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The main purpose of this paper is to compare the actual transpiration rates from tomato crop, as measured at leaf scale and estimated by a macroscopic approach in an agro-hydrological model, named FLOWS-HAGES, under variable soil properties and water availability. To this aim, sixteen plots were cultivated with tomatoes in Metaponto, Southern Italy. Soil hydraulic properties (SHP) were obtained using a fast in-situ characterization method. Leaf-area index (LAI) was measured using a leaf-area meter. SHP and LAI were then used in the physically-based FLOWS-HAGES which allowed calculating the macroscopic transpiration rates, Ta,m. Single-leaf transpiration rates, Ta,l, and stomatal conductance, gs,l, were measured in situ. For comparing with Ta,m, gs,l was upscaled by Big-Leaf approach to canopy scale stomatal conductance, gs,c, which was applied to Penman-Monteith model to obtain the canopy-scale transpiration, Ta,c. Finally, multiple linear regression (MLR) was used to find the statistical relationship between Ta,m and Ta,c, and the SHP and gs,c. Results showed that the macroscopic approach smooths the spatial variability of transpiration rates. Ta,c increased with the saturated water content, θs, and the slope of the water retention curve, n, while Ta,m decreased with increasing θs and n. MLR improved significantly by introducing gs,c to predict Ta,m.

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices is a tedious task hindering the full exploitation of soil moisture tensiometers. This research develops and lab validates a low cost IoT soil moisture tensiometer. The IoT-prototype is capable of measuring tension up to −80 Kpa with R2 = 0.99 as compared to the same tensiometer equipped with a mechanical manometer. It uses an ESP32 MCU, BMP180 barometric sensor and an SD card module to upload the measured points to a cloud service platform and establishes an online soil water potential curve. Moreover, it stores the reading on a micro-SD card as txt file. Being relatively cheap (76 USD) the prototype allows for more extensive measurements and, thus, for several potential applications such as soil water matric potential mapping, precision irrigation, and smart irrigation scheduling. In terms of energy, the prototype is totally autonomous, using a 2400 mAh Li-ion battery and a solar panel for charging, knowing that it uses deep sleep feature and sends three data points to the cloud each 6 h.

Global population growth and climate change raise a challenge to agriculture, which, combined with the issues concerning the use of chemical fertilizers, have generated increasing attention in the use of plant-associated bacteria as a sustainable strategy in agri-food systems. The objective of this study is to evaluate the ability of five bacterial strains, previously isolated from the rhizosphere or endosphere of plants adapted to harsh environmental conditions, to act as potential plant biofertilizers in different conditions of water availability. The strain biosafety for a deliberate environmental release was investigated through a literature survey and antibiotic resistance testing. The selected strains were first characterized for their plant growth–promoting (PGP) and rhizocompetence-related traits through in vitro assays and then on short-term in vivo experiments on tomato plants. A long-term greenhouse experiment was further conducted to monitor the PGP effect of the bacteria during the entire life cycle of tomato plants subjected to full irrigation or to severe water deficit conditions, aiming to assess their actual effect on plant productivity, which is the ultimate target of the agricultural sector. Some of the strains showed a potential in improving water use efficiency and mitigating plant water stress. Under severe irrigation deficit, four of the tested strains, Micrococcus yunnanensis M1, Bacillus simplex RP-26, Pseudomonas stutzeri SR7-77, and Paenarthrobacter nitroguajacolicus 2–50, significantly increased the number of productive plants in comparison to non-bacterized control ones. Two of them, Bacillus simplex RP-26 and Paenarthrobacter nitroguajacolicus 2–50, demonstrated also, under full irrigation, to significantly improve the water productivity in comparison with non-bacterized plants. Despite all the strains showed promising PGP potential in short-term assays, the positive effect of the bacterial inoculants on plant physiology and fruit yield was observed in some cases but never corroborated by statistical significance. These results highlight the importance of performing long-term in vivo experiments to define the real PGP ability of a bacterial inoculant to positively impact plant production.

Olive mill wastewater (OMW) is a compound originating from oil mills during oil extraction processes. In the Mediterranean area, more than 30 million m3 of OMW are produced each year, which represents 95-97% of the world production. Such volumes of untreated OMW are usually directly disposed of into drainage systems, water bodies (such as streams, lagoons and ponds) or are sprinkled on soils, causing potentially severe environmental problems to soils and groundwater. Consequently, there is a serious waste management problem related to the olive oil industry, because these practices no longer being acceptable. In the case of on-land OMW disposal, the characterization and the identification of this contaminant in soils is a fundamental task especially with a view to maintaining the integrity and quality of agroecosystems. In recent years, soils have been extensively studied to detect contaminants by using various geophysical methods. Among such techniques, time domain reflectometry (TDR) has shown, in different contexts, evident sensitivity and resolution capability for the characterization of contaminated soil sites. In order to further exploit the potential of the TDR technique, in this study we conducted a series of laboratory-controlled tests to explore how OMW influences the dielectric response of contaminated soils. This investigation led to the development of an empirical dielectric model to estimate the presence of OMW in variably saturated-contaminated soils with different textures and pedological features. In particular, the soils selected belong to two typical pedological units in southern Italy which account for approximately 90% of the Italian olive plantations and one of the highest concentrations of the olive oil industry in Italy. In these districts, as well as in other European and Mediterranean countries, there is the controversial habit to spread OMW on soils.

