基于马氏距离和Canopy改进K-means的交通聚类算法

在对交通数据的研究中经常会使用到聚类算法,且不同的聚类算法有不同的特性。K-means作为其中的一种聚类算法,具有较高的准确性和实用性,但其准确性易受主观选取K值和确定初始聚类中心的影响。为了优化聚类中心和K值的选取问题,提出MC-Kmeans算法。在所提方法中,首先通过Canopy算法选取K值,然后依据马氏距离的计算准则来确定初始聚类中心,最后将K值和聚类中心的值作为K-means的参数进行聚类。将MC-Kmeans算法应用到某时间段的纽约出租车交通数据中进行实际的验证。结果表明,与K-means算法比较,所提方法准确度更高,与实际交通情况更加相匹配,更能反映区域内的交通热点情况。

基于信息熵和Canopy-K-Means算法的货车驾驶风格识别

为识别货车的激进驾驶行为,保障货车行车安全,提出1种基于信息熵和Canopy-K-Means算法的货车驾驶风格识别方法。首先,从货车自然驾驶数据中提取出604个驾驶片段,根据信息熵理论计算各个驾驶片段的速度熵值、横向加速度熵值和纵向加速度熵值,构成货车驾驶风格表征指标集;其次,针对K-Means算法的聚类数量主观选取、初始聚类中心随机选取的问题,使用Canopy算法改进K-Means算法(Canopy-K-Means算法);最后,分别使用K-Means算法和Canopy-K-Means算法对货车驾驶风格进行识别。研究结果显示,Canopy-K-Means算法的轮廓系数和Calinski-Harabasz指数均大于K-Means算法,表现出更优的聚类性能。根据Canopy-K-Means算法,可将货车驾驶风格分为沉稳型、常规型和激进型3类,其中激进型货车驾驶风格的指标熵值和极差均较大,存在较高的安全隐患,需要引起相关部门的高度重视。

基于改进的Canopy-k-means的大跨屋盖表面风荷载分区方法

针对k-means聚类算法在大跨屋盖结构表面风荷载分区计算中,聚类数k值随机选取容易导致结果不稳定和计算效率低等问题,提出改进的Canopy-k-means聚类算法。首先,引入Canopy算法并对其初始阈值和聚类中心的选取方式进行改进,减少初始值选取的盲目性,以提高风荷载分区结果的可靠性;其次,通过改进Canopy算法对风荷载数据集进行预处理,快速准确地确定聚类数k值;第三,将改进Canopy算法与k-means结合使用,实现最优分类数k值的精准识别,使得改进的Canopy-k-means聚类算法进行大跨屋盖结构表面风荷载分区时能够快速准确地得到分区结果;最后,以一大跨柱面屋盖干煤棚结构为例,基于风洞试验所得结构表面风荷载数据测试结果,采用所提改进的Canopy-k-means聚类算法对其表面风荷载进行分区计算。结果表明,采用改进的Canopy-k-means聚类算法,将0°、50°和90°风向角时大跨屋盖表面风荷载划分为了3个不同的分区,其对应的SD值分别为2.36、3.51和2.52,较传统k-means聚类算法所得对应值明显降低,类内紧凑性和类间分散性明显提升。所提改进Canopy-k-means聚类算法能够快速准确地得到最优分区结果,对大跨屋盖表面风荷载分区具有工程参考价值。

核电站控制棒驱动机构Canopy焊缝焊接温度场和应力-应变场模拟

目的研究机加工和拉拔2种成形方式下得到的填充环对Canopy焊缝的影响,获取焊接焊缝成形、焊接残余应力和变形的相关数据,以指导Canopy焊缝焊接工艺。方法采用数值模拟的方法,建立Canopy焊缝焊接数值分析模型,模拟焊接温度场、焊接残余应力和焊接残余变形。结果拉拔成形环焊接熔池高度为9 mm,机加工成形环焊接熔池高度为8.3 mm;机加工成形环焊接最大残余应力为255.6 MPa,而拉拔成形环焊接最大残余应力为277.8 MPa,均出现在管座紧贴焊缝的位置;机加工成形环焊接残余变形为0.19 mm,拉拔成形环焊接残余变形为0.186 mm,最大残余变形均出现在焊接起始位置附近,在焊缝与管座交接的位置。结论熔池形貌直接影响了热影响区域的大小,拉拔Y型环焊接熔池高度更大,焊接的热影响区域更大;拉拔Y型环焊接残余应力略大于机加工Y型环焊接残余应力;机加工成形环和拉拔成形环焊接残余变形相近。

