Search Results (1,228)

Wheat constitutes a significant agricultural crop in China and shows a notable tendency to easily bioaccumulate cadmium (Cd) when cultivated in contaminated soils, thereby posing substantial risks to human health. Foliar barrier agents can reduce Cd levels in the edible portions of wheat, facilitating compliance with the established safety standards for human consumption. In the present study, spring wheat was grown in pots in moderate to lightly Cd-contaminated soil. Five treatments with four foliar barrier agents, namely P, silicon-organic fertilizer (SOF), mancozeb (MZ), and microencapsulated fertilizer (MEF), were established to determine their impact on Cd content and cumulative Cd uptake and distribution across wheat organs. All four agents reduced Cd in wheat kernels, with SOF and MZ showing the most substantial reductions of 77.7% and 77.2%, respectively, and a 12% reduction in Cd distribution across wheat organs. These agents primarily block Cd transfer from stems and leaves to grain, ensuring food safety. MZ and SOF most effectively reduced Cd accumulation in wheat. All four agents reduced the target hazard quotient, with SOF yielding the greatest decrease. Thus, SOF is the optimal foliar barrier agent for wheat, supporting safe production on Cd-contaminated farmlands and advancing food security and sustainable agricultural development. Full article

The geographical origin of aniseed (Pimpinella anisum L.) represents a key quality determinant, as it directly influences the chemical composition and commercial value of its essential oil. Agronomic traits of aniseed (plant height, umbel diameter, number of umbels per plant), productivity-related traits (number of seeds, thousand-seed weight, yield per plant, plant biomass, harvest index, yield per hectare, essential oil content and yield), and physiological traits (germination energy and total germination) exhibit variations depending on geographical origin. The study proposes an integrated framework for accurate classification by combining agronomic, productivity, and physiological data with GC-MS profiles and advanced machine learning (ML) techniques. A total of 144 samples were analyzed, based on a factorial design including three locations, six fertilizer treatments, two years, and four replications. trans-Anethole was the dominant compound in all samples (89.508–101.441%). Several classification models, including artificial neural networks, random forests, MARSplines, boosted trees, interactive trees, naïve Bayes, and support vector machines, were evaluated to discriminate samples by geographical origin using agro-meteorological and GC-MS data. The results indicate that AI and ML approaches effectively captured complex non-linear relationships. Overall, the multi-model framework highlights the strong potential of machine learning for agro-food authentication, supporting improved traceability, site-specific decision-making, and quality control. Full article

Wheat (Triticum aestivum L.) is a cornerstone of global nutrition yet yield-focused intensification has often overlooked the biological complexity of nutrient interactions and their implications for nutritional outcomes. This review synthesizes current advances in wheat nutrient management from a systems perspective, integrating nutrient interactions, fertilization practices, and genetic improvement. A key novelty of this work is the development of a conceptual framework that links nutrient interaction networks with genotype-specific and environment-dependent responses, providing a unified approach to optimizing wheat productivity. Evidence indicates that plant performance is governed by coordinated nutrient dynamics rather than isolated inputs, with interactions such as nitrogen and sulfur playing a central role in regulating nutrient-use efficiency and metabolic processes. In addition, targeted micronutrient management, particularly zinc and selenium, is highlighted as a practical pathway for agronomic biofortification and enhanced nutritional value. The review further emphasizes substantial genetic variation in nutrient-use efficiency and yield stability, supporting the integration of breeding strategies with fertilization approaches. Emerging tools, including genomic-assisted selection and gene editing, are discussed as enabling technologies. Overall, this synthesis advances a biologically informed framework for sustainable wheat production that improves yield and nutritional outcomes. Full article

Soybean is a globally important legume crop which fulfills most of its nitrogen (N) requirement through Biological Nitrogen Fixation (BNF) in symbiosis with Bradyrhizobium species, thereby reducing dependence on synthetic fertilizers and supporting more sustainable production systems. This review synthesizes current knowledge on the mechanism, capacity, and regulation of BNF in soybean, including nodule formation, nitrogenase activity and response to soil and environmental conditions. The evidence shows that BNF can provide a substantial share of the crop’s N uptake, although high-yielding systems frequently experience the “N gap”, which is a difference between a higher crop demand and a lower N supplied from BNF and existing soil reserves. This can be partially managed with strategies like inoculation, co-inoculation, re-inoculation or judicial application of N. This review further highlights the advances in microbial inoculant technologies, plant growth-promoting rhizobacteria (PGPR), soybean breeding and genetic engineering aimed at improving BNF stability, efficiency and capacity across different soil environments. Overall, the maximization of soybean BNF has strong potential to reduce synthetic fertilizer use, improve yield and seed quality, and enhance the economic and environmental sustainability of soybean-based systems. Full article

