Modeling Approaches for Pesticide Exposure Assessment in Rice Paddies

Luo, Yuzhou; Williams, W. M.; Young, Dirk F.; Watanabe, Hirozumi; Boulangé, Julien; Ritter, Amy; Thai, Phong K.

doi:10.1021/bk-2011-1075.ch014

Cited by 8 publications

(10 citation statements)

References 34 publications

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“…These can also be utilized as a fundamental dataset to compare the experimental results obtained in the paddy fields affected by site‐specific hydrology and management practices. In such situations, mathematical models can be useful because they can incorporate the effects of actual environmental conditions and management practices, especially their temporal variations, on pesticide fate and behavior in a rice paddy …”

Section: Introductionmentioning

confidence: 99%

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Inverse analysis to estimate site‐specific parameters of a mathematical model for simulating pesticide dissipations in paddy test systems

Kondo¹,

Wakasone²,

Iijima³

et al. 2019

Pest Management Science

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BACKGROUND In Japan, while experimental data for the dissipation behavior of paddy pesticides under a standardized test system are available, the application of a mathematical model is limited. This paper proposes a new model calibration procedure for inversely deriving the model parameters from the experimental data. This procedure is tested in the open software R by running an improved Pesticide Concentration in Paddy Field‐1 (PCPF‐1) model with R packages to analyze the dissipation of simetryn and molinate in flooded lysimeters and paddy fields. RESULTS The model fitting was performed by a random minimization routine. Furthermore, the uncertainties of the model parameters envisioned by the global sensitivity analysis were successfully reduced using the Markov chain Monte Carlo technique. The calibrated simulation was validated at each test plot by confirming multiple statistical indices (i.e. Nash–Sutcliffe efficiency 0.88–1.00, percent bias <±5%). The dissipation pathways of two herbicides were quantitatively clarified by the mass balance of calibrated simulations and the effect of the unexpected herbicide runoff was quantified. The case study showed that the adjustment of daily percolation rate in the lysimeter experiment is the key to simulate the actual paddy field condition more accurately, especially in a case where pesticides show higher water solubility and soil mobility. CONCLUSION The developed procedure can analyze the experimental data with acceptable accuracy and extract the unobservable information quantitatively. Our approach is applicable to the optimization of not only the model but also future experimental design. © 2018 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%

“…One reason for this may be the fact that user‐defined or empirical parameters are intensively used in paddy pesticide modeling, indicating that the models should be carefully calibrated and validated under site‐specific conditions . The user has two options for model calibration: manual calibration and automatic calibration.…”

Section: Introductionmentioning

confidence: 99%

Inverse analysis to estimate site‐specific parameters of a mathematical model for simulating pesticide dissipations in paddy test systems

Kondo¹,

Wakasone²,

Iijima³

et al. 2019

Pest Management Science

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show abstract

“…The mathematic implementations of these processes, i.e., modeling components, are identified based on the previous review and evaluation (Luo, 2011;Luo et al, 2011). The following ten modeling components are generally covered in popular rice pesticide models, including the Rice Water Quality Model (RICEWQ) , the Simulation Model for Pesticide Concentrations in Paddy Field (PCPF) , and the Pesticide in Flooded Application Model (PFAM) (Young, 2011) temperature effects on metabolism.…”

Section: Environmental Description and Pesticide Fate Processesmentioning

confidence: 99%

“…Exposure assessments at higher tiers are generally based on advanced riceepesticide models which simulate physically based processes of pesticide fate and transport in actual or hypothesized field conditions. In our previous studies (Luo, 2011;Luo et al, 2011), existing riceepesticide models were reviewed according to their similarity and differences in simulating pesticide fate and transport in rice paddy environment. Most of the models could be calibrated to reasonably capture pesticide dynamics in rice paddies (Johnson, 1991;Karpouzas et al, 2006;Watanabe and Takagi, 2000;Watanabe et al, 2006;Williams et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling complexity in simulating pesticide fate in a rice paddy

et al. 2012

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“…Many models for predicting the fate and transport of pesticide from rice paddies with different scales are available. For the rice paddy plot, there are several models available, such as (i) the Rice Water Quality (RICEWQ) model, (ii) the Pesticide in Flooded Application Model (PFAM), (iii) the Rice model v1.0, (iv) the Pesticide Paddy Field (PADDY) model, and (v) the Pesticide Concentrations in Paddy Field (PCPF‐1) model, which have been applied previously to study the fate of pesticides . For block‐scale applications, the PCPF‐B can be used .…”

Section: Introductionmentioning

confidence: 99%