Northeast Region Of China Research Articles

An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series

The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m2/m2. This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data.

Soil Degradation of Sloping Cropland in the Typical Black Soil Region in Northeast China: Risk Assessment and Uncertainty Analysis

ABSTRACTSoil degradation in the typical black soil region in Northeast China poses a threat to agricultural productivity and sustainability. However, little is known about the distribution of soil with different degradation degrees on a regional scale. This study aimed to map soil degradation status based on a newly developed soil degradation index (SDI) and assessed the uncertainty of degradation risk through the combination of ordinary Kriging and sequential Gaussian simulation (SGS) in order to enhance the accuracy and credibility of predictive results. The mean SDI was 0.445, with a coefficient of variation of 30%. The realization with 200 times by SGS was the best showed an increasing pattern of soil degradation degree from north to south in the study area. Slightly and moderately degraded sloping cropland accounted for 35.3% and 52.4% of all sloping cropland, respectively. High‐risk areas were mainly located in the Daxing'an and Changbai Mountains to the Songnen Plain. Proactive measures are necessary to control and mitigate further degradation by improving soil management and weakening the impact of runoff on sloping cropland. The developed risk assessment maps provide baseline information for regional sustainable development.

Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020

Agricultural land resources are essential for food production, and thus it is vital to examine the spatiotemporal changes in these resources and their impacts on land suitability to optimize resource allocation. In this study, we investigated the spatial evolution of cropland resources through land use change analysis by utilizing four periods of land use data from 1990 to 2020 in the black soil region of northeast China (BSRNC). Employing niche theory, we developed a cultivability evaluation model tailored to the BSRNC, which was used to assess the impact of the spatial changes in cropland patterns over the past 30 years on land suitability. Our key findings are as follows: (1) Cropland resources have generally tended to expand in the BSRNC, with an increase of 7.16 × 103 km2 in the cultivated area and a northeastward shift in the cropland center by 52.94 km, indicating significant changes in the spatial configuration of the land. (2) The region’s cultivable land resources were substantial, covering 694.06 × 103 km2, or 55.78% of the total area, with notable spatial variability, influenced by the regional climate and topography. (3) The land cultivability has slightly improved, as shown by a 0.10 increase in the cultivability index, but a significant declining trend in the cultivability of cropland was observed after 2000. Our findings provide valuable insights to help accurately assess land productivity in the BSRNC and facilitate the sustainable use and conservation of black soil.

Revealing the rural multifunctionality declining and its causes in depopulated regions of Northeast China: a case study of Heilongjiang Province

As the main form of global rural decline, the impact of rural population loss on the overall level and trend of rural multifunction is still unclear. Thus, it is crucial to reveal the characteristics and influencing factors of rural multifunction changes in depopulation areas which is important to actively deal with depopulation and promote sustainable development in rural areas. Based on an evaluation of rural multifunctionality from 2000 to 2020, the spatial statistical analysis method and a geographical model were used to reveal the declining characteristics of rural multifunction in the depopulation area, as well as the effects of the influencing factors in the whole region and under various population size areas. The results show that demographic changes had a significant impact on rural multifunction development. Compared to areas with population growth, the weakening of rural multifunctionality was more likely to occur in counties with depopulation. The negative impact of population shrinkage on rural multifunction development was shown in our results as an inverted “U” shape. In the process of demographic shrinkage, the dominant factors affecting rural multifunctionality in the whole region tended to be diversified, but the population urbanization rate was always the main factor. Among regions with different population sizes, natural factors played important roles in the development of rural multifunction, and depopulation enhanced the impact of economic factors. The interaction effect of natural, social, and economic factors was stronger than that of a single factor in rural multifunction. In the process of depopulation, the interaction effect was more and more polarized. The interaction effect of economic and natural factors enhanced, but which was the most stable in mid-population-size areas. Thus, to alleviate rural decline and realize rural revitalization, the trend of demographic change should be given attention to keep a moderate population size, as well as we should actively deal with the impact of population shrinkage on rural multifunction according to different stages of population shrinkage.

