Search

The Chinese government encourages rural economic entities to use farmland management rights as collateral for loans, which helps to alleviate multi-level financing needs in rural areas. Based on the panel data of counties in Hubei Province, this paper adopts the Difference-in-Differences (DID) and the intermediary effect model to evaluate the impact of farmland management rights mortgage loans (FMRML) on the agri-food industrial agglomeration (AIA) in China. The study found that the pilot policy has significantly promoted the AIA. Moreover, the regression results remain robust after conducting the placebo test and the Propensity Score Matching Difference-in-Differences (PSM-DID) model, which demonstrates that the improvement effect is stable and long-lasting. From the heterogeneity analysis, it can be seen that the policy of FMRML has a more significant effect on the AIA in mountainous and hilly areas. By further analysis of the mechanism of action, it can be concluded that the pilot policy promotes the AIA by enhancing agricultural specialized production. The main findings can provide information for policymakers in China. The recommendations we have summarized encompass gradually expanding the scope of the pilot policy of FMRML, advancing the institutionalization and legalization of the policy, and promoting agricultural production specialization. [ABSTRACT FROM AUTHOR]

The transformation of the government into a digital entity is imperative, serving not only as a catalyst for the modernization of China's governance system and capacity but also as a cornerstone for advancing the digital economy and the establishment of a digital China. This paper presents a multi-level analytical framework designed to assess the digital transformation performance of local governments. Utilizing a dataset comprising macro-regional and micro-individual data from Hubei province, we conduct an extensive analysis to examine the underlying mechanisms that influence the digital transformation performance of local governments and employ the hierarchical linear model (HLM) as the primary analytical instrument. The results of our analysis show that individual-level government–citizen interactions, government image, and district-level department collaborative capacities exert substantial and positive influences on the digital transformation performance of local governments. Furthermore, it is worth noting that department collaborative capacity plays a significant and positive moderating role in the relationship between government image and the digital transformation performance of local governments. These findings not only offer valuable insights for optimizing policy formulation but also contribute to a more comprehensive understanding of the mechanisms underlying the digital transformation performance of local governments. [ABSTRACT FROM AUTHOR]

The rapid urbanization in China in the past four decades has significantly affected traditional villages, resulting in a series of problems such as limited space, damaged style, weak sense of place, and loss of skills. This paper aims to establish a new paradigm for the sustainable development of village architecture in the Tujia ethnic area of southwestern Hubei province in order to address the issues of development and renovation of village architecture in this region. To achieve this goal, first, through field investigations and records of typical villages in the Tujia ethnic area of southwestern Hubei province, the characteristics of Tujia ethnic villages and the real challenges faced by village architecture are summarized. Secondly, the Delphi method is used to conduct expert interviews, construct a multi-level evaluation index system for sustainable development, and use the analytic hierarchy process to determine the weights of each index. Based on this, four design paradigms for sustainable development are proposed, including flexible layout, integrated interface composition, localized housing construction, and modular unit construction. These four paradigms cover the entire design stage from function to form, and from overall scene to structure. At the same time, multiple modular options are provided for each stage. Compared to the traditional experience-based construction model, the comprehensive benefits and promotability are greatly improved. Therefore, the research findings provide a reference for the sustainable development of traditional village architecture in other ethnic areas of China. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

WeChat is a social media widely used in rural China because of its operability and simplicity. This paper contributes to the role of WeChat in rural e-commerce. Based on 406 orange farmers' survey data from Hubei Province, China, we used the Heckman model to analyze the factors that influence farmers' participation in WeChat e-commerce and estimate the impacts of WeChat e-commerce on farmers' sales performance. We found that information literacy and social connections are important factors affecting farmers' participation in WeChat e-commerce. WeChat e-commerce has a positive effect on farmers' sales performance, and the social connections between farmers and consumers can reinforce this effect. Particularly, the beneficial effects are more prominent for farmers with a higher level of Internet development, who operate on a smaller scale, and who are less educated. The results show that WeChat e-commerce improves sales performance mainly by increasing the quantity of sales and the profit rate. These findings will help countries like China to use daily social media to develop rural e-commerce. [ABSTRACT FROM AUTHOR]

Land transfer and its socio-economic impact are key areas of research interest. Such an examination can help to enhance the sustainability of farming livelihoods, maximise livelihood strategies, and achieve sustainable development. This paper establishes a sustainable livelihood evaluation index for rural households based on sustainable livelihood theory. It measures the degree of sustainability in the livelihoods of farmers based on field research data from 650 rural households in Hubei Province, China, and analyses the impact of land transfer using a multiple linear regression model. A number of control variables were identified and introduced into the analysis. It also uses the regression decomposition approach to investigate the impact of each factor on the sustainable livelihoods of rural households. The findings revealed that (1) land transfer can significantly increase the sustainability of rural households' livelihoods; (2) livelihood sustainability increases with the size of the land transfer area; and (3) the primary elements determining the ability of rural households to maintain sustainable living are land transfers, the amount of land transferred, and the size of the family. Based on these findings, this study argues for the need to improve land transfer management, accelerate agricultural industrialisation and promote the transfer of land contract management rights to improve the livelihoods of rural households. [ABSTRACT FROM AUTHOR]

To overcome the difficulty that existing hydrological models cannot accurately simulate hydrological processes with limited information in irrigated paddy areas in southern China, this paper presents a prediction model combining the Ensemble Empirical Mode Decomposition (EEMD) method and the Long Short-Term Memory (LSTM) network. Meteorological factors were set as the multivariate input to the model. Rainfall, regarded as the main variable affecting runoff, was decomposed and reconstructed into a combination of new series with stronger regularity by using the EEMD and K-means algorithm. The LSTM was used to explore the data laws and then to simulate and predict the runoff of the irrigated paddy areas. The Yangshudang (YSD) watershed of the Zhanghe Irrigation System (ZIS) in Hubei Province, China was taken as the study area. Compared with the other models, the results show that the EEMD-LSTM multivariate model had better simulation performance, with an N S E above 0.85. Among them, the R 2 , N S E , R M S E and R A E of the EEMD-LSTM (3) model were the best, and they were 0.85, 0.86, 1.106 and 0.35, respectively. The prediction accuracy of peak flows was better than other models, as well as the performance of runoff prediction in rainfall and nonrainfall events, while improving the N S E by 0.05, 0.24 and 0.24, respectively, compared with the EEMD-LSTM (1) model. Overall, the EEMD-LSTM multivariations model is suited for simulating and predicting the daily-scale rainfall–runoff process of irrigated paddy areas in southern China. It can provide technical support and help decision making for efficient utilization and management of water resources. [ABSTRACT FROM AUTHOR]

