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Predicting rice pesticide fate and transport following foliage application by an updated PCPF-1 model

机译:通过更新的PCPF-1模型预测叶片应用后的稻瘟病和运输

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摘要

The Pesticide Concentration in Paddy Field (PCPF-1) model has been successfully used to predict the fate and transport of granular pesticides applied to the paddy fields. However, it is not applicable for pesticides in foliar formulation while previous studies have reported that foliar application may increase the risks of rice pesticide contamination to the aquatic environment due to pesticide wash-off from rice foliage. In this study, we developed and added a foliar application module into the PCPF-1 model to improve its versatility regarding pesticide application methods. In addition, some processes of the original model such as photodegradation were simplified. The updated model was then validated with data from previous studies. Critical parameters of the model were calibrated using the Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm. The calibrated model simulated pesticide dissipation trend and concentrations with moderate accuracy in the two paddy compartments including rice foliage and paddy water. The accuracy of the predicted soil concentrations could not be evaluated since no observed data were available. Although the p-factor and r-factor obtained using the SUFI2 algorithm indicated that the uncertainty encompassed in the predicted concentrations was rather high, the daily predicted pesticide concentrations in rice foliage and paddy water were satisfactory based on the NSE values (0.36-0.89). The updated PCPF-1 model is a flexible tool for the environmental risk assessment of pesticide losses and the evaluation of agricultural management practices for mitigating pesticide pollution associated with rice production.
机译:稻田(PCPF-1)模型中的农药浓度已成功用于预测应用于稻田的粒状农药的命运和运输。然而,它不适用于叶面配方中的农药,而前一项研究报告说,由于稻草的农药冲洗,叶面申请可能会增加水稻农药污染的风险。在这项研究中,我们开发并添加了一个叶面应用模块进入PCPF-1模型,以改善其关于农药应用方法的多功能性。另外,简化了诸如光降解的原始模型的一些过程。然后使用以前研究的数据验证更新的模型。使用顺序不确定性拟合版本2(SUFI-2)算法进行校准模型的临界参数。校准的模型模拟了两种稻草舱中适度精度的杀虫剂耗散趋势和浓度,包括稻草和水稻水。由于没有可用的数据,因此无法评估预测的土壤浓度的准确性。虽然使用SUFI2算法获得的p系数和R型因子表明预测浓度所包围的不确定性相当高,但在水稻叶子和水稻水中的日常预测农药浓度基于NSE值(0.36-0.89)令人满意。更新的PCPF-1型号是对农药损失环境风险评估的灵活工具,以及评估减轻与水稻生产相关的农药污染的农业管理实践。

著录项

  • 来源
    《Journal of Environmental Management》 |2021年第1期|111356.1-111356.11|共11页
  • 作者单位

    United Graduate School of Agricultural Science Tokyo University of Agriculture and Technology Tokyo 183-8509 Japan Research Center for Climate Change Nong Lam University HoChiMinh 700000 Viet Nam;

    Center for Global Environmental Research National Institute for Environmental Science Onogawa 16-2 Tsukuba Japan;

    Queensland Alliance for Environmental Health Sciences The University of Queensland 20 Cornwall Street Woolloongabba Queensland 4102 Australia;

    Institute of Agricultural Machinery National Agriculture and Food Research Organization 1-40-2 Nisshin Kita-ku Saitama City Japan;

    Graduate School of Agriculture Tokyo University of Agriculture and Technology Tokyo 185-8509 Japan;

    Hazardous Chemicals Division Institute for Agro-Environmental Sciences National Agriculture and Food Research Organization 3-1-3 Kannondai Tsukuba Ibaraki 305-8604 Japan;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PCPF-1; Pesticide fate and transport; SUFI-2; Foliar application; Tricyclazole;

    机译:PCPF-1;农药命运和运输;SUFI-2;叶面申请;三环唑.;

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