Search

This study discusses the mechanism of social trust and legal institutions and their impact on farmers' contract selection in the farmland transfer market from the perspective of contract governance. Using data from a survey of 128 villages in Heilongjiang, Henan, Zhejiang, and Sichuan provinces, this study empirically tests the impact of social trust and legal institutions on the binding force of contracts, and the proportions of paper and long-term contracts in the farmland transfer market. The results showcase, first, that improvement in social trust and legal institutions can strengthen the binding force of farmland contracts. The strength of legal institutions, as embodied in regulation files and execution, and of social trust, as embodied in village neighborhood relations and loan relations, have significant positive impacts on the binding force of contracts in the farmland transfer market. Second, the binding force of contracts positively impacts both paper and long-term contracts in the farmland transfer market. Whether contract execution or dispute resolution rates are selected as the proxy variables for the binding force of contracts, the stronger the contract binding force, the higher the proportion of both paper and long-term contracts in the farmland transfer market. Therefore, improving formal and informal social systems to enhance contractual binding force is of great importance in standardizing contracts and improving the efficiency of market resource allocation. [ABSTRACT FROM AUTHOR]

As the main body that unites farmers internally and connects with markets externally, professional farmer cooperatives are playing an increasingly important role around the world. In order to investigate the significant factors influencing this subject in agricultural socialization services, 487 cooperatives in Heilongjiang Province were selected for investigation. The field survey found that many of the better-developed cooperatives have a certain degree of inadequacy in the performance of their service functions. This paper proposes that viability and government support are important factors influencing the realization of the service function of farmer professional cooperatives. Based on the empirical analysis of sample survey data and econometric models, it was demonstrated that the experience of the chairman, the number of board members, the distribution of members, the scale of land operation, profitability, and the institutional arrangement of the cooperatives are an important embodiment of the viability of the cooperatives. These factors significantly influence the service function of cooperatives in different sections, including pre-production, mid-production, and post-production. Moreover, obtaining external support from the government is necessary for cooperatives at their primary stage of development, especially regarding relevant training, which can facilitate the realization of the service functions of cooperatives in all aspects. [ABSTRACT FROM AUTHOR]

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model's learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics. [ABSTRACT FROM AUTHOR]

Black soil plays an important role in maintaining a healthy ecosystem, promoting high-yield and efficient agricultural production, and conserving soil resources. In this paper, a typical black soil area of Keshan Farm in Qiqihar City, Heilongjiang Province, China, is used as a case study to investigate the black soil farmland productivity evaluation model. Based on the analysis of the composite index (CI) model, productivity index (PI) model and various machine learning models, the soil productivity evaluation method was improved and a prediction model was established. The results showed that the support vector machine regression model based on simulated annealing algorithm (SA-SVR), as well as the Gaussian process regression model (GPR), had obvious advantages in data preprocessing, feature selection, and model optimization compared to the modified composite index model (MCI), the modified productivity index model (MPI), and the coefficients of determination (R2) of their modelling, which were up to 0.70 and 0.71, respectively, and these machine learning prediction models can reflect the effects on maize cultivation and its yield through soil parameters even with small datasets, which can better capture the nonlinear relationship and improve the accuracy and stability of yield prediction, and is an effective method for guiding agricultural production as well as soil productivity evaluation. [ABSTRACT FROM AUTHOR]

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset. [ABSTRACT FROM AUTHOR]

It is widely recognized that urban resilience is one of the core goals of urban development. As an important part of a city, the resilience level of urban human settlements directly affects the development trend of urban resilience. However, at present, research results on the resilience of urban human settlements are very rare, are mainly concentrated in the central region of China, and rarely take into account the economically backward northeastern region. Therefore, in order to better improve the anti-risk ability of the urban human settlement environment system in three provinces of Northeast China, fully implement the strategic goal of "Comprehensive Revitalization of Northeast China", and achieve high-quality urban development, this paper focuses on 34 prefecture-level cities in three provinces of Northeast China and proposes an urban human settlement resilience evaluation system with 36 indicators in five dimensions, namely, the natural system, human system, housing system, supporting system, and social system. Using the entropy weight method, the Dagum Gini coefficient, and a geographical probe model, the changes in the resilience level of each city from 2005 to 2020 were measured, and the urban living environment was assessed in terms of the adaptability and resilience of the development level in each subsystem based on the temporal and spatial evolution law and its influencing factors. The following conclusions were drawn: (1) The development level of urban human settlement resilience in the three provinces in Northeast China showed an N-shaped development trend from 2005 to 2020, but the regional differences were significant, and the overall spatial pattern was "high in the south and low in the north". (2) In terms of the overall difference, the overall difference in urban human settlement resilience in the three northeastern provinces of China was small: the inter-regional difference was the main source of the difference, and the intra-regional difference was the secondary source. The regional differences were in the order of Heilongjiang Province > Liaoning Province > Jilin Province, indicating that Jilin Province had the smallest difference and that the resilience level of urban human settlements does not show a balanced development trend. In terms of the average Gini coefficient between regions, the order of difference was Liaoning Province–Heilongjiang Province > Jilin Province–Liaoning Province > Jilin Province–Heilongjiang Province, indicating that the difference between Liaoning Province and Heilongjiang Province was the most significant. (3) The "natural system", "human system", "living system", "supporting system", and "social system" had significant spatial and temporal heterogeneity and significantly affected the resilience level of urban human settlements in the three provinces in Northeast China. Among them, the "social system" has always been the main factor affecting the resilience level of urban human settlements. [ABSTRACT FROM AUTHOR]

Heilongjiang Province, as the largest production and supply base for high-quality soybeans in China, plays a vital role in optimizing the layout of soybean production and promoting the revitalization of the soybean industry. Soybean yield is used as a key indicator of soybean production. This study integrated soybean yield data from agricultural reclamation systems and local authorities. A variety of statistical analysis methods, such as barycenter analysis, the Mann–Kendall test, the space–time cube, and grey relational analysis, were used to research the spatiotemporal evolution and influencing factors of soybean production in Heilongjiang Province from 2011 to 2021. This paper revealed the spatiotemporal evolution mechanism and explored the reasons for the differences in the effects of influencing factors. The results were as follows. (1) During the period between 2011 and 2021, the center of gravity of county-level soybean yield in Heilongjiang Province moved towards the northwest over a distance of 16.82 km. The soybean yield in the province experienced a mutation in approximately 2018, from a downward trend to an upward trend. (2) The spatiotemporal hot spots of county-level soybean yield in Heilongjiang Province were concentrated along the line from Hailun to Aihui. The types of hot spots included consecutive hot spots, intensifying hot spots, sporadic hot spots, and new hot spots. (3) The spatiotemporal agglomeration patterns of county-level soybean yield in Heilongjiang Province included only high-high clusters, only low-low clusters, only high-low outliers and multiple types. (4) The temporal changes in soybean yield in various counties of Heilongjiang Province had obvious regional characteristics. (5) Socioeconomic factors had aftereffects on soybean planting decisions. (6) Sunlight hours, the price ratio of local soybeans to local maize, average temperature, the number of soybean patents, the price ratio of imported soybeans to local soybeans, soybean cultivation income, local soybean prices, and the number of newly established soybean enterprises were primary influencing factors. Precipitation and soybean import volume were secondary influencing factors. The income difference between maize and soybeans, crops-hitting disaster area, and maize yield were general influencing factors. This study aims to offer new pathways for alleviating the structural contradiction between soybean supply and demand and to provide a reference for the formulation of national soybean industry policies and food security strategies. [ABSTRACT FROM AUTHOR]

