Characterization of the soil compaction based on the mapping of the apparent electrical conductivity

Based on the measurement of soil penetration resistance (PR), it is possible to identify compacted soil layers, where root growth may be harmed, affecting crop development and yield. The objective of this work was to analyze the use of management zones (MZ), delimited on the basis of mapping of the spatial variability of the soil apparent electrical conductivity (ECa), in the differentiation of soil compaction levels. The work was carried out in a 25.8-ha no-tillage area, cultivated under a center pivot. The ECa was measured under two soil moisture conditions (13.7 and 16.45%), using the Terram® equipment. Soil penetration resistance (PR) was measured using the SoloStar PLG5500 penetrograph. Based on the spatial variability ECa mapping, management zones (2, 3, and 4 zones) were delimited. The mean PR values ??of each MZ were compared by the t-test of means. It was possible to differentiate mean values ??of penetration resistance (PR), which vary from 0.9 to 2.10 MPa, from the characterization of management classes generated on the basis of the ECa spatial variability. The highest stratification of PR values ??was obtained as a function of sampling directed at delimited management zones when the soil had lower moisture content (13.7%). The highest mean PR values ??were obtained for the split of the ECa map into at least three classes. It was identified that for the study area there is no need to perform any mechanical decompaction operation.

Assessing the dynamics of soil salinity with time-lapse inversion of electromagnetic data guided by hydrological modelling

Irrigated agriculture is threatened by soil salinity in numerous arid and semi-arid areas of the world, chiefly caused by the use of highly salinity irrigation water, compounded by excessive evapotranspiration. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected irrigated lands and evaluate the performance of management strategies. In this study, we report on the results of an irrigation experiment with the main objective of evaluating time-lapse inversion of electromagnetic induction (EMI) data and hydrological modelling in field assessment of soil salinity dynamics. Four experimental plots were established and irrigated 12 times during a 2-month period, with water at four different salinity levels (1, 4, 8 and 12 dS m−1) using a drip irrigation system. Time-lapse apparent electrical conductivity (σa) data were collected four times during the experiment period using the CMD Mini-Explorer. Prior to inversion of time-lapse σa data, a numerical experiment was performed by 2D simulations of the water and solute infiltration and redistribution process in synthetic transects, generated by using the statistical distribution of the hydraulic properties in the study area. These simulations gave known spatio-temporal distribution of water contents and solute concentrations and thus of bulk electrical conductivity (σb), which in turn were used to obtain known structures of apparent electrical conductivity, σa. These synthetic distributions were used for a preliminary understanding of how the physical context may influence the EMI-based σa readings carried out in the monitored transects as well as being used to optimize the smoothing parameter to be used in the inversion of σa readings. With this prior information at hand, we inverted the time-lapse field σa data and interpreted the results in terms of concentration distributions over time. The proposed approach, using preliminary hydrological simulations to understand the potential role of the variability of the physical system to be monitored by EMI, may actually allow for a better choice of the inversion parameters and interpretation of EMI readings, thus increasing the potentiality of using the electromagnetic induction technique for rapid and non-invasive investigation of spatio-temporal variability in soil salinity over large areas.

Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.