Effects of dust controls on respirable coal mine dust composition and particle sizes:case studies on auxiliary scrubbers and canopy air curtain

Control of dust in underground coal mines is critical for mitigating both safety and health hazards.For decades,the National Institute of Occupational Safety and Health(NIOSH)has led research to evaluate the effectiveness of various dust control technologies in coal mines.Recent studies have included the evaluation of auxiliary scrubbers to reduce respirable dust downstream of active mining and the use of canopy air curtains(CACs)to reduce respirable dust in key operator positions.While detailed dust characterization was not a focus of such studies,this is a growing area of interest.Using preserved filter samples from three previous NIOSH studies,the current work aims to explore the effect of two different scrubbers(one wet and one dry)and a roof bolter CAC on respirable dust composition and particle size distribution.For this,the preserved filter samples were analyzed by thermogravimetric analysis and/or scanning electron microscopy with energy dispersive X-ray.Results indicate that dust composition was not appreciably affected by either scrubber or the CAC.However,the wet scrubber and CAC appeared to decrease the overall particle size distribution.Such an effect of the dry scrubber was not consistently observed,but this is probably related to the particular sampling location downstream of the scrubber which allowed for significant mixing of the scrubber exhaust and other return air.Aside from the insights gained with respect to the three specific dust control case studies revisited here,this work demonstrates the value of preserved dust samples for follow-up investigation more broadly.

Long-term thinning decreases the contribution of heterotrophic respiration to soil respiration in subalpine plantations

Interest in the dynamics of soil respiration(R_(S))in subalpine forest ecosystems is increasing due to their high soil carbon density and potential sensitivity to environmental changes.However,as a principal silvicultural practice,the long-term impacts of thinning on R_(S) and its heterotrophic and autotrophic respiration components(R_(h) and Ra,respectively)in subalpine plantations are poorly understood,espe-cially in winter.A 3-year field observation was carried out with consideration of winter CO_(2) efflux in middle-aged sub-alpine spruce plantations in northwestern China.A trench-ing method was used to explore the long-term impacts of thinning on Rs,Rn and R_(a).Seventeen years after thinning,mean annual Rs,Rn and R_(a) increased,while the contribu-tion of R_(h) to R_(s) decreased with thinning intensity.Thinning significantly decreased winter R,because of the reduction in R_(n) but had no significant effect on Ra.The temperature sensitivity(Q_(10))of R_(h) and R_(a) also increased with thinning intensity,with lower Q_(10) values for R_(h)(2.1-2.6)than for Ra(2.4-2.8).The results revealed the explanatory variables and pathways related to R_(n) and R_(a) dynamics.Thinning increased soil moisture and nitrate nitrogen(NO_(3)^(-)-N),and the enhanced nitrogen and water availability promoted R_(h) and R_(a) by improving fine root biomass and microbial activity.Our results highlight the positive roles of NO_(3)^(-)-N in stimulating R_(s) components following long-term thinning.Therefore,applications of nitrogen fertilizer are not recommended while thinning subalpine spruce plantations from the perspective of reducing soil CO_(2) emissions.The increased Q_(10) values of R_(s) components indicate that a large increase in soil CO_(2) emissions would be expected following thinning because of more pronounced climate warming in alpineregions.

