modelling the fate of pesticides in paddy rice-fish pond farming systems in northern vietnam

Research ArticleReceived: 8 August 2012 Revised: 22 January 2013 Accepted article published: 8 March 2013 Published online in Wiley Online Library:

(wileyonlinelibrary.com) DOI 10.1002/ps.3527

Modelling the fate of pesticides in paddyrice–fish pond farming systems in northernVietnamNguyen La,a,b Marc Lamers,a∗ Vien V Nguyenc and Thilo Strecka

Abstract

BACKGROUND: In Vietnam, paddy rice fields have been identified as a major non-point source of pesticide pollution of surface-and groundwater which is often directly used for domestic purposes. One strategy to assess the risk of pesticide pollution is touse process-based models. Here, we present a new model developed for simulating short-term pesticide dynamics in combinedpaddy rice field–fish pond farming systems. The model was calibrated using the Gauss–Marquardt–Levenberg algorithm andvalidated against measured pesticide concentrations of a paddy field–fish pond system typical for northern Vietnam.

RESULTS: In the calibration period, model efficiencies were 0.82 for dimethoate and 0.87 for fenitrothion. In the validationperiod, modelling efficiencies slightly decreased to 0.42 and 0.76 for dimethoate and fenitrothion, respectively. Scenariosimulations revealed that a field closure period of 1 day after pesticide application considerably reduces the risk of pond andsurface water pollution.

CONCLUSION: These results indicate that the proposed model is an effective tool to assess and evaluate management strategies,such as extended field closure periods, aiming to reduce the loss of pesticides from paddy fields.c© 2013 Society of Chemical Industry

Keywords: pesticides; rice fields; fish ponds; modelling; water quality; south-east Asia; Vietnam

1 INTRODUCTIONThe use of pesticides has contributed to secure agriculturalfood production in many countries of the world. Nonetheless,pesticides may be lost to adjacent environmental compartmentssuch as surface water or groundwater, where they may severelyaffect non-target organisms, degrade the quality of drinkingwater and enter the human food chain.1,2 The transfer ofpesticides from agricultural fields to adjacent compartments ismainly determined by surface and sub-surface run-off.3,4 However,measured disappearance of pesticides may also be due tovolatilisation and spray drift. Paddy rice is a special situationin that paddy fields are submerged during most of the cropgrowth cycle. Water depths commonly range between 2 and10 cm depending on water availability. Most rice varieties cantolerate a ponding water depth of at least 15 cm without adverseeffects on grain yield.5 Paddy fields are typically designed asflow-through systems. They are interconnected by a complexsystem of canals and weirs enabling water flow by gravity. Sincepest management is commonly accomplished during submergedconditions, a considerable fraction of the applied pesticides entersthe ponding surface water with or shortly after application.6 Therequirement for large amounts of irrigation water increases thelikelihood for contamination of surface water and groundwater bymeans of controlled drainage, overflow or leaching. Various studiesdemonstrate that paddy rice pesticides are the main source of non-point pollution of surface water in the major paddy rice-growing

regions in Europe,7,8 Japan9–11 and south-east Asia.12–14

During transport in surface water, pesticides essentially undergothe same processes as in soil. The most important are sorption tosediment and suspended particles as well as degradation. Sorptionleads to retardation of the contaminant front, degradation todissipation of the pesticides. Measured disappearance may alsobe due to volatilisation,15 formation of bound residues16 andleaching.1,17–19

Quantifying and predicting pesticide losses from paddy fieldsto non-target compartments is a prerequisite for assessing therisk of water pollution and for evaluating management strategiesaiming at reducing this risk. However, field measurements aretime consuming, costly and logistically demanding. Accordingly,risk assessment in remote areas such as in northern Vietnamis hampered by limited field data. One way to cope with thisshortcoming is to use process-based models. During recentdecades, substantial efforts have been made to develop and

∗ Correspondence to: Marc Lamers, University of Hohenheim, Institute of SoilScience and Land Evaluation, Biogeophysics Section, 70593 Stuttgart, Germany,E-mail: [email protected]

a University of Hohenheim, Institute of Soil Science and Land Evaluation,Biogeophysics Section, 70593, Stuttgart, Germany

b Soils and Fertilizers Research Institute, Department of Land Use Research, DongNgac, Tu Liem, Hanoi, Vietnam

c Hanoi University of Agriculture, Institute of Plant Pathology, Gia Lam Hanoi,Vietnam