The agricultural marketing behavior of farmers is crucial for the realization of production value. Based on survey data from 406 citrus farmers in Hubei Province, this paper empirically examines the effects of risk aversion and Internet use on farmers' marketing behavior in terms of fresh produce. The results show that, first, farmers are generally reluctant to sell, with reluctant sellers accounting for about one-third of the total sample, and most report high levels of Internet use. Second, risk aversion and Internet use have a significant impact on farmers' reluctance to sell. The higher the level of farmer risk aversion, the lower the reluctance to sell, while Internet use significantly increases the probability of farmers being reluctant to sell. Third, Internet use weakens the inhibiting effect of risk aversion on reluctance to sell. These findings help to clarify the factors influencing farmers' reluctance to sell and provide reference suggestions for promoting high-quality agricultural development and rural industrial revitalization. [ABSTRACT FROM AUTHOR]

Timely and accurate crop yield information can ensure regional food security. In the field of predicting crop yields, deep learning techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN) are frequently employed. Many studies have shown that the predictions of models combining the two are better than those of single models. Crop growth can be reflected by the vegetation index calculated using data from remote sensing. However, the use of pure remote sensing data alone ignores the spatial heterogeneity of different regions. In this paper, we tested a total of three models, CNN-LSTM, CNN and convolutional LSTM (ConvLSTM), for predicting the annual rice yield at the county level in Hubei Province, China. The model was trained by ERA5 temperature (AT) data, MODIS remote sensing data including the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and Soil-Adapted Vegetation Index (SAVI), and a dummy variable representing spatial heterogeneity; rice yield data from 2000–2019 were employed as labels. Data download and processing were based on Google Earth Engine (GEE). The downloaded remote sensing images were processed into normalized histograms for the training and prediction of deep learning models. According to the experimental findings, the model that included a dummy variable to represent spatial heterogeneity had a stronger predictive ability than the model trained using just remote sensing data. The prediction performance of the CNN-LSTM model outperformed the CNN or ConvLSTM model. [ABSTRACT FROM AUTHOR]

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies. [ABSTRACT FROM AUTHOR]

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. [ABSTRACT FROM AUTHOR]

The external environment has an important impact on farmers' willingness to withdraw from rural homesteads. Based on the data from 392 farmers in Wuhan and Suizhou City from July to November 2021, we explored the significant role that the external environment played in farmers' willingness to withdraw from rural homesteads. The empirical results, according to the binary Logit model, showed that the number of homesteads, location, and economy positively affect farmers' intention to withdraw from their rural homesteads, and the effect in turn decreases. Famers in a village with no tourism resources are more likely to be willing to withdraw from their homesteads. Based on the empirical findings, this paper finally proposed that local governments should adopt a targeted policy to encourage farmers to orderly withdraw from their rural homesteads. [ABSTRACT FROM AUTHOR]

In view of the differences of land use structure and ecological environment changes caused by land consolidation projects with different features and landforms, this paper uses the modified ecosystem service value estimation model to quantitatively evaluate the change patterns of the ecosystem service value (ESV) of land consolidation, development and arrangement projects in Hubei Province under plains, hills and mountains to provide a theoretical basis for the ecological transformation of land consolidation. The results show that, compared with the pre-land consolidation period, (1) the total amount of ESV in the land consolidation project areas has increased, whereas the land development and arrangement project areas have decreased. Under the same nature, the growth rate of land consolidation project areas is as follows: hills < plains < mountains, and the loss rate of land development and arrangement project areas are as follows: mountains > plains > hills and hills > mountains > plains, respectively. Under the same landform, the total loss rate of ESV in land development project areas is slightly lower than that in land arrangement project areas. (2) The total amount of ESV in the land consolidation project areas under different natures and landforms of the supply service function shows a decreasing trend, and the total amount of ESV of the adjustment service, support service and cultural service function shows an increasing trend in the land consolidation project areas, whereas the land development and arrangement project areas show a decreasing trend. There is an obvious value transformation process of "ecology for production" in the land development and arrangement areas. [ABSTRACT FROM AUTHOR]

In China, rural construction land refers to the construction land of townships (town) and villages. The construction land of townships (town) and villages refers to the land used by township (town) and village collective economic organizations and rural individuals to invest or raise funds for various non-agricultural construction. Rural collective construction land is divided into three categories: homestead, land for public welfare public facilities, and land for business. Complete market transaction rules can make the market of rural collective construction land run more openly, fairly, and justly, thus protecting the rights of villagers and collective economic organizations and, at the same time, reducing the transaction cost of the village collective. In this paper, we choose the formal rules of the urban and rural land linking quota trading market in Ezhou, Hubei Province, China, to analyze the three dimensions of the market transaction rules and villagers' willingness. We also provide suggestions for the development of the RCCL market and the improvement of trading rules. The research results of the logistic model show the following: (1) Under the arrangement of the transaction rules of the RCCL, there are high transaction costs, especially labor costs and time costs, so the villagers' willingness to enter the market is not very strong. (2) Asset specificity affects villagers' willingness. The larger the area of RCCL involved in the transaction, the higher the transaction cost, the lower the villagers' willingness to support the RCCL transfer. (3) The greater the uncertainty of the RCCL transaction environment—mainly due to the problems of the openness, equity, and justice of the transaction rules, which lies in the transaction information not being transparent and the income distribution and the pricing being unreasonable, resulting in villagers' rights being denied—the more the villagers are unwilling to support the RCCL entering the market. Therefore, we suggest that more open, fair, and just trading rules of the RCCL market should be formulated to protect the security of villagers' property rights. Moreover, the land value should be displayed reasonably, which can also standardize the good trading order and trading environment, thus making the market development more effective and stable. Our work provides some insights for improving the efficiency of the land market, which will contribute to the development of the worldwide RCCL market. [ABSTRACT FROM AUTHOR]

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth's water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work's results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field's overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications. [ABSTRACT FROM AUTHOR]

Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback systems or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES = 0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback systems. [ABSTRACT FROM AUTHOR]

Grain Scale Management (GSM) is a crucial factor in ensuring national food security. However, in countries facing rigid resource constraints and complex land tenure relationships, the strategy of promoting large-scale grain management through land management rights transfer may not be sustainable. Therefore, based on the traditional agricultural division of labor theory, we analyze the mechanism and rationality of Agricultural Production Socialization Services (APSS) with scale characteristics to promote GSM and propose a new approach to GSM with empirical evidence from China. Using county-level panel data from Hubei province spanning from 2010 to 2021, we construct a multi-period double difference model based on the difference in the time of the establishment of pilot agricultural production socialization service counties in Hubei province. Our empirical results demonstrate the role of APSS in promoting GSM at the macro level. Specifically, the establishment of pilot counties for APSS significantly improves the level of local GSM. Furthermore, our study reveals that the degree of local financial intervention, regional industrial structure, and regional topography have heterogeneous effects on the implementation of the policy. [ABSTRACT FROM AUTHOR]

