Search Results (111)

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

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R² values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data. Full article

The improvement of saline-alkali land plays a key role in ensuring food security and promoting agricultural development. Saline soils modifies the response of the soil microbial community, but research is still limited. The effects of applying phosphogypsum with rice cultivation (PRC) on soil physicochemical properties and bacterial community in soda saline-alkali paddy fields in Songnen Plain, China were studied. The results showed that the PRC significantly improved the physicochemical properties of soil, significantly reduced the salinity, increased the utilization efficiency of carbon, nitrogen, and phosphorus, and significantly increased the activities of urease and phosphatase. The activities of urease and phosphatase were significantly correlated with the contents of total organic carbon and total carbon. A redundancy analysis showed that pH, AP, ESP, HCO₃⁻, and Na⁺ were dominant factors in determining the bacterial community structure. The results showed that PRC could improve soil quality and enhance the ecosystem functionality of soda saline-alkali paddy fields by increasing nutrient content, stimulating soil enzyme activity, and regulating bacterial community improvement. After many years of PRC, the soda-alkali soil paddy field still develops continuously and healthily, which will provide a new idea for sustainable land use management and agricultural development. Full article

Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm². During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development. 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

Climate change and aridification have increased the risk of salinization and organic carbon loss in dryland soils. Enrichment using biochar and organic fertilizers has the potential to reduce salt toxicity and soil carbon loss. However, the effects of biochar and organic fertilizers on CO₂ and CH₄ emissions from saline soils in dryland areas, as well as their microbial mechanisms, remain unelucidated. To clarify these issues, we performed a 5-month incubation experiment on typical soda-type saline soil from the western part of the Songnen Plain using five treatments: control treatment (CK), 5% urea (U), straw + 5% urea (SU), straw + 5% urea + microbial agent (SUH), and straw + 5% urea + biochar (SUB). Compared with the SU treatment, the SUH and SUB treatments reduced cumulative CO₂ emissions by 14.85% and 35.19%, respectively. The addition of a microbiological agent to the SU treatment reduced the cumulative CH₄ emissions by 19.55%, whereas the addition of biochar to the SU treatment increased the cumulative CH₄ emissions by 4.12%. These additions also increased the relative abundances of Proteobacteria, Planctomycetes, and Ascomycota. Overall, the addition of biochar and organic fertilizer promoted CO₂ emissions and CH₄ uptake. This was mainly attributed to an improved soil gas diffusion rate due to the addition of organic materials and enhanced microbial stress due to soil salinity and alkalinity from the release of alkaline substances under closed-culture conditions. Our findings have positive implications for enhancing carbon storage in saline soils in arid regions. 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

The precise quantification of soil salinity and the spatial distribution are paramount for proficiently managing and remediating salinized soils. This study aims to explore a pioneering methodology for forecasting soil salinity by combining the spectroscopy of soda saline–alkali soil with crack characteristics, thereby facilitating the ground-based remote-sensing inversion of soil salinity. To attain this objective, a surface cracking experiment was meticulously conducted under controlled indoor conditions for 57 soda saline–alkali soil samples from the Songnen Plain of China. The quantitative parameters for crack characterization, encompassing the length and area of desiccation cracks, together with the contrast texture feature were methodically derived. Furthermore, spectral reflectance of the cracked soil surface was measured. A structural equation model (SEM) was then employed for the estimation of soil salt parameters, including electrical conductivity (EC_1:5), Na⁺, pH, HCO₃⁻, CO₃²⁻, and the total salinity. The investigation unveiled notable associations between different salt parameters and crack attributes, alongside spectral reflectance measurements (r = 0.52–0.95), yet both clay content and mineralogy had little effect on the cracking process due to its low activity index. In addition, the presence of desiccation cracks accentuated the overall spectral contrast of salt-affected soil samples. The application of SEMs facilitated the concurrent prediction of multiple soil salt parameters alongside the regression analysis for individual salt parameters. Nonetheless, this study confers the advantage of the swift synchronous observation of multiple salt parameters whilst furnishing lucid interpretation and pragmatic utility. This study helps us to explore the mechanism of soil salinity on the surface cracking of soda saline–alkali soil in the Songnen Plain of China, and it also provides an effective solution for quickly and accurately predicting soil salt content using crack characteristics, which also provides a new perspective for the hyperspectral measurement of saline–alkali soils. Full article

