
2.5
50
2.5
100
4
Horizon thickness (cm)
Compartment thickness (cm)
4
4
3.5
3.5
22.5
7.5
7.5
7.5
22.5
7.5
60
Consensus
Horizon thickness (cm)
Compartment thickness (cm)
4
0.5
3.5
0.5
22.5
1.5
22.5
1.5
52.5
7.5
105
22.5
1.25
4
5
Total core (cm)
22.5
2.5
45
5
105
200
took into account the experimental large volatilisation losses that were showed for this
compound in data set in the first week.
Table 4 shows the numerical discretisation of the soil profile. The core depth
(from 60 to 200 cm) varied between users; the data set included information on the
soil core to a depth of 2 m, however the presence of a fluctuating water table, which
PRZM is not designed to simulate, probably accounts for the different core depths
chosen.
The number and the thickness of horizons and the thickness of horizon compartments selected by different users were also different. Subdivisions are important
because numerical dispersion can arise from using too small or too large a com-
323
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 5
Decay rate (per day) in dissolved (DWRATE) and adsorbed (DSRATE) phase and partition coef®cient (KD) (ml/
g) of bentazone (B) and ethoprophos (E) for each horizon used in calibrated and consensus simulations
Horizon
Parameter
User 1
User 2
User 3
User 4
Consensus
1
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160
4.5
0.004
0.012
0.004
0.012
4.29
0.11
0.04
0.009
0.003
0.006
1.8
0.1
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
2
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160a
4.5a
0.002
0.002
0.002
0.002
0.206
0.005
0.04
0.009
0.003
0.006
1.8
0.1
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
3
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0030
0.0064
0.0030
0.0064
160a
4.5a
0.002
0.002
0.002
0.002
0.225
0.006
0.04
0.009
0.003
0.006
1.8
0.10
0.0034
0.002
0.0034
0.002
3.62
0.11
0.0032
0.0034
0.0032
0.0034
4.24
0.11
4
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0011
0.0000
0.0011
0.0000
160a
4.5a
0.02
0.0045
0.0025
0.003
0.74
0.04
0.0008
0.0011
0.0008
0.0011
0.17
0.04
0.0022
0.0002
0.0022
0.0002
1.11
0.02
5
DWRATE E
DWRATE B
DSRATE E
DSRATE B
KD E
KD B
0.0011
0.0000
0.0011
0.0000
160a
4.5a
0.01
0.002
0.002
0.0002
0.08
0.004
0.0008
0.0011
0.0008
0.0011
0.17
0.04
0.0015
9.2 10ÿ5
0.0015
9.2 10ÿ5
0.21
0.01
a
As Koc value.
partment size. The choice of horizon thickness can be made from the soil data provided
with the data set, although there was considerable variation between users for these
values. Compartment size within these horizons however, is a matter of personal choice
based upon experience and this may account for the wide range of compartment sizes
(Table 4).
Although differences in the soil properties selected can be seen between the users, the
discrepancies are not large enough to have caused a significant difference to model
output.
Table 5 shows the decay rates and sorption coefficients. PRZM-2 requires only the
input of linear sorption coefficients and a first-order decay rate, nonetheless there
was variability between users in the choice of these parameters which are vital to
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
the prediction of leachate concentration. Note that one user entered a Koc value
which the model then uses to derive a Kd value based upon the percentage carbon in the
horizon.
2.4. Comparison
Evaluation of performance of model are carried out using graphical method and
statistical indices. The indices used are chosen to evaluate the overall fit (model
efficiency, EF), the prediction of total soil residues (coefficient of residual mass, CRM)
(Vanclooster et al., 1999) and the prediction of the distribution of residues in soil (mean
depth ratio, MDR) (Walker et al., 1995). It should be noted that these indices must be
used carefully and the way people have to interpret them must not adapted to their interest
and purpose. However they do provide a useful method of comparing the degree of fit of
model outputs produced by different users to measured data. For the purposes of
graphical presentation confidence at the 95% level limits were calculated.
Indices of model performance were calculated for each user for both the calibrated and
uncalibrated runs and also for all of the simulations grouped together (global indices). We
have included the consensus simulation in the graphical data to indicate probably the best
performance of PRZM-2 model.
3. Results and discussion
Soil moisture profiles were sufficiently in agreement with observed data after
calibration of field capacity and wilting point values. In Fig. 1 the experimental data and
PRZM-2 simulation for the first layer (0±30 cm) are reported. Discrepancies could be due
to the error in the parametrisation of field capacities and wilting point values and in the
lack of the observed pan evaporation data. The model controls water flow using only
these parameters.
Solute transport was poorly predicted by all PRZM-2 simulations, as showed in
Fig. 2. The experimental high content of bromide in the top soil layer was poorly
predicted by all simulations. The high content of bromide in the winter wheat plants
(Boesten and Van der Pas, 1996) and the turnover of plant litter may have played a key
role in the release of bromide during the study, leading to a steady content of solute in the
top soil layer.
The model output did not match the measured data for bentazone well. In general the
model tended to over-estimate the concentration of bentazone in the 30±60 cm zone
which may indicate that the model underestimated the mobility of this chemical for the
Vredepeel site. Figure 3 shows that the consensus simulation lay outside the global 95%
confidence limits of experimental data for this chemical at some depths. Table 6 shows
that the model efficiency for all the simulations is poor for this chemical and the model
only adequately described its distribution of residues along the profile, as indicated by
MDR index near at 1 for user 2 and 3.
