Temporal dynamics of sapota pest damage and Phytophthora disease: insights from time series and machine learning models

Introduction Sapota ( Manilkara zapota L.) is a major tropical fruit crop prone to damage by bud borer ( Anarsia achrasella ), seed borer ( Trymalitis margarias ), and fruit rot caused by Phytophthora species. Climatic variability strongly influences these biotic stresses, yet long-term temporal patterns remain poorly quantified. Methods A decade-long dataset (2014–2022) from 21 major sapota-growing districts of Maharashtra, India, was analyzed to study pest and disease dynamics. Statistical and machine learning approaches, including ARIMA, SARIMA, and VAR time-series models, along with Random Forest feature importance analysis, were applied to quantify climatic influences and forecast severity trends. Correlation analyses were used to assess weather–pest/disease associations. Results Trend analysis revealed fluctuating bud and seed borer damage, while Phytophthora disease severity remained relatively stable. Bud borer incidence was positively correlated with rainfall (r = 0.69), seed borer with maximum temperature (r = 0.47), and Phytophthora with minimum temperature (r = 0.64). The ARIMA model provided accurate forecasts for bud borer (MSE = 8.03) and Phytophthora (MSE = 0.20), while the VAR model performed best for seed borer (MSE = 17.96). Random Forest analysis identified minimum temperature as the most critical driver of bud borer and Phytophthora severity, whereas relative humidity was most influential for seed borer. Discussion The integration of statistical and machine learning models provides robust insights into sapota pest and disease epidemiology under climatic variability. These findings highlight the importance of temperature, humidity, and rainfall in shaping pest–pathogen interactions and provide predictive tools to design timely, targeted, and climate-resilient management strategies for sapota cultivation.