烟田土壤质地的空间变异性研究

为了解烟田土壤质地的空间变异情况进而为烟草精准施肥提供依据,本研究在地统计学和GIS的支持下,以半方差函数为基本工具,分析了平顶山地区典型烟区耕层土壤质地的空间变异特征,并运用普通克里格(Kriging)进行最优无偏线性插值,制作了黏粒、砂粒和粉粒的空间分布图。结果表明,研究区域内不同土壤颗粒在较大范围内存在空间自相关性,黏粒、砂粒和粉粒的空间自相关距离分别为609m、657m和429m;黏粒和砂粒由结构性因素引起的空间变异达80%。各向异性分析都表明,黏粒和砂粒存在较强烈的各向异性,粉粒各向异性则较小。对试验土壤来说,土壤质地具有较强的空间自相关性和变异性,这可能是影响土壤养分空间变异性的主要原因,因此在设计施肥方案时应考虑土壤质地空间变异性的影响。     

黄土土质对溅蚀特征的影响

土壤质地是土粒溅蚀量及溅出方式的重要影响因素。以黄土高原4种土壤(土、黑垆土、砂黄土、黄绵土)为试验用土,通过人工模拟降雨试验研究了黄土土质对溅蚀量、溅出土粒颗粒组成的影响。结果表明:(1)溅蚀量随降雨历时增加呈幂函数趋势增加。降雨开始时溅蚀量取决于土表松散颗粒含量及土粒均匀程度。随后溅蚀量增加速率则取决于黏粒(0.002mm)、细粉粒(0.002~0.02mm)及吸湿性黏土矿物含量,二者综合作用使得溅蚀量增加幅度减小。(2)溅蚀过程中溅出土粒的粒径组成决定于供试土壤的颗粒组成,0.25mm颗粒含量高的土壤溅出土粒粒径分布均匀,而0.25mm颗粒含量低的土壤溅出土粒集中分布于粗粉粒、细砂粒粒级内。(3)通过溅出土粒实际与原始颗粒含量的比值可判断土壤颗粒溅出方式。供试土壤黏粒、细粉粒、粗粉粒(0.02~0.05mm)的实际与原始颗粒含量比值1,均以团粒形式溅出,不受土壤类别影响;而土、黑垆土砂粒的实际与原始颗粒含量的比值1,以团粒形式溅出;砂黄土、黄绵土土壤砂粒实际与原始颗粒含量比值≈1,以单粒形式溅出。     

北江干流河岸带不同植被类型土壤粒径分形特征

结合野外调查与室内试验,以北江干流河岸带竹林、桉树林、混交林和草地土壤为对象,对比分析了不同植被类型土壤的颗粒组成与分形维数(D)特征及其与其他土壤理化性质的相关性。结果表明:北江干流河岸带土壤结构良好(D=2.79),质地均一,粒径分布均匀(粉粒、砂粒、黏粒百分比分别为40.66%,36.59%,22.75%),但随粒级增大,空间变异增强;D值与土壤颗粒组成显著相关,随黏粒和粉粒含量升高、砂粒含量降低,D值升高;不同植被类型土壤分形与颗粒组成差异明显,其中砂粒含量表现为竹林混交林草地桉树林,D值、粉粒和黏粒含量均表现为桉树林草地混交林竹林,竹林和桉树林对土壤结构的影响差异有显著(p0.05),其他植被类型影响差异不显著;D值、黏粒含量均与硝态氮(NO-3-N)和全磷(TP)含量分别呈显著负相关和显著正相关(p0.05)关系,土壤砂粒含量与含水量(SM)呈显著负相关(p0.05),土壤粉粒含量只与SM呈显著正相关,另外,D值、砂粒、粉粒和黏粒含量与总有机碳(TOC)和全氮(TN)的相关性均不显著。河岸带土壤受多种因素影响,其土壤理化性质间的相互关系与其他景观差异明显。     

