Bioscience Evidence 2026, Vol.16, No.4, 249-263 http://bioscipublisher.com/index.php/be 259 a single method, especially repeated use of chemical insecticides (Iftikhar et al., 2023). Because pest populations vary among years, crop growth stages, and local environments, family farms should use flexible combinations of control measures instead of following one fixed management approach. In Bangladesh, an IPM program that combined egg mass collection, sweep-net monitoring, perching poles, and insecticide applications based on economic thresholds achieved better pest control and higher rice yield than routine preventive management. In India, combining proper plant spacing, natural enemy conservation, botanical pesticides, entomopathogenic fungi, sticky traps, and selective insecticides significantly reduced planthopper damage compared with traditional farming practices. The benefit-cost ratios reached 1:7.6 and 1:6.85 in two consecutive years (Longkumer et al., 2024). Family farms should therefore establish integrated management systems that combine pest monitoring, habitat management, resistant rice varieties, physical control, and selective chemical control instead of relying on a single "universal solution." 6.4 Keep simple field records Effective IPM depends on connecting pest occurrence with rice growth stages, weather conditions, field history, and previous management practices. Decisions based on field records are generally more reliable than decisions based only on experience. Even when intensive monitoring is not possible, recording simple information such as pest species, crop damage, survey date, rice growth stage, rainfall, and control measures can help farmers better understand pest patterns and avoid unnecessary pesticide applications (Husin et al., 2024). Using monitoring records to guide management decisions improves the accuracy of pest control. When monitoring information is shared among farmers, its value becomes even greater than when it is used by individual farms alone. A pest early-warning program involving more than 4 000 smallholder farmers in Africa showed that access to pest warning information increased IPM adoption by 8~32 percentage points and improved both yield and farm income by 18%~26% (Khonje et al., 2026). Rice pest forecasting and early-warning systems are also becoming more advanced. When pest populations exceed the economic threshold, these systems can quickly alert farmers and support timely pest management decisions. 7 Future Perspectives 7.1 Wider adoption of eco-friendly pest management The wider adoption of eco-friendly pest management in high-quality rice production should focus not only on increasing yield but also on improving grain quality, food safety, and ecological sustainability. IPM shares many of the same goals as sustainable agriculture. Its main strength lies in combining biological control, habitat management, insect-resistant rice varieties, plant-based pesticides, and precise intervention to reduce pesticide residues while maintaining important ecosystem services. Plant-based pesticides are biodegradable, have a lower environmental impact, and are generally safer for non-target organisms. These characteristics make them an important alternative for reducing chemical pesticide use in high-quality rice production (Ganesan et al., 2025). At the same time, conserving and using natural enemies should remain a major priority. Rice fields already contain abundant beneficial organisms that can naturally suppress many pests. Unnecessary insecticide applications often disrupt this ecological balance and increase the risk of pest resurgence. 7.2 Digital monitoring technologies Modern technologies, including sensors, intelligent trapping devices, drones, remote sensing, and data analysis systems, can continuously monitor crop health, environmental conditions, and pest population dynamics. These technologies support early warning, hotspot detection, and timely intervention based on economic thresholds (Mansoor et al., 2025). Combining Internet of Things (IoT) technologies with unmanned aerial vehicles (UAVs) allows pest monitoring at both ground level and field scale. By integrating field images with weather information, these systems can better explain the relationship between environmental conditions and pest outbreaks. When combined with artificial intelligence, machine learning, and remote sensing, digital monitoring systems can identify damaged areas, predict future pest outbreaks, determine the best control timing, and support more precise pest management with lower pesticide inputs.
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