Search Results (83)

The main soil type in the Sanjiang Plain of Northeast China, dryland albic soil is of great significance for studying nutrient distribution characteristics. This study focuses on 852 Farm in the typical dryland albic soil area of the Sanjiang Plain, using a combination of paired t-test, geostatistics, correlation analysis, and principal component analysis to systematically reveal the spatial differentiation of soil nutrients in the black soil layer and white clay layer of dryland albic soil, and to clarify the impact mechanism of plow layer nutrient characteristics on crop productivity. The results show that the nutrient content order in both the black and white clay layers is consistent: total potassium (TK) > organic matter (OM) > total nitrogen (TN) > total phosphorus (TP) > alkali-hydrolyzable nitrogen (HN) > available potassium (AK) > available phosphorus (AP). Both layers exhibit a spatial pattern of overall consistency and local differentiation, with spatial heterogeneity dominated by altitude gradients—nutrient content increases with decreasing altitude. Significant differences exist in nutrient content and distribution between the black and white clay layers, with the comprehensive fertility of the black layer being significantly higher than that of the white clay layer, particularly for TN, TP, TK, HN, and OM contents (effect size > 8). NDVI during the full maize growth period is significantly positively correlated with TP, TN, AK, AP, and HN, and the NDVI dynamics (first increasing. then decreasing) closely align with the peak periods of available nitrogen/phosphorus and crop growth cycles, indicating a strong coupling relationship between vegetation biomass accumulation and nutrient availability. These findings provide important references for guiding rational fertilization, agricultural production layout, and ecological environmental protection, contributing to the sustainable utilization of dryland albic soil resources and sustainable agricultural development. Full article

With accelerating urbanization and agricultural modernization, the scale, structure, and land use conditions of rural settlements in China’s three northeastern provinces (TNPs) have changed dramatically, impacting regional food production and sustainable rural development. Based on multitemporal land use datasets and socioeconomic statistics, we used spatial pattern analysis, machine learning models, and the Shapley additive explanation (SHAP) method to investigate the spatial evolutionary characteristics and driving factors of rural settlements in China’s TNPs from 1980 to 2020. The results show that (1) the spatial evolution of rural settlements followed a four-stage “expansion–stabilization–re-expansion–restabilization” trend; arable land conversion was the primary source of expansion, with limited conversion from forests, grasslands, and water bodies. (2) Rural settlements demonstrated marked agglomeration, with the spatial distribution evolving from “single-center clustering” to “multiregional contiguous clustering”. Rural settlements in the Sanjiang Plain evolved into large patch clusters, while those in the lower Liaohe River Basin became small patch clusters. (3) Rural settlements at low elevations and near roads and waterways presented a large-scale, agglomerative distribution, while settlements at high elevations and far from rivers and roads showed a small-scale, high-agglomeration pattern. (4) The rural population, total power of agricultural machinery, total grain output, and primary industry value added predominantly drove settlement spatial expansion, with an “initial suppression, then promotion” trend, while the urbanization rate and GDP per capita had a negative impact, with the opposite trend. The interaction effects among high-contributing factors transitioned from suppressive to promoting. Our results provide theoretical insights for spatial planning and sustainable development in agricultural rural settlements. Full article

Albi-boric argosols, mainly distributed in the Sanjiang Plain of Heilongjiang Province, China, accounting for over 80% of the total cultivated land area, is characterized by a nutrient-deficient layer beneath black soil. This study addresses the challenges of modern agriculture by investigating the impact of straw incorporation on soil dissolved organic carbon (DOC) and its structures in albi-boric argosols, profiles, using fluorescence excitation–emission spectroscopy and parallel factor analysis (PARAFAC). Three treatments were applied: undisturbed albi-boric argosols (C), mixed albic and illuvium layers (M), and mixed albic and illuvium layers with straw (MS). Results showed that the yield of M and MS increased by 9.9% and 13.0%, respectively. There was a significant increase in DOC content, particularly in the MS treatment. Fluorescence index (FI) values ranged from 1.65 to 1.86, biological index (BIX) values were less than 1, and humification index (HIX) values were below 0.75, indicating a mix of plant and microbial sources for DOC, autochthonous characteristics, and weaker humification degree. PARAFAC identified two/three individual fluorophore moieties that were attributed to fulvic acid substances, soluble microbial products, and tyrosine-like substances, with microbial products as the dominant component. This study demonstrates the effect of improving barrier soil and maintaining sustainable agriculture by enhancing soil quality. Full article