Rice canopy temperature is affected by nitrogen fertilizer

Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehensively examined. We conducted a two-year field experiment with three rice varieties(HD-5, NJ-9108, and YJ-805) and three nitrogen treatments(zero-N control(CK), 200 kg ha~(–1)(MN), and 300 kg ha~(–1)(HN)). We measured canopy temperature using a drone equipped with a high-precision camera at the six stages of the growth period. Generally,canopy temperature was significantly higher for CK than for MN and HN during the tillering, jointing, booting, and heading stages. The temperature was not significantly different among the nitrogen treatments between the milky and waxy stages. The canopy temperature of different rice varieties was found to follow the order: HD-5>NJ-9108>YJ-805, but the difference was not significant. The canopy temperature of rice was mainly related to plant traits, such as shoot fresh weight(correlation coefficient r=–0.895), plant water content(–0.912), net photosynthesis(–0.84), stomatal conductance(–0.91), transpiration rate(–0.90), and leaf stomatal area(–0.83). A structural equation model(SEM) showed that nitrogen fertilizer was an important factor affecting the rice canopy temperature.Our study revealed:(1) A suite of plant traits was associated with the nitrogen effects on canopy temperature,(2) the heading stage was the best time to observe rice canopy temperature, and(3) at that stage, canopy temperature was negatively correlated with rice yield, panicle number, and grain number per panicle. This study suggests that canopy temperature can be a convenient and accurate indicator of rice growth and yield prediction.

Drought events influence nutrient canopy exchanges and green leaf partitioning during senescence in a deciduous forest

The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced leaves.However,the effects of drought events on this partitioning and the consequences for the maintenance of tree nutrition are poorly documented.An experiment in a beech forest in Meuse(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence period.The aim was to identify potential nutritional consequences of droughts for trees.Monitoring nutrient dynamics,including resorption,chemistry of green and senesced leaves,foliar absorption and leaching in mature beech stands from 2012 to 2019 allowed us to compare the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by almost a third of the potassium(K)partitioning to resorption(i.e.resorption efficiency),thus reducing the K reserve in trees for the next growing season.This result suggests that with the increased drought frequency and intensity expected for the coming decades,there will be a risk of potassium deficiency in trees,as already observed in a rainfall exclusion experiment on the same study site.Reduced foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also observed.In addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is more likely to improve tree nutrition.The calcium(Ca)negative resorption decreased,with no apparent consequence in our study site.Our results show that nutrient exchanges in the canopy and the partitioning of the green leaf pool can be modified by drought events,and may have consequences on tree nutrition.

The influences of canopy temperature measuring on the derived crop water stress index

Crop water stress index(CWSI)is widely used for efficient irrigation management.Precise canopy temperature(T_(c))measurement is necessary to derive a reliable CWSI.The objective of this research was to investigate the influences of atmospheric conditions,settled height,view angle of infrared thermography,and investigating time of temperature measuring on the performance of the CWSI.Three irrigation treatments were used to create different soil water conditions during the 2020-2021 and 2021-2022 winter wheat-growing seasons.The CWSI was calculated using the CWSI-E(an empirical approach)and CWSI-T(a theoretical approach)based on the T_(c).Weather conditions were recorded continuously throughout the experimental period.The results showed that atmospheric conditions influenced the estimation of the CWSI;when the vapor pressure deficit(VPD)was>2000 Pa,the estimated CWSI was related to soil water conditions.The height of the installed infrared thermograph influenced the T_(c)values,and the differences among the T_(c)values measured at height of 3,5,and 10 m was smaller in the afternoon than in the morning.However,the lens of the thermometer facing south recorded a higher T_(c)than those facing east or north,especially at a low height,indicating that the direction of the thermometer had a significant influence on T_(c).There was a large variation in CWSI derived at different times of the day,and the midday measurements(12:00-15:00)were the most reliable for estimating CWSI.Negative linear relationships were found between the transpiration rate and CWSI-E(R^(2)of 0.3646-0.5725)and CWSI-T(R^(2)of 0.5407-0.7213).The relations between fraction of available soil water(FASW)with CWSI-T was higher than that with CWSI-E,indicating CWSI-T was more accurate for predicting crop water status.In addition,The R^(2)between CWSI-T and FASW at 14:00 was higher than that at other times,indicating that 14:00 was the optimal time for using the CWSI for crop water status monitoring.Relative higher yield of winter wheat was obtained with average seasonal values of CWSI-E and CWSI-T around 0.23 and 0.25-0.26,respectively.The CWSI-E values were more easily influenced by meteorological factors and the timing of the measurements,and using the theoretical approach to derive the CWSI was recommended for precise irrigation water management.

Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning

Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data.

Spatiotemporal Variability and Environmental Controls of Temperature Sensitivity of Ecosystem Respiration across the Tibetan Plateau

Warming-induced carbon loss via ecosystem respiration(R_(e))is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of R_(e)(Q_(10)).However,little is known about the patterns and controlling factors of Q_(10)on the plateau,impeding the comprehension of the intensity of terrestrial carbon-climate feedbacks for these sensitive and vulnerable ecosystems.Here,we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q_(10)values in diverse climate zones and ecosystems,and further explore the relationships between Q_(10)and environmental factors.Moreover,structural equation modeling was utilized to identify the direct and indirect factors predicting Q_(10)values during the annual,growing,and non-growing seasons.The results indicated that the estimated Q_(10)values were strongly dependent on temperature,generally,with the average Q_(10)during different time periods increasing with air temperature and soil temperature at different measurement depths(5 cm,10 cm,20 cm).The Q_(10)values differentiated among ecosystems and climatic zones,with warming-induced Q_(10)declines being stronger in colder regions than elsewhere based on spatial patterns.NDVI was the most cardinal factor in predicting annual Q_(10)values,significantly and positively correlated with Q_(10).Soil temperature(Ts)was identified as the other powerful predictor for Q_(10),and the negative Q_(10)-Ts relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming.Note that the interpretations of the effect of soil moisture on Q_(10)were complicated,reflected in a significant positive relationship between Q_(10)and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season.These findings are conducive to improving our understanding of alpine grassland ecosystem carbon-climate feedbacks under warming climates.

Analysis on the Application and Improvement of Canopy without Sunshade Curtain

Nowadays,in order to expand the roof view to bring passengers closer to nature,more and more new energy vehicles are opting for canopy designs without sunshades.However,after removing traditional sunshades,new solutions must be sought to address issues such as heat insulation,glaring sunlight,and interior reflections from the roof glass during the summer months.This paper conducts an in-depth analysis of the technical advantages and shortcomings of sunshade-free canopy in terms of heat insulation and interior reflections during summer,from both theoretical analysis and experimental comparison perspectives,and proposes improvement strategies.The research results indicate that although the panoramic roof enhances the vehicle's interior view and technological appeal,it still has shortcomings in terms of heat insulation and the problem of interior reflections caused by direct sunlight.The proposed improvement strategies can effectively mitigate these issues,and offers consumers more comfortable and intelligent driving experiences.

不同Y形环应用于核电站控制棒驱动机构Canopy焊缝焊接工艺及焊缝性能的研究

试验采用两种机械加工Y形环和一种拉拔Y形环应用于核电站控制棒驱动机构Canopy焊缝,研究不同Y形环对Canopy焊缝焊接工艺及焊缝性能的影响。结果表明:机械加工Y形环为整圈环,相比拉拔Y形环,更有利于定位焊;机械加工Y形环(控公差)与拉拔Y形环装配间隙范围相当,比机械加工Y形环装配间隙范围大,更有利于工程应用;3种Y形环熔成的Canopy焊缝组织主要由铁素体和柱状奥氏体组成,无明显差异;3种Y形环熔成的Canopy焊缝经23.1~23.3MPa水压试验,焊缝表面均无渗漏,无可见变形。该研究为机械加工Y形环应用于核电站控制棒驱动机构Canopy焊缝提供了宝贵的经验。

