Research on the indirect value of rice production has predominantly focused on ecosystem services, often overlooking the importance of negative externalities. Addressing these externalities is crucial for ecological regulation and the promotion of sustainable agriculture. This study employed market valuation and spatial autocorrelation methods to evaluate the negative externality value of rice production in China, using panel data from 2000 to 2021 across various provinces. The analysis covered greenhouse gas emissions, pollution from fertilizers and pesticides, plastic film residue, water resource consumption, and energy pollution from agricultural machinery. The findings reveal that: (1) The mean annual value of negative externalities in national rice production was approximately 2080.27×10⁸ yuan, with the contributions from greenhouse gas emissions, fertilizers, pesticides, plastic waste, water resource consumption, and agricultural machinery energy pollution being 35.93%, 20.71%, 10.27%, 9.04%, 17.44%, and 6.61%, respectively. (2) The total value of negative externalities showed an initial increase followed by a decrease, with 2012 marking a turning point. This trend reflects the impact of new era of ecological control measures. (3) There was significant spatial heterogeneity in the distribution of negative externalities, mainly concentrated to the east of the Hu Line. The annual mean values in rice-producing regions were as follows: central China (1025.45×10⁸ yuan), south China (426.96×10⁸ yuan), southwest China (329.36×10⁸ yuan), northeast China (221.52×10⁸ yuan), and north China (61.99×10⁸ yuan). There was a positive global correlation between external cost values and geographical space, with local spatial autocorrelation evolving from high-high clustering in the southeast to low-low clustering and insignificance toward the northwest. High-high clusters were predominantly found in central and southern rice-growing areas of China, while low-low clusters were mainly in the northern region of China. This paper presents a novel approach to assessing negative externalities in rice production, offering a comprehensive and dynamic global perspective. It also proposes strategic responses involving government, market, and farmer-led initiatives.

With the rapid global climate change and the swift urban development in the Weihe River Basin, the vegetation ecosystem in this region faces numerous challenges. Investigating the spatiotemporal changes in vegetation and their response to soil moisture conditions is crucial. Utilizing normalized difference vegetation index (NDVI) and land surface temperature (LST) data from MODIS for the years 2001 to 2020, this study inverted the soil moisture conditions in the Weihe River Basin, northwest China. Through linear regression, residual analysis, and contribution analysis, we thoroughly examined the spatiotemporal characteristics of vegetation cover during the growing seasons and the driving factors and contributions to soil moisture conditions from 2001 to 2020. The results indicate: (1) During the period from 2001 to 2020, the overall trend of the growing season NDVI mean values in the Weihe River Basin exhibited a fluctuating increase, with an average trend rate of 0.47×10^-2·a^-1. The vegetation showed a recovery trend. However, during the years 2012 to 2016, the growing season NDVI mean values experienced a declining trend, attributed to the inhibitory effect of human activities. (2) The impact of soil moisture conditions and human activities on the growing season NDVI in the Weihe River Basin diverged significantly. The influence of soil moisture conditions primarily exhibited a relatively weak and slow growth effect, while the impact of human activities was mainly characterized by promoting vegetation recovery. (3) The contributions of soil moisture conditions and human activities to the changes on the growing season NDVI mean values in the Weihe River Basin were mainly concentrated in the same direction. Negative contributions, accounting for 19.77%, were solely attributed to soil moisture conditions. On the other hand, positive contributions, indicating higher influence, primarily originated from human activities. This suggests that human activities are the primary driving force behind vegetation cover changes in the Weihe River Basin. (4) The overall vegetation in the Weihe River Basin is influenced by a dual promotion from both soil moisture conditions and human activities. Inhibitory effects are primarily concentrated in the agricultural ecological zone of the Fenwei Basin, which corresponds to a high contribution rate from human activities in the same direction. This suggests that current human activities are the main factor inhibiting the growth of vegetation cover. This study can provide a more accurate scientific basis for ecological conservation and sustainable development in the Weihe River Basin.

Timely and accurate understanding of crop distribution within irrigation areas is essential for the efficient allocation of irrigation water resources and precise field management. This study focuses on the Qingtongxia irrigation area in Ningxia, China, employing multitemporal Sentinel-2 satellite data to analyze early characteristics of rice and maize. Key “flooding” and “vegetation” signals are extracted, and a time-series dataset comprising the modified normalized difference water index (MNDWI) and normalized vegetation index (NDVI) is constructed. By analyzing sample thresholds for these key features, a decision tree model for the early planting distribution of rice and maize is established, facilitating the extraction of the spatial distribution for rice and maize planting in the Qingtongxia irrigation area in 2022. The results reveal the following: (1) During the latter half of the maize and rice seedling stages, from May 15 to 31, flooding and vegetation signals are crucial for differentiating between the two crops. (2) Based on the early crop phenological characteristics, the mapping accuracy of rice and corn images obtained from May 16 to May 31 was higher than 90%, with user accuracy exceeding 91% and overall accuracy exceeding 90%. The Kappa coefficient was higher than 0.88, significantly higher than the classification accuracy of the random forest classification method during the same period. (3) The proposed method demonstrates strong applicability in the early extraction of rice and maize planting distribution, requiring fewer ground samples for extension across both spatial and temporal scales. Therefore, this method provides significant support for early investigations of rice and maize planting distribution in the Qingtongxia irrigation area.

Soil moisture is a crucial abiotic factor that limits the growth and development of plants and the ecological construction of sandy areas in semi-arid regions. In this study, continuous observations were performed on soil moisture at depths of 0-100 cm in shifting, semifixed, and fixed sandy land in the Mu Us Sandy Land during the growing seasons from 2008 to 2010 and from 2018 to 2021 (April to October). The dynamic changes in soil moisture and its response to rainfall were systematically analyzed. The results are as follows: (1) Affected by seasonal changes in rainfall, the seasonal variation of soil moisture in shifting, semifixed, and fixed sandy land exhibited a generally “∽”-shaped or double-peaked pattern. The soil moisture in the 10- and 30-cm depth ranges exhibited greater fluctuations, whereas that in the 60- and 100-cm depth ranges showed smaller fluctuations. (2) The dynamic differences in soil moisture during the growing season were significant among the three degrees of fixation. Overall, the shifting sandy land had the best soil moisture status with smooth changes, whereas the fixed sandy land had the worst soil moisture status with the most drastic changes. The semifixed sandy land fell between the two. The soil moisture in the 10-30 cm depth range of the fixed sandy land was better than that of the semifixed and shifting sandy lands, whereas the situation was opposite at depths of 30-100 cm. (3) The pattern of rainfall was the main factor determining the spatiotemporal distribution of soil moisture. As rainfall events occur and the amount of rain increases, the depth of rainwater infiltration gradually increases. However, deep replenishment of soil moisture in fixed sandy land requires higher rainfall amounts and longer periods. During the growing season, small rainfall events were dominant, resulting in greater fluctuations in soil moisture in the surface layers. At the beginning of the growing season, soil moisture below 10 cm was not replenished because of low rainfall and small rainfall events, resulting in poor soil moisture conditions. The shifting and semifixed sandy lands had better soil moisture at depths of 10-30 cm than at depths of 30-100 cm, whereas the opposite was true for the fixed sandy land. These results provide a scientific basis for the restoration of near-natural vegetation and the stable maintenance of sand-fixing vegetation on sandy decertified land in semi-arid regions.