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographic regions. In this study, 220 samples, consisting of sterile swabs from palmar skin and oral and nasal cavities were collected from Chinese Han individuals living in Shanghai, Chifeng, Kunming, and Urumqi, representing the geographic regions of east, northeast, southwest, and northwest China. The full-length 16S rRNA gene of the microbiota in each sample was sequenced using the PacBio single-molecule real-time sequencing platform, followed by clustering the sequences into operational taxonomic units (OTUs). The analysis revealed significant differences in microbial communities among the four regions. Cutibacterium was the most abundant bacterium in palmar samples from Shanghai and Kunming, Psychrobacter in Chifeng samples, and Psychrobacillus in Urumqi samples. Additionally, Streptococcus and Staphylococcus were the dominant bacteria in the oral and nasal cavities. Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. The prediction accuracy using hypervariable regions (V3-V4 and V4-V5) was comparable with that of using the entire 16S rRNA. Overall, our study highlights the distinctiveness of the human microbiome in individuals living in these four regions. Furthermore, the microbiome can serve as a biomarker for geographic origin inference, which has immense application value in forensic science.IMPORTANCEMicrobial communities in human hosts play a significant role in health and disease, varying in species, quantity, and composition due to factors such as gender, ethnicity, health status, lifestyle, and living environment. The characteristics of microbial composition at various body sites of individuals from different regions remain largely unexplored. This study utilized single-molecule real-time sequencing technology to detect the entire 16S rRNA gene of bacteria residing in the palmar skin, oral, and nasal cavities of Han individuals from four regions in China. The composition and structure of the bacteria at these three body sites were well characterized and found to differ regionally. The results elucidate the differences in bacterial communities colonizing these body sites across different regions and reveal the influence of geographical factors on human bacteria. These findings not only contribute to a deeper understanding of the diversity and geographical distribution of human bacteria but also enrich the microbiome data of the Asian population for further studies.

Declining trends and regional variations of organophosphate ester contamination in indoor dust from mainland China: Insights from a filed study and meta-analysis.

This study investigated the presence of 20 organophosphate esters (OPEs) in indoor dust samples collected from the Chinese cities of Lanzhou, Xining, and Lhasa. The results demonstrate the ubiquitous presence of most OPEs in these three cities, with the highest concentrations of ΣOPEs found in Xining. We also summarized the occurrence of OPEs in indoor environments from 38 studies with 1875 samples collected across various regions of mainland China from 2012 to 2023. The weighted-median concentration of ΣOPEs in indoor dust exhibited region-specific variations, range from 381.9 to 6622.5ng/g. Chloroalkyl-OPEs (Cl-OPEs) (e.g., tris(2-chloroethyl) phosphate (TCEP), tri(1-chloro-2-propyl) phosphate (TCIPP), and tri (1,3-dichloro-2-propyl) phosphate (TDCIPP)) predominated in all seven regions (range: 38.9%-71.4%). TCIPP was predominant in the Central China, North China, Northeast China, Northwest China, Southwest China, and Southwest China regions, while TCEP dominated in the Eastern China region. A significant downward trend in OPE concentrations in indoor environments was observed during the investigated period. Dust ingestion was identified as the predominant pathway of human exposure to OPEs indoors. The hazard quotients for Cl-OPEs were below the non-carcinogenic threshold, suggesting significant health risks are unlikely. This study underscores the widespread occurrence of OPEs in indoor dust across mainland China, emphasizing the necessity for ongoing monitoring and regulation of these chemicals.

Evolutionary characteristics, regional differences and spatial effects of coupled coordination of rural revitalization, new-type urbanization and ecological environment in China

