Agricultural flood disaster risk assessment plays a vital role in agricultural flood disaster risk management. Extreme precipitation events are the main causes of flood disasters in the Midwest Jilin province (MJP). Therefore, it is important to analyse the characteristics of extreme precipitation events and assess the flood risk. In this study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method was used to determine the threshold of extreme precipitation events. The total duration of extreme precipitation and the total extreme precipitation were selected as flood indicators. The copula functions were then used to determine the joint distribution to calculate the bivariate joint return period, which is the flood hazard. Historical data and flood indicators were used to build an agricultural flood disaster vulnerability surface model. Finally, the risk curve for agricultural flood disasters was established to assess the flood risk in the MJP. The results show that the proposed approaches precisely describe the joint distribution of the flood indicators. The results of the vulnerability surface model are in accordance with the spatiotemporal distribution pattern of the agricultural flood disaster loss in this area. The agricultural flood risk of the MJP gradually decreases from east to west. The results provide a firm scientific basis for flood control and drainage plans in the area.

The study of soil environment in drainage fields is important for environmental management and ecological restoration, and there is currently a knowledge gap in understanding the impact of soil microbial communities in the Shengli coalfield drainage fields and the corresponding ecological effects. To investigate the changes in rhizosphere soil microbial communities of different dominant plants after years of restoration, this study examines the improvement effects of different dominant plants on the soil environment. This study is based on high-throughput sequencing to restore the slope of coal mine spoil after 15 years as the sampling site. The rhizosphere soil of five dominant plants was selected for microbial community analysis, and functional prediction of the microbial community was conducted. The dominant plants selected included Erect Milkvetch (Astragalus adsurgens), Lemongrass (Caragana korshinskii), Alfalfa (Medicago sativa), Phyllanthus pinnatifida (Elymus dahuricus), and Brassica Rapa (Brassica campestris). The results showed that after 15 years of restoration, the soil physicochemical properties in the Phyllanthus pinnatifida group were better than those in the other groups overall, but some of them were inferior to those in the lemon-stripped mallard group. Abundant saprophytic fungal communities were found in different dominant plant groups, mainly belonging to the phyla Ascomycota and Basidiomycota, resulting in significantly higher organic matter content in the dominant plant groups compared to the CK group. The bacterial communities were dominated by the phyla Actinobacteriota, Proteobacteria, Chloroflexi, and Firmicutes. Among these microbial phyla, the Phyllanthus pinnatifida group had higher abundance, which is beneficial for vegetation colonization. Redundancy analysis showed that soil pH was significantly correlated with microbial communities. Organic matter content and pH are the main factors influencing the composition of soil microbial communities, significantly affecting the composition of microorganisms in different groups. After years of restoration, the environment of the Shengli Coalfield's spoil heap has been greatly improved. The planting of various beneficial plants has resulted in significant improvements to the soil microbial community and physicochemical properties, with Phyllanthus pinnatifida having the most positive impact. This lays the foundation for the subsequent restoration of the slope of the spoil heap.

Due to climate change and global warming, the frequency of sandstorms in northern China is increasing. Stipa breviflora, a dominant species in Eurasian grasslands, can help prevent desertification from becoming more serious. Studies on S. breviflora cover a wide range of fields. To the best of our knowledge, the present study is the first to sequence, assemble, and annotate the S. breviflora genome. In total, 2,781,544 contigs were assembled, and 2,600,873 scaffolds were obtained, resulting in a total length of 649,849,683 bp. The number of scaffolds greater than 1 kb was 70,770. We annotated the assembled genome (>121 kb), conducted a selective sweep analysis, and ultimately succeeded in assembling the Matk gene of S. breviflora. More importantly, our research identified 26 scaffolds that may be responsible for the drought tolerance of S. breviflora Griseb. In summary, the data obtained regarding S. breviflora will be of great significance for future research.

Dominant species occupy a pivotal role in plant community, influencing the structure and function of the ecosystem. The spatial distributions of dominant species can react to the effect of different grazing intensities, thereby reflecting their tolerance and adaptive strategies toward grazing. In this study, geostatistical methods were mainly used to study the spatial distribution characteristics of Stipa krylovii Roshev. and Leymus chinensis (Trin.) Tzvel. species at two interval scales (quadrat size 5 m × 5 m, 10 m × 10 m) and two treatments (free grazing, FG, 1.66 sheep·ha− 1·a− 1; control, CK, 0 sheep·ha− 1·a− 1) in typical steppe of Inner Mongolia. A systematic sampling method was used in each 100 m × 100 m representative sample plots to obtain the height, coverage, and density of all species in the community. The results showed that grazing altered the concentrated distribution of S. krylovii and the spatial mosaic distribution pattern of S. krylovii and L. chinensis while having no effect on the spatial clumped distribution of L. chinensis. It also found that the spatial distributions of dominant species are primarily affected by structural factors, and random factors such as long-term grazing led to a transition of S. krylovii from a concentrated distribution to a small patchy random pattern should not be overlooked. Our findings suggest that long-term grazing alters the spatial distribution pattern of dominant species and that adaptive strategies may be the key for maintaining the dominant role of structural factors.

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R² = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions.

Leymus chinensis is a widely distributed species of Eurasian grasslands that is well adapted to a range of environmental conditions. In this study, we measured soil and plant chemical elements to examine the basis of the ecological adaptations of L. chinensis to temperate steppe. We sampled leaves of L. chinensis and soil layers at different latitudes in temperate steppe for three consecutive years from 2012 to 2014. One-way analysis of variance and linear regression were used to analyze the variation in foliar nitrogen (N) and phosphorus (P) contents and the N:P ratio in L. chinensis. The correlations between the foliar N:P ratio and the variation in soil total nitrogen (TN) and total P contents with latitudinal gradient were measured to explore the homeostasis stoichiometry of foliar N and P in L. chinensis. With increasing latitude, both the foliar N and P contents of L. chinensis showed parabolic trends with significant negative correlations. The population of L. chinensis was limited by N, and the soil TN content showed a significant increasing trend with the increase in the foliar N:P ratio. The variation in the foliar N:P ratio of L. chinensis was determined through the variation in the soil N content. In different latitude intervals, the foliar N, P, and N:P ratio of L. chinensis showed absolute stability and strong internal stability, and the values of H_N:P were high and similar in different latitudes. In conclusion, the population of L. chinensis in the temperate steppe is highly adaptable; and, thus, can maintain a dominant position in the variable environment and has a wide distribution range.