Long‐Term Population Dynamics of the Spotted Stem Borer ( Chilo partellus ) in Contrasting Maize Farming Systems Under Variable Climatic Conditions in East Africa

The spotted stem borer ( Chilo partellus ) is one of the most destructive pests of maize in sub‐Saharan Africa, causing substantial yield losses and threatening food security among smallholder farming communities. This study investigated the long‐term relationships between climatic variability and C. partellus incidence across contrasting organic and conventional farming systems in two agroecological zones. Biweekly monitoring observations of C. partellus incidence were summarized quarterly for temporal analyses across four long‐term farming systems at Chuka and Thika: conventional high‐, organic high‐, conventional low‐, and organic low‐input. The resulting analytical series represented 32 and 28 quarterly positions at Chuka and Thika, respectively. Climatic variables (temperature, rainfall, and relative humidity) were obtained from on‐site weather stations. Spearman’s rank correlation, cross‐correlation, ARIMA/SARIMA time‐series models with exogenous climatic predictors (ARIMAX/SARIMAX), and Bayesian generalized linear mixed models were used to evaluate climatic associations and farming systems effects. Temperature emerged as the climatic variable most consistently associated with C. partellus incidence, showing significant negative relationships across all farming systems at Chuka and within the organic farming systems at Thika, whereas rainfall and relative humidity exhibited comparatively weak associations. Cross‐correlation analyses indicated predominantly contemporaneous and short lag associations (0–2 quarters) between climatic variables and pest incidence. Time‐series models were evaluated using descriptive measures of in‐sample agreement between observed and fitted values. Across farming systems, R ² values ranged from 0.039 to 0.650, indicating substantial variation in the degree to which the fitted models represented the observed temporal patterns. These measures describe in‐sample model fit and should not be interpreted as evidence of out‐of‐sample predictive or forecasting performance. Bayesian analyses further indicated that C. partellus incidence was higher in the organic than in the conventional high‐input systems after accounting for climatic variability, whereas the 95% credible intervals for farming system effects in the low‐input systems overlapped zero. Overall, these findings suggest that climatic variability and farming system management are associated with long‐term variation in C. partellus incidence. The findings may inform the development of climate‐informed pest‐monitoring and integrated pest management strategies, while independently validated models would be required before application for pest forecasting under sustainable maize production in East Africa.