Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China

Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.

Effects of Artificial Vegetation Restoration on Soil Physicochemical Properties in Southern Edge of Mu Us Sandy Land

[Objective] This study aimed to investigate the artificial vegetations on soil physicochemical properties of sandy land. [Method] The soil physicochemical proper- ties in five representative lands respectively covered by Artemisia ordosica, Salix cheilophila, Hedysarum scoparium, Populus simonii and Amorpha fruticosa, all of which were planted artificially at the same year were measured in the present study, using a bare soil as the control. [Result] Artificial vegetation improved the soil physicochemical properties by different extents in the lands covered by different plants. The soil physicochemical properties such as bulk density under A. Fruticosa and H. Scoparium were improved greatly. The frequency distribution of soil particle size under artificial vegetations exhibited a bimodal curve. The average soil particle size under A. fruticosa was the smallest, and the soil was very poorly sorted. The soil nutrients in the sandy land were not significantly improved by artificial vegeta- tion. [Conclusion] Artificial vegetation has a certain impact on soil properties in sandy land, as it greatly improves the soil physical properties but not the chemical properties.

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

Principal Chemical Properties of Artificial Soil Composed of Fly Ash and Furfural Residue

To solve soil shortage in reclaiming subsided land of coal mines, the principal chemical properties of artificial soil formed by mixing organic furfural residue and inorganic fly ash were examined. The results indicated that the artificial soil was suitable for agriculture use after irrigation and desalination, the available nutrients in the artificial soil could satisfy the growth demand of plants, and the pH tended to the neutrality.

Prediction Modeling and Mapping of Soil Carbon Content Using Artificial Neural Network, Hyperspectral Satellite Data and Field Spectroscopy

Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.

Soil restoration research advances of artificial sand-binding vegetation ecosystem in the Tengger Desert, Northern China

Soil plays an important role in desert ecosystem, and is vital in constructing a steady desert ecosystem. The management and restoration of desertified land have been the focus of much discussion. The soil in Shapotou desert region has developed remarkably since artificial sand-binding vegetation established in 1946. The longer the period of dune stabilization, the greater the thickness of microbiotic crusts and subsoil. Meanwhile, proportion of silt and clay increased significantly, and soil bulk density declinced. The content of soil organic matter, N, P, and K similarly increased. Therefore, soil has developed from aeolian sand soil to Calcic-Orthic aridisols. This paper discusses the effects brought about by dust, microbiotic soil crust and soil microbes on soil-forming process. Then, we analyzed the relation between soil formation and sand-binding vegetation evolution, in order to provide a baseline for both research on desert ecosystem recovery and ecological environment governance in arid and semi-arid areas.

New artificial boundary condition for saturated soil foundations

Anew artificial boundary model based on multi-directional transmitting and viscous-spring artificial boundary theories is proposed to absorb stress waves in a saturated soil foundation in dynamic analysis. Since shear waves （S-waves） are the same in a saturated soil foundation and a single-phase medium foundation, a tangential visco-elastic boundary condition for a single-phase medium foundation can also be used for saturated soil foundations. Thus, the purpose of the artificial boundary proposed in this paper is primarily to absorb two types of P-waves in a saturated soil foundation. The main idea is that the stress of the P-waves in the saturated soil foundation is decomposed into two types. The first type of stress, δra＇ is absorbed by the first artificial boundary. The second type of stress, δrb, is balanced by the stress generated by the second artificial boundary. Ultimately, both types of P-waves （fast-P-waves and slow-P-waves） are absorbed by the artificial boundary model proposed in this paper. In particular, note that the fast-P-waves and slow-P-waves are absorbed at the position of the first boundary. Thus, the artificial boundary model proposed herein can simultaneously absorb P-fast waves, P-slow waves and shear waves. Finally, a numerical example is given to examine the proposed artificial boundary model, and the results show that it is very accurate.

Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network

The effect of soil nutrient content on fruit yield and fruit quality is very important.To explore the effect of soil nutrients on apple quality we investigated 200 fruit samples from 40 orchards in Feng County,Jiangsu Province.Soil mineral elements and fruit quality were measured.The effect of soil nutrient content on fruit quality was analyzed by artificial neural network(ANN)model.The results showed that the prediction accuracy was highest(R2=0.851,0.847,0.885,0.678 and 0.746)in mass per fruit(MPF),hardness(HB),soluble solids concentrations(SSC),titratable acid concentration(TA)and solid-acid ratio(SSC/TA),respectively.The sensitivity analysis of the prediction model showed that soil available P,K,Ca and Mg contents had the greatest impact on the quality of apple fruit.Response surface method(RSM)was performed to determine the optimum range of the available P,K,Ca,and Mg contents in orchards In Feng County,which were 10∼20 mg⋅kg^(−1),170∼200 mg⋅kg^(−1),1000∼1500 mg⋅kg^(−1),and 80∼200 mg⋅kg^(−1),respectively.The research also concluded that improving the content of available P and available Ca in orchard soil was crucial to improve apple fruit quality in Feng County,Jiangsu Province.

Assessment of strength development in cement-admixed artificial organic soil with GX07

To explore the stabilization effect of stabilizing agent GX07 on treating organic soil and the influence of organic matter on the strength development of stabilized soil,artificial organic soil with various organic matter content was obtained by adding different amounts of fulvic acid into non-organic clay,and then liquid-plastic limit tests were carried out on the artificial organic soil.Meanwhile,unconfined compressive strength(UCS) tests were performed on cement-only soil and composite stabilized soil,respectively.The test results indicate that the plastic limit of soil samples increases linearly,and the liquid limit increases exponentially as the organic matter content increases.The strength of stabilized soil is well correlated with the organic matter content,cement content,stabilizing agent content and curing time.When the organic matter content is 6%,as the cement content varies in the range of 10%-20%,the strength of cement-only soil increases from 88.5 to 280.8 kPa.Once 12.6% GX07 is added into the mix,the strength of stabilized soil is 4.93 times compared with that of cement-only soil.GX07 can obviously improve the strength of cemented-soil and has a good economic applicability.A strength model is proposed to predict strength development.

Seismic wave input method for three-dimensional soil-structure dynamic interaction analysis based on the substructure of artificial boundaries

The method of inputting the seismic wave determines the accuracy of the simulation of soil-structure dynamic interaction. The wave method is a commonly used approach for seismic wave input, which converts the incident wave into equivalent loads on the cutoff boundaries. The wave method has high precision, but the implementation is complicated, especially for three-dimensional models. By deducing another form of equivalent input seismic loads in the fi nite element model, a new seismic wave input method is proposed. In the new method, by imposing the displacements of the free wave fi eld on the nodes of the substructure composed of elements that contain artifi cial boundaries, the equivalent input seismic loads are obtained through dynamic analysis of the substructure. Subsequently, the equivalent input seismic loads are imposed on the artifi cial boundary nodes to complete the seismic wave input and perform seismic analysis of the soil-structure dynamic interaction model. Compared with the wave method, the new method is simplifi ed by avoiding the complex processes of calculating the equivalent input seismic loads. The validity of the new method is verifi ed by the dynamic analysis numerical examples of the homogeneous and layered half space under vertical and oblique incident seismic waves.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

A field study of advanced municipal wastewater treatment technology for artificial groundwater recharge

Field studies were conducted to investigate the advanced treatment of the municipal secondary effluent and a subsequent artificial groundwater recharge at Gaobeidian Wastewater Treatment Plant, Beijing. To improve the secondary effluent quality, the combined process of powdered activated carbon adsorption, flocculation and rapid sand filtration was applied, which could remove about 400 dissolved organic carbon （DOC） and 70% adsorbable organic halogens. The results of liquid size exclusion chromatography indicate that in the adsorption unit the removed organic fraction was mainly low molecular weight compounds. The fractions removed by the flocculation unit were polysaccharides and high molecular weight compounds. The retention of water in summer in the open recharge basins resulted in a growth of algae. Consequently, DOC increased in the polysaccharide and high molecular weight humic substances fraction. The majority of the DOC removal during soil passage took place in the unsaturated area. A limited reduction of DOC was observed in the aquifer zone.

