Forecasting occurrence of palm weevil Rhynchophorus palmarum L. (Coleoptera, Curculionidae) using Autoregressive Integrated Moving Average modeling

Abstract

Oil palm (Elaeis guineensis L.) is a crucial crop in Ecuador, considerably affected by black palm weevil Rhynchophorus palmarum L. (Coleoptera: Curculionidae) for several years. Despite its importance, the behavior of the black weevil in Ecuador is not well comprehended presently. Therefore, this study aimed to predict infestation patterns of the black palm weevil in Ecuador using a mathematical model based on monitoring data. Data on the number of insects per trap from a commercial oil palm farm in Quinindé, Ecuador, was collected every two weeks for five years (2016-2020) and analyzed using the Classical Fourier (CF) spectrum and the Dickey-Fuller test to determine seasonality. The trend component of the data dropped from 16.33 in January 2017 to 11.96 in January 2019, with a fluctuation ranging from -0.11 to 2.50 observed for the entire data set. The results obtained after fitting the model ranged from -0.11 to 3.19, with a maximum of 5.30. The augmented Dickey-Fuller (ADF) test for the black weevil time series yielded a result of -5.60 (P<0.01). The partial autocorrelation ranged from -0.35 to 0.1. Based on our model, we projected the occurrence of black palm weevil from 2021 to 2024, with a fluctuation in the number of insects per trap ranging from 12.68 in January 2021 to 13.023 in November 2023. This model can be used to predict future insect occurrences in Ecuador, providing valuable insights into the behavior of the black weevil and using it for effective development control measures for this pest.

Keywords

• Rhynchophorus palmarum
• Oil palm
• ARIMA
• Modeling
• Insect pest forecasting
• SARIMA