The carrying capacity of water resources can provide a reference index for regional economic construction and development. Hubei produces 13.2% of China's hydroelectric energy and 4% of China's water resources, highlighting that the reservoir group in Hubei province is relatively developed. In the current research on water resources carrying capacity, only the amount of water resources was considered; the benign mutual feeding effect of regional reservoirs on regional water resources carrying capacity was not reflected upon. In order to guide social and economic activities better, this paper proposes the addition of reservoir water storage to the calculation of water resources carrying capacity as a separate indicator. In this paper, the cloud model method was used to calculate the water resources carrying capacity of Hubei province and the Dematel method was used to determine the degree of importance of reservoir water storage. Finally, the degree of obstacles was also considered to discuss the main factors affecting the water resources carrying capacity of Hubei province. In the system discussed in this paper, the degree of influence and the affected degree of reservoir water storage were found to be 1.2915 and 0.5759, respectively. The calculation results showed that Hubei province's water resources carrying capacity has been increasing every year and the amount of water resources per unit area was the main restricting factor, with the obstacle degree reaching 19.24% of the average annual level. [ABSTRACT FROM AUTHOR]

The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small. [ABSTRACT FROM AUTHOR]

As a developing country with insufficient water resources, China's water environment management and performance evaluation have important research value. The three provinces (Henan, Hubei, and Hunan) in central China with typical significance in geographical location and water resources governance were selected as research objects in this paper. Based on the principal component analysis (PCA) method and the pressure-state-response (PSR) model, a comprehensive evaluation system for the water environment in those three provinces during 2011–2017 was established in this paper. The evaluation results show that: (1) The water environment management and performance evaluation of the three provinces in central China were generally poor in 2011–2012, but the overall trend was rising; (2) in 2013–2014, the situation was improved compared to the previous two years, but needed further enhancement; (3) in 2015–2017, the water environment management and performance of the three provinces showed significant improvement. Among them, the Hubei Province had the highest water environment evaluation value (1.692), and the Henan Province had the most significant progress (from 0.043 to 1.671). The contributions of this paper are: (1) The comprehensive evaluation model based on PCA and the PSR model was constructed to analyze the sustainable development of water environment in central China; (2) the performance evaluation system for water environment management, which could comprehensively evaluate the performance of water environment treatment and effectively reveal the correlation between various indicators, was established. The principal factors in water environment management can be obtained by this evaluation system. Based on the analysis of the reasons underlying the above changes, the corresponding policy recommendations for improving water environment management and performance in central China were suggested in order to provide a reference for further improvement of water environment management in developing countries. [ABSTRACT FROM AUTHOR]

In recent years, air pollution has received serious concerns from researchers, media, and the public sectors, but air pollution from agricultural production activities has not received enough attention. This paper focuses on agricultural air pollution in central China, which is aggravated by the ongoing rural labor migration trend. With a set of panel data released from Hubei and Hunan provinces in China, we adopt the mediating effect model to explore the relationship between rural labor migration and air pollution caused by agricultural activity in China. First, we use the inventory analysis method and principal component analysis method to calculate the comprehensive index of the air pollution of agriculture in 152 counties and districts from Hubei and Hunan provinces, and we empirically test the impact of labor migration on air pollution with a mediating effect model as well as carry out regional heterogeneity analysis on the pollution effect of these two provinces mentioned above. The analysis above indicates that the increase of labor migration has intensified the comprehensive index of air pollution caused by agricultural activity by changing the supply of labor force in the agricultural sector, the budget line of rural residents, and the scale of agricultural production and crop planting structure, but there is a difference in the indirect total effect between the two provinces mentioned above according to our regional heterogeneity analysis. This study is a necessary extension to studies on alleviating and controlling air pollution in China. [ABSTRACT FROM AUTHOR]

Under the background of new-type urbanization and rural revitalization strategy, how to promote the development of urban–rural integration has become an important issue in today's society. This paper designed a new evaluation mechanism for the development level of urban–rural integration. Specifically, a three-level evaluation index system of urban–rural integration development level was established from four aspects: spatial integration, economic integration, social integration and living environment integration. By combining the entropy weight method with the ranking method, a combination weighting method was proposed to determine the weight of each index in the index system. Furthermore, an improved TOPSIS method based on relative entropy and grey relational degree was proposed to evaluate the development level of urban–rural integration, which considering proximity from the perspectives of distance and shape and solving the problem that some situations cannot be compared through the original model. Then, the established evaluation mechanism was applied to make an empirical analysis for evaluating the development level of urban–rural integration in Hubei Province, China. Cluster analysis and obstacle factor analysis were used to further analyze the evaluation results. Finally, according to the evaluation results, some effective countermeasures and policy implications were provided to improve the development level of urban–rural integration in Hubei Province. [ABSTRACT FROM AUTHOR]

With the rapid development of China's urbanization process and the promotion of the 'double carbon' strategy, green buildings will become an inevitable trend in the future development of the construction industry. Among the various building evaluation criteria, it is important to discuss how to promote the development of green buildings more efficiently and adaptively according to the characteristics of personnel needs. This study constructed a questionnaire to assess building use satisfaction based on China's national standards. Field research was conducted on 23 projects in six cities in Hubei Province, China, and a total of 2251 questionnaires were collected. The survey evaluated satisfaction with the current use of green buildings across different age groups and genders. A new satisfaction evaluation model is constructed through fuzzy comprehensive evaluation to provide guidance for the differentiated development of green buildings in different cities. [ABSTRACT FROM AUTHOR]

In recent years, China's government has encouraged the adoption of the TOT (Transfer-Operate-Transfer) model to realize the marketization of China's public service stock projects. The TOT model is a cooperation mechanism through sharing investment, revenue and risks between the government and private partner. Therefore, a fair and reasonable revenue sharing method (RSM) is the key to the success of the TOT project. This paper aims to provide a fair and reasonable RSM based on a modified Shapley value with a triangular symmetric fuzzy structure element, which has better motivation, flexibility, forecasting function and dynamic precise distribution function. According to the factors that affect revenue sharing, the Shapley value is improved with initial correction coefficient composed of investment ratio, risk-sharing ratio, execution degree, and fuzzy payment to achieve fairness and reasonableness. The methodology is illustrated by a case study of a TOT project selected from Laohekou city of Hubei province, China. The results testify that the revenue-sharing ratios of participants is positively correlated with the initial correction coefficient, which make the RSM more motivating; and the Shapley value with fuzzy payment by using triangular symmetric fuzzy element function make the RSM more flexible, and it has both forecasting function and precise dynamic distribution function under project revenue uncertainty. [ABSTRACT FROM AUTHOR]