Rural revitalization (R), new-type urbanization (U) and the ecological environment (E) are a unity of complementary advantages and mutual promotion and coexistence, the coupling and coordination of the three is the key to realizing the Chinese modernization and the harmonious coexistence of human beings and nature. Based on China’s provincial panel data from 2011 to 2022, this paper constructed a comprehensive evaluation index for the R-U-E system, using the entropy method, coupled coordination model, Dagum’s Gini coefficient, kernel density estimation, spatial correlation analysis, and spatial Durbin model to explore the level of comprehensive development, spatial-temporal evolutionary characteristics of coupled coordination, regional differences and spatial effects of the three systems. The following results were observed: (1) The comprehensive index of rural revitalizationand new-type urbanization exhibits a growing trend and significant regional differences, with East China outperforming other regions, but the average value of below 0.5 needs to be further improved. The ecological environment index exhibits a smoother growth trend, higher in South China than in other regions. (2) The level of R-U-E coupling coordination has increased year on year, from barely coordinated to primary coordination. (3) The spatial imbalance in coupling coordination has improved both nationally and in the seven regions, with the largest intra-regional differences in South China and the smallest intra-regional differences in Central China. The differences between the Northeast and Central China regions exhibit a widening trend, while the differences between all other regions exhibit a decreasing trend. Interregional variations are the main factor affecting overall variance, but their impact on the overall variance is gradually diminishing. (4) The level of coupling coordination has obvious spatial correlation, and the “high-high” and “low-low” cluster aggregation characteristics was evident. (5) Government behavior and the levels of economic growth, human capital, and digitization significantly contribute to the coupled and coordinated development of the region and, at the same time, have a significant spatial spillover effect on neighboring provinces; the degree of openness to the outside world contributed to the coordinated development of the region, while it had an inhibitory effect on the neighboring provinces; and the drive to innovate has only a certain contributing effect on the neighboring regions. Targeted policy measures in response to this paper’s empirical findings may provide policymakers with a reference point for achieving regional sustainable development goals.

A Three-Layer Scheduling Framework with Dynamic Peer-to-Peer Energy Trading for Multi-Regional Power Balance

This paper addresses the critical challenges of renewable energy integration and regional power balance in smart grids, which have become increasingly complex with the rapid growth of distributed energy resources. It proposes a novel three-layer scheduling framework with a dynamic peer-to-peer (P2P) trading mechanism to address these challenges. The framework incorporates a preliminary local supply–demand balance considering renewable energy, followed by an inter-regional P2P trading layer and, ultimately, flexible resource deployment for final balance adjustment. The proposed dynamic continuous P2P trading mechanism enables regions to autonomously switch roles between buyer and seller based on their internal energy status and preferences, facilitating efficient trading while protecting regional privacy. The model features an innovative price update mechanism that initially leverages historical trading data and dynamically adjusts prices to maximize trading success rates. To address the heterogeneity of regional resources and varying energy demands, the framework implements a flexible trading strategy that allows for differentiated transaction volumes and prices. The effectiveness of the proposed framework is validated through simulation experiments using k-means clustered typical daily data from four regions in Northeast China. The results demonstrate that the proposed approach successfully promotes renewable energy utilization, reduces the operational costs of flexible resources, and achieves an efficient inter-regional energy balance while maintaining regional autonomy and information privacy.

Morphological characteristics of permanent gullies and their relationship with topography in two typical agro‐geomorphic regions of Northeast China

AbstractGully erosion poses significant challenges to land degradation and food security. The Mollisols region of Northeast China (MNC) is the main grain‐producing region in China. The low mountainous and hilly region (MFI) and the rolling hilly region (RFI), as the prominent topographic units in MNC, have suffered significantly from severe gully erosion. However, the actual situation and influencing factors of gully erosion in both regions are still unclear. Thus, we selected two field investigation regions of 103.4 km2 in MFI and 123.9 km2 in RFI to obtain a detailed inventory of gullies. Results showed that the gully number density in MFI was 25.7 No. km−2, 8.3 times greater than in RFI. Conversely, the gully average gully length (511.7 m), width (7.8 m) and depth (2.8 m) in RFI were 156.8%, 112.6% and 37.4% greater than those in MFI, respectively. The three types of gullies, developed in different land uses (GF, farmland; GW, woodland; GR, unpaved road), had significantly varying morphologies. In RFI, GF was the longest, and GW was the widest and deepest. Conversely, in MFI, GR was the longest but exhibited the least width and depth. Notably, farmland posed the highest risk of gully erosion in both MFI and RFI, with 74.4% and 89.6% of the gullies developed there, respectively. Topography was the critical factor influencing the differences in gully erosion between the two regions. To effectively control gully erosion, topographic conditions most susceptible to gully erosion should be prioritized for attention, e.g., slopes of 8–10° and 2–4° were the slope intervals most likely to contribute to gully development in MFI and RFI, respectively. We provide the potential for large‐scale prediction of gully erosion. Corresponding prediction models based on gully area were provided and the effectiveness of the predictions was improved by separating different land uses. The study's findings could serve as a valuable foundation for gully erosion control and prediction for MNC.

