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Pest and disease damage to forests cannot be underestimated, so it is essential to detect diseased trees in time and take measures to stop their spread. The detection of discoloration standing trees is one of the important means to effectively control the spread of pests and diseases. In the visible wavelength range, early infected trees do not show significant color changes, which poses a challenge for early detection and is only suitable for monitoring middle and late discolor trees. The spectral resolution of hyperspectral restricts the improvement of its spatial resolution, and there are phenomena of different spectral of the same and foreign objects in the same spectrum, which affect the detection results. In this paper, the method of hyperspectral and CCD image fusion is used to achieve high-precision detection of discoloration standing trees. This paper proposes an improved algorithm MSGF-GLP, which uses multi-scale detail boosting and MTF filter to refine high-resolution data. By combining guided filtering with hyperspectral images, the spatial detail difference is enhanced, and the injection gain is interpolated into the difference of each band, so as to obtain high-resolution and high-quality hyperspectral images. This research is based on hyperspectral and CCD data obtained from LiCHy, Chinese Academy of Forestry, Maoershan Experimental Forest Farm, Shangzhi City, Heilongjiang Province. The evaluation framework is used to compare with the other five fusion algorithms to verify the good effect of the proposed method, which can effectively preserve the canopy spectrum and improve the spatial details. The fusion results of forestry remote sensing data were analyzed using the vegetation Normalized Difference Water Index and Plant Senescence Reflectance Index. The fused results can be used to distinguish the difference between discoloration trees and healthy trees by the multispectral vegetation index. The research results can provide good technical support for the practical application of forest remote sensing data fusion, and lay the foundation for promoting the scientific, automatic and intelligent forestry control. [ABSTRACT FROM AUTHOR]

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]

Addressing the excessive input and inefficient use of agrochemicals are crucial for global food security, environmental protection, and human health. This paper offers a new idea from the perspective of outsourcing agricultural production. The impact of outsourcing on the frequency and intensity of agrochemical inputs were theoretically analyzed and empirically tested using a field survey of 1211 farmers in Heilongjiang, Henan, and Hunan, the major food-producing areas in China. A Logit regression framework was used to analyze the effect, a conditional mixture process (CMP) method was used to address potential endogeneity concerns, and a mediation effect model was used to dissect the mechanism. The results show that the effect of outsourcing on both input frequency and input intensity of agrochemicals was positive at the 1% significance level. The positive effect conclusion still holds even after addressing the potential endogeneity concerns, and in the sub-sample estimates for maize, wheat, and rice. We conclude that outsourcing can improve the utilization efficiency of agrochemicals by increasing the frequency of agrochemical inputs, but fail to solve the excessive agrochemical inputs and even leads to a further increase in the intensity of agrochemical inputs. Moreover, the mechanism for an increase in agrochemical input intensity due to outsourcing was explored, and it is more likely to be caused by inhibiting farmers' investment in soil improvement measures. [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]

The economic subsystem, social subsystem, and natural ecosystem of forest towns constitute forest towns' complex ecosystem. Forest towns' complex system provides good ecological welfare for the residents' survival and development, especially the beautiful ecological environment has played a positive role in promoting the residents' healthcare. First of all, this paper expounds on the concept of forest towns' ecological welfare from the construction goals of forest towns. Secondly, it analyzes the formation mechanism of forest towns' ecological welfare based on the DPSIR conceptual model. Finally, taking forest towns in Heilongjiang Province as an example, the index system for ecological welfare evaluation is constructed, and the influence degree of the evaluation index is analyzed through using the analytic hierarchy process. The results show that employment and income are the most important indicators that affect the ecological welfare of forest towns in Heilongjiang Province. This paper provides a theoretical basis for evaluating the level of ecological welfare level, level of healthcare level, and planning development path of Chinese forest towns in the future. [ABSTRACT FROM AUTHOR]

