GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 3.2 - POSTER GNGTS 2023 reference survey to the following ones. The inversion process has been done with ResIPy (Blanchy et al., 2020a), working with R2/R3t codes based on an Occam’s inversion method (Binley, 2015). EMI data were collected using the GF Instruments CMD-Mini Explorer (GF Instru-ments, Czech Republic) operating at 30 kHz with a combination of three coil spacing (0.32m, 0.71m, 1.18m), and paired with coordinates obtained from ProXT GPS receiver (Trimble, USA). For each survey, the device was carried at the soil surface, placed on a dedicated wood sledge, pulled by a tractor and linked to it by a 4-m long rope. The measured data were filtered from outliers (values outside the mean ± 3 standard deviations), decimated applying a smoothing window (size = 5) and corrected for temperature (Ma et al., 2011). The time-lapse inversion was performed with EMagPy (McLachlan et al., 2021), using the Cumulative Sensitivity (CS) forward model and the L-BFGS-B (Broyden– Fletcher–Goldfarb–Shanno) optimization method (Byrd et al., 1995). Figure 2. Comparison of time-lapse EC differences between the background (2017-12-19) and the following surveys respectively for the conservative (left) and the conventional treatment (right). The aim of this study was to draw qualitative interpretation of EM and ERT performing a time-lapse analysis, used for the first time to compare agriculture practices. In that sense our findings are not only in line with the literature and with traditional direct measurements, but try to go a bit further, paving the way for more refined models to identify which soil parameters are key to control spatial and temporal changes in soil water content. During the 41st National Conference of the GNGTS 2023 in Bologna, we will present the result of this experiment, which demonstrated that both ERT and EMI provided sufficient information to distinguish between the effects of CONV and CONS treatments.