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- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 12, Issue 12 (2023)
Statistical evaluation of stepwise regression method for earwig population in groundnut (Arachis hypogaea L.)
Author(s):
P Minruhi, P Lavanya Kumari, Santosha Rathod, B Ramana Murthy and K Devaki
P Minruhi, P Lavanya Kumari, Santosha Rathod, B Ramana Murthy and K Devaki
Abstract:
Background: Groundnut (Arachis hypogaea L.) is a widely cultivated oilseed crop with significant economic and nutritional value. Among the pests, Earwig (Anisolabis stali) are nocturnal insects known to feed on groundnut plants, causing damage that can result in yield losses and reduced crop quality. The matured pods were infested to an extent of 44.1% and the intensity of infestation in the case of immature pods was 52.1% in Asirya muitunde, a variety of groundnut. In any crop, weather parameters are one of the major causes of pest infestation. Further, which weather parameter influences the best at higher extent need to be known before prediction of pest as per the weather parameters. To effectively manage earwig populations in groundnut fields, it is essential to develop accurate predictive models that can assist farmers in implementing targeted pest control strategies. Stepwise regression is one of the statistical methods that was commonly employed in agricultural research for building predictive models.
Background: Groundnut (Arachis hypogaea L.) is a widely cultivated oilseed crop with significant economic and nutritional value. Among the pests, Earwig (Anisolabis stali) are nocturnal insects known to feed on groundnut plants, causing damage that can result in yield losses and reduced crop quality. The matured pods were infested to an extent of 44.1% and the intensity of infestation in the case of immature pods was 52.1% in Asirya muitunde, a variety of groundnut. In any crop, weather parameters are one of the major causes of pest infestation. Further, which weather parameter influences the best at higher extent need to be known before prediction of pest as per the weather parameters. To effectively manage earwig populations in groundnut fields, it is essential to develop accurate predictive models that can assist farmers in implementing targeted pest control strategies. Stepwise regression is one of the statistical methods that was commonly employed in agricultural research for building predictive models.
Methods: (i) Data: Secondary data on Earwig (EW) light trap catches during kharif was collected from Regional Agricultural Research Station (RARS) Tirupati, for the period of 15 years, from 2008 to 2022 during Kharif from 26 SMWs (June) to 43SMWs (October). Weather parameters viz., Maximum temperature (MAXT), Minimum temperature (MINT), Rainfall (RF), Morning Relative Humidity (RHM), Evening Relative Humidity (RHE), Sunshine hours (SSH) and Wind speed (WS) for the respective standard meteorological weeks (SMWs) also collected from Automated Weather Station (AWS) situated at RARS Tirupati.
(ii) Statistical analysis:In the stepwise regression model, the weekly average values of MAXT, MINT, RHM, RHE, RF, SSH and WS were employed as independent variables, whereas the EW population served as dependent variables.
Result: By stepwise regression analysis, the study revealed that the response variable, EW population was significantly influence by WS, MAXT, MINT, RHM whereas SSH and RF showed non-significant influence, the model R2 value for the fitted regression was low, indicating that the model was not a strong fit due to non-linearity and high heterogeneity in insect pests.
Pages: 397-403 | 244 Views 147 Downloads
How to cite this article:
P Minruhi, P Lavanya Kumari, Santosha Rathod, B Ramana Murthy, K Devaki. Statistical evaluation of stepwise regression method for earwig population in groundnut (Arachis hypogaea L.). Pharma Innovation 2023;12(12):397-403.
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
