Modelling ethoprophos and bentazone fate in a sandy
humic soil with primary pesticide fate model PRZM-2
M. Trevisan
a,*, G. Errera
a, G. Goerlitz
b, B. Remy
c, P. Sweeney
daIstituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
bHoechst Schering AgrEvo GmbH, Werk Hochst, D-65926 Frankfurt am Main, Germany
cMinisteÁre 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
dZeneca 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 PRZM-2 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 PRZM-2 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-Agricultural Water Management 44 (2000) 317±335
*Corresponding author.
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
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.
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 evapotran-spiration was extracted from the soil profile. Evapotranevapotran-spiration 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 1
Meteorological input parameters for the PRZM-2 model
PRZM-2 parameters User 1 User 2 User 3 User 4 Consensus
Pan factor 0.7 0.85 1 1 1
Snowmelt factor (cm/8C above freezing) 0.4 0.46 0 0 0 Minimum depth of which evaporation is extracted (cm) 20 10 10 10 10 Average duration of rainfall produced by storm (h) 5.4
Pan factor ¯ag (zero daily pan evaporation data read; one temperature data read)
1 1 0 1 0
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
Maximum areal coverage of the canopy (%) 100 Surface condition after harvest Residue Maximum dry weight at full canopy (kg/m2) 2.1 Maximum canopy height at maturation (cm) 50
Wheat
Emergence date 06/12/1990 22/11/1990 03/12/1990 06/12/1990 06/12/1990 Maturation date 31/07/1991 10/07/1991 31/07/1991 25/07/1991 31/07/1991 Harvest date 14/08/1991 14/08/1991 14/08/1991 14/08/1991 14/08/1991
Maximum interception storage (cm) 0.008 0.1 0.15 0.1 0.15
Maximum rooting depth (cm) 100 140 100 60 40
Maximum areal coverage of the canopy (%) 85 100 90 80 100
Surface condition after harvest Fallow Residue Residue Residue Fallow
Maximum dry weight at full canopy (kg/m2) 0 1.4 0 0 0
Maximum canopy height at maturation (cm) 80 80 80 80 80
Yellow mustard
Emergence date 29/09/1991 19/09/1991 25/09/1991 15/09/1991 29/09/1991 Maturation date 29/11/1991 29/11/1991 29/11/1991 28/11/1991 28/11/1991 Harvest date 29/11/1991 29/11/1991 29/11/1991 29/11/1991 29/11/1991
Maximum interception storage (cm) 0.2 0.5 0.15 0.1 0.20
Maximum rooting depth (cm) 45 70 40 30 30
Maximum areal coverage of the canopy (%) 90 100 70 80 100
Surface condition after harvest Fallow Cropping Fallow Residue Fallow
Maximum dry weight at full canopy (kg/m2) 0 0.53 0 0 0
Maximum canopy height at maturation (cm) 75 30 40 35 40
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 com-partments selected by different users were also different. Subdivisions are important because numerical dispersion can arise from using too small or too large a
com-Table 3
Management input parameters for the PRZM-2 model
PRZM-2 parameters User 1 User 2 User 3 User 4 Consensus
Application date 22/11/1990 22/11/1990 23/11/1990 23/11/1990 23/11/1990
Depth of application (cm) 4 0 0 0 0
Total application of bentazone (kg/ha) 0.8 0.8 0.73 0.73 0.63 Total application of ethoprophos (kg/ha) 3.35 3.35 3 3 1.33 Total depth of soil core (cm) 105 200 100 60 105
Plant uptake factor 0 0 1 0.8 1
Method of characteristics No No No No No Diffusion coef®cient for pest in air
(cm2/day)
Numerical discretisation of the soil pro®le used in calibrated and consensus simulations
User Horizon 1 2 3 4 5 Total core (cm)
