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

RBD severity in different rice ecosystems of Karnataka

Based on the observations made during the exploratory surveys of 2018 and 2019 (Table 1 and Fig. 1), it was found that RBD severity significantly varied across studied areas and districts (Fig. 2). The disease severity was highest in Chikmagalur, followed by Kodagu, Shivamogga, Mysore, and Mandya districts which belong to Hilly and Kaveri ecosystems. At the same time, the lowest severity was documented in Udupi, Gulbarga, Gadag, Dakshin Kannad, Raichur, and Bellary districts of coastal, UKP, and TBP ecosystems (Fig. 3A).

Hierarchical cluster analysis using the average linkage method for RBD severity among the 18 administrative districts of diverse rice ecosystems of Karnataka identified two main clusters, namely, cluster I and cluster II (Fig. 3B). Cluster I consist of two subclusters, cluster IA and IB. Subcluster IA consists of Mandya, Dharwad, Mysore, Hassan, Shivamogga, Haveri, and Belgaum; While, Kodagu, and Chikmagalur districts were clustered in IB. Similarly, Cluster II was divided into cluster IIA and cluster IIB. Subcluster IIA comprises Udupi, Gulbarga, Gadag, Raichur, Dakshin Kannad, Uttar Kannad, Koppal and Bellary, and Davanagere district was grouped under cluster IIB.

Spatial point pattern analysis of RBD

The cluster and outlier analysis was done using Local Moran’s I and p-values. The analyses have identified RBD cluster patterns at the district level during 2018 and 2019, representing dispersed and aggregated clusters of severity (Fig. 4). Based on positive I value, most of the districts were clustered together (at I > 0), except the coastal districts such as Uttar Kannad, Udupi, Dakshin Kannad, and interior districts such as Dharwad, Davanagere, and Chikmagalur, which exhibited negative I value (at I < 0). Similarly, the positive spatial autocorrelation was observed in the districts of Coastal, Hilly, Bhadra, and UKP ecosystems, at higher p-values, whereas at lower p-values, the districts of TBP and Kaveri ecosystems were clustered together.

Further, to characterize the strength of spatial dependence at spatial point pattern analysis, Ripley’s K function was utilized. In both the years of study, statistically significant clustering was observed at larger distances (Fig. 5). Each point under consideration exhibited a greater number of neighbors with increased evaluation distances. The average numbers of neighbors were greater at distances 0.4 and 0.8 representing the significant cluster distribution.

Surface interpolation to the explicit spatial distribution

IDW interpolation approach

Inverse distance weighted (IDW) interpolation identifies the cell values using a linearly weighted combination of a set of sample points. Contour maps created using the IDW procedure exhibited the RBD distribution pattern in different rice ecosystems of Karnataka (Fig. 6). During both the years of evaluation, the Hilly ecosystem, middle and southern parts of Karnataka has posed a potential risk to RBD with higher disease proportions (> 70%), with focal points at Chikmagalur, Kodagu, and Shivamogga districts followed by Kaveri and Bhadra ecosystems with 50–60 percent severity. Upper Krishna Project (0–10%) and coastal (0–10%) ecosystems were less disease-prone areas for RBD with relatively reduced disease indices. However, the TBP ecosystem had moderate disease severity (20–30%). It is evident from the maps of both years that the disease hot spots are majorly in the middle and Southern Karnataka, and cold spots are in Coastal and Northern Karnataka.

The IDW results were further validated by a scatter plot for predicted severity against observed severity during 2018 and 2019 (Fig. 7). From the plot, the predicted and observed severity almost lies along the line, excluding the errors during both years. The plot values representing the RBD during 2018 and 2019 exhibited a similar severity with RMSE values of 13.37 and 13.11, respectively.

Ordinary and indicator kriging

Spatial patterns of RBD severity observations were determined by semivariogram experimental models, such as spherical, exponential, and Gaussian. Among the models, the spherical model was found to be the best fit based on cross-validation of the semivariogram results (Table 2) that exhibited lower mean square error (MSE), root mean square standard error (RMSE), and average standard error (ASE) values (Fig. 8).

In the spherical model, MSE, RMSE, and ASE values for 2018 were 693.11, 26.327, and 0.789, respectively. The nugget, range (in degrees), and partial sill values were similar in all the models (Table 2). The spherical model was also found fit for the 2019 data with lower MSE (719.3061), RMSE (26.8199), and ASE (0.7957) values.

RBD severity in different rice ecosystems of Karnataka during 2018 and 2019 followed a normal distribution, as revealed by the Kolmogorov–Smirnov test, which was depicted through histograms and normal QQ plots of the dataset (Fig. 9). Before kriging and interpolation, a slight global trend in the data was removed using the first-order nominal trend removal function.

As with the IDW interpolation technique, ordinary kriging (OK) and indicator kriging (IK) were used to find the spatial surface areas of RBD in different rice ecosystems by considering the severity observations (n = 120). The OK map revealed the maximum severity of RBD in the Chikmagalur, Shivamogga, and Kodagu districts of the Hilly ecosystem with 60–80 per cent severity during 2018 and 2019 (Fig. 10). Districts of Kaveri (Mysore, Mandya, and Hassan), Bhadra (Davangere), Varada (Haveri), and part of the Hilly (Dharwad) ecosystem were found to be with 40–60 per cent severity. At the same time, districts of the Coastal ecosystem and TBP ecosystem exhibited less severity of RBD.

However, in the case of IK, the RBD was more severely distributed (during both 2018 and 2019) around the Hilly (Chikmagalur, Dharwad, Kodagu, and Shivamogga), Bhadra (Chikmagalur, Davanagere, and Shivamogga), Varada (Haveri), and Kaveri (Mandya and Mysore) ecosystems (Fig. 11). Very little distribution was observed in UKP (Belgaum and Gulbarga), TBP (Bellary, Gadag, Koppal, and Raichur), and Coastal ecosystem (Uttar Kannad, Udupi, and Dakshin Kannad). The perusal of results from OK and IK indicated that irrigated ecosystems comprising Hilly, Bhadra, Varada, and Kaveri belts had shown potential risk areas to RBD, and certainly, these areas need utmost attention to reduce and contain further spread to neighboring districts or ecosystems.

Source: Ecology - nature.com