RESUMO
BACKGROUND: The citrus red mite, Panonychus citri (McGregor) is a globally distributed agricultural pest. Of late, resistance to common acaricides has raised concerns that chemical control of P. citri is an inefficient means of control. Fluralaner, a highly toxic isoxazoline insecticide used to treat various ectoparasites, presents one potential alternative. However, little information has been reported about the effect of fluralaner on the citrus red mite. This study aims to evaluate the toxicity, sublethal and transgenerational effects of fluralaner on P. citri. RESULTS: In both laboratory and field populations of P. citri, we found fluralaner to be more toxic than conventional alternatives, including fenpropathrin, bifenazate, azocyclotin and chlorpyrifos. Interestingly, fluralaner proved more toxic to female adults than to the eggs of P. citri, with median lethal concentrations (LC50 ) of 2.446 and 122.7 mg L-1 , respectively. Exposure to sublethal concentrations of fluralaner (LC10 , LC20 and LC30 ) significantly reduced the fecundity and longevity of female adults P. citri individuals. Although concentrations of fluralaner applied to the parental female adults (F0 ) led to some changes in the developmental parameters, there were no significant changes in most of the life table parameters or population growth of the F1 generation. CONCLUSION: Our results indicate that fluralaner is highly toxic to P. citri, and a significant sublethal effect on F0 could suppress the population growth of P. citri, but not for F1 . Fluralaner may be considered as a pesticide for the future management of the citrus red mite. © 2024 Society of Chemical Industry.
RESUMO
Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.
