Spatial and temporal variability of soil apparent electrical conductivity

Abstract

Spatial and temporal variability of the soil’s apparent electrical conductivity (ECa) and other soil attributes can be analyzed using specific digital platforms for precision agriculture, contributing to agricultural management decision-making. Understanding these variations enables more efficient and sustainable management practices tailored to each area’s characteristics, leading to higher crop yields and reduced environmental impacts. A critical question arises: should ECa measurement be done regularly or just once? This study aims to evaluate the spatial and temporal variability of soil’s apparent electrical conductivity to determine if a single ECa measurement can characterize spatial soil variability. The experiment was conducted in two areas under different management practices in Céu Azul, PR, Brazil. One area operates under a direct planting system, cultivating soybeans in the summer and rotating with wheat or corn during the winter. The second area is used as pasture during the winter and planted with corn or soybeans in the summer. ECa data from 2013 to 2016, along with chemical and physical soil attributes from 2013, were retrieved from our laboratory database. Additionally, ECa data were collected on 19/05/2022, 18/10/2022, and 10/03/2023. All ECa measurements were performed using an EM38-MK2 conductivity meter in horizontal dipolar and drag mode. ECa normalization methods such as range, average, and standard score were employed to mitigate temporal influences partially. Data was processed using the AgDataBox web platform, which included data cleaning, data interpolation, creation of thematic maps, delineation of management zones, and spatial correlation matrix procedures. Thematic maps revealed that ECa spatial variability exhibited a stable pattern. Both areas showed significant cross-correlation among topography and most soil chemical and physical attributes. The study concluded that ECa measurement could be performed once as a co-variable for interpolating other variables since the ECa pattern remained stable in both areas. The average method was the most effective normalization method in both areas. Furthermore, management zones (MZs) were delineated using equivalent normalized ECa (ECa_Eq) (mS/m) with the three data normalization methods. The agreement between MZs was sufficient to conclude that the influence of the normalization methods can be ignored.