Predictive model for Red Palm Weevil population in coconut using environmental variables and regression method
In Malaysia, coconut is one of the commercialized crops, and has become the most important industrial crop. Furthermore, Johor has the largest coconut plantation area in Peninsular Malaysia, which is 14,931.6 hectare. Johor was also the largest producer of coconuts in Malaysia in 2018, with the prod...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/100135/1/NorraishaMdSabtuMFABU2021.pdf |
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| Summary: | In Malaysia, coconut is one of the commercialized crops, and has become the most important industrial crop. Furthermore, Johor has the largest coconut plantation area in Peninsular Malaysia, which is 14,931.6 hectare. Johor was also the largest producer of coconuts in Malaysia in 2018, with the production number amounting to 112,122 hectares. However, the increasing population of Red Palm Weevil (RPW) in Malaysia is very worrying. RPW is known to be a very dangerous pest on coconut trees, especially found in the Middle East. This study was conducted to model the incident potential of RPW in coconut plantations. The first objective of this study is to examine temporal factors of RPW population. The second objective is to develop a model that can predict the occurrence population of RPW on a coconut plantation based on significant environmental variables. The third objective is to validate the model that has been developed in the second objective. The fourth objective is to produce a susceptibility map of RPW population. This study has been divided into eight phases. This phase includes a site visit to the coconut plantation attacked by RPW, and an interview session conducted with the plant biosecurity officer to address objective 1. A model linear model (GLM) was used to model events based on poisson regression. The model was then validated using root mean square error (RMSE) and mean absolute error (MAE) to address objectives 2 and 3. Finally, the location of the appropriate RPW population infestation was described using weighted Overlay analysis (WOA). Based on the model, five statistically significant variables were determined, which contributed to the total RPW. The variables are humidity, rainfall, wind direction, distance of the trap from the river and road. Based on the value of R squared, the most significant variable was distance from the river (R=5.8, p<0.5), followed by humidity (R=2.9, p <0.5), wind direction (R=1, p<0.5), rain (R=0.2, p<0.5), and distance from the road (R=0.1, p <0.5). This study shows that the places with the lowest probability of being infected by RPW in Mersing are Kg. Semaloi and Tg. Resang, because the two trap locations are far from roads and rivers. Meanwhile, the locations with the highest probability of being attacked by RPW are Kg. Penyabong and certain areas in Kg. Sungai Berbatu, Kg. Belukar Juling, and Kg. Semanyir, because all locations are close to roads and rivers. In conclusion, this model predicts the presence of RPW with errors of 4 (RMSE) and 3 (MAE) for each trap. This model can be developed further, which will assist the authorities in planning and providing an early warning system to identify areas that are likely to be attacked with RPW. |
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