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.
Calibration Cold Temperature