Research Advance on Carbon Storage of Artificial Grassland in China

As an essential part of the grassland ecological system,study on the carbon storage has great significances to the carbon reduction in grassland ecological system.The carbon storage in biomass,carbon storage in soil and soil respiration are summarized in this paper to provide scientific reference for the evaluation of carbon storage in artificial grassland.

Variation in water source of sand-binding vegetation across a chronosequence of artificial desert revegetation in Northwest China

Water is the most important limiting factor in arid areas,and thus water resource management is critical for the health of dryland ecosystems.However,global climate change and anthropogenic activity make water resource management more difficult,and this situation may be particularly crucial for dryland restoration,because of variation in water uptake patterns associated with artificial revegetation of different ages and vegetation type.However,there is lacking longterm restorations that are suitable for studying this issue.In Shapotou area,Northwest China,artificial revegetation areas were planted several times beginning in 1956,and now form a chronosequence of sand-binding landscapes that are ideal for studying variability in water uptake source by plants over succession.The stable isotopesδ18O andδ2H were employed to investigate the water uptake patterns of the typical revegetation shrubs Artemisia ordosica and Caragana korshinskii,which were planted in different years.We compared the stable isotope ratios of shrub stem water to groundwater,precipitation,and soil water pools at five layers(5−10,10−40,40−80,80−150,and 150−300 cm).The results indicate that Artemisia ordosica derived the majority of their water from the 20−150 cm soil layer,whereas Caragana korshinskii obtained water from the 40−150 cm soil layer.The main water sources of Artemisia ordosica and C.korshinskii plants changed over time,from deeper about 150 cm depth to shallow 20 cm soil layer.This study can provide insights into water uptake patterns of major desert vegetation and thus water management of artificial ecosystems,at least in Northwest China.

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model’s accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

Shear resistance characteristics and influencing factors of root-soil composite on an alpine metal mine dump slope with different recovery periods

Artificial vegetation restoration is the main measure for vegetation restoration and soil and water conservation in alpine mine dumps on the Qinghai-Tibet Plateau,China.However,there are few reports on the dynamic changes and the influencing factors of the soil reinforcement effect of plant species after artificial vegetation restoration under different recovery periods.We selected dump areas of the Delni Copper Mine in Qinghai Province,China to study the relationship between the shear strength and the peak displacement of the root-soil composite on the slope during the recovery period,and the influence of the root traits and soil physical properties on the shear resistance characteristics of the root-soil composite via in situ direct shear tests.The results indicate that the shear strength and peak displacement of the rooted soil initially decreased and then increased with the increase of the recovery period.The shear strength of the rooted soil and the recovery period exhibited a quadratic function relationship.There is no significant function relationship between the peak displacement and the recovery period.Significant positive correlations(P<0.05)exists between the shear strength of the root-soil composite and the root biomass density,root volume density,and root area ratio,and they show significant linear correlations(P<0.05).There are no significant correlations(P>0.05)between the shear strength of the root-soil composite and the root length density,and the root volume ratio of the coarse roots to the fine roots.A significant negative linear correlation(P<0.05)exists between the peak displacement of the rooted soil and the coarse-grain content,but no significant correlations(P>0.05)with the root traits,other soil physical property indices(the moisture content and dry density of the soil),and slope gradient.The coarse-grain content is the main factor controlling the peak displacement of the rooted soil.

Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia

Since its arrival in late November 2022,ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned,conducted,and published using a generative artificial intelligence approach.ChatGPT-4 was released four months later and became more popular in November 2023.However,there is little study about the perception of scientists of these chatbots,especially in soil science.This article presents the new findings of a brief research investigating soil scientists’responses and perceptions towards chatbots in Indonesia.This artificial intelligence application facilitates conversation-based interactions in text format.The study evaluated ten ChatGPT answers to fundamental questions in soil science,which has developed into a normal science with a mutually agreed-upon paradigm.The evaluation was carried out by seven soil scientists recognized for their expertise in Indonesia,using a scale of 1-100.In addition,a questionnaire was distributed to soil scientists at the National Research and Innovation Agency of the Republic of Indonesia(BRIN),universities,and Indonesian Soil Science Society(HITI)members to gauge their perception of ChatGPT’s presence in the research field.The study results indicate that the scores of ChatGPT answers range from 82.99 to 92.24.ChatGPT-4 is better than both the paid and free versions of ChatGPT-3.5.There is no significant difference between the English and Indonesian versions of ChatGPT-4.0.However,the perception of general soil scientists about the level of trust is only 55%.Furthermore,80%of soil scientists believe that chatbots can only be used as digital tools to assist in soil science research and cannot be used without the involvement of soil scientists.

Spatial interpolation method based on integrated RBF neural networks for estimating heavy metals in soil of a mountain region

A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.

Bioactive Characteristics of Soil Microorganisms in Different-aged Orange (Citrus reticulate) Plantations

[Objective] The paper was to study the bioactive characteristics of soil microorganisms in different-aged orange plantations. [Method] Taking 010 cm deep soil in 3 orange plantations with different planting years in suburb of Yichang City as the test object, the variation rule of total organic carbon, total nitrogen, soil pH value, microbial biomass carbon, microbial biomass nitrogen, number of 3 main types of soil microbial flora, basal respiration, microbial entropy and metabolic entropy in differentaged orange plantations was studied. [Result] With the increase of planting years, the soil acidification of different-aged orange plantations was aggravating; total organic carbon and total nitrogen content increased first and then decreased; the total number of soil microorganism showed a downtrend, of which the number of bacteria decreased significantly, the number of actinomycetes had small changes, the number of fungi increased significantly, and the ratio of bacteria and fungi in soil （B/F） showed a decreasing trend. Soil microbial biomass carbon was fluctuated within a small range, whereas soil microbial biomass nitrogen decreased significantly; soil microbial entropy decreased significantly, and metabolic entropy showed an increasing trend. This indicated that the decrease of soil pH value affected the changes of soil microbial flora, microbial activity, microbial biomass carbon and nitrogen and soil major nutrients, and further affected the normal exertion of soil function. [Conclusion] The study explores soil nutrient characteristics and changes of microbial flora in test area, which will provide scientific basis for further study on orchard soil and orchard management.

林下植被对桢楠幼林生长和土壤质量的影响

探究林下植被去除对桢楠幼林生长和土壤质量的影响,明确驱动桢楠生长的潜在立地因子,为桢楠幼林林下植被管理提供科学依据。以重庆市永川国有林场8年生桢楠人工幼林为研究对象,采用野外控制试验,设置林下植被去除和自然保留两种处理,研究林下植被去除对桢楠生长、光合生理和土壤理化性质的影响,使用曼特尔检验分析土壤特性和桢楠生长关联性,应用随机森林模型确定影响桢楠生长的关键土壤因子,利用土壤质量指数量化土壤环境因子和生长的关系。结果表明:林下植被去除后,桢楠树高、地径、冠幅和叶面积显著增长,综合生长指数提高了169.23%,光合参数(净光合速率、气孔导度、蒸腾速率)和光合色素(叶绿素、类胡萝卜素)含量显著提升,土壤特性除有效磷、速效钾外均得到显著改善,土壤质量指数提高了107.10%。桢楠生长主要受土壤氮循环及其可利用性调控,脲酶和碱解氮是驱动桢楠生长的主要因子。林下植被去除能够通过提升土壤质量促进桢楠生长,建议在桢楠人工幼林管理时及时去除林下植被,以促进桢楠人工林可持续经营。