美国有关土壤粒度测定的简便方法

评价土壤质量和土壤对农业管理措施承受力 ,至少要进行土壤质地分析。然而 ,传统的土壤质地分析方法既昂贵又费时。美国科学家T .A .Kettler等人建立了一个快速、简便的测定土壤颗粒分布的方法 ,这一方法运用过筛和沉降 ,既可单独作为质地分析方法 ,又可同时分析颗粒有机物 (MPO)。并在美国大平原上的 6个地点 ,采集不同质地和不同有机物的土壤 ,用此方法和比重计法及标准吸管法测定土壤中砂粒、粉粒和黏粒含量。检测结果 ,对于砂粒、粉粒和黏粒 ,此方法与标准吸管法之间的绝对差 <2 % ,相对差分别为 6 %、- 1%和 - 4 % ,变异系数平均 <5 % ,回归分析r2 各为 0 99、0 98和 0 93,对所有的颗粒为 0 98。这一方法对于估测土壤质地、评价土壤质量既便宜又可靠     

不同取样方式下土壤质地空间插值的精度分析

为研究土壤质地的合理取样方式,进而研究其空间变异情况,为田间施肥及灌溉提供依据,本试验利用地统计学方法和GIS技术,在重庆市彭水县重庆烟草试验站,利用289个表层土样,研究了16 m间距的栅格取样法(对照,253个土样,扣除36个验证样点)、34 m间距的栅格取样法(115个土样)和随机取样法(115个土样)3种取样方式下土壤质地的空间插值精度。3种土壤颗粒指标中粉粒占68.43%,砂粒含量最少,占12.68%,黏粒含量略高于砂粒。砂粒和黏粒具有中等强度的变异性,粉粒具弱变异性,且数据符合正态分布。地统计分析显示,在分析该区域土壤质地时,采用栅格取样方法应适当增大取样间距,而采用随机取样方法可适当缩小取样间距。交叉检验显示,土壤质地成分在3种取样方式下的插值精度均以对照最大,栅格取样次之,随机取样最小。综合考虑插值误差、样品采集和分析成本及时效性等因素,本研究建议在该区域进行土壤质地空间变异规律分析为生产服务时应采用随机取样。     

红壤丘陵区坡地土壤颗粒组成的空间分布特征研究

通过分析红壤丘陵区农田坡面14个0~100 cm剖面的土壤颗粒组成,结合研究区土壤侵蚀等相关资料,拟阐明坡面尺度土壤砂粒、粉粒和黏粒含量的空间分布特征,揭示自然条件下土壤颗粒组成在水平和垂直方向上的分布规律。结果表明:坡面尺度土壤砂粒、粉粒和黏粒均呈现出中等的空间异质性,变异系数分别介于17.6%~23.2%、10.7%~15.8%和13.5%~17.0%。由于粗颗粒的沉积,花生地和橘园地均表现出坡下的砂粒含量显著高于坡上和坡中(P0.05),黏粒含量坡下显著低于其他坡位(P0.05);由于黏粒更容易随入渗过程向深层运动,两种植被类型均表现出土壤砂粒含量随深度增加而降低(P0.05)、黏粒含量随深度增加而增加的趋势(P0.05)。无论在水平方向还是垂直方向上,粉粒含量均无明显变化规律(P0.05)。砂粒含量随坡位和土壤深度的变化程度均大于粉粒和黏粒。植被类型及相应的耕作制度影响土壤颗粒的分布,土壤砂粒在水平方向上的运动在花生地表现得强于橘园地;橘园地土壤黏粒含量在垂直方向上的迁移速率大于花生地,而对粉粒含量的分布规律影响不大。     

Vis-NIR光谱信息辅助的土壤质地协同克里格预测制图

土壤质地传统获取方法费时费力,土壤反射光谱信息是土壤综合信息的反应,以光谱信息为辅助变量的土壤质地协同克里格预测制图值得研究。利用采样点的土壤光谱数据主成分分析综合得分作为协同克里格协变量,并结合GS+软件提供的最佳地统计模型预测土壤质地的空间变异。结果表明以光谱信息为辅助变量的协同克里格方法能提高制图精度,减少样本数量。在主变量减少至60个的情况下,砂粒,粘粒含量的预测值和实测值的均方根误差分别为7.85%,3.03%。     

