Search Results (828)

The transition toward sustainable agriculture requires innovative biostimulants that enhance crop performance while reducing reliance on synthetic agrochemical inputs, such as mineral fertilizers and chemical pesticides, including herbicides, insecticides, and fungicides. Microalgae have gained considerable attention as sustainable biostimulants due to their diverse biochemical composition, including primary metabolites, phenolic compounds, and phytohormones. This work examined how Chlorococcum sp. extract influences seed germination, early seedling development, pigment content, and biochemical composition of soybean (Glycine max L.). Biochemical characterization of the extract revealed it was rich in polyphenols and flavonoids. Seeds were treated with four concentrations (0.1–2 g L⁻¹) and compared to an untreated control. The extract significantly improved the final germination percentage, from 57% in the control to 70% (0.5 g L⁻¹) and 80% (1 and 2 g L⁻¹), with a similar trend observed for the seed vigor index. Radicle and hypocotyl lengths increased significantly. Results also showed that pigment levels increased, especially under the 0.5 g L⁻¹ treatment. Seedling biochemical parameters, including carbohydrates, proteins, reducing sugars, phenolic compounds, and DPPH radical-scavenging activity, increased under microalgal treatments. Overall, the findings highlight the potential of microalgae-derived biostimulants as renewable and environmentally friendly alternatives to conventional crop inputs, supporting more sustainable soybean production and contributing to the development of resilient and resource-efficient agricultural systems. Full article

Sugar transporters play a pivotal role in photoassimilate partitioning and biomass accumulation in plants. However, how whole-genome duplication (WGD) has reshaped the evolutionary trajectory of the sugar transport system in Bambusoideae remains elusive. Here, we systematically identified and characterized the SUT (Sucrose Transporters) and MST (Monosaccharide Transporters) gene families across 14 bamboo genomes representing four major lineages: herbaceous, temperate woody, neotropical woody and paleotropical woody bamboos. Our results suggest that WGD acted as the primary driving force behind the expansion of these gene families, with woody bamboos possessing significantly higher gene counts than herbaceous bamboos. Notably, extensive gene loss was observed in hexaploid lineages, aligning with the gene dosage balance hypothesis. Phylogenetic and sequence analyses demonstrated that core genes were subjected to purifying selection. Intriguingly, 21 groups of identical protein sequences across species were identified in paleotropical woody bamboos, suggesting high sequence conservation. Transcriptomic and syntenic analyses further unveiled the mechanisms of post-polyploidization expression divergence: homeologs exhibited marked expression asymmetry across subgenomes. Distinct from the broad expression pattern in herbaceous bamboos, which rely on SUT5 and a few STP (Sugar Transporter Protein) genes, woody bamboos have achieved fine-tuned expression partitioning and tissue-specific specialization during different culm developmental stages through significantly expanded SUT and STP family members. This study reveals an evolutionary pattern characterized by “WGD-driven expansion, post-polyploid fractionation, and subgenome expression divergence.” This pattern suggests a potential link to the enhanced sugar allocation efficiency required for the explosive growth of woody bamboos, offering valuable gene resources and a foundation for future functional studies in Poaceae crops. Full article

Plant biostimulants have emerged as key tools for sustainable agriculture by improving crop productivity, resource-use efficiency, stress resilience, and food quality while reducing dependence on external agricultural inputs. Despite their remarkable diversity in origin and composition, increasing evidence suggests that structurally distinct biostimulants may influence overlapping conserved regulatory networks controlling plant growth and environmental adaptation. This review proposes a mechanistic convergence framework to explain how structurally distinct plant biostimulants may influence overlapping regulatory networks controlling plant growth and environmental adaptation. We examine how humic substances, seaweed extracts, protein hydrolysates, amino acids, chitosan, silicon, and microbial biostimulants regulate extracellular perception, intracellular signalling, phytohormonal crosstalk, transcriptional reprogramming, and metabolic integration, ultimately enhancing root development, nutrient and water use efficiency, photosynthesis, carbon and nitrogen metabolism, redox homeostasis, stress tolerance, crop productivity, and food quality. We further discuss how transcriptomics, proteomics, metabolomics, epigenomics, and computational biology are identifying recurring signalling and metabolic responses that may help define conserved regulatory processes and potential molecular biomarkers associated with the physiological responses induced by chemically diverse biostimulants. This systems-level framework provides a conceptual basis for the rational development of evidence-based precision biostimulants for sustainable and climate-resilient agriculture. Full article