Conservational tillage (NT) is widely recognized globally for its efficacy in mitigating soil loss due to wind and water erosion. However, a systematic large-scale estimate of NT’s impact on soil loss reduction in Northeast, China’s primary granary, remains absent. This study aimed to investigate the spatial and temporal variability of soil erosion under NT compared to conventional tillage (CT) in the black soil region and to analyze the underlying mechanisms driving these erosions. The Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) models were employed, incorporating previously published plot/watershed data to estimate the potential reduction of water and wind erosion by NT in this region. Results indicated that under CT practices, water- and wind-induced soil losses were widely distributed in the arable land of Northeast China, with intensities of 2603 t km⁻² a⁻¹ and 34 t km⁻² a⁻¹, respectively. Furthermore, the erosive processes of water and wind erosion were significantly reduced by 56.4% and 91.8%, respectively, under NT practices compared to CT. The highest efficiency in soil conservation using NT was observed in the mountainous regions such as the Changbai Mountains and Greater Khingan Mountains, where water erosion was primarily driven by cropland slopes and wind erosion was driven by the wind speed. Conversely, the largest areas of severe erosion were observed in the Songnen Plain, primarily due to the significant proportion of arable land in this region. In the plain regions, water-induced soil loss was primarily influenced by precipitation, with light and higher levels of erosion occurring more frequently on long gentle slopes (0–3°) than on higher slope areas (3–5°). In the temporal dimension, soil loss induced by water and wind erosion ceased during the winter under both tillage systems due to snow cover and water freezing in the soil combined with the extremely cold climate. Substantial reductions were observed under NT from spring to autumn compared to CT. Ultimately, the temporal and spatial variations of soil loss under CT and NT practices were established from 2010 to 2018 and then projected onto a cropland map of Northeast China. Based on this analysis, NT is recommended as most suitable practice in the southern regions of Northeast China for maintaining soil health and crop yield production, while its suitability decreases in the northern and eastern regions. Full article

For the main grain-producing areas worldwide that balance multi-tasks of grain production, ecological protection, and economic development, quantitatively revealing the correlation between human activity intensity (HAI) and ecosystem service value (ESV) is conducive to formulating adapted ecological protection policies and promoting the coordinated development of the regional economy, society, and ecosystem. In this paper, we took the Songnen Plain of China as an example, employed a modified Equivalent Factor Method (integrating socio-economic data, the normalized difference vegetation index (NDVI), and land use data), and the HAI Assessment Model (based on the data of land use, night-time light, and population spatial distribution) to measure the ESV and HAI in the Songnen Plain of China for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. We further applied the standard deviational ellipse method, the coupled coordination degree model, and the bivariate spatial autocorrelation models to reveal the spatiotemporal dynamics and correlation characteristics of ESV and HAI. The results showed the following: (1) Temporally, the ESV declined from 950.96 billion yuan in 1990 to 836.31 billion yuan in 2015, and then increased to 864.60 billion yuan in 2020, with the total loss attributed to the significant decline in the ESV of the natural ecosystem. Spatially, the ESV in the western and northeastern regions was relatively high, with a significant increase in the northeast. (2) HAI showed an upward trend from 1990 to 2020, while the high HAI levels gradually shrank after reaching the peak in 2000. Low HAI levels were mainly distributed in the northeast and southwest, aligning with the ecological space, while high HAI levels were distributed in the middle and southeast. (3) The interaction between ESV and HAI was marked by a negative correlation, transitioning from conflict to coordination. The spatial pattern of HAI and ESV showed H (HAI)-L (ESV) and L-H clustering, with H-H and L-L scattered distributions. This study contributes to providing a framework, methods, and suggestions for the sustainable planning and utilization of land and ecological protection in order to offer scientific references for the Songnen Plain, other major grain-producing areas, and related studies. Full article

Hyperspectral technology is widely recognized as an effective method for monitoring soil salinity. However, the traditional sieved samples often cannot reflect the true condition of the soil surface. In particular, there is a lack of research on the spectral response of cracked salt-affected soils despite the common occurrence of cohesive saline soil shrinkage and cracking during water evaporation. To address this research, a laboratory was designed to simulate the desiccation cracking progress of 57 soda saline–alkali soil samples with different salinity levels in the Songnen Plain of China. After completion of the drying process, spectroscopic analysis was conducted on the surface of all the cracked soil samples. Moreover, this study aimed to evaluate the predictive ability of multiple linear regression models (MLR) for four main salt parameters. The hyperspectral reflectance data was analyzed using three different band screening methods, namely random forest (RF), principal component analysis (PCA), and Pearson correlation analysis (R). The findings revealed a significant correlation between desiccation cracking and soil salinity, suggesting that salinity is the primary factor influencing surface cracking of saline–alkali soil in the Songnen Plain. The results of the modeling analysis also indicated that, regardless of the spectral dimensionality reduction method employed, salinity exhibited the highest prediction accuracy for soil salinity, followed by electrical conductivity (EC) and sodium (Na⁺), while the pH model exhibited the weakest predictive performance. In addition, the usage of RF for band selection has the best effect compared with PCA and Pearson methods, which allows salt information of soda saline–alkali soils in Songnen Plain to be predicted precisely. Full article

