Sustainable urban agriculture plays a very important role in the management of food security and environmental issues of rapidly expanding cities. Inefficient irrigation, poor choice of crops and a lack of real-time monitoring are major challenges that are affecting traditional rooftop gardening. To address these issues, this work suggests a smart Internet of Things-based, eco-friendly rooftop garden sensor and machine learning-based planting suggestion system that runs on electricity. The system incorporates a group of low-cost sensors to measure soil moisture, pH, temperature, humidity, and rainfall and an automated irrigation system where the harvested rainwater is used to manage water in an efficient and sustainable manner. The architecture is centered on a Random Forest machine learning module which analyzes the real-time environmental data and decides if it is possible to plant and suggests crops that best fit the current microclimate conditions of the location of the rooftop. The system is designed to work in three synchronous levels of sensing, processing and user interface, with a responsive GrowGreen web dashboard displaying real-time monitoring, irrigation notifications and recommended crops in a ranking order. The experimental findings prove that the Random Forest model attained as high as 92% prediction accuracy and irrigation efficiency of 95%, which proves the practical feasibility of the framework proposed. This platform contributes to retro-friendly rooftop farming by helping to streamline water usage, increase the potential of crops and promoting the growth of intelligent, resilient green cities due to the innovative incorporation of IoT and machine learning technologies.

Integrating Community Perception, Climate Vulnerability Index, and Driver-Pressure-State-Impact-Response Approaches for Climate Risk Assessment of the Persepolis World Heritage Site.

Predicting soil saturated hydraulic conductivity (Ks) is essential for understanding water movement in soils and designing efficient irrigation and drainage systems. Thus, our paper proposes the mutated grasshopper optimization (MGRO) algorithm- convolutional neural network (CNN)- kernel ridge regression (KRR)-error correction (ERC) method to predict Ks. The MGRO-CNN-KRR-ERC (MCKE) model is executed in several steps. First, the MGRO algorithm optimizes the hyperparameters of the CNN and KRR techniques. Then, the CNN technique is trained on the input data to produce a set of outputs called the summary vector, which represents the compact and meaningful descriptor of the original dataset for subsequent KRR processing. Next, the summary vector is fed into KRR to produce predicted Ks data. Finally, the ERC method reduces the difference between these data and measured Ks values to improve the overall prediction accuracy. The model uses four soil properties as input data. Our study uses the mutual information index to select the most informative features, and the results indicate that all four properties significantly affect the precision of predictions. The current research also utilizes the kernel estimation method to determine the uncertainty of the predicted Ks values. The study results demonstrate that the MCKE model significantly outperforms KRR, CNN, and hybrid approaches in terms of predictive accuracy, error reduction, and robustness. Specifically, the MCKE model achieves a reduction of 51-93% in mean absolute percentage error (MAPE to all other predictive models. In general, the MCKE model provides a robust and reliable framework for predicting Ks under diverse environmental conditions.

ABSTRACT The water rebound effect (WRE) challenges sustainable irrigation by offsetting gains from improved irrigation efficiency. Using a DID approach and panel data from 1999 to 2022, this study finds that China’s Comprehensive Agricultural Water Price Reform (CAWPR) significantly reduces the WRE in Western China. The reform promotes water-saving irrigation while limiting the reuse of saved water, turning efficiency gains into actual regional water conservation. These findings show that CAWPR can address the irrigation efficiency paradox and support agricultural water sustainability.

A driver-pressure-state-impact-response and system dynamics framework for climate change adaptation in vulnerable wetlands.

