Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermo-gravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.
A soil moisture content map is important for providing information about the distribution of moisture in a given area. Moisture content directly influences agricultural yield thus it is crucial to have accurate and reliable information about moisture distribution and content in the field. Since soil is a porous medium modified generalized Archie’s equation provides the basic formula to calculate moisture content data based on measured ECa. In this study we aimed to find a more accurate and cost effective method for measuring moisture content than manual field sampling. Locations of 25 sampling points were chosen from our research field as a reference. We assumed that soil moisture content could be calculated by measuring apparent electrical conductivity (ECa) using the Veris-3100 on-the-go soil mapping tool. Statistical analysis was carried out on the 10.791 ECa raw data in order to filter the outliers. The applied statistical method was ±1.5 interquartile (IRQ) distance approach. The visualization of soil moisture distribution within the experimental field was carried out by means of ArcGIS/ArcMAP using the inverse distance weighting interpolation method. In the investigated 25 sampling points, coefficient of determination between calculated volumetric moisture content data and measured ECa was R2 = 0.87. According to our results, volumetric moisture content can be mapped by applying ECa measurements in these particular soil types.
Soil compaction causes important physical modifications at the subsurface soil, especially from 10 to 30 cm depths. Compaction leads to a decrease in infiltration rates, in saturated hydraulic conductivity, and in porosity, as well as causes an increase in soil bulk density. However, compaction is considered to be a frequent negative consequence of applied agricultural management practices in Slovakia.
Detailed determination of soil compaction and the investigation of a compaction impact on water content, water penetration depth and potential change in water storage in sandy loam soil under sunflower (Helianthus annuus L.) was carried out at 3 plots (K1, K2 and K3) within an experimental site (field) K near Kalinkovo village (southwest Slovakia). Plot K1 was situated on the edge of the field, where heavy agricultural equipment was turning. Plot K2 represented the ridge (the crop row), and plot K3 the furrow (the inter–row area of the field). Soil penetration resistance and bulk density of undisturbed soil samples was determined together with the infiltration experiments taken at all defined plots.
The vertical bulk density distribution was similar to the vertical soil penetration resistance distribution, i.e., the highest values of bulk density and soil penetration resistance were estimated at the plot K1 in 15–20 cm depths, and the lowest values at the plot K2. Application of 50 mm of water resulted in the penetration depth of 30 cm only at all 3 plots. Soil water storage measured at the plot K2 (in the ridge) was higher than the soil water storage measured at the plot K3 (in the furrow), and 4.2 times higher than the soil water storage measured at the most compacted plot K1 on the edge of the field. Results of the experiments indicate the sequence in the thickness of compacted soil layers at studied plots in order (from the least to highest compacted ones): K2–K3–K1.