[他誌掲載論文]
Journal of Pesticide Science, 2016, 41(4), 152～162 より転載

Development and validation of the SPEC model for simulating
the fate and transport of pesticide applied to Japanese uplandagricultural soil
Julien Boulange1, Dang Quoc Thuyet2, Piyanuch Jaikaew1, Satoru Ishihara3
and Hirozumi Watanabe1
1Department of International Environmental and Agricultural Sciences, Tokyo University of Agriculture
and Technology, 3–5–8 Saiwaicho Fuchu Tokyo 183–8509, Japan
2

Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1–1–1 Yayoi, Bunkyo, Tokyo 113–8657, Japan

3

Food and Agricultural Materials Inspection Center, Agricultural Chemicals Inspection Station,
2–772 Suzuki-cho, Kodaira, Tokyo 187–0011, Japan・本論文の版権は、日本農薬学会が所有していますが、版権所有者の許可を得て転載しています.

J. Pestic. Sci. 41(4), 152–162 (2016)
DOI: 10.1584/jpestics.D16-027

Original Article

Development and validation of the SPEC model for simulating the fate
and transport of pesticide applied to Japanese upland agricultural soil
Julien Boulange,1 Dang Quoc Thuyet,2 Piyanuch Jaikaew,1Satoru Ishihara3 and Hirozumi Watanabe1,*
Department of International Environmental and Agricultural Sciences, Tokyo University of Agriculture and Technology,
3–5–8 Saiwaicho Fuchu Tokyo 183–8509, Japan
2
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1–1–1 Yayoi, Bunkyo, Tokyo 113–8657, Japan
3
Agricultural Chemicals Inspection Station, Food and Agricultural Materials Inspection Center, Tokyo, Japan
1(Received March 1, 2016; Accepted July 20, 2016) ​

A pesticide fate and transport model, SPEC, was developed for assessing Soil-PEC (Predicted Environmental Concentrations inagricultural soils) for pesticide residues in upland field environments. The SPEC model was validated for predicting the watercontent and concentrations of atrazine and metolachlor in 5-cm deep soil. Uncertainty and sensitivity analyses were used toevaluate the robustness of the model’s predictions. The predicted daily soil water contents were accurate regarding the number ofobservation points (n=269). The coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) were equal to 0.38 and 0.22,
respectively. The predicted daily concentrations of atrazine and metolachlor were also satisfactory since the R2 and NSE statistics
were greater than 0.91 and 0.76, respectively. The field capacity, the saturated water content of the soil and the Q10 parameter
were identified as major contributors to variation in predicted soil water content or/and herbicide concentrations. © Pesticide
Science Society of Japan
Keywords: SPEC model, pesticide, fate and transport, upland soil.

cerns about the increased probability of exceeding the MRLs incrops. Indeed, in agricultural fields where crop rotation is used,
the residues of pesticides applied to the previous crop may betaken up by the next crop depending on the persistence and uptake characteristics.
To conduct realistic assessments of pesticide residues in soil
for the purpose of adapting to the new regulations, a model simulation could be one practical alternative to pesticide monitoringand experiments that are often expensive and time consuming.
By taking into account the major processes involved in the environmental fate of pesticides (sorption, degradation, leaching, volatilization, and runoff), models can be used for pesticide registration, mitigation, risk assessment, and screening purposes.8–10)
Modeling approaches vary in complexity and can be classified asdeterministic or stochastic models with two subcategories, mechanistic and functional.11) Models based on simple lumped parameters are limited to the relative ranking of hazardous chemicals
but have the potential to be used for preliminary risk assessment.
In contrast, models based on distributed parameters are morecomprehensive in the level of detail and can account for the heterogeneity of the environment. In practice, however their use islimited due to impractical data requirements.2)
In Japan, the development of pesticide models for investigating the fate and transport of pesticides applied to lowland rice
paddy fields has been reported.12–17) However there is not yet a

Introduction
Pesticides have been commonly used in agriculture since thesecond half of the twentieth century.1,2) The widespread use ofpesticides, however, has resulted in drift, leaching, and the runoff of pesticide from target crops to off-target areas which canaversively impact the environment.2) Indeed, agriculture has
been reported as the main source of groundwater contaminationand numerous monitoring studies have highlighted the presenceof pesticides in agricultural soils and surface and ground bodies
of water.3–5) In Japan, the persistence of pesticides in agricultural soils is evaluated in accordance with the test guidelines prescribed by the Ministry of Agriculture, Forestry and Fisheries6)
in limited conditions or simple scenarios. Meanwhile, the Ministry of Health, Labor and Welfare announced new standards
for Maximum Residue Limits (MRLs) for pesticides in food andfood additives (Positive List System) in 2003.7) In the so-called
Positive List System, MRLs of 0.01 ppm were assigned for allof the registered combinations of crops and pesticides that had
been previously neglected. These low MRLs have raised con* To whom correspondence should be addressed.
E-mail: pochi@cc.tuat.ac.jp
Published online November 6, 2016
© Pesticide Science Society of Japan12

Vol. 41, No. 4, 152–162 (2016)

Development and validation of the SPEC 153

pesticide fate and transport model that simulates the behaviorof pesticides applied to Japanese upland agricultural soil. This
could be due to the necessity of validating models through testing against high-quality field data sets.9) The aim of this research
was thus: (1) to develop a pesticide fate and transport model, theSPEC model, (2) to evaluate the predictions of the model using
field monitoring data and, and (3) to conduct uncertainty andsensitivity analyses of the SPEC model.

