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.

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.

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.

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.

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.

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.

Cadmium Release in Contaminated Soils due to Organic Acids

There is limited information on the release behavior of heavy metals fromnatural soils by organic acids. Thus, cadmium release, due to two organic acids (tartrate andcitrate) that are common in the rhizosphere, from soils polluted by metal smeltersor tailings andsoils artificially contaminated by adding Cd were analyzed. The presence of tartrate or citrate at alow concentration (<= 6 mmol L^(-1) for tartrate and <= 0.5 mmol L^(-1) for citrate) inhibited Cdrelease, whereas the presence of organic acids in high concentrations (>= 2 mmol L^(-1) for citrateand >= 15 mmol L^(-1) for tartrate) apparently promoted Cd release. Under the same conditions, theCd release in naturally polluted soils was less than that of artificially contaminatedsoils.Additionally, as the initial pH rose from 2 to 8 in the presence of citrate, a sequentialvalley and then peak appeared in the Cd release curve, while in the presence of tartrate the Cdrelease steadily decreased. In addition, Cd release was clearly enhanced as the electrolyteconcentration of KNO_3 or KC1 increased in the presence of 2 mmol L^(-1) tartrate. Moreover, ahigher desorption of Cd was shown with the KCl electrolyte compared to KNO_3 for the sameconcentration levels. This implied that the bioavailability of heavy metals could be promoted withthe addition of suitable types and concentrations of organic acids as well as reasonable fieldconditions.

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.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

This study has provided an approach to classify soil using machine learning.Multiclass elements of stand-alone machine learning algorithms(i.e.logistic regression(LR)and artificial neural network(ANN)),decision tree ensembles(i.e.decision forest(DF)and decision jungle(DJ)),and meta-ensemble models(i.e.stacking ensemble(SE)and voting ensemble(VE))were used to classify soils based on their intrinsic physico-chemical properties.Also,the multiclass prediction was carried out across multiple cross-validation(CV)methods,i.e.train validation split(TVS),k-fold cross-validation(KFCV),and Monte Carlo cross-validation(MCCV).Results indicated that the soils’clay fraction(CF)had the most influence on the multiclass prediction of natural soils’plasticity while specific surface and carbonate content(CC)possessed the least within the nature of the dataset used in this study.Stand-alone machine learning models(LR and ANN)produced relatively less accurate predictive performance(accuracy of 0.45,average precision of 0.5,and average recall of 0.44)compared to tree-based models(accuracy of 0.68,average precision of 0.71,and recall rate of 0.68),while the meta-ensembles(SE and VE)outperformed(accuracy of 0.75,average precision of 0.74,and average recall rate of 0.72)all the models utilised for multiclass classification.Sensitivity analysis of the meta-ensembles proved their capacities to discriminate between soil classes across the methods of CV considered.Machine learning training and validation using MCCV and KFCV methods enabled better prediction while also ensuring that the dataset was not overfitted by the machine learning models.Further confirmation of this phenomenon was depicted by the continuous rise of the cumulative lift curve(LC)of the best performing models when using the MCCV technique.Overall,this study demonstrated that soil’s physico-chemical properties do have a direct influence on plastic behaviour and,therefore,can be relied upon to classify soils.

Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.

Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

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.