Search Results (13)

Wheat, as a staple crop, holds paramount importance in global food security, and its grain color significantly influences market value, nutritional quality, and consumer preferences. This review critically summarizes recent advances in the genetic basis of wheat grain color, encompassing the biochemical pathways responsible for pigment production and the implications of these traits on nutritional quality. Additionally, we explore the influence of environmental factors on grain color and its prospective role in breeding programs aimed at enhancing the nutritional profile of wheat. Recent findings highlight the growing interest in colored wheat due to its health benefits, further driven by the rise in natural food trends. The review concludes with a discussion on future research directions and the importance of integrated breeding strategies. Full article

Against the backdrop of the rural revitalization strategy after poverty alleviation in China, this paper takes the Dianfanghe community in Zhaochuan Town, Shangnan County, Shangluo City, Shaanxi Province as the research object and explores the role of universities in constructing a mechanism to prevent poverty reversion. A mixed-methods approach was adopted for 646 households, combining field surveys, household surveys, and spatial analysis to identify key vulnerabilities. The study found that there are five dimensions of the risk of poverty reversion in the community after poverty alleviation: unbalanced human structure, fragile physical capital, constraints of the natural environment, single source of income, and weak social capital. To address these risks, a three-party collaborative monitoring mechanism involving the government, universities, and society was proposed, which effectively enhanced the community’s risk resistance ability. This model provides a replicable paradigm for universities’ participation in consolidating poverty alleviation achievements in the new era and has important reference value for similar mountainous communities. Full article

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems. Full article

Scientifically evaluating net carbon dioxide (CO₂) emissions is the pivotal strategy for mitigating global climate change and fostering sustainable urban development. Shaanxi Province is situated in central China, and boasts robust energy resources in the north and a significant carbon-sink zone in the southern Qinling Mountains. Therefore, uncovering the spatial distributions of net CO₂ emissions and identifying its influencing factors across cities in Shaanxi Province would furnish a crucial theoretical foundation for advancing low-carbon development strategies. In this research, the net CO₂ emissions of cities in Shaanxi Province from 2005 to 2020 are calculated using the carbon-emission-factor calculation model, then the Geodetector is utilized to evaluate the single-factor explanatory power and two-factor interactions among the fourteen various influencing variables, and then the spatial econometric model is employed to analyze the spatial spillover effects of these key factors. The results show the following: (1) The net CO₂ emissions present significant regional differences among the ten cities of Shaanxi Province, notably Xi’an City, Yulin City, and Weinan City, which have recorded remarkable contributions with the respective totals reaching 72.2593 million tons, 76.3031 million tons, and 58.1646 million tons. (2) Regarding temporal trend changes, the aggregate net CO₂ emissions across whole province underwent a marked expansion from 2005 to 2019. Yulin City and Shangluo City exhibit remarkable surges, with respective average annual growth rates soaring at 7.38% and 7.39%. (3) From the perspective of influencing factors, GDP exhibits the most pronounced correlation spanning the entire province. Meanwhile, foreign investment emerges as a significant contributor specifically in Xi’an and Yulin City. Moreover, interaction detection reveals most factor combinations exhibit bi-enhancement, while a few exhibits intricate and non-linear enhancement. (4) The SDM regression and fixed-effect analysis reveal that city GDP had a positive spillover effect on neighboring cities’ net CO₂ emission, while investment in scientific research and technology services, along with per capita construction land, exhibit notable negative spillovers, suggesting potential emission reduction benefits across cities. Full article

