<![CDATA[Abstract. Ginting TY, Triwidodo H, Winasa IW, Maryana N. 2025. Population dynamics modelling of Scirpophaga incertulas based on climatic factors in Mardingding, Karo, North Sumatra, Indonesia. Biodiversitas 26: 5003-5011. The yellow stem borer (Scirpophaga incertulas) is a major rice pest in Southeast Asia whose outbreaks are strongly influenced by climatic conditions. This study developed and evaluated dynamic models of S. incertulas populations in Mardingding Sub-district, Karo District, North Sumatra, Indonesia, during a single cropping season (January-April 2025). Field monitoring was conducted weekly at three sites using light traps, accompanied by assessments of infestation intensity and incidence. Climate data, including temperature, relative humidity, and rainfall, were obtained from NASA POWER (Prediction of Worldwide Energy Resources). To assess whether population differences among sites were statistically significant, an Analysis of Variance (ANOVA) was performed. The results revealed a significant difference in S. incertulas populations across locations (F = 8.10, F-critical = 3.28, p < 0.05), underscoring the influence of site-specific agroecosystem conditions. The relationships between climate variables and pest dynamics were further analyzed using multiple linear regression and a discrete logistic growth model. Results indicated that the logistic model provided more accurate estimates of S. incertulas population dynamics compared to linear regression, with the lowest prediction error (18.45%) observed at Tanjung Pamah. At this site, populations remained low (1.5-4.5 individuals) under average conditions of 22.89°C, 89.44% relative humidity, and 5.90 mm rainfall. Peak infestation intensity and incidence across sites occurred approximately six weeks after planting, under mean conditions of 23°C, 89% relative humidity, and 5-6 mm rainfall. These findings demonstrate that integrating climatic drivers with logistic models improves forecasting of S. incertulas outbreaks, particularly in relatively homogeneous rice systems. Such approaches support early-warning systems, reduce reliance on chemical control, and strengthen biodiversity-friendly rice pest management.]]>
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