Biochar obtained from eucalyptus, rice hull, and native bamboo as an alternative to decrease mobility of hexazinone, metribuzin, and quinclorac in a tropical soil.

Mobile herbicides have a high potential for groundwater contamination. An alternative to decrease the mobility of herbicides is to apply materials with high sorbent capacity to the soil, such as biochars. The objective of this research was to evaluate the effect of eucalyptus, rice hull, and native bamboo biochar amendments on sorption and desorption of hexazinone, metribuzin, and quinclorac in a tropical soil. The sorption-desorption was evaluated using the batch equilibrium method at five concentrations of hexazinone, metribuzin, and quinclorac. Soil was amended with eucalyptus, rice hull, and native bamboo biochar at a rate of 0 (control-unamended) and 1% (w w-1), corresponding to 0 and 12 t ha-1, respectively. The amount of sorbed herbicides in the unamended soil followed the decreasing order: quinclorac (65.9%) > metribuzin (21.4%) > hexazinone (16.0%). Native bamboo biochar provided the highest sorption compared to rice hull and eucalyptus biochar-amended soils for the three herbicides. The amount of desorbed herbicides in the unamended soil followed the decreasing order: metribuzin (18.35%) > hexazinone (15.9%) > quinclorac (15.1%). Addition of native bamboo biochar provided the lowest desorption among the biochar amendments for the three herbicides. In conclusion, the biochars differently affect the sorption and desorption of hexazinone, metribuzin, and quinclorac mobile herbicides in a tropical soil. The addition of eucalyptus, rice hull, and native bamboo biochars is a good alternative to increase the sorption of hexazinone, metribuzin, and quinclorac, thus, reducing mobility and availability of these herbicides to nontarget organisms in soil.

Interaction between herbicides applied in mixtures alters the conception of its environmental impact.

Herbicide mixtures have often been used to control weeds in crops worldwide, but the behavior of these mixtures in the environment is still poorly understood. Laboratory and greenhouse tests have been conducted to study the interaction of the herbicides diuron, hexazinone, and sulfometuron-methyl which have been applied alone and in binary and ternary mixtures in the processes of sorption, desorption, half-life, and leaching in the soil. A new index of the risk of leaching of these herbicides has also been proposed. The sorption and desorption study has been carried out by the batch equilibrium method. The dissipation of the herbicides has been evaluated for 180 days to determine the half-life (t1/2). The leaching tests have been carried out on soil columns. The herbicides isolated and in mixtures have been quantified using ultra-high performance liquid chromatography coupled to the mass spectrometer. Diuron, hexazinone, and sulfometuron-methyl in binary and ternary mixtures have less sorption capacity and greater desorption when compared to these isolated herbicides. Dissipation of diuron alone is slower, with a half-life (t1/2) = 101 days compared to mixtures (t1/2 between 44 and 66 days). For hexazinone and sulfometuron-methyl, the dissipation rate is lower in mixtures (t1/2 over 26 and 16 days), with a more pronounced effect in mixtures with the presence of diuron (t1/2 = 47 and 56 and 17 and 22 days). The binary and ternary mixtures of diuron, hexazinone, and sulfometuron-methyl promoted more significant transport in depth (with the three herbicides quantified to depth P4, P7, and P7, respectively) compared to the application of these isolated herbicides (quantified to depth P2, P4, and P5). Considering the herbicides' desorption and solubility, the new index proposed to estimate the leaching potential allowed a more rigorous assessment concerning the risk of leaching these pesticides, with hexazinone and sulfometuron-methyl presenting a higher risk of contamination of groundwater.

Machine learning models as an alternative to determine productivity losses caused by weeds.

BACKGROUND: Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models have a low generalization capacity to understand different scenarios. However, computational development facilitated the implementation of supervised machine learning models, as artificial neural networks (ANNs), capable of understanding complex relationships. The objectives of our work were to evaluate the ability of ANNs to estimate yield losses in onion (model crop) due to weed interference and compare with multiple linear regression (MLR) and empirical models. RESULTS: MLR constructed from non-destructive and destructive methods show R2 and root mean square error (RMSE) values varying between 0.75% and 0.82%, 13.0% and 19.0%, respectively, during testing step. The ANNs has higher R2 (higher than 0.95) and lower RMSE (less than 6.95%) compared to MLR and empirical models for training and testing steps. ANNs considering only the coexistence period and system have similar performance to MLR models. However, the insertion of variables related to weed density (non-destructive ANN) or fresh matter (destructive ANN) increases the predictive capacity of the networks to values close to 99% correct. CONCLUSION: The best performing ANNs can indicate the beginning of weed control since they can accurately estimate losses due to competition. These results encourage future studies implementing ANNs based on computer vision to extract information about the weed community.

Impact of Pyrolysis Temperature on the Properties of Eucalyptus Wood-Derived Biochar.

Pyrolysis conditions directly influence biochar properties and, consequently, influence the potential use of biochar. In this study, we evaluated the effects of different pyrolysis temperatures (450, 550, 650, 750, 850, and 950 °C) on the hydrogen potential, electrical conductivity, ash content, yield, volatile matter content, elemental analysis, Fourier-transform infrared spectroscopy results, X-ray diffraction results, scanning electron microscopy results, specific surface area, and micropore volume of eucalyptus wood-derived biochar. The degree of linear association between pyrolysis temperatures and biochar properties was examined using the Pearson correlation coefficient. The results showed a positive correlation of the pyrolysis temperature with the hydrogen potential value, electrical conductivity, and elemental carbon. There was a negative correlation of the pyrolysis temperature with the yield, volatile matter content, elemental oxygen, elemental hydrogen, surface area, aromaticity, hydrophilicity, and polarity indexes. The Fourier-transform infrared spectroscopy data indicated an increase in aromaticity and a decrease in the polarity of high-temperature biochar. The increased pyrolysis temperature caused the loss of cellulose and crystalline mineral components, as indicated by X-ray diffraction analysis and scanning electron microscopy images. These results indicated that changing the pyrolysis temperature enables the production of biochar from the same raw material with a wide range of physicochemical properties, which allows its use in various types of agricultural and environmental activities.

Multivariate analysis and multiple linear regression as a tool to estimate the behavior of hexazinone in Brazilian soils.

Weed control efficiency and the environmental contamination potential of herbicides depend on soil sorption and desorption. Among the indexes that evaluate the soil adsorption processes, the coefficients sorption (Kfs) and desorption (Kfd) obtained by Freundlich isotherms can provide accurate information about the behavior of an herbicide in the soil. The values of Kfs and Kfd of an herbicide vary according to the physicochemical characteristics of the soil, so it is possible to estimate these coefficients with high precision if good predictive mathematical models are constructed. Therefore, our objective aimed to evaluate the use of multiple regression models (MLR) associated with multivariate techniques to estimate the coefficient Kfs and Kfd for the hexazinone based on the chemical and physical attributes of soils. The correlation analyses, principal components, and clustering analysis allowed the multiple linear regression technique to generate models with higher adjustment coefficient (R2) for Kfs (0.73 to 0.99) and Kfd (0.94 to 0.99), and lower root mean squared error (RMSE) for Kfs (0.003 to 0.065) and Kfd (0.018 to 0.120). Regression models created from groups of soils showed greater prediction performance for Kfs and Kfd. The organic matter followed by the cation exchange capacity was the most important attributes of soils in sorption and desorption processes of hexazinone.

Use of neural networks to estimate the sorption and desorption coefficients of herbicides: A case study of diuron, hexazinone, and sulfometuron-methyl in Brazil.

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.