Pest Manag Sci (2013) www.soci.org c© 2013 Society of Chemical Industry

www.soci.org N La et al.

apply simulation models to predict pesticide behaviour in soil andin soil–water–plant systems (e.g. CREAMS,20 PRZM,21 GLEAMS22).Nonetheless, most of the models were designed for use in non-flooded cropping conditions and cannot be directly appliedto paddies. For paddy rice, only a few models are applicable,among them PADDY23 and PCPF-1,24 developed in Japan, andRICEWQ25 from the USA. The models have the same mathematicalstructure. They are formulated as sets of ordinary differentialequations, which apply the principle of mass balance to therelevant system compartments. Special features of the differentmodels are that PADDY considers the dissolution of granules,PCPF-1 considers daily fluctuations of paddy water depth andphotochemical degradation in paddy water, whereas RICEWQ canaccount for particle setting and resuspension. The PADDY modelhas been extended for use at large scale (PADDY-large).26 PCPF-1was coupled to SWMS-2D to simulate water flow and pesticidetransport in a soil profile in a Japanese paddy field.27 RICEWQ waslinked to a transport model that simulates the fate of pesticidesin tributary systems (RIVWQ).28 RICEWQ was further linked toVADOFT, a vadose zone transport model to predict pesticide

concentrations in soil, run-off and groundwater.28–32

The above models have been successfully applied to data sets

from Europe and Japan.8,15,23,28–30,33,34 At the time of the study,however, they were not open-source. This limits their flexibilityto be modified and adapted to specific environmental conditionsoutside the scope for which they were developed. In mountainregions of south-east Asia, for example, many paddy farmingsystems include ponds in which farmers raise fish to produceadditional food and income. Often, irrigation water is first usedin paddy fields before it flows to a fish pond and further tostreams.6 When farmers apply abundant pesticides to the ricecrops, fish production in the ponds may suffer. We have thereforeset up a new model which includes fish ponds as separatecompartments. The model was calibrated (spring season) andvalidated (summer–autumn season) against measured data froma field study conducted in northern Vietnam by Anyusheva et al.in 2008.6

2 MATERIALS AND METHODS2.1 Field studyThe field study of Anyusheva et al.6 was conducted in the ChiengKhoi watershed, Yen Chau District, Son La province, northernVietnam (20◦ 37′ 0′′ N, 106◦ 4′ 60′′ E). The study area is mountainouswith elevations ranging from 300 to 1000 m above sea level. Theclimate is sub-tropical with a rainy season from April/May toSeptember/October, and a relatively dry, cold winter. Averagetemperature and annual precipitation are 21 ◦C and 1200 mm,respectively. In the Chieng Khoi watershed, there are two riceseasons per year, a spring crop season from January to June anda summer–autumn season from July to November. The waterdemand of the first is mainly met by irrigation, that of the latterby rain. Each household usually cultivates one to several paddyrice fields and at least one fish pond. Detailed information aboutthe study area can be found in Lamers et al.,14 Schad et al.35 andAnyusheva et al.6

The field study was conducted in 2008 during both rice cropseasons. The experimental site (paddy field, 550 m2; fish pond,150 m2) was located at the bottom-most position of a rice fieldtoposequence. The irrigation water was supplied by a bamboopipe connected to an irrigation channel that receives water froma nearby reservoir. From the paddy field the water flows to a fish

pond and then to a natural river. The study included continuousmeasurements of water flows between the compartments of thesystem (irrigation channel, paddy field, fish pond and stream)and frequent sampling of the water for pesticide analysis. In closecollaboration with the farmer, a mixture of 22 g a.i. dimethoateand 14 g a.i. fenitrothion was applied once during each croppingseason. Water samples for pesticide analysis were taken frompaddy and pond surface water; soil samples were taken from theupper 5 cm of the paddy field shortly before and 1 h, 1, 2, 3, 4, 5, 6,9 and 14 days after pesticide application. A thorough evaluationand discussion of the measurement results can be found inAnyusheva et al.6,36

2.2 Model philosophyApproaches and algorithms to model the key underlyingbiogeochemical processes were mainly adopted from theliterature (e.g. Williams et al.25). Our model consists of threemajor sub-modules focusing on water balance and pesticidedynamics in the paddy field and in the fish pond. The modelruns in hourly time steps. Results are valid at the plot scale(paddy field and fish pond scale). All fluxes are one-dimensional,either in the vertical or in the horizontal direction. Input datainclude: (1) hourly evapotranspiration (ET0) or weather (e.g.hourly air temperature, solar radiation, wind speed, humidity)and site-specific data (e.g. elevation, longitude, latitude) tocalculate ET0, (2) precipitation, (3) soil-related variables (e.g. bulkdensity), (4) water management information, and (5) pesticideapplication data.

