Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

Root-lesion nematodes, Pratylenchus sp., are recognized as one of the most important plant parasitic nematodes. They infest various crop plants and cause severe root damages which lead to the yield loss of agricultural crops. There are 72 species recorded in the world, but 14 species in Korea. Soil samples were collected from chrysanthemum cultivars in various regions. Morphological and molecular diagnosis firstly recognized two species as Pratylenchus kumamotoensis and P. pseudocoffeae in Korea. Morphologically, P. kumamotoensis characterized by pharyngeal gland lobes overlapping with intestine dorsally. P. pseudocoffeae, which is similar with P. coffeae, but pharyngeal gland lobes are longer than P. coffeae. In molecular diagnosis, nucleotide sequences of D2-D3 region (700-800 bp) within 28S rDNA and ITS region including ITS1, 5.8S and ITS2 (700-1,000 bp) between 18S and 28S rDNA were determined from collected samples. NCBI database comparison of D2-D3 of P. kumamotoensis and P. pseudocoffeae were 99% similar with those of corresponding species, respectively. ITS of P. pseudocoffeae was 99% similar within species. ITS sequence of P. kumamotoensis was firstly determined in this study. Distribution of P. kumamotoensis was only reported in Japan but P. pseudocoffeae is present in Japan and USA. Here we firstly report the presence of these two species in Korea.

In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for screening target loci with agricultural traits. We propose the variation block method, a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing short-read DNA sequences of the cultivar to a reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are named as variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybeans and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color. We suggest that the variation block method is an efficient genomics method for recombination block-level comparison of crop genomes. We expect that this method holds the prospect of developing crop genomics by bringing genomics technology to the field of crop breeding.

In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for screening target loci with agricultural traits and for developing designed cultivars. We propose the molecular breeding platform based on the variation block (VB) method, which is a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are termed variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybeans and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color. In addition, we identified recombination hot spots of soybean genome in 614 recombinant inbred lines (RILs) using VB-specific indel markers. We expect that this platform facilitate the development of designed cultivars by introducing precise target loci into plant genomes.