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

Eduardo L.  Pacheco-Sánchez; Lenin A.  Guamani-Quimis; Cinara  Ewerling da Rosa; Diego  Portalanza; Alejandro E.  Mieles; Felipe R.  Garcés-Fiallos

doi:10.17268/sci.agropecu.2023.015

Autores/as

Eduardo L. Pacheco-Sánchez Postgraduate Institute, Technical University of Manabí, Av. José María Urbina y Che Guevara, Portoviejo, EC13132, Manabí.
Lenin A. Guamani-Quimis Postgraduate Institute, Technical University of Manabí, Av. José María Urbina y Che Guevara, Portoviejo, EC13132, Manabí.
Cinara Ewerling da Rosa Center of Natural and Exact Sciences, Federal University of Santa Maria, Roraima Av. 1000, Santa Maria.
Diego Portalanza Carrera de Ingeniería Ambiental, Facultad de Ciencias Agrarias, Instituto de Investigación “Ing. Jacobo Bucaram Ortiz, Ph.D”, Universidad Agraria del Ecuador (UAE), Avenida 25 de Julio, Guayaquil.
Alejandro E. Mieles Facultad de Ciencias Ambientales, Universidad Estatal del Sur de Manabí, Km 0.5 vía Jipijapa-Noboa, Jipijapa, Manabí.
Felipe R. Garcés-Fiallos Laboratory of Phytopathology, Experimental Campus La Teodomira, Faculty of Agronomic Engineering, Technical University of Manabí, Santa Ana, EC130105, Manabí.

DOI:

https://doi.org/10.17268/sci.agropecu.2023.015

Palabras clave:

Rhynchophorus palmarum, Oil palm, ARIMA, Modeling, Insect pest forecasting, SARIMA, Time series analysis

Resumen

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.

Citas

Adebayo, F. A., Sivasamy, R., & Shangodoyin, D. K. (2014). Forecasting Stock Market Series with ARIMA Model. Journal of Statistical and Econometric Methods, 3(3), 65-77.

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014. https://doi.org/10.1155/2014/614342

Andrew, M. L. (1993). Geostatistics and geographic information systems in applied insect ecology. Annual Review of Entomology, 38(1), 303–328. https://doi.org/10.1146/annurev.ento.38.1.303

Antony, B., Johny, J., Montagné, N., Jacquin‐Joly, E., Capoduro, R., et al. (2021). Pheromone receptor of the globally invasive quarantine pest of the palm tree, the red palm weevil (Rhynchophorus ferrugineus). Molecular Ecology, 30(9), 2025-2039. https://doi.org/10.1111/mec.15874

Breiman, L. (2001). Statistical Modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199-231. https://doi.org/10.1214/ss/1009213726

Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151-163. https://doi.org/10.1016/j.neucom.2019.05.099

Camargo, M. I., Sánchez, P., Denardi, S. E., & Caetano, F. H. (2010). Rhyncophorus palmarum L. (Linnaeus, 1758): A morphological and histological study of the female reproductive system. Microscopy Research and Technique, 74(9). https://doi.org/10.1002/jemt.20969

Charemza, W. W., & Syczewska, E. M. (1998). Joint application of the Dickey-Fuller and KPSS tests. Economics Letters, 61(1), 17-21. https://doi.org/10.1016/S0165-1765(98)00149-9

Chen, J., Yang, J., Huang, S., Li, X., & Liu, G. (2023). Forecasting tourist arrivals for Hainan island in China with decomposed broad learning before the COVID-19 pandemic. Entropy, 25(2), 338. https://doi.org/10.3390/e25020338

Chiu, L.-Y., Arcega Rustia, D. J., Lu, C.-Y., & Lin, T.-T. (2019). Modelling and forecasting of greenhouse whitefly incidence using time-series and ARIMAX analysis. IFAC-PapersOnLine, 52(30), 196-201. https://doi.org/10.1016/j.ifacol.2019.12.521

Cryer, J. D., & Chan, K.-S. (2008). Time Series Analysis, Springer Texts in Statistics.

Springer New York, New York, NY. https://doi.org/10.1007/978-0-387-75959-3

Dalbon, V. A., Acevedo, J. P. M., Ribeiro Junior, K. A. L., Ribeiro, T. F. L., da Silva, J. M., Fonseca, H. G., Santana, A. E. G., & Porcelli, F. (2021). Perspectives for synergic blends of attractive sources in South American palm weevil mass trapping: Waiting for the red palm weevil Brazil invasion. Insects, 12(9), 828. https://doi.org/10.3390/insects12090828

de Oliveira, W. K., de França, G. V. A., Carmo, E. H., Duncan, B. B., de Souza Kuchenbecker, R., & Schmidt, M. I. (2017). Infection-related microcephaly after the 2015 and 2016 Zika virus outbreaks in Brazil: a surveillance-based analysis. The Lancet, 390(10097), 861-870. https://doi.org/10.1016/S0140-6736(17)31368-5

Elango, K., Nelson, S. J., & Aravind, A. (2020). Rugose spiralling whitefly, Aleurodicus rugioperculatus Martin (Hemiptera, Aleyrodidae): An invasive foes of coconut. Journal of Entomological Research, 44(2), 261. https://doi.org/10.5958/0974-4576.2020.00046.8

Esparza-Díaz, G., Olguin, A., Carta, L. K., Skantar, A. M., & Villanueva, R. T. (2013). Detection of Rhynchophorus palmarum (Coleoptera: Curculionidae) and identification of associated nematodes in South Texas. Florida Entomologist, 96(4), 1513-1521. https://doi.org/10.1653/024.096.0433

FAOSTAT. (2019). FAOSTAT: Statistical database. FAOSTAT: Statistical Database.

