Search Results (1,526)

The grain-filling stage is crucial for determining yield and quality in rice. This process, and the pronounced disparity in development between superior and inferior grains, is orchestrated by a dynamic network of endogenous phytohormones. However, an integrated synthesis of their synthesis, transport, signaling, and crosstalk—particularly in the context of modern high-yield cultivation—is lacking. This review comprehensively analyzes the roles of auxin, cytokinin, gibberellin, abscisic acid, ethylene, brassinosteroids, and polyamines, with emphasis on their spatiotemporal dynamics and interactions in shaping grain fate. We explicitly link these hormonal mechanisms to agronomic and chemical regulation practices, such as nitrogen management and alternate wetting-drying irrigation. By synthesizing this knowledge, we aim to propose a unified model of grain filling regulation. This framework provides an actionable theoretical foundation for designing precise strategies to manipulate hormonal balances, thereby improving grain filling uniformity, yield, and quality in rice. Full article

This review integrates current knowledge on how greenhouse conditions regulate the nutritional quality and shelf life of tomato, cucumber, and sweet pepper. Preharvest environmental factors jointly shape fruit composition, firmness, and storage performance through their control of photosynthesis, assimilate partitioning, and structural stability. Across all variables, light intensity and fruit temperature emerge as the dominant determinants of overall quality and shelf life potential. Relative air humidity (RH), irrigation regime, and nutrient balance primarily affect firmness, water loss, and physiological disorders, while CO₂ enrichment, shading, and mineral or biostimulant inputs exert secondary yet consistent effects. Comparative evaluation shows that tomato is most sensitive to temperature and RH, cucumber to water status and epidermal stress, and sweet pepper to radiation for color and antioxidant development. These distinctions confirm that no single climatic optimization can be universally applied, and management must therefore target species-specific physiological constraints to sustain both nutritional excellence and storage performance. Major knowledge gaps remain, particularly regarding the combined effects of interacting environmental drivers and the integration of physiological responses with postharvest behavior. Future research should adopt multifactorial designs and predictive modeling to support climate-smart greenhouse strategies that optimize quality and storability under variable growing conditions. Full article

In the optimal design and operation of moistube irrigation systems, a wetted body and its components are important factors. This study presents an analytical characterization of the soil wetted body under horizontal moistube irrigation. In the laboratory experiment, the temporal and spatial changes in the wetted body during irrigation were observed. Specifically, the maximum wetting distances in the horizontal, vertical upward, and vertical downward directions on the soil profile were measured every 30 min. Additionally, images documenting the wetted body’s changes at different time points were recorded throughout the experiment. On this basis, by locating the soil profile of the wetted body in a coordinate system, the main motion equations describing the temporal and spatial changes in the wetted body’s soil profile were derived. Through integral processing of these motion equations, an analytical model for the wetted body under horizontal moistube irrigation was constructed. Finally, the model was validated using the experimental data. The results show that the model outcomes are consistent with the natural movement of water in the soil. Therefore, when characterizing the size of the wetted body under horizontal moistube irrigation using the soil profile area, the proposed method, which involves analyzing the shape and components of the wetted body’s soil profile at different time points and determining its soil profile size by integrating four distinct parabolas, is feasible. Full article

This study aimed to evaluate the effect of selenium concentrations in mitigating salt stress on the physiology, growth, and yield of okra plants irrigated with brackish water. Treatments consisted of four irrigation water salinity levels (ECw: 0.4, 1.3, 2.2, and 3.1 dS m⁻¹) combined with four selenium concentrations (0, 5, 10, and 15 mg L⁻¹), arranged in a randomized block design in a 4 × 4 factorial scheme, with three replicates and one plant per plot. Increasing irrigation water salinity from 0.4 dS m⁻¹ reduced relative water content, gas exchange, initial chlorophyll a fluorescence, plant growth, and production of okra, while increasing the percentage of electrolyte leakage. Irrigation Water salinity levels above 0.4 dS m⁻¹ impaired plant water status, gas exchange, growth, chlorophyll a fluorescence, yield, and water-use efficiency, while increasing electrolyte leakage. Salinity above 1.0 dS m⁻¹ also inhibited photosynthetic pigment synthesis. Selenium did not mitigate salinity-induced reductions in chlorophyll and carotenoids. However, foliar Se at 8.6–15 mg L⁻¹ enhanced gas exchange, chlorophyll a fluorescence, growth, and fruit yield under salinity up to 3.1 dS m⁻¹. These results support Se induced attenuation of salinity stress, warranting further mechanistic studies. Full article