Regulating the number of breeding sows (NBS) is crucial for pork supply–demand balance. Current forecasting methods for NBS fail to consider the principle of pig's months of age (MOA) transfer and the impact of factors like diseases and policies on NBS fluctuations, leading to unsatisfactory accuracy. To bridge the research gap, a two-part forecasting model for the NBS was developed. In the first part, a recurrence forecasting model was established according to the growth characteristics of pigs and the principle of pig's MOA transfer. In the second part, the random disturbance term was introduced to consider the influence of plague, policy and other factors on the NBS, and a forecasting method for random disturbance term based on Improved Flower Pollination Algorithm-Back Propagation Neural Network (IFPA-BPNN) was given. Subsequently, the proposed IFA and other newer optimization algorithms were evaluated on CEC 2017 test suite to verify the effectiveness and superiority of IFA. Lastly, the proposed model was employed to forecast the NBS in Heilongjiang Province and Anhui Province of China from 2009 to 2021. Compared to other time series forecasting models, the proposed model showed superior accuracy, confirming its scientific and effective nature. Relevant managerial insights were provided at the end of this paper. [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]

The work of traditional intangible cultural heritage analyzes the intangible cultural works in restricted scales way. So, it is difficult to multi-scale understand the closely related to intangible cultural heritage. In this paper, visual analytics approaches are proposed for the preserve and protection those historic cultural heritages, which include multi-dimensional visualization, temporal visualization, and geospatial visualization. We take national intangible cultural heritage in Heilongjiang province as an example. Through visual analysis of intangible cultural heritage in Heilongjiang province, experts can form multi-dimensional understanding the culture of Heilongjiang province, including the categories, numbers and distributions. Furthermore, those methods we proposed can effective help experts make an in-depth analysis of those intangible cultural heritage, and provide them a new insight in a comparative way. [ABSTRACT FROM AUTHOR]

The cultural industry of ethnic minorities carries the wisdom of many ancients. In today's modernization, ethnic minority cultural industry products can still impress people's hearts with exquisite handicrafts, natural materials, and strong culture. The rise in demand for ethnic cultural products will bring about an increase in the demand for raw materials for handmade products, which is bound to have an impact on the environment. This paper is aimed at modeling and analyzing the ecological level of minority cultural industries based on the background of the Internet of Things (IoT) and environmental responsibility. In the experiment, this paper analyzed the ecological level of the traditional fish skin industry by taking the Hezhe people in Heilongjiang as an example. Aiming at the cultural industry of the Hezhe people, this paper selected the fish skin industry as the object of empirical analysis from the fish culture it represents. According to the ecological level of the industry, this paper selected the three first-level indicators of production energy saving level, ecological environmental protection level, and production emission reduction level and its 12 subordinate second-level indicators to model and analyze the fish skin industry of the Hezhe people. The analysis results showed that the industrial ecological level of the Hezhe fish skin industry in the past 16 years reached the standard rate of more than 75%. However, the ecological level index for the three years of 2018, 2019, and 2020 was between 0.3 and 0.5, which indicated that the ecological level in these three years was very low. This was caused by the mechanization of the handicraft industry in recent years. [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]

In accordance with the topographic features and other relevant factors, data affecting wind power generation in engineering practice may be difficult to obtain, and three short-term wind power forecasting methods are proposed based on incomplete data. Most wind power forecasting studies are based on wind speed data, but wind power forecasting needs more influential factors in addition to wind speed data, and it is difficult to obtain these data in actual production and life. Therefore, this paper proposes the wind power forecasting under the condition of incomplete data. First, this paper makes theoretical analysis on multi-state space Markov chain wind power forecasting, backpropagation (BP) neural network wind power forecasting, and genetic algorithm (GA)-BP neural network wind power forecasting, and the corresponding wind power forecasting models are constructed. Second, with the actually measured data of a wind farm in the Heilongjiang province as the research object, the historical data are preprocessed first and then imported into three wind power forecasting models for simulation, and the curves of relative error and absolute error of total wind power forecasting in this area are obtained. Finally, the simulation results of three methods based on incomplete data are analyzed and evaluated. The results show that under the condition of incomplete data, the maximum error of the GA-BP neural network wind power forecasting model improved by the genetic algorithm is reduced from 6.8% to 1.6%, and the forecasting accuracy is greatly improved. [ABSTRACT FROM AUTHOR]