Synergistic Matching and Influencing Factors of Grain Production and Cropland Net Primary Productivity in the Black Soil Region of Northeast China

Exploring the spatiotemporal dynamics, spatial mismatch, and complex influencing mechanism of grain production and cropland productivity in the black soil region of northeast China (BSRNC) is essential for the synergistic protection and utilization of black soil cropland and sustainable grain production. The BSRNC has realized cropland expansion and grain production increases in the past decades. This implied a substantial investment has been made in the region’s agriculture. However, at present, knowledge on the spatial mismatch and influencing factors of grain production and cropland productivity is still unclear. This study analyzed the spatial–temporal mismatch characteristics of grain production and cropland net primary productivity (CNPP) using the gravity center model, spatial autocorrelation analysis, and spatial mismatch index (SMI), and identified the spatial heterogeneity and prediction–response relationships of influencing factors based on a geographically and temporally weighted regression (GTWR) model and boosted regression tree (BRT) machine learning algorithm. The findings indicated that grain production and CNPP have been increasing, but the overall spatial pattern of cold hotspots has not changed obviously in the BSRNC from 2000 to 2020. The SMI has shown a decreasing trend, indicating that the synergistic development of grain production and CNPP has been obvious, which plays an important role in sustainable food supply capacity. Agricultural production and the natural environment have always been critical factors influencing the spatial mismatch. Specifically, the marginal impact of fertilizer application has undergone a shift. This study may provide new clues for the formulation of regional strategies for sustainable food supply and black soil cropland system protection.

Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.

Effects of forest fire on successional trajectory of understory vegetation in boreal permafrost region in Northeast China

Effects of forest fire on successional trajectory of understory vegetation in boreal permafrost region in Northeast China

The Spatiotemporal Characteristics and Driving Factors of Soil Degradation in the Black Soil Region of Northeast China

The Northeastern Black Soil Region in China is recognized as one of the three major black soil regions globally and is often regarded as a cornerstone of national food security. However, prolonged agricultural practices have led to increasingly severe soil degradation, and the mechanisms and driving factors behind the degradation of soil quality remain unclear. Therefore, this study examines the historical and current characteristics of soil quality, focusing on major influencing factors, such as the 70-year history of reclamation and climate change. By accessing different databases, reviewing the relevant literature, and performing Pearson correlation and redundancy analyses (RDA), this study investigated the variation patterns of significant soil quality indicators and their driving factors in the 0–20 cm soil layer along the latitudinal direction (Nenjiang, Beian, Hailun, and Harbin) in the typical black soil region of Northeast China. The main conclusions are as follows: the soil organic matter (SOM) content experienced a rapid decline in the 30 years preceding cultivation (1950~1980), with the greatest decline rate in the Beian area (about 1.10 g/kg per year). The SOM in the Beian, Hailun, and Harbin areas decreased from north to south, changing at rates of 9.40–21.67 g/kg/degree and 0.15–0.34 g/kg/m with latitude and elevation, respectively. Elevation impacts the annual rate of change in soil quality indicators through its influence on the annual mean maximum temperature (AMXT) and annual atmospheric pressure (AP). AMXT and AP exhibit a linear relationship with elevation, based on which regression models were established. The key factors influencing soil quality indicators in the black soil region include cultivation years (Y), annual mean maximum and minimum temperatures (AMXT and AMNT), annual relative humidity (ARH), and AP. An increase in chemical fertilizer application is among the critical factors affecting soil pH. Additionally, the extensive use of agricultural machinery can reduce soil porosity and cause water and salt accumulation, ultimately leading to a decline in soil pH. This study offers theoretical support for mitigating soil degradation in Northeast China’s black soil region, thereby contributing to national food security and promoting sustainable development.