Skarn-hosted deposits are commonly recognized as the consequence of magma intrusion within the Earth's upper crust. The Ergu Pb-Zn deposit can be regarded as a typical skarn-hosted deposit in the hydrothermal ore-forming system within the central Lesser Xing'an Range (LXR), Heilongjiang, China. Although extensive studies were conducted to understand the ore-forming system associated with the Ergu Pb-Zn deposit through using the traditional geoscience methods, the ore-forming process involved in this deposit has not been justified in a strictly scientific manner to date. In this paper, the hydrothermal ore-forming process of the Ergu Pb-Zn deposit is computationally simulated through using the dual length-scale approach associated with the finite element method (FEM). The related computational simulation results have demonstrated that: (1) the pore-fluid convection provides continuous ore-forming fluid and material sources for the Ergu Pb-Zn deposit at the quartz-Pb-Zn sulfide stage; (2) the convective flow of the pore fluid is the main dynamic mechanism, which controls the temperature, chemical species and pore-fluid velocity distributions in the Ergu Pb-Zn deposit; (3) the localized structure plays a key role in controlling the localized pore-fluid flow pattern, which can further control the location and formation of the orebody grade in the Ergu Pb-Zn deposit; (4) the dual length-scale approach associated with the FEM is very useful in dealing with the computational simulation of the hydrothermal ore-forming mechanism involved in the Ergu Pb-Zn deposit. [ABSTRACT FROM AUTHOR]

The response characteristics of the mesostructure and macro-characteristics of the soil–rock mixture under repeated freeze–thaw action have an important influence on the safety and stability of the dump slope in low-temperature environments. In order to further understand the multi-scale response behavior of a soil–rock mixture under freeze–thaw cycles, this paper carried out indoor freeze–thaw cycles, uniaxial compression, and electrochemical impedance spectroscopy tests on a soil–rock mixture taken from a graphite mine dump in Jixi City, Heilongjiang Province, China. Combined with the simulation calculation of discrete element numerical software (PFC2D 7.0), the effects of freeze–thaw cycling on electrochemical impedance spectrometry (EIS) mesoscopic parameters, uniaxial compressive strength, and crack propagation of soil–rock mixtures were analyzed. The intrinsic relationship between mesoparameters and macroscopic mechanical properties was established. The results showed that as the number of freeze–thaw cycles increases from 0 to 15, the mesopores inside the soil–rock mixture gradually increase, and the angular similarity of distribution characteristics increases by 5.25%. The uniaxial compressive strength and the peak secant modulus increase exponentially with the increase in the number of freeze–thaw cycles, the uniaxial compressive strength decreases by 47.62%, and the peak secant modulus decreases by 75.87%. The peak strain and pore compaction stage showed an exponential increase and an increasing trend, respectively, and the peak strain increased from 2.115% to 4.608%. The failure mode was basically similar in different cycles; the failure cracks extended from the corners to the middle and lower parts before the failure finally occurred. The types of failure cracks were mainly tensile cracks, followed by tensile shear cracks and the fewest compression shear cracks. The similarity and uniaxial compressive strength conformed to a good linear relationship with the number of freeze–thaw cycles, with the uniaxial compressive strength decreasing linearly with the increase in similarity. [ABSTRACT FROM AUTHOR]

Marsh wetlands are significant ecosystems located between land and water bodies which can both protect species diversity and provide habitats. Changes in the hydrological situation of marsh wetlands as a result of climate change and human activities have led to the degradation of wetland landscapes. Taking the Honghe National Nature Reserve (HNNR) in Heilongjiang Province, China, as an example, this paper gathered information on the reserve's wetland landscape before and after dam construction. The information was obtained using field survey data and a random forest classification method based on Landsat data powered by the Google Earth Engine (GEE) cloud platform. Then, on the basis of the water level data and the digital elevation model, the wetland landscape dynamics of HNNR under three water level conditions were simulated. The findings were as follows: (1) From 1998 to 2008, the area of marsh and meadow had a downward trend, while the area of forest, farmland and water showed a gradual and upward trend; a marked rise in the area of marsh and a continued rise in the area of forest, farmland and water, and a sharp decline in the area of meadow during 2008 to 2018 was observed. (2) There was a significant increase in the area of marsh under the 20 and 40 cm water level simulation scenarios, with a decrease in the number of patches, and an increase in the aggregation index with rising water levels; in contrast, when the water level rose to 60 cm, the area of marsh and the number of patches decreased, but the aggregation index continued to increase. (3) The correlation between wetland landscape and the water level was a nonlinear one. The area of marsh increased and then decreased with increasing water level, reaching a maximum at the 40 cm water level; therefore, 40 cm was the optimal water level regulation scenario. Hydrological processes are the most fundamental ecological processes in marsh wetlands. Understanding the scientific pattern of the spatial pattern characteristics of species as a function of water level environment is important for scientifically guiding the restoration of marsh vegetation. [ABSTRACT FROM AUTHOR]

In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei–Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei–Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical–physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. [ABSTRACT FROM AUTHOR]

The fatigue safety of cable-girder anchorage structures in cable-stayed bridges under long-term service has attracted much attention. For bridges located in seasonally cold regions, the effect of low-temperature environments should be considered when evaluating fatigue performance. Using the Heilongjiang Bridge in China as a case study, a room-temperature fatigue test with a numerical simulation that considers the low-temperature effect on both load effect and fatigue resistance was proposed. A fatigue test with increased testing load amplitude was performed on a 1:3.75 ratio specimen. After 3.2 million loading cycles and using an acoustic emission technique, no fatigue crack was observed in the anchorage structure. The extended finite element method was then adopted to analyze the anchorage zone's fatigue crack initiation position and propagation path. Finally, based on the fatigue characteristics of bridge steel, the fatigue resistance to the crack propagation of the vulnerable area was evaluated under three different service conditions. The results show that the fatigue performance of the anchorage zone at low temperatures is sufficient. Moreover, this paper provides a more widely applicable and cost-effective approach for the fatigue evaluation of steel bridges. [ABSTRACT FROM AUTHOR]

Population shrinkage has had a certain negative impact on urban and rural development in many aspects. The impact of population shrinkage on urban–rural integration has become one of the core scientific issues that needs to be addressed in the current research on promoting the goal of common prosperity in China. Northeast China is a typical region in China that is experiencing a decrease in population and economic activity. Investigating the integrated development of urban and rural areas in this region is highly important for revitalizing Northeast China. This research paper focuses on 32 prefecture-level cities in Northeast China and utilizes spatial correlation analysis and the Geographical Weighted Regression model to uncover the evolving spatial patterns and influential factors affecting integrated urban–rural development in the context of population decline. The findings revealed the following: (1) The level of integrated urban–rural development in Northeast China continues to rise despite the shrinking population. During the comprehensive population decline stage, the growth rate of the urban–rural coupling coordination degree surpasses that of the initial stage. The areas with high values of urban–rural coupling coordination degree shift from northeastern Heilongjiang to four sub-provincial cities. The spatial correlation between urban–rural coupling and coordinated development weakens, with the main type being low–low agglomeration. (2) Factors such as economic development level, labor force size, urbanization level, level of openness, urban–rural accessibility, and proportion of built-up areas significantly correlate with urban–rural coupling and coordination. The influence of each factor varies in magnitude and direction across different locations. Labor force size and urban–rural accessibility have the most-significant impact on integrated urban–rural development, with labor force size having a positive effect and urban–rural accessibility having a negative effect. The impact of the economic development level follows a pattern of initial increase and subsequent decrease as the population shrinks. (3) Although population decline does not hinder integrated urban–rural development in Northeast China, it is closely connected to changes in the factors influencing such development. To capitalize on the development opportunities presented by national policies, Northeast China should adopt a model of urban–rural development that promotes rural growth through cities. This entails attracting talented individuals to return, enhancing the flow of urban–rural development elements in both directions, and creating a spatial development pattern characterized by "big city, big agriculture, and big ecosystem". By doing so, the revitalization of Northeast China can be achieved. [ABSTRACT FROM AUTHOR]