Irrigation scheduling plays a pivotal role in bridging water resource allocation and farmland production management. For decades, scheduling in irrigation districts has predominantly relied on operators’ experience and relatively rigid water delivery plans, making it difficult to simultaneously meet the demands for timely responsiveness and precise water allocation under the combined influence of meteorological variability, changing crop water requirements, and the dynamic adjustments of water conveyance and distribution systems. The advancement of digital technologies, such as the Internet of Things, machine learning, deep reinforcement learning, and digital twins, has opened new technical pathways for optimizing irrigation scheduling. Focusing on the development of smart irrigation and scheduling optimization in irrigation districts, this paper systematically reviews the relevant literature published from January 2000 to June 2026 and delineates its evolution into three stages. Early-stage research was grounded in physical models, empirical rules, and hydraulic simulations, establishing fundamental methods for evapotranspiration estimation, crop water requirement calculation, and canal water delivery simulation. The middle stage, marked by the introduction of the Internet of Things and machine learning, enabled real-time monitoring of hydrological conditions, soil moisture, and meteorological data and promoted a data-driven transformation of water demand forecasting methods. The recent stage is characterized by the integration of deep reinforcement learning, digital twins, and knowledge graphs, which extends irrigation district scheduling from isolated single-point optimization toward multi-agent coordination and closed-loop management. Existing evidence confirms that digital technologies have yielded water-saving and yield-increasing benefits at the field scale and improved water distribution efficiency in several demonstration irrigation districts; however, their wider deployment at the district scale still faces bottlenecks such as inadequate sensing of physical execution processes, underdeveloped multi-objective trade-off mechanisms, and limited model transferability and long-term operational sustainability. To address these challenges, this paper proposes future research directions oriented toward real-time perception of water delivery and distribution status, multi-objective robust optimization, explainable artificial intelligence, and human–machine collaborative decision-making, thereby providing a reference for the theoretical development, engineering deployment, and operational management of smart irrigation district scheduling systems. Full article

Saline water irrigation is a potential strategy to address agricultural water scarcity, but its effects on soil respiration and carbon balance are not well understood. To clarify these effects and promote the safe utilization of saline water resources, this study investigated five irrigation water salinity levels EC_iw: 1.3, 3.4, 7.1, 10.6, and 14.1 dS·m⁻¹ (i.e., 1, 2, 4, 6, 8 PSU; 1000, 2000, 4000, 6000, 8000 mg·L⁻¹) in a winter wheat–summer maize rotation during 2024–2025. The results indicated that saline water irrigation caused salt accumulation during the wheat season, whereas salt leaching occurred during the maize season. When EC_iw ≤ 3.4 dS·m⁻¹, no notable decreases were observed in dry matter accumulation, water productivity, and carbon emission efficiency for both crops. In contrast, when EC_iw > 3.4 dS·m⁻¹, the crop yields and net carbon input of the crop rotation system were suppressed to a considerable extent. Furthermore, under saline water irrigation, the average soil respiration rate decreased by 4.1–25.2% during the wheat growing season, while that for maize decreased by 7.4–30.7%. Soil respiration in wheat was negatively correlated with soil salinity and pH, and positively correlated with soil moisture (p < 0.01). In maize, soil respiration was negatively correlated with salinity (p < 0.01), and positively correlated with soil moisture (p < 0.05) and temperature (p < 0.01). The entropy-weighted TOPSIS model identified 3.4 dS·m⁻¹ as the appropriate irrigation salinity threshold for maintaining yield and carbon sink function in this rotation system. Full article

Precision agriculture (PA) has emerged as a data-driven approach for improving maize (Zea mays L.) production through the integration of remote sensing, Geographic Information Systems (GISs), Global Navigation Satellite Systems (GNSSs), the Internet of Things (IoT), and machine learning (ML). This systematic review evaluates the application of PA for yield optimisation and resource-use efficiency in maize production between 2020 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 464 records were identified from Scopus and Web of Science, of which 129 studies met the inclusion criteria. The reviewed literature comprised field experiments (28.6%), remote sensing and PA integration studies (25.5%), machine learning applications (19.4%), climate-informed PA strategies (23.4%), and soil degradation studies (3.1%). Remote sensing and integrated multi-technology systems were the most extensively investigated approaches, followed by ML-based models for yield prediction and crop monitoring. Overall, PA technologies enhanced maize productivity, nutrient-use efficiency, water-use efficiency, and yield prediction accuracy through site-specific management and data-driven decision support. Despite its considerable potential, the adoption of PA remains constrained by high implementation costs, technical complexity, and limited data availability. Collectively, these findings demonstrate that precision agriculture provides an effective framework for sustainable maize intensification by improving productivity, optimising resource-use efficiency, and strengthening resilience to climate variability. Full article