The Sanjiang Plain (SJP) in Northeast China, a crucial black soil region, serves as a quintessential example of a high-intensity agricultural development zone and stands as China’s largest commercial grain production base. In the context of global climate change, pronounced global warming and increased vegetation greening are expected to significantly impact the agricultural water resource supply and its alignment with crop water requirements in the SJP. This study assesses how climate change and vegetation greening affect the crop water supply–demand relationship in the SJP, addressing the critical question of whether natural precipitation can sustain regional agricultural development. Using the extensively validated ESSI-3 distributed hydrological model, integrated with reanalysis and multi-source satellite data, we analyzed data from 1982 to 2018. The results indicate a statistically significant rise in the regional temperature and leaf area index (p < 0.05), with a notable shift around 2000. Key findings include (1) an increase in crop irrigation water requirements (IWR) post-2000, with significant spatial variation; the central and western regions experienced the highest increases, while the eastern region saw reduced risk to crop water security. Furthermore, (2) climate change accounted for approximately 37.9% of the increased IWR in central and western regions, with vegetation greening contributing about 21.2%. Conversely, in the eastern region, vegetation dynamics had a more pronounced effect (28.6%), while climate change contributed less (12.3%). These results suggest a shift in crop water deficit risk boundaries toward the east and north. To optimize water use, expanding high-water-demand crops in the eastern regions and reducing their cultivation in the west is recommended, enhancing alignment between natural precipitation and crop water needs. Full article

In this study, we investigated the changes in the communities of arbuscular mycorrhizal fungi (AMF) and their driving factors across eight vegetation succession stages in the Sanjiang Plain, Northeast China, original natural wetland (NW), wetland edge (EW), shrub-invaded wetland (IW), shrub-dominated wetland (DW), young-Betula forest (YB), mature-Betula forest (MB), Populus and Betula mixed forest (PB), and conifer forest (CF), using Illumina MiSeq sequencing. As this research has revealed, significant differences exist in soil physicochemical indicators, including moisture content (MC), pH, soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), and available phosphorus (AP). As vegetation succession progresses, the diversity and structure of AMF communities also undergo changes, with the Simpson diversity index being highest in coniferous forests (CF) and the Abundance-based Coverage Estimator (ACE) and Chao1 indices being elevated in shrub-dominated wetlands (PB). Non-metric multidimensional scaling (NMDS) analysis reveals distinct differences in AMF communities across various succession stages. Furthermore, stacked bar charts indicate that the genus Glomus dominates in most wetland and forest succession stages but is nearly absent in CF, where it is replaced by the genus Paraglomus. Canonical correspondence analysis (CCA) demonstrates that SOC has a more significant impact on AMF communities during the EW stage of succession, while AP and TP exert greater influence during the CF stage as well as the MB and YB stages. AN, on the other hand, plays a more prominent role in shaping AMF communities during the IW and NW stages. PICRUSt2 predictions reveal that enzymes such as alcohol dehydrogenase and L-aminoadipate-semialdehyde dehydrogenase are most abundant in YB, whereas pathways like 4-amino-2-methyl-5-diphosphomethylpyrimidine biosynthesis are most enriched in IW. These findings uncover the close interplay between soil physicochemical properties and AMF community dynamics, aiming to deepen our understanding of the relationships among soil physicochemical properties, AMF community changes, and succession dynamics in wetland and forest ecosystems. Full article

Understanding the logic of farmers’ black soil conservation behaviors and promoting these actions are crucial measures for enhancing soil quality and ensuring national food security. This article uses survey data from 676 farmers in typical black soil areas of the Sanjiang Plain, China, employing binary logistic regression and mediation effect models to empirically examine the impact of protective policies on farmers’ black soil conservation behaviors and the mediating effect of their perceptions. The results indicate: (1) Protective policies have a significant positive effect on farmers’ black soil conservation behaviors. (2) Farmers’ perception of black soil conservation plays a crucial mediating role in the impact of policy guidance on conservation behaviors. Protective policy not only directly influences farmers’ behaviors but also indirectly promotes conservation actions by shaping farmers’ sense of responsibility and awareness of obligations. (3) There are variations in the mediating effects of farmers’ cognition based on different types of cultivated land and operational scales. Therefore, efforts should be made to strengthen the promotion and implementation of black soil protection policies and subsidies to enhance the sustainability and effectiveness of conservation practices. Greater emphasis should be placed on policy advocacy to raise farmers’ sense of responsibility and awareness of the importance of black soil conservation. Additionally, the diverse needs of different types of farming households should be considered, and a range of measures should be implemented to encourage active participation in black soil conservation. Full article

