Vertical Migrating and Cluster Analysis of Soil Mesofauna at Dongying Halophytes Garden in Yellow River Delta

For the first time, we used Tullgren method made a study on vertical migrating and cluster analysis of the soil mesofauna in Dongying Halophytes Garden in the Yellow River Delta （YRD）, Shandong Province. The results showed that the soil mesofauna tended to gather on soil surface in most samples at most times, but the vertical migrating greatly varied in different seasons or environment conditions. Acari was the dominant group. The index of diversity of the soil fauna was correlated with the index of evenness. The Acari＇s number of individuals infected other species and numbers. Dominant group-Aeari made greater contribution to the result of cluster analysis, and there were significant differences between communities in different habitats by cluster analysis with both Bray-Curtis and Jaccard similarity coefficient.

The Heterogeneity and Its Influencing Factors of Soil Nutrients in Peak-Cluster Depression Areas of Karst Region

By selecting a typical peak-cluster depression area of karst region in Southwest China, we evaluated the effect of land use types and topographic factors on soil nutrients. Grid and line sampling methods were used to sample soil in depression and slope lands respectively, and classical statistical tools were applied to analyze the spatial variability character of soil organic carbon （SOC）, total nitrogen （TN）, total phosphorus （TP）, total potassium （TK）, available nitrogen （AN）, available phosphorus （AP）, available potassium （AK）, pH, and C/N. It was found that land use type was the dominant factor that effected the spatial heterogeneity of SOC, TN, TP, TK, AN, and AP. The content of SOC, TN, and AN decreased with the increase of land use intensity. Due to high fertilizer input, TP and AP in tillage fields were higher than those in the other land use types. TK had no obvious change trend among various land use types. Topographic factors had a significant effect on SOC, TN, TP, AN, AP, AK, and pH. Habitat factor was the dominant factor that effected AK. Altitude factor was the dominant factor for pH. However, all of these factors had no significant effect on C/N. Tillage practice had important effect on soil nutrients loss and soil degradation in the fragile karst ecosystem, and the input of organic manure should be increased in this region.

Optimization of constitutive parameters of foundation soils k-means clustering analysis

The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.

Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets

Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.

The features of soil erosion and soil leakage in karst peak-cluster areas of Southwest China

Through utilizing water flow monitoring, rock scratching, soil wood piles and radionuclide ^(137)Cs tracing in the Longhe karst ecological experimental site(hereinafter referred to"Longhe site"), Pingguo County, Guangxi Province, the features and values of soil erosion and soil leakage in different geomorphologic locations and land uses in the karst peak-cluster depressions are showed clearly. There are four kinds of geomorphologic locations in the karst peak-cluster depression, namely peaks, strip, slopes and depression. The soil leakage modulus in the peaks and strips respectively occupy 92.43% and 96.24% of the total mean soil erosion modulus at experimental sites. On the slope, soil leakage accounted for about 75%. At the bottom of depression, surface water was the main factor of soil erosion, and at last most soil leaked into underground rivers from sinkholes. The total soil erosion modulus and the contribution rates of relative surface soil erosion in regard of peaks, slopes and depressions gradually increased. There are also five major types of land use in the karst peak-cluster depressions, farmland, Kudingcha tea plantations, young Lignum Sappan fields, shrub-grassland and pastures. The soil erosion modulus of slope farmland has the highest value with an increasing trend year by year. But soil erosion modulus of other four land use types decreased by year, which shows the "grain for green" will result in better soil protection. By handling with rocky desertification and ecological rehabilitation in Longhe site, the mean soil erosion modulus of the karst peak-cluster depression has decreased about 80% from 2003 to 2015.

Effect of corylus clusters on the physicochemical properties of soil

Soil sample plots were specified and the soil in layer A0, A1 and AB were collected in Maoershan-Forest Experiment Farm of Northeast Forestry University for study of the effect of corylus clusters on soil in 1999. The result shows that the pH value, contents of organic matter, total nitrogen, alkali-discomposed nitrogen and total phosphorus under the corylus clusters are higher than that under the non-corylus clusters, except the available phosphorus content. The number of soil granular aggregates or the water stable aggregates under corylus clusters is more than that under the non-corylus clusters. The corylus clusters play an important role in improving the physicochemical properties of the soil, which should be conserved and developed in the forestry production.

