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https://doi.org/10.1007/978-94-007-0394-0_33

has been cited by the following article:

• TITLE: Temporal Comparisons of Apparent Electrical Conductivity: A Case Study on Clay and Loam Soils in Mississippi

  AUTHORS: Reginald S. Fletcher

  KEYWORDS: Time-Based, Clustering, Mapping

  JOURNAL NAME: Agricultural Sciences, Vol.13 No.8, August 16, 2022

  ABSTRACT: On-the-go soil sensors measuring apparent electrical conductivity (ECa) in agricultural fields have provided valuable information to producers, consultants, and researchers on understanding soil spatial patterns and their relationship with crop components. Nevertheless, more information is needed in Mississippi, USA, on the longevity of ECa measurements collected with an on-the-go soil sensor system. That information will be valuable to users interesting in employing the technology to assist them with management decisions. This study compared the spatial patterns of ECa 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. Apparent electrical conductivity shallow (0 - 30 cm) and deep (0 - 90 cm) measurements were obtained with a mobile system. Descriptive statistics, Pearson correlation analysis, paired t-test, and cluster analysis (k-means) were used to compare the data sets. Similar trends were evident in both datasets; apparent electrical conductivity deep measurements were greater (P a shallow measurements; a strong positive correlation (P 0.90) existed between the ECa shallow and deep measurements. Also, a high correlation (r ≥ 0.79) was observed between the ECa measurements and the y-coordinates recorded by a global positioning system, indicating a spatial trend in the north and south direction (vice versa) of the plot. Comparable spatial patterns were observed between the years in the ECa shallow and deep thematic maps developed via clustering. Apparent electrical conductivity data measurement patterns were consistent over the five years of this study. Thus the user has at least a five-year window from the first data collection to the next data collection to determine the relationship of the ECa data to other agronomic variables.