Seasonal Population Dynamics of Sucking Insect Pests and Pink Bollworm on Cotton and Their Correlation with Meteorological Parameters

A two-year field investigation was conducted at the Cotton Research Station, Multan during Kharif seasons of 2024 and 2025 to study the seasonal population dynamics of major sucking insect pests jassid (Amrasca biguttula biguttula Dist), whitefly (Bemisia tabaci Genn.), aphid (Aphis gossypii Glov.) and thrips (Thrips tabaci Lind.), along with pink bollworm (Pectinophora gossypiella Saund.) on cotton variety MNH-1020. Pest counts were recorded on a weekly basis from June to October using standard sampling methods. Simultaneously, meteorological data including maximum temperature, minimum temperature, relative humidity and rainfall were recorded from the nearest agro meteorological observatory. The results revealed that all sucking pests reached their peak population during the second week of August in both years, with whitefly (Bemisia tabaci) recording the highest count of 23.0 ± 1.77 adults/leaf in 2024 (and 20.4 ± 1.57 adults/leaf in 2025). Jassid (Amrasca biguttula biguttula) peaked at 7.1 ± 0.55 nymphs/leaf in 2024 and 6.4 ± 0.49 nymphs/leaf in 2025. Aphid (Aphis gossypii) attained maximum populations of 15.8 ± 1.21 individuals/leaf in 2024 and 13.1 ± 1.01 individuals/leaf in 2025, while thrips (Thrips tabaci) reached 25.7 ± 1.97 individuals/plant in 2024 and 22.3 ± 1.72 individuals/plant in 2025. Pink bollworm (Pectinophora gossypiella) attained maximum larval infestation of 22.5 ± 1.73 larvae/100 bolls in 2025 and 17.5 ± 1.34 larvae/100 bolls in 2024. Statistical analysis showed highly significant positive correlations of jassid, whitefly and aphid populations with temperature (r = 0.847–0.876) and relative humidity (r = 0.741–0.858). Pink bollworm showed significant positive correlation with both temperature (r = 0.684–0.712) and humidity (r = 0.698). Rainfall exhibited a negative relationship with sucking pests but a positive relationship with pink bollworm larval populations. Simple linear regression models explained up to 76.7% of the variance in pest populations attributable to meteorological factors. These findings provide a scientific basis for forecasting pest outbreaks and planning timely integrated pest management interventions in the cotton agro-ecosystem of Southern Punjab.