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Spatio-temporal time series forecasting with trap catch data of oriental fruit moth (Grapholita molesta) in peach (Prunus persica) orchards in South Korea

  • 0Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, United States.
Frontiers in Plant Science +

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December 22, 2025

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December 22, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study forecasts oriental fruit moth (OFM) populations in South Korea using time series models, revealing a shift towards earlier emergence due to climate change. The Prophet model proved superior for predicting OFM dynamics and guiding regional pest management.

Area Of Science

  • Agricultural Entomology
  • Pest Management
  • Time Series Analysis

Background

  • The oriental fruit moth (OFM) inflicts significant economic damage on peach and stone fruit crops in South Korea.
  • Understanding OFM population dynamics is crucial for effective pest management strategies.

Purpose Of The Study

  • To analyze spatio-temporal OFM patterns in South Korea.
  • To forecast OFM populations using time series models (SARIMA and Prophet).
  • To provide data-driven guidance for region-specific OFM management.

Main Methods

  • Utilized ten years of bimonthly sex pheromone trap data (2016-2025).
  • Compared Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet models for prediction.
  • Conducted spatio-temporal analysis across three key peach-producing provinces.

Main Results

  • The Prophet model demonstrated superior predictive performance over SARIMA across all provinces.
  • Observed a phenological shift in OFM emergence from multi-peak to single-peak patterns, occurring earlier in May.
  • Identified regional variations in OFM population trends, influenced by climate and pesticide use.

Conclusions

  • Climate change and pesticide strategies are altering OFM phenology and population dynamics.
  • Region-specific pest management, focusing on early generations, is essential.
  • Predictive time series models are vital for developing smart, proactive integrated pest management systems.

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