To clarify the relationship between plant biomass and soil nutrients of dominant plants in desert grasslands, this study selected five dominant plant species: Peganum harmala, Setaria viridis, Festuca sinensis, Puccinellia distans, and Agropyron cristatum. We measured their biomass and root-zone soil nutrients to explore biomass allocation and its relationship with soil nutrients. The results are as follows: (1) There were significant differences in total biomass and root-shoot ratio among the five dominant plant species (P<0.05), with Peganum harmala having the highest total biomass and Puccinellia distans having the lowest. The perennial plants Peganum harmala, Puccinellia distans, Festuca sinensis, and Agropyron cristatum had most of their biomass allocated below ground, whereas the annual plant Setaria viridis had most of its biomass above ground. The order of root-shoot ratio among the five plants was as follows: Puccinellia distans>Agropyron cristatum>Festuca sinensis>Peganum harmala>Setaria viridis. (2) There were significant differences (P<0.05) in root-zone soil organic carbon, available nitrogen, available potassium, total nitrogen, total phosphorus, total potassium, and their stoichiometric characteristics among the five plant species. The order of soil C:N ratio among the five plants was as follows: Puccinellia distans>Setaria viridis>Agropyron cristatum>Peganum harmala>Festuca sinensis. (3) Variations in plant biomass, root-shoot ratio, and soil nutrients varied among the plants. The root-zone soil total potassium of Peganum harmala, Setaria viridis, and Festuca sinensis and the root-zone soil moisture of Puccinellia distans and Agropyron cristatum exhibited weak variation, whereas the other plant characteristics, soil nutrients, and stoichiometric characteristics exhibited moderate variation. The biomass of the five dominant plant species exhibited a positive correlation with the root-zone soil available nitrogen and total potassium (P<0.05). The allocation of plant biomass and soil nutrient composition significantly vary among different species and life histories in the desert grassland ecosystem. In the future, it will be necessary to restore degraded desert ecosystems by applying appropriate fertilization based on the nutrient requirements of different dominant plant species.

To obtain timely and accurate information about crop cultivation in arid zones, this study used the PIE-Engine Studio platform to extract 14 vegetation indices in the Yanqi Basin, Xinjiang, China based on 2022 Sentinel-2 images and 1948 field location sampling data during the crop reproduction period. Crop planting information was extracted using the See5.0 decision tree, random forest (RF), and multiple regression (MR) models to select feature parameters. Each model was combined with support vector machine (SVM) algorithms to construct five classification models and five sample segmentation schemes. The best classification scheme was determined by visual interpretation and confusion matrix comparison. The results are as follows: (1) The overall accuracy (OA) and Kappa coefficients of all classification models are above 92.20% and 0.9037, respectively, indicating that it is feasible to extract crop information using the SVM algorithm in the PIE platform. (2) The mean OA and Kappa coefficients of SVM-with-red-edge are 93.77% and 0.9236, which are 0.96% and 0.0120, respectively. (3) The introduction of vegetation index improved the OA and Kappa coefficients of SVM-RF, SVM-MR, and SVM-See5.0 compared with the SVM-with-red-edge method. (4) The mean OA and Kappa coefficient relationships for the five classification models were SVM-RF>SVM-MR>SVM-See5.0>SVM-with-red-edge>SVM-without-red-edge, showing that the inclusion of the red-edge band and vegetation index significantly improved the accuracy of crop identification, with SVM-RF (8:2) being the best classification model with OA and Kappa coefficients of 98.72% and 0.9866, respectively. These results provide new ideas and references for accurate and rapid access to large-scale arid zone crop information.

This study used Dengyi No. 2 maize as the experimental material to assess the effects of furrow cover rainfall collection mode on the microbial community structure and diversity of maize rhizosphere soil. A single-factor completely-randomized experimental design was adopted, and conventional plain planting without mulching was used as the control (CK). Soil microbial community composition and diversity were analyzed using Illumina high-flux sequencing technology. Six treatments comprising ridges covered with the following were set: ordinary black mulch (HL), liquid mulch (YL), straw (NJ), liquid mulch (YJ), and ordinary black mulch (HJ). The results showed that: (1) The rain-harvesting mode of ridge and furrow mulching was beneficial in increasing corn yield and water use efficiency. Among the six treatments, the number of rows per ear, 1000-grain weight, yield, and water use efficiency of treated HJ were the highest: 11.22%, 31.31%, 88.02%, and 79.83% higher than that of control CK, respectively. There were significant differences between the NJ treatment and CK (P<0.05); however, the yield and water use efficiency of NJ treatment were lower than those of the CK treatment. (2) Each treatment of ridge and furrow mulching except for NJ significantly increased microbial community diversity and changed the microbial structure. (3) The microbial community composition of each treatment was affected by the precipitation harvesting mode at the phyla and class levels. The main bacterial phyla in the soil microbial community were discovered to be Proteobacteria, Acidobacteria, Gemmatimonadetes, and Bacteroidetes. The main dominant Bacteroidia were Gammaproteobacteria (25.8%), Bacteroidia (8.4%), and Alphaproteobacteria (7.7%). Therefore, ridge mulching can improve soil microbial richness, diversity, and evenness index. In other words, the corrugation and rain-harvesting modes can change the structure and composition of the soil microbial at the phyla and class levels to increase corn yield.

Surface energy balance is the crucial link in the interaction between the earth and the atmosphere, and it is essential to study its characteristics with different underlying surfaces to understand heat transfer at the surface. However, limited information is available on the variation characteristics of the energy balance in the formation process of salt-crusted soil. The main difficulty is the limited calculation method for the albedo of the soil surface during the development of salt crust, which remarkably affects the accurate quantitative description of heat transport in saline soil. Therefore, a simulation experiment combined with an energy balance model and an albedo calculation method was applied to quantitatively analyze the characteristics of dynamic variations in the energy balance during the formation and development of salt-crusted soil. The results are as follows: (1) The formation and development process of salt crust on the soil surface could be expressed using the logistic growth model (R²=0.99). (2) Under continuous irradiation (1000 W·m^-2, 16 d), the albedo of the salt-crusted soil was 0.15-0.41 higher than that of the control treatment with continuous development of the salt crust, which decreased the absorbed heat by the soil, consequently reducing the surface temperature of the salt-crusted soil by 16 °C (mean value) compared to the control treatment. (3) The net radiation, sensible-heat flux, latent-heat flux, and soil-heat flux of the salt-crusted soil decreased by 47.9%, 52.4%, 46.8%, and 47.4% (mean value), respectively, compared to that of the control treatment. This reduction happened under the influence of albedo and surface temperature, which further affected the soil profile temperature. The results provided a notable scientific value for further investigation of soil water-heat transport under the influence of the salt crust.