Drought is a major problem affecting upland rice growth worldwide, including in northeast Thailand, with insufficient irrigation, where drought stress leads to reduced yields and may affect the functional compound content of rice grains. This research aimed to study the efficacy of arbuscular mycorrhizal fungi (AMF) Rhizophagus variabilis KS-02 and endophytic fungi (EPF) Trichoderma zelobreve PBMP16 on promoting the growth and accumulation of functional substances in upland black rice under drought conditions. Factorial experiments in a randomized complete block design (RCBD) were conducted by cultivating rice inoculated with AMF and EPF as well as co-inoculated with AMF+EPF under three watering conditions: 100% field capacity (FC), 66% FC, and 33% FC. The results show that both AMF, EPF improved some plant growth parameters and physiological performance under both well-watered and water-limited conditions. Inoculating plants with fungi increased the production of enzymes APX, CAT, and GR, as well as proline, which helps plants tolerate water deficit stress. Functional grain quality, including phenolic compounds, anthocyanins, and antioxidant activity, was also increased by fungal inoculation. While co-inoculation provided advantages for certain parameters, particularly antioxidant activity and biomass, single inoculation with AMF or EPF was equally effective or superior for specific traits depending on the level of water stress. Overall, this report shows that both AMF and EPF contribute to improving the productivity and functional quality of upland black rice under drought conditions, with treatment effects varying according to fungal type and water availability. Full article

To address the issue of low fertilizer proportioning accuracy in irrigation and fertilization systems due to neglecting the influence of target ions in raw water, this study designed a high-precision online automatic water–fertilizer mixing device that can directly mix raw water (without water purification treatment) with fertilizer stock solution. This device is capable of preparing mixed fertilizer solutions containing N, K, and Ca elements. It employs ion-selective electrodes and flow meters for online detection and feedback of target ion concentrations in the fertilizer solution and flow rate information, and adopts an online fertilizer mixing control strategy that uses a constant raw water flow rate and a fuzzy PID control method to dynamically adjust the pulse frequency of metering pumps, thereby changing the injection volume of nutrient solution. Simulation and experimental analyses show that the piping system of the device is reasonably designed, ensuring stable and smooth fertilizer injection. The temperature-compensated concentration detection models for the three target ions in the fertilizer solution, constructed using a stepwise fitting method, achieve average relative detection errors of 1.94%, 1.18%, and 2.87% for K⁺, NO₃⁻, and Ca²⁺, respectively. When preparing single-element or mixed fertilizer solutions, the device achieves an average steady-state error of no more than 4% and an average steady-state time of approximately 40 s. Compared with deionized water, the average relative errors for potassium ions, nitrate ions, and calcium ions when preparing fertilizer solutions with raw water are 1.33%, 1.12%, and 1.19%, respectively. Compared with the theoretical errors of fertilizer preparation with raw water, the fertilizer proportioning errors of this device for potassium ions, nitrate ions, and calcium ions can be reduced by a maximum of 10.55%, 66.84%, and 62.71%, respectively, which is superior to the performance requirements for water–fertilizer integration equipment specified in the national industry standard DG/T 274-2024. Additionally, the device achieves accurate and stable fertilizer proportioning with safe and reliable operation during 6 h of continuous operation. This device significantly reduces the impact of raw water on fertilizer proportioning accuracy, improves the adaptability of the device to irrigation water sources, and provides theoretical basis and technical support for water-fertilizer integration systems in cost-sensitive agriculture. Full article

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

Efficient water management is increasingly critical in vineyard operations, particularly in the context of climate change and the rising demand for sustainable agricultural practices. Regulated deficit irrigation has emerged as a promising technique that allows significant water savings while sustaining or improving the quality of grapes. However, its effective implementation requires timely and precise information on vine water status and environmental conditions (pluviometry, humidity, radiation, etc.). This study presents the methodology of a decision-support tool that tested the application of several artificial intelligence regression models. Among the algorithms evaluated, an Extreme Gradient Boosting (XGBoost) regression model showed the best performance and was adopted as the core predictive engine of the ViñAI tool to optimize deficit irrigation in vineyards. Based on the developed methodology, the ViñAI tool integrates open-access environmental data such as weather forecasts and satellite-based estimates of evapotranspiration. Furthermore, ViñAI is designed with the potential to integrate sensor-based field data. Overall, the results demonstrate that ViñAI offers a scalable, data-driven approach to support climate-smart irrigation decisions in vineyards, particularly in sensor-sparse or resource-limited contexts, and provides a robust basis for further multi-season and multi-region validation. Full article