Soilless systems are increasingly important in protected horticulture because they improve water and nutrient-use efficiency, support year-round production, and reduce dependence on field conditions. However, the root zone is still commonly managed as a passive nutrient reservoir using mainly electrical conductivity and pH set points. This review presents the root zone as an actively engineered biological environment in which dissolved oxygen, root-zone temperature, nutrient-solution chemistry and hydraulics, microbiomes and biofilms, and sensor-guided control interact to determine crop performance. These domains converge on root respiration and ATP production, membrane transport, aquaporin activity, hydraulic conductance, calcium delivery, oxidative balance, and microbial or pathogen selection. Their combined effects influence fresh mass, tissue hydration, nutrient uptake, phytochemical composition, tipburn incidence, disease resilience, and overall system stability. Recent evidence indicates that active aeration, targeted root-zone heating or cooling, optimized flow scheduling, and calcium-focused interventions can improve the yield and quality of leafy vegetables, although responses vary with crop species, cultivar, developmental stage, and production-system architecture. Current evidence also indicates important uncertainties, including crop- and cultivar-specific response thresholds, architecture-dependent performance, energy and resource costs, and the still-limited predictability of microbiome manipulation. Emerging sensing, machine learning, digital-twin, and predictive-control approaches could enable a transition from threshold-based correction to physiology-informed root-zone state management. Nevertheless, wider commercial translation is constrained by inconsistent reporting of sensor location, hydraulic conditions, nutrient composition, microbial status, and resource use. We therefore propose a minimum reporting framework and research priorities for developing reproducible, energy-aware, microbiologically robust, and crop-specific root-zone management strategies for closed soilless horticulture. Full article

Hydrogels (HGs) have emerged as promising multifunctional materials for sustainable agriculture due to their high water retention capacity and their ability to act as controlled-release platforms for agrochemicals, nutrients, microorganisms, and bioactive compounds. Their application has gained increasing attention in response to global challenges associated with climate change, water scarcity, soil salinization, and the low efficiency of conventional fertilizers, which contribute to environmental degradation and reduce crop productivity. This review provides a critical overview of hydrogel-based systems for agricultural applications, with particular emphasis on the encapsulation of bioactive components for soil remediation and crop protection under abiotic stress conditions. A systematic literature review following PRISMA guidelines was conducted using the Scopus database, resulting in the analysis of 548 studies published between 2003 and 2024. Bibliometric analysis revealed a marked increase in research activity since 2021, mainly driven by advances in water-retention technologies, nanocomposite hydrogels, controlled-release systems, and bioactive encapsulation strategies. The review discusses the main factors governing hydrogel functionality, including swelling behavior, crosslinking density, biodegradability, and interactions with soil–plant systems. Particular attention is given to recent developments involving the incorporation of microorganisms, nanoparticles, micronutrients, and agrochemicals into biodegradable hydrogel matrices to improve nutrient availability, mitigate salinity stress, and reduce agrochemical losses. Finally, the challenges associated with scalability, environmental stability, and field validation are discussed, highlighting the potential of hydrogel-based bioactive delivery systems for developing resilient and resource-efficient agricultural systems. Full article

Plant diseases pose a significant challenge to global agriculture, with early diagnosis particularly problematic for resource-limited farmers. This study introduces an AI-powered web application for classifying leaf health in two key medicinal plants, Ricinus communis (Eranda) and Vitex negundo (Nirgundi). The tool categorizes leaf images into three health states—Healthy, Medium Healthy, and Unhealthy to facilitate timely and informed decision making in crop management. A dataset comprising 2834 images of Ricinus communis and 3796 images of Vitex negundo under various conditions was created and used to train the model. Advanced transfer learning architectures, including VGG16, ResNet50, MobileNetV2, Xception, EfficientNetB0, and VGG19, were employed to enhance the classification accuracy of the system. Notably, VGG16, EfficientNetB0, and VGG19 achieved highest level of accuracy, whereas the other models showed comparatively lower level of accuracies. This work performs real-time assessment of leaf health for sustainable Ayurvedic agriculture. Full article

