Dynamic modeling of pesticide residue in proso millet under multiple application situations

Abstract

Proso millet (Panicum miliaceum L.) is a cereal crop with potential resistance to drought and heat stress, making it a promising alternative crop for regions with hot and dry climates. Because of its importance, it is crucial to investigate pesticide residues in proso millet and assess their potential risks to the environment and human health to protect it from insects or pathogens. This study aimed to develop a model for predicting pesticide residues in proso millet using dynamiCROP. The field trials consisted of four plots, with each plot containing three replicates of 10 m². The applications of pesticides were conducted two or three times for each pesticide. The residual concentrations of the pesticides in the millet grains were quantitatively analyzed using gas and liquid chromatography-tandem mass spectrometry. The dynamiCROP simulation model, which calculates the residual kinetics of pesticides in plant-environment systems, was employed for predicting pesticide residues in proso millet. Crop-specific, environment-specific, and pesticide-specific parameters were utilized to optimize the model. Half-lives of pesticides in grain of proso millet, which were needed to input for dynamiCROP, were estimated using a modified first-order equation. Proso millet-specific parameters were obtained from previous studies. The accuracy of the dynamiCROP model was assessed using statistical criteria, including the coefficient of correlation (R), coefficient of determination (R²), mean absolute error (MAE), relative root mean square error (RRMSE), and root mean square logarithmic error (RMSLE). The model was then validated using additional field trial data, which showed that it could accurately predict pesticide residues in proso millet grain under different environmental conditions. The results demonstrated the accuracy of the model in predicting pesticide residues in proso millet after multiple applications.

Keywords: DynamiCROP; Modified first-order equation; Panicum miliaceum L.; Pesticide kinetics; Real agricultural situation; Residual prediction model.