An accurate estimation of carbon stocks in terrestrial ecosystem and their future changes in relation to land use and land cover change (LUCC) is important for regional territorial spatial optimization and low-carbon development. In this paper, we integrated the System Dynamics (SD) model, PLUS model and InVEST model to simulate land use dynamics and corresponding carbon stocks in Heilongjiang Province from 2030 to 2050 under three SSP-RCP scenarios proposed by the CMIP6. The findings revealed significant variations in land use demand projections across different SSP-RCP scenarios, with increases observed in farmland, construction land and unused land while decreases in woodland and grassland, and the SSP585 scenario showed the highest increment or decrease. Under the SSP126 scenario, the expansion of farmland was due to a reduction in construction land, with little change observed in woodland and grassland, which resulted in a carbon stock increase of 102.71 × 106 Mg at the highest rate; conversely, under the SSP585 scenario, rapid expansion of farmland, construction land and unused land came at the expense of forest and grassland, leading to a significant carbon stock decrease of 204.64 × 106 Mg. The increase in farmland and the decrease in woodland under the SSP245 scenario was relatively moderate with little change observed in construction, resulting in a carbon stock increase of 108.10 × 106 Mg. Regardless of any scenario considered here, forests remain an important carbon sink contributing significantly to carbon sequestration as well as other ecosystem services in Heilongjiang Province. Enhancing territorial spatial planning and ecological environment construction, while promoting an eco-economic development model, will significantly contribute to the achievement of carbon neutrality and regional sustainable development goals. [ABSTRACT FROM AUTHOR]

Drought is complex and is also one of the main disasters affecting China. Exploring the response of agricultural drought and meteorological drought to climatic factors helps us to understand the causes of drought. In this paper, we evaluated the temporal and spatial characteristics of soil drought and meteorological drought (SMD) and explored their responses to climatic factors and latent heat fluxes (LHF), and then explained their variation from the perspective of atmospheric circulation. The following results were obtained. (1) Meteorological drought has gradually increased in the Liaohe River Basin, on the North China Plain, and on the Loess Plateau while average soil moisture has been maintained at only approximately 25%. The impacts of drought are very serious in these regions. (2) LHF response to short time-scale (3-month scale) drought performance is high in the dry season, and the regions with high correlation coefficients are spatially distributed and concentrated in the monsoon climate zone. The regions with high correlation coefficients between drought and LHFs on long time scales (12-month scale) are concentrated in the coastal basin of southeast China. (3) Short- and long-term SMDs showed highly responsive and significant relationships with PDO, showing variations in the southeast coastal basin, the Pearl River basin, the northwest inland basin and the eastern part of the Heilongjiang basin, with a maximum correlation coefficient of 0.21 (p < 0.01). The short-term SMD in the northwestern inland region was significantly negatively correlated with AMO (correlation coefficient of −0.19, p < 0.01). the Nino3.4 index is significantly positively correlated with the SMD in the southeast coastal region of China, with a maximum correlation coefficient of 0.23 (p < 0.01). The decrease in convective precipitation led to a stronger association between soil and meteorological drought and climatic factors. This study helps to reveal the changing patterns of SMDs and can also be used globally to identify the local development patterns of drought under climate change. [ABSTRACT FROM AUTHOR]

Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms. The Heihe area of Heilongjiang Province is one of the key forest areas and forest-fire-prone areas in China. Based on daily historical forest-fire data from 1995 to 2015, daily meteorological data, topographic data and basic geographic information data, the main forest-fire driving factors were first analyzed by using RF importance characteristic evaluation and logistic stepwise regression. Then, the prediction models were established by using the two machine learning methods. Furthermore, the goodness of fit of the models was tested using the receiver operating characteristic test method. Finally, the fire-risk grades were divided by applying the kriging method. The results showed that 11 driving factors were significantly correlated with forest-fire occurrence, and days after the last rain, daily average relative humidity, daily maximum temperature, daily average water vapor pressure, daily minimum relative humidity and distance to settlement had a high correlation with the risk of forest-fire occurrence. The prediction accuracy of the two algorithms in regard to fire points was higher than that for nonfire points. The overall prediction accuracy and goodness of fit of the RF and BPNN algorithms were similar. The two methods were both suitable for forest-fire occurrence prediction. The high-fire-risk zones were mainly concentrated in the northwestern and central parts of the Heihe area. [ABSTRACT FROM AUTHOR]

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth. [ABSTRACT FROM AUTHOR]

The Heilongjiang Duobaoshan area is located at the confluence of the Great Xing'an Range and the Lesser Xing'an Range, and the area has undergone a complex magmatic and tectonic evolutionary history resulting in a complex and diverse geological background for mineralization. As a result of this geological complexity and the multi-period nature of mineralization, the geochemical data of the area are usually not satisfied with a single statistical distribution form, so traditional statistical methods cannot adequately explore and identify the distribution of deep-seated information in the geochemical data. Based on the above problems, this paper adopts a multivariate component data analysis method to process 14 mass fraction data elements, namely Ag, As, Au, Bi, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, W, and Zn, in the 1:50,000 soil geochemical data from the Duobaoshan area of Heilongjiang. The spatial distribution and internal structural characteristics of raw, logarithmic transformation and isometric logarithmic ratio (ILR) transformed data were compared using exploratory data analysis (EDA); robust principal component analysis (RPCA) was applied to obtain the PC1 and PC2 principal component combinations associated with mineralization, and a spectrum–area (S–A) fractal model was further used to decompose the geochemical anomalies of the PC1 and PC2 principal component combinations as composite anomalies. The results show the following: (i) The data transformed by the isometric logarithmic ratio (ILR) eliminate the influence of the original data closure effect, and the spatial scale of the data is more uniform; the data are approximately normally distributed, based on which RPCA can be applied to better explore the correlation between elements and the pattern of co-associated combinations. (ii) The S–A method was further used to decompose the composite anomalies of the PC1 and PC2 principal component combination in the study area. The anomalous and background fields of the screened-out PC1 and PC2 principal component combinations reflect anomalous information on mineralization dominated by Au mineralization. Moreover, the anomaly and background information after extraction were in good agreement with the known Au deposits (points), and many geochemical anomalies with prospecting potential were obtained in the periphery, providing a theoretical basis and exploration focus for the next step in the searching and exploring of the study area. [ABSTRACT FROM AUTHOR]

Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy. [ABSTRACT FROM AUTHOR]

Recently, with the development of geophysical exploration technology, geophysical engineering instruments and methods have also improved. The multi-electrode resistivity method is widely used in engineering exploration. In this paper, multi-electrode resistivity tests were carried out in a seasonal frozen soil area in Heilongjiang Province, to provide an optimized multi-electrode resistivity method under the conditions of frozen soil. Combined with shear wave velocity tests and standard penetration tests, multi-electrode resistivity tests were used to comprehensively analyze and evaluate the physical and mechanical properties of Tertiary semi-diagenesis rocks. The results show that the high resistivity due to the frozen surface layer acting as a shield can be eliminated by technical means. It is feasible to test the resistivity through the frozen surface layer. The multi-electrode resistivity method can visually reflect the interface between saturated sand and semi-diagenetic rocks. Dividing the interface between saturated sand and semi-diagenetic rocks is advantageous as the morphology of the resistivity curve has a significant curvature change. There is a strong correlation between the resistance and shear wave velocity of a strata in which the Pearson correlation coefficient is as high as 0.99. The multi-electrode resistivity method test used in combination with the shear wave velocity test and the standard penetration test could give the bearing capacity and frictional resistance of semi-diagenetic rocks, which saves a lot of time and material costs in engineering exploration. [ABSTRACT FROM AUTHOR]

Detailed spatial distribution of soil organic matter (SOM) in arable land is essential for agricultural management and decision making. Based on digital soil mapping (DSM) theory, much attention has been focused on the selection of environmental covariates. However, the importance of human activity factors in SOM prediction has not received enough attention, especially in arable soil. Moreover, due to the insufficient amount of soil sampling data used to train and validate the DSM model, the prediction results may be questionable, and some even contradictory. This paper explores the effectiveness of the human footprint, amount of fertilizer application, agronomic management level, crop planting type, and irrigation guarantee degree in SOM mapping of arable land in Heilongjiang Province. The results show that the model only including environmental covariates accounts for 41% of the variation in SOM distribution. The model combining the five human activity factors increases the SOM spatial prediction by 39% in terms of R2 (coefficient of determination), 12% in terms of RMSE (root mean square error), 15% in terms of MAE (mean absolute error), and 11% in terms of LCCC (Lin's concordance correlation coefficient), showing better prediction accuracy and performance. This indicates that human activity factors play a crucial role in determining SOM distribution in arable land. In the SOM prediction, soil moisture is the most important environmental covariate, and the amount of fertilizer application with a relative importance of 11.36% (ranking 3rd) is the most important human activity factor, higher than the annual average precipitation and elevation. From a spatial point of view, the Sanjiang Plain is a difficult area for prediction. [ABSTRACT FROM AUTHOR]