Remote sensing image segmentation of gully erosion in a typical black soil area in Northeast China based on improved DeepLabV3+ model

Gully erosion, a cause of soil degradation and reduced crop yields is widespread in the diffuse river and hillock regions of Northeast China. The surface conditions in gully erosion areas are complex, and manual extraction methods are inefficient, hindering gully erosion monitoring progress. Accurate extraction of gullies is essential for effective environmental monitoring and management. In this study, we propose an improved deep learning network, named DD-DA (the DeepLabV3+ deep network with dual attention mechanism), for automatic and precise gully erosion extraction from 2 m resolution Gaofen-6 remote sensing images from the Heshan Farm area. The DD-DA model builds upon the DeepLabV3+ network architecture with targeted optimizations. Firstly, ResNet50 replaces the original backbone network of DeepLabV3+, enhancing feature extraction capabilities. Second, the Convolutional Block Attention Module (CBAM) integrates spatial and channel attention, improving the model's capacity to capture the shape and texture features of gully erosion. Finally, the Dice loss function is adopted to address the limitations of the traditional cross-entropy loss function, particularly in handling unbalanced datasets. To promote deep learning applications in gully erosion monitoring in Northeast China, a gully erosion dataset was constructed. Experiments on gully erosion datasets show that DD-DA can achieve high Mean Intersection over Union (MIoU) (79.21 %), Pixel Accuracy (96.17 %), Recall (87.66 %), F1-Score (86.48 %), and Precision (86.08 %) simultaneously, which significantly outperform traditional image processing methods and recently deep learning networks. Overall, DD-DA efficiently and accurately segment gully erosion in agricultural fields, significantly improving the extraction of gully erosion features from high-resolution remote sensing images, particularly in the black soil regions of northeastern China. Meanwhile, this study offers a solution for the rapid monitoring of gully erosion and provides substantial technical support for extracting other land degradation features, such as soil salinization and desertification.

Black Soil Quality After 19 Years of Continuous Conservation Tillage

Conservation tillage is a practice adopted worldwide to prevent soil degradation. Although there have been many studies on the impact of conservation tillage on soil quality, most studies on cultivated land in the black soil region of Northeast China are based on the physical and chemical indicators of soil. In addition, the experiment time is generally short, so there is a lack of information about long-term conservation tillage from the perspective of the physical, chemical, and biological integration of soil. A comparative analysis of the physical, chemical, and biological characteristics of soil was conducted under no-till (NT) with straw mulching and conventional tillage (CT) treatments after 19 years of field experiments. By using membership functions to normalize and render all the indicators dimensionless, and calculating the weight of each indicator through principal component analysis, the comprehensive index of soil quality can be calculated as a weighted summation. The results indicate that NT had no significant effect on soil bulk density at a soil depth of 0–20 cm. NT increased the field water-holding capacity of the 0–5 cm layer, reduced the total porosity of the 5–10 cm soil layer, and decreased the non-capillary porosity of the 0–20 cm soil layer. Compared to CT, NT significantly increased the organic carbon content of the soil in the 0–5 cm layer, comprehensively improved the total nutrient content of the soil, and significantly increased the contents of ammonium nitrogen, nitrate-nitrogen, and available phosphorus in the soil. It also significantly improved the total phosphorus content in the 5–20 cm soil layer. NT improved the microbial carbon and nitrogen content of the soil, significantly enhanced the microbial nitrogen content in the 0–5 and 5–10 cm soil layers, and reduced the bacterial species diversity in the 5–10 cm soil layer. However, the soil enzyme activities showed no significant differences between different treatments. Under the NT treatment, the evaluation of soil quality indicators, such as mean weight diameter, field water-holding capacity, non-capillary porosity, microbial biomass nitrogen, total nutrients, and available nutrients, was relatively successful. Based on the weight calculation, the organic carbon, catalase activity, fungal richness, and bacterial diversity indicators are the most important of the 22 soil quality indicators. In terms of the comprehensive index of soil fertility quality, NT increased the soil quality comprehensive index by 34.2% compared to CT. Long-term conservation tillage improved the physical, chemical, and biological properties of the soil, which significantly enhanced the quality of the black soil.

