Rice is an important cereal crop rich in starch and carbohydrates grown around the globe. Despite its significance, rice exhibits substantial genetic variation, particularly under environmental stresses such as salinity. This study investigates the genetic diversity of F3 segregating populations of rice under normal and salt stress. Various segregating genotypes were evaluated, demonstrating statistically significant differences (p<0.01 and p<0.05, ANOVA) in morphological and physiological parameters. The genotypes Kharagnjia and L-12 performed well in normal soils, while Shua-92 and L-20 showed better performance in tiller plant-1 and panicle length. The cluster analysis grouped rice genotypes into four major clusters based on genetic similarity. Principal Component Analysis (PCA) identified tillers per plant, panicle length, grain yield per plant, and leaf area as key contributors to genetic variation. The highest variability was observed in PC-XII (100%) and PC-XI (98.3%). These findings provide valuable insights for breeding programs aimed at enhancing salt tolerance in rice.

In the context of climate change, exploring and predicting the spatio-temporal distribution of flood disasters is crucial for developing effective flood risk management and disaster reduction strategies. This study tackles the shortcomings of traditional methods used to measure the risk of regional flood disasters, which often lack precision. A series of machine learning models enhanced by Particle Swarm Optimization-Machine learning (PSO-ML) models were developed and integrated with General Circulation Model (GCM) data to analyze the temporal and spatial characteristics of flood disasters under different scenarios in Shanxi Province, China. Results indicate that the frequency of days with precipitation exceeding 50 mm in the study area increased from 23 in 1981 to 71 in 2021. The northernmost city, DT, experienced 18 extreme precipitation days, while the southernmost city, YC, recorded 71 such days. A gradual increasing trend in extreme precipitation days was observed from north to south and from distant to near areas. The PSO-ML models demonstrated notably improved performance compared to traditional models across all indices. PSO-XGBoost, PSO-RF, and PSO-KNN exhibited higher prediction accuracy than conventional single models, with AUC values of 0.98, 0.95, and 0.94, respectively. Land use change, elevation, and slope emerged as the most influential factors, with weights of 10.37%, 10.01%, and 8.76%, respectively. Across four scenarios (SSP126, SSP245, SSP370, and SSP585), flood-prone areas were projected to shift southward, with varying degrees of increase in risk areas. The SSP370 scenario showed gradual growth, projecting 7660.116 km² of at-risk area by 2100. The SSP585 scenario exhibited the most rapid growth, with a projected peak of 13,933.69 km² by 2070. This research proposes a novel approach to flood disaster risk assessment and offers insights for effectively mitigating regional flood risks.

Analysing the patterns and impacts of land-use changes in the production–living–ecological space (PLES) of the Fenhe River Basin (FRB 39,721 km²), China, is necessary to support sustainable development. Based on remote sensing images from 1990 to 2020, we aimed to analyse the PLES land-use changes. Industrial production and living spaces continuously encroached on the agricultural production and ecological spaces between 1990 and 2022 owing to industrialisation and urbanisation, and the ecological land area decreased by 699.21 km², while the industrial production land area increased by 521.32 km². We used the soil and water assessment tool (SWAT) model to quantitatively analyse the impact of PLES changes on runoff in the FRB. With the continuous expansion of production and living spaces, the extensive use of concrete in cities has led to ground hardening, making it difficult for precipitation to infiltrate, with surface runoff increasing by 0.3 mm annually. The reduction in ecological space has led to a reduction in forests and grasslands, weakening the water-holding capacity of the watershed and affecting groundwater storage. This study provides a scientific basis for watershed management and the integrated development of PLES.