As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water and direction is proposed. It is suitable for the study area which possesses rich water resources. To reconstruct the historical shape of the river network, (1) water-related toponyms and direction-related toponyms are extracted as two datasets based on the key words in each village toponym; (2) the feasibility of the river network reconstruction based on these toponyms is validated via a quantitative analysis, according to the spatial distributions of toponyms and rivers; (3) the reconstructed historical shape of the river network can be obtained via qualitative knowledge and geometrical analysis; and (4) the reconstructed rivers are visualized to display their general historical trends and shapes. The results of this paper demonstrate the global correlation and local differences between the toponyms and the river network. The historical river dynamics are revealed and can be proven by ancient maps and local chronicles. The proposed method provides a novel way to reconstruct historical river network shapes using toponym datasets. [ABSTRACT FROM AUTHOR]

Under the background of global climate change, accurate monitoring and comprehensive assessment of droughts are of great practical significance to sustain agricultural development. Considering multiple causes and the complexity of the occurrence of drought, this paper employs multiple input variables, i.e., precipitation, temperature, evaporation, and surface water content to construct a modified composite drought index (MCDI) using a series of mathematical calculation methods. The derived MCDI was calculated as a multivariate drought index to measure the drought conditions and verify its accuracy in Hubei Province in China. Compared with the existing multivariate drought index, i.e., meteorological drought composite index (CI), there was a high level of correlation in monitoring drought events in Hubei Province. Moreover, according to the drought historical record, the significant drought processes monitored by the MCDI were consistent with actual drought conditions. Furthermore, temporal and spatial analysis of drought in Hubei Province was performed based on the monitoring results of the MCDI. This paper generalizes the development of the MCDI as a new method for comprehensive assessments of regional drought. [ABSTRACT FROM AUTHOR]

Recognizing the spatial effects of regional poverty is essential for achieving sustainable poverty alleviation. This study investigates these spatial effects and their determinants across three distinct administrative levels within Hubei Province, China. To analyze the spatial patterns and heterogeneity of multi-scale regional poverty, we employed various spatial analysis techniques, including the global and local Moran's I statistics, the Lineman, Merenda, and Gold (LMG) method, as well as Multiscale Geographically Weighted Regression (MGWR). We found that: (1) Regional poverty exhibits significant spatial dependence across various scales, with a higher level of spatial dependence observed at higher administrative levels. (2) The spatial distribution of poverty is primarily influenced by geographical factors, encompassing first-, second-, and third-nature geographical elements. Notably, first-nature geographical factors make substantial contributions, accounting for 36.99%, 42.23%, and 23.79% at the county, township, and village levels, respectively. (3) The influence of geographical factors varies with scale. Global effects of various factors may transcend scales or remain confined to specific scales, while the local impacts of different factors also exhibit variations across scales. These results underscore the necessity for collaborative efforts among government entities at different levels with the anti-poverty measures tailored to local contexts. [ABSTRACT FROM AUTHOR]

Agricultural extension services play an important role in promoting pesticide reduction and green production, although the mismatch between farmers' needs and service supply in rural China seriously affects the application and promotion of biological pesticides, which has been generally ignored by existing studies. Taking 1160 rice farmers in Hubei Province, China, as an example, this study constructs a need-supply fit model of agricultural extension services from the perspectives of need and supply. We further use the logit model and propensity score matching method (PSM) to estimate the effect of service need-supply fit on farmers' biological pesticide adoption behavior. The specific results are as follows: First, the level of need-supply fit for agricultural extension services was low for the overall sample. This indicates that the agricultural extension service supply is not consistent with the actual needs of farmers. Second, the agricultural extension service need-supply fit significantly and positively affects farmers' biological pesticide adoption behavior. Third, there are significant differences in farmers' biological pesticide adoption behaviors under different need-supply fit. When the level of service need-supply fit exceeds the 0.2 threshold, the positive effect of service need-supply fit on farmers' biological pesticide adoption behavior gradually increases. Fourth, the agricultural extension service need-supply fit indirectly increases farmers' motivation to adopt biological pesticides by regularizing biological pesticide use behavior and alleviating path dependency. Therefore, the main body of agricultural extension services should optimize the adjustment of the supply mode of agricultural extension services to fully grasp the reality of farmers' need for biological pesticides in order to promote the application of biological pesticides. [ABSTRACT FROM AUTHOR]

This study addresses how site index may affect crown shape by developing a crown shape regression model for the planted L. kaempferi at high altitudes in the subtropical climate of China. A total of 9241 branches from 78 trees, including 39 dominant trees and 39 intermediate trees, were measured in Hubei Province, southern China. The branch characteristics, including branch length, branch angle, and branch chord length of all living branches, were measured by felling trees. The models that showed good performance in crown shape modeling were used and compared in the present study. The goodness of fit statistics and stability of parameter estimation of the modified Kozak equation were better than those of the segmented polynomial equation. A nonlinear mixed-effect crown shape model was developed based on the modified Kozak equation. In addition to the tree level variables of diameter at the breast height (DBH), crown ratio (CR), and tree height-to-DBH ratio, an attempt was made to incorporate the site index into the crown shape model for the planted L. kaempferi. However, the site index was not a significant variable in the crown shape model. The purpose of our study was to lay the foundation for further study of the growth of the trees and the effects of crown morphology on stem growth in the future. [ABSTRACT FROM AUTHOR]

The ratoon rice cropping system (RR) is developing rapidly in China due to its comparable annual yield and lower agricultural and labor inputs than the double rice cropping system (DR). Here, to further compare the greenhouse effects of RR and DR, a two-year field experiment was carried out in Hubei Province, central China. The ratoon season showed significantly lower cumulative CH4 emissions than the main season of RR, the early season and late season of DR. RR led to significantly lower annual cumulative CH4 emissions, but no significant difference in cumulative annual N2O emissions compared with DR. In RR, the main and ratoon seasons had significantly higher and lower grain yields than the early and late seasons of DR, respectively, resulting in comparable annual grain yields between the two systems. In addition, the ratoon season had significantly lower global warming potential (GWP) and greenhouse gas intensity-based grain yield (GHGI) than the main and late seasons. The annual GWP and GHGI of RR were significantly lower than those of DR. In general, the differences in annual CH4 emissions, GWP, and GHGI could be primarily attributed to the differences between the ratoon season and the late season. Moreover, GWP and GHGI exhibited significant positive correlations with cumulative emissions of CH4 rather than N2O. The leaf area index (LAI) and biomass accumulation in the ratoon season were significantly lower than those in the main season and late season, and CH4 emissions, GWP, and GHGI showed significant positive correlations with LAI, biomass accumulation and grain yield in the ratoon and late season. Finally, RR had significantly higher net ecosystem economic benefits (NEEB) than DR. Overall, this study indicates that RR is a green cropping system with lower annual CH4 emissions, GWP, and GHGI as well as higher NEEB. [ABSTRACT FROM AUTHOR]

