Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity

The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (EC_a). In this study, we recorded the EC_a on three fields with an EM38 (Geonics, Canada). The EC_a data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the EC_a due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the EC_a values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the EC_a. Furthermore, in soils with differently textured layers, EC_a was used to estimate the thickness of the uppermost loess layer. A comparison of EC_a and large‐scale soil maps reveals some pros and cons of EC_a measurements. The main advantages of EC_a recordings are the high spatial resolution in combination with low efforts. Yet, the EC_a signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the EC_a pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and EC_a patterns within a field indicates that the EC_a signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, EC_a surveys can be a powerful tool to facilitate and improve conventional soil mapping.     

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.     

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.     

Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil

Spatial variability and relationship between soil apparent electrical conductivity (EC_a), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. EC_a technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil EC_a within a Malaysian rice cultivation area using VerisEC sensor. The EC_a values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil EC_a was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly.     

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.     

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.     

World's Oldest Cotton Experiment: Relationships between Soil Chemical and Physical Properties and Apparent Electrical Conductivity

Abstract

Measuring and mapping apparent soil electrical conductivity (EC_a) is a potentially useful tool for delineating soil variability. The “Old Rotation,” the world's oldest continuous cotton (Gossypium hirsutum L.) experiment (ca. 1896), provides a valuable resource for evaluating soil spatial variability. The objectives of this study were to determine the relationship between soil chemical and physical properties and EC_a in the Old Rotation, to determine spatial differences in these properties, and to relate differences in these properties to long‐term management effects. Soils at the site classified as fine, kaolinitic, thermic Typic Kanhapludults. Soil EC_a was measured at 0–30‐ and 0–90‐cm depths (EC_a‐30 and EC_a‐90) using a Veris^® 3100 direct contact sensor with georeferencing. Soils were grid sampled (288 points) at close intervals (1.5×3.0 m) for chemical properties and grid sampled (65 cells, 7.5×6.9 m) for soil texture. Soil organic carbon (SOC) and total nitrogen (N), extractable phosphorus (P), potassium (K), calcium (Ca), pH, buffer pH, and estimated cation exchange capacity (CEC_est) were measured at two depths (0–5‐ and 5–15‐cm). Soil EC_a was highly spatially correlated. The EC_a‐30 was more highly correlated with clay content (r=0.58, P≤0.01) and P(r=0.43, P≤0.01) than other soil properties. Total nitrogen and SOC had little or no relationship with EC_a‐30. Cropping systems affected chemical properties in the Old Rotation, indicating crop rotation and cover crops are beneficial for soil productivity. The relatively poor relationship between soil chemical parameters and EC_a suggest that mapping plant nutrients and SOC using EC_a is problematic because of strong dependence on clay content.     

Spatial Distribution and Management Zones for Sulphur and Micronutrients in Shiwalik Himalayan Region of India

Agricultural land degradation due to nutrient deficiencies is a threat to agricultural sustainability. As nutrients availability is influenced by soil heterogeneity, climatic conditions and anthropogenic activities; hence, delineation of nutrient management zones (MZs) based on spatial variability could be an effective management option at regional scale. Thus, the present study was carried out to delineate MZs in the Shiwalik Himalayan region of India by capturing spatial variability of soil properties and secondary and micronutrients status because of the emerging nutrient deficiencies. For the study, a total of 2575 geo‐referenced representative surface (0–15 cm depth) soil samples were collected from the study region covering an area of 53,483 km². The soils were analysed for pH, electrical conductivity, soil organic carbon, available sulphur (S) and micronutrients (Zn, Fe, Cu, Mn, B and Mo) concentrations. There was a wide variation in soil properties with coefficient of variation values of 14 (for pH) to 86% for available Mo. Geostatistical analysis revealed spherical, Gaussian, exponential, stable, circular and K‐Bessel best‐fit models for soil properties. Most of the soil properties were having moderate spatial dependence except soil pH and S (strong spatial dependence) and Zn (weak spatial dependence). About 49%, 10%, 2%, 13%, 11%, 12% and 8% area of the study region were found to be deficient (including acute and marginal deficiency) in S, Zn, Fe, Cu, Mn, B and Mo, respectively. The principal component analysis and fuzzy c‐mean clustering were performed to develop the MZs. Four principal components with eigenvalues greater than 1 and accounting 65·4% of total variance were retained for further analysis. On the basis of fuzzy performance index and normalized classification entropy, four potential MZs were identified. Analysis of variance confirmed the heterogeneity in most of the studied soil properties among the MZs. The study indicated that the methodology of delineating MZs can be effectively used in site‐specific S and micronutrients management in the Shiwalik Himalayan region of India. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Establishing soil electrical conductivity ‐ Depth relations from electromagnetic induction measurements

Abstract

Principles of electromagnetic induction (EM) and field calibration approaches are discussed as they pertain to the application of EM to soil systems for the purpose of deriving soil electrical conductivity ‐ depth relations. Evidence is provided to support the utility of EM‐derived estimates of EC_a‐depth relations. Limitations of using electromagnetic induction to determine EC_a for discrete depth intervals through the soil are discussed. Current research designed to increase the accuracy of EC_a‐depth determinations by dealing with the spatial variability problem associated with salinity in soil and by mitigating some of the inherent limitations of the calibration approaches is described.     

