Proximal or on-the-go sensors measuring soil electrical conductivity have shown good relationships with soil properties affecting crop production, including texture [1] [2], cation exchange capacity [3] [4], soil water content [3] [5], drainage condition [6], salinity [7] [8] [9], and subsoil characteristics [8] [10] [11]. The systems have shown promise in improving and developing soil boundaries because of their links to the global positioning system [2] [12]. Their popularity is increasing because of the various configurations available for integrating them with farm and commercial equipment [8] [9]. They can acquire large amounts of data cheaply and more quickly than manual surveys, thus, providing more detail on the spatial variability of an area [13]. Other systems such as ground penetrating radar [14], multispectral and hyperspectral imagery [6] [14], and time domain reflectometry [15] have shown potential to map soil spatial variability. Nevertheless, apparent electrical conductivity (EC_a) has been evaluated more than any other technique for studying soil spatial patterns [9] and has been recognized as a valuable tool to study soil spatial variability in agricultural settings [7] [16]. At the current rate, apparent electrical conductivity (EC_a) systems will be the staple equipment for mapping soil spatial variability now and in the immediate future.

Yet, a better understanding of the stability of EC_a measurements is needed so that users of the equipment have insight into the temporal stability of the readings. Using electromagnetic induction or the Veris mobile platform [17] [18] [19] [20] reported EC_a stability ranging from 2 months to 4 years. Also, more information is needed on comparing EC_a maps derived from over a period of time because the information would be valuable to farmers, consultants, and researchers interested in using the equipment to make management decisions.

Mississippi, USA, consists of various soils with different characteristics. Research is lacking in this area on the stability of apparent electrical conductivity measurements over time on soils used for agricultural production. The objective of this study was to compare spatial patterns of EC_a data collected at two different periods to determine the temporal stability of map products derived from the data. The study focused on data collected in 2016 and 2021 from a field plot consisting of clay and loam soils in Mississippi.

The descriptive statistics are summarized in Table 1 for both years. The EC_a shallow mean (t = 117.85, df = 1997, P < 0.05, 2016; t = 106.01, df = 1764, P < 0.05, 2021), median, minimum, and maximum values were less than the EC_a deep mean, median, minimum, and maximum values. The EC_a shallow readings were more variable than the EC_a deep readings according to the coefficient of variation values. Statistically significant positive correlation coefficients (Table 2)

^an = number of samples, Min = minimum, Max = maximum, and CV = coefficient of variation; for each year, mean values followed by a different letter represent statistical significance at P < 0.05 according to paired t-test.

^an = number of samples, *statistically significant at P < 0.05.

were observed between each measurement data’s EC_a shallow and deep readings. Also, both EC_a measures had a strong positive relationship with the y-coordinate, indicating a linear trend in the dataset based on direction.

Figure 2 and Figure 3 illustrate the map derived from clustering the EC_a shallow and deep values collected in 2016 and 2021, respectively. The lowest values were observed in the southeastern corner of the field. In contrast, the highest values were detected in the northern section of the plot. Low-Medium, Medium, and Medium-High values occurred from south to north in the field. A noticeable trend was observed on the maps; the values were similar in the southwest to the northeast direction (the other way around) and more variable in the south to the north direction (the other way around).

Descriptive statistics derived from the EC_a readings of the clusters are presented in Table 3. The clustering algorithm assigned the lowest number of points to the lowest zone for each dataset. These points were clustered in the southeastern section of the field (Figure 2 and Figure 3). The second-highest number of points was assigned to the Medium-High to High zones established by the clustering algorithm for this field. These areas were primarily in the northern part of the field (Figure 2 and Figure 3). The difference between the low and high clusters means was approximately 67.2 mS·m⁻¹.

The descriptive statistics patterns were consistent between the EC_a shallow and deep values measured in 2016 and 2021. That pattern was higher mean, median, minimum, and maximum values for the EC_a deep readings. Others documented the same pattern for soil EC_a shallow and deep readings in The Republic of Trinidad and Tobago [20], Belgium [25], Canada [26], and Spain [27]. Also, these findings agreed with the findings of [20] [28], who observed positive, statistically significant correlations between EC_a shallow and deep measurements. The correlation values between EC_a shallow and deep values were greater than 0.80,

^an = number of samples, Min = minimum, Max = maximum, LM = low-medium, M = medium, and MH = medium-high.

indicating good quality data [9]; the ratio between the EC_a shallow and deep values were less than one signifying a regular soil profile, meaning for this field, soil properties that may be correlated with EC_a increases with depth [9].

The relationship between EC_a data and soil physical and chemical properties can be complex [8] [9]. Apparent electrical conductivity measurements have provided a general estimate of soil texture and have shown promise for mapping soil spatial variability [9]. On non-saline soils, increases in clay content were associated with increases in EC_a shallow and deep readings [19]. The soil in this study’s field was not classified as being saline [22]. Hence, it was assumed that the northern portions of the field contained more clay than the southern sections of field. Points in the northern section of the field were grouped into the High EC_a zone. Furthermore, similarities and differences were apparent in the spatial patterns of the EC_a shallow and deep measurements.

Furrow irrigation was used to supply water to the crops grown in this field when needed. Yearly, the plot was irrigated from south to north. Some of the variability seen on the field maps could be attributed to irrigation. Furthermore, irrigation and other farm management practices would have affected the topsoil more than the subsoil, thus contributing to the topsoil having more variability than the subsoil (EC_a deep readings).

The results supported the theory that soil maps derived from the EC_a data should show the same pattern over time [19]. They are additional to the times reported in other studies reporting comparisons ranging from 2 months to 4 years [17] [18] [19] [20]. Others have reported positive results of using georeferenced EC_a data to be reliable for developing soil sampling strategies for non-point source pollutants [29], soil quality [30], and variations in crop yields [31]. Sampling schemes can be easily developed from the maps produced in this study.

The soil spatial variability map derived from the EC data is totally different from the field’s USDA soil survey map. The EC_a map shows smoother transitions compared with the hard breaks between the soil survey map units. It is essential to point out that those differences are not new for maps derived with EC_a data. Nevertheless, the EC_a and USDA soil survey map provides important information for agricultural production.

The findings of this case study indicated that EC_a measurements have a longevity of at least five years, supporting backward compatibility between EC_a data and other types of data collected in the past. For example, a producer may have yield monitor data collected in 2020. However, apparent electrical conductivity data were not collected by the producer, consultant, etc., of the field until 2021. Thus, if the patterns observed on the 2021 EC_a maps are good for five years or more, then the producer, consultant, etc., should be able to evaluate the relationships between the different datasets. Future research will continue to focus on that point and others to determine the agronomic significance of EC_a maps derived from mobile sensors in agricultural fields in Mississippi, USA, over various time periods.

The author thanks Milton Gaston, Jr., for his assistance in collecting the apparent electrical conductivity data. This research was supported in part by the US Department of Agriculture, Agricultural Research Service. The findings and conclusions in this publication are those of the author and should not be construed to represent any official USDA or US Government determination or policy.

The author declares no conflicts of interest regarding the publication of this paper.

Fletcher, R.S. (2022) Temporal Comparisons of Apparent Electrical Conductivity: A Case Study on Clay and Loam Soils in Mississippi. Agricultural Sciences, 13, 936-946. https://doi.org/10.4236/as.2022.138058