Promoting ratoon rice is a critical measure for ensuring food security in China. Understanding the mechanism underlying farmers' decision-making involving ratoon rice production may contribute to the design and implementation of extension policies. This study examined the impact of off-farm employment and outsourced machinery services on farmers' ratoon rice production behavior. We used a representative household survey of 1752 rice farmers in Hubei province in central China and a multinomial endogenous treatment effect model to address potential self-selection biases from both observable and unobservable factors. Our estimates suggest that the probability of ratoon rice production decreases by 17.2% for farmers with off-farm employment, while the probability of ratoon rice production increases by 78.9% and 57% for farmers with outsourced machinery services and those with simultaneous off-farm employment and outsourced machinery services, respectively. Moreover, we found that outsourced machinery services can mitigate the negative impact of agricultural labor aging and feminization on ratoon rice production to some extent. Outsourced machinery services have regional heterogeneity effects as farmers in non-plain areas are more likely to engage in ratoon rice production. [ABSTRACT FROM AUTHOR]

The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the MAIAC AOD throughout Hubei Province. The reconstructed AOD dataset, exhibiting an R2/RMSE of 0.76/0.18, compared to AERONET AOD, was subsequently used for PM2.5 estimation. Our research breaks from traditional methodologies that solely depend on latitude and longitude information. Instead, it emphasizes the use of climate feature as an input for estimating PM2.5 concentrations. This strategic approach prevents potential spatial discontinuities triggered by geolocation information (latitude and longitude), thus ensuring the precision of the PM2.5 estimation (sample/spatial CV R2 = 0.91/0.88). Moreover, we proposed a method for identifying the absolute feature importance of machine-learning models. Contrasted with the relative feature-importance property typical of machine-learning models (a minor difference in the order of top three between geolocation-based and climate-feature-based models, and the slight difference in the top three: 0.08%/0.17%), our method provides a more comprehensive explanation of the absolute significance of features to the model (maintaining the same order and a larger difference in the top three: 0.99%/0.72%). Crucially, our findings demonstrated that AOD reconstruction can mitigate the overestimation of annual mean PM2.5 concentrations (ranging from 0.52 to 9.28 µg/m3). In addition, the seamless PM2.5 dataset contributes to reducing the bias in exposure risk assessment (ranging from −0.11 to 9.81 µg/m3). [ABSTRACT FROM AUTHOR]

Biopesticides are perceived as a feasible alternative to chemical pesticides, providing an effective approach to pest management while mitigating the undesirable effects of chemical pesticide overuse. Yet, due to the distinctive attributes of the two types of pesticides, farmers often adopt a combination of both. This study aimed to probe the interdependent relationship underlying the mixed use of chemical pesticides and biopesticides by farmers in the rice–crayfish integrated system (RCIS) in Hubei province, China. We also sought to identify influencing factors and associated consequences of this practice. Given that the adoption of chemical pesticides and biopesticides by farmers is not mutually exclusive, a multivariable probit model was utilized to estimate simultaneous pesticide applications. Survey data from 736 RCIS farmers revealed that 10.50% of the sample farmers employ a combination of chemical pesticides and biopesticides. A substitution relationship was observed between the adoption of chemical pesticides and biopesticides by farmers, whereas no significant complementary relationship was found in the application of the two types of pesticides. The primary motivation for the mixed use of pesticides by farmers is to achieve superior pest control efficacy. Notably, when integrating the use of pesticides, farmers pay less attention to factors such as resource endowments and multiple production objectives. Further analysis indicated that farmers display significant concern for pesticide attributes including insecticidal efficacy, toxicity, insecticidal spectrum, and validity period. The mixed use of pesticides has resulted in a decreased frequency of pesticide application but has escalated the cost. While the mixed use of pesticides elevated the selling price of rice, no significant improvement was observed in yield and output value. The insights garnered from this study offer strategic implications for policymakers on how to enhance the judicious adoption of pesticides by farmers. [ABSTRACT FROM AUTHOR]

Efficient and accurate landslide recognition is crucial for disaster prevention and post-disaster rescue efforts. However, compared to machine learning, deep learning approaches currently face challenges such as long model runtimes and inefficiency. To tackle these challenges, we proposed a novel knowledge distillation network based on Swin-Transformer (Distilled Swin-Transformer, DST) for landslide recognition. We created a new landslide sample database and combined nine landslide influencing factors (LIFs) with remote sensing images (RSIs) to evaluate the performance of DST. Our approach was tested in Zigui County, Hubei Province, China, and our quantitative evaluation showed that the combined RSIs with LIFs improved the performance of the landslide recognition model. Specifically, our model achieved an Overall Accuracy (OA), Precision, Recall, F1-Score (F1), and Kappa that were 0.8381%, 0.6988%, 0.9334%, 0.8301%, and 0.0125 higher, respectively, than when using only RSIs. Compared with the results of other neural networks, namely ResNet50, Swin-Transformer, and DeiT, our proposed deep learning model achieves the best OA (98.1717%), Precision (98.1672%), Recall (98.1667%), F1 (98.1615%), and Kappa (0.9766). DST has the lowest number of FLOPs, which is crucial for improving computational efficiency, especially in landslide recognition applications after geological disasters. Our model requires only 2.83 GFLOPs, which is the lowest among the four models and is 1.8242 GFLOPs, 1.741 GFLOPs, and 2.0284 GFLOPs less than ResNet, Swin, and DeiT, respectively. The proposed method has good applicability in rapid recognition scenarios after geological disasters. [ABSTRACT FROM AUTHOR]

Hybrid sturgeon is an important economic fish species in China. In 2021, a bacterium was isolated from the liver and kidneys of freshwater-farmed hybrid sturgeon in Yichang City, Hubei Province, causing a disease with high mortality and surface bleeding. Through morphological observation, 16S rDNA gene sequence analysis, pathogenicity, an antimicrobial sensitivity test, as well as serum physiological and biochemical analysis, it was identified as Staphylococcus saprophyticus and named E702. The 16S rDNA gene sequence of E702 is highly homologous to S. saprophyticus in GenBank. Phylogenetic analysis showed that E702 and S. saprophyticus clustered into one clade. The 50% lethal dose of E702 was 2.14 × 105 CFU/g. The percentages of monocytes and eosinophils were markedly increased in the diseased sturgeon's blood, whereas the percentages of platelets and lymphocytes were decreased. The activity levels of aspartate aminotransferase, alanine aminotransferase and alkaline phosphatase in the diseased fish were significantly increased. The diseased fish suffered obvious damage to many tissues and organs, especially the liver and kidney, showing swelling, hyperemic and inflammatory cell infiltration. E702 was sensitive to antibiotics such as neomycin, cefazolin, norfloxacin, carbenicillin, gentamicin and ciprofloxacin. The study not only proved that S. saprophyticus was responsible for a great deal of hybrid sturgeon deaths, but also shed light on its potential risks in hybrid sturgeon farming. The research results provided the theoretical basis for the diagnosis as well as prevention of this disease. [ABSTRACT FROM AUTHOR]

