Use of Soil Electroconductivity in a Multistage Soil-Sampling Scheme

The inherent variability of pasturelands makes it difficult to sample soils and accurately characterize a pasture. Indirect methods such as soil electroconductivity (EC) can be used to rapidly, noninvasively, and inexpensively quantify soil variability. The objective of this study was to determine if rapidly collected, georeferenced soil information could be used to propose an accurate, multistage sampling scheme for five soil variables in a central Iowa pasture. Results from this study suggest that the use of noninvasively collected soil EC and topographic data along with fuzzy k-means clustering can be used to delineate relatively homogeneous sampling zones. Consequently, these easily defined sampling zones can beneficially serve as a more directed approach to soil sampling. Introduction Devising a soil-sampling scheme in a pasture situation is difficult due to the inherent variability in pasture landscapes (Fig. 1). The design of traditional sampling schemes, such as grid and triangular, overlook a very important truth in field studies: certain areas of a field are more similar than other areas of the field. This fact is also the fundamental principle of geostatistics: points that are located close together are often more similar than points located far apart. This principle can be applied to generating a sampling scheme for a field situation that is more efficient. A “good” sampling scheme should be able to quantify field variability as accurately as possible and with as few sampling points as possible. An initial step in sampling a field may be to divide the field into a number of homogeneous strata (5). Stratified sampling requires taking one or multiple samples within each stratum (15). If the areas within the strata were homogeneous, this would support the principle of geostatistics by grouping together points within a field that are similar. Two-stage sampling designs begin with an initial sampling of primary units and then secondary units are selected from each of the selected primary units (15). Further stages of sampling from the secondary or higherorder units may follow and are termed multistage sampling. However, how does one know if areas of a field are similar if samples have not yet been taken? One way to quickly measure field variability is by use of electromagnetic induction (EMI). Soil electrical conductivity (EC) can be measured on an Fig. 1. Rolling topography of pastures. Crop Management 29 October 2003 extremely small grid in a rapid, easy and nondestructive manner by the use of EMI. Soil EC is affected by a number of soil characteristics including soil water content, dissolved salt content, clay content and mineralogy, and soil temperature (11). Soil EC, as measured by EMI, has been correlated with clay content (20), soil water content (8), sand deposition (9), total soluble salts (20), yield (7), and soil available N (6). Benefits from the relationship between soil EC and various soil properties include improved soil mapping, prediction, and management. In this study, the coupling of soil EC measurements with global positioning systems (GPS) was used to rapidly, easily, and nondestructively collect georeferenced data. By using this information to measure field variability within the pasture, a sampling scheme was devised. Sampling efficiency could be improved by sampling a field more densely in areas that are heterogeneous and less densely in areas that are homogeneous; thus, a stratified sampling scheme was used. The objective of this study was to determine if rapidly collected, georeferenced soil information could be used to propose an accurate, multistage soil-sampling scheme. Pasture Site Description Research was conducted at the Iowa State University Rhodes Research Farm (41°52'N, 93°10'W) in central Iowa. The Wisconsin loess-covered landscape has an underlying Yarmouth-Sangamon paleosol. The soils are primarily slope and erosion phases of the Fayette (Fine-silty, mixed, superactive, mesic Typic Hapludalfs) and Clarinda (Fine, smectitic, mesic Vertic Argiaquolls) series (T. E. Fenton, personal communication, 2001). The pasture site of the study included topographically distinct summit, sideslope, toeslope, backslope, and opposite summit landscape positions (Fig. 2). Sampling Methods A dense soil-sampling grid consisting of 116 points was devised for a 1-acre, nongrazed grass-legume pasture (Fig. 3). Sampling points were arranged in a triangular grid with interand intra-row separation distances of 19.7 ft. In order to obtain data from samples located closer than 19.7 ft, an additional point was sampled within each row at randomly chosen 3.3 or 6.6 ft separation distances. This short range variation in soil samples was investigated in order to obtain a more reliable experimental semivariogram model (3). Soil EC estimates were obtained