Precipitation is an important parameter in water resource management, urban flood warning systems, and hydrological analyses. Precipitation forecasting can provide a decision-making basis for relevant organizations, such as those in the agricultural sector and water conservancy departments. In this paper, a modified grey self-memory model (MGSM) was constructed by combining a self-memory function and grey theory. To verify the precision of the model in cases in which measured data are not available in the forecasting stage, a self-test method based on the scale effect in the precipitation forecasting stage was proposed. Ultimately, the model was verified based on three precipitation scales—the annual scale, the crop growth period, and the monthly scale—in the crop growth period from 1961 to 2018 in the Songnen Plain area, Heilongjiang Province. The results showed that the MGSM yielded higher fitting accuracy than the original GM(1,1) and grey self-memory models. Furthermore, the precipitation in the study area was predicted with the MSGM at the three different scales above from 2019 to 2023. The accuracy of forecasting meets the relevant requirements, and the model can be used to forecast precipitation trends at different time scales in the future. The results provide a reference for formulating scientific and rational agricultural water use strategies and guiding agricultural production practices. [ABSTRACT FROM AUTHOR]

To evaluate the state of an agricultural development more comprehensively, a vulnerability assessment is introduced into agricultural water and land resources system, and it is expected that the vulnerability assessment can provide a basis for improving system structure and function and realizing sustainable development. In the study, 27 evaluation indicators are selected from the agricultural water and land resources system (AWLRS), socio-economic system and ecological structure system to construct the evaluation index system for agricultural water and land resource system vulnerability (AWLRSV). Seagull optimization algorithm (SOA) is used to calibrate the parameters of the random forest (RF) model. SOA-RF model is applied to measure the AWLRSV of Heilongjiang Province in China. The results show that the SOA-RF model has higher accuracy and stronger stability than the traditional RF model and DA-RF model. The value of AWLRSV in Heilongjiang Province presents a downward–upward–downward trend from 2008 to 2018. The vulnerability levels are mainly level II and III, and level III is mainly distributed northwest and southeast of Heilongjiang Province. The novelty of this paper is to regard the agricultural water and land resources system as a compound system, put forward the vulnerability assessment framework. The findings may provide reference for regional sustainable development from a new research perspective. [ABSTRACT FROM AUTHOR]

In this paper, we selected the northeast region as a study area from the perspective of soil nutrient demand, calculated the livestock carrying capacity of farmland under three scenarios where nitrogen nutrient accounts for 35% (low level), 45% (medium level), and 55% (high level) of fertilization, and carried out a risk analysis. The results show that the scale of husbandry breeding is expanding and the scale of the planting industry has remained basically unchanged. Under the three scenarios, there were 23 regions where the livestock manure exceeded the maximum value that could be absorbed by farmland in 2008 and 28 regions in 2019. These regions in the potential area are mostly located in Heilongjiang province and the regions in the restricted area are mostly located in Liaoning Province. On the whole, the northeast region is generally faced with the problem of livestock overloading, and the insufficient utilization and treatment capacity of livestock manure poses a huge threat to regional ecological security. Based on this, adjusting the structure of regional planting and breeding, promoting the development of the livestock manure processing industry, enhancing the production capacity of organic fertilizer, and constructing an integrated pattern of regional planting and breeding are effective ways to realize the sustainable utilization of farmland in northeast China. [ABSTRACT FROM AUTHOR]

Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring. [ABSTRACT FROM AUTHOR]

Simple Summary: Cryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi are among the most common diarrhea-causing pathogens in humans and livestock. This was the first study to systematically and molecularly detect the prevalence of three intestinal protozoa in cattle in Heilongjiang Province, Northeast China. Our findings indicate that a variety of species/genotypes are prevalent in cattle in Heilongjiang Province and that some of them are zoonotic, indicating a risk of zoonotic disease transmission in endemic areas. This study helps to understand the epidemiology of Cryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi in cattle in Heilongjiang Province. Crytosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi are important diarrheal pathogens with a global distribution that threatens the health of humans and animals. Despite cattle being potential transmission hosts of these protozoans, the associated risks to public health have been neglected. In the present study, a total of 1155 cattle fecal samples were collected from 13 administrative regions of Heilongjiang Province. The prevalence of Cryptosporidium spp., G. duodenalis, and E. bieneusi were 5.5% (64/1155; 95% CI: 4.2–6.9), 3.8% (44/1155; 95% CI: 2.7–4.9), and 6.5% (75/1155; 95% CI: 5.1–7.9), respectively. Among these positive fecal samples, five Cryptosporidium species (C. andersoni, C. bovis, C. ryanae, C. parvum, and C. occultus), two G. duodenalis assemblages (E and A), and eight E. bieneusi genotypes (BEB4, BEB6, BEB8, J, I, CHS7, CHS8, and COS-I) were identified. Phylogenetic analysis showed that all eight genotypes of E. bieneusi identified in the present study belonged to group 2. It is worth noting that some species/genotypes of these intestinal protozoans are zoonotic, suggesting a risk of zoonotic disease transmission in endemic areas. The findings expanded our understanding of the genetic composition and zoonotic potential of Cryptosporidium spp., G. duodenalis, and E. bieneusi in cattle in Heilongjiang Province. [ABSTRACT FROM AUTHOR]

Well-defined and stable property rights play a pivotal role in shaping human economic behavior by averting the tragedy of the commons. This study employs micro-survey data from Heilongjiang Province, China, to empirically investigate the impact and mechanisms of land approval on the adoption of straw-returning technology by farmers. Utilizing the Probit model and mediation and moderation effect testing methods, the findings reveal the following: (1) Land approval significantly promotes the adoption of straw-returning techniques by farmers, with a marginal effect of 0.288. This view is further validated through counterfactual inference constructed using the propensity score matching method. (2) Endowment effects mediate the relationship between land approval and farmers' adoption of straw-returning technology. (3) Digital skills and farming scale negatively moderate the policy's impact on farmers' adoption of straw-returning technology. (4) In terms of control variables, the age of farmers and the dispersion of cultivated land have a significant negative impact on the adoption of straw-returning technology by farmers, while training related to agricultural straw-returning skills and government technology promotion significantly positively affects the use of straw-returning technology by farmers. Therefore, the clarity of land property rights helps to harness the policy effects of land approval and provides a research approach for countries with communal land ownership to implement actions for soil quality conservation. [ABSTRACT FROM AUTHOR]