This article discusses the issues caused by traditional water resource development and utilization, as well as policy issues in China that have led to a water crisis. The article proposes a theoretical approach along with a quantitative accounting of water resources, in order to solve these problems. To improve the value accounting method for water resources, the study focuses on a unified accounting perspective of water quantity and quality, allowing for an evaluation of water use efficiency and quality. The study uses prefecture-level cities in Hubei Province as a case study and finds that the water use efficiency of these cities has constantly improved, while water quality has shown an annual improvement. Water resource assets, liabilities, and net assets have increased, but with fluctuations. The study shows differences in water resource assets, liabilities, and net assets in the eastern, central, and western regions of Hubei Province. The unified accounting perspective of water quantity and quality provides a new idea and method for the preparation of water resource balance sheets and will effectively improve the management level and efficiency of water resources. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. At 10:05 a.m. on 4 February 2022, the perilous rock mass Yizhuxiang in Leizu Town, Yuan'an County, Hubei Province, China collapsed on a large scale. The Yizhuxiang collapse was about 35 m in length, 52 m in height, and 29 m in maximum thickness. The volume of the collapse was 5.32 × 104 m3. It threatened the transport of the national trunk road, destroyed the branch road and some cement mixing station workshops, and caused serious economic losses. The rock mass exposed in the collapse was dolomite with developed joint fractures. Under the effects of mining and unloading, fractures occurred in the rock mass; the top of the rock mass was bent and deformed towards the free face; and tension cracks were formed on the rear edge. The safety ore pillar directly below the collapsed body was compressed and deformed under the action of gravity, thus accelerating the internal deformation of the rock mass and leading to the formation of multiple dominant joints inside the rock mass. In the winter of 2021, the weather was extreme, with heavy snowfall and low temperatures. Under the effects of freeze–thaw, the strength of the rock mass declined and the tension cracks further expanded so that the rock mass experienced an accelerated deformation and finally collapsed. Causes of the collapse include mining activity beneath the collapsed mass and heightened extreme weather. The cause of the landslide disaster in this area is, however, freeze–thaw, which deserves the attention of scholars and the vigilance of the local government. [ABSTRACT FROM AUTHOR]

The Chinese water administration department has continuously explored and formulated regulatory and market-oriented water control policies to alleviate the contradiction between water shortage and economic and social development and promote the new idea of 'water-saving first' water control. Among them, implementing a water resources tax policy as a price means has achieved initial success. The water-saving effect of water resources tax collection is one of the important bases for determining whether the tax reform will be promoted nationwide in the next stage. Based on this, taking Hubei Province, the first tax reform pilot in China, as an example, water resource elements are integrated into the economic system and a dynamic stochastic general equilibrium model (DSGE) is constructed, embedded in water resources tax to simulate the persistent impact of such a tax on water saving objectives. The research shows that: (1) A water resources tax can effectively achieve the goal of water-saving and improve the utilization efficiency of water resources. (2) Levying a water resources tax helps to improve the water-saving awareness of enterprises and residents and promotes enterprises to optimize their production structure. (3) Rational and efficient use of special water resources protection funds is the basis for ensuring the effective implementation of a water resources tax. It can also improve the recycling capacity of water resources. This means that the government should speed up the exploration of the relationship between supply and demand for comprehensive water resources, to establish a reasonable range of water resources tax rates to guarantee people's livelihoods, and to accelerate the construction of water resources tax guarantee measures, in order to achieve a relatively steady-state of water resources utilization and protection, realizing the dual goal of sustainable economic development and sustainable use of water resources. [ABSTRACT FROM AUTHOR]

Golden snub-nosed monkeys (Rhinopithecus roxellanae) belong to Class A, the highest level of endangered primate species. Exploring the infection status of potential pathogens in golden snub-nosed monkeys is important for controlling associated diseases and protecting this species. The objective of this study was to investigate the seroprevalence for a number of potential pathogens and the prevalence of fecal adenovirus and rotavirus. A total of 283 fecal samples were collected from 100 golden snub-nosed monkeys in December 2014, June 2015, and January 2016; 26 blood samples were collected from 26 monkeys in June 2014, June 2015, January 2016 and November 2016 at Shennongjia National Reserve in Hubei, China. The infection of 11 potential viral diseases was examined serologically using an Indirect Enzyme-linked Immunosorbent Assay (iELISA) and Dot Immunobinding Assays (DIA), while the whole blood IFN-γ in vitro release assay was used to test tuberculosis (TB). In addition, fecal Adenovirus and Rotavirus were detected using Polymerase Chain Reaction (PCR). As a result, the Macacine herpesvirus-1 (MaHV-1), Golden snub-nosed monkey cytomegalovirus (GsmCMV), Simian foamy virus (SFV) and Hepatitis A virus (HAV) were detected with the seroprevalence of 57.7% (95% CI: 36.9, 76.6), 38.5% (95% CI: 20.2, 59.4), 26.9% (95% CI: 11.6, 47.8), and 7.7% (95% CI: 0.0, 84.2), respectively. Two fecal samples tested positive for Adenovirus (ADV) by PCR, with a prevalence of 0.7% (95% CI: 0.2, 2.5), and further, the amplification products were sequenced. Phylogenetic analysis revealed that they belonged to the HADV-G group. However, other pathogens, such as Coxsackievirus (CV), Measles virus (MeV), Rotavirus (RV), Simian immunodeficiency virus (SIV), Simian type D retroviruses (SRV), Simian-T-cell lymphotropic virus type 1 (STLV-1), Simian varicella virus (SVV), Simian virus 40 (SV40) and Mycobacterium tuberculosis complex (TB) were negative in all samples. In addition, a risk factor analysis indicated that the seroprevalence of MaHV-1 infection was significantly associated with old age (≥4 years). These results have important implications for understanding the health status and conservation of the endangered golden snub-nosed monkey population at Shennongjia Nature Reserve. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigated Reeves' turtle (Mauremys reevesii) using radiotelemetry to determine the beginning and end dates of, and habitats selected for, hibernation, and to inform future conservation strategies for protection of this species during the hibernation season. Hibernation began in late October 2021 and arousal began in March 2022. Reeves' turtles mainly hibernate in abandoned ponds or lands. The terrestrial hibernation sites had more herbage cover and were close to the field edge, and the aquatic hibernation sites were covered with herbage, which provides shelter and protection and thermal stability for the turtles during hibernation. We suggest vigilantly protecting this unique resource to provide Reeves' turtles with secure hibernaculum sites and avoiding redevelopment of these areas during hibernation. Hibernation protects turtles from extreme winter conditions. Reeves' turtle (Mauremys reevesii) is a medium-sized aquatic turtle that lives in freshwater habitats in lowland areas with still or slowly moving water. Currently, little is known regarding its overwintering behavior. In the current study, 20 Reeves' turtles from the wild were investigated using radiotelemetry in the field to determine the beginning and end dates of, and habitat selected for, hibernation. Hibernation began in late October 2021 and arousal began in March 2022. Reeves' turtles do not appear to be limited in their selection of suitable hibernation habitats, which included fish ponds, abandoned ponds (ponds not being used for farming), marshes, and abandoned fields (fields not being used for farming). In the aquatic hibernation habitats, only herbage cover was significantly different between the selected and random habitats (t = 2.525, df = 9, p = 0.033). In the terrestrial hibernation habitats, there were significant differences in the canopy (Z = −2.201, p = 0.028), slope gradient (Z = −2.032, p = 0.042), herbage cover (Z = −2.379, p = 0.017), and distance from the habitat edge (Z = −2.524, p = 0.012) between the selected and random habitats. This indicates that Reeves' turtles prefer to hibernate at the soft edges of flat habitats with low canopy and high herbage cover when hibernating in terrestrial habitats and prefer to hibernate at sites with high herbage cover when hibernating in aquatic habitats. To the best of our knowledge, this is the first study to investigate hibernation in wild Reeves' turtles in the field, and the results identify key ecological variables correlated with habitat selection during hibernation. This knowledge could inform local conservation measures related to farming activities. [ABSTRACT FROM AUTHOR]

