基于土壤电导率时空变异性的管理分区技术研究

A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity （ECb） measurements were performed thrice in situ in the topsoil （0-20 cm） across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity （EC）, to identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for sitespecific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be managed in a site-specific way.     

基于多源数据的盐碱地精确农作管理分区研究

为了便于对盐碱地实施变量管理和精确农作，以海涂围垦区盐碱土为研究对象，以NDVI数据、盐分数据以及作物产量数据作为分区变量，对一面积为15 hm²的盐碱地农田进行了基于多个数据源的精确农作管理分区研究。利用模糊c均值聚类方法进行分类分区，引入了模糊聚类指数(FPI)和归一化分类熵(NCE)作为最佳分区数目的判断标准，通过单项方差分析对分区结果进行比较和评价。研究发现，对本研究区，最佳的分区数目为三个。不同管理分区之间土壤化学性质(EC1:5,有机质，速效磷，速效钾，全氮，碱解氮以及阳离子交换量)的均值都存在着统计意义上的显著差异性，其中子区3具有最高的肥力水平和作物生产能力而子区1最低。利用所选取的三个变量，模糊c均值聚类算法可以较好地进行精确农作管理分区划分。分区结果不但可以指导采样, 而且可以作为变量管理的决策单元用于田间变量管理作业中,为精确农业变量投入的实施提供有效手段和决策依据。     

Assessing soil spatial variability and delineating site-specific management zones for a coastal saline land in eastern India

ABSTRACT

The present study was to delineate management zones (MZs) in salt affected Mahakalpada block in eastern India by capturing both spatial variability of soil parameters along with satellite derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Grid wise 237 soil samples collected from the study area were analyzed and spatial maps were generated for physicochemical properties, DTPA extractable micronutrients, i.e. iron, zinc, copper, and manganese and major nutrients, i.e. available nitrogen (AN), phosphorous (AP), and potassium (AK). Soil electrical conductivity and AK showed a high CV of 100% and 56.7%, respectively. Principal component analysis was performed using the soil spatial maps, NDVI and EVI maps and only four principal components which produced eigenvalues > 1 and accounting for 75.4% of the total variability were retained for further analysis. Further, fuzzy c-mean clustering was used to delineate the MZs based on fuzzy performance index (FPI) and normalized classification entropy (NCE) was used for identifying the three MZs. There was a significant difference between MZ1 and MZ2 for all the variables except AN and EVI whereas all the variables were significantly different between MZ1 and MZ3 highlighting the usefulness of MZs delineation technique for site-specific nutrient management.     

Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area

Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of: (i) top- and subsoil samples taken at 110 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO₃ and pH were analysed, (ii) soil apparent electrical conductivity (EC_a) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two EC_a variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, EC_a that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, EC_a and elevation can be considered as key properties to delineate potential management classes.     

Multiscaling analysis of soil roughness variability

Soil surface roughness (SSR) is a parameter highly suited for the study of soil susceptibility to wind and water erosion. The development of a methodology for quantifying SSR has typically been based on field techniques to obtain data, rather than on the indexes used for interpreting soil roughness variability. One of the most used indexes to evaluate SSR is the random roughness (RR), easily calculated from the heights obtained with a pin meter. The RR index was obtained from soil elevation measurements collected at the intersections of a 2 × 2-cm² grid in a 100 × 400-cm² plot from three different types of soil. SSR values for all soil types were obtained after passing three different tillage tools (chisel, tiller, and roller) through three types of soils at field conditions. The RR index was calculated using the standard deviation (SD) of the lines parallel to the direction of tillage. Lines were 20 mm apart.Since RR assumes vertical random roughness without correlation, the variability of SSR was assessed using structure function (SF) to complement the study. Therefore, the main objective of this analysis was to better illustrate the variability of SSR in relation to spatial distribution. The SF was highly sensitive to soil roughness variability and depended on the tillage tool treatments and soil types, thereby illustrating the origin of the soil roughness variability, either from the soil itself or from the tillage tool used. We also demonstrate that the concept of a generalised Hurst exponent derived from the SF improves our ability to differentiate among the cases.     

