Department of Plant Pathology, University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, India.
Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India.
Sci Rep. 2022 May 6;12(1):7403. doi: 10.1038/s41598-022-11453-9.
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.
水稻是一种全球重要的作物，极易感染稻瘟病（RBD）。我们研究了 RBD 的空间分布，考虑了印度卡纳塔克邦不同水稻生态系统中 120 个采样点的两年探索性数据。进行了点格局和表面插值分析，以确定 RBD 的空间分布。通过空间自相关和 Ripley 的 K 函数生成 RBD 的空间聚类。此外，还利用反距离加权（IDW）、普通克里金（OK）和指示克里金（IK）方法，通过利用邻近观测值预测未访问位置的值来生成空间图。使用平均链接方法的层次聚类分析确定了 RBD 严重程度的两个主要聚类。从局部 Moran's I 来看，除了沿海和内陆地区（I < 0）外，大多数地区都聚集在一起（I > 0）。在沿海、丘陵、巴德拉和上克里希纳项目生态系统中观察到正空间相关性（p > 0.05），而在通加巴德拉和卡维里生态系统地区，p < 0.05 的地区聚集在一起。从克里金来看，丘陵生态系统、卡纳塔克邦的中部和南部地区容易受到 RBD 的影响。这是印度首次使用地统计学方法对 RBD 空间分布进行的深入研究，该研究的结果有助于制定针对 RBD 的特定生态系统管理策略。
