98%
921
2 minutes
20
The pesticide application method is one of the important factors affecting its effectiveness and residues, and the risk of pesticides to non-target organisms. To elucidate the effect of application methods on the efficacy and residue of cyenopyrafen, and the toxic effects on pollinators honeybees in strawberry cultivation, the efficacy and residual behavior of cyenopyrafen were investigated using foliar spray and backward leaf spray in field trials. The results showed that the initial deposition of cyenopyrafen using backward leaf spray on target leaves reached 5.06-9.81 mg/kg at the dose of 67.5-101.25 g a.i./ha, which was higher than that using foliar spray (2.62-3.71 mg/kg). The half-lives of cyenopyrafen in leaves for foliar and backward leaf spray was 2.3-3.3 and 5.3-5.9 d, respectively. The residues (10 d) of cyenopyrafen in leaves after backward leaf spray was 1.41-3.02 mg/kg, which was higher than that after foliar spraying (0.25-0.37 mg/kg). It is the main reason for the better efficacy after backward leaf spray. However, the residues (10 d) in strawberry after backward leaf spray and foliar spray was 0.04-0.10 and < 0.01 mg/kg, which were well below the established maximum residue levels of cyenopyrafen in Japan and South Korea for food safety. To further investigate the effects of cyenopyrafen residues after backward leaf spray application on pollinator honeybees, sublethal effects of cyenopyrafen on honeybees were studied. The results indicated a significant inhibition in the detoxification metabolic enzymes of honeybees under continuous exposure of cyenopyrafen (0.54 and 5.4 mg/L) over 8 d. The cyenopyrafen exposure also alters the composition of honeybee gut microbiota, such as increasing the relative abundance of Rhizobiales and decreasing the relative abundance of Acetobacterales. The comprehensive data on cyenopyrafen provide basic theoretical for environmental and ecological risk assessment, while backward leaf spray proved to be effective and safe for strawberry cultivation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.envpol.2024.123601 | DOI Listing |
PLoS One
August 2025
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.
Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers.
View Article and Find Full Text PDFPLoS One
May 2025
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
The four-rotor plant protection Unmanned Aerial Vehicle (UAV) is an important piece of equipment for the efficient plant protection field. However, the flight-coupled wind field is unclear, which has become the bottleneck factor limiting the improvement of the spray quality. When the multi-rotor plant protection UAV flew and sprayed, the liquid droplets were accelerated, the stems and branches were shaken, and the leaves were turned over in the disturbance area of the spiral backward flight-coupled wind field.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
July 2025
School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China. Electronic address:
FT-NIR and chemometrics are vital analytical tools for medicinal plant quality control. This study aimed to establish a rapid technique for quantifying sennoside A and B in Cassia plants (i.e.
View Article and Find Full Text PDFComput Biol Med
March 2025
Yuan Ze University, Artificial Intelligent Center, Taoyuan, 320, Taiwan. Electronic address:
A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of cardiovascular diseases. However, the olive oil industry confronts hurdles arising from olive tree diseases, despite the numerous health advantages associated with its consumption. In pursuit of research goals, this study endeavors to employ cutting-edge intelligent computing paradigms, specifically nonlinear autoregressive exogenous neural networks utilizing the Levenberg-Marquardt scheme (NNLMS), to comprehensively analyze the complex dynamic interactions of the fractional-order olive disease control (FO-ODC) model.
View Article and Find Full Text PDFPlants (Basel)
November 2024
Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia.
Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance.
View Article and Find Full Text PDF