The safeguarding of cultivated land is paramount in ensuring national food security, sustainable economic and social development, and the preservation of the ecological environment. Rapid and accurate acquisition of cultivated soil salinity and spatial distribution information is imperative for the protection of cultivated land. This study focuses on cultivated land in Pingluo County, Ningxia, China, as the research object and discusses the feasibility of combining optical remote sensing and microwave remote sensing to predict the accuracy of soil salt content compared with single remote sensing data. The methodology involved the extraction of the spectral indices from Landsat 9 OLI and radar polarization combination indices from Sentinel-1. Variable projection importance and gray correlation degree were used to screen and combine characteristic variables. Three machine learning algorithms (back propagation neural network, support vector machine, and random forest) were used to construct the soil salt content prediction model. The best model was used to predict the spatial distribution of the soil salt content in cultivated land. The results show the following facts: (1) The model, validated using the variable projection importance method for screening variables, generally exhibited a higher determination coefficient (R²) than the model established using the gray correlation method for characteristic variables. (2) Using the random forest algorithm, the model combining the spectral index and radar polarization combination index demonstrated the best effect. The modeling set exhibited an R² of 0.791 and a root mean square error (RMSE) of 1.016. This represented an increase in R² by 0.065 and 0.085 compared with the single data source model, with corresponding decreases in RMSE by 0.147 and 0.189. The validation set showed an R² of 0.780 and an RMSE of 1.132, indicating a respective increase in R² by 0.091 and 0.237 and a decrease in RMSE by 0.175 and 0.377 compared with the single data source model. (3) The distribution range of mildly salinized and moderately salinized soil of cultivated land in Pingluo County covered wide areas, accounting for 23.77% and 33.54%, respectively, whereas severely salinized soil constituted 15.37%. This underscores the effectiveness of modeling by combining multisource remote sensing data in improving the prediction accuracy of soil salt content. The outcomes offer a valuable technical reference for predicting soil salt content in arid areas and contribute to the sustainable development of local agriculture.

It is critical to ensure timely and accurate monitoring of leaf water content (LWC) when assessing the growth status of cotton. To accurately estimate cotton LWC, hyperspectral data, and leaf water data from cotton leaves in the oasis of the Ugan River-Kuqa River Delta, Xinjiang, China, were selected and processed using fractional differentiation of raw spectra. The sample were analyzed through correlation coefficient analysis, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), Monte Carlo uninformative variables elimination (MC-UVE), and a combination of CARS and SPA to filter the feature bands. The modeling of the LWC inversion was executed through random forest regression (RFR) based on the whale optimization algorithm (WOA), and independent samples were used for validation analysis. The results show that: (1) The disparities in the number and positions of the feature bands obtained using the different feature band screening methods are different, where the number of feature bands obtained through MC-UVE is 8 while CARS produced 38. The positions of the characteristic bands identified through the SPA, GA, and CARS-SPA methods are considerably consistent and fundamentally concentrated in the near-infrared range of 950-1050 nm. (2) The CARS-SPA-WOA-RFR model has the best inversion with an R² of 0.93 and a root mean square error of 0.032. This model can provide a decision basis for accurate and rapid monitoring of cotton drought and precision irrigation.

Desert vegetation within the Shiyang River Basin of China plays an important role in mitigating the convergence of the Badain Jaran and Tengger Deserts. It plays a vital role in maintaining the ecological security of the regional oasis. A comprehensive understanding of the types, distribution, and survival of desert vegetation in the Shiyang River Basin was pursued through a multiyear field survey aided by satellite images. This endeavor classified desert vegetation types, draw vegetation distribution maps, and analyze the quantitative characteristics of typical desert vegetation communities. These findings serve as a basis for the conservation and sustainable use of desert vegetation and its species diversity in the Shiyang River Basin. The study yielded the following key insights: (1) The Shiyang River Basin boasts diverse desert vegetation types, organized into 4 vegetation type groups, 6 vegetation types, 7 vegetation subtypes, and 40 vegetation groups. These exhibit typical desert and steppe desert characteristics. The spatial distribution of desert vegetation within the basin considerably varies. The eastern part predominantly features drifting sands and a homogeneous vegetation type, while the northern part is characterized by a rich distribution of Gobi areas interspersed with different vegetation types and species composition. (2) The species composition of desert vegetation in the Shiyang River Basin is considerably rich, encompassing 486 species in 255 genera of 57 families, mainly consisting of temperate desert plants, perennial herbs, annual herbs, and shrubs, accounting for 49.59%, 25.31%, and 18.93% of the total number of species, respectively. The desert vegetation structure in the Shiyang River Basin is simple, with shrubs and semi-shrubs often serving as community-building species. (3) The number of dominant species within typical plant communities in the Shiyang River Basin varies considerably. These communities exhibit species richness ranging from 2.1 to 16.3, with an average of 7.6 species. The Reaumuria songarica+Nitraria sphaerocarpa community is the richest, with 16.3 species. Following closely is the Caroxylon passerinum+Nitraria sphaerocarpa+Kalidium cuspidatum community, which encompasses 14.5 species. Conversely, the Nitraria tangutorum, Sympegma regelii, Kalidium foliatum, and Artemisia ordosica communities had the lowest number of species, with 2.7, 2.6, 2.1, and 2.6 species, respectively. In summary, the Shiyang River Basin harbors diverse desert vegetation types and a rich variety of constituent species. However, community cover, species richness, and diversity index are low. Therefore, the protection and restoration of desert vegetation and its fragile habitats should be strengthened to maintain the diversity, stability, and sustainability of the desert ecosystem while harnessing its full ecological protection potential.

