Agrometeorological determinants of population dynamics of semilooper, Trichoplusia ni (Hubner) and cabbage butterfly, Pieris brassicae (Linnaeus) on cabbage
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2022-04-27 |
| Journal | Journal of Agrometeorology |
| Authors | Purti, Krishna Rolania |
| Institutions | Chaudhary Charan Singh Haryana Agricultural University |
| Citations | 1 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive SummaryâThis study focuses on developing predictive models for the population dynamics of two major cabbage pests, the semilooper (Trichoplusia ni) and the cabbage butterfly (Pieris brassicae), based on agrometeorological parameters. The findings are critical for optimizing Integrated Pest Management (IPM) strategies through environmental data correlation.
- Modeling Efficacy: Multiple linear regression models successfully accounted for 84% to 87% of the total variability in the larval population of the semilooper (T. ni).
- Semilooper Drivers: Semilooper larval population exhibited a strong, highly significant positive correlation (r > 0.79) with both maximum and minimum temperatures across both study years.
- Humidity Constraint: Semilooper population showed a strong, highly significant negative correlation (r < -0.81) with morning and evening relative humidity (RH).
- Cabbage Butterfly Drivers: The cabbage butterfly population showed generally weak correlation with most weather parameters, except for a strong positive correlation with sunshine hours (r = 0.83) in the 2017-18 season.
- Biotic Factor Influence: The population of generalist predators (spiders) showed a strong positive correlation with both semilooper (r > 0.88) and cabbage butterfly (r > 0.90) populations, indicating density-dependent predation.
- Peak Incidence Timing: Peak larval populations for both pests consistently occurred around the 13th Standard Meteorological Week (SMW), corresponding to the first week of April.
Technical Specifications
Section titled âTechnical SpecificationsâThe following table summarizes the key correlation coefficients and modeling results derived from the analysis using SPSS 20.0.
| Parameter | Value | Unit | Context |
|---|---|---|---|
| Semilooper R2 (2017-18) | 0.87 | N/A | Variability explained by weather parameters |
| Semilooper R2 (2018-19) | 0.84 | N/A | Variability explained by weather parameters |
| Semilooper Correlation (Max Temp) | 0.92 | N/A | Highly significant positive correlation (Both years) |
| Semilooper Correlation (Min Temp) | 0.79 to 0.85 | N/A | Highly significant positive correlation |
| Semilooper Correlation (Evening RH) | -0.88 to -0.92 | N/A | Highly significant negative correlation |
| Cabbage Butterfly Correlation (Sunshine) | 0.83 | N/A | Significant positive correlation (2017-18 only) |
| Spider Correlation (Cabbage Butterfly) | 0.97 | N/A | Strongest positive correlation (2018-19) |
| Cabbage Butterfly Peak Population | 38.80 | larvae/plant | Maximum recorded average (13th SMW, 2017-18) |
| Experimental Area Size | 100 | m2 | Cabbage cultivation plot |
Key Methodologies
Section titled âKey MethodologiesâThe study utilized a field experiment approach combined with statistical modeling to establish relationships between environmental inputs and biological outputs.
- Experimental Setup: Cabbage seedlings (variety âGolden Acreâ) were transplanted in late December during the rabi seasons of 2017-18 and 2018-19.
- Plot Configuration: The 100 m2 area was divided into four 5m x 5m quadrates. Standard spacing was 60 cm x 45 cm.
- Treatment Control: All insecticidal treatments were strictly excluded throughout the crop cycle to ensure observations reflected natural pest dynamics.
- Pest Data Collection: Larval populations of semilooper and cabbage butterfly were recorded weekly using the direct visual count method on tagged plants, observing all open leaves and heads.
- Meteorological Data Acquisition: Data on maximum temperature, minimum temperature, relative humidity (morning and evening), wind speed, sunshine hours, and rainfall were obtained from the dedicated Agrometeorological Observatory.
- Statistical Analysis: Correlation coefficients and regression coefficients were calculated using Multiple Linear Regression analysis via SPSS 20.0 software to quantify the influence of weather parameters on population fluctuations.
Commercial Applications
Section titled âCommercial ApplicationsâThe derived predictive models and correlation data are valuable for engineering and data science applications focused on environmental monitoring and agricultural optimization.
- Precision Agriculture Systems: Development of real-time, weather-driven decision support tools for farmers, allowing precise timing of non-chemical or chemical interventions based on predicted pest outbreaks (especially semilooper, which is highly predictable by T and RH).