Heilongjiang Province is the main grain producing region in China and an important part of Northeast China Plain, which is one of the three black soil belts in the world. The cultivated region of black soil accounts for 50.6% of the black soil region in Northeast China. Due to the obvious rise of temperature and uneven distribution of precipitation in the 20th century, it has been considered to be one of the important reasons for agricultural drought and aridity. Under the background of climate change, understanding the multiyear changes and occurrence characteristics of cultivated land drought in different agricultural regions in Heilongjiang Province is of great significance for the establishment of agricultural drought prediction and early warning system in the future, guiding agricultural high-standard farmland irrigation in different regions, promoting black soil protection, and then improving grain yield. This paper calculates the temperature vegetation drought index (TVDI) based on the normalized difference vegetation index (NDVI) and surface temperature (TS) product data of MODIS from 2000 to 2021. Taking TVDI as the drought evaluation index, this paper studies the temporal and spatial variation distribution characteristics and occurrence frequency of drought in the whole region and four agricultural regions of Heilongjiang Province: Daxing an Mountain and Xiaoxing an Mountain (region I), Sanjiang Plain (region II), Zhangguangcai Mountains (region III), and Songnen Plain (region IV). The results show that medium drought generally occurred in Heilongjiang Province from 2000 to 2021, accounting for about 70% of the total cultivated land. The drought was severe from 2000 to 2009 and weakened from 2010 to 2021. In the 110 months of the crop growing season from 2000 to 2021, about 63.84% of the region suffered more than 60 droughts. It is found that the frequency of drought varies from region to region. More than 80 droughts occurred in the west of region IV and the middle of region II. The characteristics of region IV are large sandstorm, less precipitation, and lack of water conservancy facilities, resulting in frequent and strong drought. It is also found that the occurrence frequency, degree grade and regional distribution of drought are closely related to seasonal changes. In spring, the occurrence grade and frequency of drought in region IV are the strongest and the drought phenomenon is serious. In autumn, drought is frequent and distributed in all regions, but the grade is not strong (mainly medium drought), and the drought phenomenon is medium. It is humid in summer. Crops in Heilongjiang Province are one crop per annual. Spring drought seriously restricts the water content of crops. Long-term drought will lead to poor crop development and reduce yield. Therefore, only by clarifying the characteristics of regional time drought, monitoring accurate drought events and accurately predicting the occurrence of drought, can we guide high-standard farmland precision irrigation, improve crop yield and ensure national food security. At the same time, severe drought will affect the terrestrial ecosystem, resulting in the distribution of crops and microorganisms, and the transformation between carbon sink and carbon source. [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]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 "carbon peak and carbon neutral" emission target has set new requirements for the supply chain structure, exacerbated by the complexity and vulnerability of the agricultural sector and global supply chains after the COVID-19 outbreak. Under these circumstances, the resilience evaluation of the agricultural supply chain based on green transformation became an important subject. Based on this topic with outstanding problems, this paper evaluates the resilience level of the agricultural supply chain in northeast China with the goal of achieving the "double carbon" objective. By focusing on its resilience, we selected Heilongjiang, Jilin, and Liaoning provinces as research subjects and collected the agricultural supply chain-related data from 2010 to 2019. Based on the relationship between supply chain nodes and the operation system, an evaluation model containing three levels was established. The entropy weight method and TOWA operator were used to carry out secondary weighting on the evaluation system and time levels containing 12 indicators, following the evaluation of the resilience level of the agricultural supply chain in three static and dynamic dimensions using the grey correlation method. The results show that the importance of the evaluation level of "internode resilience level" has always been a priority during the decade. Therefore, it is pointed out that the resilience construction and green transformation of the agricultural supply chain should be strengthened and implemented from the following three aspects: strengthening agricultural modernization, optimizing supply chain operation, and the implementation and promotion of effective policies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scarcity and irreplaceable attributes of water resources and the increasing demand for water resources are exacerbating the water crisis in China. The "Most Stringent Water Resource Management System" was implemented in 2011, then a series of regulations and policies had been issued centering on it. In this paper, based on rigid constraints from the Most Stringent Water Resource Management System, taking economic development, per capita welfare level, and so on into consideration, the water resource carrying capacity (WRCC) model was constructed. The results show that rigid water resources constraints would have carried more population than actual respectively during the periods 2015–2019, at the national level. At the provincial level, the maximum population under rigid constraints for Heilongjiang, Jiangsu, Anhui, and Xinjiang are overloaded. And we analyzed the overloaded causes from the aspects of water resources endowment, social and economic development, and water resources utilization. In conclusion, the rigid constraints from the Most Stringent Water Resource Management System are not reasonable. In this paper, the WRCC model can be used to assess water safety or water resources sustainability based on water policy. [ABSTRACT FROM AUTHOR]