The rice production system in China is facing challenges, including declining soil fertility and a stagnant rice yield. This study aimed to test whether integrating the return of straw to fields with less power puddling could simultaneously enhance soil fertility and rice yields. Therefore, field experiments were conducted in Heilongjiang Province, a key rice-growing region in China, from 2017 to 2021, using three different planting methods: control group (CK), straw return (SR) and straw return integrated with less power puddling (SR + LP). The results showed that small soil aggregates (particle diameter < 0.25 mm) and soil bulk density were significantly decreased when straw return was integrated with less power puddling. These changes contributed to the preservation of soil structure. Simultaneously, this approach significantly increased soil ammonium nitrogen content from 9.9 to 10.9 mg kg−1, organic matter content from 35.0 to 36.2 g kg−1, available nitrogen content from 140.5 to 147.0 mg kg−1 and available potassium content from 128.6 to 136.8 mg kg−1 at mature stage on average. Consequently, the post-heading stored assimilates accumulation of rice was increased from 6.12 to 6.43 t ha−1, and the nitrogen, phosphorus and potassium accumulation of rice were increased by 7.85 kg ha−1, 1.13 kg ha−1 and 5.68 kg ha−1, respectively. These changes ultimately resulted in a higher 1000 g weight and filled grain rate, providing the foundation for higher yields (an increase from 9.31 t ha−1 to 9.55 t ha−1). Furthermore, this approach also increased the net income for farmers by USD 14 t ha−1. In summary, this study demonstrates that integrating straw return with less power puddling can enhance soil's nutrient supply and retention capacity. This enhancement may boost the absorption and transportation of nutrients, ultimately establishing the groundwork for higher yields and economic benefits by enhancing the 1000 g weight and filled grain rate. Future research should delve deeper into its applicability across different ecosystems and investigate the yield-increasing mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: When holocyclic aphids, Aphis glycines Matsumura, were exposed to a diurnal of 35 °C lasting for seven days, the development, reproduction, and morphogenesis were affected significantly. These results are important for predicting the dynamics of A. glycines in this region. This also provides evidence that shows short-term heatwaves are useful for controlling the occurrence of A. glycines. The soybean aphid, A. glycines, is an important soybean pest. Harbin, in the Heilongjiang Province, is an area with increasing temperatures in China that faces frequent short-term heatwaves. In this study, the development, reproduction, and morph differentiation of A. glycines have been studied when they were exposed to diurnal at 35 °C for seven days, beginning at different developmental stages. The nymph stage duration of A. glycines was longer, the adult lifespans and total lifespans were shorter, and their bodies were smaller when exposed to a diurnal of 35 °C beginning at the 1st to 4th stadium. The adult reproduction period was shorter, and the adult fecundity and intrinsic rate of increase were smaller than those of aphids reared at a diurnal of 25 °C. A higher and lower proportion of gynoparae and males were deposited as offspring on day 13 by adults when exposed to diurnal at 35 °C, beginning at the 1st to 4th stadium, respectively, than those of aphids reared at a constant of 20 °C. These results are important for predicting the dynamics of A. glycines in Harbin soybeans. This provides evidence that short-term heatwaves are probably useful for controlling A. glycines, by inhibiting development and male morphogenesis. [ABSTRACT FROM AUTHOR]

Heilongjiang Province, the largest commercial grain base in China, experiences significant challenges due to the environmental effects on its soil. The freezing and thawing cycle in this region leads to the transport of water and heat, as well as the exchange and transfer of energy. Consequently, this exacerbates the flooding disaster in spring and severely hampers farming activities such as plowing and sowing. To gain a better understanding of the freezing and thawing mechanisms of farmland soil in cold regions and prevent spring flooding disasters, this study focuses on Heilongjiang Province as a representative area in northeast China. The research specifically investigates the frozen and thawed soil of farmland, using a large-scale low-temperature laboratory to simulate both artificial and natural climate conditions in the cold zone. By employing the similarity principle of geotechnical model testing, the study aims to efficiently simulate the engineering prototypes and replicate the process of large-span and long-time low temperatures. The investigation primarily focuses on the evolution laws of key parameters, such as the temperature field and moisture field of farmland soil during the freeze–thaw cycle. The findings demonstrate that the cooling process of soil can be categorized into three stages: rapid cooling, slow cooling, and freezing stabilization. As the soil depth increases, the variability of the soil temperature gradually diminishes. During the melting stage, the soil's water content exhibits a gradual increase as the temperature rises. The range of water content variation during thawing at depths of 30 cm, 40 cm, 50 cm, and 80 cm is 0.12% to 0.52%, 0.47% to 1.08%, 0.46% to 1.96%, and 0.8% to 3.23%, respectively. To analyze the hydrothermal coupling process of farmland soil during the freeze–thaw cycle, a theoretical model of hydrothermal coupling was developed based on principles of mass conservation, energy conservation, Darcy's law of unsaturated soil water flow, and heat conduction theory. Mathematical transformations were applied after defining the relative degree of saturation and solid–liquid ratio as field functions with respect to the relative degree of saturation and temperature. The simulated temperature and moisture fields align well with the measured data, indicating that the water–heat coupling model established in this study holds significant theoretical and practical value for accurately predicting soil temperature and moisture content during the spring sowing period, as well as for efficiently and effectively utilizing frozen soil resources in cold regions. [ABSTRACT FROM AUTHOR]

Fungal endophytes are well-known for their ability to promote plant growth and hinder fungal diseases, including Fusarium head blight (FHB) caused by Fusarium graminearum. This study aimed to characterize the biocontrol efficacy of strain J4-3 isolated from the stem of symptomless wheat collected from Heilongjiang Province, China. It was identified as Epicoccum layuense using morphological characteristics and phylogenetic analysis of the rDNA internal transcribed spacer (ITS) and beta-tubulin (TUB). In a dual culture assay, strain J4-3 significantly inhibited the mycelial growth of F. graminearum strain PH-1 and other fungal pathogens. In addition, wheat coleoptile tests showed that lesion symptoms caused by F. graminearum were significantly reduced in wheat seedlings treated with hyphal fragment suspensions of strain J4-3 compared to the controls. Under field conditions, applying spore suspensions and culture filtrates of strain J4-3 with conidial suspensions of F. graminearum on wheat spikes resulted in the significant biocontrol efficacy of FHB. In addition, wheat seedlings previously treated with spore suspensions of strain J4-3 before sowing successfully resulted in FHB reduction after the application of conidial suspensions of F. graminearum at anthesis. More importantly, wheat seedlings treated with hyphal fragments and spore suspensions of strain J4-3 showed significant increases in wheat growth compared to the controls under greenhouse and field conditions. Overall, these findings suggest that E. layuense J4-3 could be a promising biocontrol agent (BCA) against F. graminearum, causing FHB and a growth-promoting fungus in wheat. [ABSTRACT FROM AUTHOR]

Simple Summary: Gut microbiota is diverse and complex and is a key environmental factor affecting the immune homeostasis of the host. The results showed significant differences in gut microbiota community structures between semi-free-range and captive red-crowned cranes. Comparing the differences in gut microbiota function and composition of captive and semi-free-range red-crowned cranes is critical for conservation management and policy making, as well as the projects focusing on the release of captive red-crowned cranes into the wild. The wild populations of red-crowned cranes (Grus japonensis) in west China are gradually decreasing, necessitating the optimization of reintroduction measures. This study used 16S rRNA high-throughput sequencing technology to compare the gut microbiota communities of cranes living in two modes (captive and semi-free-range) before their reintroduction in Zhalong National Nature Reserve, Heilongjiang Province, China. The results showed that Proteobacteria (74.39%) and Firmicutes (25.29%) were the dominant gut bacterial phyla inhabiting these cranes. Significant differences were found in the gut microbiota community composition between semi-free-range and captive cranes (p < 0.01). Psychrobacter, Sporosarcina, and Lactococcus were significantly enriched in captive cranes (p < 0.05), while Pseudomonadaceae_Pseudomonas, Pantoea, Lysobacter, and Enterobacteriaceae_Pseudomonas were more abundant in semi-free-range cranes (p < 0.05). The functions and community structure of gut microbiota were affected by feeding patterns (p < 0.05). The metabolic pathways of ethylbenzene degradation, PPAR signaling pathway, betalain biosynthesis, systemic lupus erythematosus, and shigellosis were up-regulated in semi-free-range cranes (p < 0.05). [ABSTRACT FROM AUTHOR]