不同坡面位置土壤水分差异规律分析

不同坡向土壤湿度差异大。试区内，土壤储水量以北坡为最高，次之是西向坡与南向坡。从坡顶到坡脚，3m土层平均土壤湿度由小到大;在垂直方向上，各坡面湿度可分成浅层和深层两个层次。在浅层内，湿度值从坡顶到坡脚逐渐减小或差别不大;在深层内，从坡顶到坡脚逐渐增大，水土保持工程措施和生物措施可缓解降水的再分配。     

Spatial variability and temporal dynamics analysis of soil erosion due to military land use activities: uncertainty and implications for land management

Human activities, such as military off‐road vehicular traffic, disturb ground and vegetation cover of landscapes and increase potential rainfall related runoff and soil erosion. On military lands, soil erosion is of major concern in order to sustain training lands and thus there is a need for land condition maps for planning training activities and land management. In this study, we presented a conditional co‐simulation algorithm to generate annual time series maps of soil erosion status from 1989 to 2001 for an army installation. The spatial variability and temporal dynamics of land condition were analyzed. This algorithm modeled soil erosion as realizations of a random function by combining a set of permanent plot data and Landsat Thematic Mapper (TM) images. In addition to estimation maps of soil erosion status, we obtained the maps of uncertainties including the variance of each pixel estimate and the probability of poor land condition. The results and maps are useful tools for land managers and decision‐makers to guide military training programs and to generate management plans for sustaining training lands. Copyright © 2007 John Wiley & Sons, Ltd.     

农田土壤颗粒组成及其剖面分层的空间变异分析

对60m55m的农田尺度上100个取样点土壤颗粒组成及剖面层次的空间变异性的传统统计分析表明,各属性的变异系数属中等强度。半方差函数模型均为球状模型,各属性具有一定的空间相关性,并表现有一定的各向异性。在对土壤颗粒组成及剖面层次空间分布趋势分析的基础上,根据半方差函数模型,对各属性选择不同趋势和异向性的普通Kriging内插值比较的结果表明,考虑变量在空间分布趋势和异向性的插值结果比不考虑要好。在以上分析基础上,利用Kriging内插值绘制各层深度和颗粒组成的等值线图,并分析了其空间分布规律。     

Detecting zones of abrupt change in soil data, with an application to an agricultural field

We propose a new method for estimating and testing the zones where a variable has discontinuities or sharp changes in the mean. Such zones are called Zones of Abrupt Change (ZACs). Our method is based on the statistical properties of the estimated gradient of the variable. The local gradient is first interpolated by kriging. Then we test whether the estimated local gradient exceeds some critical threshold computed under the null hypothesis of a constant mean. The locations where the local test is rejected define the potential ZACs, which are then tested globally. Using this method, we analysed soil data from an agricultural field. The analysis of the main soil components of the ploughed layer (clay, silt and sand particles and calcium carbonate content) reveals the structural variations in the field, linked to boundaries between soil types. Its application to non‐permanent variables (soil water and mineral nitrogen content of the soil profile to 120 cm taken at several dates) shows that water content has the same ZACs for all dates, whereas mineral nitrogen has none.     

基于聚类及PCA分析的红壤坡耕地耕层土壤质量评价指标

为准确评价红壤坡耕地耕层土壤质量特征,该文采用聚类分析法(CA)和主成分分析法(PCA)分别建立了南方红壤丘陵区坡耕地耕层质量诊断最小数据集(minimum data set,MDS),并利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和soil quality index-PCA,SQI-PCA)和全量数据集土壤质量指数(soil quality index-total,SQI-T)对坡耕地耕层特征进行分析。结果表明:1)红壤坡耕地耕层土壤质量变化特征差异明显,其中耕层平均厚度(19.93±4.9)cm,接近作物生长适宜水平;土壤有机质、全氮平均含量分别为(17.43±8.71)和(0.97±0.42)g/kg,处于中度贫瘠化水平;土壤有效磷和速效钾含量丰富,平均含量分别为(26.1±22.22)和(155.46±88.35)mg/kg;p H均值为(5.34±0.77),土壤呈弱酸性。2)红壤坡耕地耕层土壤质量评价最小数据集由耕层厚度、土壤容重、土壤贯入阻力、土壤有机质、p H值和有效磷组成。基于不同数据集的耕层土壤质量评价结果差异明显,土壤质量指数变化范围、均值表现为SQI-TSQI-CASQI-PCA,变异系数表现为SQI-TSQI-CASQI-PCA,SQI-CA与SQI-T的Nash有效系数和相关度高于SQI-PCA,而相对偏差系数和平均相对误差则低于SQI-PCA,这表明基于聚类分析最小数据集(MDS-CA)较基于主成分分析最小数据集(MDS-PCA)更适合替代全量数据集(total data set,TDS)对耕层土壤质量进行评价。3)从保水、保土、保肥及增产潜力角度看,红壤坡耕地合理耕层诊断指标适宜性阈值为耕层厚度≥20.39 cm,土壤容重0.92~1.21 g/cm3,土壤贯入阻力≤1.21 kg/cm3,土壤有机质含量≥18.82 g/kg,p H值5.04~5.38,有效磷≥28.83 mg/kg。合理深松是构建合理耕层的有效措施之一。该研究结果可为南方红壤丘陵区坡耕地耕层质量恢复、农作物生产适宜性调控和坡耕地水土流失阻控提供参考,有利于红壤丘陵区坡耕地资源持续利用。     