In our model the relevant components of pesticide behaviourare represented by a total of seven stocks (Fig. 1). The temporalchange of each stock is formulated as ordinary differential equation(ODE). The resulting set of ODEs was numerically solved usingthe Runga Kutta (4th order) method37 as implemented in theODE solver Berkeley Madonna (BM) (Version 8.0.1).38 BerkeleyMadonna was originally developed to enhance the efficiency ofmodels developed in the STELLA language. The developers addedthe language-unique features and a user-friendly graphical userinterface.38 Berkeley Madonna has been proven to be a powerfultool for setting up dynamic models in various scientific disciplines(e.g. Ingwersen et al.,39 Kahl et al.40 Treydte et al.41).

The ODEs used in our model are listed in Table 1. Equationsapplied to calculate the mass fluxes linking the seven stocks aregiven in Table 2. They are described in detail in the followingsections.

In principle, our model is based on the following assumptions:(1) sprayed pesticides are either intercepted by the canopy,lost by wind drift or directly reach the paddy surface water;(2) pesticides are immediately mixed when entering anycompartment; (3) the paddy and pond compartments consistof a surface water layer and a sub-surface soil layer: the sub-surface layer is 5 cm thick and comprises both soil water andsoil matrix; and (4) the key water budget processes – irrigationand drainage – are not explicitly calculated but are based onmeasured data and can vary depending on management activitiesof farmers.

2.2.1 Water balanceWe use a storage model concept to calculate the water balanceof both the paddy field and the fish pond. The change in waterstorage (Vpa) over time is equal to the sum of inflows minus the sumof outflows. Water inflow to the paddy field comprises irrigation

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Modelling the fate of pesticides in Vietnamese rice fields www.soci.org

Figure 1. Conceptual pools and fluxes of the pesticide model. Boxes indicate reservoirs. Pesticide fluxes are labeled according to Table 2.

Table 1. Differential equations used to calculate pesticide dynamics in various environmental compartments

Definition Equation No.

Paddy field

Pesticide mass on foliage (mg)∂Mf∂t = If _ap − If _dg − If _wo 1.1

Pesticide mass in surface water (mg)∂Mpa_aq

∂t = Ipa_ap + If _wo − Ipa_dg − Ipa_vo − Ipa_dr − Ipa_sp 1.2

Pesticide mass absorbed to soil solid particles (mg)∂Mpa_sd

∂t = Isw_sd 1.3

Pesticide mass in soil water (mg)∂Mpa_sw

∂t = Ipa_sp − Isw_dg − Isw_up − Isw_sd − Isw_lea 1.4

Fish pond

Pesticide mass in surface water (mg)∂Mpd_aq

∂t = Ipa_dr − Ipd_vo − Ipd_dr − Ipd_sp − Ipd_dg 1.5

Pesticide mass absorbed to soil solid particles (mg)∂Mpd_sd

∂t = Ipd_sd 1.6

Pesticide mass in soil water (mg)∂Mpd_sw

∂t = Ipd_sp − Ipd_dg_sw − Ipd_sd + Ipd_sw_lea 1.7

Equations for the respective fluxes are given in Table 2.

(Vir_pa) and precipitation (Vpr_pa). Water outflow is the result ofevapotranspiration (Vet_pa), drainage to the fish pond (Vdr_pa) andinfiltration (Vsp_pa):

∂Vpa

∂t= Vir_pa + Vpr_pa − Vdr_pa − Vet_pa − Vsp_pa (1)

For the water storage (Vpd) in the fish pond, water inflow ismade up of the drainage from the paddy field (Vdr_pa) andprecipitation (Vpr_pd), while water outflow includes evaporation(Vep_pd), drainage to the receiving stream (Vdr_pd) and infiltration(Vsp_pd):

∂Vpd

∂t= Vdr_pa + Vpr_pd − Vdr_pd − Vep_pd − Vsp_pd (2)

Irrigation, drainage and precipitation were measured. Hourlyreference evapotranspiration (ET0) was determined according to

the FAO Penman–Montheith method:42

ET0 = 0.408· �· (Rn − G) + γ · 900T+273 u2 (es − ea)

� + γ · (1 + 0.34· u2)(3)

where � denotes the slope of the vapour pressure curve (kPa◦C−1), Rn is the net radiation at the crop surface (MJ m−2 h−1),G the soil heat flux density (MJ m−2 h−1), γ the psychometricconstant (kPa ◦C−1), T the air temperature (◦C), u2 the windspeed at 2 m height (m s−1), and es and ea denote the saturationand actual vapour pressure, respectively (kPa). The effective cropevapotranspiration for the paddy is then calculated by multiplyingET0 with a crop coefficient of 1.2.42 The infiltration rate wascalculated from the mass balance.