The southern region of Xinjiang has great potential for land resource utilization and is a crucial reserve supplying cultivated land resources in China. Evaluating soil salinization susceptibility in this area is necessary, as it aids in making informed decisions regarding water and land selection and land zoning management in southern Xinjiang. Sources, including total dissolved solids (TDS) in groundwater, groundwater depth, soil texture, geomorphic type, land-use type, and soil salt content, were collected via field surveys. By employing data-driven evidence weight, the spatial correlations of a single factor contributing to soil salinization were analyzed, followed by a multifactor comprehensive quantitative evaluation of soil salinization using a Logistic regression model. The results showed the following: (1) Shallow groundwater depth, high groundwater TDS, fine-grained soil, alluvial (lacustrine) plain geomorphology, and grassland land-use types showed strong positive spatial correlation with high soil salt content. (2) High soil salinization-prone areas were identified, encompassing the ancient channel in the alluvial plain and ancient lake marsh in the lacustrine plain, covering ~29 km² and accounting for 1.3% of the total area. Additionally, abandoned farmland around the irrigated area was a prone area of soil salinization, covering ~453 km², accounting for 20.5% of the total area. In contrast, the vast desert and water areas around the irrigated area exhibited a low salinization-prone area of ~1726 km², accounting for 78.2% of the total irrigated area. (3) The comprehensive evaluation using the Logistic regression model was validated with field investigation, affirming its accuracy and reliability. This comprehensive quantitative assessment of soil salinization susceptibility provides a scientific basis for soil salinization prevention and control measures and for making informed decisions regarding the selection of reserved cultivated land resources in southern Xinjiang.

Owing to rapid urban expansion and industrialization in China, the core urban areas of large cities have suffered from varying degrees of heavy metal pollution in the soil. Herein, the accumulation characteristics and ecological risks of six heavy metals (Cd, Cr, Cu, Pb, Zn, and Ni) in the soils of two distinct urban land types (urban park and transportation hub green lands) within the core urban areas of Urumqi City were systematically studied. The study yielded multiple key results: (1) Soil heavy metal concentrations in the green land soils of transportation hubs were higher than urban parks. Compared with the soil background values of heavy metals in Urumqi City, urban park green land of the core urban area exhibited light pollution, while the green land in transportation hubs demonstrated moderate pollution. (2) The comprehensive pollution index for soil heavy metals within urban park green land varied among different administrative regions, with Toutunhe District (2.85) showing the highest level, followed by Shuimogou District (2.13), Tianshan District (1.91), Xinshi District (1.85), and Midong District (1.23). Conversely, for green land in transportation hubs, the comprehensive pollution index of soil heavy metals differed across administrative regions, with Midong District (4.17) being the most heavily affected, followed by Sayibak District (3.24), Xinshi District (2.84), Shuimogou District (2.70), Tuotunhe District (2.50), and Tianshan District (2.37). (3) Regarding the potential ecological risk index for heavy metals in green soil of urban parks, the rankings among different administrative regions were as follows: Shuimogou District (101.68) >Toutunhe District (98.83) >Xinshi District (88.56) >Tianshan District (73.43) >Midong District (58.24). In addition, for transportation hubs green land, the potential ecological risk index of heavy metals for each administrative region exhibited regional disparities, with Midong District (177.60) being the most adversely affected, followed by Shuimogou District (131.75), Sayibak District (120.25), Xinshi District (105.76), Toutunhe District (105.63), and Tianshan District (82.12). The largest comprehensive pollution index and potential ecological risk index for urban park green land were observed in the Toutunhe and Shuimogou Districts, respectively, while the largest values for urban transportation hubs green land were documented in Midong District. (4) Spatial distribution characteristics revealed that high-value urban park green land was generally distributed in the central urban area, resembling islands of green amidst the urban landscape. Conversely, high-value heavy metal pollution of green land in transportation hubs was distributed in an island shape in the southwestern region of Midong District and the eastern part of Sayibak District, except for Ni. This study identifies key factors affecting the varying degrees of soil heavy metal pollution in different administrative regions, including the concentration of industrial enterprises, dense transportation arteries, and high population density. This study potentially provides a scientific basis and reference for the protection of the soil ecological environment in the core urban area.

An accurate and quantitative evaluation of crop water productivity is the basis for the improvement of crop yield in arid regions and is of great significance to alleviate water shortage and drive sustainable agricultural development. We have considered the Ugan-Kuqa River Oasis on the north bank of the Tarim Basin of China as the subject area, and the watershed SEBAL remote sensing evapotranspiration model and cotton distribution identification model are established employing the Google Earth Engine cloud platform from 2009 to 2020. The production estimation model was used to evaluate the moisture productivity of cotton in the watershed. The following results were observed. (1) The cotton production in the Ugan-Kuqa River Oasis ranges from 1610.10 kg·hm^-2 in 2009 to 1855.05 kg·hm^-2 in 2020, with a growth rate of 13.20%. The cotton planting area extends to the edge of oasis year by year, and the center of gravity of cotton production moves 2485 m from west to east. (2) The average evapotranspiration (ET) value during the cotton growth period was 686.80 mm in 2009 and was 738.66 mm in 2020, indicating an overall upward trend, with a growth rate of 7.02%. The maximum value of ET during the cotton growth period was observed during the flowering and boll-opening stages. The higher ET value is mainly distributed in the interior of the oasis and at the edge of the north bank of the Tarim River. (3) The average water productivity in 2009 and 2020 was 0.21 kg·m^-3 and 0.25 kg·m^-3, respectively, indicating a growth rate of 16% in 12 years. In terms of space, the gravity center of water productivity in the Ugan-Kuqa River Oasis moved 1832 m from the northeast to the southwest of Hongqi Town, with an average annual moving speed of 152.67 m·a^-1. The water productivity of oasis cotton showed an expanding trend in the east-west direction compared with that in the north-south direction. The trend of spatial distribution direction had been enhanced, and the spatial pattern tended to be agglomerated. (4) After 12 years, the increased yield rate exceeded the increased evapotranspiration rate, promoting the increase of water productivity. Moreover, water productivity is closely related to the cotton planting area and reasonable water irrigation technology. High water productivity is mainly distributed in Xinken Farm in Xayar County and Sangtamu Farm in Xinhe County, respectively. The large-scale planting and intensive management of farms promoted and improved cotton production, stable distribution, and efficient use of agricultural water resources.

The Huangshui River Basin is an important part of the Huangshui Valley. Additionally, collaborative environmental factors that predict the spatial distribution of soil nutrients are particularly important for managing soil nutrients. Moreover, less attention is paid to the effect of model parameters on the results obtained from soil nutrient inversion studies. In this study, the Huangshui River Basin in Qinghai Province (China) was selected as the study area, and 28 factors, including elevation, aspect, slope, plane curvature, section curvature, relief degree of land surface, topographic wetness index, soil pH, and spectral reflectance, were selected. In addition, these factors were combined with the Bayesian optimization algorithm (BOA) to construct artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGBoost) machine learning models for predicting the spatial distribution of four soil nutrients in farmlands: soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK), respectively. Further, the prediction accuracy of these three models was evaluated based on the model coefficient of determination (R²), root-mean-square error (RMSE), and relative percent deviation (RPD). The results revealed that: (1) All four soil nutrients exhibited a moderate degree of variability, with TN showing the highest variability of 69.481%. The XGBoost model based on the Bayesian optimized hyperparameter combination was better than other models in predicting the TN content (R², RMSE, and RPD were 0.893, 0.359, and 2.470, respectively). The R² values of the XGBoost model validation set for estimating the SOM, AK, and AP contents were 0.801, 0.509, and 0.442, respectively, and the corresponding RPD values were 2.152, 1.210, and 1.274, respectively. Moreover, this model exhibited a better prediction capability. (2) The comparison of the number of optimizations and errors of the three models revealed that the BOA-XGBoost model exhibited minimum number of parameter optimizations, higher efficiency, and better robustness. The ANN and SVM models demonstrated different prediction accuracies for different nutrients; additionally, the SVM model predicted the SOM content with high accuracy (RPD=1.580), while the ANN model predicted TN efficiently (RPD=2.460). Based on Landsat 8 remote sensing images, the XGBoost inversion model developed by combining 28 factors of the Huangshui River Basin was found to be more suitable for application in soil nutrient inversion research; furthermore, it can more accurately describe the spatial distribution pattern of the soil nutrient inversion in the Huangshui River Basin, better ensure precise agriculture fertilization, improve the fertilizer utilization rate and crop yield, and provide a reference for precise agriculture fertilization in the Huangshui River Basin.