Granites associated with hydrothermal uranium deposits provide critical insights into the processes governing uranium enrichment and mobilization within the continental crust. The Yangjiaonao deposit, situated in the Lujing ore field within the Nanling Metallogenic Belt (South China), is a typical granite-related hydrothermal vein-type uranium deposit. This study presents integrated zircon U-Pb geochronology, whole-rock geochemistry, whole-rock Nd isotopes and zircon Hf isotopes for the medium-to-coarse-grained porphyritic biotite (MCB) and medium-to-fine-grained two-mica (MFM) granites from the Yangjiaonao (YJN) granitic pluton. Both units yielded Triassic ages (~235–233 Ma), indicating synchronous emplacement during the Early Mesozoic period. However, they exhibit distinct metallogenic fertilities rooted in their petrogenesis. MCB granite, derived from greywacke-dominated sources, shows typical S-type characteristics, whereas uranium remained mineralogically sequestered in refractory accessory phases (e.g., zircon, monazite) during differentiation, evidenced by high and stable Th/U ratios. Conversely, MFM granite represents L-type peraluminous systems originated from felsic, arkose-like protoliths. Advanced fractionation in the MFM system triggered significant Th-U decoupling, driving Th/U ratios down to ~0.5 and promoting uranium enrichment in the residual melt. This differentiation-driven concentration of ‘leachable’ uranium identifies MFM granite as the primary fertile source for the Yangjiaonao hydrothermal mineralization. Full article

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article

Eggshell waste generated by the poultry processing industry represents a significant yet underutilized biogenic resource with substantial potential for sustainable agricultural and environmental applications. Globally, several million metric tons of eggshell residues are produced annually, consisting predominantly of calcium carbonate (CaCO₃) in the form of calcite, along with minor quantities of organic matrices and trace minerals. These physicochemical characteristics make eggshells a promising renewable alternative to conventional mineral sources for use as fertilizers, soil amendments, and biomaterials. Recent studies have shown that finely ground eggshell powder (ESP) is an effective liming material that can regulate soil chemical conditions and improve agronomic performance under acidic soil conditions. Furthermore, eggshell-derived materials have been incorporated into composting systems, biochar composites, and nanostructured fertilizers to enhance nutrient dynamics, immobilization of contaminants, and microbial activity. Advances in nanotechnology have facilitated the synthesis of nano-calcium carbonate (NCC) and nanohydroxyapatite (nHAP) fertilizers with improved nutrient supply and controlled-release properties. However, challenges associated with nanosafety evaluation, large-scale processing technologies, regulatory harmonization, and long-term field validation remain. Therefore, this review critically synthesizes the structural, biochemical, and physicochemical properties of eggshells and eggshell membranes, examines their applications in sustainable agriculture and environmental remediation, and identifies the key research priorities required to advance eggshell valorization within circular bioeconomy strategies. Full article

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization. Full article

Soil degradation in both agricultural and urban environments is accelerating due to intensive land use, plastic pollution, construction practices, and climate change, threatening ecosystem stability, food security, and carbon storage capacity. This review synthesizes current advances in biopolymeric materials as regenerative alternatives to conventional soil management approaches. Biopolymers derived from natural sources—including polysaccharides, proteins, and lignin-based compounds—are examined for their multifunctional roles in improving soil structure, enhancing water retention, optimizing nutrient delivery, stabilizing slopes, and supporting pollutant immobilization. Recent developments highlight the emergence of stimuli-responsive hydrogels, controlled-release fertilizer matrices, and composite soil conditioners capable of simultaneously addressing water stress, salinity, erosion, and contamination. In parallel, biodegradable agricultural films and in-soil degradable materials offer pathways to reduce microplastic accumulation while maintaining agronomic performance. Beyond agriculture, bio-based construction materials and bio-receptive design strategies extend biopolymeric interventions into the built environment, promoting soil permeability, microbial diversity, and circular material flows. The review emphasizes the need for context-specific formulation, long-term field validation, and life-cycle assessment to ensure environmental safety and scalability. By integrating soil science, polymer chemistry, and regenerative design, biopolymeric systems are described here as tools for restoring soil health and fostering resilient urban–rural ecosystems under conditions of environmental change. Full article

DNA methylation is a fundamental epigenetic modification that regulates gene expression, genome stability, and cell identity across vertebrate development. Disruption of DNA methylation homeostasis contributes to a wide spectrum of human diseases, including developmental disorders, neurological conditions, and cancer. Understanding how DNA methylation patterns are established, maintained, and dynamically remodeled during development is therefore essential for elucidating disease mechanisms and identifying therapeutic opportunities. The zebrafish (Danio rerio) has emerged as a powerful vertebrate model for investigating DNA methylation dynamics in vivo. Its external fertilization, optical transparency, rapid embryogenesis, and high fecundity enable direct observation and experimental manipulation of epigenetic processes at developmental stages that are difficult to access in mammalian systems. In addition, the core enzymatic machinery governing DNA methylation, including DNA methyltransferase (DNMT) and ten-eleven translocation (TET) protein families, is evolutionarily conserved between zebrafish and humans. In this review, we summarize current knowledge of the zebrafish methylome and the enzymatic regulators that control DNA methylation dynamics. We discuss how DNA methylation shapes early embryonic development, organogenesis, and cell fate decisions, and highlight insights gained from zebrafish models of human disease. Finally, we examine emerging technologies that are enabling increasingly precise interrogation of epigenetic regulation in vivo. Together, these advances position zebrafish as an important platform for bridging developmental epigenetics with human disease biology and therapeutic discovery. Full article

