Testing PESTLA using two modellers for bentazone
and ethoprophos in a sandy soil
J.J.T.I. Boesten
a,*, B. GottesbuÈren
baAlterra Green World Research, Wageningen University and Research Centre,
PO Box 47, 6700 AA Wageningen, Netherlands
bBASF AG (APD/RB), Landwirtschaftliche Versuchsstation, PO Box 120, 67114 Limburgerhof, Germany
Abstract
Two modellers tested the PESTLA model (version 2.3.1) against results of a ®eld study on bentazone and ethoprophos behaviour in a sandy soil. Both modellers achieved an acceptable description of the measured moisture pro®les but only after calibration of the soil hydraulic properties. Both could describe the bromide-ion concentration pro®les measured at the end of the ®rst winter reasonably well. However, both predicted that practically all bromide had leached out of the top 50 cm of the soil at the end of the second winter, whereas about 10% of the bromide dose remained in this layer. This is attributable to a systematic deviation of bromide transport from the concept assumed in the convection/dispersion equation and/or to the release of bromide from dead root remnants. Both modellers derived pesticide transformation and sorption parameters from laboratory studies with soil from the ®eld. Both described bentazone movement reasonably well. Modeller 1 described the concentration pro®les reasonably well, whereas Modeller 2 strongly overestimated the concentrations at the end of the study. This difference was mainly attributable to a difference in interpretation of the temperature dependence of the transformation rate of bentazone. Only Modeller 2 simulated ethoprophos behaviour. He simulated the persistence of ethoprophos in the top 20 cm very well during the ®rst 200 days. However, thereafter the transformation in the ®eld proceeded much faster than simulated. This is probably caused by accelerated transformation resulting from exposure of the top soil layer to about 1 mg kgÿ1of ethoprophos over 200 days. Simulated penetration of ethoprophos was deeper than measured. By including accelerated transformation (admittedly on an ad-hoc basis) within the simulations, good agreement was achieved between measured and simulated penetration of ethoprophos. Calculations showed that the effect of calibrating water ¯ow was substantial for bentazone but small for ethoprophos. However, the effect of calibration of water ¯ow for bentazone was much smaller than the effect of the difference between the transformation rate parameters derived by the two modellers. We recommend that the guidance for deriving pesticide±soil input parameters be improved in order to
*Corresponding author. Tel.:31-317-47-4343; fax:31-317-42-4812.
E-mail address: j.j.t.i.boesten@alterra.wag-ur.nl (J.J.T.I. Boesten).
reduce differences between modellers because a large in¯uence of the person of the modeller on the outcome of model tests is unacceptable for methodological reasons.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Pesticide leaching; Modelling; Modeller subjectivity
1. Introduction
Mathematical leaching models are useful tools for assessing the risk of groundwater contamination resulting from the agricultural use of pesticides. In general, the validation status of such models needs to be improved (FOCUS Regulatory Modelling Workgroup, 1995) via testing them against high-quality field data sets. In the Dutch pesticide registration procedure, the PESTLA model has been used since 1989 for a first assessment of pesticide leaching potential. This assessment is based on the Dutch standard scenario as described by Boesten and van der Linden (1991). This scenario is based on a sandy soil profile which is considered representative of sandy areas in the Netherlands where groundwater is pumped up to be used as drinking water. The Vredepeel data set (described by Boesten and van der Pas, 1999, 2000) was collected to test PESTLA for a realistic field situation similar to the Dutch standard scenario. The aim of the present study is to test the PESTLA model against this data set. The test is performed by two modellers (the authors) in order to take into account the possible effect of modeller subjectivity on the result of the model test (Brown et al., 1996; Boesten, 2000). The modellers are indicated by their initials: JB and BG.
van den Bosch and Boesten (1994) performed the first test of PESTLA against the Vredepeel data set. The PESTLA version they used (described by van der Linden and Boesten, 1989; Boesten and van der Linden, 1991) only includes equilibrium sorption. It has already been recognised for some time that non-equilibrium sorption may influence leaching considerably (Walker, 1987; Boesten et al., 1989). Therefore, the Vredepeel data set contains pore water concentrations from which non-equilibrium sorption parameters can be derived. So, in the present study a PESTLA version is used that includes non-equilibrium sorption (version 2.3.1). This version will be replaced in the near future by a PESTLA-3 version (van den Berg and Boesten, 1999) which contains the same description of non-equilibrium sorption.