沙柳沙障对沙丘土壤颗粒粒径及分形维数的影响

统计分析了库布齐沙漠流动沙丘、设置沙柳活沙障和死沙障沙丘的土壤颗粒粒径分布、分形维数及其与土壤砂粒(>0.05 mm)、粉粒(0.05~0.002 mm)和黏粒(<0.002 mm)含量的关系。结果表明:与流动沙丘相比,设置沙障沙丘的粉粒和黏粒含量增加,且随着土层的加深而表现为下降趋势,不同部位则均呈沙丘下部>上部>中部的趋势。土壤颗粒分形维数因设置沙障而呈增大趋势,且表现为活沙障沙丘>死沙障沙丘>流动沙丘;垂直分布上,设置沙障沙丘的土壤分形维数随土层加深而逐渐减小,而流动沙丘表现为表层与下层大而中层小的特征,沙丘不同部位的分形维数则均表现为沙丘下部>上部>中部。土壤颗粒分形维数大小与土壤质地的细粒化有一致的变化趋势,且与砂粒含量呈极显著负相关关系,而与黏粒含量、粉粒含量呈极显著正相关关系。<0.05 mm粒径物质含量的增加和>0.05 mm粒径物质含量的降低共同导致了土壤颗粒分形维数在设置沙障后的增大。     

荒漠-绿洲边缘区生态过渡带的土壤颗粒分形特征

通过实地采样和实验室分析,测定了黑河中游荒漠绿洲边缘区生态过渡带的土壤粒级分布、有机碳(SOC)、全氮(TN)、全磷(TP)、速效氮(AN)和速效磷(AP)的含量,并采用1993年杨培岭等提出的用粒径的重量分布代替数量分布的土壤分形模型,计算了土壤颗粒分形维数;最后利用统计分析法,研究了颗粒分形维数与土壤粒级分布及各养分指标的关系.结果表明:从荒漠边缘向绿洲延伸的过渡带,土壤中砂粒含量占绝对优势,粉粒居中,黏粒最少,采样区土壤质地较粗;土壤颗粒分形维数在2.105～2.609之间变动,处于极低水平,与土壤各养分指标具有相似的变化趋势,除靠近绿洲区存在一个"生态裂谷"外,整体呈上升趋势;利用相关分析法得出,土壤颗粒分形维数与砂粒含量呈显著负相关,与黏粉粒及各养分指标均呈显著的正相关;利用回归分析法进行定量分析,砂粒含量每增加1g/kg,颗粒分形维数D降低0.0019个单位;粉粒和黏粒含量每增加1g/kg,颗粒分形维数D分别增加0.002和0,023个单位,对土壤颗粒分形维数变化幅度的影响依次为黏粒>粉粒>砂粒,分形维数对黏粒含量的变化最为敏感.因此,可以将土壤颗粒分形维数作为衡量荒漠-绿洲边缘区土壤养分变化状况的指标之一,用于反映荒漠绿洲区土壤的退化状况.     

土壤不同粒径有机无机复合体对丁草胺的吸附特性

为了解土壤不同粒径组分对农药吸附-解吸行为的影响和吸附贡献率,以及不同粒径组分中有机无机组分的结合方式和复合程度如何影响有机质对农药的吸附,选取我国6个省区的7种理化性质差别较大的土壤,并采用物理方法提取该7种土壤的三个粒径有机无机复合体(黏粒:0.002mm;粉粒:0.02~0.002 mm;砂粒:0.05~0.02 mm)为研究材料,采用批量平衡法研究丁草胺在不同土壤和不同粒径有机无机复合体固/液界面的分配规律。同时,定量计算土壤各粒径组分对丁草胺的吸附贡献率,并从有机无机复合体角度探讨不同粒径组分中总有机碳(TOC)对丁草胺的吸附特性。结果表明:土壤黏粒组分对丁草胺具有最大的吸附量和较小的解吸率,而砂粒组分对丁草胺则具有较小的吸附量和最大的解吸率。土壤黏粒、粉粒和砂粒组分对丁草胺的吸附贡献率分别为36.7%~72.4%、21.7%~50.5%和10%。TOC是影响各粒径组分对丁草胺吸附的主要原因,但其影响程度受各粒径组分中TOC的理化性质以及其与无机矿物的复合程度控制。     

基于地表昼夜温差的平原区土壤质地模糊聚类与预测制图

The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.     

基于高时间分辨率农田土壤湿度观测的绿洲农业区土壤质地制图

Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated. Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error (RMSE) values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error (RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas used in this study.     