The growing demand for food production has increased the pressure on soil and fertilizer use, often leading to nutrient losses, soil degradation, and environmental pollution. Green chemistry offers practical solutions to these challenges by encouraging cleaner, safer, and more efficient ways of producing and using fertilizers. This review summarizes recent advances in multi-nutrient sustainable fertilizers developed through green chemistry principles, including renewable raw materials, low-toxicity synthesis methods, and environmentally friendly delivery systems. Different approaches, such as controlled-release carriers, nano-enabled formulations, chelated nutrients, and bio-based coatings, are discussed with a focus on how they reduce nutrient losses and improve soil and plant health. The review also highlights the benefits and limitations of these technologies, gaps in current research, and the need for long-term field studies to assess their safety and effectiveness. Overall, green chemistry-guided fertilizer development shows strong potential to support sustainable agriculture by improving nutrient efficiency while reducing environmental impacts. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

The complexity of physicochemical properties in iron ore tailings has led to extensive and varied study avenues. Moreover, changes in these features resulting from source discrepancies have complicated the identification of consistent patterns in study findings, thereby hindering the standardization and advancement of resource exploitation technologies. This paper provides a comprehensive analysis of the utilization pathways for iron tailings. It identifies the mainstream recovery processes for rare earth minerals, a relatively less-researched direction. It also describes research progress on the use of iron tailings for the preparation of fertilizers and soil conditioners, as well as their application as cementitious materials or aggregates in building materials and mine backfilling engineering. It incorporates various activation methods for the preparation of cementitious materials from iron tailings into a unified comparative framework and quantifies the key performance indicators of different activation pathways through a summary table. It also summarizes studies on the ecological reclamation of tailings ponds based on bioremediation techniques. The essential physicochemical properties of iron deposits are meticulously analyzed, and this is followed by a specialized overview of the principal treatment techniques, critical performance indicators, and their foundational mechanisms. The current application of various technical approaches is examined to identify key problems, and future development opportunities are outlined. Full article

As China advances high-quality agricultural development, promoting green production among farmers has become an important policy priority. Using survey data from 1787 rice farmers in seven major rice-producing provinces in southern China, this study examines whether enterprise-led contract farming can promote farmers’ green production behavior. Green production behavior is measured by a composite index based on six practices, including green control technology, soil testing and formulated fertilization, organic fertilizer substitution, water-saving irrigation, agricultural film recycling, and straw return. Empirical analysis results show that enterprise-led contract farming can significantly promote farmers’ green production behavior. Further analysis suggests that food safety certification, planting technology training, and lower perceived price volatility are important pathways through which contract farming is linked to green production practices. The promoting effect is weaker among older farmers, stronger for farmers cultivating land with medium soil fertility, and more pronounced among small-scale rice farmers. These findings highlight the role of enterprise-led contract farming in promoting farmers’ green production and offer policy implications for encouraging wider participation in green production practices. Full article

Age-related decline in oocyte quality is closely associated with mitochondrial dysfunction and oxidative imbalance, which disrupt redox-sensitive meiotic signaling and compromise embryo developmental competence. Rescue in vitro maturation (r-IVM) enables the utilization of immature oocytes retrieved during conventional in vitro fertilization (IVF) cycles. However, the developmental potential of r-IVM oocytes remains limited, particularly in women of advanced maternal age. This study evaluated whether transient caffeine supplementation during r-IVM improves the developmental competence of immature human oocytes in clinical assisted reproduction technology cycles. Immature oocytes obtained during conventional IVF were cultured with or without short-term caffeine exposure during r-IVM prior to standard culture conditions. After maturation, metaphase II oocytes underwent intracytoplasmic sperm injection, and embryonic development was assessed by fertilization rate, day 3 good-quality embryo formation, and blastocyst development. Although caffeine supplementation did not significantly affect nuclear maturation rates, it significantly increased fertilization efficiency and the proportion of good-quality embryos compared with controls. These effects were most pronounced in women aged ≥37 years. Time-lapse morphokinetic analysis further revealed more synchronized developmental kinetics in embryos derived from caffeine-treated oocytes, resembling those derived from in vivo-matured oocytes. Collectively, these findings suggest that transient caffeine exposure during r-IVM enhances post-fertilization developmental competence. The underlying mechanisms remain to be elucidated, and future studies are required to determine whether redox-sensitive meiotic pathways and mitochondrial function are involved. Full article