Exploring the characteristics and driving factors of carbon storage change in different terrain gradient variations can provide important insights for formulating the agricultural ecological protection policy for regional development. Previous studies have used the fixed value of carbon density to evaluate the change characteristics of carbon storage but ignored the spatio-temporal heterogeneity of carbon storage at the block scale and the impact of policy factors. Thus, this paper takes Sanjiang Plain, Heilongjiang Province, China, as a study area, and the spatio-temporal variation of carbon storage at different topographic gradients was revealed using hot and cold spot analysis and zonal statistics. Through the geographic detector and estimation of the soil carbon density model, the driving factors and intensity of carbon storage spatial distribution are revealed from 1990 to 2020. We conducted analyses on aboveground biomass, underground biomass, and soil carbon storage across three elevation levels (0–200 m, 200–500 m, 500–999 m) to reveal the quantitative distribution features of carbon storage. The study analysis finds that carbon storage indicates a sawtooth evolution during the study period. Carbon storage was dominant at elevation I (range is 0–200 m), slope I (range is 0–2°), and relief amplitude I (range is 0–30 m). Additionally, the carbon storage losses were severe at elevation II (range is 200–500 m), slope II (2–6°), and relief amplitude II (30–70 m). In contrast, the carbon storage losses at elevation III (500–999 m), slope III (6–15°), and relief amplitude III (70–186 m) were insignificant. The spatial pattern of carbon storage varies significantly under different topographic gradients from 1990 to 2020. The most critical driving factors influencing the spatial distribution pattern of carbon storage were land use and annual average temperature. Distance to urban centers and soil texture also moderately influence the distribution of carbon storage. As the topographic gradient increases, the dominant factors of carbon storage gradually change from annual mean temperature and the extent of land use to policy factors and other socio-economic factors. Therefore, this study emphasizes the importance of implementing policies that convert farmland to forests and wetlands and promote the green transformation of agriculture. Full article

Isolated marsh wetlands are crucial for maintaining regional hydrological connectivity and biological contiguity. The Sanjiang Plain is the most typical area of marsh wetland change in China. A large number of isolated marshy wetlands have been formed here due to natural and anthropogenic influences. However, there have been few quantitative studies of the dynamics of isolated marsh wetlands and their drivers at the regional scale. This study used Landsat series image data provided by the Google Earth Engine. Through field surveys, combined with visual interpretation and the Random Forest Algorithm, the distributional changes in isolated marsh wetlands, non-isolated marsh wetlands, and natural marsh wetlands in the Sanjiang Plain from 1975 to 2020 were identified and extracted. The dynamic change characteristics as well as the patch importance values (dIIC) of isolated and non-isolated marsh wetlands were analyzed using the dynamic degree, standard deviation ellipse model, and the integral index of connectivity (IIC). Finally, the driving factors and interactions affecting the distribution of isolated marsh wetlands were analyzed by the Geodetector model. The results show that (1) the temporal dynamics of the three types of marsh wetlands are less than 0 from 1975 to 2020, and the temporal dynamics of isolated marsh wetlands are the largest. The lost wetlands were concentrated in the northeastern and east–central regions of the Sanjiang Plain. The center of mass of the standard deviation ellipse moved from northeast to southwest, and the isolated marsh wetlands moved the most. (2) The IIC of non-isolated marsh wetlands and natural marsh wetlands decreased and then increased, and the non-isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern and east–central regions. On the other hand, the IIC of isolated marsh wetlands increased and then decreased, and the isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern region. (3) The elevation is the most important driving factor affecting the distribution of isolated marsh wetlands in the Sanjiang Plain. The interaction between the driving factors had a significantly higher effect on the distribution of isolated marsh wetlands than that of a single driving factor, with the strongest interaction between aspect and elevation in 1975, 1986, 2000, and 2010, and between aspect and slope in 2020. Full article

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China’s Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately. Full article

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R² = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R² = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R² = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km² and 1336 km², and NT = 559 km² and 585 km², respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km² and NT was 1265 km²). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems. Full article