Lavender (Lavandula angustifolia) is widely recognized as a low-input and environmentally adaptable plant; however, the environmental impacts associated with greenhouse-based seedling production remain insufficiently quantified. This study aimed to evaluate the carbon footprint of lavender seedling production under greenhouse conditions using a life cycle-based approach. This study provides a focused assessment of the seedling production stage, which is often overlooked in life cycle-based evaluations of agricultural systems. The system boundary was limited to operational processes within a single production cycle, and emissions were calculated using activity data and emission factors derived from the IDEMAT 2025 database. Primary data were collected from a 200 m2 greenhouse facility, including water consumption, electricity use, agricultural inputs, transportation, and waste management processes. Emission calculations were performed using relevant emission factors to estimate total greenhouse gas emissions in CO2-equivalent terms. The total carbon footprint was calculated as 265.32 kg CO2-eq per production cycle, corresponding to 0.03 kg CO2-eq per seedling. The results showed that water consumption and electricity use were the dominant contributors to total emissions, whereas agricultural inputs and transportation had relatively minor impacts. Negative emission values associated with waste management and end-of-life processes reflect emission savings (avoided emissions) achieved through recovery and recycling processes within the system. These findings demonstrate that emission intensity in greenhouse-based lavender seedling production is highly dependent on resource use, particularly water and energy. Improving irrigation efficiency and optimizing energy use are critical for reducing carbon emissions. These approaches also support the development of sustainable and resource-efficient agricultural production systems.

Abstract. Fivintari FR, Nurmalita, Ikhsan J, Mulyono, Ekawati FI, Yunanto, Ardila RA. 2026. Efficiency of shower and sprinkler irrigation system on curly red chili in coastal area Kulon Progo, Indonesia, using a stochastic frontier approach. Asian J Agric 10 (1): g100161. https://doi.org/10.13057/asianjagric/g100161. Coastal agriculture faces significant production challenges due to sandy soil characteristics, limited water retention, and increasing pressure on water resources. Improving irrigation efficiency is therefore essential to enhance productivity and sustainability in these environments. This study aims to evaluate the technical efficiency of curly red chili production under shower and sprinkler irrigation systems in the coastal area of Kulon Progo, Indonesia. The analysis employs a Cobb–Douglas production function estimated using the Stochastic Frontier Analysis (SFA) approach, which allows for the separation of random shocks and technical inefficiency in agricultural production. Both irrigation groups were technically efficient, with average technical efficiency levels above 0.7. However, sprinkler irrigation was more efficient than shower irrigation, with average scores of 0.902 and 0.795, respectively. Overall, the curly red chili farming business can achieve a maximum production of around 82.2% by using a combination of various production factors, suggesting that output could still be increased by approximately 17.8% through better input management and technology adoption without expanding resource use. The findings further demonstrate that irrigation technology plays a critical role in determining production efficiency. In particular, sprinkler irrigation is found to be more technically efficient than shower irrigation, reflecting its advantages in water distribution and labor use in sandy coastal conditions. These results highlight the importance of appropriate irrigation system selection as a key strategy for improving farm performance in coastal agriculture. The study provides empirical evidence to support the promotion of more efficient irrigation technologies and targeted extension programs aimed at enhancing technical efficiency. Overall, the findings contribute to the literature on irrigation efficiency in coastal farming systems and offer practical insights for farmers and policymakers seeking to improve productivity under challenging environmental conditions.

Excessive water use in flooded irrigation systems poses a significant challenge in rice cultivation. Therefore, it is essential to adopt irrigation management techniques that conserve water, enhance water productivity, and address water scarcity. This experiment aimed to evaluate the yield and water productivity of various crop establishment methods combined with different irrigation management techniques. The experiment was designed using a split-plot layout with three replications. The main plots comprised three crop establishment techniques: the System of Rice Intensification (SRI), Direct Seeding (DSR), and Transplanting (TPR). The five irrigation regimes were randomized to the subplots implemented, including: continuous submergence (flooding) up to a depth of 3cm (I1); irrigation application of 24L m- 2 four days after the ponded water had disappeared during crop growth (I2); irrigation application of 24L m- 2 four days after the ponded water disappeared until panicle initiation, followed by submergence until the dough stage (I3); irrigation application of 24L m- 2 four days after the disappearance of ponded water until flowering, then submergence until the dough stage (I4); and saturation throughout until the dough stage (I5). The Shalimar Rice-4 variety was utilized for the study. The results indicated that the SRI method of crop establishment yielded the highest grain yields of 7.72 t ha^-1 and 7.93 t ha^-1, alongside straw yields of 9.64 t ha^-1 and 9.98 t ha^-1, in the years 2021 and 2022, respectively. Furthermore, the highest total crop water productivity was observed in the saturation treatment compared with continuous flooding. Consequently, the System of Rice Intensification with saturation emerged as the most efficient irrigation strategy, as it retained the highest total crop water productivity under the temperate conditions of Kashmir.

