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Climate change and intensive agriculture are responsible for the increasing frequency and intensity of abiotic stresses generating conditions of water scarcity. Currently, there is the need to select and release, in a short time, plants adaptable to the current and future environmental conditions and resistant to biotic and/or abiotic stress. This study presents the design and validation of a High-Throughput Screening (HTS) system for the continuous and simultaneous monitoring of the plant stress response to drought in a semi-controlled environment.Structurally, the HTS-system is formed by three hardware segments to detect with high-frequency the agrometeorological variables (i.e., atmometry), the weights (i.e., gravimetry), and the soil water content (SWC) (i.e., time domain reflectometry, TDR) of sixteen pots in which the medicinal crop Salvia officinalis L (sage) was grown. Two irrigation treatments, one based on full irrigation and the second on soil water deficit conditions, were applied following a feedback control irrigation scheduling protocol, and an automated micro-irrigation system was designed to manage them.The system was able to model the sage water stress function following the root water uptake (RWU) macroscopic approach. The threshold of soil water status below which crop water stress occurred was also identified. The gravimetric-based daily evapotranspiration (ETc act) and the time domain reflectometry (TDR) -based RWU rates showed a high correlation, which allowed validating the RWU indicators based on soil moisture sensors to estimate the ETc act fluxes.Keywords. Agro-hydrological modelling, high-throughput systems; root water uptake; sage; water stress function

The EM-38 is a Handheld Electrical Magnetic Induction (EMI) device, non-invasive and commonly used for monitoring salinity, mapping bulk soil properties, and evaluating soil nutrient status. The measured data is an electrical conductivity, [mS/m], which was referred to a representative soil volume of 1.0-1.5 m depth and 2.0 m width.The Data AcQuisition (DAQ) system for EM-38 (Geonics Inc.) conductivity meter, used for recording the spatial variability of the soil bulk electrical conductivity (EC), is expensive, according to the proprietary software, and do not provide detailed, modifiable circuit schematics.To address these issues, we developed an easy-to-use, modifiable, and inexpensive data acquisition (DAQ) system for EC data recording and spatializing. In particular, this work investigates the feasibility of using a low-cost open-source DAQ system to be installed on an EM-38 conductivity meter (Geonics Inc.). This DAQ system is based on Raspberry Pi and allows collecting speed and position of the EM-38 device by carrying it on a specific designed sled system.The EM-38 data (± 200mV) were acquired by A/D converter 24 bit and GPS data ($GPGGA, $GPRMC [NMEA 0183]) were obtained by serial input RS232. The Data logger (Raspberry Pi 4) stored the acquired data and transferred it to the server using an internet connection (Router 4G). The control Firmware has been written in Python language. To ensure the accuracy and reliability of the collected data, the system has been evaluated and tested in well-known open-filed (CIRAAA-a reserch center of the University of Pisa), where the spatial variability of the main soil physical properties (i.e. soil texture, organic matter, electrical conductivity) are known.The performance has been evaluated by comparing the data string with the one generated by a professional DAQ system. The latter includes a CR1000 data logger (Campbell Scientific) to control and store the EC data and integrats a GPS receiver (GPS16X-HVS, Garmin Inc.) which provides the position, velocity, and timing information.First results allowed to approve the possibility to extract the analogical signal from the device, which is strongly responsive to the variation of the physical properties of the soil environment. Moreover, the device is able to estimate accurately the spatial patterns of the investigated soil physical properties. Keywords: Precision farming, Zoning, EMI sensors, soil bulk electrical conductivity, open source DAQ

In the last two decades, research on water resource monitoring and management has mainly been aimed at reducing irrigation water volume and energy consumption. At the same time, the effects of climate change and agricultural policies have also been major research interests. Therefore, there is an interest in focusing on the assessment of irrigation performance to improve water management and to increase the sustainability of irrigated agricultural territories. Recent advances in optoelectronics, mechatronics, communication, and information technologies have allowed for the implementation of low cost, easy to operate, and virtual free maintenance of data acquisition systems to be used in soil-crop water status monitoring, as well as in smart irrigation systems. Agro-hydrological models have been recognized as an economic and simple tool to quantify crop water requirements in the decisionmaking processes for both farm and basin scales. They can simulate the mass and/or energy exchange processes in the soil-plant-atmosphere continuum under different spatial and temporal scales. In combination with new technologies such as sensors and remote sensing, these models are promising techniques that have accelerated spatial data collection substantially. Remote sensing and wireless sensor networks can cover scales from a single leaf to complete irrigation systems and can create data sets for large numbers of agricultural families and farming conditions. Hence, the agro-hydrological sensor-model based approach must be properly chosen and calibrated to increase its use on larger commercial farms, even using remote sensing data. This Special Issue consists of a collection of seven papers that cover a broad range of advances on modelling and partitioning evapotranspiration, modelling soil water and salt content, and modelling irrigation systems, with the aim to increase the sustainability of irrigation derived from the adoption of monitoring and optimum management practices