Exploratory Analysis of Crop Disease Incidence Data in Imo State Using Geospatial Techniques

Mapping crop disease incidence using geospatial analysis enables targeted agricultural extension responses that improve smallholder farmer livelihoods in South East Nigeria. This study conducted a geospatial exploratory analysis of cassava mosaic virus, yam mosaic virus, and maize streak virus incidence data collected by agricultural extension officers across 27 Local Government Areas in Imo State, South East Nigeria. Disease incidence records for the 2020 to 2022 farming seasons were merged with soil type, altitude, and rainfall geospatial data layers. Kernel density estimation, spatial autocorrelation (Moran's I), and choropleth mapping were applied using QGIS and GeoPandas. Spatial clustering of cassava mosaic virus incidence was identified in the eastern LGAs bordering Abia State, with Moran's I of 0.48 indicating significant positive spatial autocorrelation. Rainfall above 1,800 millimetres per year was strongly associated with disease clustering. Yam mosaic virus showed a more dispersed spatial pattern without significant autocorrelation. The analysis identified five priority intervention zones requiring urgent extension advisory deployment. Farmer practice data indicated that use of certified disease-free planting materials was below 22 percent in high-incidence zones. Limitations included variation in recording quality across extension officers and incomplete spatial coverage in remote LGAs. Recommendations include developing a geo-tagged disease surveillance application for extension officers, establishing certified seed distribution points in disease hotspot zones, and training extension personnel in geospatial data collection protocols.

Keywords: geospatial analysis, crop disease, Imo State, cassava mosaic virus, spatial autocorrelation