2.2.2 Pesticide dynamicsPesticide transport and behaviour is controlled by the followingkey processes: interception by foliage, wash-off from foliage to

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Table 2. Differential equation used for calculate fluxes described in various environmental compartments

Definition Equation No.

Paddy fieldDegradation rate in paddy soil water (mg h−1) Isw_dg = Mpa_sw .K1

w 2.1

Degradation rate in surface water (mg h−1) Ipa_dg = Mpa_aq.K1w 2.2

Degradation rate on foliage (mg h−1) If _dg = Mf .K1f 2.3

Pesticide loss to deeper soil layer though seepage (mg h−1) Isw_lea = Vsp_paMpa_sw

θ.arpa .SLd2.4

Pesticide loss through paddy drainage (mg h−1) Ipa_dr = Vdr_paMpa_aq

Vpa2.5

Pesticide loss though paddy seepage (mg h−1) Ipa_sp = Vsp_paMpa_aq

Vpa2.6

Pesticide loss by wind drift (mg h−1) Idrift = APP · DRIFT 2.7

Pesticide mass intercepted by foliage (mg h−1) If _ ap = APP(1 − DRIFT)ITC 2.8

Pesticide mass intercepted by surface water (mg h−1) Ipa _ ap = APP(1 − DRIFT)(1 − ITC) 2.9

Pesticide uptake through transpiration (mg h−1) Isw_up = ETr1000 arpa.Mpa_sw .swup.S 2.10

Sorption–desorption between soil water and paddy soil solid particles (mg h−1) Isw_sd = α(

kd.Cnfpa_sw − Cpa_sd

)arpa.SLd.ρs.1000 2.11

Volatilisation rate (mg h−1) Ipa_vo = KvoMpa_aq

WLpa2.12

Wash-off rate from foliage (mg h−1) If _ wo = Mf · WASH · PCP 2.13

Fish pondDegradation rate in soil water (mg h−1) Ipd_dg_sw = Mpd_sw · K1

w 2.14

Degradation rate in surface water (mg h−1) Ipd_dg = Mpd_aq· K1w 2.15

Pesticide loss to deeper soil layer through seepage (mg h−1) Ipd_sw_lea = Vsp_paMpd_sw

θ ·arpd ·SLd2.16

Pesticide loss through pond drainage (mg h−1) Ipd_dr = Vdr_pdMpd_aq

Vpd2.17

Pesticide loss through seepage (mg h−1) Ipd_sp = Vsp_pdMpd_aq

Vpd2.18

Sorption–desorption between soil water and pond soil solid particles (mg h−1) Ipd_sd = α(

kd· Cnfpd_sw − Cpd_sd

)arpd· SLd· ρs· 1000 2.19

Volatilisation rate (mg h−1) Ipd_vo = KvoMpd_aq

WLpd2.20

Equation numbers (No.) are given for reference.

paddy surface water driven by precipitation, volatilisation frompaddy and pond water, adsorption to and desorption from solidparticles, leaching from paddy and pond water and degradationon foliage, in paddy field pond water and soil (Fig. 1).

The loss of pesticides by volatilisation is calculated as a functionof evaporation (Table 2, Eq. 2.12). The pesticide fraction lost bywind drift is specified by the user (Table 2, Eq. 2.7). The allocationbetween crop foliage and paddy water is based on the interceptionpotential of foliage at the time of application (Table 2, Eqs 2.8 and2.9).25 The wash-off of pesticides from the foliage to the paddysurface water is calculated as a function of precipitation accordingto Cohen and Steinmetz (Table 2, Eq. 2.13).43 In the paddy water,pesticides are exposed to degradation, leaching, volatilisation ortransport to the fish pond by surface drainage (Table 1, Eq. 1.2).Degradation is assumed to follow a first order reaction (Table 2,Eq. 2.2). Pesticide loss from the paddy to the pond by drainageis calculated directly by multiplying measured drainage rates andsimulated pesticide concentrations in the paddy surface water(Table 2, Eq. 2.5). In the soil compartment, pesticides may besorbed to and desorbed from the soil matrix. Equilibrium sorptionis represented by a Freundlich sorption isotherm (Table 2, Eq. 2.11).Pesticides dissolved in the soil water are leached to deeper soillayers, degraded or taken up by crop roots through transpiration.The latter process is considered with systemic pesticides only.Pesticides enter the surface water compartment of the pondby drainage from the paddy field. In the fish pond, pesticide