In recent years, machine learning methods have garnered significant attention in the field of crop recognition, playing a crucial role in obtaining spatial distribution information and understanding dynamic changes in planting areas. However, research in smaller plots within mountainous regions remains relatively limited. This study focuses on Shangzhou District in Shangluo City, Shaanxi Province, utilizing a dataset of high-resolution remote sensing images (GF-1, ZY1-02D, ZY-3) collected over seven months in 2021 to calculate the normalized difference vegetation index (NDVI) and construct a time series. By integrating field survey results with time series images and Google Earth for visual interpretation, the NDVI time series curve for maize was analyzed. The Random Forest (RF) classification algorithm was employed for maize recognition, and comparative analyses of classification accuracy were conducted using Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Artificial Neural Network (ANN). The results demonstrate that the random forest algorithm achieved the highest accuracy, with an overall accuracy of 94.88% and a Kappa coefficient of 0.94, both surpassing those of the other classification methods and yielding satisfactory overall results. This study confirms the feasibility of using time series high-resolution remote sensing images for precise crop extraction in the southern mountainous regions of China, providing valuable scientific support for optimizing land resource use and enhancing agricultural productivity. Full article

[Objective] This study focused on the primary tea-producing regions of Shangluo City (ranging from 108°34′20″ E to 111°1′25″ E and 33°2′30″ N to 34°24′40″ N), which include Shangnan County, Zhen’an County, Zhashui County, Danfeng County, and Shanyang County. The aim was to explore the characteristics and influencing factors of soil nutrient content variation across different tea gardens in the area. The study involved an analysis of various soil nutrient indicators and an investigation of their correlations to assess the nutrient status of tea gardens in Shangluo City. [Method] A total of 228 soil samples from these tea gardens were quantitatively analyzed for pH, soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (AK), as well as clay, silt, and sand content. Additionally, the soil texture was qualitatively analyzed. Statistical methods including analysis of variance (ANOVA), correlation analysis, principal component analysis (PCA), and regression analysis were performed using SPSS software to examine the relationships between soil nutrients and texture in relation to altitude, latitude, and fertility status. [Results] The results indicated that the pH of tea garden soils in Shangluo City was relatively stable, ranging from 4.3 to 7.6, with the mean of 5.9 and a coefficient of variation of 11.0%. The soil organic matter (SOM) content varied from 7.491 to 81.783 g/kg, exhibiting a moderate variability with a coefficient of variation of 38.75%. The mean values for total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), available potassium (AK), clay, silt, and sand were 1.53 g/kg, 213 mg/kg, 0.85 g/kg, 49.1 mg/kg, 5.5 g/kg, 110 mg/kg, 3.99, 44.89, and 51.11, respectively. AN and AP displayed higher coefficients of variation at 57% and 120.1%, respectively. Significant differences in pH, SOM, TN, TP, TK, silt, and sand were observed at varying elevations, while TN, TP, TK, clay, silt, and sand varied significantly across different latitudes. Principal component analysis (PCA) results revealed that altitude had four principal components with eigenvalues greater than 1, accounting for 71.366% of the total variance, whereas latitude exhibited five principal components with eigenvalues exceeding 1, explaining 76.304% of the total variance. Regression analysis indicated that altitude exerted a stronger influence on soil indicators, as demonstrated by a well-fitting model (Model 4), where the coefficients of principal components 1, 3, and 4 were positive, while that of principal component 2 was negative. In contrast, latitude influenced soil indicators most effectively in Model 3, where the coefficient of principal component 5 was positive, and the coefficients of principal components 1 and 4 were negative. [Conclusions] The variation in soil nutrients and pH in the tea gardens of Shangluo City is closely associated with altitude and latitude. Notably, there is no discernible trend of pH acidification. Therefore, tea garden management should prioritize the rational application of soil nutrients at varying altitudes and focus on enhancing soil texture at different latitudes to adapt to the diverse soil characteristics under these conditions, thereby promoting sustainable development in tea gardens. Full article