核电站控制棒驱动机构Canopy焊缝组织和性能研究

试验采用专用自动TIG焊机焊接核电站控制棒驱动机构Canopy焊缝,选用机械加工Y形环作为填充材料,研究Canopy焊缝组织和性能。结果表明:通过自动TIG焊,机械加工Y形环熔成的Canopy焊缝,成形良好,宽度适中,焊缝与母材冶金熔合良好;焊缝组织主要由板条状或骨架状铁素体与快速凝固的柱状奥氏体组成;经23.1~23.3MPa水压试验,Canopy焊缝表面无渗漏,无可见变形;维氏硬度结果表明:焊缝硬度在210HV左右,焊缝硬度分布均匀。该研究为核电站控制棒驱动机构Canopy焊缝用机械加工Y形环提供了宝贵的经验。

屏蔽主泵Canopy密封环自动焊接技术研究

为了降低作业人员在AP1000机组屏蔽主泵在役检修拆装过程中的辐照剂量,研究了与实际作业工序相同的Canopy密封环自动焊接工艺,开发了可远程控制、对称施焊的自动焊接装置,并在模拟体上进行了焊接试验。试验结果表明,在优化的焊接参数下,Canopy密封环打底焊成形良好,多层多道焊缝无层间未熔合等缺陷,起弧、收弧过渡良好。该自动焊接装置结构紧凑,可实现模拟件的稳定焊接,焊接质量良好,能够满足屏蔽主泵在役检修更换时的自动焊接需求。

基于加权密度Canopy的K-means文本聚类

针对现有文本聚类性能不高的问题,提出了一种改进质心初始化的K-means文本聚类算法.该算法首先利用Canopy算法进行文本预聚类,并且对Canopy算法的阈值选取策略进行改进,定义加权密度进行Canopy中心的选取,得到更准确的聚类数以及初始聚类中心;然后将所得结果作为K-means算法的初始化参数进行后续迭代聚类,有效解决了传统算法因随机选取初始聚类中心而陷入局部最优解的问题,减少了算法的迭代次数,提高了聚类准确性.实验结果表明:与其他同类型算法相比,该算法在文本聚类分析中更具优势.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Effect of the L-D_(1)alleles on leaf morphology,canopy structure and photosynthetic productivity in upland cotton(Gossypium hirsutum L.)

One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on achieving high photosynthetic efficiency traits in order to enhance the yield.Leaf shape is a significant agronomic trait of upland cotton that affects plant and canopy architecture,yield,and other production attributes.The major leaf shape types,including normal,sub-okra,okra,and super-okra,with varying levels of lobe severity,are controlled by a multiple allelic series of the D-genome locus L-D_(1).To analyze the effects of L-D_(1)alleles on leaf morphology,photosynthetic related traits and yield of cotton,two sets of near isogenic lines(NILs)with different alleles were constructed in Lumianyan 22(LMY22)and Lumianyan 28(LMY28)backgrounds.The analysis of morphological parameters and the results of virus-induced gene silencing(VIGS)showed that the regulation of leaf shape by L-D_(1)alleles was similar to a gene-dosage effect.Compared with the normal leaf,deeper lobes of the sub-okra leaf improved plant canopy structure by decreasing the leaf area index(LAI)and increasing the light transmittance rate(LTR),and the mid-range LAI of sub-okra leaf also guaranteed the accumulation of cotton biomass.Although the chlorophyll content(SPAD)of sub-okra leaf was lower than those of the other two leaf shapes,the net photosynthetic rate(Pn)of sub-okra leaf was higher than those of okra leaf and normal leaf at most stages.Thus,the improvements in canopy structure,as well as photosynthetic and physiological characteristics,contributed to optimizing the light environment,thereby increasing the total biomass and yield in the lines with a sub-okra leaf shape.Our results suggest that the sub-okra leaf may have practical application in cultivating varieties,and could enhance sustainable and profitable cotton production.

