Universidade Federal Rural do Semi-Árido, Centro de Ciências Vegetais, Departamento de Ciências Agronômicas e Florestais, Av. Francisco Mota, 572, CEP 59625-900, Mossoró, RN, Brazil.
Universidade Federal Rural do Semi-Árido, Centro de Ciências Vegetais, Departamento de Ciências Agronômicas e Florestais, Av. Francisco Mota, 572, CEP 59625-900, Mossoró, RN, Brazil.
Chemosphere. 2019 Dec;236:124333. doi: 10.1016/j.chemosphere.2019.07.064. Epub 2019 Jul 11.
The use of herbicides in Brazil has been carried out based on the manufacturer's recommendation, often disregarding the high variability of soil attributes. The use of statistical methods to predict the herbicide retention processes in the soil can contribute to the improvement of weed control efficiency associated with the lower risk of environmental contamination. This research evaluated the use of Artificial Neural Networks (ANNs) to predict soil sorption and desorption, as well as the environmental contamination potential of diuron, hexazinone and sulfometuron-methyl herbicides in Brazilian soils. The sorption and desorption coefficients of the three herbicides were determined in laboratory tests for 15 soils from different Brazilian states. To predict the sorption and desorption of diuron, hexazinone and sulfometuron-methyl were used a multilayer perceptron ANNs (MLP). The inputs were the characteristics of the herbicides and the physical and chemical attributes of the soils, and the outputs of were the sorption and desorption coefficients (Kfs and Kfd). The risk of leaching of diuron, hexazinone, and sulfometuron-methyl herbicides were evaluated considering the sorption values observed and those estimated by the models. The Artificial Neural Network (ANN) models were efficient for the prediction of sorption and desorption of diuron, hexazinone, and sulfometuron-methyl herbicides. The physicochemical properties of the herbicides were more important for the modeling of multilayer perceptron ANNs than the soil attributes. The herbicides diuron, hexazinone, and sulfometuron-methyl have a high potential risk for contamination of groundwater in different Brazilian states.
巴西使用除草剂是基于制造商的建议进行的，往往忽略了土壤属性的高度可变性。使用统计方法来预测除草剂在土壤中的保留过程，可以有助于提高与降低环境污染风险相关的杂草控制效率。本研究评估了人工神经网络（ANNs）在预测巴西土壤中敌草隆、六嗪酮和磺酰脲甲酯除草剂的土壤吸附和解吸以及环境污染潜力方面的应用。在实验室测试中，为来自巴西不同州的 15 种土壤确定了这三种除草剂的吸附和解吸系数。为了预测敌草隆、六嗪酮和磺酰脲甲酯的吸附和解吸，使用了多层感知器人工神经网络（MLP）。输入是除草剂的特性以及土壤的物理和化学特性，输出是吸附和解吸系数（Kfs 和 Kfd）。考虑到观察到的和模型估计的吸附值，评估了敌草隆、六嗪酮和磺酰脲甲酯除草剂淋失的风险。人工神经网络（ANN）模型可有效地预测敌草隆、六嗪酮和磺酰脲甲酯除草剂的吸附和解吸。对于多层感知器人工神经网络模型，除草剂的物理化学性质比土壤属性更为重要。敌草隆、六嗪酮和磺酰脲甲酯这三种除草剂在巴西不同州对地下水污染具有很高的潜在风险。
