The Second Competition on Spatial Statistics for Large Datasets

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our {\em ExaGeoStat} software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team

Genetic diversity assessment in jute (<em>Corchorus </em>species) utilizing inter simple sequence repeat and simple sequence repeat markers

316-326Fifteen inter simple sequence repeat (ISSR) and five simple sequence repeat (SSR) markers were amplified in ninety eight accessions of both the cultivated species of jute Corchorus olitorius and Corchorus capsularis with the objective to study the genetic diversity of both the species at molecular level. Allelic variability, total number of alleles, and higher polymorphic information content (PIC) suggested ISSR as efficient marker system utilized for the study. Jaccard’s similarity coefficients based on ISSR and combined ISSR-SSR analysis gave useful information for appropriate parent selection for future hybridization programme. Principal coordinate analysis (PCoA) supported UPGMA based cluster analysis and species speciation occurred at molecular level