Vaccinium corymbosum L., commonly known as blueberry, is a valuable small fruit tree in terms of its economic significance and is primarily found in China within the regions of Heilongjiang and Jilin provinces. Additionally, it thrives in the areas spanning the middle and lower reaches of the Yangtze River. Beyond their economic value, blueberries play a crucial role within forest ecosystems, serving as a significant energy source and source of nutrients. Their presence contributes significantly to fostering stability, enhancing biodiversity, and acting as an indicator of environmental quality within forest ecosystems. Since August 2022, an unknown leaf disease has been found on a large scale in blueberry fields in Nanjing, Jiangsu Province, China. The disease causes leaf curling, wilting, and even early defoliation, severely reducing the yield and production value of blueberries. The pathogenicity test confirmed the virulence of the isolates (NG5-1, NG5-2, NG5-3, NG5-4, N2-1, and N2-2) against V. corymbosum. The two pathogens were identified as Colletotrichum fructicola and C. aenigma by observing the morphological characteristics of the isolates and combined with multilocus phylogenetic analyses (ITS, CAL, ACT, TUB2, ApMat, and GAPDH). Blueberry anthracnose, caused by C. aenigma, is the first report of this disease in China. The biological characteristics of C. aenigma were investigated under different conditions, including temperature, pH, light conditions, culture medium, and carbon and nitrogen sources. The optimal temperature for growth was determined to be within the range of 25–30 °C; C. aenigma exhibits optimal growth at a pH of 7–8. Mycelial growth is favored under conditions of partial light, whereas complete darkness promotes spore production. It was found that PDA medium was the most favorable medium for C. aenigma mycelial growth, and MM medium was the best medium for spore production; the most suitable carbon sources for colony growth and spore production were sorbitol and glucose, respectively, and the most suitable nitrogen source was peptone. This study furnishes a theoretical foundation for a more scientifically informed approach to the prevention and control of anthracnose on V. corymbosum. [ABSTRACT FROM AUTHOR]

Differentiated nitrogen (N) application styles and dosages can directly affect crop yield and planting costs. To maximize the efficiency of N fertilizer utilization and solve the problem of increasing production without increasing income, the Decision Support Systems for Agrotechnology Transfer (DSSAT) model was used in this study to carry out experiments simulating different N application styles and dosages for potatoes at 15 meteorological observation stations in the northeast region of China during 2013–2017. The present study aims to analyze the relationship among yield, cost, and net gains under different N fertilizer management scenarios by combining the cost-benefit method. The main conclusions are as follows: (1) the staged and incremental application of N fertilizer during the potato growth period can promote a yield increase. The lowest yield was achieved by a one-time basal application of N fertilizer at sowing, and the highest yield was observed when N fertilizer was applied in a 1:1 ratio at sowing and the end of tuber formation, respectively. (2) The range for N fertilizer application to achieve the highest potato yield at each station is 275–330 kg·ha−1. (3) The highest potato yield under sufficient N application treatments did not correspond to the highest net gains. Potato net gains at all stations showed an increasing-then-decreasing trend with the increase in N application. The highest gains were obtained when 220 kg·ha−1, 220 kg·ha−1, and 275 kg·ha−1 of N fertilizer were applied in a 1:1 ratio as a basal fertilizer and supplementary fertilizer at sowing, and the end of tuber formation in Heilongjiang, Jilin, and Liaoning provinces, respectively. This study provides a reference program for potato planting with the highest benefits for regional governments, farmers, and enterprises and aims to improve the status quo of potato planting, increasing production without increasing income. [ABSTRACT FROM AUTHOR]

Urban–rural integration relies on the rational flow of factors between urban–rural areas. Land represents a closely related factor between urban–rural areas, so the effective utilization of land resources can promote the flow of urban–rural factors. Therefore, there is a certain correlation between land use function and urban–rural integration. The purpose of this study is to explore the coupling and coordination relationship between the two systems and to find out the spatial–temporal differentiation characteristics in the process of land use function and urban–rural integration. The main conclusions are as follows: (1) The comprehensive level of land use function and urban–rural integration in Heilongjiang Province shows an overall upward trend, but there is a large differentiation on a municipal scale. (2) The coupling coordination degree of the two systems in Heilongjiang Province shows a spatial distribution pattern of "high in the north and low in the south, high in the middle and low in the east and west". From 2013 to 2022, except for Harbin and Yichun, the overall trend in other regions is gradually upward. (3) The obstacle degree analysis of land use function and urban–rural integration in Heilongjiang Province shows that there is a close correlation of obstacle factors between the two systems. [ABSTRACT FROM AUTHOR]

Research Objectives: Considering the complex challenges arising from urbanization, population growth, and the consequential strain on China's cultivated land resources, we integrate insights from advanced artificial intelligence (AI) technology. The primary objective is to quantitatively assess the cultivated land-holding responsibility of local governments in China from a fairness perspective. Our aim is to integrate the cultivated land responsibility ownership amount into the compensation mechanism for cultivated land protection, thereby promoting fairness, cultivated land conservation, and sustainable agricultural development across Chinese provinces while ensuring efficient agricultural product distribution. Research Methodology: Beyond a traditional literature review and model analysis, we incorporated AI technology for data analysis and model optimization. Findings: ① The findings demonstrate a surplus in both food security and ecological security within cultivated land. ② In-depth analysis via AI revealed that the provinces with the largest surplus include Heilongjiang, Jilin, and Hubei. ③ With the precision provided by AI, we delineated the minimum value of the cultivated land responsibility ownership amount for each province. Eleven provinces met this criterion, primarily located in the northeast and central regions and including Yunnan in the west. ④ From a fairness perspective, coupled with AI insights, we established a compensation mechanism for cultivated land protection that is better suited to the Chinese context. Based on this mechanism, the national average compensation standard was calculated to be 26,900 CNY per hectare. The highest standards were observed in Shanghai, Beijing, and Jiangsu, while the lowest were in Shanxi, Heilongjiang, and Gansu. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the impact of climate change on forestry efficiency (FRE) and total factor productivity change (TFPC) in 31 provinces of China for a study period of 2001–2020. Additionally, the study aims to evaluate the success level of governmental initiatives used to mitigate climate change. Using the DEA-SBM, this study estimates the forestry efficiency for 31 Chinese provinces and seven regions. Results indicate that the average forestry efficiency score obtained is 0.7155. After considering climatic factors, the efficiency level is 0.5412. East China demonstrates the highest average efficiency with a value of 0.9247, while the lowest score of 0.2473 is observed in Northwest China. Heilongjiang, Anhui, Yunnan, and Tibet exhibit the highest efficiency scores. Mongolia, Heilongjiang, Sichuan, Hebei, and Hunan are the five provinces most affected by climate change. This study's findings indicate that the average total factor forestry productivity (TFPC) is 1.0480, representing an increase of 4.80%. The primary determinant for change is technology change (TC), which surpasses efficiency change (EC). Including climate variables reduces total factor productivity change (TFPC) to 1.0205, mainly driven by a decrease in TC. The region of South China exhibits the highest total factor productivity change (TFPC) with a value of 1.087, whereas both Northeast China and Central China observe falls below 1 in TFPC. The Mann–Whitney U test provides evidence of statistically significant disparities in forestry efficiency and TFPC scores when estimated with and without incorporating climate factors. Kruskal–Wallis found a statistically significant difference in FRE and TFPC among seven regions. [ABSTRACT FROM AUTHOR]

As one of the major new agricultural business entities, agricultural leading enterprises (ALEs) are responsible for ensuring national food security, leading agricultural and rural modernization, and increasing farmers' employment prospects and incomes. From the perspective of headquarters and branches, this study used a point pattern analysis, the local Moran's index, the rank-size rule, and the geographical detector to depict the spatial pattern of ALEs in Heilongjiang Province, detect influencing factors, and reveal the spatial layout mechanism. The main conclusions are as follows. (1) ALE headquarters and branches in Heilongjiang Province had different location requirements, and their layout orientation, clustering areas, and influencing factors were different. (2) The headquarters displayed a pronounced urban and agglomeration orientation, while branches exhibited a significant farm dependence and raw material orientation. (3) Both the headquarters and the branches showed a significant trend towards spatial clustering. The headquarters were mainly in the Harbin municipal district and surrounding counties, including Wuchang, Zhaodong, and Beilin, which showed a high–high cluster pattern. The branches were mainly in the Sanjiang Plain. Tongjiang, Fujin, Hulin, Mishan, Raohe, Baoqing, and Suibin showed a high–high cluster pattern, while the Harbin municipal district and Bei'an showed a high–low outlier pattern. (4) The ALEs' regional connection network in Heilongjiang Province was radially distributed with the Harbin municipal district as the centre. The agricultural reclamation system deeply influenced it. (5) The number of supporting enterprises, number of permanent residents, gross domestic product, railway mileage, number of people with Bachelor's degrees or above, and distance from the provincial capital were the main influencing factors of the headquarters spatial pattern in Heilongjiang Province. The number of state farms in Heilongjiang Province, the per capita grain yield, highway mileage, and distance from the provincial capital were the main influencing factors of the branch spatial patterns in Heilongjiang Province. The interaction results indicated that the explanatory power of two-factor interaction was stronger than that of a single factor regardless of headquarters or branches, and most interaction types were bilinear enhancements. This study aims to provide a decision-making reference for the long-term development of ALEs in Heilongjiang Province at the present stage and accelerate the development of agricultural industrialization in major grain-producing areas. [ABSTRACT FROM AUTHOR]