The Shule River Basin is an important grain-producing area in northwest China. Exploring the influence of climate change and human activities on grain production in this region can provide an important scientific basis for promoting regional food security. In this paper, Yumen City, located in the middle reaches of the Shule River Basin, was used as a representative research area. The sensitivity of maize traits and maize yield per unit area to natural factors (maximum temperature, minimum temperature, solar radiation, wind speed, and precipitation) and human factors (policy, irrigation amount, and fertilizer amount) was analyzed using meteorological, field management, soil property, and yield data from 1990 to 2020, based on which 2017 was selected as a typical year. The DSSAT-CERES-Maize model was used to quantitatively analyze the influence of natural and human factors on maize traits and climatic yield per unit area, and the most suitable conditions for maize growth and development in the study area were thus identified. The results showed that: (1) Middle reaches of the Shule River Basin have a warm and humid climate. Fertilizer application showed an increasing-decreasing trend and effective irrigated area showed a continuously increasing trend. (2) Actual yield per unit area showed a slightly increasing trend, and yield per unit area showed an increasing-decreasing-stable trend due to the influence of policies promoting high-quality maize varieties and farmland water conservation measures. Climatic yield per unit area showed a decreasing trend with strong positive sensitivity to the highest moderate precipitation and a strong negative sensitivity to the lowest low temperature. Maize traits showed strong positive sensitivity to fertilizer application quota, effective irrigated area, and lowest temperatures at ear and flowering stages, and strong negative sensitivity to highest temperatures at ear and flowering stages. (3) The optimum conditions for maize growth and development in the study area were found to be as follows: a highest temperature range of 14.80-38.56 ℃, a lowest temperature range of -0.38-22.16 ℃, a solar radiation range of 3.93-25.15 MJ·m^-2), a wind speed range of 0-3.81 m·s^-1, an irrigation amount of 15 mm, and a fertilizer application rate of 10 kg·hm^-2. Within these ranges, the relationship between solar radiation and maize traits and yield was U-shaped, while the relationships between maximum temperature, minimum temperature, wind speed, and water and fertilizer factors and maize traits and yield were inverted U-shaped.

Because the cover affects the water and heat distribution of mine tailings, studying the effect of plant fiber blanket cover on the hydrothermal distribution of tailing sand is critical for regulating hydrothermal conditions in microbial induced calcite precipitation (MICP) technology to mitigate tailings pollution. In the summer of 2022, a field simulation experiment was conducted using plant fiber blankets of various materials (jute, straw, coconut silk, and palm) and specifications (300 g·m^-2, 500 g·m^-2, 700 g·m^-2, and 900 g·m^-2) to cover tailing sand at the Mosuo Wan Desert Research Station, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. The influence of the plant fiber blanket covering on the water and heat distribution of tailing sand was investigated by measuring the change in the temperature, water content, and evaporation of tailing sand. The results revealed that (1) The plant fiber blanket covers reduced the temperature, daily temperature difference, and daily variation of tailing sand at a depth of 0-20 cm. The 900 g·m^-2 straw fiber blanket (D9) exhibited the strongest cooling effect and smallest daily variation. (2) The plant fiber blanket covers improved water retention and reduced the water evaporation loss of tailing sand at a depth of 0-30 cm. (3) The plant fiber blanket covers can inhibit the evaporation of water from tailing sand. The test results revealed that cumulative evaporation inhibition efficiency under the same specification except D9 was slightly greater than 900 g·m^-2 jute fiber blanket (H9), and followed the order jute>straw>palm>coconut silk. The evaporation efficiency of all materials increased with the increase in specifications, with D9 inhibiting evaporation efficiency up to 71.3%. (4) D9 was the best water saving and cooling solution for plant fiber blanket covering tailing sand in arid areas. The plant fiber blanket covers can effectively save water and reduce temperature. Furthermore, the results of the study can provide theoretical support for the application of MICP technology in arid areas to control hydrothermal conditions in the pollution dispersion of tailings.

Mining subsidence in the Shanxi-Shaanxi-Inner Mongolia border region of northwestern China has caused ecological damage by strongly disturbing the surface soil. To explore the mechanism by which mining subsidence influences soil water and salt transport, physical soil subsidence and numerical soil-water and salt-transport models were established in the HYDRUS-2D water-salt model using soil bulk density data from different stress areas of the subsidence profile. The variation in soil moisture, total salt, and different solute-ion content with soil depth and migration time in different subsidence stress regions during soil evaporation was studied. The results showed that: (1) At 0-40 cm depth, the effect of subsidence and tension significantly enhanced soil evaporation, resulting in soil water content in the extrusion zone significantly higher than the tension zone. (2) The total soil salt content in each subsidence stress region varied strongly with depth, and salt accumulation was observed in the 20-40 cm and 60-80 cm soil layers. Moreover, the salt accumulation depth in the right tensile area showed a downward migration trend. (3) The accumulation concentrations of Ca²⁺, SO₄^2-, Mg²⁺, and Cl^- in the subsidence tensile zone were greater than the extrusion zone. The concentrations of Ca²⁺, SO₄^2-, and CO₃^2- showed a single peak of accumulation with depth and a significant decrease in accumulation depth in the subsidence and extension areas. (4) The HYDRUS-2D water-salt model can accurately simulate soil water and salt transport in mining subsidence soil profiles. Compared with the measured soil salt and water content values, the average relative error, root mean square error, and coefficient of determination of the simulated values were respectively as follows: ≤0.5, ≤0.5, and >0.95. In the mining subsidence areas of the Shanxi-Shaanxi-Inner Mongolia border region, salt migration through the subsidence tensile stress area of the soil can greatly alleviate the problem of land salinization caused by strong evaporation. This study thus provides an important theoretical basis for implementing science-based ecological restoration projects in mining subsidence areas, for establishing differentiated ecological restoration models, and for accelerating ecological self-healing capabilities.