Different types of distresses affect cement concrete pavement at different degrees. The determination of dominant distresses of the pavement preventive maintenance (PM) and its judgement standard can provide corresponding basis for PM decision. In this paper, 22 military airports in Northeast China, such as Heilongjiang Province, Jilin Province, and Liaoning Province, were selected to collect the data of pavement distresses. Based on the structural equation model (SEM), the structural relationship between the influencing factors of each distress and the pavement damage was established, and the goodness-of-fit of the model was tested. In addition, through path analysis, the influence degree of five kinds of latent variables such as joint distress, surface distress, vertical distress, repair distress, and fracture distress on pavement damage was obtained. Four distresses, such as corner peeling, surface peeling, surface crack, and interplate slip, were identified as the dominant distresses of PM of cement concrete pavement. On this basis, a binary classification model of confusion matrix was constructed. The basic evaluation index, receiver operating characteristic (ROC) curve, and Kolmogorov–Smirnov (KS) curve were used to comprehensively determine the judgement standard of the dominant distresses of pavement PM from multiple evaluation angles (corner peeling rate ≤ 35%, surface peeling rate ≤ 30%, surface crack rate ≤ 8%, and interplate slip rate ≤ 0.5%). The judgement standard can be combined with the corresponding prediction model to determine the optimal timing of PM of cement concrete pavement and provide pavement maintenance managers with the support of decision-making. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion, which seriously threatens the food security and ecological security of this region. In this paper, based on the daily observation data of 124 meteorological stations in study area from 1961 to 2020, seasonal and monthly wind erosion climate factor (C) in spring (March to May) were calculated by using the method proposed by the Food and Agriculture Organization of the United Nations (FAO), the wind erosion characterization in spring were systematically analyzed based on C by various statistical analysis methods. The results showed that in the past 60 years, spring wind erosion climate factor (CSp) and monthly C of the whole region and each province (region) all showed highly significant decreasing trend, but they began to show rebounded trend in the middle or late 2000s. CSp of the study area showed a significant upward trend since 2008 with an increase of 4.59 (10a)−1. The main contributors to this upward trend are the changes of C in March and in April. For the four provinces (regions), CSp in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia all showed rebounded since 2008, 2011, 2008 and 2009, respectively. The rebounded trend of CSp in eastern Inner Mongolia was the most obvious with a tendency rate of 11.27 (10a)−1, and its mutation occurred after 1984. The rebound trend of CSp in Heilongjiang Province takes the second place, with a trend rate of 4.72 (10a)−1, but there's no obvious time mutation characteristics. The spatial characteristics of CSp and monthly C are similar, showing decreasing characteristics centered on the typical black soil belt of Northeast China. Compared with 1961–1990, in the period from 1991 to 2020, the proportion of high value areas (CSp>35, monthly C>10) has decreased to varying degrees, while the proportion of low value areas (CSp ≤10, monthly C ≤4) has increased. The trends of seasonal and monthly C in 82.2%∼87.7% of the stations show significant decreases at 95% confidence level. CSp is closely related to wind speed at 2m height, temperature difference, minimum temperature and precipitation in the same period, of which the correlation between CSp and wind speed is the strongest, indicating that the main control factor for CSp in the study area is wind speed, but the impact of the change of temperature and precipitation on CSp cannot be ignored. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solving the contradiction between agricultural economic growth and agricultural environmental problems is a difficult problem in regional environmental governance. Based on the panel data of 31 provinces, municipalities, and autonomous regions in China from 2000 to 2019, spatial Dubin model (SDM) is used to analyze the influence of agricultural economic growth and other factors on planting non-point source pollution. Innovate from the perspective of research objects and research methods, and the research results show (1) In the past 20 years, the amount of fertilizer applied and crop straw yield increased continuously. Through the fertilizer and farmland solid waste discharge of ammonia nitrogen (NH3–N), total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD), calculation of the equal-standard discharges of planting non-point source pollution shows that China's planting non-point source pollution is serious. Among the investigated areas in 2019, the equal-standard discharges of planting non-point source pollution in Heilongjiang Province were the highest and have reached 24.35 × 1010 m3. (2) The global Moran index of 20 years in the study area shows obvious spatial aggregation and diffusion characteristics, and has a significant positive global spatial autocorrelation, indicating that planting non-point source pollution discharges of the study area have potential interdependence in space. (3) SDM time-fixed effect model showed that the equal-standard discharges of planting non-point source pollution had a significant negative spatial spillover effect, and the spatial lag coefficient was − 0.11. Among the influencing factors, agricultural economic growth, technological progress, financial support to agriculture level, consumption capacity, industrial structure, and risk perception have significant spatial spillover effects on planting non-point source pollution. The results of effect decomposition show that the positive spatial spillover effect of agricultural economic growth on adjacent areas is greater than the negative effect on the local area. Based on the analysis of significant influencing factors, the paper provides direction guidance for the formulation of planting non-point source pollution control policy. [ABSTRACT FROM AUTHOR]