The escalating ambient ozone (O3) pollution in China has garnered significant attention, necessitating an intensified focus on O3 pollution control and the coordinated management of PM2.5 and O3. This study reviews and analyzes the spatiotemporal characteristics of O3 and PM2.5 concentrations in 13 cities within Heilongjiang Province from 2019 to 2021. The analysis is based on data sourced from the ecological environment monitoring network. In addition to this, correlation analyses were executed to explore the interaction between the two pollutants. The findings reveal a declining trajectory in PM2.5 concentration over the past three years, while O3 concentration has exhibited an upward trend. Temporally, both O3 and PM2.5 concentrations display pronounced seasonal variations, with peaks evident during the spring and summer (May to July), as well as in the winter (January, February, and December). From a spatial standpoint, elevated O3 concentrations were identified in the southwestern cities of Harbin, Daqing, and Suihua, while the northwestern cities of Daxinganling and Heihe exhibited comparatively lower O3 concentrations, but the difference was not significant. Conversely, PM2.5 concentrations demonstrated substantial variation among the 13 cities (districts). Regarding their correlation, a noteworthy positive correlation between the two pollutants was observed in April and May, contrasted by a negative correlation in November and December. Weather categories such as excellent, good, lightly polluted, moderately polluted, and other weather showed a lower correlation, whereas heavily polluted and severely polluted categories demonstrated a stronger correlation. Furthermore, the correlation with severe pollution is greater than that with heavily polluted, further indicating that heavier air pollution is more conducive to the coexistence of O3 and PM2.5 to form composite pollution. On a provincial scale, the correlation between the two pollutants is progressively increasing annually. This signifies a closely intertwined and intricate interaction and transformation relationship between O3 and PM2.5, accentuating the urgency for synergistic control measures. [ABSTRACT FROM AUTHOR]

This study aimed to determine the influence of time and precipitation as covariates on the flood frequency distribution in the Songhua River tributaries under the nonstationarity assumption and to investigate the possibility of nonstationary models' application in river management scope demarcation work. Nonstationary flood frequency analysis (NS-FFA) was conducted in three typical basins of the Songhua River (in Northeastern China) based on the generalized additive models for location, scale, and shape (GAMLSS), and stationary flood frequency analysis was used as a comparison. Under the stationarity assumption, the Pearson type Ⅲ (P-Ⅲ) distribution is the main theoretical distribution for the flood extremum at hydrological stations, followed by a lognormal (LN) distribution. Under the nonstationarity assumption, when time is considered a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 63.75%), followed by the Weibull distribution (with 18.75%). When precipitation is considered as a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 57.5%). We attempted to apply several FFA methods to calculate the design frequency in this study, referring to the work requirements for river management scope demarcation in three typical basins, and came to the following conclusions. From the simulation results of the p = 10% flood at the export stations of typical basins, it can be seen that time-covariate NS-FFA obtained the best simulation results. Two cases of the simulation under the stationarity assumption are positive, which will lead to a high design scale. The time-covariate GAMLSS in NS-FFA has the advantages of higher calculation accuracy and simpler processes. To better balance construction costs and disaster protection requirements, NS-FFA can be used to determine the design scale of water conservation projects; additionally, it can be used to demarcate the scope of river management. The accuracy of GAMLSS for FFA is also influenced by the complexity of the terrain, with basins with relatively simple terrain having higher calculation accuracy. [ABSTRACT FROM AUTHOR]

Compound microbial fertilizer is a new type of environmentally friendly slow-release fertilizer that can effectively improve the physical and chemical properties of the soil, significantly improve the ecological environment, and promote the sustainable development of agriculture. In this study, we conducted a field experiment to evaluate the impact of different applications of chemical fertilizer combined with composite microbial fertilizer on soil physical properties and soybean yields at Heshan Farm, Heilongjiang Province, China, during 2021–2022. Soybean varieties "Jinyuan 55" and "Keshan 1" were treated with three treatments implemented as follows: T1 (conventional fertilization), T2 (50% N fertilizer + compound microbial fertilizer), and T3 (0 N fertilizer + compound microbial fertilizer). Compared to conventional fertilization (T1 treatment), the application of composite microbial fertilizers (T2 and T3) resulted in a decrease in soil bulk density and an increase in porosity. Notably, we observed that moderate application of the composite microbial fertilizer (T3) led to a decrease in the volume fraction of clay particles and an increase in the volume fraction of sand particles. Furthermore, all treatments exhibited high content of agglomerates larger than 5 mm at 0–20 cm. The application of composite microbial fertilizers (T2 and T3) promoted the formation of large soil agglomerates and reduced the presence of micro-agglomerates smaller than 0.25 mm. In 2021–2022, The soybean yield increased by 13.02% in the T2 treatment compared with the T1 treatment and decreased by 9.34% in the T3 treatment. We concluded that the appropriate application of compound microbial fertilizer can help protect black soil, enhance the self-repair capability of black soil, and improve soybean quality in abnormal precipitation years. These results provide an actionable basis for constructing and developing green fertilizer systems for the soybean industry. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) play a crucial role in safeguarding biological resources and preserving ecosystems. However, the lack of standardized and highly operational criteria for evaluating their conservation effectiveness, particularly across different ecological types, remains a significant gap in the literature. This study aims to address this gap by constructing a conservation effectiveness evaluation model for two distinct types of PAs in Heilongjiang Province, China: the Zhalong National Nature Reserve (ZlNNR), a wetland ecological reserve; and the Mudanfeng National Nature Reserve (MdfNNR), a forest ecological reserve. We employed various methods, including land use dynamic index, visual analysis of landscape patterns, remote sensing inversion, and a multi-factor comprehensive assessment model, to assess changes in conservation effectiveness from 2000 to 2020. Our findings reveal a contrast between the two PAs. In the ZlNNR, croplands and water bodies increased significantly by 4069.4 ha (K = 1.5820%) and 2541.58 ha (K = 3.2692%). In the MdfNNR, impervious lands increased greatly by 65.35 ha (K = 7.4021%), whereas forest lands decreased by 125 ha (K = −0.067%). The core area of the two PAs displayed increased landscape regularity, whereas the experimental area showed heightened landscape diversity. In ZlNNR, the MPSL value increased by 134.91%, whereas the PDL value decreased by 57.43%, indicating a more regular landscape pattern. In MdfNNR, the SHDIL value decreased by 110.7%, whereas the PDL value increased by 52.55%, indicating a more fragmented landscape pattern. The area with improved vegetation trends in ZlNNR was 8.59% larger than in MdfNNR, whereas the area with degraded vegetation trends was 4.86% smaller than in MdfNNR. In all years, the high effectiveness area was larger in ZlNNR than in MdfNNR, whereas the medium and low effectiveness areas were smaller in ZlNNR compared to MdfNNR. This study provides a scientifically rigorous assessment method for evaluating the conservation effectiveness of different types of PAs, laying a solid theoretical foundation and practical guidance for future conservation strategies. [ABSTRACT FROM AUTHOR]