Estimation of soil texture from permanent wilting point measured with a chilled‐mirror dewpoint technique

Soil texture is an important factor governing a range of physical properties and processes in soil. The clay and fine fractions of soil are particularly important in controlling soil water retention, hydraulic properties, water flow and transport. Modern soil texture analysis techniques (x‐ray attenuation, laser diffraction and particle counting) are very laborious with expensive instrumentation. Chilled‐mirror dewpoint potentiameters allows for the rapid measurement of the permanent wilting point (PWP) of soil. As the PWP is strongly dictated by soil texture, we tested the applicability of PWP measured by a dewpoint potentiameter in predicting the clay, silt and sand content of humid tropical soils. The clay, silt, and sand content, organic matter and PWP were determined for 21 soils. Three regression models were developed to estimate the fine fractions and validated using independent soil data. While the first model showed reasonable accuracy (RMSE 16.4%; MAE 13.5%) in estimating the clay, incorporating the organic matter into the equation improved the predictions of the second model (RMSE 17.3%; MAE 10.9%). When used on all soil data, the accuracy of the third model in predicting the fine fraction was poor (RMSE 31.9%; MAE 24.5%). However, for soils with silt content greater than 30%, the model prediction was quite accurate (RMSE 7–12%; MAE 7–9%). The models were used to estimate the sand content and soil textures of soils, which proved relatively accurate. The dewpoint potentiometer can serve a dual purpose of rapidly estimating the PWP and the clay, fine fraction, and soil texture of soils in a cost efficient way.     

典型黑土区耕作土壤质地遥感时间窗口及影响因素分析

了解黑土区耕作土壤质地的空间分布对于黑土区农业精准管理以及耕地保护至关重要。遥感技术是快速获取土壤质地空间分布的有效方法。该研究以黑龙江省友谊农场耕地为研究对象,评估研究区土壤质地遥感反演的最佳时间窗口并分析其影响因素。筛选覆盖研究区的2019-2021年25幅Sentinel-2影像,将每幅影像的波段和构建的光谱指数输入随机森林模型,建立土壤质地遥感反演模型,比较不同时期影像反演土壤质地的模型精度,确定土壤质地遥感反演的最适宜影像,并分析造成反演土壤质地精度变化的原因,获取友谊农场土壤质地空间分布。结果表明：1）友谊农场反演土壤质地的最佳时间窗口为4月下旬至5月中旬;2）在25幅Sentinel-2影像中,2020年5月7日反演粉粒和砂粒的模型精度最高（粉粒的R2为0.785,均方根误差为6.697%;砂粒的R2为0.776,均方根误差为8.296%）;2019年5月3日反演黏粒的模型精度最高（R2为0.776,均方根误差为1.6%）;3）不同时期的Sentinel-2影像对土壤质地反演的准确性有很大的影响,而土壤含水量和秸秆覆盖是造成不同时期土壤质地预测精度差异的重要原因。研究为确定土壤质地遥感反演的最佳时间窗口、实现区域尺度土壤质地制图提供关键技术。     

激光法与湿筛－吸管法测定土壤颗粒组成的转换及质地确定

湿筛-吸管法是测定土壤颗粒组成(PSD)的传统方法,而激光法则是新兴的土壤颗粒测定方法,为了明确二者测定数据间的转换关系,应用两种方法分别测定了中国6个主要土纲的265个土壤样品。结果表明,激光法测定的土壤黏粒含量明显地小于湿筛-吸管法测定的数据,激光法测定的土壤粉粒含量明显地大于湿筛-吸管法测定的数据,而对于土壤砂粒含量的测定结果二者互有高低。两种方法测定的黏粒、粉粒和砂粒间均分别具有很好的相关性,甚至按照美国的7级分类标准,每个粒度级别在两种方法间均具有很好的相关性。按照激光法和吸管法测定数据间的转换关系式得出了用激光法测定数据的砂土、壤土和黏土质地划分界限,从而能够应用激光法测定的数据直接进行质地划分,这对于推动激光法在土壤学中的进一步应用和推动土壤科学的发展均具有重要意义。     