Integrating agrivoltaics into smallholder farming systems to enhance food production and irrigation efficiency under climate stress

Abstract Since the 1950s, global irrigated areas have expanded dramatically, with complex effects on regional climate worldwide. Although the North China Plain (NCP) is among the most intensively irrigated regions in the world, the effects of irrigation expansion on heat stress over the past multidecadal timescale remain poorly understood. Based on long‐term meteorological records, we identified an enhanced cooling rate of 0.187°C decade −1 due to irrigation expansion during April–June from 1961 to 2005. The cooling effect weakened since 2005 due to improved irrigation efficiency and slowed irrigation expansion. Conversely, irrigation amplified nighttime temperature by 0.117°C decade −1 until 2005 and exacerbated daily moist heat stress by 0.269°C decade −1 after 1980, primarily due to increased humidity at night. Projections to 2050 suggest that irrigation will continue to alleviate heatwaves through cooling with a negligible impact on exacerbating extreme moist heat stress, which remains predominantly driven by climate change.

Subsurface drip irrigation (SDI) is an efficient irrigation method widely used in arid and semi-arid regions; however, its effectiveness largely depends on soil physical properties and the resulting soil water distribution. This study investigated soil wetting pattern dynamics under SDI and developed empirical models to predict wetted soil geometry as influenced by emitter discharge, installation depth, irrigation time, soil texture, and bulk density. Laboratory experiments were conducted using two representative soils from the Kurdistan Region of Iraq (Semel—silty clay and Zakho—clay loam). Emitters with discharge rates of 2 and 4 L h⁻¹ were installed at depths of 12.5, 25, and 37.5 cm, and equal water volumes (8 L) were applied to monitor the advancement of the wetting front. Nonlinear regression models were developed to estimate maximum horizontal wetted diameter (H) and average vertical wetted depth (V) as functions of emitter discharge (q), irrigation time (t), clay content (c), and bulk density (ρb). The models showed strong predictive performance, with coefficients of determination (R²) ranging from 0.87 to 0.98. Model validation indicated acceptable accuracy, with root mean square error (RMSE) values of 1.676–5.033 cm, mean absolute error (MAE) of 4.531–10.745 cm, and mean absolute percentage error (MAPE) of 5.468–10.613%. The index of agreement (d) ranged from 0.968 to 0.995, while mean bias error (MBE) values were close to zero, indicating minimal systematic error. The results demonstrated that wetting front expansion was primarily governed by irrigation time, whereas emitter discharge had a comparatively smaller influence, particularly at shallow depths. Increased clay content reduced both horizontal and vertical wetting dimensions, while the effect of bulk density varied with emitter depth. Lower discharge applied over longer durations enhanced lateral water movement, whereas higher discharge promoted vertical flow and deep percolation. The developed models provide practical tools for predicting soil water distribution and can support the optimization of emitter spacing, irrigation scheduling, and water-use efficiency in SDI systems under semi-arid conditions.

