Bioscience Methods 2024, Vol.15, No.4, 196-206 http://bioscipublisher.com/index.php/bm 2 04 8 Concluding Remarks The field of pest management in cotton crops has seen significant advancements over recent years, driven by a combination of biotechnological innovations, integrated pest management (IPM) strategies, and ecological approaches. One of the most notable advancements is the widespread adoption of Bt cotton, which has significantly reduced the reliance on chemical insecticides and has led to regional suppression of pest populations. The development of multi-gene pyramiding and RNA interference (RNAi) technologies has further enhanced the effectiveness of genetically engineered crops in managing pest resistance. Integrated pest management has evolved to incorporate a systems approach that emphasizes pest ecology and biology, insecticide resistance management, and the use of selective insecticides as a last resort. This approach has been successfully embedded within the farming systems, particularly in the Australian cotton industry, leading to a dramatic decline in the amount of insecticide active ingredient applied per hectare. Additionally, the use of cover crops has been shown to improve early-season natural enemy recruitment, thereby reducing pest pressure and the need for insecticide applications. Advances in controlled release pesticide formulations have also shown promise in reducing the environmental impact of pest management practices. These formulations aim to optimize the delivery and efficacy of pesticides, thereby minimizing their adverse effects on human health and ecosystems. Furthermore, plant training techniques, which involve inducing plant defenses through artificial injury, have emerged as a supplementary tool for IPM, particularly beneficial for smallholders. The future of sustainable pest management in cotton crops lies in the continued integration of biotechnological innovations with ecological and agronomic strategies. One of the key areas for future research is the development of new transgenic traits that can delay pest resistance. This can be achieved by increasing the prevalence of refuges and enhancing the implementation of IPM practices. Additionally, there is a need for ongoing surveillance of insect resistance and monitoring of grower compliance to ensure the sustainable use of Bt cotton and other genetically engineered crops. The role of genomics and bioinformatics in understanding plant-pest interactions will be crucial in developing new pest management strategies that are both effective and environmentally friendly. Advances in plant secondary metabolism and immunity, as well as microbiome science, offer promising avenues for enhancing crop resistance to insect pests. Moreover, the adoption of habitat management practices, such as the use of cover crops, can provide stable environments that support natural enemy communities and reduce pest populations. To achieve sustainable development goals (SDGs) in cotton production, it is essential to promote the adoption of innovative pest management technologies through industry-wide extension campaigns and grower education programs. This will require concerted efforts from researchers, policymakers, and the agricultural industry to address the challenges of public misperception and regulatory inaction. By fostering a collaborative approach, the cotton industry can continue to advance towards more sustainable and resilient pest management practices. Acknowledgments BioSci Publisher appreciates the valuable feedback from the reviewers. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Reference Alves A., Souza W., and Borges D., 2020, Cotton pests classification in field-based images using deep residual networks, Comput. Electron. Agric., 174: 105488. https://doi.org/10.1016/j.compag.2020.105488 Azfar S., Nadeem A., Ahsan K., Mehmood A., Almoamari H., and Alqahtany S., 2023a, IoT-Based cotton plant pest detection and smart-response system, Applied Sciences, 13(3): 1851. https://doi.org/10.3390/app13031851
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