Characterization of some Middle European soil textures by gamma‐spectrometry

Information about the variability of different soil attributes within a field is essential for sustainable land management and precision agriculture. Mobile proximal gamma‐ray spectrometry can map soil characteristics of vast areas at different scales rapidly and cost‐effectively. This study aims at investigating reliability and capability of mobile‐gamma‐spectrometry (radiometrics) data to map typical soils of Middle Europe. In this paper, we investigate relationships between the radioelement concentrations (K, U, Th, and dose rate) and soil parameters (texture, CEC, pH, and organic‐C content) at four different field sites and soil textures. The data reliability is confirmed at the survey start. Mobile data have an excellent linear correlation (nearly 1:1) with the stationary readings (of identical devices, acquisition setups, and soil conditions) but moderate correlation with laboratory data (of different devices, setups, and sample conditions). Dried lab samples have systematically higher radioelement concentrations than the field soils (normally wet). Consequently, the mobile‐gamma‐spectrometric data is sufficiently accurate for soil mapping, and its calibration by laboratory data is less useful due to the varying environmental conditions. Single absolute radioelement concentrations show only moderate correlations with the different soil parameters, particularly clay content and CEC. This may be related to varying environmental conditions (soil moisture, soil structure, vegetation, land use, etc.) between the study sites. Investigations of the ratios of radioelement concentrations yield a clear improvement of their correlations to soil parameters, especially for sand and clay contents, CEC, and organic C. Additionally, multiple‐linear‐regression models were established using the element concentrations of potassium and thorium to predict silt content and pH. The results of the highly correlated models were confirmed by comparing with clay and silt content and pH value, respectively, to six additional independent field samples. Briefly, applications of gamma‐ray data for soil mapping offers the possibility of the development of quantitative relationships regarding soil parameters like sand and clay contents, CEC, and organic C. Classification of soil textures by gamma‐ray data seems to be promising, though a broader database of soils is needed for further research. We recommend gamma‐ray mapping as a complementary or even an alternative to common mapping techniques.     

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC_a), reflectance in the visible and near‐infrared (vis‐NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC_a reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐NIR sensor measurements with measurements of EC_a and or IF.     

Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Within the southern Ecuadorian Andes, landslides have an impact on landscape development. Landslide risk estimation as well as hydrological modelling requires physical soil data. Statistical models were adapted to predict the spatial distribution of soil texture from terrain parameters. For this purpose, 56 soil profiles were analysed horizon-wise by pipette and laser method. Results by pipette compared to laser method showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. The performance of regression tree (RT) and Random Forest (RF) models was compared by hundredfold model runs on random Jackknife partitions. Digital soil maps of sand, silt and clay percentage mean and standard deviation indicate model variability and prediction uncertainty.RF models performed better than RT models. All terrain factors considered in the analysis influenced soil texture of the surface horizon, but altitude a.s.l. was assigned the highest variable importance during model construction. Shallow subsurface flow is considered responsible for increasing sand/clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network by removing clay particles downslope. Deeper soil layers are not influenced by this process and therefore, did not show the same texture properties. However, the influence of parent material and landslides on the spatial distribution of soil texture cannot be neglected. Model performance, most probably, could be improved by a bigger dataset.     

土壤持水特性参数与土壤质地尺度关系的联合多重分形分析

Soil water-retention characteristics at measurement scales are generally different from those at application scales, and there is scale disparity between them and soil physical properties. The relationships between two water-retention parameters, the scaling parameter related to the inverse of the air-entry pressure (αvG, cm-1) and the curve shape factor related to soil pore-size distribution (n) of the van Genuchten water-retention equation, and soil texture (sand, silt, and clay contents) were examined at multiple scales. One hundred twenty-eight undisturbed soil samples were collected from a 640-m transect located in Fuxin, China. Soil water-retention curves were measured and the van Genuchten parameters were obtained by curve fitting. The relationships between the two parameters and soil texture at the observed scale and at multiple scales were evaluated using Pearson correlation and joint multifractal analyses, respectively. The results of Pearson correlation analysis showed that the parameter αvG was significantly correlated with sand, silt, and clay contents at the observed scale. Joint multifractal analyses, however, indicated that the parameter αvG was not correlated with silt and sand contents at multiple scales. The parameter n was positively correlated with clay content at multiple scales. Sand content was significantly correlated with the parameter n at the observed scale but not at multiple scales. Clay contents were strongly correlated to both water-retention parameters because clay content was relatively low in the soil studied, indicating that water retention was dominated by clay content in the field of this study at all scales. These suggested that multiple-scale analyses were necessary to fully grasp the spatial variability of soil water-retention characteristics.