As described by Vanclooster et al. (2000), testing of pesticide leaching models should proceed in three steps: firstly test the water flow sub-model, then test the solute transport sub-model using a tracer and finally test the pesticide sub-model. This procedure is followed: Section 3 deals with water flow, Section 4 with tracer movement, Section 5 with bentazone and Section 6 with ethoprophos. BG simulated bentazone behaviour but made no calculations for ethoprophos.
2. Description of the 2.3.1 version of PESTLA
(1978), Belmans et al. (1983) and de Jong and Kabat (1990). It assumes one-dimensional (vertical), transient, unsaturated/saturated water flow in a heterogeneous soil-root system using the Darcy flow equation and the conservation equation (including a sink term for water uptake by the crop). Soil temperatures are simulated using Fourier's law assuming a heat conductivity and a heat capacity that are functions of the volume fraction of water in the soil. The temperature at the soil surface is assumed to be equal to the daily average of air temperature. The boundary condition at the bottom of the soil system is defined as a constant soil temperature at 10 m depth.
The mass concentration of the pesticide in the soil system,c* [M Lÿ3], is described as
c ycLr XEXNE (1)
in whichyis the volume fraction of water in soil,cLthe mass concentration of pesticide in the liquid phase [M Lÿ3], r the dry soil bulk density (M Lÿ3),XE the equilibrium content sorbed of pesticide [M Mÿ1] and XNE the non-equilibrium content sorbed of pesticide [M Mÿ1]. The equilibrium content sorbed is described with a Freundlich sorption isotherm
in whichmOMis the mass fraction of soil organic-matter, KOM,E is the organic-matter/ water distribution coef®cient [L3Mÿ1] for the equilibrium sorption sites, cL,REF is a reference value of cL (set at 1 mg dmÿ3) and N is the Freundlich exponent. The non-equilibrium content sorbed is described by ®rst-order kinetics assuming a Freundlich isotherm for the non-equilibrium sorption sites organic-matter/water distribution coef®cient [L3Mÿ1] for the non-equilibrium sorption sites. Boesten and van der Pas (1988) give the theoretical background of Eq. (3) and Boesten (1991) gives an example of the in¯uence of adding Eq. (3) on the amount leached. The introduction of the parameter cL,REF in Eqs. (2) and (3) eliminates the problem that the productmOMKOMhas a unit that is a function ofN. Pesticide transport in soil is described with the convection/dispersion equation and pesticide plant uptake with the concept of the transpiration-stream concentration factor (see Boesten and van der Linden, 1991).
The rate of transformation in soil,RT[M Lÿ3Tÿ1], is described with the first-order rate equation
RTkc (4)
at the non-equilibrium site at a particular soil depth, the transformation rate is described as
RTkLycL (5)
in whichkLis the rate coef®cient for transformation in the liquid phase [Tÿ1] which is calculated as
in whichkL,MAXis the maximal transformation rate coef®cient in the liquid phase [Tÿ1]. The value ofkL,MAXis set at 2 per day in the model (which is an arbitrary high rate). Eqs. (5) and (6) imply that the description is identical to Eq. (4) as long as this corresponds to a transformation rate coef®cient in the liquid phase of less than 2 per day. If the rate becomes higher (because a large fraction of the pesticide is sorbed on the non-equilibrium sites), the limit of 2 per day will prevent too high a rate of transformation. This somewhat complicated construction is needed because it is conceptually not probable that the amount sorbed at the non-equilibrium sites is subject to transformation.
The procedure of the numerical solution of the pesticide part of the model is the same as described by Boesten and van der Linden (1991). For the bentazone simulations, both BG and JB used compartments that were 5 cm thick. Only JB performed ethoprophos simulations. He used compartments of 2 cm in the top 30 cm and of 5 cm below 30 cm. The thinner compartments for ethoprophos were needed to obtain a solution of sufficient accuracy for its narrower concentration pattern.
