Predicting phyllody disease and leafhopper species populations in sesame using weather variables: An ARIMAX time series framework

Abstract

Leafhopper species, viz., Hishimonus phycitis, Orosius albicinctus, and Amrasca bigutulla bigutulla, and sesame phyllody disease, damage sesame in tropical regions of India. Among the leafhopper species, H. phycitis incidence was maximum during 2022–23, peaked (70.36 nos./3 leaves) during the 40th Standard Meteorological Weeks (SMW), followed by O. albicinctus, which peaked in the 33rd SMW (53.14 nos./3 leaves) during 2021–2022. The impact of weather factors on the leafhoppers showed that minimum temperature (MnT) positively correlated with H. phycitis (r = 0.33***), O. albicinctus (r = 0.37***), populations A. bigutulla bigutulla (r = 0.22*), and phyllody percentage disease incidence (PDI) (r = 0.16). Rainfall (RF) and wind speed (WS) were negatively associated with leafhoppers, O. albicinctus, and A. bigutulla bigutulla, respectively. Morning (RHm) and evening (RHe) relative humidity were positively associated with all leafhopper species. The MnT was inversely associated with O. albicinctus (r = 0.37***) populations. The PDI was positively associated and significantly associated with the weather factors except WS. The autoregressive integrated moving average model with exogenous variables (ARIMAX) (0, 1, 0) of H. phycitis showed that MnT, RHe, and RF greatly affected the pest’s growth. The ARIMAX (3, 0, 1) of O. albicinctus showed that MnT and RHe significantly impacted the incidence. The ARIMAX (3, 0, 2) model of A. bigutulla bigutulla showed that MxT, MnT, RHm, and RHe substantially affected their occurrence. The ARIMAX (2, 0, 1) model of PDI revealed that MxT, MnT, RHm, RHe, and RF substantially influenced the disease’s incidence. Insect pests exhibit varied patterns of occurrence and severity in multi-cropping systems due to substantial differences in agro-climatic variables between locations. Comprehending the impact of weather patterns on sesame leafhoppers and PDI is crucial for developing successful management methods. Based on weekly events and climatic factors, the ARIMAX model was developed to anticipate the presence of leafhopper species and PDI on sesame.