This study presents and evaluates a real-time decision support system (DSS) for site-specific irrigation scheduling based on soil moisture forecasting with SWIM 2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model). The SWIM 2 framework integrates a soil water balance model with in situ sensor data and soil moisture samples through Bayesian inverse modeling to generate probabilistic 10-day soil moisture forecasts. We assess the performance of the soil moisture forecasts and the irrigation DSS by integrating the model parameter ensemble with either deterministic or ensemble-based probabilistic weather forecasts, providing insights into their benefits and trade-offs in real-time irrigation management. Both approaches resulted in high detection rate and accuracy in predicting water stress triggering the irrigation threshold. The full ensemble yielded slightly better reliability at longer lead times whereas the probability distribution of the soil moisture predictions at short lead times was dominated by the SWIM 2 parameter uncertainty. Simulation of different irrigation treatments using the calibrated SWIM 2 -based model illustrated and confirmed its potential for evaluating water use efficiency and crop response. Overall, this work illustrates the application and practical advantages of a probabilistic, ensemble-based modeling framework in supporting site-specific, data-informed irrigation strategies. • SWIM 2 enables real-time, site-specific irrigation advice using probabilistic soil moisture forecasts. • Soil moisture uncertainty shifts from model parameter to weather uncertainty dominance with longer lead times. • Deterministic and ensemble weather forecasts were compared in water stress predictions for irrigation support. • Simulated irrigation strategies reveal impacts on water use efficiency, irrigation efficiency, and productivity.

Combining dynamic Bayesian prediction of the crop coefficient with automated lysimetry for highly accurate water-use control

Given the limited water resources and the challenge of emitter clogging in irrigation systems, this research aimed to design, construct and optimize an integrated and automated automatic filtration system to overcome the problems of conventional systems with operator dependence and suboptimal water and energy consumption. In this regard, a new system was designed in which a hydrocyclone and a screen filter were integrated into a single chamber and a pressure difference-based control algorithm was implemented to automatically activate the washing operation. The performance of this system was evaluated under the influence of three factors: operating pressure (100, 200, and 300 kPa), suspended solids concentration in water (4, 8, 12%), and screen pore diameter (0.1, 0.2, 0.3 mm) in the form of 81 experiments. The obtained data including filtration efficiency, water and energy consumption, and washing time were analyzed using statistical methods and Random Forest modeling, and a multi-objective optimization process was performed using the NSGA-II algorithm. A multi-objective optimization framework was implemented across three distinct operational scenarios: a quality-oriented scenario prioritizing maximum filtration efficiency; a resource-saving scenario emphasizing the minimization of wash water and energy consumption; and a balanced scenario aiming to achieve a compromise between performance and resource use. The optimization results revealed a set of optimal solutions that clearly illustrate the trade-offs among the considered objectives. The balanced scenario is reported as a representative operational case, achieving filtration efficiencies in the range of 88–98% with wash water consumption of 21–24 L and energy consumption of 80–90 kWh per cycle.

Abstract A comprehensive analysis of crop water footprint (WFP), water use efficiency (WUE) and economic water productivity (EWP) between 2010 and 2020 in two climatically and economically diverse agricultural regions: Punjab, Pakistan and Western Australia, Australia was undertaken. Using the Water Footprint Network method and climate, soil and agricultural production data, the study quantified green, blue and grey water footprints for five staple crops in each region. Normalization by local water scarcity and irrigation share was used to compare between the two regions with different water stress levels and irrigation dependence. Results showed that the total crop water requirements in Punjab increased by 12% during the study period due to changing climatic conditions. Cotton and rice had the highest WFP and dependence on irrigation, however generated relatively low economic returns per unit of water, showcasing inefficiency in the current agricultural system. In contrast, rain-fed crops in WA showed lower absolute crop water requirements and lower increases in normalized WFP, with stable WUE and EWP with cereals, showcasing efficient water use under climate-driven limitations. Normalization highlighted the difference in water stress, identifying that Punjab’s irrigation-intensive crops put much greater stress on local water resources than WA crops. Integration of biophysical and economic indicators, when normalizing for water scarcity, provides a strong basis to assess water practice of various agricultural systems. The results indicate the necessity for location-specific approaches: increased irrigation efficiency and suitable crop selection in Punjab; and climate-resilient cropping to maximize water use productivity and economic return in WA.