基于图神经网络聚类的土壤监测点位优化

在土壤污染的监测过程中,由于需要大面积的监测点位布设,所以需要大量的人力和物力,同时原有的土壤监测点位存在冗余和代表率低的问题,对监测结果的准确性和全面性产生了影响。使用一种基于自编码器降维和图卷积网络(GCN)推理的土壤点位优化方法,通过将原有监测点位聚类成不同簇,来实现用尽量少的监测点位全面地表现区域污染情况的目的。使用GCN推理结合自编码器降维的土壤点位优化方法,该方法同时利用土壤监测点位数据的结构信息和高维特征表示,将监测点位聚类成不同簇。通过对相关性进行分析,发现聚类后的簇中心点位可以作为原始簇的代表,从而降低土壤监测点位的冗余性,实现了监测点位的优化。实验证明GCN方法可以有效地减少监测点位的数量,同时保持监测结果的准确性和全面性。

重庆市植烟土壤质量评价指标体系的构建与应用

为准确评价重庆市烟区土壤质量,以重庆2 513个代表性植烟单元(每个单元面积约6.7 hm^(2))的典型烟田土壤理化指标数据为基础,采用聚类分析法和主成分分析法建立了土壤质量评价最小数据集,构建了植烟土壤质量评价指标体系。结果表明:(1)基于主成分分析法建立的土壤质量评价体系更适合重庆烟区土壤质量评价,评价指标从原来的16个减为7个,具体为:p H、有机质、有效磷、有效铁、有效锌、有效硫和水稳性大团聚体。(2)烟区土壤酸化较为严重,有35.1%的烟田土壤偏酸;土壤氮素较为适宜,而有效磷和速效钾偏高的烟田占比分别为62.9%和75.8%,其他元素缺乏和过量并存,水稳性大团聚体偏低的烟田占比47.2%。(3)对基于最小数据集构建模型进行的合理性验证发现,基于主成分分析法确定的最小数据集(MDS-PCA)模型的R2比基于聚类分析法确定的最小数据模型的R~2高,前者的MAE、RMSE相比后者更小,说明MDS-PCA能够替代全量数据集对烟田土壤质量进行评价;基于MDS-PCA得到的土壤质量指数平均值为0.61,表明烟区土壤质量总体处于中等偏低水平,45.04%的烟田土壤质量急需提升。(4)土壤质量评价指标隶属度值雷达图表明,pH、有效磷、有效铁、有效硫和水稳性大团聚体的隶属度比较小,为烟区土壤质量限制性因子。

基于因子-聚类分析的火龙果园土壤养分状况评价

为了解海南省火龙果产区土壤养分现状,调查测定了184个土壤样品的营养成分水平,通过单个营养指标等级评估和因子聚类方法进行全面评估。等级评价结果表明:调研区域土壤酸化现象较严重,pH <6.5的酸性土壤占72.82%,土壤碱解氮含量普遍较低,在调研的全部地块中,碱解氮含量处于缺乏状态(碱解氮小于0.01 mg·kg^(-1));速效钾、全氮和有机碳在部分地块含量较低,分别为23.37%、23.37%和33.7%,处于低及以下等级水平;土壤速效磷含量最高,处于中等及以上等级地块的比例占100%。因子分析表明,测定的6项养分指标可提取出2个公因子,其中,因子1和因子2表征的全氮、有机碳、速效钾、碱解氮、pH是作为主要影响火龙果园土壤综合营养状况的因子。通过对土壤营养综合指数IFI(Integrated fertility index)的聚类分析了解,海南省火龙果园土壤IFI值范围为-0.904~1.405,均值为-0.171,其中仅有41.3%的土壤处于中等及以上肥力水平。乐东、东方、三亚、陵水这四个海南火龙果主要果园地区中,东方和陵水的土壤IFI值有80.33%、50%的土壤处于低等肥力水平;三亚地区有61.9%的土壤IFI值处于中等以上肥力水平;整体来说,海南省火龙果园大部分地区(除三亚外)土壤肥力状况处于中等以下水平。