The sustainable development of rural tourism requires not only active participation from the government and enterprises but is also closely tied to the attitudes of local residents. This study, grounded in the theories of relative deprivation and social comparison, focuses on the residents living near the Jinshi Gorge Scenic Area in Shangluo City. We constructed a structural equation model to explore how residents’ perceptions of the social environment in rural tourism influence their sense of relative deprivation, enhance their happiness, and ultimately promote the sustainable development of rural tourism. The study’s findings reveal the following: (1) that demographic characteristics, including age, education level, and annual income, significantly influence residents’ perceptions of their social environment, particularly their sense of group identity, social support, and feelings of inequality. (2) Levels of relative deprivation vary significantly across different demographic groups. (3) There is a strong positive correlation between individual cognitive relative deprivation and individual emotional relative deprivation. Similarly, group cognitive relative deprivation positively predicts group emotional relative deprivation. (4) Experiences of discrimination, feelings of inequality, and strength of group identity emerge as significant predictors of both individual and group-level cognitive and emotional relative deprivation. (5) Social support has a significant negative effect on individual cognition, individual emotions, group cognition, and group emotional relative deprivation. Full article

The evaluation of rural environmental quality plays an important role in improving farmers’ quality of life and in realizing a livable, workable, and beautiful countryside. Taking Shangluo City in Shaanxi Province as the study area, 16 indicators across five systems were selected to evaluate the rural environmental quality. The following methods were used in the evaluation: the hierarchical analysis method, the expert scoring method, and the fuzzy comprehensive evaluation method. The results show the following: (1) The rural environmental quality assessment value of Shangluo City is adequate. (2) In the system layer, the toilet renovation and infrastructure scores were high; however, the household sewage treatment and the construction and management mechanisms need to be improved. (3) According to an IPA quadrant diagram, the importance and satisfaction values for each index varied significantly. The management of black, foul-smelling water bodies and action on environmental remediation emerged as key to improving rural environmental quality. This study can provide a reference for the comprehensive improvement of rural environmental quality in other areas of Shaanxi Province. Full article

In recent years, “Jin Gao Yu” that has been traded as a kind of jade has appeared in areas of the Luonan and Shangnan counties, Shangluo City, Shaanxi Province, attracting the attention of scholars and consumers for its delicate texture and warm color. In this study, infrared spectroscopy, Raman spectroscopy, X-ray diffraction analysis, and electron microprobe analysis were used to conduct a systematic gemmological test and an analysis of “Jin Gao Yu”. The results show that “Jin Gao Yu” is a compact mineral aggregate dominated by dolomite, which contains quartz mineral inclusions. The color of “Jin Gao Yu” is grayish-white to earthy-yellow, the refractive index is about 1.54, and the relative density is about 2.86. Its crystal structure is basically the same as that of dolomite, both of which are trigonal systems with granular crystalloblastic textures. It has good crystallinity. The recrystallization phenomenon can be seen under a polarizing microscope. This study determined the species of “Jin Gao Yu”, improved its gemological basic data, provided a theoretical basis for the identification of “Jin Gao Yu” in the future, and, also, provided a new direction for the use of dolomite. Full article

Sustainable livelihoods are those that are able to cope with and recover from stress and shocks, and that maintain or strengthen livelihood capacity and livelihood capital, without damaging the foundations of the natural environment. In this paper, the IPHACT framework was constructed by improving the classic IPAT equation, and the key factors affecting production output and livelihood sustainability, as well as the factor differences among different livelihood strategy groups, were analyzed. We took 24 counties and districts of Ankang, Shangluo and Hanzhong in the Qin-Ba Mountain area of southern Shaanxi Province as examples, using a survey of farmers’ livelihood status and livelihood capital accounting. The results show that the amplification effect of population size on the environmental impact of livelihood output is widespread and generally significant. Both livelihood sustainability and livelihood benefit passed the significance test in the multi-model analysis, and the negative effect of livelihood sustainability proved the negative correlation between the environmental impact of livelihood output and livelihood sustainability, that is, the higher the livelihood output dependent on natural capital, the greater the environmental impact. The livelihood transformation of Hanzhong City is developing in the direction of reducing the environmental impact of livelihood output, and farmers have successfully practiced green livelihood transformation by changing their livelihood strategies. On the road to common prosperity, livelihood demand will inevitably increase. Reducing the dependence on natural capital is the key to effectively enhancing the sustainability of livelihoods. Full article