Drought significantly reduces global crop yields and agricultural productivity. This study aims to isolate drought-tolerant PGPR strains and evaluate their effects, both individually and in combination with salicylic acid (SA), on cowpea plants growth, physiological traits, antioxidant enzymes, and mineral content under both drought stress and non-stress conditions. Among fifteen bacterial isolates, AO7, identified as Streptomyces diastaticus subsp. ardesiacus PX459854 through 16S rRNA sequencing, demonstrated significant plant growth promotion in cowpea under gnotobiotic conditions. On the other hand, varying salicylic acid concentrations (0.5, 1.0, and 2.0 mM) was exposed to assess the plant growth of cowpea plants in a gnotobiotic system. A pot experiment in 2023 used a split-plot design with treatments for irrigation (unstressed and stressed) and different soaking treatments (control, S. diastaticus, salicylic acid (2 mM), and a combination). After 60 days, the combination treatment enhanced growth metrics, outpacing the control under stress. The microbial community in the T4 treatment exhibited the highest counts, while T8 (combination, stressed) showed lower counts but the highest chlorophyll content at 6.32 mg g⁻¹ FW. Notable increases in proline and significant changes in enzyme activities (PO, PPO, CAT, and APX) were observed, particularly in treatment T8 under stress, indicating a positive response to both treatments. Mineral content of cowpea leaves varied with soaking treatments of S. diastaticus and SA (2.0%) especially under drought stress which the highest values were 1.72% N, 0.16% P, and 2.66% K with treatment T8. Therefore, T8 (combination, stressed) > T6 (S. diastaticus, stressed) > T7 (salicylic acid, stressed) > T5 (control, stressed) for different applications under stressed conditions and T4 (combination, unstressed) > T2 (S. diastaticus, unstressed) > T3 (salicylic acid, unstressed) > T1 (control, unstressed) for the other applications under normal conditions. Thus, using S. diastaticus and SA (2.0%) in combination greatly enhanced the growth dynamics of cowpea plants under drought stress conditions. Full article

Chickpeas are a pulse crop that originated in Eurasia and are a source of protein for many people. The objective of this research is to select stable, high-yielding chickpea genotypes using uni- and multivariate methods of adaptability and stability analysis. Fifteen genotypes were tested in the 2020 and 2021 agricultural years. The experimental design was a completely randomized block design with three replications. The collected data were yield (kg/ha) values, and the stability analyses were performed using Eberhart and Russell’s, Lin and Binns’s modified by Carneiro’s, additive main effects and multiplicative interaction (AMMI), and weighted average of absolute scores (WAASB) methods. The average sum of ranks (ASR) was then calculated by ranking genotypes according to their yield and stability indices. The AMMI analysis of variance showed significant effects (p < 0.05) for environments, genotypes, and the interaction between genotypes and environments. From AMMI, the first three principal components (PCs) had significant effects, and the cumulative variance on the PC1 and PC2 axes was 86%. FLIP02-23C, FLIP03-109C, and Jamu 96 had the lowest ASR, indicating that these genotypes are the most stable and productive chickpea genotypes. According to AMMI2, genotypes FLIP03-109C, FLIP03-35C, FLIP02-23C, and FLIP06-155C could be adapted to irrigated environments. Full article

Phosphorus (P) characteristics significantly affect crop yield and P use efficiency (PUE). It is unclear whether different types of acidic phosphate fertilizers can enhance the availability of phosphorus in liming soil and soybean yields. In this field experiment in 2022 and 2023 in Xinjiang, China, four phosphate fertilization treatments, including no phosphate fertilization (CK), application of monoammonium phosphate (MAP), application of urea phosphate (UP), and application of a mixture of monoammonium phosphate and urea phosphate (8:2, M8U2), were designed. Then, the impacts of the four phosphate treatments on the PUE, growth, and yield of the high-oil soybean variety Kennong 23 under drip irrigation were explored. The results showed that the application of phosphate fertilizers significantly increased the soil inorganic P, available P, and total P content compared with CK, promoting the growth and yield formation of soybeans. The soil Ca2-P content of the UP treatment was higher than that of the MAP treatment. The soil Ca8-P content of the M8U2 treatment was higher than that of the MAP treatment, but the soil phosphorus fixation was lower. The soil available P content, soybean plant P accumulation, leaf photosynthetic capacity, and dry matter accumulation all reached the maximum in the M8U2 treatment. The soybean yield, net revenue, and PUE of the M8U2 treatment were 6.04%, 9.37%, and 14.16% higher than those of the MAP treatment, and 7.64%, 16.59%, and 23.50% higher than those of the UP treatment, respectively. Therefore, the combined application of acidic phosphate fertilizers (MAP and UP) can increase soil available P content and plant P absorption in liming soil and stimulate photosynthesis, enhancing soybean yield and PUE. This study will provide a technical reference for the P reduction and soybean yield enhancement in arid areas. Full article