基于多源环境变量的橡胶园土壤管理分区

为了提高大尺度范围内橡胶园土壤管理的针对性,以海南省国营八一农场橡胶园为研究对象,以地形因子(海拔、坡度和坡向)、成土母质、气候变量(平均降雨量和平均气温)和植被指数为数据源,对橡胶园进行土壤管理分区。利用模糊C均值聚类法进行分区,以模糊性能指数(fuzzy performance index,FPI)和归一化分类熵(normalized classification entropy,NCE)作为判断最佳分区数的标准,并通过单因素方差分析和分区前后土壤属性以及环境变量变异系数对比对分区结果进行评价和验证。研究结果表明,橡胶园管理分区的最佳数目为3个。不同管理分区之间土壤属性(pH值、有机质、全氮、有效磷、速效钾、有效硫、交换性钙、交换性镁、有效铜、有效铁和有效锰)和环境变量(高程、坡度、降雨量、平均温和归一化植被指数)的差异性都达到了极显著水平(P0.01),同时,3个分区中土壤属性和环境变量变异系数的均值比分区前明显下降。这就验证了在大尺度范围内,利用较易获取的多源环境变量进行橡胶园土壤管理分区是可行的,同时依据不同分区的特点制定了相应的土壤管理措施,提高了大范围区域内橡胶园土壤管理的针对性。     

基于GIS和多种土壤属性的烟田养分分区管理研究

以平顶山典型烟区烟田土壤为研究对象,用111个样点耕层土壤（0 ~ 20 cm）的pH、有机质、总N、碱解N、速效P、速效K、活性有机质、阳离子交换量等数据对烟田进行管理分区研究。利用主成分分析从繁杂的数据中提取3个主成分,利用MZA软件进行模糊聚类分析从而实现分区,采用FPI和NCE来确定最佳分区数。结果表明研究区的最佳分区数为3,模糊指数为1.5。各分区内土壤养分的变异系数都较整个研究区有所降低,而分区间土壤养分差异显著。研究区的平均混乱度指数为0.37,不同模糊类别交叠程度较小,地理空间上土壤的隶属关系相对明确。通过模糊聚类分析法可以较好地进行管理分区的划分,分区结果可以作为变量施肥的单独作业单元进行肥料管理。     

离群样点对土壤养分空间变异分析的影响研究

<正>土壤采样是估测区域土壤特性统计参数和空间变异分析模型的重要方式[1],由于实验或输入错误等原因可能导致样点数据中存在与大部分样点值有明显不同的离群样点。这将影响样点数据集的质量,常可以使参数估测分析结果与真实状态值     

An approach to assessment of management impacts on agricultural soil carbon

Agroecosystems contain about 12% of the terrestrial soil C and play an important role in the global C cycle. We describe a project to evaluate the degree to which management practices can affect soil C in agroecosystems. The objectives of the project are to determine if agricultural systems can be managed to conserve and sequester C and thereby reduce the accumulation of CO₂ in the atmosphere, and to provide reference datasets and methodologies for agricultural assessments.     

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site‐specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (EC_a) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and EC_a. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.     