Soil organic matter (SOM) in cultivated land is vital for land quality and food security. This study examines SOM distribution and influencing factors in northeastern China, providing insights for sustainable agriculture. Utilizing 10 m resolution SOM data, the analysis covers regions including the Greater and Lesser Khingan Mountains, Liaohe Plain, Sanjiang Plain, Songnen Plain, the northwest semi-arid region, and the low hilly areas of Paektu Mountain. The Geodetector method is employed to assess various influencing factors. The key findings are as follows: (1) The average SOM content in Northeast China (37.70 g/kg) surpasses the national average, is highest in the Greater and Lesser Khingan Mountains (49.32 g/kg), and lowest in the northwest semi-arid region (26.15 g/kg). (2) SOM content is maximized in regions with high altitudes, steep slopes, low temperatures, and moderate precipitation. (3) The annual average temperature is the primary factor influencing SOM distribution, with a combination of temperature and administrative divisions providing better explanatory power. (4) SOM trends vary across protected areas, with slope being critical in semi-humid plains, elevation in arid regions, and no dominant factors identified in the Sanjiang Plain. These findings underscore the need for tailored black soil protection policies to effectively leverage local resources and preserve ecosystem integrity. Full article

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables. Full article

Alfalfa is a perennial herbaceous forage legume that is significantly and adversely affected by monocropping. Crop rotation is the most effective measure to overcome continuous cropping obstacles. However, the mechanisms of how bacterial communities are affected and the potential links between these effects and cropping systems remain poorly understood. Based on a long-term field experiments with continuous alfalfa and forage crops with alfalfa rotation in the black soil region of the western Songnen Plain in Northeast China, the alterations in soil bacterial community structure using high-throughput sequencing of the 16S rRNA gene and soil chemical properties and enzyme activities were analyzed. The alfalfa–forage oats–silage maize–alfalfa and alfalfa–silage maize–forage oats–alfalfa system significantly increase the levels of total phosphorus and available phosphorus, and promote the activities of acid phosphatase, β-glucosidase, leucine aminopeptidase, and N-acetyl-β-glucosaminidase in comparison to continuous alfalfa. While alfalfa crop rotation did not affect the α-diversity of soil bacteria, it significantly altered the bacterial community composition and structure. Some key taxa are significantly enriched in the crop rotation system soils, including Bacillus, Sphingobium, Paenibacillus, Hydrogenispora, Rubrobacter, Haliangium, and Rubellimicrobium. Additionally, crop rotation with alfalfa increased the stability and complexity of the soil bacterial co-occurrence network. Based on our findings, we recommend promoting the alfalfa–forage oats–silage maize–alfalfa and alfalfa–silage maize–forage oats–alfalfa rotation systems as ideal practices for overcoming the challenges associated with continuous cropping of alfalfa. These systems not only enhance soil nutrient content and enzyme activities but also foster a beneficial microbial community, ultimately improving soil functionality and crop performance. Full article

Saline-alkali land is an important component of terrestrial ecosystems and may serve as a carbon sink but its net contribution to the overall terrestrial carbon sink is unknown. Using methods recommended by the IPCC, this study evaluates the impacts of interconverting saline-alkali and non-saline-alkali land on terrestrial carbon stocks by measuring two major carbon pools (soil organic carbon and vegetation carbon) in the saline-alkali land of China’s Songnen Plain. Distinct phases in the evolution of the region’s terrestrial carbon stock were delineated, factors contributing to transitions between phases were identified, and the effects of changes in the saline-alkali land carbon stock on the overall terrestrial carbon sink were estimated. Between 2005 and 2020, the region’s saline-alkali land carbon stock initially increased, then declined, and finally increased again. However, the overall terrestrial carbon stock decreased by 0.5 Tg (1 Tg = 10¹² g), indicating that the increase in the saline-alkali land carbon stock was due primarily to expansion of the saline-alkali land area. The conversion of non-saline-alkali land to saline-alkali land was a carbon-emitting process; consequently, in areas undergoing saline-alkali land change, the lower carbon density bound was equal to the carbon density of unconverted saline-alkali land and the upper bound was equal to the carbon density of unconverted non-saline-alkali land. In general, changes in the carbon stock of saline-alkali land correlated negatively with changes in the overall terrestrial carbon stock. The conversion of saline-alkali land into grassland and cropland through biochar improvement and the planting of saline-tolerant crops (Leymus chinensis, salt-tolerant rice) has a positive effect on promoting the enhancement of terrestrial carbon stocks. Full article