MACRO), SPEC development focuses on having minimum
input parameter requirements while maintaining physically
based processes. The mass balance equation used by the SPECmodel to calculate the amount of water in the soil layers is:Materials and Methods

WCi +1,1 =WCi ,1 + Raini − Runoffi ,1 − INFi ,1 − ETi ,1
WCi +1,2 =WCi ,2 + INFi ,1 − INFi ,2 − ETi ,2(1)

WCi , j =10 ⋅ depth j ⋅ ρb ⋅ θi , j(2)where the subscripts i and j specify the day and the soil layer ofthe variables. To clearly display the processes that are consideredin soil layers 1 and 2 in Eq. (1), the subscript j was explicitly replaced by the soil layer number (1 or 2). WCi+1,j and WCi,j are thewater contents expressed as water depths (using Eq. (2)) for day
i+1 and i of the soil layer j (mm), respectively; Raini is the amountof rainfall that occurred during day i (mm), INFi,1 and INFi,2 are
the amount of infiltration on day i from soil layers 1 and 2 (mm),respectively; ETi,1 and ETi,2 are the amounts of water removed
from soil layers 1 and 2 (mm) due to evapotranspiration; depthj
is the depth of the soil layer j (cm); ρb is the bulk density of thesoil (g cm−3); and θi,j is the volumetric water content of soil layer
j for day i (cm3/cm3). The methodology implemented to calculate
each process is detailed in the next section while the processes
considered to simulate pesticide fate and transport including pesticide loss through percolation, runoff, and biochemical and photochemical degradations are presented in Section 3.

1. Model description
The SPEC model was designed to assess the Soil-PEC (Predicted
Environmental Concentrations in agricultural soils) of pesticides. The model, coded in Visual Basic for Application in MS
Excel, is a lumped parameter, one-dimensional, field scale, anddaily time-scale model. The properties of the soil layers are assumed to be homogeneous; a maximum of two soil layers canbe defined in the model while a maximum of three successive
applications of pesticide can be scheduled. The depth of the soil
layers is defined by the user. Groundwater flow or recharge is
not considered in the model. Then, the soil water content andpesticide concentrations are calculated successively, from top tobottom. The SPEC model does not simulate the subsurface lateral flow, macropore flow, bypass flow, or tile drainage. Fig. 1
shows the current conceptual SPEC model and the various hydrological and pesticide fate and transport processes consideredby the model. The SPEC model estimates water runoff, leaching,
and associated pesticide loading. The Soil Conservation Service(SCS) curve number technique developed by the USDS is used
to estimate runoff whereas infiltration is determined by using astorage routing methodology. Such a scheme is often referred toas “tipping bucket” in the literature.18) As compared with otherpollutant fate and transport fate models (PRZM, HYDRUS,

2. Field-scale hydrological processes
2.1. Infiltration
The daily infiltration of water is related to the current water content of the soil and the soil’s ability to hold water. Water infiltrates from a soil layer to the soil layer below if the water content
of the soil layer exceeds the field capacity of that layer and theFig. 1. Conceptual hydrological and pesticide fate and transport processes considered by the SPEC model in a bare soil, upland field. Plain arrows represent the hydrological processes while dashed arrows characterize the pesticide fate and transport processes.

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154 J. Boulange et al.

layer below is not saturated. The amount of water available for
infiltration in a soil layer is therefore given by:

WCXi , j = max(0, WCi , j − FC j )CN 2adjust =(3)

where CN2adjust is the CN2 value adjusted for slope and slp is theaverage slope of the field (%). CN1 and CN3 are respectively thelowest and highest boundaries of the CN value. They are evaluatedonce, at the beginning of the simulation, using Eqs. (8) and (9):where WCXi,j is the drainable volume of water through infiltration in soil layer j on day i (mm), and WCi,j and FCj are the watercontent and field capacity at iteration i of soil layer j (mm), respectively. Next, the amount of water that actually moves fromone soil layer to the soil layer below is calculated the storage
routing methodology19):− Δt ⋅ Ksat j 
INFi , j = WCXi , j  1 − exp
SATj − FC j 

CN 1 = CN 2 −(Raini − Ia )2(Raini − I a + Si )20 ⋅ (100 − CN 2)
100 − CN 2 + exp(2.533 − 0.0636(100 − CN 2))

CN 3 = CN 2 exp(0.00673(100 − CN 2))

(4)

(8)
(9)

CN1 and CN3 remain constant during the whole simulation andcan be seen as properties of the soil. The retention parameter (Si)varies depending on the daily moisture of the soil and is re-evaluated at each computation iteration using the following equation:

where INFi,j is the amount of water that infiltrates from soil layer
j to the underlying soil layer at iteration i (mm), Δt is the lengthof the time step (h), Ksatj is the saturated hydraulic conductivity
for layer j (mm/h), SATj is the saturated water content of layer j(cm3/cm3), and the other parameters are as previously defined.
2.2. Surface runoff
Pesticide losses through surface runoff depend on the amountsof available pesticides in the soil surface, their chemical properties, and the intensities of rainfall and runoff.20) In the SPECmodel, surface runoff is only computed for the topsoil layer,using the SCS curve number procedure. The SCS curve method
is an empirical method developed through more than 20 yearsof studies involving rainfall-runoff relationships across theUSA.21) The method was developed to take into account different categories of land use and soil type. While the SCS curve
method was reported to be appropriate for Japanese soil conditions on a watershed scale, there have been few reports regarding its application on a field scale.22) The SCS curve number
equation is defined as23):

Runoffi ,1 =CN 3 − CN 2
[1 − 2 ⋅ exp(−13.86 ⋅ slp)]+ CN 2 (7)3WCi − Wres
(10)
Si = Smax  1 − WCi − Wres + exp(w1 − w 2 (WCi − Wres )) 
where Si is the retention parameter at time i (mm), Smax is the
maximum value the retention parameter can achieve on any
given day (mm), WCi is the soil water content of the soil layer(mm), Wres is the water residue of the soil layer (mm), and w1
and w2 are shape coefficients. Smax can be calculated from Eq. (5)by replacing CNi with CN1. Once the retention parameter of thesoil for the day is known, the value of the daily curve number
can be calculated by rearranging Eq. (6):CN i =