1 Horizon thickness (cm) 7.5 7.5 22.5 22.5 45 105 Compartment thickness (cm) 0.1 0.1 1.25 2.5 5 2 Horizon thickness (cm) 50 50 100 200
Compartment thickness (cm) 2.5 2.5 2.5
3 Horizon thickness (cm) 4 3.5 25 17.5 50 100 Compartment thickness (cm) 4 3.5 2.5 2.5 2.5 4 Horizon thickness (cm) 4 3.5 22.5 7.5 22.5 60
Compartment thickness (cm) 4 3.5 7.5 7.5 7.5 Consensus Horizon thickness (cm) 4 3.5 22.5 22.5 52.5 105
Compartment thickness (cm) 0.5 0.5 1.5 1.5 7.5
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
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 0.0030 0.004 0.04 0.0034 0.0032 DWRATE B 0.0064 0.012 0.009 0.002 0.0034 DSRATE E 0.0030 0.004 0.003 0.0034 0.0032 DSRATE B 0.0064 0.012 0.006 0.002 0.0034
KDE 160 4.29 1.8 3.62 4.24
KDB 4.5 0.11 0.1 0.11 0.11
2 DWRATE E 0.0030 0.002 0.04 0.0034 0.0032 DWRATE B 0.0064 0.002 0.009 0.002 0.0034 DSRATE E 0.0030 0.002 0.003 0.0034 0.0032 DSRATE B 0.0064 0.002 0.006 0.002 0.0034
KDE 160a 0.206 1.8 3.62 4.24
KDB 4.5a 0.005 0.1 0.11 0.11
3 DWRATE E 0.0030 0.002 0.04 0.0034 0.0032 DWRATE B 0.0064 0.002 0.009 0.002 0.0034 DSRATE E 0.0030 0.002 0.003 0.0034 0.0032 DSRATE B 0.0064 0.002 0.006 0.002 0.0034
KDE 160a 0.225 1.8 3.62 4.24
KDB 4.5a 0.006 0.10 0.11 0.11
4 DWRATE E 0.0011 0.02 0.0008 0.0022
DWRATE B 0.0000 0.0045 0.0011 0.0002 DSRATE E 0.0011 0.0025 0.0008 0.0022 DSRATE B 0.0000 0.003 0.0011 0.0002
KDE 160a 0.74 0.17 1.11
KDB 4.5a 0.04 0.04 0.02
5 DWRATE E 0.0011 0.01 0.0008 0.0015
DWRATE B 0.0000 0.002 0.0011 9.210ÿ5
DSRATE E 0.0011 0.002 0.0008 0.0015 DSRATE B 0.0000 0.0002 0.0011 9.210ÿ5
KDE 160a 0.08 0.17 0.21
KDB 4.5a 0.004 0.04 0.01
aAsKocvalue.
the prediction of leachate concentration. Note that one user entered a Koc value
which the model then uses to derive aKdvalue 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
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.
M.
T
revisan
et
al.
/
Agricultu
ral
W
ater
Managem
ent
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.
T
revi
san
et
al.
/
Agricultur
al
W
ater
Manageme
nt
44
(2000)
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.
M.
T
revisan
et
al.
/
Agricultu
ral
W
ater
Managem
ent
44
(2000)
317±335
Fig.
3.
(
Co
ntinued
).
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-of-magnitude 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.
Table 6
Statistical model performance indicators for simulated ethoprophos and bentazone pro®lesa
Index Bentazone profile Ethoprophos profile
Global EFb 108 Poorc 135 Poor
CMRb 108 1.371 135 0.514
User 1 EF 12 Poor Poor 15 Poor Poor
MDRb 12 0.436 1.184 15 2.289 2.094
User 2 EF 12 Poor 15 0.362
MDR 12 1.082 15 1.481
User 3 EF 12 Poor Poor 15 0.204 0.262
MDR 12 1.039 1.114 15 2.854 2.690
User 4 EF 12 Poor Poor 15 Poor Poor
MDR 12 0.500 0.982 15 2.054 2.054
Consensus EF 12 Poor Poor 15 0.771 0.758
MDR 12 1.150 1.120 15 1.282 1.280
aThe number of data points used to calculate the indicator are reported. User 2 has not performed calibrated
simulations.
bEF Ð model ef®ciency; CRM Ð coef®cient of residual mass; MDR Ð mean depth ratio. cPoor Ð when the value of index becomes negative, the ®t is unacceptably poor.
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.
M.
T
revi
san
et
al.
/
Agricultur
al
W
ater
Manageme
nt
44
(2000)
Fig.
4.
(
Co
ntinued
).
Fig.
4.
(
Co
ntinued
).
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.
M.
T
revisan
et
al.
/
Agricultu
ral
W
ater
Managem
ent
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,
PRZM-2 and VARLEACH: variability between model users in prediction of pesticide leaching using a standard data set. Pestic. Sci. 47, 249±258.
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.