蔗渣生物炭对岩溶峰丛洼地石灰土土-水特征曲线的影响

岩溶地区岩石裸露、土壤浅薄且持水能力较低,其土壤水分保持和运动状况是其水土漏失治理的重要因素。生物炭拥有多孔结构和高比表面积,能改变土壤持水能力,但其种类、粒径、添加量和土壤类型等因素对土壤持水能力产生正面或负面作用,为了研究添加不同比例蔗渣生物炭对岩溶峰丛洼地石灰土持水特性的影响,选取位于广西河池地区岩溶峰丛洼地的石灰土壤作为试验样本,分别添加不同比例生物炭(0%,2%,4%,6%),利用滤纸法测定生物炭-石灰土混合土在0~40MPa吸附能力下土水特性曲线,并使用扫描电子显微镜(SEM)和核磁共振波谱法(NMR),观察生物炭-石灰土混合土的微观孔隙结构。研究结果表明:当土壤含水量超过30%时,不同比例生物炭掺入量的持水能力相似,土壤的持水性并不会随着生物炭掺入量的增加而持续提升。在添加生物炭0%,2%,4%时混合土的持水能力是在逐渐提高的,添加4%和6%时混合土的持水能力基本一致,这表明添加生物炭存在一个最优值和适用范围。建议岩溶峰丛地区石灰土含水率在30%以下时,施加4%~6%的蔗渣生物炭,可提高土壤的持水能力。

喀斯特峰丛洼地3个建群树种“植物-凋落物-土壤”系统氮同位素特征

以喀斯特峰丛洼地3种常见次生林及其建群树种为研究对象,测定3种林分建群种的不同器官及凋落物、土壤的氮含量及氮同位素丰度值(δ^(15)N),探讨植物-凋落物-土壤氮含量及稳定氮同位素组成变化及内在联系。结果显示:3种次生林建群种植物中叶片氮含量最高,茎的氮含量最低,3种次生林建群树种叶片氮含量均显著高于掉落物和土壤氮含量,0~30 cm土壤氮含量显著高于30~90 cm土壤氮含量,土壤养分主要集中于表层土壤;白栎、栓皮栎和光皮桦器官中δ^(15)N变化范围分别为-2.82‰~14.94‰、-1.37‰~9.35‰和-4.39‰~26.06‰,3种建群种器官间氮同位素组成均表现出显著差异(P<0.05);3种建群种群落表层土壤δ^(15)N差异显著,3种林分中0~30 cm土壤δ^(15)N均显著低于30~60 cm和60~90 cm的土壤,δ^(15)N可能是这些群落土壤氮循环过程中的敏感指标,光皮桦叶片氮含量略大于白栎和栓皮栎,光皮桦林土壤表层δ^(15)N大于白栎林和栓皮栎林,光皮桦可能比白栎和栓皮栎更为适应喀斯特生境。

基于主成分和聚类分析的云南文山典型石漠化区土壤肥力分析

对云南砚山县和西畴县典型石漠化区土壤肥力状况进行丰缺诊断和综合肥力评价,以期为研究区域土壤品质提升提供科学依据。测定土壤pH、有机质、碱解氮、有效磷、速效钾、电导率6个土壤肥力指标,对土壤肥力指标变化规律进行分析,通过主成分分析计算土壤肥力综合得分,应用系统聚类法对土壤肥力进行分类。结果表明,研究区域土壤有效磷含量属于中等偏下水平,碱解氮含量属于高水平。采用主成分分析将土壤肥力指标提取为3个主成分,可反映土壤全部信息的80.52%。第一主成分以有机质和pH贡献大,第二主成分以速效钾、碱解氮、有效磷贡献大,第三主成分以电导率贡献大。土壤综合肥力可分为2类,第一类土壤综合肥力属于中等水平,第二类土壤综合肥力水平较低。总而言之,云南砚山县和西畴县典型石漠化区土壤综合肥力属于中低水平,土壤有效磷、有机质和pH是影响土壤综合肥力高低的关键因子,需加强土壤养分管理,合理施肥,提升土壤综合肥力水平。

Soil profile characteristics of high-productivity alluvial cambisols in the North China Plain