25400
Si + 254(11)

where CNi and Si are the curve number and the retention parameter for day i (mm), respectively. In practice, the method isimplemented as follows: first, the CN1 and CN3 of the soil arecomputed (Eqs. (8) and (9)). Then, every computation iteration,
S, is calculated using Eq. (10), and the amount of runoff is evaluated using Eq. (5).
An option to cancel runoff has been implemented in themodel. Indeed, during field experiments conducted to validate
the model, borders were installed between fields to avoid the potential cross-contamination of pesticides. As a result the surface
runoff of each plot was confined within that plot.24) When the“no-runoff ” option is used, water that was supposed to be lost
due to water runoff is routed to infiltration therefore increasing
the amount of infiltrating water. Note that, in the case of irrigation, the amount of irrigation water was added to the amount ofprecipitation as input water.
2.3. Evapotranspiration
Two options have been implemented in the SPEC model regarding evapotranspiration (ET). The first option is used when nodata are available. A constant daily value is used throughout thesimulation. The second option uses the Penman-Monteith equation to predict daily evapotranspiration (ETc). The procedure forcalculating all variables can be found in Allen et al.25)
Since ETc is computed by assuming that the plant had opti-(5)

where Runoffi,1 is the runoff amount generated at time i by the
topsoil layer (mm), Si is the retention parameter of the soil on
day i (mm), and Ia is the initial abstraction which includes surface storage, interception and infiltration prior to runoff (mm).
For runoff to occur, the condition Raini>Ia must be met. In theSPEC model, Ia was approximated as 0.2S as it is commonly reported in the literature.19) The curve number of the soil is related
to the retention parameter of the soil Si, as illustrated in Eq. (6):

 1000
Si = 25.4 − 10 
(6)
 CN i
where CNi is the curve number for day i of the top soil layer(dimensionless). Three moisture conditions are defined in the
SCS curve number method: dry (CN1), average (CN2), and wet(CN3). CN2 is required as a parameter input; an appropriate
value can be extracted from the literature for various combinations of land use and soil type.19,23) Note that these CN values arerecommended for a 5% slope; if the slope of the field is different,
the CN number must be adjusted.

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Development and validation of the SPEC 155

Oc
(15)
100
where Koc is the soil organic-water partitioning coefficient of thepesticide (L/kg) and Oc is the percentage of soil organic carbon(%). Note that the transport of pesticide sorbed to soil particles
with surface runoff is not considered in the current model.
3.2. Pesticide transport via vertical percolation
In the SPEC model, the amount of pesticide that is transportedwith percolating water is a function of infiltration:

mum soil water conditions, the actual evapotranspiration of the
field must be adjusted to reflect current field conditions. The actual amount of water removed from the two soil layers by evapotranspiration is proportional to the depth of the soil layers and is
calculated using Eq. (12):depth1ETi ,1 = min  WCi ,1 − FC1 , ETcdepth1 + depth2 depth2ETi ,2 = min  WCi ,2 − FC1 , ETcdepth1 + depth2 K d = K oc

(12)Mperci , j = Area ⋅ INFi , jwhere ETi,1 and ETi,2 are the actual evapotranspiration losses
at day i from soil layers 1 and 2 (mm), respectively, WCi,1 andWCi,2 are the amounts of water held in soil layers 1 and 2 (mm),respectively, FC is the soil field capacity (mm) of the soil layer,and depth1 and depth2 are the depths of soil layers 1 and 2 (cm),respectively．

Mbioi , j =10 ⋅ depth j ⋅ Area ⋅ ρb ⋅ kbio ⋅ Cs j

(17)

where Mbioi,j is the mass of pesticide loss from soil layer j at iteration i by biochemical degradation (mg), ρb is the bulk densityof the soil (g/cm3), and kbio is the first-order rate constant of thepesticide biochemical degradation in the soil (1/day). The firstorder rate constant degradation is calculated from the half-life of
the pesticide’s biochemical degradation:

Mpi ,1 = Mpi −1,1 − Mrunoffi ,1 − Mperci ,1 − Mphotoi ,1 − Mbioi ,1(13)
Mpi ,2 = Mpi −1,2 + Mperci ,1 − Mperci ,2 − Mbioi ,2kbio =

where Mpi,1 and Mpi,2 are the mass of pesticide in soil layers 1
and 2 at time i (mg), respectively, Mpi−1,1 Mpi−1,2 are the massesof pesticide in soil layer 1 and 2 at time i−1 (mg), respectively,
Mrunoffi,1 is the mass of pesticide lost through runoff from thetopsoil layer at time i (mg), and Mperci,1 and Mperci,2 are themasses of pesticide lost through percolation at time i by soil layers 1 and 2 (mg), respectively. Mphotoi,1, is the mass of pesticide loss through photodegradation in the topsoil layer at timei (mg), and Mbioi,1, Mbioi,2 and are the masses of pesticide loss
trough biochemical degradation at time i (mg), in soil layers 1
and 2, respectively.
3.1. Pesticide transported by surface runoff
The mass of pesticide loss from the soil top layer through waterrunoff is calculated by:

Cs1
Kd

(16)

where Mperci,j is the mass of pesticide loss from soil layer j at iteration i (mg), INFi,j is the amount of water that percolates fromthe layer j (mm), and all other variables are as previously defined. The mass of pesticide loss by percolation by soil layer j isadded to the mass of pesticide in the soil layer j+1 (Eq. (13)).
3.3. Pesticide biochemical degradation
Pesticide biochemical degradation was describe by a first-orderequation:

3. Pesticide fate and transport
Pesticide is introduced in the model by scheduling a pesticide
application. The user is required to input the date and the ratepesticide application. Then, the fate and transport of the pesticide in the field are simulated by considering various pesticidedegradations, loss of pesticide by surface runoff, and pesticidetransport through vertical percolation in and out of the soil layers. Consequently, the equation used to predict pesticide concentrations in the two soil layers is:

Mrunoff i ,1 = Area ⋅ Runoffi ,1

Cs j
Kd

ln(2)
HLbio

(18)

where HLbio is the pesticide half-life of biochemical degradation(day).
The influence of temperature on the degradation rate can be
accounted for in the temperature data as: (1) two average temperatures with their corresponding periods, (2) daily averagetemperatures, and (3) hourly average temperatures. Using thefirst option, two degradation rates are computed and used during the appropriate periods. In contrast, when using options
2 and 3, the degradation rate can change on a daily or hourly
basis. The equation used to adjust the half-life of a pesticide due
to temperature is given as26):(t1 −25)/10
kbio = kbioref Q10(19)

where kbioref is the reference pesticide’s half-life at 25°C (day),Q10 is the change of half-life given a 10°C change in temperature(unitless), and t1 is the temperature at which the half-life of thepesticide must be calculated (°C).
3.4. Photochemical degradation
Photodegradation was reported to be one of the most destructive pathways for pesticides after their release into the environment.27) This process in soil surfaces is only significant if there is
no foliage covering the ground. In the SPEC model, this process
is only considered in 2-mm depth of the topsoil layer. To accu-(14)

where Csj is the pesticide concentration in soil layer j (mg/kg),
Kd is the soil adsorption coefficient of the pesticide in the soil (L/kg) and the other parameters are as previously defined. The soil
adsorption coefficient of the pesticide in the soil is related to thesoil organic content, Oc (%). The relation is given as:

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156 J. Boulange et al.

rately determine the loss of pesticide by photodegradation, thelevel of solar radiation reaching the ground must be known. Thisis evaluated using the following equation which was originallydeveloped for paddy fields15):

RUVB -a − RUVB -b= f R-ab ⋅ t
RUVB -ametolachlor were applied twice, on June 10, 2013 and December
6, 2013 to the whole surface of the plots. The commercial formulation of the herbicides (Geza non gold® Syngenta, Tokyo, Japan)
was diluted with distilled water and applied at the recommended
rates of 771.3 and 732 g a.i./ha for atrazine and metolachlor, respectively. Neither herbicide was applied to the field prior to theexperiment and no crops were grown on the plots. In addition,no irrigation water was applied to the field during the entire duration of the experiment.24) Precipitation, soil temperature, andsoil moisture contents at 5.0 cm deep were recorded hourly.24)
Soil samples were collected at a depth of 5 cm at specified intervals from the three plots using soil cores 5 cm in diameter.
A composite sample was created for each plot by mixing threesamples taken randomly from each plot. The procedure used toclean up the composite samples and extract the pesticide can befound in the literature.23)(20)

where RUVB-a and RUVB-b are the daily UV-B radiation above andbelow the plant canopy (MJ/m2), respectively, fR-ab is the slopeof the fitted line obtained from the relative difference of the radiation above and below the plant canopy that accounts for thelight attenuation by the growing crop, and t is the time (day).
The UV-B radiation reaching the ground can be calculated as:

RUVB -b = fUS R S -a(1 − f R -ab ⋅ t )(21)

where fUS is the fraction of the UV-B radiation over the solar
radiation, and RS-a is the solar radiation below the plant canopy.
When no plants are growing in the field (bare soil condition),fR-ab is equal to 0 and the UV-B radiation above and below theplant canopy is identical. The final equation used to compute themass of pesticide loss by photodegradation is:

Mphotoi ,1 = 2 ⋅ Area ⋅ RUVB -b ⋅ k photo ⋅ Cs1

5. Model parameterization
The input parameters used for predicting soil moisture and concentrations of atrazine and metolachlor at the TUAT experimental farm are reported in Table 1. Soil layers 1 and 2 were
1 cm and 4 cm deep, respectively. The data used to parameterize the model were taken from the literature or database. Whenno data were available, the inputs were calibrated.21,24,28,29) The
curve number value used in the simulation was extracted fromthe tables provided by the SCS Engineering Division and is appropriate for a 5% slope with bare soil (no crop residue) and asoil with moderate infiltration rate.19) Previous analysis indicatedthat the curve number method was applicable for the andisol
soil plot scale with bare soil. The method was, however, sensitive
regarding the initial moisture content of the soil.30) Nevertheless, further validation of the method of application in Japan forother combinations of land cover and soil conditions is necessary. Hourly monitored precipitation and temperature data wereused for the simulation. Daily average solar radiation as well as
minimum, maximum, and average daily air temperature data
was downloaded from the AMEDAS weather station locatedabout 500 m from the monitoring site in Fuchu City, Tokyo(Japan).31) These data were used to calculate the daily amount of
evapotranspiration from the TUAT experimental farm.25)(22)

where Mphotoi,1 is the mass of pesticide loss by photodegradation (mg), kphoto is the first-order rate coefficient of photochemical degradation with respect to cumulative UV-B radiation(m2M/J), and all other parameters are as previously defined. The
first-order rate coefficient of photochemical degradation withrespect to cumulative UV-B radiation can be calculated from thehalf-life of pesticide photodegradation.