The exploration of the spatiotemporal variations and influencing factors of grain yield in major grain-producing regions is greatly important to maintain stable and sustainable agriculture. Taking Henan Province and Heilongjiang Province as examples, this study reveals the spatiotemporal characteristics of grain yield at the county level by using multisource data on the economy, society, and natural geography from 2000 to 2021 and employing methods such as coefficients of variation, standard deviational ellipses, and spatial autocorrelation analysis. Moreover, geographical detector and geographically weighted regression models are combined to explore the differences in significant influencing factors between these provinces and the spatial heterogeneity of regression coefficients, respectively. The following findings are drawn: (1) Grain yield in both provinces gradually increased, with notable differences in the annual growth rate, the proportion, and at the county level. (2) The number of high-yield counties significantly increased and their spatial distribution became more concentrated, indicating a notable shift in the main regions. (3) The overall spatial correlation of grain yield steadily increased, and the local spatial correlation transitioned from random distribution to gradual aggregation. (4) There were significant differences in the influencing factors, where geographical environment, socio-economic factors, and input factors all affected both provinces. In summary, this study provides a scientific reference for governments worldwide to formulate rational and effective food production policies, thereby contributing to global food security and sustainable social development. [ABSTRACT FROM AUTHOR]

Using the water footprint (WF) approach to evaluate the water-use efficiency in agricultural production is crucial for assessing the sustainable use of water resources and mitigating water scarcity and pollution. This study calculated the blue, grey, green and total water footprints of spring maize production in Northeast China in 2019 and 2020 and compared the water footprint values at the provincial and municipal scales. In addition, this study analyzed the spatial variation and drivers of the water footprint. The results show that the average water footprints of spring maize production in Northeast China in 2019 and 2020 were 1.78 m3kg−1 and 2.00 m3kg−1, out of which the grey water footprint contributed the most, accounting for 55.19% and 49.85% of the total water footprint, respectively, while the blue water footprint contributed the least, accounting for only 17.44% and 18.68% of the total water footprint. At the provincial level, the water footprint of spring maize production in Northeast China was spatially clustered, with the lowest total water footprint in Heilongjiang Province and the highest total water footprint in Jilin Province. The spatial distribution difference of the spring maize unit yield was the fundamental factor explaining the difference in the water footprint. The precipitation, surface water resources, average temperature, effective irrigated area and the proportion of effective irrigated area also had impacts on the water footprint. This study provides a scientific basis for optimizing the distribution of spring maize production in Northeast China, formulating appropriate sustainable water resource management plans, improving water-use efficiency and realizing sustainable water resource management in Northeast China. [ABSTRACT FROM AUTHOR]

The ice flood phenomenon frequently occurs in frigid locations of high latitude and high altitude, which triggers ice dam or ice jam flooding thus endangering personal and property safety. Hence, a scientific risk evaluation with enough consideration of each factor is a basic and necessary requirement for preventing ice flood disaster risks. This study establishes a risk evaluation system for ice flood disasters based on the catastrophe theory and utilizes the Pearson correlation coefficient to screen underlying indicators to evaluate the risk of ice flood in the upper Heilongjiang River region. Considering the correlation between different indicators, a hierarchical cluster analysis is invoked to simplify the indicator set and to select typical years. The results of the evaluation system indicate that the catastrophe membership values in the Mohe, Tahe, and Huma regions from 2000 to 2020 ranged from 0.86 to 0.93. Based on the membership values and the actual disaster situations, a four-level classification of risk ratings is conducted. The comparison between the results obtained from the catastrophe theory evaluation method and the fuzzy comprehensive evaluation method reveals similar risk levels, which verifies the effectiveness and practicality of the catastrophe theory applied to the ice flood risk evaluation and presents a novel method for the study of ice floods. [ABSTRACT FROM AUTHOR]

An evaluation of soil quality sustainability can support decision making for the sustainable use of land resources. However, certain current problems associated with these evaluations remain unaddressed, e.g., the evaluation indicators do not fully reflect soil quality risks and the evaluation scale is not sufficiently small. In this study, 25,000 spatial grids of dimensions 3 km × 3 km are used to divide the major grain-producing regions in China, namely, the Sanjiang Plain and the Songnen Plain of Heilongjiang. Then, the soil erosion modulus, nutrient balance index, soil organic carbon (SOC) storage, heavy metal soil pollution index and crop productivity are calculated for each grid using the RULSE model, nutrient balance index model, soil type method, geoaccumulation index method and mechanism method, respectively. A spatial grid cluster analysis method is used to thoroughly evaluate and analyze the sustainability of soil quality in each grid. The results show that the overall soil status of the study area is good. The soil and water conservation levels are high, the soils show low levels of contamination, the crop production potential is high and the ratio of highly sustainable to moderately sustainable soils is approximately 2:1. Only 2.74% of the land is rated extremely unsustainable and needs to be restored to a basic level of productivity before subsequent functional restoration can be carried out. This study provides a new method for the fine-scale evaluation of soil quality and contributes to the management of land resources. [ABSTRACT FROM AUTHOR]

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers' cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers' behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China. [ABSTRACT FROM AUTHOR]

Monitoring land-use and land-cover change (LUCC) is extremely important in the sustainable development and management of terrestrial ecosystems. Taking the Heilongjiang (Amur) River Basin as the study area, this study aimed to identify the spatiotemporal characteristics of land cover at the entire basin and at the country levels of the three countries using the land-use change index, frequency statistics and land-cover change degree. The results showed that: (1) The LULC types were mainly forest land and grassland (accounting for nearly 83% in total) from 2001 to 2019. The main land-cover types in China, Russia and Mongolia were forest land, forest land and grassland, respectively. (2) The area of urban land, cropland and wetland increased significantly from 2001 to 2019, while the area of forest land and bare land decreased during this time. In general, the ecological environment has greatly improved over the last 19 years, although the relevant ecological restoration still needs to be further implemented. The findings can provide a scientific basis for ecological protection and sustainable development in the Heilongjiang (Amur) River Basin. The approaches here are also applicable to land-cover change research in other similar regions. [ABSTRACT FROM AUTHOR]

In the process of rapid advancement of agricultural production, the dynamic evolution characteristics of land and water resources in the main grain-producing areas and the influencing factors are less studied. This study takes Heilongjiang Province, the main grain-producing area in China, as an example, constructs an index system from three functions: production, life, and ecology, uses information entropy to determine the weights and importance of each index, uses the rate of change of the index to determine the basic data of the dynamic development of the system, combines the weights to determine the dynamic evolution characteristics, and compares and analyzes them with the static evolution characteristics determined by the actual data. The results showed that there were differences in the important indicators under different conditions, and the important indicators under static and dynamic conditions were the proportion of the tertiary industry to GDP(A7) and GDP per unit area(A8), with importance weights of 7.45% and 8.0%, respectively. The static evolution index of the land and water resource system increased slowly from 0.16 to 0.91, while the dynamic evolution index fluctuated and declined from 0.58 to 0.34, indicating that the ability of the land and water resource system in the study area to maximize comprehensive benefits is constantly weakening. Managers can pre-control the development speed of the important indicators under dynamic conditions and promote the sustainable development of the land and water resource system. [ABSTRACT FROM AUTHOR]

This paper assesses the occurrence, distribution, source, and toxicity of polycyclic aromatic hydrocarbons (PAHs), and their methylated form (Me-PAHs) in sewage sludge from 10 WWTPs in Northeastern China was noted. The concentrations of ∑PAHs, ∑Me-PAHs ranged from 567 to 5040 and 48.1 to 479   n g. g − 1 dw, which is greater than the safety limit for sludge in agriculture in China. High and low molecular weight 4 and 2-ring PAHs and Me-PAHs in sludge were prevalent. The flux of sludge PAHs and Me-PAHs released from ten WWTPs, in Heilongjiang province, was calculated to be over 100 kg/year. Principal component analysis (PCA), diagnostic ratios and positive matrix factorization (PMF) determined a similar mixed pyrogenic and petrogenic source of sewage sludge. The average values of Benzo[a]pyrene was below the safe value of 600   n g. g − 1 dependent on an incremental lifetime cancer risk ILCR of 10−6. Sludge is an important source for the transfer of pollutants into the environment, such as PAHs and Me-PAHs. Consequently, greater consideration should be given to its widespread occurrence. [ABSTRACT FROM AUTHOR]