The eco-innovation level of the tourism industry may solve the dilemma between tourism innovation and ecological protection. It is an effective method for promoting industry upgrade and sustainable development of tourism economy. This paper established an index system based on pressure-stateresponse- PSR model and evaluated the dynamic change and spatial difference of the eco-innovation level of the tourism industry in Heilongjiang Province by using entropy weight method. The results show that tourism eco-innovation level has been increasing year by year with greater population and environmental pressure from 2005 to 2016. There are obvious differences in each region. In Harbin, tourism eco-innovation level is the highest and the pressure is also greater. The following region is Daxinganling which lacks innovation investment. The index values of other regions are lower and the ability of tourism eco-innovation is weaker. The research results offer references for making policy on tourism innovation and ecotourism development in Heilongjiang Province under the background of ecological civilization. [ABSTRACT FROM AUTHOR]

As an old northeast industrial base and forestry region, Heilongjiang Province is facing problems such as urban decline, aging population and great pressure on environmental conservation. However, the development of ecological tourism is increasing year by year, which is an effective way to solve social problems such as employment and economic development in various regions. In view of this, this paper determines the index weight through expert scoring, and evaluates the overall sustainable development ability of forest tourism in Heilongjiang Province by using analytic hierarchy process, so as to provide reference for the development of forest tourism industry in Heilongjiang Province. The research showed that the sustainable development of forest tourism in Heilongjiang Province should mainly consider the resource support system, focus on improving the quality of forest tourism resources, preserving the characteristic tourism culture, improving the level of tourists' consumption and the per capita growth rate of residents' tourism income, and increasing infrastructure investment. [ABSTRACT FROM AUTHOR]

Excessive application of mineral fertilizers and synthetic pesticides poses a substantial threat to the soil and water environment and food security. Organic fertilizer and biopesticides have gradually become essential technology for reducing mineral fertilizer and pesticide inputs. In the process, the technical environment is critical for promoting farmer behavior related to the adoption of organic fertilizer and biopesticides. This paper analyzes the influence of the technical environment on farmer behavior related to the adoption of organic fertilizer and biopesticides based on a survey of 1282 farmers in Heilongjiang Province, China, using the bivariate probit model. The results indicate that (1) farmer behavior related to the adoption applying organic fertilizer and biopesticides were positively correlated; (2) the technical environment had a significant positive impact on farmer behavior related to the adoption of organic fertilizer and biopesticides; and (3) the technical environment had a heterogeneous effect across different groups of farmers. This research provides insights useful for promoting organic fertilizer and biopesticides to farmers. It can be helpful to bundle relevant environmental technologies, conduct technology training for farmers and strengthen the construction of rural information networks. [ABSTRACT FROM AUTHOR]