Fire is a key ecological factor in marshes, significantly influencing the nitrogen (N) cycle. The impacts of different fire regimes on marshes have garnered increasing attention. This study aims to reveal the effects of fire regimes on N distribution in marshes. We conducted field experiments with fixed–point prescribed burning in typical Sanjiang Plain freshwater marshes, exploring the influences of various fire regimes on the distribution of N in marshes. We found that in the spring–burned plots, the soil ammonium (${\mathrm{NH}}_4^{+}–\mathrm{N}$) content increased by 318% with thrice–burned approaches compared to once–burned, and by 186% with thrice–burned compared to twice–burned. In the autumn–burned plots, ${\mathrm{NH}}_4^{+}–\mathrm{N}$ content increased by 168% and 190%, respectively. Similarly, the soil nitrate (${\mathrm{NO}}_3^{–}–\mathrm{N}$) content three years subsequent to burning increased by 29.1% compared to one year since burning, and by 5.96% compared to two years since burning in the spring–burned plots (73.8% and 32.9% increases, respectively, in the autumn–burned plots). The plant stem–N content of the autumn burns increased by 30.9%, 119%, and 89.1% compared to the spring burns after one, two, and three years since burning, respectively. Our results indicate that high fire–frequency promotes marsh N cycling within the span of three years. The marsh soil conversion of ${\mathrm{NH}}_4^{+}–\mathrm{N}$ to ${\mathrm{NO}}_3^{–}–\mathrm{N}$ was enhanced with increased time since burning. High fire–frequency promotes plant growth, exacerbating competition between plant populations, with this effect being more significant in autumn–burned plots than in spring–burned plots. Full article

Soil organic matter (SOM) is essential for assessing land quality and enhancing soil fertility. Understanding SOM spatial and temporal changes is crucial for sustainable soil management. This study investigates the spatial and temporal variations and influencing factors of SOM content in the Songnen Plain (SNP) and Sanjiang Plain (SJP) of Heilongjiang Province, China, based on high-precision SOC content data (RMSE = 4.84 g/kg⁻¹, R² = 0.75, RPIQ = 2.43) from 1984 to 2021, with geostatistical analyses and geodetector models. This study aims to quantitatively reveal and compare the long-term spatial and temporal characteristics of SOM changes and their influencing factors across these two plains. The results show that SOM content in both plains has decreased over the past 37 years. In the SNP, the average SOM decreased from 48.61 g/kg to 45.6 g/kg, representing a reduction of 3.01 g/kg, or a 6.10% decrease; SOM decreased spatially from northeast to southwest, covering 63.1% of the area. In the SJP, the average SOM declined from 48.41 g/kg to 44.31 g/kg, a decrease of 4.1 g/kg, or an 8.50% decrease; no pronounced spatial pattern was observed, but the declining area comprises 67.49%. Changing SOM hotspots are concentrated in southern SNP and central and northwestern SJP, showing clear heterogeneity across counties. Geodetector model analysis indicates annual mean temperature as the primary driver of SOM variations in SNP; while elevation is the main driver in SJP, the combined explanatory power of multiple factors surpasses individual ones. There is a positive correlation between SOM and temperature in SNP, and policy protection positively influences SOM in both plains. These findings provide insights into the differential protection of SOM in SNP and SJP. Full article

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China. Full article

Jiamusi is situated in Heilongjiang Province, China, in the center of the Sanjiang Plain. The 1980s’ overplanting of paddy fields resulted in a decrease in groundwater levels, scarcity of groundwater resources, and frequent earth collapses. Examining and safeguarding the groundwater resources in this region has emerged as a crucial subject. In light of this, this paper uses the remote sensing water balance method and the SWAT distributed hydrological model to calculate groundwater resources in the Jiamusi area. It also conducts scientific experiments by examining various factors, including rainfall, the degree of water supply, soil type, and land use. The measured monthly runoff of Jiamusi City’s Tongjiang and Fuyuan City’s hydrology stations was utilized to establish the model parameters for the SWAT model. A preliminary assessment of the distribution features of shallow groundwater in the Jiamusi area is conducted using the two methodologies mentioned above, and the following results are reached: (1) Tongjiang Hydrological Station and Fuyuan Hydrological Station both had good runoff modeling results, with R² and NS values of 0.81, 0.77, and 0.77, 0.75, respectively. (2) The SWAT model works well for assessing groundwater resources. Between 2010 and 2016 (two preheating years), Jiamusi’s average groundwater recharge was 61.03 × 10⁸ m³, with a recoverable amount of 27.4 × 10⁸ m³. (3) Based on the remote sensing water balancing approach, the average exploitable quantity of groundwater recharge in the Jiamusi area between 2008 and 2016 is 23.94 × 10⁸ m³, while the average recharge in the area is 53.2 × 10⁸ m³. (4) The Jiamusi metropolitan area is the core of the groundwater phreatic reservoir water reserves, which progressively decline in both the northeast and southeast directions. It falls to the southwest as Fuyuan City’s center. The Songhua River’s main stream area near Tongjiang City has the least volume of water reserves in the phreatic layer, and the area’s groundwater reserves converge to the southeast and northwest, where surface water makes up the majority of the water resources. Full article

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management. Full article