processes are similar to those in the paddy water compartment(Table 2). The pesticides are exposed to degradation, leaching,volatilisation and transport to the river by surface drainage(Table 1, Eq. 1.7).

2.3 Sensitivity analysis and model calibrationSensitivity analysis and automatic parameter estimation wereperformed externally by linking our model with PEST (Model-Independent Parameter Estimation).44 PEST performs inversesimulations by minimising a weighted least-squares objectivefunction using the Gauss–Marquardt–Levenberg algorithm. PESTfurther calculates important statistics of the estimates, e.g. theparameter correlation matrix and linear confidence regions.To automate the data input during the parameter estimationprocess via the graphical user interface of Berkeley Madonna,we used the freeware scripting language AutoIT Version 3.0.45

Details of the linkage can be found in Ingwersen et al.39

and Lamers et al.46 PEST calculates composite parametersensitivities, si, as:44

si = (JtQJ

)0.5ii m−1

where J denotes the Jacobian matrix, Q stands for the co-factor matrix, a diagonal matrix whose elements are the squaredobservation weights, and m is the number of observationswith non-zero weights. Relative composite parameter sensitivities



Table 3. Definition of parameters, their relative sensitivities, default and calibrated value

Dimethoate Fenitrothion

Parameter Definition

Default

(value range)

Relative

sensitivity

Calibrated

value

Default

(value range)

Relative

sensitivity

Calibrated

value

App Application mass (mg) 22 000c — — 14 025c —

DRIFT Fraction loss by drift (%) 0.024 (0.02–0.028)d 0.001 — 0.024 (0.02–0.028)d 0.002 —

ITC Fraction of interceptionpotential of foliage (%)

0.425 (0.1–0.75)d 0.006 0.1 0.425 (0.1–0.75)d 0.038 0.232

Kd Water–sediment partitioncoefficient (mL g−1)

0.58 (0.28–0.88)e 0.011 0.88 5.41 (4.28–6.53)e 0.025 0.653

Kf Foliar decay rate constanta(h−1) 0.01 (0.009–0.011)f 0.0019 — 0.01 (0.009–0.011)f 0.019 —

Kw Water decay rate constantb(h−1) 0.048 (0.0001–0.96)g 0.078 0.04 1.045 (0.026–2.063)g j 0.14 0.107

n_f Freundlich exponent of sorptionisotherm (−)

1 (0.97–1.05)h 0.044 1.05 1 (0.86–1.1)h 0.061 0.96

S Pesticide index (1 = systemic;0 = non-systemic) (−)

1h — — 0h —

WASH Wash-off per mm ofprecipitation (mm−1)

0.025 (0.014–0.036)i 0.0023 0.036 0.025 (0.014–0.036)i 0.021 0.014

α_Di Rate coefficient (−) 0.001 0.0003 — 0.001 0.003 —

Default and value ranges were based on a literature review.

a Foliar decay rate constant: Kf =0.693/DT50_f

24 .

b Water decay rate constant: Kw =0.693/DT50_w

24 .c Measured.d Linders et al.60

e Calculated from Koc value reported by EFSA.56,57

f Willis and McDowell.67

g HSDB.48

h PPDB.54,55

i Cohen and Steinmetz.43

j Maguire and Hale.49

are then obtained by multiplying si by the magnitude of theparameter value. In this study, all observations were weightedequally. The increment used for calculation of derivatives withrespect to any parameter is calculated as 0.01 times the currentvalue of that parameter. More detailed information is given byDoherty.44

We tested the sensitivity of the following variables: (1) thepesticide fraction lost by wind drift (DRIFT), (2) the pesticidefraction intercepted by foliage (ITC), (3) Freundlich coefficient (Kd),(4) Freundlich exponent (nf), (5) pesticide decay rate on foliage(Kf), (6) pesticide decay rate in water (Kw), (7) pesticide wash-offrate from foliage (WASH), and (8) rate coefficient for sorption(α). We calibrated the model with three data sets of pesticideconcentrations in paddy surface water, pond surface water andpaddy soil measured during the spring rice crop season in 2008.For model calibration we normalised the observations betweenthe three data sets.