BACKGROUND: Heilongjiang Province is a frontier province with distinctive characteristics, fertile land and rich products. OBJECTIVE: This study provides a new method for qualitatively studying flavonoids in traditional Chinese medicine and a new auxiliary means for identifying flavonoid isomers. METHODS: The flavonoids in Polygala fallax Hemsl were identified by ultra-performance liquid chromatography-photo-diode array (PDA)-quadrupole-electro- static field orbitrap mass spectrometry tandem by UV Spectrum, primary and secondary high-resolution mass spectrometry (MS 1 /MS 2) cleavage of fragments combined with databases, mass spectrometry cleavage patterns and literature. RESULTS: The established QSRR model was used to verify the flavonoids identified from the Polygala fallax Hemsl. CONCLUSION: The structure of multiple Polygala fallax Hemsl has been identified using various spectral methods. The tumor cytotoxic activity of the isolated compounds was evaluated. This paper is of great significance for further elucidating the pharmacodynamic substance basis and further developing and utilizing Polygala fallax Hemsl. [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]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a new method of measuring social benefits of bioethanol supply chains, which considers job creation, biomass purchase and different impact of economic activities in different locations. A multi-objective MILP model is developed to address the optimal design of a bioethanol supply chain which maximizes both economic and social benefits. The ε-constraint method is employed, and a set of Pareto-optimal solutions is obtained. The developed model is applied to case studies in Heilongjiang Province in Northeast China. Actual data are collected as practical as possible for the feasibility and effectiveness of the result. The result shows the bioethanol supply chain can bring both economic and social benefits in the given area and offer governments a possible better and more efficient way to bring more social benefits. The effect of the government subsidy on enterprises' decisions about economic and social benefits is discussed. [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]

In this paper, a novel fingerprint method was established for the quality control of Rana chensinensis eggs (RE) by high-performance liquid chromatography (HPLC). Cluster analysis and principal component analysis were performed. Besides, the antitussive effect of RE was explored. The analysis was achieved on a Kromasil 100-5C18 (4.6 mm × 250 mm, 5 μm) column by gradient elution using methanol-0.1% phosphoric acid solution as the mobile phase. The influence of RE on cough latent periods and cough times of mice was investigated via an ammonia cough-inducing experiment. The validated HPLC method was precise, reproducible, and stable. The HPLC fingerprints of 10 batches of RE samples displayed 31 well-resolved common peaks in the chromatogram. Three of these peaks were identified and assigned to 1-methyl hydantoin, estradiol, and 4-cholestene-3-one. The similarities of 10 batches of samples were more than 0.95. RE from different origins could be classified into three groups via SPSS 23.0 software, suggesting RE samples from various provinces (Jilin, Liaoning, and Heilongjiang) can be well distinguished via the established method. High dose and middle dose of the RE group can significantly prolong the cough latent periods of mice (P < 0.05 or P < 0.01) and inhibit the cough times of mice (P < 0.01), indicating RE had a good antitussive effect. HPLC fingerprint combined with multicomponent determination can be an efficient and useful method for monitoring the quality of RE. This study also provided a more comprehensive strategy for the quality evaluation of RE. [ABSTRACT FROM AUTHOR]

Heilongjiang Province, as a major grain-planting province in China, under the condition of limited production level and cognitive level, the food and agriculture industry often adopts the "high input-high output" production model to achieve grain yield and increase production. As one of the important material input elements in agricultural production, chemical fertilizer plays an irreplaceable role in increasing crop output and farmers' income. The reduced application of chemical fertilizer can improve the soil and water source, improve the production environment from the internal agricultural production, and ensure the quality and safety of agricultural products from the source, which is beneficial to the sustainable development of agriculture in China. In this paper, Probit model is used to analyze the risk preference and risk perception of grain farmers in Heilongjiang Province. The results showed that high degree of risk preference had a negative effect on decision behavior of fertilizer application, while high natural risk perception had a positive effect on fertilizer application behavior of grain farmers. At the same time, the results showed that the cultivated land area owned by farmers, the total income of agricultural production, the training of fertilizer technology, and the cognition of the impact of fertilizer on the environment all had significant effects on the chemical fertilizer application behavior of grain farmers. Finally, according to the results of this study, some feasible suggestions are put forward. [ABSTRACT FROM AUTHOR]