Mineral substrates for indoor horticulture systems critically determine plant water availability and irrigation demand. However, integrative assessments linking pore structure, water retention, and evaporation dynamics of commonly used mineral growing media remain scarce. A total of nine distinct mineral substrates were investigated: expanded clay, expanded slate, pumice, perlite, zeolite, vermiculite, lava granules, brick chips, and clay granules. To assess the impact of granulometry, pumice was tested in three different grain sizes (1–3 mm, 4–7 mm, 7–14 mm), resulting in a total of 11 experimental samples. Samples were characterized using scanning electron microscopy (SEM), suction experiments, and evaporation tests at 30%, 50%, and 70% relative humidity (RH) at 23 °C. Bulk density ranged from <0.12 g·cm−3 (perlite, vermiculite) to >0.99 g·cm−3 (zeolite, brick chips), while volumetric water content varied from 11.0 vol.% (expanded clay) to 46.6 vol.% (vermiculite). Plant-available water content (AWC) ranged from 2.7 vol.% (expanded clay) to 30.9 vol.% (clay granules). These results demonstrate that pore interconnectivity, rather than total porosity, is the decisive driver of hydraulic performance. Finer pumice fractions increased water retention by ~16% compared to coarser fractions. All substrates exhibited a two-phase evaporation profile, with initial rates ranging from 1.9 to 5.6 g·h−1 at 30% RH. Clay granules showed the most temporally stable evaporation, with only a 37% rate reduction over 48 h, compared to 66% for perlite. While conducted under controlled laboratory conditions, these findings provide a quantitative basis for targeted substrate selection and blending to optimize root-zone hydration, irrigation efficiency, and hygrothermal performance in permanent indoor horticulture systems.

Efficient use of water in agriculture is becoming increasingly critical, particularly in semi-arid regions like Anantapuramu district. Anantapuramu district (14.6819° N latitude and 77.6006° E longitude), which covers a geographical area of 1.913 million hectares. The district has a semi-arid climate, with hot and dry conditions for most of the year. This study focuses on estimating crop water use efficiency (WUE) of bush bean under different irrigation methods using the CROPWAT 8.0 model in the semi-arid conditions of Anantapuramu district. The weather data used for CROPWAT 8.0 model was collected for Madakasira mandal of Anantapuramu district for the years 2011 to 2020. The crop water requirement for bush bean was determined. The results clearly shows the relationship between water use, crop yield, and water use efficiency (WUE) under different irrigation methods. Flood irrigation used the highest amount of water (328.9 mm) but resulted in a relatively low yield (1000 kg/ha) and the lowest water use efficiency (3.04 kg/ha-mm), indicating poor water management and significant losses. Full (100%) irrigation improved yield significantly (1281.25 kg/ha) with slightly reduced water use, resulting in higher efficiency (4.27 kg/ha-mm), making it suitable where water availability is not a constraint. Deficit irrigation reduced water use (231.5 mm) while maintaining a relatively high yield (1187.5 kg/ha), leading to improved efficiency (5.13 kg/ha-mm), thus providing a balance between water saving and productivity. Partial Root Zone Drying (PRD) irrigation used the least water (141.2 mm) while maintaining yield (1000 kg/ha) and achieving the highest water use efficiency (7.08 kg/ha-mm), making it the most efficient irrigation method. There is a modify backward relationship between facility use and facility use efficiency. PRD was the most efficient method, followed by deficit irrigation, while flood irrigation was the least efficient and unsuitable under water-scarce conditions. For sustainable nutrient management, PRD and deficit irrigation methods are advisable, especially in regions grappling irrigate limitations.