The model was then validated against an independent data setobtained during the subsequent summer/autumn season 2008.To assess model performance beyond the visual valuation, weused statistical criteria as proposed by Loague and Green.47 Thesecriteria were modelling efficiency (EF), root mean square error(RMSE), and coefficient of residual mass (CRM):

EF =

(n∑

i=1

(Oi − O

)2 −n∑

i=1

(Pi − Oi)2

)

n∑i=1

(Oi − O

)2

(4)

RMSE =√√√√(

1

n

n∑i=1

(Pi − Oi)2

).100

Oi(5)

CRM =

n∑i=1

Oi −n∑

i=1

Pi

n∑i=1

Oi

(6)

where Pi are predicted values, Oi observed values, O the meanof the observed data, and n the number of observations. If allpredicted values match the observations, the evaluation wouldyield: EF = 1.0; RMSE = 0.0; and CRM = 0.0. Note that both EF andCRM can become negative.

3 RESULTS3.1 Sensitivity analysisThe simulated pesticide concentrations were sensitive to all testedparameters. Table 3 lists the relative parameter sensitivities.For dimethoate, parameter sensitivities were clearly rankedin the order: Kw > nf > Kd > ITC > WASH > Kf > DRIFT > α. Forfenitrothion the ranking yields a slightly different result:Kw > nf > ITC > Kd > WASH > Kf > α > DRIFT. In summary, thesorption rate coefficient (α), the pesticide fraction lost by wind drift(DRIFT) and the decay rate on the foliage (Kf) are the least sensitive.We therefore excluded these parameters from the calibration.



Figure 2. Measured (O) and simulated ( – ) concentration of dimethoate (left) and fenitrothion (right) in the calibration period (spring crop).

Hence, the following five parameters were used to calibrate themodel: ITC, Kd, Kw, nf and WASH.

3.2 Model calibrationThe results of the model calibration for pesticide concentrationsin all three compartments are shown in Fig. 2. Concentrations ofdimethoate and fenitrothion in the paddy surface water peaked1 h after application. Maximum concentrations were 614 and266 µg L−1, respectively. Both peaks were accurately captured bythe model. After reaching the peak concentrations, both pesticidesdisappeared very rapidly from the paddy water, reaching lowconcentrations of 53 µg L−1 (dimethoate) and 10 µg L−1 (fenitroth-ion). Both the temporal pattern and the magnitude of pesticidedissipation are matched well by the simulation, yielding highmodelling efficiencies of 0.97 (dimethoate) and 0.96 (fenitrothion).

The measured pesticide concentrations in the paddy soilpeaked after 24 h, followed by an exponential decreaseto minimum concentrations of 44 µg kg−1 (dimethoate) and2 µg kg−1 (fenitrothion). The model roughly reproduced thetemporal concentration pattern, yielding modelling efficienciesof 0.42 (dimethoate) and 0.83 (fenitrothion). However, the modelunderestimated the peak dimethoate concentration by almost50%. Moreover, the subsequent dissipation was slightly over-estimated. The fenitrothion peak was reproduced better, but thelow concentration after 100 h were underestimated by the model.

Measured and simulated concentrations of dimethoate andfenitrothion in the pond surface water were in good agreement,yielding modelling efficiencies of 0.96 and 0.81, respectively(Table 4). One reason is that the pesticide concentration in the pond

surface water depends mainly on the drainage from the paddyfield. The measurements showed that (Fig. 2) both pesticides werenot detected in the pond water 48 h after application. After thepaddy water was discharged from the paddy field to the pond,the concentration of both pesticides rapidly increased, yieldingpeaks of 16 and 0.56 µg L−1. Simulated concentrations matchedfairly well the observations with regard to the temporal patternand the magnitude of concentrations. However, the simulatedpeak concentrations were much higher than the observed ones. Itis possible that the temporal resolution of the measurement wasinsufficient to capture such high concentration.

The modelling efficiencies calculated for the entire data set were0.82 and 0.87 for dimethoate and fenitrothion, respectively. Weconclude that measured and modelled pesticide concentrationswere in general agreement with regard to both magnitude andtemporal pattern.

