Water is considered one of the vital natural resources and factors for performing short- and long-term agricultural practices on Earth. Meanwhile, globally, most of the available freshwater resources are utilized for irrigation purposes in agriculture. Currently, many world regions are facing extreme water shortage problems, which can worsen if not managed properly. In the literature, numerous methods and remedies are used to cope with the increasing global water crises. The use of precision irrigation water-saving systems (PISs) for efficient water management under climate change is one of them and is a highly recommended approach by researchers. It can mitigate the adverse effects of changing climate and help enhance water use efficiency, crop yield, and environmental footprints. Thus, the present study aimed to comprehensively examine and review PISs, focusing on their development, implementation, and positive impacts on sustainable water management. In addition, we searched the literature using different online search engines and reviewed and summarized the main results of the previously published papers on PISs. We discussed the traditional irrigation method and its modernization for enhancing water use efficiency, PIS monitoring and controlling, architecture, data sharing communication technologies, the role of artificial intelligence for irrigation water-saving, and the future prospects of the PIS. Based on the brief literature review, the present study concluded that the future of PISs seems bright, driven by the need for efficient irrigation water management systems, technological advancements, and increasing environmental awareness. As the water scarcity problem intensifies due to climate change and population growth, the PIS is poised to play a critical role in optimizing and modernizing water usage, increasing water use efficiency, and reducing environmental footprints, thus ensuring sustainable agriculture development.

Plastic products in plant production and protection help farmers increase crop production, enhance food quality, and reduce global water use and their environmental footprint. Simultaneously, plastic has emerged as a critical ecological issue in recent years, and its pollution has significantly impacted soil, water, and plants. Thus, this review examines the multifaceted problems of plastic pollution in agriculture as a risk to food security, the ecosystem, and the environment. The study’s objective was to review and present the most recent information on using different plastic products in agriculture, the sources of plastic pollution, the advantages and drawbacks of using plastic products, and the strategies for mitigating plastic pollution in agriculture. Furthermore, after examining current plastic applications, benefits, adverse effects, and risks to soil, plants, and the environment, we addressed the requirements for technological advancements, regulations, and social processes that could contribute to mitigating plastic pollution in our ecosystems. We identified different pathways toward more sustainable use of plastics in agriculture and discussed future research directions.

Rapid urbanization, climate variability, and land degradation are increasingly challenging traditional open-field farming systems. Soilless farming (SLF) has emerged as a complementary approach to enhance horticultural resilience in space-constrained and climate-stressed environments. This review critically evaluates the role of SLF within the broader framework of climate-smart agriculture (C-SA), with a particular focus on its applications in urban and peri-urban settings. Drawing on a systematic review of the existing literature, the study explores how SLF technologies contribute to efficient resource use, localized food production, and environmental sustainability. By decoupling crop cultivation from soil, SLF enables precise control over nutrient delivery and water use in enclosed environments, such as vertical farms, greenhouses, and container-based units. These systems offer notable advantages regarding water conservation, increased yield per unit area, and adaptability to non-arable or degraded land, making them particularly relevant for high-density cities, arid zones, and climate-sensitive regions. SLF systems are categorized into substrate-based (e.g., coco peat and rock wool) and water-based systems (e.g., hydroponics, aquaponics, and aeroponics), each with distinct design requirements, nutrient management strategies, and crop compatibility. Emerging technologies—including artificial intelligence, the Internet of Things, and automation—further enhance SLF system efficiency through real-time data monitoring and precision control. Despite these advancements, challenges remain. High setup costs, energy demands, and the need for technical expertise continue to limit large-scale adoption. While SLF is not a replacement for traditional agriculture, it offers a strategic supplement to bolster localized food systems and address climate-related risks in horticultural production. Urban horticulture is no longer a peripheral activity; it is becoming an integral element of sustainable urban development. SLF should be embedded within broader resilience strategies, tailored to specific socioeconomic and environmental contexts.

Drought and heat stresses are severe threats to crop production and food security in arid and semi-arid regions of the globe, especially during the summer. This study investigates the effects of micro-spray on micro-climatic and physiological characteristics of cucumber and tomato plants for four growing seasons (two growing seasons each (cucumber: 2017 and 2018 and tomato: 2021 and 2022)). The experiment was conducted in a Venlo-type greenhouse where plants were irrigated with a combined micro-spray and drip irrigation (MSDI) system (1 min for tomato (seven times a day) and 2 mins (four times a day) for cucumber) and drip irrigation (DI). Both plants’ growth and physiological parameters were recorded at the end of the experiment. Moreover, the experimental results showed that plant height and stem diameter increased under MSDI in all seasons. In addition, a micro-spray duration of 2 min decreased the average daily air temperature (T_a) and leaf temperature (T₁) by 0.8 °C and 4.9 °C, respectively, and increased the average daily relative humidity (RH) by 4.3%. However, a micro-spray duration of 1 min reduced the average daily T_a and T₁ by 0.76 °C and 4.6 °C, respectively, but the increase in RH (2.7%) was much lower than that in the single micro-spray duration of 2 min. In addition, the net leaf photosynthetic rate (P_n), the effective quantum yield of PSII (Φ_PSII), and the stomatal conductance (G_s) of both plants increased under MSDI compared with DI. A K-means analysis showed that MSDI could eliminate the adverse effects of sunlight stress on cucumbers and tomatoes. MSDI increased the yield of cucumber and tomato plants by 18.9% and 40.4%, respectively. The fruit weight of cucumber (2017) and tomato (2021) under MSDI did not increase significantly compared to DI, which indicates that MSDI mitigated heat stress, prevented flowers from being burned, and increased the number of fruits. The results also suggested that the total soluble solids in the cucumbers and tomatoes showed no significant differences (p > 0.05) between the two treatments in four seasons. In conclusion, the MSDI system can be an appropriate strategy for the irrigation needs and climate control of plants grown in greenhouses during the hot season.

Determination of evaporation (E) and transpiration (T) in tea fields separately is important in developing precise irrigation scheduling and enhancing water use efficiency. In this study, the Shuttleworth–Wallace (S-W) model was applied to simulate the variations of E and T based on the data from 2015 to 2018 in a tea field in southeast China. The dual crop coefficient (D-K) method recommended by FAO-56 was also applied to calculate E and T, using the same data set to compare with the S-W model. The measured crop coefficient (K_c) ranged from 0.43 to 1.44 with the average value was 0.90 during 1–150 DOY (days of year), and the measured K_c tended to be stable with the average value of 0.83 during 151–365 DOY in 2015. The S-W model estimated ET_c with root mean square error (RMSE) and R² of 0.45 mm d⁻¹ and 0.97, while for the D-K method the values were 0.61 mm d⁻¹ and 0.95. Therefore, both approaches could estimate the E and T separately in tea fields in southeast China, however, the D-K method had a slightly poorer accuracy compared to the S-W model in the estimation of ET_c.