Abstract Efficient water management is a critical requirement in modern agriculture due to increasing water scarcity and the need for sustainable farming practices. Traditional irrigation systems rely on manual operation and fixed schedules, which often result in over-irrigation, water wastage, and reduced crop productivity. This paper presents an IoT-Based Smart Irrigation System using ESP32, designed to automate irrigation based on real-time environmental conditions. The system utilizes a soil moisture sensor, temperature sensor, and water level sensor to continuously monitor field parameters. The ESP32 microcontroller processes the sensor data and controls a water pump through a relay module, ensuring that water is supplied only when required. The proposed system also incorporates IoT technology to enable wireless monitoring and control through a mobile or web interface. This allows users to access real-time data and manage irrigation remotely. The system is low-cost, energy-efficient, and easy to implement, making it suitable for small-scale farms, gardens, and greenhouse applications. The results demonstrate that the system effectively reduces water wastage, minimizes human intervention, and improves irrigation efficiency, thereby contributing to smart and sustainable agricultural practices. Keywords: IoT, Smart Irrigation System, ESP32, Soil Moisture Sensor, Temperature Monitoring, Water Level Sensor, Automated Irrigation, Wireless Monitoring, Precision Agriculture, Sustainable Water Management.

Background: Water scarcity and climatic challenges in Iraq necessitate the adoption of efficient irrigation systems such as drip and subsurface drip irrigation to improve water use efficiency and reduce soil degradation. This study evaluates the effectiveness of the subsurface nano-drip irrigation system. Methods: A field experiment was conducted in 2025, which included measuring the uniformity of the emission and calculating the coefficient uniformity values, in addition to the percentage of variation in the drip discharge before planting under three levels of operating pressures 50, 100 and 150 kPa. A discharge rate of 2.26 L h-1 was adopted at 150 kPa. Result: The operating pressure of 150 kPa gave moral values for the distribution homogeneity coefficient, emission uniformity and discharge variance ratio, reaching 98.13%, 96.57% and 6.44% respectively, with an actual dripper discharge of 2.26 L h-1. At the end of the growing season, the operating pressure of 150 kPa resulted in a discharge rate of 2.19 L h-1, while the values of distribution uniformity (DU), emission uniformity (EU) and coefficient of variation (CV) were 97.83%, 96.39% and 9.74%, respectively. Operating at 150 kPa provides optimal discharge characteristics, with minimal degradation observed after seasonal operation. These findings confirm the suitability of nano-drippers for subsurface irrigation applications, supporting their efficiency and sustainability in agricultural water management.

ABSTRACT Multi‑scenario flowchart showing CMIP6 climate models → bias correction → CROPWAT model → wheat irrigation projections for Bangladesh under SSP1‑2.6 to SSP5‑8.5, with temperature as dominant driver. Climate change poses a significant threat to wheat production in Bangladesh, a staple food. This study projects future irrigation requirements for wheat in Mymensingh by integrating an ensemble of five CMIP6 climate models with the FAO CROPWAT model, employing rigorous quantile-mapping bias correction and out-of-sample split-sample validation. Four emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were analyzed across near (2026–2050), mid (2051–2075), and far (2076–2100) futures. Projections indicate progressive warming, with January maximum temperatures increasing by up to 21.71% under SSP5-8.5 by late century. Consequently, potential crop water requirements rise from 1.38–3.87% near-term to 15.06% under SSP5-8.5 in the far future. While some precipitation increases were noted, their benefits are offset by rising evapotranspiration. Irrigation scheduling will be compressed, with the third irrigation advancing by one week under higher emission scenarios. Sensitivity analysis identified temperature as the dominant driver, increasing irrigation demand by up to 17.19% when isolated; this represents an upper-bound estimate as CROPWAT excludes CO2-mediated stomatal effects. A 20% reduction in soil moisture significantly altered irrigation efficiency. Substantial inter-model variability underscores projection uncertainty, reinforcing the necessity for adaptive, climate-resilient water management strategies to safeguard wheat productivity and national food security in Bangladesh's evolving agricultural landscape.