k photo =ln(2)HL photo ⋅ fUS ⋅ Solar(23)

where HLphoto is the photochemical degradation half-life of thepesticide (day), and Solar is the average solar radiation measured during the experiment duration (MJ/m2/day). While determining kphoto experimentally at a site is preferable for accurately predicting the photodegradation of pesticides, Eq. (22)can be used to derive kphoto from existing pesticide databases.
4. Field experiments
We attempted to validate the SPEC model so as to predict: (1)soil water content and (2) the concentrations of two herbicides:
atrazine and metolachlor. All observed data were acquired overa two-year monitoring period (2013–2014) at the experimental
farm of Tokyo University of Agriculture and Technology (TUAT
experimental farm), Tokyo, Japan. Details of the experiment canbe found elsewhere.24) Briefly, the field was divided into threeexperimental plots that were surrounded by plastic borders buried approximately 10 cm in the ground. Note that the borders
prevented surface runoff from the plots. The texture of the soil
was identified as clay-loam while its taxonomic order is andisol. It contained 29.6% sand, 33.4% silt, and 23.4% clay. Somecharacteristics of the soil are reported in Table 1. Atrazine and6. Sensitivity and uncertainty analyses
The possible application of any model and its validation procedures are largely determined by the model’s sensitivity.11) Indeed, input parameters are variable which can be attributed to(1) protocols and analytical methods and (2) spatial variability
that occurs naturally.11) Since input parameters must be estimated whenever data are missing, characterizing and ranking
input parameters as to their influence on model predictions are
absolutely necessary to correctly interpret a models’ output. Uncertainty and sensitivity analyses were performed by applying
Monte Carlo (MC) techniques to the SPEC model. To noticeably
recognize the effects of uncertainty included in input parameterson the predicted soil water content and pesticide concentrations,16Vol. 41, No. 4, 152–162 (2016)

Development and validation of the SPEC 157

two MC scenarios were created. The first MC scenario (MC scenario 1) included only input parameters related to soil properties while the second MC scenario (MC scenario 2) consisted of
input parameters related to pesticide characteristics.
The water residue (Wres), the saturated hydraulic conductivity(KSat), the saturated water content (SATj) and the field capacity (FCj) of the soil were included in the first MC scenario fora total of four investigated input parameters. Three parameters
were considered in the second MC scenario: the photodegradation half-life (HLphoto), the Q10, and the soil organic-water partitioning coefficient of the pesticide (Koc). To avoid redundancy,
the kbio was not included in the analysis, since it is nested with
the Q10 parameter (Eq. (18)). In addition, accurate kbio data fromlaboratory experiments (unpublished data, Table 1) were available for both atrazine and metolachlor. The sample size used for
the MC simulations was 250 for both soil and pesticide parameter scenarios. This sample size proved sufficient for a pesticide
fate and transport model in the case of pesticide applied in ricepaddies.32) Uniform distributions were given to all investigatedparameters. All parameters except the Q10 parameter were allowed to vary a maximum of ±10% from the values used in thedeterministic scenario presented in Table 1. The range of the Q10parameter was 1.0 to 2.2. A value of 1.0 indicates that temperature has no effect on the degradation half-life. A value of 2.2 wasrecommended for use when no site data was available.26) Notethat the maximum range of the saturated water content of thesoil was to 1.0 as values higher than 1.0 are not physically possible. For evaluating model response the soil water content andherbicide concentrations, target outputs were selected 24 days
after the herbicide applications. This corresponds to the half-life period of appreciable herbicide concentrations; therefore thedata set is representative of each season.
To visualize the evolution of output uncertainty every computation step, the 95th percentiles of the predicted soil watercontent and herbicide concentrations were plotted together withthe predictions of the deterministic scenario.
The method used to measure input sensitivity was reported
previously.32,33) The method relies on a stepwise regression analysis that computes standard rank regression coefficients (SRRCs)
for the predictors (inputs) that have the most significant influence on the predictions (outputs). By ranking the input parameters by absolute values of SRRCs, the model’s most sensitive
parameters can be highlighted.
7. Model evaluation
The model’s accuracy regarding the predictions of soil watercontent and herbicide concentrations was evaluated using statistical indices. The coefficient of determination (R2) which
describes the degree of collinearity between the simulated andmeasured data was reported to be extremely sensitive to extremely high values (outliers) and insensitive to additive andproportional differences between model predictions and measured data.34) Therefore to appropriately interpret the accuracyof a model, it is necessary to report additional statistical indicessuch as the Nash-Sutcliffe efficiency (NSE). NSE is a normalized
statistic that compares the measured data variance to the relative
magnitude of the residual variance.35) NSE statistic range between−∞ and 1.0, the latter being the optimal value. Positive values
are generally viewed as acceptable levels of performance. In
contrast, negative values indicate that the mean of the observed

Table 1. List of the input parameters used to simulate the soil water content and the concentrations of atrazine and metolachlor in TUAT experimental
farm
SPEC model inputs

Abbreviations

Units

Atrazine

Metolachlor

Organic carbon

Oc%

6.9524)

Bulk density

ρb

g/cm

0.524)

Saturated water content

SAT

3cm /cm

0.9524)

Water residue

Wres

3

cm /cm0.10a

Saturated hydraulic conductivity

Ksat

Cm/h

10.80aCurve number

CN

—

8623)

Field

Slope

slp

%

524)

Field capacityFC

cm3/cm

0.4024)

Date of applications

—

Date

10 June 2013, 6 December 2013

Application rate

—

g/ha

771.324)732.524)

Partitioning water organic coefficient

Kd

L/kg

28)

12024)

Half-life biochemical degradation

HLbio

28)

24.728)Half-life photo-degradation

HLphoto

day

29)

199a

Average solar radiation

Solar

kJ/m

1424)

14a

Q10

Q10

—

1.3524)1.4224)

Pesticideday

Note: a Input was calibrated.