3.3 Model validationMeasured and predicted pesticide concentrations duringvalidation are shown in Fig. 3. Simulated concentrations in thepaddy surface water matched the observations well, resulting inhigh modelling efficiencies (Fig. 3). The simulation captured peakconcentrations well. With regard to the paddy soil concentration,the agreement was less satisfying. The model systematicallyunderestimated dimethoate concentrations, which manifests itselfin a high positive value of CRM (0.7) and a drop in efficiency to0.08 (Table 4). The fenitrothion concentration in the paddy soilwas slightly underestimated, resulting in a modelling efficiency of0.33. The simulated dimethoate concentration in the pond water



Table 4. Model performance during calibration (spring crop) and validation (summer/autumn crop)

Dimethoate Fenitrothion

Statistical criteria Paddy water Paddy soil Pond water Total Paddy water Paddy soil Pond water Total

Calibration periodEF 0.97 0.42 0.96 0.82 0.96 0.83 0.81 0.87

RMSE 45.96 93.12 33.55 71.8 56.92 46.13 82.27 61.6

CRM −0.17 0.59 0.15 0.24 −0.22 0.17 0.06 0.05

Validation periodEF 0.94 0.08 0.14 0.42 0.93 0.33 0.76 0.76

RMSE 51.93 107.94 136.06 113.4 76.07 54.18 130.13 76.92

CRM 0.18 0.7 −0.38 0.24 −0.3 0.25 −0.54 0.01

EF, model efficiency.RMSE, root mean square error.CRM, coefficient of residual mass.

Figure 3. Model performance, measured (O) and simulated ( – ) concentration of dimethoate (left) and fenitrothion (right) in the validation period(autumn/summer crop).

matched the peak concentration quite well, but the subsequentconcentrations were not reproduced well, which manifests itself innegative value of CRM (−0.38) and a significant drop in efficiencyto 0.14. The simulation yielded better results with fenitrothion interms of magnitude and timing. The observed rapid decline offenitrothion concentration in pond water within 24 h was wellreproduced by the simulation.

In general, model validation showed smaller accuracies,reducing EF and increasing RMSE compared to the calibrationperiod. For the entire data set, EF slightly decreased to 0.76(fenitrothion) or dropped to 0.42 (dimethoate) (Table 4). Regarding

the pesticide concentration in surface water, however, modelaccuracy was satisfactory, expressing itself in a high EF value of0.93 for fenitrothion and 0.94 for dimethoate.

3.4 Scenario modellingThe validated model was applied to assess the effect of the closureperiod of the drainage between paddy field and fish pond. Weran the model with varying closure periods of the drainage afterpesticide application. The modelled scenarios were: no closing andclosure periods of 5, 10, 15, 20 and 25 h after application. Figure 4presents dimethoate and fenitrothion concentrations in the pond



Figure 4. Simulated concentrations of dimethoate (left) and fenitrothion (right) in the pond water for scenarios of 0, 5, 10, 15, 20, 25 h of closure afterpesticide application.

Figure 5. Application mass lost (%) from paddy field to fish pond, simulatedfor scenarios of 0, 5, 10, 15, 20, 25 h of closure after pesticide application.

water for the different scenarios. The results given in Fig. 5 areexpressed as the fractions of pesticide mass lost to the pond. Theconcentrations were significantly reduced with extended closingperiods. Pesticide losses can be reduced by a factor of 10 if thedrainage from the paddy field to the fishpond is closed for 25 hafter pesticide application. In conclusion, we strongly recommendclosing the drainage from the paddy field to the fish pond afterpesticide application for a period of at least one day.

4 DISCUSSIONThe simulations in both the calibration and validation periodsagreed well with measured concentrations in the paddy surfacewater yielding modelling efficiencies higher than 0.93. The modelsimulations, however, were less accurate with regard to paddy soilconcentrations. This may reflect a lack of precise infiltration data.The infiltration rate was not measured but calculated from thewater budget.