Delineation of Management Zones for Southern Root-Knot Nematode using Fuzzy Clustering of Terrain and Edaphic Field Characteristics

Management zones (MZs) for southern root-knot nematode (RKN) from the integration of terrain (TR) and edaphic (ED) field features might facilitate variable rate nematicide applications. This study was conducted on 11 coastal plain fields in the USA. The relationships between RKN populations and five soil ED and TR attributes (apparent soil electrical conductivity [shallow (EC_a-s) and deep (EC_a-d)], elevation (EL), slope (SL), and changes in bare soil reflectance) were analyzed using canonical correlation. Using two ED and TR data sets, canonical predictors were used for zone delineation. Although the results showed that the zones with RKN population above the RKN field average were associated with the lowest values of EC_a-s, EC_a-d, normalized difference vegetation index (NDVI), and SL with respect to field average values, zone segregation was enough using EC_a-s and EC_a-d data. The results suggest the potential for using soil properties to identify RKN risk zones.     

Spatial variability of soil properties and wheat yields in an irrigated field

Abstract

An irrigated farmer's field at Hafizabad village in Dera Ismail Khan District of Northwest Frontier Province of Pakistan was sampled at a regular grid spacing of 50x15 m from surface (15 cm) to study the spatial variability of soil properties and wheat yield. The farm measured 250x75 m. Soil samples collected were analyzed for soil pH, lime content, organic matter, mineral nitrogen (N), ammonium bicarbonate (AB)‐DTPA‐extractable phosphorus (P) and potassium (K), and soil texture. A uniformly trial on wheat with a uniform rate of 120 kg N ha^‐1, 90 kg P₂O₅ ha^‐1, and 60 kg K₂O ha^‐1 was laid out. The results showed that the soil P had the highest coefficient of variation (CV 46%) followed by organic matter (36.20%) and clay content (33.81%). Grain yield had also a considerable variation in the field (CV=31.84%). Geostatistical technique of semivariogram analysis showed that mineral N, AB‐DTPA‐extractable K, sand, silt, and clay content had the strong spatial structure. Maps of soil fertility and crop productivity of the farm was prepared using modern geostatistical technique of kriging. The farm was divided into different management zones based on these maps for fertility management.     

Soil salinity and its distribution determined by soil sampling and electromagnetic techniques

Abstract. Diagnosis of soil salinity and its spatial variability is required to establish control measures in irrigated agriculture. This article shows the usefulness of electromagnetic (EM) and soil sampling techniques to map salinity. We analysed the salinity of a 1‐ha plot of surface‐irrigated olive plantation in Aragon, NE Spain, by measuring the electrical conductivity of the saturation extract (EC_e) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EM_H) and vertical (EM_V) dipole positions. EM_H and EM_V values had asymmetrical bimodal distributions, with most readings in the non‐saline range and a sharp transition to relatively high readings. Most salinity profiles were uniform (i.e. EM_H=EM_V), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EM_H > EM_V, and by EC_e being greater in shallow than in deeper layers. The regressions of EC_e on EM readings predicted EC_e with R² > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 EC_e values estimated from the electromagnetic readings and the 22 measured values of EC_e. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted EC_e values between 3.9 dS m^?1 and 4.2 dS m^?1), although the electromagnetically estimated EC_e improved the mapping of details. Whereas soil sampling is preferred for analysing the vertical distribution of soil salinity, the electromagnetic sensor is ideal for mapping the lateral variability of soil salinity.     