The prevalence of suboptimal health status among Chinese secondary school students and its relationship with family health: the mediating role of perceived stress and problematic internet use

BackgroundThe health status of secondary school students has received widespread attention, and family plays an extremely important role in protecting and promoting their health. However, the relationship between family health and suboptimal health status (SHS) among secondary school students and its underlying mechanisms are unclear. This study aims to understand the prevalence of SHS among Chinese secondary school students and analyze the relationship between family health and SHS, and examine the mediating roles of perceived stress and problematic internet use.MethodsThe 2,094 secondary school students (52.6% boys, 47.4% girls, Mage ± SD = 15.74 ± 1.78) of this study came from “2022 Psychology and Behavior Investigation of Chinese Residents, PBICR”. The chi-square test and t-test were used to analyze the prevalence of SHS in secondary school students with different characteristics. The SPSS PROCESS macro software was used to explore the relationship between family health and SHS and the mediating effects of perceived stress and problematic internet use.ResultsThe prevalence of SHS among Chinese secondary school students was 40.7%, and older students, girls, non-only child, high school students, smokers, drinkers, and students living in the Northeast region of China had higher prevalence. Family health, SHS, perceived stress, and problematic internet use were significantly related (P < 0.001). Family health had a negative effect on SHS (β = -0.127, 95% CI: -0.173 to -0.080). At the same time, perceived stress mediated the relationship between family health and SHS (chain mediation model 1: β = -0.109, 95% CI: -0.133 to -0.087; chain mediation model 2: β = -0.098, 95% CI: -0.120 to -0.078) and problematic internet use also mediated their relationship (chain mediation model 1: β = -0.034, 95% CI: -0.056 to -0.014; chain mediation model 2: β = -0.077, 95% CI: -0.099 to -0.055). The chain mediating effect of perceived stress and problematic internet use was also found in this study and the chain path from family health to perceived stress to problematic internet use to SHS was superior (β = -0.043, 95% CI: -0.054 to -0.033).ConclusionsMany secondary school students are experiencing SHS. Improving family health, such as strengthening parent–child communication and fostering positive parenting practice, is critical to improving their health. Helping secondary school students relieve perceived stress and reduce their problematic internet use is key to implementing family-oriented health interventions.

Effects of different tillage methods on soil properties and maize seedling growth in alternating wide and narrow rows rotation mode in the Songliao Plain of China

The Songliao Plain is the main maize (Zea mays L.) producing region in Northeast China. The no-tillage22NT: no-tillage. (NT) method in alternating wide and narrow rows rotation mode is widely used for maize planting in this region. However, in spring, the NT method in this mode suffer from low soil temperature and high soil water content33SWC: soil water content. (SWC), which severely restrict maize seed germination and seedling growth. We pioneered the research and practice of the ridge tillage44RT: ridge tillage. (RT) method and strip tillage55ST: strip tillage. (ST) method in this mode. We developed a new tillage machine to enable the implementation of RT and ST methods in alternating wide and narrow rows rotation mode, which has not been previously reported in this region. In this mode, the relative research of the RT method and ST method has not been conducted, and the effects of the RT method and ST method on the soil properties and maize seedling growth have not been clarified. Therefore, a 3-year field experiment was conducted at 7 randomly selected experimental sites in this region to research the effects of RT, ST, and NT methods on soil properties and maize seedling growth. During the spring sowing period, for soil pH, organic matter66OM: organic matter. (OM), available nitrogen, available phosphorus, and available potassium, both the RT method and ST method resulted in greater values than the NT method, and there was no significant difference between the RT method and ST method. This indicated that prolonged NT was not conducive to even distribution of OM, resulting in slightly lower available nitrogen, phosphorus, and potassium compared to the RT and ST methods. Within 30 days after spring sowing, for soil temperature, RT method value > ST method value > NT method value; for SWC, NT method value > RT method value > ST method value. For mean seedling emergence time77MET: mean seedling emergence time. (MET), RT method value < ST method value < NT method value; for seedling emergence rate88ER: seedling emergence rate. (ER), plant height, stem thickness, and plant dry weight, RT method value > ST method value > NT method value. Higher soil temperature, more suitable SWC, and better nutrient availability were beneficial in shortening MET, promoting dry matter accumulation, which ultimately increased plant height, stem thickness, and plant dry weight of maize seedlings. In RT, ST, and NT methods, soil temperature and plant dry weight were positively and linearly correlated in the 10–22 °C soil temperature interval. In the RT method and ST method, SWC and plant dry weight were positively and linearly correlated in the 15–24 % SWC interval. In the NT method, SWC and plant dry weight were negatively and linearly correlated in the 27–35 % SWC interval. Moreover, maize yield responses were positive for the RT method and ST method compared with the NT method. The RT method and ST method well solved the problems of low soil temperature and high SWC that existed in NT fields, which affected the early growth of maize seedlings, and ensured the stability and improvement of maize yields. The results suggest that both the RT method and ST method may provide significant improvements over the existing NT method.