17

100

23.5
100

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158 J. Boulange et al.

value is a better predictor than is the simulated value.36)
The root mean square error (RMSE) is an error statistic because it indicates error in the units of the variable of interest(cm3/cm3 for the soil water content and mg/L for herbicideconcentrations).36) A value of 0 indicates a perfect fit. Having
an RMSE value of less than half the standard deviation of themeasured data was reported to be appropriate.37) The coefficientof residual mass (CRM) indicates if the model overestimate orunderestimate the observations, a perfect fit is indicated by avalue of 0. Positive values indicate that the model has a tendency
to underestimate the data while negative values indicate that themodel tends to overestimate the observations.38) The equations
used to compute the different indices have been commonly reported in the literature.36,38)

used during the entire simulation period (dotted line in Fig. 2).
In contrast, for the second scenario, daily ETs computed by thePenman-Monteith algorithm were used in the model (solid line
in Fig. 2). The effect of ET on the simulated soil water content
was particularly clear during the winter season (Fig. 2). Indeed,
the default ET value (0.1 cm/day) seems to be too high during
the winter season (dotted line in Fig. 2). The average ETs calculated using the Penman–Monteith method were 0.1 and 0.06 cm/
day for the summer and winter seasons, respectively. As a result,
too much water is removed from the soil, which results in theunderestimation of soil water content during this period. In contrast, using daily ET values greatly improved the accuracy of thesimulations of soil water content.
The statistical evaluations of the SPEC model for the two scenarios are reported in Table 2. The CRM statistic indicates that themodel has a slight tendency to overestimate the soil water content.
The NSE, and RMSE statistics were similar for the two scenarios
using constant and daily ET values. In contrast, the R2 value increased significantly for the simulation using daily ET values. Indeed, the high linear relationship between the predicted and observed soil water content can be observed graphically in Fig. 2.
While the predicted daily soil water content values did not always
match the observed values, the general trend of the observations
is very well captured by the model’s simulation. In general, regarding the number of observation points (n=269), the temporal andspatial variations of the observed water contents and the daily predictions for both scenarios were classified as good.

Results and Discussion
1. Model validation for the prediction of soil water content
During field monitoring, the daily volumetric average of the soil
water content varied from 0.34 to 0.40 cm3/cm3 during the summer and winter seasons, respectively.24) There is a clear correlation between precipitation and increased the soil water content (Fig. 2). In major precipitation events, soil water content
increased sharply, since runoff amounts were rerouted to thepercolation component, as indicated previously. In the field, nosign of ponding was observed during these intense precipitation
events, indicating that significant runoff was unlikely to haveoccurred. However, the validation of runoff and the corresponding pesticide discharge components of the SPEC model is required. In general, the SPEC model accurately predicted the soil
water content of the TUAT experimental farm for the durationof monitoring (Fig. 2). The sensor used to record the soil watercontent failed starting on the March 5, 2014 (Fig. 2). Consequently, the evaluation of the model is based on recording prior
to the sensor’s failure.
Two scenarios were used to simulate the soil water content.
In the first scenario, a constant value for ET (0.1 cm/day) was

2. Model validation for the prediction of atrazine and metolachlor concentrations
Atrazine and metolachlor concentrations were simulated from
June 10, 2013 to May 5, 2014, using the input parameter values reported in Table 1 and the scenarios for constant and daily
ET values. The deterministic simulations using the daily ET values are reported in Fig. 3 while the statistical evaluations of themodel for both scenarios are reported in Table 2. The predicted

Fig. 2. Predicted and observed daily water content in 5-cm deep soil at TUAT experimental farm from 10 June 2013 to 5 May 2014. The grey band indicates the 95th percentile confidence interval of the predicted soil water content acquire from the MC simulation 1.

18

Vol. 41, No. 4, 152–162 (2016)

Development and validation of the SPEC 159

Fig. 3. Predicted and observed concentrations of atrazine (A) and metolachlor (B) in 5-cm deep soil for the 1st MC scenario (parameter related to soil
properties). Grey bands indicate 95th percentile confidence interval.
Table 2. Statistical evaluation of the SPEC model regarding the prediction of soil water content, atrazine and metolachlor concentrations
Outputs
Evapotranspiration

Water content
Const. ET

Atrazine

Metolachlor

Daily ETs

Const. ET

Daily ETs

Const. ET

Daily ETs

R2

0.16

0.34

0.93

0.92

0.91

0.93

NSE

−3.88

−1.06

0.91

0.89

0.82

0.76

RMSE

0.09

0.05

0.41

0.45

0.53

0.61

CRM

0.09

−0.003

0.12

0.07

−0.21

−0.27

Table 3. Percentage of atrazine and metolachlor dissipated by various processes as compared to herbicides applied mass for the summer and winter seasons
Processes

Unit

Atrazine

Metolachlor

Summer

WinterSummer

Winter57

Biochemical degradation

%

39

49

57

Photo-degradation

%

3

5

5

7

Percolation

%

58

46

39

33

Runoff

%

0

0

0

0

Residual dissolved into soil-water

%

<0.1

<0.1

<0.1

0.1

Residual sorbed onto soil-particles

%

<0.1

0.2

<0.1

2

Note: Runoff simulation was disabled for this simulation.

herbicide concentrations for the two ET scenarios were similar,
since the statistical evaluations of the two scenarios yield similar
statistics for atrazine and metolachlor (Table 2). The model was
flagged by the CRM statistics as slightly underestimating atrazine concentrations and overestimating metolachlor concentrations. Those trends were also confirmed by a visual inspection of
the deterministic simulations of atrazine and metolachlor (Fig.
3). Nevertheless, the predicted herbicide concentrations are in
range of the observations. Moreover, the NSE was positive forall scenarios while the R2 was higher than 0.90 for all scenarios.
Thus, the model accurately simulated atrazine and metolachlor
concentrations on the TUAT experimental farm.

