Abstract
Soil apparent electrical conductivity (ECa) sensors have been used to detect spatial variability because they correlate with soil attributes. Studies with soil attributes have shown that the number of subsamples and sampling points influences mapping. However, there are no studies that investigated the influence of sampling or subsampling density on ECa maps. Therefore, this study verified the influence of ECa readings per sample point on the semivariance and kriging analysis. The data were collected from an area (2.5 ha) of coffee plants. One hundred sampling points were measured considering 20 readings each. 1, 5, 10, 15, and 20 sample point readings were tested. The influence of the number of readings per sampling point on the ECa mapping was determined using linear regression analysis at a significance level of 5%. The results obtained showed that ECa readings per sampling point significantly influence ECa maps. In addition, they demonstrated that reducing the number of readings per sampling point increases prediction errors by kriging. Thus, ECa maps determined with the highest readings per sampling point were mostly accurate.
Highlights
Detecting spatial variability of soil attributes and determining management zones using soil apparent electrical conductivity (ECa) sensors have helped reduce cost attached to soil sampling (Stadler et al, 2015; Corwin & Scudiero, 2020)
The sensor was developed using a BeagleBone Black (BBB) single-board computer with Debian 7.9, connected to a 7" LCD (Liquid Crystal Display) Cape touch screen manufactured by 4D Systems (Minchinbury, Australia), an electronic circuit for signal conversion and amplification, an electronic circuit to determine the potential difference caused by the soil, and a conditioning circuit to adjust the signal between 0 and 1.8 V
The increase in the number of readings per sample point caused an increase in the proportion between the contribution (C) and plateau (C0+C) and a reduction in the number of errors based on the estimation done using kriging during cross-validation (Table 1)
Summary
Detecting spatial variability of soil attributes and determining management zones using soil apparent electrical conductivity (ECa) sensors have helped reduce cost attached to soil sampling (Stadler et al, 2015; Corwin & Scudiero, 2020). This is because ECa presents reliable data of easy and fast measurements at a low cost (Corwin & Scudiero, 2020). Both types of sensors generate readings that correlate with soil attributes and can be used to direct soil sampling and define management zones
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