In arid regions characterized by extreme water scarcity, it is important to synergistically optimize both crop yield and water use. Irrigation strategies based on empirical knowledge overlook crops’ dynamic water needs and may cause water waste and yield loss. To address this issue, this paper proposes an intelligent irrigation scheduling method based on a crop growth model and an improved deep reinforcement learning (DRL) agent. We construct a high-fidelity cotton growth environment using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The model was calibrated with local data from the Shihezi region, Xinjiang, to provide a reliable simulation platform for DRL agent training. We developed a temporal state representation module based on a Bidirectional Long Short-Term Memory (BiLSTM) network and an attention mechanism. This module captures dynamic trends in historical environmental information to focus on critical decision factors. The Soft Actor–Critic (SAC) algorithm was improved by integrating a feature attention mechanism to enhance decision-making precision. A dynamic reward function was designed based on the critical growth stages of cotton to incorporate agronomic prior knowledge into the optimization objective. Simulation results demonstrate that our proposed method can improve water use efficiency (WUE) by 39.0% (with an 8.4% increase in yield and a 22.1% reduction in water consumption) compared to fixed-schedule irrigation strategies. An ablation study further confirms that each of our proposed modules—BiLSTM, the attention mechanism, and the dynamic reward—makes a significant contribution to the final performance. Full article

A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability. Full article

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. Using satellite-derived LULC scenarios (2001, 2010, 2020) to drive the WRF-Hydro/Noah-MP modeling framework, we provide a holistic assessment of water dynamics in Ethiopia’s Awash Basin. The model was calibrated and validated with observed streamflow (R² = 0.80–0.89). Markov analysis revealed rapid cropland expansion and urbanization (2001–2010), followed by notable woodland recovery (2010–2020) linked to national initiatives. Simulations show that early-period changes increased surface runoff, potentially enhancing reservoir storage for large-scale irrigation. In contrast, later changes promoted subsurface flow, indicating a shift towards enhanced groundwater recharge, which is critical for small-scale and well-based irrigation. Evapotranspiration (ET) trends, validated against GLEAM (monthly R² = 0.88–0.96), reflected these shifts, with urbanization suppressing water fluxes and woodland recovery fostering their resurgence. This research demonstrates that land use trajectories directly alter the partitioning of agricultural water sources. The findings provide critical evidence for designing sustainable land and water management strategies that balance crop production with forest conservation to secure irrigation water and support initiatives like Ethiopia’s Green Legacy Initiative. Full article

A clear understanding of water droplet formation and distribution dynamics is fundamental to improving the hydraulic performance and operational efficiency of sprinkler irrigation systems. This study presents an experimental investigation of droplet characteristics using high-speed photography under controlled laboratory conditions. The objective was to analyze droplet diameter, ellipticity, frequency, and velocity at working pressures of 0.2, 0.25, and 0.3 MPa. Median droplet diameters measured at 6–8 m from the nozzle were 2.79 mm, 3.41 mm, and 3.68 mm at 0.2 MPa, with a reduction of up to 17.7% as pressure increased to 0.3 MPa. Smaller droplets were predominantly concentrated near the nozzle and decreased with radial distance, influencing water application uniformity. Morphological parameters such as uniformity (1.3), ellipticity (2.13), and circularity (0.81) were quantified. Cumulative frequency curves revealed 12% droplet fragmentation at 7–8 m under higher pressures, illustrating the dynamic nature of droplet breakup. A strong linear correlation between droplet diameter and calibrated reference diameter confirmed the reliability of the measurement technique. These findings demonstrate that high-speed photography is a robust method for droplet characterization and provides accurate, repeatable data essential for optimizing sprinkler designs to reduce water loss due to evaporation and wind drift. The study contributes to precision irrigation research by offering a detailed understanding of droplet behavior under varying operating pressures. Full article