The tobacco whitefly, Bemisia tabaci, is a notorious pest affecting various crops globally, and it exhibits high levels of resistance to various insecticides.  Afidopyropen is a recently commercialized pyropene insecticide for B. tabaci control with high selectivity and a novel mode of action.  We previously identified a high level of afidopyropen resistance in a field-collected population after selection in the lab, and named it the HD-Afi strain.  In the present study, minimal cross-resistance in the HD-Afi strain was found between afidopyropen and other common chemical agents.  However, the P450 enzyme activity in HD-Afi was 2.18 times the level in susceptible strain HD-S.  Expression analysis revealed that two of 12 candidate P450 genes, namely CYP6DW3 and CYP4C64, were significantly up-regulated in HD-Afi.  Silencing CYP6DW3 and CYP4C64 by RNA interference (RNAi) substantially increased the susceptibility of whitefly adults, confirming their involvement in afidopyropen resistance.  Homology modeling and molecular docking analyses demonstrated stable binding of afidopyropen to CYP6DW3 and CYP4C64, with binding free energies of –6.87 and –6.11 kcal mol^–1, respectively.  The findings of this study suggest that the induction of CYP6DW3 and CYP4C64 facilitates afidopyropen detoxification, contributing to the development of resistance in B. tabaci.

Rice‒rape, rice‒wheat and rice‒garlic rotations are common cropping systems in Southwest China, and they have played a significant role in ensuring ecological and economic benefits (EB) and addressing the challenges of China’s food security in the region.  However, the crop yields in these rotation systems are 1.25‒14.73% lower in this region than the national averages.  Intelligent decision-making with machine learning can analyze the key factors for obtaining better benefits, but it has rarely been used to enhance the probability of obtaining such benefits from rotations in Southwest China.  Thus, we used a data-intensive approach to construct an intelligent decision‒making system with machine learning to provide strategies for improving the benefits of rice–rape, rice–wheat, and rice–garlic rotations in Southwest China.  The results show that raising the yield and partial fertilizer productivity (PFP) by increasing seed input under high fertilizer application provided the optimal benefits with a 10% probability in the rice–garlic system.  Obtaining high yields and greenhouse gas (GHG) emissions by increasing the N application and reducing the K application provided suboptimal benefits with an 8% probability in the rice–rape system.  Reducing N and P to enhance PFP and yield provided optimal benefits with the lowest probability (8%) in the rice‒wheat system.  Based on the predictive analysis of a random forest model, the optimal benefits were obtained with fertilization regimes by reducing N by 25% and increasing P and K by 8 and 74%, respectively, in the rice–garlic system,  reducing N and K by 54 and by 36%, respectively, and increasing P by 38% in rice–rape system, and reducing N by 4% and increasing P and K by 65 and 23% in rice–wheat system.  These strategies could be further optimized by 17‒34% for different benefits, and all of these measures can improve the effectiveness of the crop rotation systems to varying degrees.  Overall, these findings provide insights into optimal agricultural inputs for higher benefits through an intelligent decision-making system with machine learning analysis in the rice–rape, rice‒wheat, and rice–garlic systems.