Moso bamboo expansion decreased soil heterotrophic respiration but increased arbuscular mycorrhizal mycelial respiration in a subtropical broadleaved forest

Moso bamboo(Phyllostachys Pubescens)expansion into adjacent forests has been widely reported to affect plant diversity and its association with mycorrhizal fungi in subtropical China,which will likely have significant impacts on soil respiration.However,there is still limited information on how Moso bamboo expansion changes soil respiration components and their linkage with microbial community composition and activity.Based on a mesh exclusion method,soil respirations derived from roots,arbuscular mycorrhizal(AM)mycelium,and free-living microbes were investigated in a pure Moso bamboo forest(expanded),an adjacent broadleaved forest(nonexpanded),and a mixed bamboo-broadleaved forest(expanding).Our results showed that bamboo expansion decreased the cumulative CO_(2)effluxes from total soil respiration,root respiration and soil heterotrophic respiration(by 19.01%,30.34%,and 29.92%on average),whereas increased those from AM mycelium(by 78.67%in comparison with the broadleaved forests).Bamboo expansion significantly decreased soil organic carbon(C)content,bacterial and fungal abundances,and enzyme activities involved in C,N and P cycling whereas enhanced the interactive relationships among bacterial communities.In contrast,the ingrowth of AM mycelium increased the activities ofβ-glucosidase and N-acetyl-β-glucosaminidase and decreased the interactive relationships among bacterial communities.Changes in soil heterotrophic respiration and AM mycelium respiration had positive correlations with soil enzyme activities and fungal abundances.In summary,our findings suggest that bamboo expansion decreased soil heterotrophic respiration by decreasing soil microbial activity but increased the contribution of AM mycelial respiration to soil C efflux,which may potentially increase soil C loss from AM mycelial pathway.

Response of soil respiration to short-term changes in precipitation and nitrogen addition in a desert steppe

Changes in precipitation and nitrogen(N)addition may significantly affect the processes of soil carbon(C)cycle in terrestrial ecosystems,such as soil respiration.However,relatively few studies have investigated the effects of changes in precipitation and N addition on soil respiration in the upper soil layer in desert steppes.In this study,we conducted a control experiment that involved a field simulation from July 2020 to December 2021 in a desert steppe in Yanchi County,China.Specifically,we measured soil parameters including soil temperature,soil moisture,total nitrogen(TN),soil organic carbon(SOC),soil microbial biomass carbon(SMBC),soil microbial biomass nitrogen(SMBN),and contents of soil microorganisms including bacteria,fungi,actinomyces,and protozoa,and determined the components of soil respiration including soil respiration with litter(RS+L),soil respiration without litter(RS),and litter respiration(RL)under short-term changes in precipitation(control,increased precipitation by 30%,and decreased precipitation by 30%)and N addition(0.0 and 10.0 g/(m^(2)·a))treatments.Our results indicated that short-term changes in precipitation and N addition had substantial positive effects on the contents of TN,SOC,and SMBC,as well as the contents of soil actinomyces and protozoa.In addition,N addition significantly enhanced the rates of RS+L and RS by 4.8%and 8.0%(P<0.05),respectively.The increase in precipitation markedly increased the rates of RS+L and RS by 2.3%(P<0.05)and 5.7%(P<0.001),respectively.The decrease in precipitation significantly increased the rates of RS+L and RS by 12.9%(P<0.05)and 23.4%(P<0.001),respectively.In contrast,short-term changes in precipitation and N addition had no significant effects on RL rate(P>0.05).The mean RL/RS+L value observed under all treatments was 27.63%,which suggested that RL is an important component of soil respiration in the desert steppe ecosystems.The results also showed that short-term changes in precipitation and N addition had significant interactive effects on the rates of RS+L,RS,and RL(P<0.001).In addition,soil temperature was the most important abiotic factor that affected the rates of RS+L,RS,and RL.Results of the correlation analysis demonstrated that the rates of RS+L,RS,and RL were closely related to soil temperature,soil moisture,TN,SOC,and the contents of soil microorganisms,and the structural equation model revealed that SOC and SMBC are the key factors influencing the rates of RS+L,RS,and RL.This study provides further insights into the characteristics of soil C emissions in desert steppe ecosystems in the context of climate change,which can be used as a reference for future related studies.