Zeolites can be extensively employed in agricultural activities because they improve soil properties such as infiltration rates, saturated hydraulic conductivity, water holding capacity, and cation exchange capacity. Natural and synthetic zeolites can efficiently hold water. Zeolites are also believed to have the ability to lose and gain water reversibly, without changing their crystal structure. In the present study, several laboratory tests were carried out using: (i) zeolite synthesized from coal fly ash (a waste product from burning coal in thermoelectric power plants), (ii) a silty loam soil, typically found in Southern Italy, and (iii) sunflower as a reference plant. The selected soil was amended with different percentages of zeolite (1, 2, 5, and 10%) and the effects of the synthetic mineral addition on the hydrophysical properties of the soil and plant growth were evaluated. The results indicated that soil–zeolite mixtures retained water more efficiently by pore radius modification. However, this causes a variation in the range of plant-available water towards higher soil humidity values, as the amount of added zeolite increases. These data confirm that zeolite addition modifies the selected hydrophysical properties of the soil with the effect of decreasing the soil drainage capacity, making the soil less habitable for plant growth.

This paper dealt with the calibration of an EMI sensor for monitoring the time dynamics of root zone salinity under irrigation with saline water. Calibration was based on an empirical multiple regression approach largely adopted in the past and still applied in practice for its relative simplicity. Compared to the more complex inversion approaches, it requires an independent dataset of local σb measured within discrete depth intervals, to be compared to horizontal and vertical electrical conductivity (ECaH and ECaV) readings for estimating the parameters of the empirical regression equations. In this paper, we used time domain reflectometry (TDR) readings to replace direct sampling for these local σb measurements. When using this approach, there is the important issue of taking into account the effect of the different sensor observation volumes, making the readings not immediately comparable for empirical calibration. Accordingly, a classical Fourier’s filtering technique was applied to remove the high frequency part (at small spatial scale) of the original data variability, which, due to the different observation volume, was the main source of dissimilarity between the two datasets. Thus, calibration focused only on the lower frequency information, that is, the information at a spatial scale larger than the observation volume of the sensors. By this analysis, we showed and quantified the degree to which the information of the set of TDR readings came from a combination of local and larger scale heterogeneities and how they have to be manipulated for use in EMI electromagnetic induction sensor calibration.

This paper mainly aims to illustrate an irrigation management tool to simulate scheduling of district-level water needs over the course of an irrigation season. The tool is mostly based on a daily model for simulating flow of water (and solutes) in heterogeneous agri-environmental systems (called FLOWS-HAGES). The model produces information on the daily evolution of: soil water contents and pressure potentials in the soil profile; water uptake and actual evapotranspiration; stress periods for each crop; return fluxes to the groundwater and their quality in terms of solute concentrations (e.g., nitrates). FLOWS-HAGES provides a daily list of hydrants to be operated according to water or crop-based criteria. The daily optimal sequence of hydrant use may thus be established by passing the volumes to be delivered on to the model for simulating the hydraulics of the irrigation network, in order to ensure that the discharges flowing inside the network of distribution pipes are delivered under optimal pressure head distribution in the system. All the above evaluations can be carried out in a stochastic framework to account for soil heterogeneity and climate changes. To illustrate the potential of FLOWS-HAGES, a case study was considered for a selected sector of the Irrigation District 10 in the “Sinistra Ofanto„ irrigation system (southern Italy, Apulia region). In a 139 ha area (Sector 6 of the Irrigation District), soil profiles were analyzed for characterization of hydraulic properties variability. Hydraulic properties were determined by a combination of field and laboratory measurements. Model simulations were validated by comparing soil water storage simulated and measured by a sensor based on electromagnetic induction technique. Irrigation water volumes and frequency calculated by the model were compared to the volumes actually supplied by the farmers. Compared to the farmers behavior, the model simulates more frequent irrigations with lower irrigation volumes. Finally, some indexes of irrigation performance were calculated for each farm under study. The resulting maps provide useful information on the spatial distribution of farmer behavior, indicating the abuse or underuse of water as well as the fraction of the water lost by drainage following the irrigation method applied.