3. Water ¯ow
3.1. Procedure and results of uncalibrated runs
JB divided the soil into three layers (0±30, 30±60 and 60±200 cm) and derived hydraulic characteristics from the site-specific laboratory measurements described by Boesten and van der Pas (2000) using the RETC package to fit the Van-Genuchten parameters. Details of the fitting procedure are described by van den Bosch and Boesten (1994). BG divided the soil into four layers (0±25, 25±50, 50±100 and 100±200 cm) and estimated the Van-Genuchten parameters via combining information from pedo-transfer functions (Vereecken et al., 1989), a Dutch database (WoÈsten, 1987) and the site-specific measurements. Differences between the estimated hydraulic characteristics were sometimes very large (see the uncalibrated moisture-retention curves for the top layer in Fig. 1 as an example).
that is calculated viaqaeÿbhin whicha[L Tÿ1] andb[Lÿ1] are parameters andhis the distance [L] between the groundwater table and the soil surface. This option results in a simulated groundwater level. The value ofawas 8 mm per day andbwas 0.03 cmÿ1. BG prescribed the groundwater level as a function of time on the basis of the measurements by Boesten and van der Pas (1999).
The moisture profiles were not simulated well in the uncalibrated runs: JB overestimated the volume fraction of liquid in the 30±60 cm layer and BG overestimated the moisture profiles in almost all layers (see Fig. 2). JB simulated a too shallow groundwater table for most of the experimental period as is shown in Fig. 3. The systematic difference between measured and calculated moisture profiles shown in Fig. 2a and c is especially remarkable for the calculations by JB because these were based on the hydraulic properties measured in the laboratory with soil samples from this particular experimental field. A possible explanation is an inadequate handling of spatial variability of soil hydraulic properties. As described by Boesten and van der Pas (2000), the core samples for the measurements of the soil hydraulic properties were taken from only one pit. It can be expected that the spatial variability within one field is considerable (as is also indicated by the ranges of the measured moisture profiles in Fig. 2). So measuring soil hydraulic properties using samples from one pit only may not be reliable. However, it remains remarkable that JB overestimated the moisture contents in the 30±60 cm layer so strongly in the uncalibrated runs.
3.2. Procedure and results of calibrated runs
JB changed the moisture-retention curve for the 0±30 cm layer to increase water retention and he assumed that the hydraulic properties of the 30±60 cm layer were equal
to those estimated for the 60±200 cm layer. The values ofa and b were changed into 12 mm per day and 0.025 cmÿ1, respectively, to increase the water flux at the bottom of the system (resulting in a deeper groundwater table). BG considered both the effects of changes in hydraulic properties and of the root extraction pattern and the root depth. He concluded that the effect of the root parameters could practically be ignored. He adjusted
the hydraulic properties by dividing the soil into two layers (0±25 and 25±200 cm) and taking Van Genuchten parameters estimated by a Dutch database (WoÈsten, 1987). The difference between the hydraulic relationships calibrated by BG and JB was small (as is illustrated for the moisture-retention curve of the top layer in Fig. 1). Both modellers achieved a good description of the measured moisture profiles via the calibration (see Fig. 2b and d). JB also obtained a reasonably good description of the groundwater level (Fig. 3), although the level in the field reacts more quickly to rainfall events than is simulated.
4. Bromide movement
4.1. Procedure and results of uncalibrated runs
The uncalibrated runs for bromide-ion were made with the uncalibrated water flow input. A bromide dose of 111 kg haÿ1was used (Boesten and van der Pas, 2000). JB used a dispersion length of 5 cm which was also assumed for the calculations on the Dutch standard scenario for a soil with similar properties (Boesten and van der Linden, 1991). BG assumed a dispersion length of 3 cm based on van den Bosch and Boesten (1994). Both JB and BG assumed a transpiration-stream concentration factor of 1.0. Both assumed no sorption and no transformation of bromide. JB obtained a reasonable description of the bromide concentration profiles after 103 and 278 days; after 474 days the calculated bromide concentrations in the upper 50 cm were below 0.5 mg dmÿ3, whereas the average measured value was about 2 mg dmÿ3. BG calculated too little bromide movement after 103 days (as a result of the overestimated water retention). After 278 days, he calculated much lower bromide concentrations than measured . Probably this was caused by the plant uptake being too high (about 80% of the dose after 278 days). After 474 days, the shape of the concentration profile obtained by BG was similar to that calculated by JB, but the concentration level was lower.