with a Geonics EM-38 (Geonics, Ontario, Canada) as it was pulled behind a four-wheel drive vehicle in a nonconductive cart. The EM-38 was operated in the vertical dipole orientation at 0.65 ft above the soil surface. The EM-38 integrates over an area approximately equal to its Fig. 2. Wireframe map illustrating topographic variation of pasture site (Golden Software, Inc., Golden, Colorado). Units of feet shown above mean sea level. Fig. 3. Initial dense sampling scheme (n = 116). Crop Management 29 October 2003 length of 3.3 ft and over a depth of approximately 9.8 ft (12). However, the measurement is primarily influenced by the 0-to-4.9-ft depth increment (12). Given the speed of travel and rate of EC data collection, an EC reading was logged approximately every 13 ft along transects spaced 6 ft apart throughout the pasture. Each soil EC measurement was georeferenced using a Trimble GPS Pathfinder Pro XR receiver (Trimble, Sunnyvale, CA), and all GPS locations were differentially corrected (DGPS) to obtain 3-to-6-ft accuracy. EM-38 measurements were recorded via a direct connection to Trimble System Controller 1 Asset Surveyor software (Trimble, Sunnyvale, CA) in the GPS datalogger. Positional data for the soil EC values were corrected for the lag distance between GPS receiver and the EM-38 instrument. Soil EC values at each of the 116 grid points were interpolated from the dense data set of the 834 georeferenced EC points. Soil samples were collected via coring following the EM-38 measurements. At each of the 116 sampling sites, five 6-inch soil cores were collected and combined for soil pH and available P and K analysis in the Iowa State University Soils Testing Laboratory (Ames, IA). Organic matter was analyzed from a single core composed of three depth increments to 18 inches, using a dry combustion method. Organic matter percentage averaged across all three depth increments was reported. To quantify soil moisture, a single core composed of seven 6-inch samples was taken. Each 6-inch sample was analyzed using the gravimetric moisture method (2), and average percent soil moisture was reported. Elevation data were recorded using a Leica System 500 real time kinematic (RTK) system (Leica, Switzerland), and slope data were calculated from this using ArcView 3.2 Spatial Analyst (ESRI, Redlands, CA). Geostatistical analyses were performed using ArcView 8.1 ArcGIS Geostatistical Analyst (ESRI, Redlands, CA). Multistage Sampling Scheme Multistage sampling was examined as a stepwise method to create relatively homogeneous sampling zones. Using recorded soil EC and topographic data, a fuzzy k-means algorithm was implemented as the next step in the multistage sampling scheme. The algorithm was used to stratify the field into relatively homogeneous zones based on the densely collected soil EC and topographic data. The fuzzy k-means method has been utilized for classifying soil and landscape data when binary or strictly discrete groupings are not adequate to describe natural systems (4). Given the continuous nature of soils, fuzzy set classification provides a suitable means of classifying areas of a field. Elevation, slope, and apparent soil EC were used to delineate zones using the software program Management Zone Analyst (18). Based on the software output, five zones appeared optimal for establishing strata of homogeneity in the pasture (Fig. 4). Within these five strata, two intensities of a ranked set sampling scheme (10) were analyzed: n = 30 and n = 15 (Figs. 5 and 6). Ranked set sampling was first described by McIntyre (10) as a method for obtaining more precise and unbiased measurements of forage yield. The five strata delineated by clustering corresponded to the sets in ranked set sampling. The points within each of the five strata were ranked in order of EC value magnitude. Soil EC was chosen as a concomitant variable because it was the variable most easily and accurately collected (14), and it was correlated with the soil variables of interest. When constructing the n = 30 scheme, six points were selected in each of the five strata. With this scheme, the six points were chosen based on maximizing the within-zone variation of soil EC. The six points were selected based on choosing the minimum, maximum, and four in-between quantile values of soil EC within each zone. Maximum within-zone variation was sought in order to maintain the variability identified throughout the field. Similarly, when devising the n = 15 scheme, three points were selected in each of the five strata with the goal of maximizing the within-zone variation of the concomitant variable, soil EC. The three points were selected based on choosing the minimum, maximum, and median values of soil EC within each zone. Crop Management 29 October 2003 Field Characterization The statistical summaries of topographic and soil attributes for the initial, dense grid (n = 116) sampling