In recent years, China has made great progress in the construction and management of national nature reserves such as the Daqingshan National Nature Reserve in Inner Mongolia. However, the associated ecological benefits have not been effectively assessed. The purpose of this study is to assess whether the establishment of the Daqingshan National Nature Reserve has contributed to the ecological recovery and improvement of the area. Examining changes in vegetation dynamics can be an effective tool for regional ecological engineering assessment, and this study analyzes such changes using the normalized difference vegetation index (NDVI). Based on the Google Earth Engine (GEE) cloud platform, the study uses Landsat remote sensing imagery to extract NDVI data for the nature reserve for the years 1995 to 2020. Spatial and temporal variation in NDVI and variation drivers before and after the establishment of the reserve were analyzed using Pearson correlation, residual analysis and the Lindeman-Merenda-Gold (LMG) model. A decreasing NDVI trend was identified during the 1995—2008 period in 69.04% of the studied area, while an increasing NDVI trend was identified during the 2008—2020 period in 94.98% of the studied area. These results indicate that the quality of vegetation in the study area has improved significantly since it became a national nature reserve. Negative impacts from human activities and climatic warming during the 1995—2008 period led to decreases in NDVI in the studied area, with climate warming being the dominant factor. An increase in precipitation and positive impacts from human activities drove increases in NDVI in the studied area during the 2008—2020 period, with positive human impacts arising from ecological-environmental protection engineering implementation being the main reasons for vegetation recovery during this period. The selection of Landsat remote sensing images and the use of the GEE integrated computing environment enabled the study to obtain vegetation monitoring data over a long time span and at a high spatial resolution. Spatiotemporal variation in vegetation was also more accurately portrayed using the NDVI measure, thereby enriching the technical means for long time-sequence and small region-scale vegetation monitoring. The results of this study provide clear evidence of the ecological benefits brought by the establishment of the Daqingshan National Nature Reserve. At the same time, the study provides basic information and technical support for future ecological-environmental management decisions concerning the protected area.

High-resolution soil salinity maps are urgently needed in arid and semi-arid regions to visualize the subtle spatial variations in salinity distribution. These maps are crucial for guiding the development of land resource management policies and water resource management policies in salt-affected and potentially salt-affected areas, aiming to prevent further soil degradation and ensure sustainable agricultural economic development and food security. Based on PlanetScope imagery, vegetation spectral indices and soil salinity indices were extracted, resulting in a total of 21 variables. These variables were used as input for the Bagging algorithm to construct a soil salinity prediction model, referred to as Model-Ⅰ. The max-relevance and min-redundancy (mRMR) method was employed to select relevant feature variables, which were then inputted into the Bagging algorithm to build a soil salinity prediction model, referred to as Model-Ⅱ. Field sampling data were used to assist in model building and validation. Model-Ⅰ and Model-Ⅱ were evaluated using model evaluation metrics. The results indicate that the prediction performance of Model-Ⅱ is better than that of Moedl-Ⅰ (mean R²=0.66, mean RMSE=18.00 dS·m^-1, mean PRIQ=3.21 for the validation set), and that mRMR effectively reduces the multidimensional feature redundancy. PlanetScope images combined with the mRMR method successfully mapped high-resolution soil salinity, which provided more detailed information on the spatial distribution of soil salinity, and the results of the study promoted the use of PlanetScope data to monitor soil salinity information.

Soil salt crust has an important impact on soil evolution and ecohydrological processes in arid areas. There are few recent studies on water and salt distribution characteristics in salt-crust soils, and the influence of salt-crust thickness is not considered, leading to great differences in research results. Therefore, in this paper, four initial salt concentration treatments (0 g·L⁻¹, 10 g·L⁻¹, 150 g·L⁻¹, and 250 g·L⁻¹) were set to obtain different salt-crust thicknesses (4.5 mm, 6.6 mm, 7.3 mm) through laboratory simulation tests, and the soil-profile dynamics of water and salt were compared and analyzed using a partial repeated stepwise withdrawal method. The results were as follows: (1) Compared with the non-salt treatment, the thicker the salt crust, the larger the soil-profile water content, and the smaller the salt-content variation range. (2) At the end of the experiment, the water content distribution characteristics of the 4.5 mm salt-crust soil were similar to those of the unsalted treatment, and the water contents of the 6.6 mm and 7.3 mm salt-crust soils were significantly higher than that of the unsalted treatment (P<0.05). (3) At the end of the test, the minimum salt contents of the 4.5 mm, 6.6 mm, and 7.3 mm salt-crust soils decreased by 90.5%, 46.3%, and 32.1%, respectively, compared with their initial salt contents. The results confirm that salt-crust thickness has a great influence on the distribution of soil water and salt. Therefore, it is suggested that the influence of salt-crust thickness should be considered comprehensively in future research on distribution characteristics of water and salt.

This article chooses the Xiaohaizi Irrigation Area of the Third Division of the Xinjiang Production and Construction Corps, Xinjiang, China, as the research object. In April 2021, 324 soil samples were collected in layers (including 0-20 cm and 20-40 cm) to determine the total salt, eight major ions, and pH values. Descriptive statistics, significance testing, and spatial interpolation were used to analyze the soil salt content and distribution characteristics in the irrigation area. The results reveal that: (1) The varying degrees of salinization affect the soil in the Xiaohaizi Irrigation Area, and the distribution of total salt and various base ions in the soil is extremely uneven in space. The salt concentration generally exhibits a low distribution pattern in the southwest and high distribution characteristic in the northeast. The areas with the most severe soil salinization hazards are the 51^st and 53^rd regiments. (2) The research area is primarily composed of chloride sulfated soil, distributed in main areas of the 51^st and 44^th regiments and some areas of the 50^th and 53^rd regiments. Sulfated soil is distributed at the junction of the 44^th, 50^th, and 51^st regiments, whereas chloride saline soil is distributed in the majority of the 49^th regiment. (3) Moderately saline soil is the most widely distributed in the surface area of the study area, followed by lightly saline soil. Severely saline soil is concentrated in the 51^st regiment, the middle of the 44^th regiment, and the junction between the 50^th and 53^rd regiments. Lightly saline soil is the soil layer with the largest distribution area of 20-40 cm, followed by moderately saline and nonsaline soil. The research results provide theoretical basis and data support for targeted management of soil salinization by clarifying the characteristics, types, and degree of soil salinity within the region and continuously improving the local irrigation and drainage management system in the future.