As China is currently in a critical period of transition to green development, a quantitative analysis of the coupled synergy between economic growth, population migration, and environmental protection performance can provide a rational reference for the formulation of macro-policies in relevant regions. Based on these objectives, this study built a matching analysis framework between gross domestic production (GDP) density (GD), population density (PD), and gross ecosystem production (GEP) density (ED) to analyze the spatial heterogeneity of these three indicators among 362 municipal units in different regions of the Chinese Mainland from 2000 to 2020 based on satellite remote sensing images and statistical data. The spatial adaptability between them was explored by employing a center-of-gravity model. The findings of this study show that: (1) the GD, PD, and ED on the Chinese Mainland exhibited varying degrees of spatial heterogeneity on both sides of the Hu Line during the investigation period, with the general feature of being higher in the southeastern region and lower in the northwestern region; (2) the centers of gravity of GD, PD, and ED were all located in Hubei Province during the investigation period. The centers of gravity of PD and GD shifted 79.39 km and 109.72 km to the southwest, respectively, whereas the center of gravity of ED, remained relatively stable during the investigation period; and (3) the center of gravity distances between PD–GD, ED–PD, and ED–GD in 2020 were 99.31 km, 247.52 km, and 346.27 km, respectively, and the percentages of highly matched units ranked among the 362 samples were 72.93%, 23.48%, and 25.69% for GD–PD, GD–ED, and ED–PD, respectively. This study concluded that a synergistic spatial pattern of the population, economic layout, and land use on the Chinese Mainland has not yet been formed. Therefore, this study suggests that future policies should be committed to promoting the northwest Chinese Mainland movement of the center of gravity of GD and PD, as well as the southeast movement of the ED center. [ABSTRACT FROM AUTHOR]

Potential prediction is an important research content of thunderstorm gale weather forecast, and it is still a challenge because the environmental field of thunderstorm gale presents different characteristics under different weather conditions. Using the 12-year thunderstorm gale data of Hubei province in central China and the reanalysis data of National Center for Environmental Prediction (NCEP), this study analyzed the percentile distribution of environmental physical quantities of thunderstorm gale, and the continuous probability method was adopted to establish the probability forecast models of thunderstorm gale in four different types of weather situation, which are in the rear of trough type, in front of trough type, in the periphery of the western Pacific subtropical high type and easterly airflow type. Finally, probability prediction was realized by objective classification criterion in operation. The results show that the method based on objective classification and continuous probability can significantly improve the probability of thunderstorm gale detection, and also reduce the missing alarm rate of thunderstorm gale. Moreover, the quantitative test of 16 weather processes under four types of weather situations also shows that the continuous probability method has a higher probability of detection than the bisection method, and significantly reduces the missing alarm of extreme wind by the bisection method. [ABSTRACT FROM AUTHOR]

Hubei province is located in the center of China with 56% total area characterized with mountainous area. Thus, flash flood caused by extreme rainfall has become one of the significant obstacles that highly affect the social and economic development of the province. In order to scientifically understand the mechanism of flash flood disasters and provide technological support to the local flood prevention and control work, the IWHR designed and developed a new distributed hydrological model named China-FFMS that can simulate the evolution of natural disasters and make an assessment by setting the flood water sources in line with the flow discharge. The FFMS was further applied to simulate the 8.12 flash flood disaster that occurred in the Liulin county of Hubei province on 12 August ("8.12") and fed by the data collected from the national flash flood disaster investigation and assessment. The calculated peak flow was 666.22 m3/s with an error of +13% compared with postdisaster investigation data (589 m3/s). The results showed that using a multisourced modelling approach, e.g., mixing spatiotemporal variables and sources, to simulate the flash flood process was able to accurately reproduce the flood process and the consistence of the flow discharge, thereby explaining the underlying reason of the disaster formation and evolution. Regarding the case of the Liulin county, the main factor leading to the disaster was the overlapped peak flow where the Dunne flood peak of three different tributaries from the upper reach met together at the same time. Moreover, the peak flow of the Lianhua river at the downstream of Liulin County also arrived at the same time as the upstream peak, which obstructed the flood progress and increased the damage of the disaster. According to the analysis, several suggestions and recommendations are proposed such as the improvement of the forecast and early warning system of the upstream areas, the optimization of the current flood defense plan, and the enhancement of the residents' awareness of flash flood disasters. [ABSTRACT FROM AUTHOR]