The observed ethoprophos concentration profile was also not well described by the
model, however here the consensus simulation gave a better fit to the data, especially for
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 1. Observed vs. predicted soil moisture content in top soil layer. Mean and 95% con®dence interval of observed data are shown. Model simulation with the
consensus parameters are also shown.
325
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M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 2. Observed vs. predicted bromide concentration in top soil layer. Mean and 95% con®dence interval of observed data are shown. Model simulation with the
consensus parameters are also shown.
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 3. Observed vs. predicted bentazone concentration pro®les. Mean and 95% con®dence interval of observed data are shown. Model simulation with the consensus
parameters are also shown.
327
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 3. (Continued ).
328
329
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Table 6
Statistical model performance indicators for simulated ethoprophos and bentazone pro®lesa
Index
Bentazone profile
Data
Not
calibrated
Ethoprophos profile
Calibrated
Data
Not
calibrated
Poorc
1.371
135
135
Poor
1.184
15
15
Poor
2.289
15
15
0.362
1.481
Calibrated
Global
EFb
CMRb
108
108
User 1
EF
MDRb
12
12
Poor
0.436
User 2
EF
MDR
12
12
Poor
1.082
User 3
EF
MDR
12
12
Poor
1.039
Poor
1.114
15
15
0.204
2.854
0.262
2.690
User 4
EF
MDR
12
12
Poor
0.500
Poor
0.982
15
15
Poor
2.054
Poor
2.054
Consensus
EF
MDR
12
12
Poor
1.150
Poor
1.120
15
15
0.771
1.282
0.758
1.280
Poor
0.514
Poor
2.094
a
The number of data points used to calculate the indicator are reported. User 2 has not performed calibrated
simulations.
b
EF Ð model ef®ciency; CRM Ð coef®cient of residual mass; MDR Ð mean depth ratio.
c
Poor Ð when the value of index becomes negative, the ®t is unacceptably poor.
the first sampling point (Fig. 4). This simulation used a reduced application rate to
account for the volatilisation losses observed with ethoprophos, some users did not
include this loss and this may be the reason for the improved prediction obtained by
consensus. Users 2 and 3 did get a reasonable value for the model efficiency, but a poor
one for the MDR index, indicating a poor prediction of distribution of residues along the
profile (Table 6).
The fit of the model output to the aeric mass of ethoprophos data showed similar
features (Fig. 5).
The model fitted the data for ethoprophos better than for bentazone and this was
especially true for the consensus simulations. The reason for this may be the greater
mobility of bentazone. In order to produce a good model fit to the data, a model would
have to accurately predict the movement of soil water; more so for a mobile than for a
more strongly adsorbed chemical such as ethoprophos. It is therefore not surprising that
the model did not predict the bentazone concentration well, but did so for ethoprophos,
which is less mobile. Whether the model would have produced better predictions with an
accurate description of water balance is a matter of conjecture.
The model gave predictions of chemical concentration that were within an order-ofmagnitude in all cases. The movement of chemicals through soil is a complex process and
no model can reproduce exactly what happens in the field. So, although the model did not
give a good fit to the data according to statistical criteria, it still gave a reasonable
prediction of the magnitude of concentrations that were observed in the field when
ethoprophos and bentazone were applied to the Vredepeel site.
330
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 4. Observed vs. predicted ethoprophos concentration pro®les. Mean and 95% con®dence interval of observed data are shown. Model simulation with the consensus
parameters are also shown.
331
Fig. 4. (Continued ).
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 4. (Continued ).
332
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
Fig. 5. Observed vs. predicted areic mass of ethoprophos as a function of time. Mean and 95% con®dence interval of observed data are shown. Model simulation with
the consensus parameters are also shown.
333
334
M. Trevisan et al. / Agricultural Water Management 44 (2000) 317±335
4. Conclusion
The PRZM-2 model was evaluated to predict pesticide transport in soil using the
Vredepeel data set. Simulations were carried out by several groups, with and without
calibration of some parameters, and finally a consensus simulation was performed.
The model did not accurately describe the behaviour of ethoprophos and bentazone.
However the model predictions of the total pesticide residues were generally within an
order-of-magnitude of those measured on the site. It was difficult to parametrise the
model to get a good fit to the water balance observed on the site, probably because key
parameters of the model, as field capacity and wilting point, were derived from retention
curve, and that increase the degree of results uncertainty.
Volatilisation routine does not simulate ethoprophos losses during first week as well as
data set shows; it is a matter of conjecture why this happen.
The volatilisation routine is affected by chemical properties and soil temperature and
wind speed. The users adopted Henry constant values in agreement with indication of
data set provider, but in Vredepeel conditions soil temperature is always less than 158C
after treatment and wind speed is slowly (300±400 cm/s); for this reason may be that
model not forecast the observed pesticide volatilisation.
The main question in application of pesticide fate model is whether the model
parameters can be derived from the data provided. This exercise shows that, given exactly
the same data, different model users will parametrise a model in very different ways. That
will lead to very different model output. The reason for this user-dependent variability
was the interpretation of the experimental data. Although the data set was extensive and
well described many parameters were derived from experimental data and their user
interpretation could be different. Control of model input and guidance for the selection of
inputs is therefore vital if a model is to be used to predict the fate of pesticides in the
environment, as has been indicated by Brown et al. (1996).
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