Element flow has gradually become an important method for studying urban spatial structure. This study examined 11 prefectural cities in the Guanzhong Plain urban agglomeration; constructed a measurement model for information, traffic, migration, and composite networks; and analyzed the spatial structure of the urban network of the urban agglomeration through social network analysis and spatial visualization. The spatial structure of the composite flow network had Xi’an as the center and Xianyang, Baoji, Weinan and Tianshui as important nodes; Yuncheng, Linfen and Qingyang were the secondary nodes, radiating to the surrounding three cities. Element flow connection strength was unbalanced, and only three city pairs were in the first level of the composite flow network. Network density was low-middle, and the network connection was weak. Xi’an was the primary central city of the Guanzhong Plain urban agglomeration with the strongest agglomeration and radiation capabilities; it could communicate with other cities without intermediate cities and was a bridge for other cities. Tongchuan, Pingliang, Shangluo, and Qingyang were at the edge of the urban agglomeration and had weak agglomeration, radiation, and intermediary capabilities. The inner cities of cohesive subgroups were closely related with weak connections between subgroups. The single-polarization of the Guanzhong Plain urban agglomeration was serious, and the single-core spatial structure centered on Xi’an had limited impact on the urban agglomeration. Development of small and medium-sized cities should be strengthened in the future. Full article

As an environment-friendly material, biochar has been used to remove heavy metals from wastewater, and the development of cost-effective biochar has been an emerging trend. However, limited studies consider the competitive adsorption of co-existing metals and the separation efficiency of absorbent and solution after adsorption. In this study, pomelo peel was used to prepare biochar (BC) and magnetic biochar (MBC) at different temperatures. Then, the physicochemical properties of the biochars were characterized and the adsorption characteristics of Cu²⁺, Pb²⁺, and Zn²⁺ on the biochars in single, binary, and ternary metal systems were investigated. The results showed that both pyrolysis temperature and magnetization could affect the adsorption capacity of biochar. The adsorption kinetic and thermodynamic processes could be well described by the pseudo-second-order kinetic model and Langmuir model. The adsorption isotherm types of Pb²⁺ and Zn²⁺ changed in the binary metal condition. The competitive adsorption order of three heavy metal ions in ternary metal adsorption was Pb²⁺ > Cu²⁺ > Zn²⁺. The MBC of 500 °C showed a good adsorption capacity to Pb²⁺ in the co-existing environment, and the maximum adsorption capacity was 48.74 mmol g⁻¹. This study also provided technical support for the utilization of pomelo peel and the engineering application of biochar. Full article

Urban efficiency can effectively measure the management and allocation level of urban factor inputs. Based on the data of 30 prefecture-level cities in Northwest China from 2006 to 2015, urban efficiency is measured by data envelopment analysis (DEA). Then the spatiotemporal evolution rule is identified by Malmquist model. The results illustrate that the overall average urban efficiency of cities in Northwest China each year from 2006 to 2015 was at the low level. Only Jiayuguan, Yulin, Yan’an, and Karamay reached the high average urban efficiency, while Dingxi, Pingliang, Guyuan, Shangluo, Tianshui, Longnan, and Baiyin were at the inefficient level. Most cities in Northwest China were still in the “growing” stage of increasing returns to scale. The scale of urban investment was relatively insufficient, and economies of scale had not yet formed. Cities with decreasing returns to scale were mainly distributed in the capital cities and the central and sub-central cities of Guanzhong-Tianshui Economic Zone with relatively abundant urban resources and capital. Cities with constant returns to scale were mainly distributed in four cities including Yan’an, Yulin, Jiayuguan, and Karamay with high efficiency. The overall comprehensive efficiency, technical efficiency, and scale efficiency of cities in Northwest China were not only low, but also showing a downward trend. The overall progress of urban technology had failed to make up for the shortfall caused by low efficiency, resulting in total factor productivity (TFP) decreasing by 0.5%. Therefore, the cities in Northwest China should continuously improve their technical efficiency and scale efficiency, and ultimately enhance the comprehensive efficiency. Full article