4.2. Procedure and results of calibrated runs
The calibrated runs for bromide were made with the calibrated water flow input. JB tried to improve the fit between measured and calculated bromide profiles by varying the dispersion length between 3 and 10 cm but this did not lead to improvements so the
dispersion length was kept at 5 cm. BG did not modify the bromide input parameters, so the only difference between his calibrated and uncalibrated runs was the improved description of water flow. The results of the calibrated runs in Fig. 4 show that JB and BG simulated bromide movement after 103 days reasonably well. After 278 days JB described the bromide profile reasonably well but BG calculated lower concentrations than measured (probably again caused by overestimation of plant uptake). JB and BG could not simulate the concentration profile measured after 474 days: according to the
model almost no bromide was present in the 0±50 cm layer whereas concentrations in the order of 1 mg dmÿ3were measured in this layer (see Fig. 4). This discrepancy is possibly caused by water moving preferentially in flow pathways which do not interact with bromide present in the finer soil pores during the leaching in the second winter. Another possibility is the release of bromide ion by dying plant roots. The total amount of bromide recovered from the soil profile after 474 days corresponds to 20% of the bromide dose of which about 10% was present in the top 50 cm. It can be expected that significant amounts of bromide were taken up by the wheat (harvested after about 270 days) and the mustard crops (grown between 300 and 380 days). Part of this bromide was present in the wheat and mustard roots at their harvest and was released by diffusion and mineralisation of the root remnants. A fraction of the bromide may still have been present in the root remnants at the sampling time and may have been released during the extraction of the soil in the laboratory. It is practically impossible to quantify the amount of bromide that was released by the plant roots but it is possible that this was in same order as the concentrations found in the top 50 cm after 474 days. In view of this interpretation problem we recommend the measurement of bromide uptake by crops in future studies.
5. Bentazone behaviour
5.1. Estimation of pesticide input parameters
JB use a bentazone dose of 0.63 kg haÿ1 (amount recovered after one day) and BG used the calculated dose of 0.80 kg haÿ1(Boesten and van der Pas, 2000). Pesticide/soil input parameters were based on the laboratory studies by Boesten and van der Pas (1999,
2000) and were not further calibrated. JB used the parameters for the equilibrium sorption isotherm (Eq. (2)) recommended by Boesten and van der Pas (2000); so
KOM,E2.1 dm3kgÿ1andN0.82. Parameters for the non-equilibrium sorption sites (Eq. (3)) were derived from the measurements in the soil pore water during the incubation of bentazone in top soil material at 5 and 158C (see Boesten and van der Pas, 1999). Values ofkDwere 0.01 per day at 58C and 0.02 per day at 158C from which an average of 0.015 per day was derived. SimilarlyKOM,NEvalues of 0.7 and 1.6 dm3kgÿ1were found at 5 and 158C, respectively, from which an average of 1.2 dm3kgÿ1was derived. BG estimated all sorption parameters from the measurements in the soil pore water during the incubation of bentazone in top soil material at 58C (see Boesten and van der Pas, 1999) using the ModelMaker 2.0 software package. He foundKOM,E1.7 dm
3
kgÿ1,N0.73,
KOM,NE2.9 dm 3
kgÿ1and kD0.015 per day. So differences between the KOM,E, N andkDvalues found by BG and JB were small. However, BG found a value ofKOM,NE that is more than two times the value found by JB.
JB used the transformation rate parameters of bentazone recommended by Boesten and van der Pas (2000): half-life in the top layer of 62 day at 208C with temperature coefficientgequal to 0.0798 Kÿ1(for definition of gsee Boesten and van der Linden, 1991), assuming no transformation between 50 and 100 cm depth and a transformation rate below 100 cm depth that is 1.44 times as high as that in the top layer. BG derived a half-life of 228 days at 58C and of 37 days at 158C. From these values, he estimated the half-life of bentazone at 208C to be 15 days which he combined withg0.08 Kÿ1(i.e. the default value in PESTLA). He assumed no transformation between 50 and 100 cm depth and a transformation rate below 100 cm depth that was 0.9 times the rate in the top layer. Both JB and BG used the default value of 0.7 for the parameterB(describing the moisture dependency of the transformation rate); for definition ofBsee Boesten and van der Linden, 1991.
