One of the most well-known green vegetables in the world, lettuce, has several purposes beyond just nourishment. Customers can select from a range of varieties in the lettuce group. Furthermore, lettuce is a great source of bioactive substances with associated health advantages, including polyphenols, carotenoids, and chlorophyll (Shi et al. 2022). They can grow in almost any system including NTF hydroponics. The nutrient film technique involves plants growing without the use of a substrate by keeping a layer of nourishing solution around their roots. When the NFT initially surfaced, it appeared to be the perfect growth system since it provided the best control over root watering without requiring the purchase of a substrate. Nevertheless, good water quality should be ensured to achieve maximum yields (van Os et al. 2019). Innovative technologies (nanobubbles and electronic water treatment) might show promise for treating high-salinity water for irrigation purposes (Zoukidis et al. 2025). This research aims to investigate the differences in growth and development of butterhead lettuce in an NFΤ hydroponic system between high-salinity water treated with nanobubbles by the combination of two innovative technologies (MAXGROW and generator of agro-nanobubbles) and non-treated high-salinity water. Specifically, the experiment includes 8 different levels of salinity (E.C. 1, 4, 6, 8, 10, 12 dS/m) with each one also including 50% Hoagland solution. The results showed that the combination of two devices can reduce the effect of salinity on the lettuce and achieve greater plant development and yield. Nevertheless, there is need for further research on these technologies and especially further investigation into the effect of the treated water on the physiological and biochemical characteristics of lettuce.

In arid and semi-arid regions, efficient water resource management depends on the accurate estimation of actual evapotranspiration (ET), a critical parameter for determining crop water needs. However, this estimation is challenging due to complex soil–vegetation–atmosphere interactions and the scarcity of reliable in situ data.

This study evaluates the use of the FAO-56 dual crop coefficient approach, which separates ET into basal crop (Kcb), soil evaporation (Ke), and water stress (Ks) coefficients. We investigate the feasibility of estimating these coefficients using freely available satellite-derived variables: Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and surface soil moisture at 5 cm depth (SSM). This remote sensing-based method addresses data limitations while ensuring reliable ET estimation. To assess its practical applicability, the methodology was tested on two wheat fields in the Haouz plain of Morocco during the 2016/2017 and 2017/2018 growing seasons, under contrasting irrigation regimes—one fully irrigated and the other experiencing water stress.

The results show that the FAO-56 coefficients can be accurately estimated from satellite data. The Kcb coefficient correlated strongly with Sentinel-2-derived NDVI (R² = 0.70), while Ke showed high correlation with SSM from Sentinel-1 (R² = 0.81). The Ks coefficient was derived from a thermal index based on Landsat LST, using reference values under stressed and well-watered conditions. ET estimates derived from these parameters were validated against eddy covariance measurements, showing strong agreement: R² = 0.77 (0.87) and RMSE = 0.68 mm (0.69 mm) for 2016/2017, and R² = 0.74 (0.70) and RMSE = 0.37 mm (0.45 mm) for 2017/2018, for the stressed and non-stressed plots, respectively.

These findings demonstrate the potential of satellite data for reliably estimating FAO-56 parameters, offering a scalable and cost-effective solution for ET monitoring and precision irrigation in data-limited, climate-vulnerable regions.

Abstract

A common and very effective method of irrigating plants in hydroponics is using a fertigation solution, which in most cases is of high salinity and needs expensive automation systems to maintain it in a reasonable and tolerable way for each species. Cucumber is one of the most popular vegetables, growing mainly in greenhouses under various hydroponics systems and with fertigation systems installed. The objective of this preliminary study was to evaluate the effect of an Electronic Water Treatment-EWT- device (MAXGROW-MG) and test its effectiveness in growing cucumbers under hydroponic solution (fertigation) in commercial greenhouses using NGS (New Growing System) hydroponics. MAX GROW is an electronic water treatment system using multiple transmissions of radio frequencies in three different frequency bands simultaneously ( ULF/LW/MW ) to tackle the problems caused by saline water and water with a high concentration of calcium carbonate ions, commonly known as limescale.

The commercial greenhouse was split in two sections of 0.4 ha each and the fertigation solution was treated using the MAXGROW system, while the other section was not (control). Various agronomics data on the cucumber and the fertigation solution were measured.

During the growing season and at three periods, the collected data included replications of 20 cucumbers, and leaves from each section were used, whilemean comparisons were performed using JMP v18 software and Student’s t-tests for reporting the results in a letter-connecting table.

The results showed that the MAXGROW system significantly reduced the EC of the fertigation solution, from ca. 2.3 to 1.3 dS/m, increased the pH to a more optimum level (ca. from 6.0 to 6.5), and increased the weight and the relative leaf chlorophyll level (SPAD units) of cucumbewr plants. Partial data are only reported here, and this study is in progress for more data collection and further validation. The next crop to be evaluated under the same greenhouse settings will be zucchini, in the net growing season, for further validation of the reported trends.