A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics

Mapping the spatial distribution of soil organic matter (SOM) content is crucial for land management decisions, yet its accurate mapping faces challenges due to complex soil-environment relationships and temporal feature capture limitations in machine learning models. This study focuses on the typical black soil region in Northeast China, specifically using Youyi Farm as the main research area and Heshan Farm as the transfer research area. A novel approach is proposed that combines the CNN-LSTM model with a Cosine Annealing Warm Restarts learning rate (CNN-LSTM-CAWR) to enhance the accuracy of SOM mapping. In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). The incorporation of the CAWR learning rate helps alleviate overfitting issues. Comparing the CNN-LSTM model, CNN model, and traditional RF model, the results show that the CNN-LSTM-CAWR model achieves the highest accuracy within research Area 1 (R2 = 0.64, RMSE = 0.54 %) and maintains strong performance in the transfer research area (R2 = 0.60, RMSE = 0.57 %). CNN-LSTM-CAWR demonstrates faster convergence, thereby improving mapping precision and effectively utilizing temporal information from features to enhance overall model performance. This study underscores the significant potential of the hybrid CNN-LSTM with CAWR model, highlighting the valuable information for SOM mapping contained within Sentinel-2 time series data.

Spatial Variation and Stock Estimation of Soil Organic Carbon in Cropland in the Black Soil Region of Northeast China

Soil organic carbon (SOC) sequestration in cropland is not only instrumental in combating climate change, but it also significantly enhances soil fertility. It is imperative to precisely and accurately quantify the SOC sequestration potential and assess the relative significance of various multiple explanatory factors in a timely manner. We studied 555 soil samples from the cropland topsoil (0–15 cm) across the black soil region in Northeast China between the years 2021 and 2022, and we identified 16 significant impact factors using one-way ANOVA and Pearson correlation coefficient analysis. In addition, the Random Forest (RF) model outperformed the Cubist model in predicting the spatial distribution of SOC contents. The predicted ranges of SOC contents span from 5.24 to 43.93 g/kg, with the average SOC content using the RF model standing at 17.24 g/kg in Northeast China. Stepwise regression and structural equation modeling revealed climate and topography as key factors affecting SOC distribution. The SOC density in the study area varied from 0.51 to 9.11 kg/m2, averaging 3.30 kg/m2, with a total SOC stock of 1226.64 Tg. The SOC sequestration potential in the study area was estimated at 3057.65 Tg by the categorical maximum method, with a remaining sequestration capacity of 1831.01 Tg. The study area has great potential for SOC sequestration. We hope to transform the theoretical value of SOC sequestration potential into actual SOC sequestration capacity by promoting sustainable agriculture and additional strategies. Our findings provide insights into the global soil conditions, SOC storage capacities, and effective SOC management strategies.

Contour Farming Suitability of the Black Soil Region in Northeast China and Its Spatial Characteristics

Contour Farming Suitability of the Black Soil Region in Northeast China and Its Spatial Characteristics