The traditional spring wheat–sunflower relay intercropping system in the Hetao Irrigation District of Inner Mongolia is constrained by restrictions on sowing time, relatively low resource use efficiency, and suboptimal strip configurations. Based on the winter-seeding technique for spring wheat, this study established an annual double-cropping system of winter-seeded spring wheat relay intercropped with sunflower. The objectives were to evaluate crop photosynthetic performance, grain yield, resource use efficiency, and economic benefits under different strip configurations and to identify a suitable strip arrangement. A two-year fixed-site field experiment was conducted from 2023 to 2025. Four winter-seeded spring wheat/sunflower relay-intercropping treatments (W9S2, W9S4, W18S2, and W18S4) were compared with sole-cropped winter-seeded spring wheat and sole-cropped sunflower. Relay intercropping increased leaf area index, SPAD values, and the net photosynthetic rate of both crops at key growth stages and also improved grain yield and resource use efficiency. Among the strip configurations, W18S2 showed the best overall performance. Its two-year average grain yields of wheat and sunflower were 13.2% and 32.2% higher, respectively, than those of the corresponding sole-cropping treatments. The land equivalent ratio and nitrogen uptake equivalent ratio of all relay-intercropping treatments were greater than 1, with the highest values observed under W18S2. In addition, W18S2 had higher light use efficiency, water use efficiency, and nitrogen partial factor productivity than the sole-cropping treatments and the other relay-intercropping configurations. The two-year average net profit of W18S2 was 46.8% and 8.0% higher than that of sole-cropped wheat and sole-cropped sunflower, respectively. Overall, the superior performance of W18S2 was associated with a more favorable canopy structure and photosynthetic performance, greater dry matter accumulation, and coordinated improvements in grain yield, resource use efficiency, and economic benefits. These findings provide a reference for the high-yield and resource-efficient cultivation of winter-seeded spring wheat relay intercropped with sunflower in the Hetao Irrigation District. Full article

Modern smart agriculture increasingly relies on edge computing for real-time, high-concurrency tasks such as wide-area drone-based crop monitoring. However, highly volatile workloads and severe environmental noise in agricultural Internet of Things (IoT) networks often lead to resource congestion and high latency when relying on traditional reactive scheduling. To address these challenges, this paper proposes a hybrid prediction-driven proactive resource scheduling method for edge computing. We construct a Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory (VMD-CNN-Attention-BiLSTM) model to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic. Furthermore, a deep reinforcement learning scheduling algorithm based on Proximal Policy Optimization (PPO) incorporates future workload trends into its state space, dynamically optimizing task offloading. To evaluate the proposed Predictive Computational Scheduling Framework (PCSF), we developed a custom edge computing simulation environment and synthesized a hybrid dataset combining real-world server logs from the Alibaba Cluster Trace with deep learning inference workloads derived from a Wheat Plant Diseases image repository. Simulations demonstrate that the prediction model achieves a Root Mean Square Error of 0.030 and a Mean Absolute Error of 0.0215. Compared to static and reactive baselines, the PCSF reduces average task timeout violations to 2.2 and total system energy consumption by nearly 40%. This proactive mechanism effectively overcomes decision-making lags, enabling efficient, low-latency computing resource allocation for modern agricultural facilities. Full article

European agriculture is increasingly required to remain economically viable while improving resource-use efficiency and reducing exposure to rising input and energy costs. This study examines the relationship between farm profitability, asset-use efficiency and energy-cost intensity across different European farming systems. The analysis is based on FADN/FSDN economic indicators reported by country and farm type for the period 2015–2023. Four types of farming were analysed separately: field crop farms, dairy farms, mixed farms and wine farms. Return on assets was used as the dependent variable, while asset turnover, economic size, fixed asset share, productivity indicators and energy intensity were included as explanatory variables. The empirical analysis combined farm-type-specific panel models with several robustness checks, including common time effects, first-difference specifications and a unified farm-type interaction model. The results show that asset turnover is the most consistently significant correlate of profitability, with a positive association across all the analysed farming systems. Energy intensity is negatively associated with profitability, with the most robust evidence identified in field crop and mixed farms. In dairy and wine farms, the association between energy intensity and profitability was not statistically significant at the 5% level in the main fixed-effects models. Fixed asset share was negatively and statistically significantly associated with profitability only in mixed farms. These findings indicate that profitability-related relationships differ across farming systems and that asset-use efficiency and energy-cost exposure should be interpreted within the structural and production characteristics of each farm type. The study does not aim to identify causal effects, but provides comparative evidence on conditional associations between profitability, accounting-based efficiency indicators and resource-use indicators in European agriculture. Full article