基于模糊C均值聚类的牛肉图像中脂肪和肌肉区域分割技术

介绍了一种自适应分割牛肉眼肌切面图像中脂肪和肌肉区域的图像处理技术。通过CCD摄像头获取以黑色平板为背景的牛肉眼肌切面彩色RGB图像。先根据彩色图像R分量的灰度直方图，利用最大方差自动取阀值法(OSTU)把黑色背景与整块牛肉图像分割开来；接着把处理后的图像变成灰度图像，用模糊C均值聚类算法(FCM)计算出牛肉脂肪像素和肌肉像素灰度值的聚类中心，以各个像素点灰度值与两个聚类中之间的绝对值距离来区分出图像中的脂肪和肌肉像素。结果表明，FCM方法是分割肌肉和脂肪区域的有效方法。     

Identifying potential management zones in a layered soil using several sources of ancillary information

Variation in soil texture has a profound effect on soil management, especially in texturally complex soils such as the polder soils of Belgium. The conventional point sampling approach requires high sampling intensity to take into account such spatial variation. In this study we investigated the use of two ancillary variables for the detailed mapping of soil texture and subsequent delineation of potential management zones for site‐specific management. In an 11.5 ha arable field in the polder area, the apparent electrical conductivity (EC_a) was measured with an EM38DD electromagnetic induction instrument. The geometric mean values of the EC_a measured in both vertical and horizontal orientations strongly correlated with the more heterogeneous subsoil clay content (r = 0.83), but the correlation was weaker with the homogenous topsoil clay content (r = 0.40). The gravimetric water content at wilting point (θg_{(?1.5 MPa)}) correlated very well (r = 0.96) with the topsoil clay content. Thus maps of topsoil and subsoil clay contents were obtained from 63 clay analyses supplemented with 117θg_{(?1.5 MPa)} and 4048EC_a measurements, respectively, using standardized ordinary cokriging. Three potential management zones were identified based on the spatial variation of both top and subsoil clay contents. The influence of subsoil textural variation on crop behaviour was illustrated by an aerial image, confirming the reliability of the results from the small number of primary samples.     

Use of an inverse method and geostatistics to estimate soil hydraulic conductivity for spatial variability analysis

A field method for determining the soil hydraulic properties using a parameter estimation technique is presented. Input data for the inverse problem are soil-water potentials and soil-water contents measured at different soil depths and different times during a field transient drainage experiment. For the water retention function the parametric relation suggested by Van Genuchten was adopted. For the hydraulic conductivity function the relation proposed by Van Genuchten and the exponential relation were adopted.

With the proposed method soil hydraulic properties along a transect of a volcanic Vesuvian soil were determined using as boundary condition the unit gradient of total potential at the bottom of the soil profile. Geostatistics were used to describe the spatial variability of hydraulic conductivity characteristics of the soil here considered.

Finally, results obtained using this method were compared with those of the simplified method suggested by Sisson and Van Genuchten based on a unit gradient water flow model.     

Field-scale spatial variability of soil calcium in a semi-arid region: Implications for soil erosion and site-specific management

Excess calcium(Ca) in soils of semi-arid and arid regions has negative effects on soil structure and chemical properties, which limits the crop root growth as well as the availability of soil water and nutrients. Quantifying the spatial variability of soil Ca contents may reveal factors influencing soil erosion and provide a basis for site-specific soil and crop management in semi-arid regions. This study sought to assess the spatial variability of soil Ca in relation to topography, hydraulic attributes, and soil types for precision soil and crop management in a 194-ha production field in the Southern High Plains of Texas,USA. Soils at four depth increments(0–2, 0–15, 15–30, and 30–60 cm) were sampled at 232 points in the spring of 2017. The Ca content of each sample was determined with a DP-6000 Delta Premium portable X-ray fluorescence(PXRF) spectrometer. Elevation data was obtained using a real-time kinematic GPS receiver with centimeter-level accuracy. A digital elevation model(DEM) was derived from the elevation data, and topographic and hydraulic attributes were generated from this DEM. A generalized least-squares model was then developed to assess the relationship between soil Ca contents of the four layers and the topographic and hydraulic attributes. Results showed that topographic attributes, especially slope and elevation, had a significant effect on soil Ca content at different depths(P 0.01). In addition, hydraulic attributes, especially flow length and sediment transport index(STI), had a significant effect on the spatial distribution of soil Ca. Spatial variability of soil Ca and its relationships with topographic and hydraulic attributes and soil types indicated that surface soil loss may occur due to water or wind erosion, especially on susceptible soils with high slopes. Therefore, this study suggests that the application of PXRF in assessing soil Ca content can potentially facilitate a new method for soil erosion evaluation in semi-arid lands. The results of this study provide valuable information for site-specific soil conservation and crop management.     

Neural network ensemble residual kriging application for spatial variability of soil properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.