scheme indicated a range in elevation of 21 ft (16). Large CVs and ranges for soil P, K, pH, OM, and moisture illustrated the magnitude of soil variability within the pasture (16). The degree to which soil EC can detect this soil variability improves our ability to delineate homogeneous sampling strata. How well soil EC identifies soil variability depends upon soil EC’s relationship with the soil parameters of interest. The results of the large database indicated a strong correlation between soil EC and soil pH, elevation and soil K (r values greater than 0.5). Soil EC was moderately correlated with soil moisture and OM values (0.10 < r < 0.50) and weakly correlated with soil P and slope (r < |0.10|) (16). These correlations were not of primary interest in our objectives, however. Of interest was determining whether soil EC could be used effectively to identify soil spatial variation and zones of homogeneity from which to sample. In the pasture of study, variability in soil EC appeared closely related to the variation in landscape position and depth to paleosol (T. E. Fenton, personal communication, 2002). Higher values of soil EC were measured in the toeslope positions. These positions have a higher moisture content and are underlain by a clay-textured paleosol (Clarinda series). Lower values of soil EC were measured upslope on the summit positions where the soils were developed entirely in loess. Middle values were measured on the backslope where the soils are formed in loess and the underlying paleosol. Because the soil was not dominated by carbonates, the variation in soil EC values was likely related to soil moisture content and textural properties (1). Textural properties influence soil parameters such as organic matter and ionic properties; thus, it was concluded that soil EC is measuring variation in soil properties related to moisture and texture. Performance of Fuzzy Classification Using elevation, slope, and apparent soil EC data, a fuzzy k-means clustering algorithm resulted in the delineation of 5 zones throughout the pasture (Fig. 4). This clustering agreed well with landscape position. Zone 1 included bottomland and backslope characteristics; Zone 2 was primarily a sideslope; Zone 3 was primarily bottomland; Zone 4 combined all three landscape positions, but with Fig. 4. Fuzzy clustering results for the initial sampling scheme (n = 116). Fig. 5. Sampling points for multistage sampling scheme (n = 30). Fig. 6. Sampling points for multistage sampling scheme (n = 15). Crop Management 29 October 2003 more gently rolling sideslopes compared to Zone 2; Zone 5 was a region consisting primarily of summit land but with some sideslope area. Cluster membership was spatially discrete with only a few points that were nonadjacent to other members of their zone. Zone 5 included points on both the west and east ends of the pasture, but this separation in zone membership was likely due to the repeating landscape pattern in the field. Zone 5 revealed a repeated summit from the repeating summit, sideslope, toeslope, and backslope pattern in the field. Table 1 quantitatively describes each of the five strata. It is worth noting that the five zones are not of equal size. The fuzzy clustering algorithm reiteratively classified each of the 116 points until each point in a zone was more similar to the cluster centroid than to any other cluster. Therefore, a relatively small amount of variability in soil measurements was expected within each zone. Zone 3 was primarily a bottomland area and it exhibited the most variability in soil P, pH, organic matter, moisture, and EC. Zone 5 showed the most variability in soil K. Table 1. Mean and standard deviation (SD) for chemical and physical soil attributes shown for each zone. * ppm is parts per million, OM is organic matter, mS/m is milliSiemens per meter. With the five strata delineated, an analysis of variance using SAS (SAS Institute, Cary, NC) was performed to determine if the strata were indeed different based upon the measured soil variables. If the zones were significantly different from one another based on the soil parameters, then they would be considered acceptable sampling zones. Results from the ANOVAs for soil P, K, pH, OM, and gravimetric moisture are shown in Table 2 based on the entire data set (n = 116). The results indicated that the effect of zone on all the soil parameters was significant. Thus, the resultant zones from the fuzzy clustering algorithm appear to have delineated significantly different zones based on the five soil attributes. Table 2. One-way ANOVA results for five zones. df = degrees of freedom, MS = Mean Square, F = F ratio * Significant at the 0.001 probability level. Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 n = 21 n = 20 n = 15 n = 37 n = 23 Mean SD Mean SD Mean SD Mean SD Mean SD Elevation (feet) 979.7 3.3 981.0 3.3 974.4 0.7 989.8 1.6 989.2 3.3