While continuously promoting agricultural water-saving and efficiency improvement, Ningxia is confronted with problems such as deepening groundwater tables and growing ecological vulnerability. Exploring the trade-off relationships and their evolutionary characteristics between socioeconomic development and water resource carrying capacity, as well as between water use efficiency improvement and groundwater-ecosystem maintenance, is of great significance for coordinated water resource governance in arid irrigation districts. Based on time-series data covering 2000–2024, this paper establishes a DPSIR evaluation model and constructs a progressive quantitative analytical framework coupling the entropy-weight-Tapio decoupling, rate-scissors difference and PLS-SEM models. During the study period, the growth rate of the response (R) dimension (13.76%) was far higher than that of the state (S) dimension (3.23%) from 2011 to 2020, confirming the objective existence of dual trade-offs. The two categories of trade-offs underwent a three-stage evolution of “latent-intensified-remediation”, showing the counter-intuitive feature of “effective total-volume control alongside continuous groundwater table deepening”. Hidden transmission barriers were identified for 2008–2016 (θ₁, θ₂ dropped to 0.46–1.32°): the transfer of water-saving dividends to industry caused groundwater extraction to rise rather than fall to a certain extent. PLS-SEM analysis reveals that structural lock-in acts as the core inhibiting factor for ecological protection. The total effect of socioeconomic development on ecology reaches 0.921, whereas structural lock-in produces a chained negative mediating effect of −0.192 by suppressing water use efficiency. Improvement in water use efficiency presents dual characteristics of overall ecological gain and localized groundwater-recharge loss. It can be concluded that engineering-only water-saving measures cannot balance water-intake reduction and recharge deficits. It is necessary to simultaneously advance low-water-consumption cropping-pattern restructuring, rigid enforcement of the 2.5 m ecological groundwater table threshold, and the substitution mechanism for saved-water volume between industry and agriculture, so as to build a coordinated “water-saving-recharge-ecology” regulation system. Full article

Sweet basil (Ocimum basilicum L.) is a popular aromatic and medicinal crop cultivated globally. While its essential oils possess inherent antimicrobial properties, the plant remains highly susceptible to various pathogens. This study investigated the impact of a beneficial synthetic microbial community (SynCom)—composed of eight strains with proven antimicrobial and plant growth-promoting activities—on basil growth, ecophysiology, and metabolic profile in an aeroponic system. Although SynCom inoculation significantly enhanced photochemical efficiency, it did not yield significant gains in total biomass. Instead, inoculated plants exhibited a strategic reallocation of resources toward structural and chemical defenses. This physiological shift was physically manifested as denser plant canopies and lower specific leaf area (SLA). Untargeted metabolomic profiling identified 167 significantly altered metabolites. Notably, SynCom inoculation increased abscisic acid (ABA) turnover and redirected tryptophan metabolism from growth-promoting auxins to defense-related indoles and kynurenine derivatives. Furthermore, inoculation enriched the leaves with essential amino acids and a diverse array of protective secondary metabolites, including phenylpropanoids, flavonoids, and terpenoids. These findings demonstrate that specialized microbial inoculants can induce a transition toward fortified phenotypes and likely enhance plant resilience, even in the absence of traditional biomass gains. Full article

Biochar has traditionally been investigated as a soil amendment for improving fertility, carbon sequestration, and nutrient retention. However, recent advances in fine milling, colloidal stabilization, and nanotechnology have enabled the development of biochar-derived materials for foliar application. Unlike conventional soil application, foliar delivery enables direct interaction with leaf tissues, potentially providing faster physiological responses, improved resource-use efficiency, and complementary functions to existing plant biostimulants. This review critically evaluates the scientific basis, agronomic performance, and regulatory implications of foliar biochar applications across diverse crop systems. We synthesize and compare major formulation types, including finely milled suspensions, aqueous extracts, nano-biochar dispersions, and biochar-based composite carriers, based on their formulation characteristics, application methods, and reported biological effects. Across multiple crops, foliar biochar has been associated with enhanced chlorophyll content, improved gas exchange, strengthened antioxidant systems, better osmotic adjustment, and increased nutrient uptake, particularly under abiotic stresses such as salinity, drought, and heat. Mechanistically, these responses are linked to surface deposition effects, redox-active functional groups, modulation of leaf microclimate, and delivery of soluble bioactive compounds. Nevertheless, outcomes remain highly context-dependent, influenced by feedstock origin, pyrolysis conditions, particle size, formulation chemistry, dose, and crop species. Potential risks including phytotoxicity, nanoparticle exposure, environmental fate, and regulatory ambiguity especially for nano-scale formulations pose additional challenges for large-scale adoption. By integrating physiological, agronomic, environmental, and legislative perspectives, this review also highlights key barriers to commercialization, including formulation stability, limited field-scale validation, environmental safety, and regulatory uncertainty, while identifying research priorities needed to determine whether foliar biochar can become a scalable and scientifically validated biostimulant for sustainable agriculture. Full article