5.2. Results of uncalibrated and calibrated runs
Only JB made uncalibrated runs for bentazone, whereas both BG and JB made calibrated runs for bentazone. Note that the calibration only dealt with the water flow input for both BG and JB. The results in Fig. 5 shows that there were distinct differences between bentazone concentration profiles as calculated by JB for uncalibrated and calibrated water flow. Calibration resulted in some deeper bentazone movement into the soil profile. This can be explained from the moisture simulation by JB shown in Fig. 2: the uncalibrated run resulted in higher moisture contents than the calibrated run. Comparison of the simulations with the measurements in Fig. 5 shows that the movement of bentazone was simulated reasonably well in all runs after 103 and 278 days (although BG overestimated the concentrations in the top layer after 278 days). JB overestimated most bentazone concentrations (average values) after 103 and 278 days. After 474 days, JB overestimated the bentazone concentrations strongly, whereas the concentration profile simulated by BG corresponded well with the measured profile (in which all concentrations were below the detection limit).
they were both based on the same laboratory studies. The difference is the result of a difference in interpretation of the half-life at the reference temperature of 208C. JB followed Boesten and van der Pas (2000) who intended to stick as closely as possible to the laboratory incubation at 58C because this was closest to the field temperature in the
relevant period (November±March). BG intended to stick as closely as possible to the official input parameter for the model, i.e. the half-life at 208C which is also used for the assessments in Dutch pesticide registration (Brouwer et al., 1994). This subtle difference between the two modellers in interpretation of the operational definition of the reference half-life has a very large effect on the result of the model test.
The comparison between measured and calculated concentration profiles of bentazone in Fig. 5 indicates that modeller subjectivity in estimating bentazone input parameters had a larger effect than the calibration of the water flow parameters: the differences between the two lines calculated by JB are smaller than the differences between the lines calculated by JB and BG for calibrated water flow.
JB used the bentazone transformation rate parameters suggested by Boesten and van der Pas (2000), which implies a half-life of 205 day at 58C at field capacity in the top 25 cm (as was measured in the laboratory incubation at this temperature). The average total amount of bentazone in the top 90 cm measured after 103 days was only 60% of the amount simulated by JB. As described by Boesten and van der Pas (2000), the average soil temperature at 2.5 cm depth over the first 103 days was 28C. So in the first 103 days, the transformation proceeded considerably faster in the field than expected on the basis of the laboratory incubation at 58C. As described by Boesten and van der Pas (2000), the laboratory incubations were carried out with soil sampled before harvesting the preceding crop (sugar beets). Between this sampling and the application of bentazone to the experimental field, the sugar beets were harvested and the leaves were chopped and incorporated into the soil. This may have caused an increased microbial activity in the field soil. Smelt (personal communication, 1997) incubated bentazone in sterilised and non-sterilised topsoil from this particular field and found that the transformation was mainly microbial. So the higher microbial activity in the field soil may have resulted in faster transformation of bentazone.
As shown by Fig. 5, the shapes of the bentazone concentration profiles simulated by BG and JB for 103 and 278 days were similar, although BG used aKOM,NEfor the non-equilibrium sorption sites that was more than two times the value used by JB (see Section 5.1). This indicates that including the non-equilibrium sorption process had only a minor influence on the calculated concentration profiles. JB tested this via an additional calculation with zeroKOM,NEand confirmed that the resulting concentration profiles of bentazone differed only slightly from those calculated with the standard value ofKOM,NE used by JB. In general, a strong influence of the non-equilibrium sorption process can only be expected if the transformation proceeds rapidly (Boesten, 1987). So if bentazone had been applied in spring or summer instead of in autumn, there would have been a larger influence of the non-equilibrium sorption process (the autumn application in the Vredepeel experiment was exceptional: the application period of bentazone is restricted to spring and summer).
