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Background and aims: The ever-increasing demand of fresh water from irrigated agriculture and the water scarcity condition that characterizes the Mediterranean region, encourages the adoption of efficient water saving strategies. In this study, the electrical resistivity imaging (ERI) technique was applied for characterizing the mass exchange mechanisms affecting the continuous soil-plant system of heterogeneous micro-irrigated orchards. Methods: Time-lapse ERI surveys were carried out in a multi-variety and mixed-age orange orchard located in Eastern Sicily (insular Italy). The time-lapse ERI monitoring was coupled with the use of independent soil water content (SWC) measurements. Results and conclusions: A clear relationship between the soil electrical resistivity (ER) and the measured SWC changes was identified in the soil under the mixed-age orchard (with an overall coefficient of determination value of 0.63). Specifically, this study highlights the dependency of SWC dynamics as function of tree age. Overall, an increase in ER was obtained in correspondence to the soil domain where the younger trees were located (< = 3 years). This behaviour was inferred to result from the higher soil evaporation process active at these locations, due to less vegetation cover of the younger trees and, consequently to their lesser root extension in comparison to the mature trees (> = 8 years). On the other hand, in mature trees, characterized by denser root biomass, there were more evident decreasing patterns of ER (i.e., greater changes in SWC), due to greater transpiration rates that caused lower initial SWC under these conditions in comparison to the younger trees. [ABSTRACT FROM AUTHOR]

The use of hydrogeophysical methods provides insights for supporting optimal irrigation design and management. In the present study, the electrical resistivity imaging (ERI) was applied for monitoring the soil water motion patterns resulting from the adoption of water deficit scenarios in a micro-irrigated orange orchard (Eastern Sicily, Italy). The relationship of ERI with independent ancillary data of soil water content (SWC), plant transpiration (T) and in situ measurements of hydraulic conductivity at saturation (Ks, i.e., using the falling head method, FH) was evaluated. The soil water motion patterns and the maximum wet depths in the soil profile identified by ERI were quite dependent on SWC (R2 = 0.79 and 0.82, respectively). Moreover, ERI was able to detect T in the severe deficit irrigation treatment (electrical resistivity increases of about 20%), whereas this phenomenon was masked at higher SWC conditions. Ks rates derived from ERI and FH approaches revealed different patterns and magnitudes among the irrigation treatments, as consequence of their different measurement scales and the methodological specificity. Finally, ERI has been proved suitable for identifying the soil wetting/drying patterns and the geometrical characteristics of wet bulbs, which represent some of the most influential variables for the optimal design and management of micro-irrigation systems. [ABSTRACT FROM AUTHOR]

An adjusted satellite-based model was proposed with the aim of improving spatially distributed evapotranspiration (ET) estimates under plant water stress conditions. Remote sensing data and near surface geophysics information, using electrical resistivity tomography (ERT), were used in a revised version of the original dual crop coefficient (Kc) FAO-56 approach. Sentinel 2-A imagery were used to compute vegetation indices (VIs) required for spatially estimating ET. The potentiality of the ERT technique was exploited for tracking the soil wetting distribution patterns during and after irrigation phases. The ERT-derived information helped to accurately estimate the wet exposed fraction (few) and therefore the water evaporated from the soil surface into the dual Kc FAO-56 approach. Results, validated by site-specific ET measurements (ETEC) obtained using the eddy covariance (EC) technique, showed that ERT-adjusted ET estimates (ETERT) were considerably reduced (15%) when compared with the original dual Kc FAO-56 approach (ETFAO), soil evaporation overestimation being the main reason for these discrepancies. Nevertheless, ETFAO and ETERT showed overestimations of 64% and 40% compared to ETEC. This is because both approaches determine ET under standard conditions without water limitation, whereas EC is able to determine ET even under soil water deficit conditions. From the comparison between ETEC and ETERT, the water stress coefficient was experimentally derived, reaching a mean value for the irrigation season of 0.74. The obtained results highlight how new technologies for soil water status monitoring can be incorporated for improving ET estimations, particularly under drip irrigation conditions. [ABSTRACT FROM AUTHOR]

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike's information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes. [ABSTRACT FROM AUTHOR]

Deep tillage can be applied to face high-density soil horizons by improving the root system's access to deeper layers. In this sense, soil and ecosystem services related to nutrients cycling and water recharge can be improved. In this study, the spatial and temporal dynamics of soil water content (θ) were inferred using two-dimensional electrical resistivity (ER) surveys, that were applied to explore the soil profile treated by deep tillage under different citrus rootstocks (RS) implantation. A seasonal scale ER monitoring (Spring 2018, Summer 2019, and Winter 2019) was carried out at a selected study area, characterized by Ultisols and located in Southeastern Brazil. Three different citrus RS-types, represented by the varieties 'Cravo Santa Cruz lemon' (CSC), 'Sunki Tropical Tangerine' (ST), and 'Citrandarin Indio' (CI). Deep tillage was adopted before RS citrus-types planting by opening furrows and subsoiling up to 0.6 m depth. The interrows citrus plants were covered with Brachiaria decumbens (Syn. Urochloa) in all citrus RS-type plots. A native forest area ('Forest') was used as reference for all θ evaluations. The model for the ER-θ relationships was determined from measurements conducted on soil samples in the laboratory. Root water uptake (RWU), in terms of θ changes, was evaluated at the different treatments under study. The obtained results proved the suitability of ER method for identifying the ER-θ dynamics in the Ultisol profiles over time. Lower and more stable θ values were obtained in the 'Forest' and interrow areas. Among the evaluated citrus RS-types, CSC showed the highest water use efficiency due to the greatest root system development. [Display omitted] • ER is a useful technique for inferring the soil water content (θ) dynamics. • ER-θ adjusted models explain 45–80 % of the θ changes in Ultisol. • ER season-based analyses allow to compare θ dynamics under citrus rootstocks. • Greater root-water uptake (RWU) was indirectly related to lower θ. • Greater RWU was observed in CSC rootstock. [ABSTRACT FROM AUTHOR]