The dissipation behavior of the two herbicides was different between the summer and winter seasons as reported by theherbicide mass balance (Table 3). At the end of the seasons, theamounts of atrazine and metolachlor remaining in the soil layers
were small. More herbicide was transported with vertical percolation during the summer season due to frequent and abundantprecipitation events as compared to the winter season (Fig. 3).
Note that since surface runoff was prevented due to the installation of borders surrounding the plot, herbicide was only transported through vertical percolation (Eq. (1)). It was anticipatedthat more herbicide mass would be lost through degradation during the summer season due to the effect of temperature on deg-

19

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160 J. Boulange et al.

herbicide concentrations in soil are displayed in Fig. 3A, B for
MC scenario 1 and in Fig. 4A, B for MC scenario 2, respectively.
The effects of the soil property uncertainties on herbicide concentrations were consistent in the summer and winter seasons,as the thickness of the 95th percentile confidence interval remained constant throughout the simulation period (Fig. 3A, B).
The 95th percentile confidence interval computed for atrazine
was greater than that for metolachlor. Since the Koc of atrazine islower than that of metolachlor (Table 1), atrazine was simulated
to be transported easily through water percolation which was
flagged as a main route for herbicide dissipation (Table 3).
The herbicide characteristic uncertainties did not affect thepredicted herbicide concentrations during the summer season(Fig. 4). In contrast, the predicted herbicide concentrations in
the winter season were greatly affected by the herbicide characteristic uncertainties, as indicated with the greater thicknessof the 95th percentile confidence interval. The Koc parameter isused to predict the amount of herbicide transported with surface runoff and vertical percolation (Eqs. (14)–(16)). The Q10parameter is used together with the soil temperature to adjust
the half-life of the biochemical degradation of herbicides (Eqs.
(17)–(19)). Both infiltration and temperature data were reported to be significantly different between summer and winterseasons at the TUAT experimental farm (Table 3).24) Therefore,
the differences in the effects of uncertainty included in herbicides’ characteristics between the summer and winter seasonson the predicted herbicide concentrations are due to different
combinations of the interrelated parameters of Koc and infiltration (Eqs. (14)–(16)) or Q10 and temperature (Eqs. (17)–(19)).
This result also suggests that it is appropriate to investigate thesensitivity of input parameters separately for summer and winterdatasets. Note that solar radiation data were similar for the summer and winter seasons, 13.6±6.6 and 12.9±6.8 MJ m−2, respectively. Consequently, the effect of the HLphoto input’s uncertainty
on herbicide concentrations is constant regardless of the sea-radation. However, the percentage of atrazine loss through biochemical degradation during the winter season was higher thanthat of the summer season. The percentages of metolachlor loss
through biochemical degradation in the summer and winter seasons were identical (Table 3). In the winter, less atrazine was lost
through percolation, resulting in more chemicals available forbiochemical degradation during the winter as compared to in thesummer (Fig. 3). The amounts of herbicides lost through photodegradation during the summer and winter seasons were similar.
On February 15, 2014, a 9.5-cm precipitation event caused agreat drop in predicted herbicide concentrations due to its transport through percolation bellow soil 5 cm deep (Fig. 3). However, the monitored herbicide concentrations, while decreasing,
did not drop suddenly as the simulation had suggested. A possible explanation is that the model does not consider the effectof snowfall and snowmelt that occur at that time of the year. It
was observed that snow melted gradually in the field and therefore, the actual amount of water that the soil received during asnowfall event was probably less than indicated in the data recorded by the logger. Note that the slight decline of observed
herbicide concentrations due to precipitation on December 27 iswell simulated by the model suggesting that the model’s assumptions are appropriate when there is no snowfall.
3. Uncertainty analyses
The effects of input uncertainty on the predicted soil water content and herbicide concentrations were investigated using two
MC scenarios which consisted of: (1) soil parameter inputs and(2) herbicide characteristic inputs. The effects of uncertainty insoil parameters on the predictions of soil water content are reported in Fig. 2. The thickness of the 95th percentile confidence
interval was constant through the simulation period, indicatingthat the influence of parameters’ uncertainty did not vary during
the summer and winter seasons.
The results of the uncertainty analysis for the prediction ofFig. 4. Predicted and observed concentrations of atrazine (A) and metolachlor (B) in 5-cm deep soil for the 2nd MC scenario (parameter related to pesticide characteristics). Grey bands indicate 95th percentile confidence interval.

20Vol. 41, No. 4, 152–162 (2016)

Development and validation of the SPEC 161

son (Eq. (22)). In the SPEC model, the parameter fUS (Eq. (21))
was constant during the simulation period. However, in practicethis parameter fluctuates; therefore photodegradation was likely
overestimated during the winter season.