The variation in fractional vegetation cover (FVC) is not only closely related to climatic factors but is influenced by human activities. Only a few studies have been conducted on the spatiotemporal characteristics of FVC in China at the provincial scale and quantitative analysis of the impact of climate factors combined with human activities on FVC. Based on the Google Earth Engine platform and Landsat data for 2000—2020, as well as contemporaneous climate and nighttime lighting data, the study is analyzed using the dimidiate pixel method, linear regression analysis, coefficient of variation, partial correlation analysis, and contribution model. The results showed the following: (1) The rate of increasing of FVC in China is 0.32%·a^-1 from 2000 to 2020. The vegetation cover area is dominated by the high cover level, accounting for 38% of the study area, with an overall decreasing trend from southeast to northwest. (2) FVC of the Loess Plateau, Yunnan Province, Tibet Autonomous Region, and western Xinjiang Uygur Autonomous Region showed an increasing trend. Interannual fluctuations in the FVC are more stable in the south than in the north and in the east than in the west. Heilongjiang Province has the highest vegetation cover at 91.7%, while Xinjiang Uygur Autonomous Region has the lowest at 14.4. The rate of variation of FVC in the Ningxia Hui Autonomous Region is 0.98%·a^-1, with significant improvement in FVC. (3) A significant spatial variability was observed in the effects of climatic factors and urbanization on FVC. Temperature and precipitation have negative and positive correlations on FVC in northern China, respectively, and urbanization mainly affects the more economically developed provinces. Temperature is the main contribution factor in the Ningxia Hui Autonomous Region, with an average contribution of 84.3%. Precipitation is the main contribution factor in Taiwan Province, with an average contribution of 71.7%. Moreover, urbanization is the main contribution factor in Shanghai, with an average contribution of 26.5%.

To explore the deep water status and root distribution of artificial rainfed planting Caragana korshinskii forest in southern Ningxia of China, the 20-year rainfed strip planting C. korshinskii forest was chosen as the research object, a similar farmland for control was selected, and a 0-1000-cm depth of soil moisture, vertical distribution of root, and correlation were analyzed. Soil moisture and root systems were investigated in the center of the C. korshinskii and farmland. The soil drying method was used to determine the soil moisture content, and the root-drill sampling method was used to investigate the root system. The results showed the following: (1) Deep soil desiccation was determined in 0-1000 cm soil layers for the 20-year C. korshinskii forest. Soil water content for the interband and intra-band of C. korshinskii forest was lower than farmland. Compared with the interband soil water, the intra-band soil moisture content was reduced by 1.46% in the 0-1000 cm soil layer. (2) In the range of 300-1000 cm soil layer, the 20-year artificial C. korshinskii appeared in different water deficiency states and soil desiccation. Soil moisture availability for the interband and intra-band were 0.21 and 0.02 and the soil water supply coefficient were 0.49 and 0.33, respectively. (3) C. korshinskii roots mainly distributed in the 0-80 cm soil layer, accounting for 46.33% and 45.56% of the total root weight for interband and intra-band dried roots, respectively. The root surface area density accounted for 66.58% (interband) and 63.51% (intra-band) of the total root surface area density, and root length density accounted for 59.54% (interband) and 58.45% (intra-band) of the total root length density. This study has positive significance for in-depth understanding of root systems, water content, and sustainable management of artificial vegetation in semiarid loess areas.

Drought and desertification have become a worldwide resource and environmental problem, causing the reduction in eolian sandy soil capability to hold water and fertilizer, lowering the soil quality, resulting in the scarcity of vegetation, and seriously limiting the agricultural and forestry production and crop growth in desertification areas. Therefore, for the healthy growth of soil desertification zones, it is crucial to develop a type of material that can not only preserve water but also improve the physical and chemical properties of soil in this area. Bentonite, a naturally occurring mineral, has excellent water absorption, water storage, and adsorption capabilities due to its distinctive crystal structure. This study selected the sand soil obtained from the Gurbantunggut Desert in Xinjiang of China as the research object and examined the effects of bentonite addition with different mass fractions and combinations (combined bentonite with organic fertilizer, humic acid) on water retention capacity, soil physical, and chemical properties (pH, conductivity, water retention, bulk density, permeability coefficient, and field capacity) improvement and plant growth of eolian sandy soil. Moreover, this study analyzed the effect and mechanism of bentonite on eolian sand soil improvement based on scanning electron microscopy (SEM) observation of eolian sand structure. The result showed the following: (1) The water content of aeolian sandy soil was significantly increased by 19.4%-21.9% (P<0.05) by the addition of 0.5% bentonite (B1), by the addition of 2.0% bentonite (B4) and by the combination of 2.0% bentonite and 1.0% organic fertilizer (BF4) (P<0.05). (2) Bentonite combinations of all treatments significantly improved the physical properties of aeolian sandy soil, and the addition of bentonite (B>0.5%) significantly decreased bulk density by 9.9%-11.1% (P<0.05), increased the total porosity by 11.7%-13.1% (P<0.05), decreased the osmotic coefficient by 56.7%-73.3% (P<0.05), and increased the field water capacity by 15.4%-16.1% (P<0.05). (3) In addition, the addition of bentonite (B>0.5%) significantly increased the pH of aeolian sandy soil (P<0.05), while the other treatments had no significant effect on pH (P>0.05). The combination of bentonite and humic acid (BA) and the combination of bentonite, humic acid, and organic fertilizer (BFA) significantly increased the conductivity of aeolian sandy soil (P<0.05). (4) The study on plant growth found that adding B and BF improved the germination rate and biomass of Sorghum sudanense. (5) The results of correlation analysis and redundancy analysis showed that bentonite and organic fertilizer played a significant role in biological growth by improving soil physical and chemical properties. (6) Moreover, the results of the SEM showed that bentonite played a role in water absorption expansion and bonding and binds sand grains together. In conclusion, adding bentonite and organic fertilizer to eolian sandy soil can improve the physical properties and improve the soil’s water retention capacity, which is conducive to the improvement of eolian sandy soil and plant recovery in desertification areas.

Pollen records have been widely employed as a significant proxy to reconstruct paleovegetation distribution as well as its spatiotemporal evolution worldwide. Due to the marked influence of many factors, such as differences among pollen productivity, transportation mode, biological characteristics of plants, climatic condition, and sedimentary environment, the associated relationship between pollen and vegetation has been shown to be fairly complicated in different regions. As a result, the information regarding the plant community reflected by the pollen assemblage is significantly different from that of the real situation. Therefore, it is clearly necessary to conduct a large number of studies to deeply explore the quantitative relationship between pollen assemblages and vegetation community. Based on vegetation survey and surface pollen data collected from 46 surface soil samples in the western Junggar Basin of Xinjiang, China, this study used various approaches to quantitatively estimate several pollen-based indices representative of vegetation, including the association index (A), underrepresentation index (U), overrepresentation index (O), representative coefficient (R), mean pollen percentage in the absence of vegetation (M_a), mean pollen percentage in the presence of vegetation (M_p), and coefficient of similarity between pollen assemblage and plant community (CC) for 19 major pollen types in our study region. The results of the analysis show that all the 19 pollen taxa can be divided into three groups. The first group includes Chenopodiaceae with A value of 1.0, O and U values of 0, and R value of 12.5. The value of M_p is much higher than M_a, therefore indicating that Chenopodiaceae pollen is over-representative for the corresponding vegetation. The second group consists of Artemisia and Ephedra, in which A values range from 0.1 to 0.4, O values vary from 0.6 to 0.9, U values are 0, and R values are greater than 18.5. Moreover, the differences in values between M_p and M_a range from 3.6% to 12.1%, implying a strong association between pollen distribution and their parent plants. The third group includes various pollen types of Nitraria, Liliaceae, Labiatae, Fabaceae, Poaceae, Asteraceae, Polygonaceae, Ranunculaceae, Rosaceae, Umbelliferae, Brassicaceae, Caryophyllaceae, Convolvulaceae, Urticaceae, and Boraginaceae, with A values of less than 0.6, U values between 0.2 and 0.9, O values between 0.1 and 1.0, and R values of less than 4.0. Moreover, the differences in values between M_p and M_a less than 4.9%, therefore showing that these pollen types are under-representative for the related vegetation. These results provide a basis to improve the reliability of pollen-based vegetation reconstruction.