The North China Plain（NCP） is one of major breadbaskets in China. Crop growth and grain yield differ significantly with spatial variations of soil properties. This study aims to identify the key soil properties in relation to the grain yield for the winter wheat（Triticum aestivum L.）-maize（Zea mays L.） cropping system in a high-productivity farmland of the NCP. The field trials were conducted in three fields with different grain yield levels in Tai＇an City, Shandong Province, China, during the 2009–2012 period. Consistent field management strategies were applied in the three fields. Fifty-one physical and chemical indicators of the soil profile as related to grain yield were evaluated. An approximate maximum of 17.8% annual average grain yield difference was observed in the fields during the period of 2009–2012. The soil indicators were classified into three clusters with specific functions using cluster analysis, and three key indicators were extracted from each cluster to characterize the different soil properties of three fields. The first cluster represented soil water retention capacity, and the key indicator was available soil water（ASW）, which ranged from 153 to 187 mm in the 1.2 m profile and was correlated positively with grain yield. The second cluster represented soil water conductivity, as measured by saturated hydraulic conductivity（K s）. The higher yield field had a greater capacity to retain topsoil water for its lower K s（1.9 cm d^–1） in the 30–70 cm soil layer as compared to the lower yield field. The third cluster represented nutrient storage and supply, as indicated by the ratio of nutrient content to silt＋clay content of the top soil layer. The ratio of soil organic matter（OM）, total nitrogen（TN）, available P, exchangeable K＋ to silt＋clay content in the 0–20 cm soil layer were 19.0 g kg^–1, 1.6 g kg^–1, 94.7 mg kg^–1, 174.3 mg kg^–1 in the higher yield field, respectively, and correlated positively with the grain yield. By characterizing the differences in soil properties among fields with different yield levels, this study offers the scientific basis for increasing grain yield potential by improving the soil conditions in the NCP.

Rainfall and Tillage Impacts on Soil Erosion of Sloping Cropland with Subtropical Monsoon Climate- A Case Study in Hilly Purple Soil area, China

Under global warming, storm events tend to intensify, particularly in monsoon-affected regions.As an important agricultural area in China, the purple soil region in the Sichuan Basin, where it has a prevailing monsoon climate, is threatened by serious soil erosion. Tillage operations alter runoff and soil erosion processes on croplands by changing the physical properties of the soil surface. To clarify the relationship between tillage and soil erosion in the purple soil region, three different tillage practices in this region were investigated at the plot scale over 4 years: bare land with minimum tillage(BL),conventional tillage(CT) and seasonal no-tillage ridges(SNTR) which was initially designed to prevent soil erosion by contoured ridges and no-tillage techniques. The results showed that although there were no significant differences in the surface runoff and soil erosion among the three practices, BL causedrelatively high surface runoff and soil erosion,followed by CT and SNTR. Classification and comparison of the rainfall events based on cluster analysis(CA) verified that the surface runoff was not significantly different between most intensive event and long intensive events but was significantly different between most intensive and short and medium-duration events. Only the rainfall events with the highest rainfall intensity could trigger serious soil erosion, up to 1000 kg ha-1 in the region. Further detailed investigations on the effects of tillage operations on the soil erosion in a subtropical region with a monsoon climate are needed to provide a basis for modeling catchments and designing better management practices.

RESEARCHES ON SOIL ENVIRONMENTAL BACKGROUND VALUES IN TIBET

The environmental background values of 13 elements of Hg, As, Se, Cr,Mn, V, Ph, Cd, Zn, Cu, Ni, F and Co in xizang soils are obtained through analyzing and determining 205 samples of surface soils in Tibet and the final data processing. The background val

Prediction of Soil Salinity Using Multivariate Statistical Techniques and Remote Sensing Tools

Soil salinity limits plant growth, reduces crop productivity and degrades soil. Multispectral data from Landsat TM are used to study saline soils in southern Tunisia. This study will explore the potential multivariate statistical analysis, such as principal component analysis (PCA) and cluster analysis to identify the most correlated spectral indices and rapidly predict salt affected soils. Sixty six soil samples were collected for ground truth data in the investigated region. A high correlation was found between electrical conductivity and the spectral indices from near infrared and short-wave infrared spectrum. Different spectral indices were used from spectral bands of Landsat data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Landsat original bands showed that the near and short-wave infrared bands (band 4, band 5 and 7) and the salinity indices (SI 5 and SI 9) have the highest correlation with EC. The use of CA revealed a strong correlation between electrical conductivity EC and spectral indices such abs4, abs5, abs7 and si5. The principal components analysis is conducted by incorporating the reflectance bands and spectral salinity indices from the remote sensing data. The first principal component has large positive associations with bands from the visible domain and salinity indices derived from these bands, while second principal component is strongly correlated with spectral indices from NIR and SWIR. Overall, it was found that the electrical conductivity EC is highly correlated (R2 = -0.72) to the second principal component (PC2), but no correlation is observed between EC and the first principal component (PC1). This suggests that the second component can be used as an explanatory variable for predicting EC. Based on these results and combining the spectral indices (PC2 and abs B4) into a regression analysis, model yielded a relatively high coefficient of determination R2 = 0.62 and a low RMSE = 1.86 dS/m.