generate less percolation (Eqs. (2) and (3)). The same parameters were retained by stepwise regression methodology that uses
herbicide concentrations as outputs. The sign of the reported
SRRCs helps to gain some insight into the model’s behavior. Increasing the field capacity of the soil decreases predicted herbicide concentrations. In contrast, increasing the saturated watercontent of the soil increases predicted concentrations of herbicide. The field capacity of the soil determines the amount ofwater that is available for infiltration (Eq. (2)) and consequently,
increasing this parameter increases the loss of herbicide due topercolation. A field’s saturated water content is primarily used todetermine the amount of percolating water (Eq. (3)). By setting
a higher SATj value, the amount of percolating water will be reduced, thereby limiting the transport of herbicide.
MC scenario 2 also produced a consistent ranking of the sensitive parameter. However, the season affected the ranking of theparameters (Table 5). For the summer season, the photodegradation half-life was flagged as the most sensitive parameter. This
result is caused by not including the biochemical degradation
rate (kbio) in the sensitivity analysis to avoid redundancy with
the Q10 parameter. In the summer, temperatures are close to thereference temperature of 25°C; consequently, the Q10 parameter does not impact the rate of kbio. The analysis of the massbalance of the two herbicides (Table 3), however revealed thatthe mass of herbicides lost through biochemical degradation is
8 to 10 times higher than that lost through photodegradation.
Consequently, accurate kbio parameters are absolutely crucial for
accurately determining the fate and transport of atrazine andmetolachlor in both summer and winter. Increasing the HLphotoslows the degradation of herbicide in the field, which results inhigher herbicide concentrations in the soil. For the winter season, the Q10 was highlighted as the most sensitive parameter.
The Q10 parameter is an indication as to what extent the half-lifeof a pesticide will deviate from its default value at 25°C when
the temperature changes by ±10°C. Indeed, the Q10 and kbio arenested together (Eq. (18)), and the high sensitivity of the Q10,
therefore, implies that the kbio has to be accurately determinedto accurately predict herbicide concentrations (Table 3). In addition, there is limited information about Q10 values for pesticide; this was reflected in the parameter’s rather wide range (1 to
2.2) which also contributed to the high overall sensitivity of theparameter (see Fig. 4 winter). During monitoring, the averagetemperature in the winter was 5±4°C.24) Since there is approximately a 20°C difference between the reference temperature of
25°C and the average temperature in winter, the half-life of theherbicides in winter was divided by the square of the Q10 (Eq.
(18)), resulting in much slower herbicide degradation. During
the summer season, the temperatures were closer to the reference temperature and, thus, the Q10 did not affect predicted herbicide concentrations.

4. Sensitivity analyses
Prior to the sensitivity analysis, all data generated by the MC
simulations was assessed and showed no evidence of skewness
or kurtosis for any of the input parameters and outputs. Consequently, a stepwise regression analysis was performed using an
SPSS software package for statistical analysis.39) There was noevidence that any of the input parameters exerted undue influence on the regression models. Moreover, no indication of multicollinearity (two or more highly correlated predictor variables)in the data was found. The standardized rank regression coefficients (SRRCs) obtained using stepwise regression methodology are presented in Tables 4 and 5 for MC scenario 1 and 2,
respectively. SRRC values can vary from −1 to 1, and high absolute values of SRRCs indicate for sensitive parameters. A positive
SRRC indicates that increasing the parameter value will increase
the output considered, and vice versa.
For MC scenario 1, the ranking of the sensitive parameters
was consistent, regardless of the season (Table 4). While the “norunoff ” option is used, the field capacity (FC) and the saturated
water content of the soil (SAT) were flagged as the most sensitive
parameters regarding the prediction of soil water content. The
SRRCs of these parameters were positive since increasing bothparameters increases the predicted soil water content. Indeed,
increasing the SAT and FC allows the soil to: (1) store morewater, (2) retain more water in periods of no rainfall, and (3)Table 4. Standardized rank regression coefficients of the SPEC model
parameters for the 1st MC scenario (parameter related to pesticide characteristics)
Outputs
Water content
Atrazine
Metolachlor

MC scenario 1

Sensitive
parameters

Summer

Winter
0.87

FC

0.87

SAT

0.30

0.30

FC

−0.60

−0.58

SAT

0.47

0.48

FC

−0.62

−0.63

SAT

0.46

0.46

Table 5. Standardized rank regression coefficients of the SPEC model
parameters for the 2nd MC scenario (parameter related to pesticide characteristics)
Outputs
Atrazine
Metolachlor

MC scenario 2

Sensitive
parameters

Summer

HLphoto

0.78

—

Q10

—

0.75

HLphoto

0.55

—

Q10

—

0.55

WinterConclusion
The SPEC model was developed to assess Soil-PEC (Predicted
Environmental Concentrations in agricultural soils). The model21

Journal of Pesticide Science

162 J. Boulange et al.

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was then validated using a field experiment carried out from
June 10, 2013, to May 5, 2014 in which the soil water content
and concentrations of atrazine and metolachlor were monitored.
The soil water content predicted were accurate regarding thetime step, and R2 and NSE statistics were equal to 0.38 and 0.22,
respectively. The predicted atrazine and metolachlor concentrations were also adequate, and the R2 and NSE statistics were higher than 0.91 and 0.76, respectively.
The performance of the model with uncertain inputs was investigated using the Monte Carlo technique. The model’s predictions
were influenced constantly throughout the simulation period dueto the uncertainty encompassed in soil properties. In contrast,
only the predicted herbicide concentrations in the winter season
were influenced by uncertainty arising from pesticide properties.
While preventing surface runoff in the model, the field capacity and the saturated water content of the soil were identified as
major contributors to variation in predicted soil water content andherbicide concentrations. In addition, the Q10 parameter was also
flagged as a major contributor to variation in predicted herbicideconcentrations, especially during the winter season.
The SPEC model therefore, has the potential to accurately predict water content and pesticide concentrations in soil. Moreover,the detailed pesticide mass balance given by the model can be used
to identify major dissipation pathways and evaluate the best options for improving environmental conditions associated with pesticide residues in agricultural soil. Future improvements include:(1) the validation of the runoff component that was disabled inthis study, (2) the creation of multiple soil layers for the improved
prediction of soil water content, and (3) the dynamic adjustment
of UV-B radiation over solar radiation’s dependence on environmental factors for improving predictions of photodegradation.
Acknowledgements
This research was supported by the Environment Research and Technology Development Fund (4-1303). DQ Thuyet is a JSPS postdoctoral
research fellow at the Graduate School of Agricultural and Life Science,
The University of Tokyo.

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