SOIL SALINITY AND SODICITY APPRAISAL BY ELECTROMAGNETIC INDUCTION IN SOILS IRRIGATED TO GROW COTTON

In the Far West Texas region in the USA, long‐term irrigation of fine‐textured valley soils with saline Rio Grande River water has led to soil salinity and sodicity problems. Soil salinity [measured by saturated paste electrical conductivity (EC_e)] and sodicity [measured by sodium adsorption ratio (SAR)] in the irrigated areas have resulted in poor growing conditions, reduced crop yields, and declining farm profitability. Understanding the spatial distribution of EC_e and SAR within the affected areas is necessary for developing management practices. Conventional methods of assessing EC_e and SAR distribution at a high spatial resolution are expensive and time consuming. This study evaluated the accuracy of electromagnetic induction (EMI), which measures apparent electrical conductivity (EC_a), to delineate EC_e and SAR distribution in two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting EC_a into EC_e and SAR were derived using the multiple linear regression (MLR) model included in the EC_e Sampling Assessment and Prediction program package developed by the US Salinity Laboratory. Correlations between EC_a and soil variables (clay content, EC_e, SAR) were highly significant (p ≤ 0·05). This was further confirmed by significant (p ≤ 0·05) MLRs used for estimating EC_e and SAR. The EC_e and SAR determined by EC_a closely matched the measured EC_e and SAR values of the study site soils, which ranged from 0·47 to 9·87 dS m⁻¹ and 2·27 to 27·4 mmol^1/2 L^−1/2, respectively. High R² values between estimated and measured soil EC_e and SAR values validated the MLR model results. Results of this study indicated that the EMI method can be used for rapid and accurate delineation of salinity and sodicity distribution within the affected area. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessing the Value of Using a Remote Sensing-Based Evapotranspiration Map in Site-Specific Management

ABSTRACT

In the glaciated regions of the northern Great Plains, water - either too much or too little - influences soil development, carbon storage, and plant productivity. Integrating site-specific water variability information directly into management is difficult. Simulation models that employ remotely sensed data can generate hard to measure values such as evapotranspiration (ET). This information can be used to identify management zones. The objective of this study was to determine if the METRIC (Mapping Evapotranspiration at High Resolution and with Internalized Calibration) model, which uses weather station and remote sensing data can be used as a tool in site-specific management. This study was conducted on a 65 ha corn (Zea mays L.) field located in east central South Dakota. The METRIC model used Landsat 7 data collected on August 4, 2001 to calculate ET values with spatial resolution of 30 m. ET values were correlated with corn yield (r = 0.85??), apparent electrical conductivity (EC_a; r = 0.71??), soil organic carbon (SOC; r = 0.32?), and pH (r = 0.28?). In the footslope positions, high ET values were associated with high corn yields, SOC, EC _a , and pH values, while in the summit/shoulder areas low ET values were associated with low yields, SOC, EC_a, and pH values. The strong relationship between ET and productivity was attributed to landscape processes that influenced plant available water, which in turn influenced productivity. Cluster analysis of the ET and EC data showed that these data bases complimented each other. Remote sensing-based ET data was most successful in identifying areas where water stress reduced corn yields, while EC_a was most successful in identifying high yielding management zones. Findings from this study suggest that remote sensing-based ET estimates can be used to improve management zone delineation.     

Spatial evaluation of soil salinity using the WET sensor in the irrigated area of the Segura river lowland

The electrical conductivity of the water within the soil pores (EC_p) measured with the WET sensor, appears to be a reliable estimate of soil salinity. A methodology combining the use of the WET sensor along with geostatistics was developed to delimit and evaluate soil salinity within an irrigated area under arid to semiarid Mediterranean climate in SE Spain. A systematic random sampling of 104 points was carried out. The association between EC_p and the saturation‐extract electrical conductivity (EC_se) was assessed by means of correlation analysis. The semivariograms for EC_p were obtained at three different soil depths. Interpolation techniques, such as ordinary kriging and cokriging, were applied to obtain EC_p levels in the unknown places. For each one of the soil depths, a model able to predict EC_se from EC_p was developed by means of ordinary least squares regression analysis. A good correlation (r = 0.818, p < 0.001) between EC_p and EC_se was found. Spherical spatial distribution was the best model to fit to experimental semivariograms of EC_p at 10, 30, and 50 cm soil depths. Nevertheless, cokriging using the EC_p of an adjacent soil depth as an auxiliary variable provided the best results, compared to ordinary kriging. An analytical propagation‐error methodology was found to be useful to ascertain the contribution of the spatial interpolation and ordinary least squares analysis to the uncertainty of the EC_se mapping. This methodology allowed us to identify 98% of the study area as affected by salinity problems within a rooting depth of 50 cm, with the threshold of EC_se value at 2 dS m^–1. However, considering the crops actually grown and 10% potential reduction yield, the soil‐salinity‐affected area decreased to 83%. The use of sensors to measure soil salinity in combination with geostatistics is a cost‐effective way to draw maps of soil salinity at regional scale. This methodology is applicable to other agricultural irrigated areas under risk of salinization.     

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。