Simple Summary: Doliutheca orientalis is revised from the Shipai Formation (early Cambrian Stage 4) of the Three Gorges area, South China. Specimens are preserved as casts in silty mudstone and as small shelly fossils in calcareous pelites. SEM and Micro-CT analyses show that Doliutheca possessed hyolithid-like skeletons (especially regarding the operculum) but significantly lack helens. This combination of features sees the genus placed within the Family Paramicrocornidae, a group of hyoliths closely related to hyolithids. Newly compressed specimens of Doliutheca from silty mudstone show some soft-part preservation with the gut clearly visible. Additionally, the highly variable apex morphology of Doliutheca is explained as a consequence of differences in preservation due to secondary deposits formed inside the shell. This finding highlights the challenge of preservational artefacts in calculating the disparity and diversity of early Cambrian skeletal fossils, and the resulting potential effects on taxonomic assessments on the diversity of skeletal taxa in the Cambrian. New hyolith specimens from the early Cambrian (Stage 4) of the Three Gorges area, western Hubei Province are described and assigned to the species Doliutheca orientalis. Doliutheca are preserved in two taphonomic modes: casts in silty mudstone revealing gross morphology and some soft parts, and internal molds in calcareous pelites, which exhibit new morphological details of the conch and operculum. SEM and Micro-CT analyses show that Doliutheca preserve well-developed platy clavicles and cardinal processes on the interior of the operculum composed of rod-shaped tubular elements. This observation and the distinct cardinal and conical shields of the operculum indicate that Doliutheca could be placed within the Family Paramicrocornidae, most recently established as a group of hyoliths closely related to hyolithids. [ABSTRACT FROM AUTHOR]

This study investigated elimination of the herbicide pendimethalin using an integrated rice and Procambarus clarkii breeding model of indoor and outdoor (pond culture) exposure tests. The pendimethalin levels in 484 samples from the primary rice and P. clarkii integrated breeding areas in Hubei province were monitored, and dietary risk assessments of pendimethalin were calculated. Pendimethalin was quantified using high-performance liquid chromatography tandem mass spectrometry, and detection levels were linear in the range of 1.0 to 10.0 μg/L, and peak areas were positively correlated with concentration, with a correlation coefficient of 0.9996. Recoveries ranged from 86.9 to 103.5%, and the limit of quantitation was 2.5 × 10−4 μg/L in water, and 1 × 10−2 μg/kg in tissues, sediments, and waterweeds. The dissipation rate of pendimethalin in tissues and water followed first-order kinetics, with half-lives of 0.51–5.64 d. In 484 samples taken from aquaculture farms, pendimethalin was detected in 8.67% of the samples at levels in the range of 1.95 to 8.26 μg/kg in Hubei province from 2018 to 2020. The maximum residue limit of pendimethalin in P. clarkii has not been established in China, but our dietary risk assessments indicated that consumption of P. clarkii from integrated rice farms was acceptable. [ABSTRACT FROM AUTHOR]

(1) Background. Conifers are the main plantation species in southern China, including Masson Pine (MP), Chinese fir (CF) and Chinese thuja (CT). Clarifying the suitable site conditions for these conifers is helpful for large-area afforestation, so as to manage forests to provide a higher level of ecosystem services. To achieve the research goals, we take the conifers in Hubei Province of southern China as a case study. (2) Methods. The situations of conifers, as well as environmental conditions of 448 sampling plots, were then investigated. The suitable growth environment of conifers in the studied area was determined by the maximum entropy algorithm, and the suitability spatial distribution of coniferous forests at the provincial level was also analyzed. (3) Results. The effect of the conifers suitability prediction model reached an accurate level, where AUC values of MP, CF and CT training set were 0.828, 0.856 and 0.970, respectively. Among multiple environmental factors, such as geography and climate, altitude is the most important factor affecting conifer growth. The contribution of altitude to the growth suitability of MP, CF and CT was 38.1%, 36.2% and 36.1%, respectively. Suitable areas of MP, CF and CT were 97,400 ha, 74,300 ha and 39,900 ha, accounting for 52.45%, 39.97% and 21.46% of the studied area, respectively. We concluded that the suitable site conditions of conifer plantations were 2800–5600 °C annual accumulated temperature, 40–1680 m a.s.l., and <40° slopes. (4) Conclusions. The study suggests that accurate spatial suitability evaluation should be carried out to provide sufficient support for the large-area afforestation in southern China. However, due to our data and study area limitations, further studies are needed to explore the above findings for a full set of plantation species in an extensive area of southern China. [ABSTRACT FROM AUTHOR]

The centimeter-scale halite rhythmites in the first member of the Shahejie Formation in the Shulu Sag of the Bohai Bay Basin are investigated, and the Eocene to early Oligocene paleoenvironmental characteristics of a typical saline lake basin are restored by reconstructing the temperature and compositional information of ancient brines. The obtained homogenization temperatures (Th) of fluid inclusions range from 6.5 to 49.2 °C, with a relative lower Th from transparent halite samples than from gray halite samples. This suggests different temperature conditions and a probable association with seasonal changes. The ion contents of halite fluid inclusions reveal the lake brine is a Na-Mg-K-Ca-Cl type and reached the initial stage of halite deposition. The transparent halite samples plotted within different phase regions than the gray halite samples on plots of ion contents and showed significant change within phase regions. Combined with the observed cm-scale rhythm in the evaporite sequences of the Shulu Sag, these results suggest a shallow water environment and frequent dilution by inflows of fresher water caused by seasonal climate change. The gray halite formed under higher temperatures and increased inflow conditions, and the transparent halite formed under lower temperatures and decreased inflow conditions. Compared with the Jiangling Sag in Hubei Province in southern China, the Shulu Sag may have been less affected by igneous rocks in the Es1 Formation due to the material source, and the concentration of trace elements such as lithium, strontium and boron in the ancient salt lake brine was lower. [ABSTRACT FROM AUTHOR]

Water is a crucial factor affecting plant growth and ecosystem processes. In the subtropical region, global climate change leads to frequent seasonal droughts. How plant water strategies and the adaptability of forest ecosystems change is an urgent issue to be discussed. In this study, four sample plots (P. massoniana for Plot 1, C. lanceolata for Plot 2, Q. acutissima for Plot 3, C. funebris and I. corallina for Plot 4) were selected in the Taizishan Mountain area of Hubei, China, including three forest types (coniferous forest, broad-leaved forest and coniferous broad-leaved mixed forest) and five dominant tree species. The δD and δ18O isotope compositions in plant and soil water were analysed, and the water use strategies of dominant species were predicted by using the MixSIAR model. The water absorption depth and proportion of the five species were significantly different in different seasons. In plot 4, I. corallina and C. funebris derived (58.8 ± 14.0% and 55.7 ± 23.4%, respectively) water from 10–40 cm soil in wet season, but C. funebris shifted to derive water from deep soil in dry season. This result indicates that the mixing of C. funebris and I. corallina can effectively prevent water competition in dry season with water deficit. From wet season to dry season, the depth of water utilisation of the P. massoniana, C. lanceolata, Q. acutissima and C. funebris with deep roots converted from shallow to deep soil, suggesting that the four species had significant dimorphic root systems and strong ecological plasticity. [ABSTRACT FROM AUTHOR]

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread. [ABSTRACT FROM AUTHOR]