Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka

Abstract

Rice is a globally important crop and highly vulnerable to rice blast disease (RBD). We studied the spatial distribution of RBD by considering the 2-year exploratory data from 120 sampling sites over varied rice ecosystems of Karnataka, India. Point pattern and surface interpolation analyses were performed to identify the spatial distribution of RBD. The spatial clusters of RBD were generated by spatial autocorrelation and Ripley’s K function. Further, inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) approaches were utilized to generate spatial maps by predicting the values at unvisited locations using neighboring observations. Hierarchical cluster analysis using the average linkage method identified two main clusters of RBD severity. From the Local Moran’s I, most of the districts were clustered together (at I > 0), except the coastal and interior districts (at I < 0). Positive spatial dependency was observed in the Coastal, Hilly, Bhadra, and Upper Krishna Project ecosystems (p > 0.05), while Tungabhadra and Kaveri ecosystem districts were clustered together at p < 0.05. From the kriging, Hilly ecosystem, middle and southern parts of Karnataka were found vulnerable to RBD. This is the first intensive study in India on understanding the spatial distribution of RBD using geostatistical approaches, and the findings from this study help in setting up ecosystem-specific management strategies against RBD.

Geospatial distribution, mapping, and identification of potential risk areas for the sterility mosaic disease of pigeonpea in India

“…To escape this limitation, the IK method was used to create probability distribution maps of SMD risk areas in different states of India. Similarly, the IK method was used to model probability maps for various crops, identifying regions of uncertinity related to disease presence, as demonstrated in previous works [24,27,28,37,38]. The probability risk maps constructed in the current investigation display potential sites of uncertainty, highlighting the highest probability zones where SMD incidence has surpassed the de ned threshold (incidence > 25%).…”

“…Several spatial disease distribution analysis techniques have been utilized to identify the spatial distribution of pathogens and the locations of diseased elds [27,28]. To establish the correlation between spatial data at various distance intervals and to develop spatial dependence, spatial autocorrelation is typically used [29,30].…”

Survey and Surveillance on Major Rice Diseases Severity in Karnataka, India

“…Although the severity of disease was varied to a greater extent in all the regions of North Eastern Karnataka. However the extent of disease incidence was noticed in all localities it may be due to use of same variety viz., Sonamahsuri (BPT-5204) and favorable environmental conditions prevailing over the locality could attributed to maximum disease incidence [12].…”

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

“…A study on the spatial spread of rice disease called rice blast and potential risk areas for this disease is being conducted in the rice ecosystem of Karnataka [72]. Crop suitability and crop pattern mapping using remote sensing GIS techniques are implemented using Landsat 8 data with NDVI and supervised classification methods.…”