基于归一化植被指数获取环境因子权重的土壤管理分区方法

在基于加权环境变量进行土壤管理分区时,常利用作物产量与环境变量之间的关系来获取环境变量的影响权重。然而,当作物产量难以获取或作物产量与环境变量之间不能建立关系模型时,这种基于作物产量获取环境变量影响权重的方法将不再适用。针对此问题,该研究基于归一化植被指数(normalized difference vegetation index,NDVI)可以反映土壤条件而土壤条件又受环境变量影响,提出一种利用NDVI获取环境变量影响权重的方法,并将其应用于橡胶园土壤管理分区。结果表明,利用该方法可以筛选出与NDVI关系紧密的15个关键环境变量,并且可以获取这些环境变量的权重,其中,前5个环境变量(坡向正弦和余弦,地形位置指数,地形湿度指数,剖面曲率)的权重几乎占到15个环境变量权重之和的一半。利用加权后的环境变量可将研究区橡胶园划分为6个分区。这些分区可以很好地区分不同土壤养分(土壤有机质、全氮、有效磷和速效钾)的丰缺水平。同时,不同分区间环境变量的差异也达到了显著水平(P<0.05)。可见,该研究提出的利用NDVI获取环境变量影响权重的方法是可靠的。研究可为土壤管理分区提供可靠的思路。

Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images

The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.

Preliminary Mapping and Characterisation of Soils in the Pirin Mountains (Bulgaria)

Soil types, humus types and vegetation as well as their hypsometric variation were analysed in terms of sequences in the northern part of the high mountains of the Pirin National Park at altitudes between 1000 and 2400 m a.s.l. The study area is characterised by a large variety of natural parameters like petrology (mainly marble and granite), morphology (different slope deposits, exposition) and the orographic climate gradient. Statistical analyses using these parameters provided a basis for the soil group classification of the sites. Based on a Digital Terrain Model (DTM) and a geological map of the Pirin National Park, the results of these statistical analyses were used to generate a "map of potential soil groups" (regionalisation using GIS). Six potential soil groups could be determined. The resulting map exhibits a confidence level of 68 % on 74.4 % of the covered area. Rendzic Leptosols, in combination with Folic Histosols and Histi-lithic Leptosols occur in the alpine and subalpine regions on calcareous substrates. With decreasing altitude they are replaced by a mosaic of Rendzic Leptosols, Phaeozems and an increasing occurrence of Cambic Umbrisols. Umbrisols found on silicatic substrates in the alpine region are replaced by Cambic Umbrisols with decreasing altitude as well. Hence, pedogenesis is characterised by increasing browning and depth of the soil profiles with decreasing altitude. The pH-level is slightly acidic to neutral in lower zones and on calcareous rocky bases. Acidification increases in the subalpine zone. Soil pH decreases down to 4 on silicate subtrates. Typical humic values in mineral topsoils are 10 to 12 %, and in organic layers of the soils above 2000 m a.s.l. they are even more. The C:N ratio closely ranges around 20 (median).

道地产区植参土壤质量等级与品质等级的相关性分析

分别以人参产量、品质(人参皂苷Rb1、Re、Rg1含量)和参地土壤质量(18项土壤理化性质)为指标,结合产量定级法、聚类分析法、主成分分析法对50个人参样地进行等级研究,探索栽培人参的土壤等级划分方法,为道地产区宜植参土壤的规划发展奠定基础。结果显示:基于产量、品质指标,研究区内土壤分级结果符合正态分布,高产、优质等级较少,以Ⅲ~Ⅴ、Ⅱ~Ⅳ等地为主。基于土壤质量指标,研究区内土壤质量指数值介于0.20~0.59之间,以Ⅲ~Ⅳ等地为主,土壤中有效磷、速效钾、有效镁、脲酶、过氧化氢酶5个指标的权重系数较高,是影响植参土壤质量的关键指标。相关分析显示,以土壤理化性质划分的土壤等级与以人参产量、品质划分的土壤等级间具有较强的正相关关系(r=0.58、0.44)。综合表明,以土壤质量划分的土壤等级具有一定代表性,基于主成分分析法构建的土壤质量评价方法在产地宜植参土壤选择与规划、土壤养护与改良、施肥管理等方面具有生产指导意义。