6. Ethoprophos behaviour
6.1. Estimation of pesticide input parameters
Only JB made calculations for ethoprophos. The ethoprophos dose was 1.33 kg haÿ1 (as recommended by Boesten and van der Pas (2000), for models that do not include volatilisation). The pesticide/soil parameters were based on the laboratory studies by Boesten and van der Pas (1999, 2000) and initially not further calibrated. JB estimated sorption parameters from the soil-suspension experiments and from the measurements in the soil pore water during the incubation of ethoprophos in top soil material at 5, 15 and 258C (see Boesten and van der Pas, 1999). The different studies for ethoprophos did not yield consistent results. Therefore, the ratio betweenKOM,NEandKOM,Ewas assumed to be equal to that for bentazone and also thekDvalue was set equal to that for bentazone. The resulting values wereKOM,E86 dm3kgÿ1,N0.87,KOM,NE47 dm3kgÿ1and kD0.015 per day.
The transformation parameters recommended by Boesten and van der Pas (2000) were used; so the half-life at 208C for the top layer was 78 days and gwas 0.093 Kÿ1. The depth-factor for the transformation rate was 1 up to 32 cm depth; it decreased to 0.42 at 50 cm depth and was kept at 0.42 below that depth. The parameterB (describing the moisture dependency of the transformation rate) was 0.7 and the transformation stream concentration factor was 0.5 (the default values in PESTLA).
6.2. Results of runs with uncalibrated and calibrated water ¯ow
resulted in a slightly closer fit than the calibrated water flow parameters. As shown by Fig. 7, the highest simulated concentrations of ethoprophos were near the soil surface. The decline of ethoprophos is almost entirely the result of transformation (simulated plant uptake was only 1±2% over the whole period). In the PESTLA model, transformation in the top layer is influenced by soil temperature and by the quotient between the current volume fraction of liquid and the volume fraction of liquid at a matric head of 100 cm (see Boesten and van der Linden, 1991). Differences between soil temperatures as simulated with calibrated and uncalibrated water flow parameters were found to be usually less than 0.18C. The quotient of the volume fractions of liquid was systematically lower for the calibrated water flow case in the period between 200 and 300 days. This explains the slightly slower transformation for the calibrated water flow case in that period as shown in Fig. 6.
Fig. 7 shows that calibrating water flow resulted in concentration profiles for ethoprophos that differed only slightly from those calculated with uncalibrated water flow parameters. So a good description of water flow is only of limited importance for simulating behaviour of pesticides such as ethoprophos (with moderate sorption). Admittedly, the model test for ethoprophos does only include concentration profiles in soil and no soil leachate concentrations. However, Boesten and van der Pas (2000) showed that soil concentrations of ethoprophos between 30 and 120 cm depth were below the detection limit of 0.2mg dmÿ3after 474 days. They found a half-life of ethoprophos of about 600 days for the soil layer between 50 and 100 cm depth. Therefore, it is unlikely that substantial concentrations of ethoprophos leached below 30 cm depth: the detection limit of 0.2mg dmÿ3 implies that less than 0.1% of the assumed dose of 1.33 kg haÿ1was present in the 30±120 cm layer after 474 days.
Fig. 7 shows that PESTLA simulated the movement into the soil profile reasonably well after 103 days. However, after 278 and 474 days the simulated penetration into the soil profile was deeper than measured and the concentrations were overestimated (especially after 474 days).