This paper targets the expansive soils in Heilongjiang and Ankang to explore the influence of initial dry density, dry-wet cycle, and freeze-thaw cycle on the soil-water characteristics. The centrifuge method was used to obtain the soil-water characteristic curves (SWCCs) with different conditions. The volumetric strain of SWCC was modified based on the shrinkage test, and the corresponding fitting equations considering different factors were established. The results show that the volumetric water content is modified to consider the volume shrinkage effect of expansive soil, and the modification is more obvious in the high matric suction range. The smaller the initial dry density is, the worse the water-holding capacity of the sample is, and the smaller the air intake value is. The greater the time of the dry-wet cycle is, the greater the saturated volumetric water content of the sample is, and the corresponding water-holding capacity is significantly reduced. When the dry-wet cycle increases to a certain extent, the structure becomes stable. With the increase of the freeze-thaw cycle, the saturated volumetric water content first decreases and then increases. Similarly, after several times of the freeze-thaw cycle, the structure is basically stable. The fitted Gardner model equations under different conditions were proved to be able to describe the SWCCs of the two targeted expansive soils. [ABSTRACT FROM AUTHOR]

Broccoli was selected as the research object in this paper to reveal the dissipation, distribution, and degradation pathway of sulfoxaflor under greenhouse and open-field cultivation conditions for the ecological risk assessment of sulfoxaflor. Results showed that the dissipation of sulfoxaflor in broccoli leaves, flowers, stems, roots, and the whole broccoli was in accordance with the first-order kinetic equation. The sulfoxaflor concentration in broccoli roots reached the maximum value after 1 day of application and then gradually decreased. The degradation half-lives of sulfoxaflor in the roots, leaves, flowers, stems, and whole broccoli were between 2.3 and 19.8 days. The longest degradation half-life of sulfoxaflor was in Heilongjiang under greenhouse cultivation. The terminal residue of sulfoxaflor in broccoli was in the range of 0.005–0.029 mg/kg, and the proportion of sulfoxaflor residue in broccoli leaves was the largest. Thirteen transformation products were separated and identified by ultrahigh-performance liquid chromatography–quadrupole time-of-flight mass spectrometry, and their kinetic evolution was studied. The cleavage of the N = S bond, C–S bond, C–O bond, and cyanide, as well as glucosylation, hydroxylation, SO extrusion, elimination, sulfhydrylation, ketonization, defluorination, and rearrangement, was inferred as the mechanism. Overall, these results can provide guidance for the supervision of the safe application of sulfoxaflor. [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]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Land cover change (LCC) significantly changed the local/regional temperature. This paper attempts to reveal the effects of cropland expansion in different ways on temperature change from the 1900s to 2010s in Heilongjiang Province. To reach this goal, we conducted four simulation research schemes with the coupled Weather Research and Forecast (WRF)-Noah model to investigate the warming/cooling effect of cropland expansion. The results show that cropland expansion exerted different effects with different land-use type conversions. In the last century, the areas with grassland-to-cropland and wetland-to-cropland transition show the warming effect, and the average surface temperature in Heilongjiang Province increased by 0.023 ℃ and 0.024 ℃, respectively. The areas with forest-to-cropland transition show the cooling effect, in which the average temperature decreased by 0.103 ℃. The variation of air temperature is mainly caused by the variation of surface reflectance and surface net radiation flux. The results provide evidence that cropland expansion changes to biophysical landscape characteristics, warming/cooling the land surface and thus enhancing/reducing the temperature, and lead to regional climate change eventually. [ABSTRACT FROM AUTHOR]