- IoT Sensor Network Design: Defining critical sensor requirements for agricultural monitoring systems, prioritizing high-accuracy temperature and relative humidity sensors over rainfall or wind speed for semilooper prediction.
- Predictive Modeling and AI: Providing validated training data sets for machine learning algorithms used in large-scale regional pest forecasting and risk mapping.
- Integrated Pest Management (IPM) Automation: Designing automated systems that trigger alerts or deploy biological controls (like spider conservation strategies) when environmental conditions (high T, low RH) favor rapid pest population buildup.
- Environmental Control Systems: Informing the design of controlled environment agriculture (CEA) systems (e.g., greenhouses) to maintain temperature and humidity profiles that are suboptimal for pest reproduction.
View Original Abstract
Insect-pests population in any crop is determined by prevailing weather parameters as they determined growth as well as reproduction of insect-pests.Cabbage (Brassica oleracea var.capitata) a member of Brassicaceae family, is an important edible cole crop grown throughout the India.It contains protein, minerals, vitamins, amino acids, essential fatty acids and dietary fibers.It is one of the best natural antioxidant and sources of vitamin C. In the local farming system, cabbage is usually part of a diversified cropping pattern and mostly grown as a cash crop for local market (Macharia et al., 2005).Cabbage is grown in winter month arranging from end of December to 1 st week of April.This period is marked with considerably variations in day and night temperature as well as photoperiod.A major constraint in the production of cabbage is the damage of insect-pests.Insect-pests are reducing 40 per cent of the total attainable yield of vegetables and nearly 60-80 per cent on an average yield loss in crucifer crops.The important insectpests that infest cabbage crop are the tobacco caterpillar, diamond back moth, cabbage semilooper, painted bug, cabbage butterfly, flea beetle, cabbage aphid, cabbage leaf webber and the mustard saw fly (Ahuja et al., 2012).The cabbage semilooper is a cosmopolitan insect that causes damage in more than 160 species of plants (Sutherland and Greene, 1984).Cabbage butterfly alone causes 40 per cent yield loss annually in India (Hasan and Ansari, 2010) in cruciferous vegetables.This transition in meteorological parameters coincides with one of the other phases of insect-pests etiology like oviposition, hatching, larval, pupal and adult longevity.Likewise, these agrometeorological parameters coincide with growth and development in cabbage crops.Tri part type interaction between agrometeorological parameters growth stages in insect-pests and host plant physiology and resistance determine the population dynamics.In view of these facts the study was conducted on corollary between agrometeorological parameters and insect-pests growth as well as population dynamics and crop variety âGolden acreâ in cabbage as a host plant.This study deals with association between weather parameters and insect-pests dynamics.The field experiment was conducted at Experimental Area, Department of Entomology, CCS Haryana Agricultural University, Hisar, during the ârabiâ season of 2017-18 and 2018-19.Cabbage seedlings of variety âGolden Acreâ were transplanted in end of December in 100 m 2 area by adopting 60Ă 45 cm spacing.The area was divided into four quadrates each of size 5m Ă 5m.All the recommended agronomic practices were followed to raise the healthy cabbage crop.The experiment excluded all kind of insecticidal treatment on any growth stage of crop for avoiding the disturbance in natural habitat of insect-pests.The population of semilooper, Trichoplusia ni and cabbage butterfly, Pieris brassicae was recorded by counting the number of larvae on whole plant by adopting the direct visual count method.All the open leaves and heads of the selected plants were observed thoroughly and count the number of larvae per plant.Only larval stage of both pests was considered and the observations were recorded at vegetative stage (28 DAT) till harvesting stage of cabbage.Plants were tagged for further observations.The observations were recorded from five randomly selected plants and subsequent observations were recorded at weekly interval.The data on important weather parameters viz., maximum temperature, minimum temperature, relative humidity (both morning and evening), wind speed, sunshine hours and rainfall were obtained from the Agrometeorological Observatory, CCS Haryana Agricultural University, Hisar.The population of semilooper and cabbage butterfly on cabbage crop was correlated with different weather parameters in both the years.In addition to correlation, regression coefficients for environmental parameters were also estimated by multiple linear-regressions.Correlation of population fluctuations of insect-pests with different meteorological parameters was worked out and multiple linear-regressions were determined by using SPSS 20.0 version. The correlation coefficient between larval population