The amount of ethoprophos sprayed onto the experimental field was about 3 kg haÿ1 (Boesten and van der Pas, 2000). We used as input the value of 1.3 kg haÿ1that Boesten and van der Pas (2000) recommend for models that ignore volatilisation. If we would
have used the 3 kg haÿ1as input, the simulation of ethoprophos persistence in soil would have been poor. So PESTLA is not valid for simulation of the initial persistence of pesticides that are sprayed onto the soil surface and that have a vapour pressure and a distribution over the soil phases like ethoprophos (Boesten and van der Pas, (2000), estimated the ratio between the concentrations of ethoprophos in gas and water to be in the order of 10ÿ6). The good correspondence between measurements and simulation in the first 200 days shown in Fig. 6, followed by the systematic deviations in the period thereafter, indicates that there was an acceleration of the transformation rate in the field after about 200 days due to some unknown cause. Smelt et al. (1996) showed via laboratory incubations that accelerated transformation of ethoprophos may occur in Dutch sandy soils after repeated application. This accelerated transformation is attributed to adaptation of soil microbial populations. Ethoprophos had never been applied to this experimental field before (Boesten and van der Pas, 2000), so accelerated transformation was not anticipated (PESTLA does not consider this acceleration process and it is therefore not meaningful to test the model for cases where this process is known to occur). The accelerated transformation was not measured in the laboratory incubations by Boesten and van der Pas (2000), although these lasted for more than 400 days. Microbial biomass may decrease considerably in laboratory studies if these last longer than three months (Anderson, 1987). So this decrease may have delayed microbial adaptation. As shown by Fig. 7, even after 278 days the highest ethoprophos concentration was found in the top 4 cm. In combination with Fig. 6, it can be estimated that the top 4 cm contained an ethoprophos content of about 1 mg kgÿ1 over a period of more than 200 days. It is therefore likely that this exposure to ethoprophos was sufficient to result in adaptation between 214 and 278 days, especially as there was a full grown wheat crop on the experimental field in that period and soil temperatures were then usually above 158C (Boesten and van der Pas, 2000).
6.3. In¯uence of including accelerated transformation on calculated ethoprophos penetration
The comparison of measured and calculated ethoprophos concentration profiles after 474 days in Fig. 7 suggests that the model not only overestimated the concentration level but also the penetration into the soil. The overestimation of the penetration may be the result of the bad simulation of the persistence between 200 and 474 days. It is meaningful
to check this, because testing of the sub-model describing the partitioning over the phases in soil (Eqs. (1)±(3)) is also relevant. Therefore, additional calculations were made in which the half-life of ethoprophos at 208C was set arbitrarily at 1 day after 250 days, to simulate the accelerated transformation (using calibrated water flow parameters). This resulted in a good fit of the total amounts of ethoprophos in the soil profile after 278 and 474 days. None of the other model parameters was changed. The comparison of measured and simulated concentration profiles in Fig. 8 shows reasonably good agreement after 278 days and excellent agreement at the end of the study (after 474 days). So after introducing accelerated transformation (admittedly on an ad-hoc basis), the model explains the observed penetration into the soil profile well. This supports the sub-model describing the non-equilibrium sorption process (Eqs. (1)±(3)) and also the description of the transformation rate with Eqs. (4)±(6): the half-life of 1 day implies that within about a week (starting from day 250) the desorption from the non-equilibrium sorption sites becomes the rate-limiting step in the transformation process. A calculation based on only equilibrium sorption (via settingKOM,NEto zero), will result in calculated concentrations that are many orders of magnitude lower than those measured after 278 and 474 days as a result of the half-life of 1 day starting from day 250.
7. Discussion and conclusions
As described in Section 1, testing of a pesticide leaching model should preferably be performed in three steps: firstly for water flow, then for solute transport via a tracer and finally for pesticide behaviour. The interpretation difficulties with the bromide concentration profile at day 474 as discussed in Section 4.2, indicate that bromide was not a perfect tracer over the whole experimental period. The possible effect of bromide uptake and release by plant roots cannot be distinguished from the possible effect of the diffusion of bromide to finer soil pores outside the flow pathways. The tracer itself then becomes a subject of the study whereas it was intended only to help with interpreting pesticide behaviour. Release by root remnants is likely to be less significant for bentazone because bentazone is transformed within the plants. In general the behaviour of any tracer in plants will not be similar to pesticide behaviour in plants. So in practice tracers can only be used without this complication in the absence of significant uptake by a crop (autumn and winter period).
and of y, the influence of the extent of sorption on solute movement can be compared with the influence of soil moisture. Assuming a reference concentrationcLof 1 mg dmÿ3, the value forr(dXE/dcL) in the top 25 cm of the soil profile is about 0.15 for bentazone and about 5 for ethoprophos. Values of the volume fraction of liquid,y, are typically in the range from 0.1 to 0.3 (Fig. 2). So for ethoprophosr(dXE/dcL) is much larger thany. This explains why the penetration of ethoprophos into the soil is so insensitive to the soil hydraulic properties.