Francq, C., & Zakoïan, J.-M. (2005). Recent Results for Linear Time Series Models with Non Independent Innovations. In Statistical Modeling and Analysis for Complex Data Problems (pp. 241-265). Springer-Verlag. https://doi.org/10.1007/0-387-24555-3_12

Gassler, B., & Spiller, A. (2018). Is it all in the MIX? Consumer preferences for segregated and mass balance certified sustainable palm oil. Journal of Cleaner Production, 195. https://doi.org/10.1016/j.jclepro.2018.05.039

Guamani-Quimis, L. A., Solís-Bowen, A. L., Portalanza, D., & Garcés-Fiallos, F. R. (2022). Can Mathematical models describe spear rot progress in oil palm trees? A five-year black weevil-disease assessment from Ecuador. Agriculture, 12(2), 257. https://doi.org/10.3390/agriculture12020257

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3). https://doi.org/10.18637/jss.v027.i03

Kannan, M., Ananthan, M., Kalyanasundaram, M., Jayaprakash, S. A., Elango, K., & Dinesh Kumar, P. (2022). Population dynamics, weather parameters interaction of insect pests on Indian bean, Lablab purpureus and prediction analysis using ARIMAX model. Journal of Environmental Biology, 43(4), 578-584. https://doi.org/10.22438/jeb/43/4/MRN-3025

Khudri, N. A. F. R. S., Mohd Masri, M. M., Maidin, M. S. T., Kamarudin, N., Hussain, M. H., Abd Ghani, I., & Jalinas, J. (2021). Preliminary evaluation of acoustic sensors for early detection of red palm weevil, Rhynchophorus ferrugineus incidence on oil palm and coconut in Malaysia. International Journal of Tropical Insect Science, 41(4), 3287-3292. https://doi.org/10.1007/s42690-021-00499-3

Kurdi, H., Al-Aldawsari, A., Al-Turaiki, I., & Aldawood, A. S. (2021). Early detection of red palm weevil, Rhynchophorus ferrugineus (Olivier), infestation using data mining. Plants, 10(1), 95. https://doi.org/10.3390/plants10010095

Lima, M. V. M. de, & Laporta, G. Z. (2020). Evaluation of the models for forecasting dengue in Brazil from 2000 to 2017: An ecological time-series study. Insects, 11(11), 794. https://doi.org/10.3390/insects11110794

Mgaya, J. F. (2019). Application of ARIMA models in forecasting livestock products consumption in Tanzania. Cogent Food & Agriculture, 5(1), 1607430. https://doi.org/10.1080/23311932.2019.1607430

Montero-Manso, P., Athanasopoulos, G., Hyndman, R. J., & Talagala, T. S. (2020). FFORMA: Feature-based forecast model averaging. International Journal of Forecasting, 36(1), 86-92. https://doi.org/10.1016/j.ijforecast.2019.02.011

Murguía-González, J., Landero-Torres, I., Leyva-Ovalle, O. R., Galindo-Tovar, M. E., Llarena-Hernández, R. C., Presa-Parra, E., & García-Martínez, M. A. (2018). Efficacy and cost of trap-bait combinations for capturing Rhynchophorus palmarum L. (Coleoptera: Curculionidae) in ornamental palm polycultures. Neotropical Entomology, 47(2), 302-310. https://doi.org/10.1007/s13744-017-0545-8

Narava, R., Kumar, S. R., Jaba, J., Kumar, A., Rao, R., et al. (2022). Development of temporal model for forecasting of Helicoverpa armigera (Noctuidae: Lepidopetra) using Arima and Artificial Neural Networks. Journal of Insect Science, 22(3). https://doi.org/10.1093/jisesa/ieac019

R Core Team. (2021). R: A language and environment for statistical computing v. 3.6. 1 (R Foundation for Statistical Computing, Vienna, Austria, 2019). Scientific Reports.

Senf, C., Seidl, R., & Hostert, P. (2017). Remote sensing of forest insect disturbances: Current state and future directions. International Journal of Applied Earth Observation and Geoinformation, 60, 49-60. https://doi.org/10.1016/j.jag.2017.04.004

Sporleder, M., Tonnang, H. E. Z., Carhuapoma, P., Gonzales, J. C., Juarez, H., & Kroschel, J. (2013). Insect Life Cycle Modelling (ILCYM) software - a new tool for regional and global insect pest risk assessments under current and future climate change scenarios. In Potential invasive pests of agricultural crops (pp. 412-427). CABI. https://doi.org/10.1079/9781845938291.0412

Stadnytska, T. (2010). Deterministic or Stochastic Trend. Methodology, 6(2), 83-92. https://doi.org/10.1027/1614-2241/a000009

Svetunkov, I., Chen, H., & Boylan, J. E. (2023). A new taxonomy for vector exponential smoothing and its application to seasonal time series. European Journal of Operational Research, 304(3), 964-980. https://doi.org/10.1016/j.ejor.2022.04.040

Tang, K. H. D., & Al Qahtani, H. M. S. (2020). Sustainability of oil palm plantations in Malaysia. In Environment, Development and Sustainability (Vol. 22, Issue 6). https://doi.org/10.1007/s10668-019-00458-6

The World Bank. (2020). INDONESIA ECONOMIC PROSPECTS The Long Road to Recovery. Iep, 81. http://ditjenbun.pertanian.go.id

Zhao, C., Hu, P., Liu, X., Lan, X., & Zhang, H. (2023). Stock market analysis using time series relational models for stock price prediction. Mathematics, 11(5), 1130. https://doi.org/10.3390/math11051130.