Introduction:
Climate change is intensifying water scarcity and increasing competition for this vital resource, especially in agricultural regions. This often leads to significant water waste and escalating territorial disputes among farmers and communities. Traditional water management approaches are proving insufficient to address these complex and interconnected challenges. This study introduces the concept of "hydro-justice," advocating for equitable and sustainable management of water resources in farming.

Methods:
We propose a smart water management framework integrating real-time hydrological data with AI-driven predictive analytics for water demand and supply forecasting. The system will leverage IoT-based sensors for precise water monitoring and blockchain technology to ensure transparent and verifiable water allocation. This framework aims to optimize irrigation schedules, detect leaks, and establish clear, immutable records of water rights and usage.

Expected Results:
We anticipate a substantial increase in water-use efficiency within agricultural settings, leading to a significant reduction in water waste. Moreover, by providing a transparent and verifiable system for water allocation, we expect a notable decrease in territorial disputes and conflicts over water resources. The AI module will enhance adaptive decision-making for farmers, while the blockchain component will foster trust and accountability.

Conclusion:
This AI- and blockchain-integrated water management system offers a novel, scalable solution for achieving hydro-justice in farming. By combining smart technology with transparent governance mechanisms, it aims to create more climate-resilient and socially equitable agricultural ecosystems, promoting both environmental sustainability and peace within farming communities.

Timely assessing drought conditions is the key to managing the risks posed by water scarcity to agriculture, the environment, and socio-economic stability. In this context, agricultural drought monitoring is vital in semi-arid regions like Tamil Nadu, India. This study aims to evaluate and compare the performance of two remote sensing-based drought indicators —the Temperature Vegetation Dryness Index (TVDI) and the Crop Water Stress Index (CWSI) over Tamil Nadu, India—during the Rabi season. Both indices were generated through Google Earth Engine (GEE) using the MODIS-based Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and evapotranspiration (ET) data at a monthly scale. A pixel-wise relationship between the NDVI and LST was used to derive the TVDI, while the CWSI was calculated using the energy balance approach. The monthly spatial and temporal dynamics of the TVDI and CWSI were analyzed across the Rabi season. The results revealed a strong positive correlation between the TVDI and CWSI, indicating consistent detection of drought stress across the region. To assess the accuracy of the indices, the Standardized Precipitation Index (SPI) was used, which shows statistically significant correlations for both indices, with the CWSI demonstrating slightly stronger agreement. Furthermore, drought severity was categorized into four levels: mild, moderate, severe, and extreme. All districts within the study area were categorized based on drought severity levels, with several key districts consistently identified as drought-prone. Overall, the results suggest that both indices are suitable for representing drought patterns; their combined application enhances the robustness of drought monitoring with minor differences in sensitivity and spatial expression. This study demonstrates the potential of integrating thermal and vegetation-based remote sensing indices for improved agricultural drought assessment in semi-arid regions like Tamil Nadu.

The combined application of fertilizers may provide essential nutrients needed by plants. This study aimed to determine the effects of the combined application of inorganic and organic fertilizers supplemented with carabao manure tea (CMT) on the growth, yield and interaction effects between hybrid and inbred of lowland rice types and fertilizer applications. A split plot-RCBD was used, with rice type (M₁- hybrid, M₂- Inbred) as the mainplot, and fertilizers (T0- No Fertilizer Application, T1- 120-90-60kg ha^-1 N-P₂O₅-K₂O, T2- 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 rice straw mixed with CRH + CMT, T3- 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 Carabao Manure + CMT, T4- 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 VermiCompost+ CMT, T5- 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 Chicken Dung + CMT, and T6- 2.5 tons ha^-1 Rice Straw-CRH Compost + 2.5 tons ha^-1 Carabao Manure + 2.5 tons ha^-1 Vermi Compost + 2.5 tons ha^-1 Chicken Dung + CMT) as the subplots. Results showed that the hybrid and inbred lowland rice types resulted indelayed heading and maturity and longer plant height, producing more productive tillers and spikelets when applied with a fertilizer combination of 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 Chicken Dung + CMT. An optimum yield of hybrid and inbred rice can be obtained with the application of 50% RR inorganic combined with any of the organic fertilizers at a rate of 60-30-30 kg ha^-1 N-P₂O₅-K₂O + 5 tons ha^-1 supplemented with manure tea. The sole application of inorganic fertilizer and 50% RR combined with 5 tons ha^-1 Chicken Dung supplemented with CMT produces greater plant height in hybrid rice, while inbred rice significantly responded to sole applications of inorganic fertilizers and 50% RR combined with any of the organic fertilizers at 5 tons ha^-1 supplemented with CMT.