Soil wind erosion is the primary stage and important component of desertification in arid and semiarid regions. Evaluating its possibility and potential risk for wind erosion control at the regional level is of considerable importance. In recent years, remote sensing and geographic information technology are often combined with mathematical methods to build a risk assessment model. However, the current risk models of wind erosion are still lacking in mechanical parameters. This study was conducted in the Hetao Plain of China, which is a typical region of wind erosion and desertification. Soil hardness and shear strength were measured in the field to determine the difference in soil erodibility among different land use types. Wind erosion risk was evaluated using fuzzy logic, analytic hierarchy process, and the weighted linear combination method based on the data of climate conditions, soil physical factors, topography, and vegetation characteristics. Then, the spatial distribution characteristics and causes of different risk areas were analyzed. The following results are presented. (1) The shear strength of land use types shows an increased tendency in the order of sandy land, grassland, woodland, cultivated land, and saline land, which agreed well with the soil hardness. The soil hardness and shear strength of sandy land are 2.05 kg·cm^-2 and 10.00 kPa, respectively, which are significantly lower than those of other land use types, indicating that the soil erodibility of sandy land is extremely high. (2) The wind erosion risk varied in spatial distribution. Wind erosion hazard is high in the west and south and low in the eastern and middle regions. Moreover, 27.51% of the total areas are found to be at a high risk of erosion. Thus, soil erodibility and vegetation coverage are essential factors affecting soil wind erosion. (3) The severe risk region is mainly distributed in most of Dengkou County, the edge of the south bank of the Yellow River, Togtoh County, and the east of Wuliangsuhai in the Urad Front Banner. Therefore, this area should be the focus of wind erosion control. The current research demonstrates strong universality and compensates for the shortcomings of existing wind erosion models, which can provide a theoretical basis for regional-scale wind erosion assessment models.

Uzbekistan is one of the important countries along the Silk Road Economic Belt and is a country with severe biodiversity loss in Central Asia. Because there has been insufficient research on biodiversity conservation in Uzbekistan by both domestic and foreign scholars, this study analyzed the challenges to biodiversity conservation, conservation status, and management system. The problems of biodiversity conservation were summarized, and suggestions were proposed for cooperation between China and Uzbekistan on biodiversity conservation: (1) coordinating economic development and biodiversity conservation as a goal; (2) ecological restoration in the key areas as the opportunity; (3) promoting biodiversity conservation through scientific research cooperation on key species; (4) reversing the use of natural resources by developing ecotourism.

In this study, the vegetation health index was selected as a measure of agricultural drought, and the Mann-Kendall test and wavelet analysis were used to investigate the drought level in the Yellow River-Huangshui River Valley (YHV) of China from 2000 to 2020. The drought degree of the crop growing season (March to November) was investigated yearly and seasonally (spring: March-May, summer: June-August, and autumn: September-November). The results are as follows: (1) Agricultural drought areas in YHV were primarily concentrated in the middle reaches of the Datong River, the main stream area of the Huangshui River, and the Yellow River Valley. (2) The agricultural drought area in YHV exhibited obvious regional differentiation and increased gradually from north to south. (3) The agricultural drought area of YHV has a trend of decreasing with periodic fluctuation in the interannual scale. 2007—2008 is the time point of abrupt changes in the agricultural drought area of YHV, and then, the agricultural drought area of YHV began to decrease rapidly. Spring is the season most severely affected by agricultural drought in YHV. This study is critical for understanding the spatial distribution and variation trend of agricultural drought in YHV and promoting the healthy development of agriculture in Qinghai Province.

On the geographic spatial scale, climate factors (such as environmental energy and precipitation) are the main driving factors of plant species diversity. However, it remains unclear whether climatic factors can explain the plant diversity in wetlands. This study discusses the influence of environmental factors, especially the effect of water and heat conditions on the distribution of wetland species. Specifically, it includes four categories and seven indicators, including spatial factors (longitude and latitude), terrain factors (altitude), water factors (annual average precipitation and evaporation), and heat factors (annual average air temperature and sunshine hours). The research objects involve 26 wetland parks in three secondary river basins in Xinjiang, China. The structural equation model is used to explore the relative importance of each indicator on wetland plant richness and their interaction. In addition, Moran’s I index is used to analyze the spatial correlation of the residuals of each variable to evaluate the impact of spatial correlation. The results show that (1) the structural equation model explains 41.8% of the variation in plant species richness. The total effect of annual average precipitation on species richness is the highest, which is 0.47, followed by the annual average sunshine hours, which is -0.42. Among them, the annual average precipitation has a positive effect, whereas the annual average sunshine hours have a negative effect. The effects of other indices on species richness are insignificant. (2) The influence of annual average precipitation on plant richness is primarily a direct effect, which is 0.39, accounting for 92.86% of the total effect. The influence of annual average sunshine hours on plant richness is primarily an indirect effect, which is -0.23, accounting for 54.76% of the total effect. (3) Spatial correlation analysis shows that there is no spatial correlation between the residuals of annual average precipitation and sunshine hours on different spatial scales, and the Moran’s I index fluctuates within the range of -0.15 to 0.10, which could be considered reliable prediction indices. (4) The direct effects of spatial factors such as longitude and latitude on plant richness are insignificant, whereas the indirect effects are significant. Longitude significantly affects the annual average precipitation, and latitude significantly affects the annual average sunshine hours and temperature, indicating that spatial factors indirectly affect the species richness by affecting the annual average precipitation and sunshine hours. In conclusion, the plant richness of the wetlands in Xinjiang is primarily driven by water and heat conditions. The role of heat depends on the water conditions. In future wetland plant diversity protection studies, the assessment and response measures of the impact of climate change on plant diversity should be strengthened.