In general, the key properties of the two pesticides areavailable in the literature but were measured for differentenvironmental and infrastructural conditions, e.g. temperatureand pH. The most sensitive parameter was Kw. The half-life ofdimethoate in water ranges from 0.8 h (Kw = 0.96 h−1) to 230 days

(Kw = 0.0001 h−1) (Table 3).48 The optimised value is in themid-range. Kw of dimethoate was 0.04 h−1 (equivalence to half-lifein water = 0.72 days). The half-life of fenitrothion was reported as0.34 h49 and 1.1 days,48 corresponding to Kw values of 0.026 and2.063 h−1, respectively. The optimised value of Kw of fenitrothionwas 0.107 h−1, which corresponds to 0.27 days. The optimisedvalues show that dimethoate and fenitrothion degradation inpaddy water is fast. At the study site, the air temperature duringthe experiment was high, on average 25◦C. The pH of the paddysurface water was around 8. Sunlight in tropical regions potentiallyenhances the degradation of organophosphorus pesticides inwater.50 The fast degradation rate recorded in the present study isin line with results presented by Sethunathan,51 Matsumura52 andRacke et al.53 for tropical conditions. Luo et al.54 used a component-based modelling system to simulate the fate of five pesticides inrice fields in the USA. Using the registrant-submitted dissipationhalf-lives, the base model estimated pesticide concentrations inpaddy water within one order of magnitude of measured data.The authors conclude that the registrant-submitted maximumvalues for the aquatic dissipation half-lives might be an option forevaluating pesticides for regulatory purposes.

The optimised values of the Freundlich exponent (n_f) ofdimethoate and fenitrothion were 1.05 and 0.96, respectively. Thevalues are in the upper range of values reported for dimethoate(from 0.965 to 1.05) and in the range of values reported forfenitrothion (0.86 and 1.1).55,56

In our model, the Kd value (Freundlich coefficient) is calculatedfrom the mass fraction of organic carbon (1.7%) and Koc (organic-carbon sorption constant). According to EFSA,57 the Koc value ofdimethoate ranges from 16.25 to 51.88 mL g−1 which, in our soil,is equivalent to Kd values between 0.28 and 0.88 mL g−1 (Table 3).The Koc value of fenitrothion ranged from 252 to 384 mL g−1,corresponding to a Kd value between 4.28 and 6.53.58 Accordingto a review by Wauchope et al.,59 however, the ratio of minimumand maximum vary between 3 and 10. The optimised Kd value offenitrothion was 0.653, which is ten times lower than the maximumliterature data. Note that under the calibration conditions the Kdvalue of fenitrothion was small and outside the range of literaturevalues. The optimised parameter ITC (pesticide fraction interceptedby foliage) is close to the lower bound of values reported by



Linders et al.60 (Table 3). The optimised value for the parameterWASH (wash-off fraction of precipitation) differed between the twopesticides: the WASH value of dimethoate was close to the upperbound (0.036), while that of fenitrothion remained in the lowerbound (0.014). The difference may be due to water solubility. Thewater solubility of dimethoate is 24 g L−1 and hence much higherthan fenitrothion (0.021 g L−1).61,62 The optimised WASH valuesagree with Cohen and Steinmetz,43 who found that more highlysoluble insecticides (e.g. methyl parathion) were washed off moreeasily than insecticides with lower solubility (e.g. fenvalerate andflucythrinate).

In the study area, many fish ponds are linked to paddy fieldsdirectly receiving the pesticide-loaded drainage water. Severalstudies highlight the influence of water management practiceson pesticide loss from irrigated paddy fields. In Asia, water-holding period and irrigation practices were identified to bethe key for controlling pesticide export from paddy fields bysurface water.34 In south-east Asia in general and in Vietnamin particular, however, the common management practice doesnot include closing the drainage from the field after pesticideapplication. Based on our scenario simulation, a minimum closureperiod of 1 day is recommended to considerably reduce therisk of surface water pollution. Watanabe et al., for example,recommended a field closure period of 10 days after pesticideapplication to be a good agricultural practice for paddy cultivationin Japan.63 Under south European conditions, field closure periodscommonly range between 2 and 7 days, depending on localmanagement practices.64 For the pesticide thiobencarb appliedon paddy fields in Australia, Quayle et al. even reported anappropriate closure period of 28 days for minimising the pesticiderun-off. 65 The State of California in the US required field closureperiods for herbicides and insecticides of up to 30 and 24 days,respectively.66

5 CONCLUSIONSThe present study shows that our model is capable of simulatingthe fate of pesticides in paddy field–fish pond farming systems.The software linkage between PEST and Berkeley Madonna hasproven to be a useful tool for automatic model calibration. Under awide range of environmental conditions, the model can be appliedto assess and evaluate management strategies aiming to reducepesticide losses from paddy fields to adjacent compartments. Thefurther development will focus on implementing an algorithm forsimulating pesticide leaching.

Another field of application, planned for the future, is toassess pesticide loads as a function of pesticide application. Suchfunctions can then be used to agree on pesticide reductionstrategies in willingness-to-pay schemes.

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