For both pesticides, there were problems with describing the transformation rate, whereas the description of sorption yielded satisfactory results (although it was difficult to get consistent results for the sorption kinetics of ethoprophos in the laboratory). So the sorption sub-model tends to be more robust than the sub-model for the transformation rate. Both for bentazone and for ethoprophos differences between the microbial population of the soil in the laboratory incubations and that in the field may have been responsible for the observed discrepancies in transformation rate.
After correcting the dose for the initial loss by volatilisation, PESTLA could simulate the persistence of ethoprophos in the top 15 cm well during the first 200 days as shown in Fig. 6. Leistra and Smelt (1981) tested a model similar to PESTLA using ethoprophos that was incorporated into the top 10 cm of three soils and found a similar correspondence between calculated and measured persistence over a period of 180 days. They did not need to correct for an initial volatilisation loss because of the incorporation into the soil. The model concepts for describing the transformation rate in the top layer in PESTLA are based on the models developed by Walker (1974) and Walker and Barnes (1981). These models have been tested extensively for persistence of herbicides in the top 15 cm layer (in total some 100 pesticide±soil combinations; e.g. Walker, 1976; Poku and Zimdahl, 1980; Walker and Zimdahl, 1981; Nicholls et al., 1982; Walker and Brown, 1983; Walker et al., 1983). These tests showed that there is roughly a probability of 50% for obtaining good correspondence between measurements and simulations (similar to the first 200 days in Fig. 6). If there were deviations, nearly always the decline in the field was faster than expected by the model. So the results obtained for ethoprophos in the first 200 days correspond well with literature experience.
Considering all results from this field study, there is no reason to reject the model concepts on which PESTLA is based for sandy soils similar to this field soil. BG obtained good agreement for bentazone. JB did not, but this may have been attributable (as suggested in Section 5.2) to the microbial activity being comparatively low at the sampling time of the soil for the incubation study in the laboratory. Variation in microbial activity over the year seems no reason to reject the model concept for the transformation rate in PESTLA. As described before, the differences found by JB for ethoprophos are probably the result of accelerated transformation. PESTLA does not include this phenomenon so this discrepancy is outside the range of validity. It will be difficult to develop models which can predict when accelerated degradation will occur.
example is the possible effect of variation in microbial activity over the year as mentioned in the preceding paragraph. Bad results of a model test are then neither attributable to the model concepts nor to the experience of the model user but are caused by the experimental procedures. This problem has to be overcome either by improving experimental procedures or by estimating the parameters from independent field studies. Pesticide leaching models play an important role in pesticide registration procedures at the EU level (FOCUS Regulatory Modelling Workgroup, 1995). In regulatory decisions that are based on model calculations, the validation status of the model needs to be taken into account. In this context the validation status is considered to be a model property which may be derived from a range of model tests. Testing of an existing model proceeds as follows: firstly estimate all input parameters, secondly obtain a numerical solution that is accurate enough and finally compare the calculated with the measured field behaviour. As described before, the testing should be performed preferably stepwise for the sub-models for water flow, solute transport and pesticide behaviour. In this line of thought, models are tested via a procedure with a reasonably reproducible outcome (the traditional paradigm). However, this study suggests that the expert judgement of the modeller (so a human factor) in a limited part of the testing process (i.e. the estimation of input parameters for the pesticide transformation rate) has such a large influence that the reproducibility of the outcome of models tests is no longer guaranteed. This conclusion is supported by the large variability in pesticide input parameters derived by different modeller-model combinations from the same laboratory studies (Boesten, 2000). Rejection of the reproducibility paradigm is not acceptable because then modelling pesticide leaching would be considered more art than science. Moreover, the validation status would be no definable property of a model (not only the model but also the modeller becomes subject of the test). So we need to find ways to restore the reproducibility of model tests. The most stringent solution would be to require that the manual of the model prescribes exactly how all input parameters need to be measured or estimated and that the results of laboratory measurements on transformation rate and sorption are processed by the model itself via separate algorithms (thus excluding the expert judgement). A less stringent and perhaps more practical solution is to improve the guidance in the model manual for estimating these input parameters to such an extent that ring tests with different modellers produce reproducible results.
Acknowledgements
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