The agricultural sector accounts for the majority of global freshwater use, particularly in the semi-arid regions of the southern Mediterranean, where water demand is especially high. This intense consumption directly contributes to the depletion of groundwater resources, making the rational management of irrigation water imperative. In this context, accurate monitoring of vegetation water status is essential for optimizing irrigation practices. Radar data are directly sensitive to soil and vegetation water status due to their dependence on the dielectric properties of both. Several previous studies on forests and annual crops have demonstrated their sensitivity to vegetation water dynamics. The objective of this study is to analyze the diurnal variations in the C-band radar backscatter coefficient (σ⁰) over an olive orchard in a semi-arid region. To this end, an in situ tower-based radar system was installed in 2020 in the Chichaoua region, in the Haouz plain of Morocco. The radar antennas were directed toward an olive orchard, and the system collected C-band radar data in VV, HH, and VH polarizations at 15-minute intervals. In parallel, measurements of evapotranspiration, sap flow, surface soil moisture, and root zone moisture were collected every 30 minutes. The results show that σ⁰ exhibits a pronounced diurnal cycle in all three polarizations, with lower values during the night and higher values throughout the day. The increase in σ⁰, observed at sunrise, coincides with the onset of evapotranspiration and sap flow. It then continues to rise, reaching its maximum in the early afternoon, before gradually decreasing and stabilizing at night. These diurnal cycles of σ⁰ are in phase with those of evapotranspiration and sap flow, highlighting the sensitivity of C-band σ⁰ to the diurnal variations in the water status of olive trees. These findings demonstrate the potential of sub-daily C-band radar data for monitoring the water status of vegetation, and thus their possible use for the early detection of water stress.

In arid and semi-arid regions like Tensift Al Haouz in central Morocco, optimizing irrigation strategies is critical due to increasing water scarcity and the high costs of field experimentation. Crop simulation models such as AquaCrop have proven valuable for evaluating water use and crop productivity, particularly for winter wheat. In this study, we develop Deep Reinforcement Learning (DRL) agents using the Proximal Policy Optimization (PPO) algorithm to learn profit-oriented irrigation policies, trained entirely within calibrated AquaCrop environments. The model is configured using local crop, soil, and weather data collected from the Tensift Al Haouz region during the 2002–2004 growing seasons and further calibrated with field measurements from nearby test sites. This simulation-based methodology enables the training of adaptive irrigation strategies without the logistical and financial constraints of real-world trials. Preliminary results show encouraging learning progress in both models, where the agents’ performance is comparable to that of experienced human irrigators. The integration of DRL with biophysical crop models demonstrates a promising path toward scalable, data-driven irrigation management in water-limited contexts.

Water scarcity is a major limiting factor for crop production in arid regions, requiring sustainable strategies to improve productivity and resource efficiency. This study uniquely investigates the combined use of compost and steel slag, an underutilized industrial by-product, as soil amendments to improve the performance of durum wheat (Triticum durum Desf.) under both full and sustained deficit irrigation (SDI) conditions. Field trials were conducted over two growing seasons in an arid area of Morocco, with two irrigation regimes, full irrigation (100% ETc) and SDI at 30% ETc, applied from 21 days after sowing. Five treatments were tested: unamended control (Ct^-), conventional agricultural practices (Ct⁺), compost (C, 4.0 t ha⁻¹), steel slag (Ss, 700 kg ha⁻¹), and the combination of steel slag and compost (Ss+C). Under full irrigation, the combined application of Ss+C significantly increased grain yield by 37% and 44% in the first (S1) and second (S2) growing seasons, respectively, compared to the control. Under deficit irrigation, Ss+C considerably improved yield by 68% in S1 and 59% in S2. The harvest index also improved in 2023, rising from 0.268 in the control to 0.285 with Ss+C. Furthermore, irrigation water use efficiency (IWUE) under SDI reached 4.44 kg/m³ (S1) and 4.85 kg/m³ (S2) with Ss+C compared to 2.65 and 3.05 kg/m³ in the stressed controls. Vegetative development, measured by the Normalized Difference Vegetation Index (NDVI) during the dough stage, increased by 66% (S1) and 51% (S2) with Ss+C. Soil micronutrients also improved, with Cu and Zn contents increasing by 35% and 52%, respectively, under full irrigation with Ss+C. These results indicate that combining compost and steel slag is an effective and sustainable soil management strategy to enhance wheat productivity, water use efficiency, and soil quality under arid and water-limited conditions.

Increasing global food demands put pressure on existing water resources for supporting water-intensive conventional agricultural systems. Hydroponic systems have emerged as a potential solution for addressing water resource utilization challenges. However, the nutrient solution used in hydroponics poses additional problems. In this research, an integrated hydroponic bioelectrochemical system facilitates simultaneous wastewater treatment, energy generation, and nutrient transport across the membrane for the hydroponic system. This configuration offers the potential to bolster crop growth by transferring valuable ions from treated municipal wastewater. A combined bioelectrochemical–hydroponic system treats municipal wastewater from the Portage Treatment Plant (Indiana) in the anode chamber via an energy-positive process while supporting lettuce growth in the cathode chamber. This is compared to a standard microbial fuel cell configuration with an air cathode. Both employ 1000-ohm resistors, cation exchange membranes (CEMs), and distilled water and wastewater as catholytes and anolytes, respectively. Plant growth in the integrated design is monitored and compared to a traditional hydroponic setup. Coulombic efficiency, chemical oxygen demand (COD), and total nitrogen removal efficiency, as well as power generation in both configurations, are evaluated. Nutrient transport pathways across the membrane and their applications to plant growth are discussed. The findings of this study provide insights into the potential of the innovative bioelectrochemical system for both wastewater treatment plants and modern agriculture in a circular economy framework.