Search Results (549)

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

Reactive molecules, including oxygen and nitrogen species, serve dual roles in human physiology. While they function as essential signaling molecules under normal physiological conditions, they contribute to cellular dysfunction and damage when produced in excess by normal metabolism or in response to stressors. Oxidative/nitrosative stress is a pathological state, resulting from the overproduction of reactive species exceeding the antioxidant capacity of the body, which is implicated in several chronic human diseases. Antioxidant therapies aimed at restoring redox balance and preventing oxidative/nitrosative stress have demonstrated efficacy in preclinical models. However, their clinical applications have met with inconsistent success owing to efficacy, safety, and bioavailability concerns. This summative review analyzes the role of reactive species in human pathophysiology, the mechanisms of action of antioxidant protection, and the challenges that hinder their translation into effective clinical therapies in order to evaluate potential emerging strategies such as targeted delivery systems, precision medicine, and synergistic therapeutic approaches, among others, to overcome current limitations. By integrating recent advances, this review highlights the value of targeting reactive species in the prevention and management of chronic diseases. Full article

In chili cultivation, obstacles to continuous cropping significantly compromise crop yield and soil health, whereas crop rotation can enhance the microbial environment of the soil and reduce disease incidence. However, its effects on the diversity of rhizosphere soil microbial communities are not clear. In this study, we analyzed the composition and characteristics of rhizosphere soil microbial communities under chili continuous cropping (CC) and chili–cotton crop rotation (CR) using high-throughput sequencing technology. CR treatment reduced the alpha diversity indices (including Chao1, Observed_species, and Shannon index) of bacterial communities and had less of an effect on fungal community diversity. Principal component analysis (PCA) revealed distinct compositional differences in bacterial and fungal communities between the treatments. Compared with CC, CR treatment has altered the structure of the soil microbial community. In terms of bacterial communities, the relative abundance of Firmicutes increased from 12.89% to 17.97%, while the Proteobacteria increased by 6.8%. At the genus level, CR treatment significantly enriched beneficial genera such as RB41 (8.19%), Lactobacillus (4.56%), and Bacillus (1.50%) (p < 0.05). In contrast, the relative abundances of Alternaria and Fusarium in the fungal community decreased by 6.62% and 5.34%, respectively (p < 0.05). Venn diagrams and linear discriminant effect size analysis (LEfSe) further indicated that CR facilitated the enrichment of beneficial bacteria, such as Bacillus, whereas CC favored enrichment of pathogens, such as Firmicutes. Fusarium solani MG6 and F. oxysporum LG2 are the primary chili root-rot pathogens. Optimal growth occurs at 25 °C, pH 6: after 5 days, MG6 colonies reach 6.42 ± 0.04 cm, and LG2 5.33 ± 0.02 cm, peaking in sporulation (p < 0.05). In addition, there are significant differences in the utilization spectra of carbon and nitrogen sources between the two strains of fungi, suggesting their different ecological adaptability. Integrated analyses revealed that CR enhanced soil health and reduced the root rot incidence by optimizing the structure of soil microbial communities, increasing the proportion of beneficial bacteria, and suppressing pathogens, providing a scientific basis for microbial-based soil management strategies in chili cultivation. Full article

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. Full article

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate climates, where variable weather conditions can significantly affect nodulation and yield. This study evaluated the effects of three nitrogen fertilisation doses (0, 30, and 60 kg N·ha⁻¹), applied in the form of ammonium nitrate (34% N) and two commercial rhizobial inoculants—HiStick Soy (containing Bradyrhizobium japonicum strain 532C) and Nitragina (including a Polish strain of B. japonicum)—on nodulation, nitrogen uptake, and seed yield. A three-year field experiment (2017–2019) was conducted in southwestern Poland using a two-factor randomized complete block design. Nodulation varied significantly across years, with the highest values recorded under favourable early-season moisture and reduced during drought. In the first year, inoculation with HiStick Soy significantly increased nodule number and seed yield compared to Nitragina and the uninoculated control. Nitrogen fertilisation consistently improved seed yield, although it had no significant effect on nodulation. The highest nitrogen use efficiency was observed with moderate nitrogen input (30 kg N·ha⁻¹) combined with inoculation. These findings highlight the importance of integrating effective rhizobial inoculants with optimized nitrogen fertilisation to improve soybean productivity and nitrogen efficiency under variable temperate climate conditions. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Suckling is crucial for piglet intestinal development and gut health, as it improves resilience during the challenging weaning phase and promotes subsequent growth. IPS, comprising Na⁺/K⁺ ions, whey protein, and glucose, has been shown to have positive effects on animal growth and intestinal health. The objectives of this study were to assess the impact of IPS consumption on the growth performance, immunity, intestinal growth and development, and microbiota structure of suckling piglets. A total of 160 newborn piglets were randomly divided into control and IPS groups, with IPS supplementation starting from 2 to 8 days after birth and continuing until 3 days before weaning. The findings revealed that IPS boosted the body weight at 24 days by 3.6% (p < 0.05) and improved the body weight gain from 16 to 24 days by 15.7% (p < 0.05). Additionally, the jejunal villus height and villus height to crypt depth ratio in the IPS group were notably increased to 1.08 and 1.31 times (p < 0.05), respectively, compared to the control group. Furthermore, IPS elevated the plasma levels of IgA and IgM, reduced the plasma levels of blood urea nitrogen (BUN), and enhanced the content of secretory immunoglobulin A (SIgA) in the jejunal mucosa of suckling piglets. Furthermore, IPS upregulated the mRNA expression of tight junction proteins GLP-2, ZO-1, and Claudin-1 in jejunal tissue, while downregulating the regulatory genes in the Toll-like pathway, including MyD88 and TLR-4 (p < 0.05). The analysis of gut microbiota indicated that IPS altered the relative abundance of gut microbes, with an increase in beneficial bacteria like Alloprevotella and Bacteroides. In conclusion, this study demonstrates that IPS supplementation enhances weaning weight, growth performance, immune function, and intestinal development in piglets, supporting the integration of IPS supplementation in the management of pre-weaning piglets. Full article

Chlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial resolution, and operational efficiency, have emerged as effective tools for estimating chlorophyll content in wheat. Although multi-source data derived from UAV-based multispectral imagery have shown potential for wheat chlorophyll estimation, the importance of multi-source deep feature fusion has not been adequately addressed. Therefore, this study aims to estimate wheat chlorophyll content by integrating spectral and textural features extracted from UAV multispectral imagery, in conjunction with partial least squares regression (PLSR), random forest regression (RFR), deep neural network (DNN), and a novel multi-source deep feature neural network (MDFNN) proposed in this research. The results demonstrate the following: (1) Except for the RFR model, models based on texture features exhibit superior accuracy compared to those based on spectral features. Furthermore, the estimation accuracy achieved by fusing spectral and texture features is significantly greater than that obtained using a single type of data. (2) The MDFNN proposed in this study outperformed other models in chlorophyll content estimation, with an R² of 0.850, an RMSE of 5.602, and an RRMSE of 15.76%. Compared to the second-best model, the DNN (R² = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R², and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. The findings of this study offer important insights into UAV-based remote sensing applications for estimating wheat chlorophyll under field conditions. Full article

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

The aim of the article is to highlight the increasing environmental burden on aquatic ecosystems in Slovakia due to continuous pollution from municipal, industrial and agricultural sources. Laboratory analyses have shown alarming exceedance of the limit values of contaminants, with nitrate nitrogen (NO₃⁻) reaching 5.8 mg/L compared to the set limit of 2.5 mg/L and phosphorus concentrations exceeding the permissible values by a factor of five, thereby escalating the risk of eutrophication and loss of ecological stability of the aquatic ecosystem. The accumulation of heavy metals is also a problem—lead (Pb) concentrations reach up to 9.7 μg/L, which exceeds the safe limit by a factor of ten. Despite the measures implemented, such as scum barriers, there is continuous contamination of the aquatic environment, with illegal waste dumps and uncontrolled runoff of agrochemicals playing a significant role. The research results underline the critical need for a more effective environmental policy and more rigorous monitoring of toxic substances in real time. These findings highlight not only the urgency of more effective environmental policy and stricter real-time monitoring of toxic substances, but also the necessity of integrating environmental logistics into the design of sustainable solutions. Logistical approaches including the optimization of waste collection, coordination of stakeholders and creation of infrastructural conditions can significantly contribute to reducing environmental burdens and ensure the continuity of environmental management in ecologically sensitive areas. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Ectomycorrhizal symbiosis is a cornerstone of ecosystem health, facilitating nutrient uptake, stress tolerance, and biodiversity maintenance in trees. Optimizing Pinus armandii–Tuber indicum mycorrhizal synthesis enhances the ecological stability of coniferous forests while supporting high-value truffle cultivation. This study conducted a pot experiment to compare the effects of three root nutrient regulations—Aolu 318S (containing N-P₂O₅-K₂O in a ratio of 15-9-11 (w/w%)), Aolu 328S (11-11-18), and Youguduo (19-19-19)—on the mycorrhizal synthesis of P. armandii–T. indicum. The results showed that root nutrient supplementation significantly improved the seedling crown, plant height, ground diameter, biomass dry weight, and mycorrhizal infection rate of both the control and mycorrhizal seedlings, with the slow-release fertilizers Aolu 318S and 328S outperforming the quick-release fertilizer Youguduo. The suitable substrate composition in this experiment was as follows: pH 6.53–6.86, organic matter content 43.25–43.49 g/kg, alkali-hydrolyzable nitrogen 89.25–90.3 mg/kg, available phosphorus 83.69–87.32 mg/kg, available potassium 361.5–364.65 mg/kg, exchangeable magnesium 1.17–1.57 mg/kg, and available iron 33.06–37.3 mg/kg. It is recommended to mix the Aolu 318S and 328S solid fertilizers evenly into the substrate, with a recommended dosage of 2 g per plant. These results shed light on the pivotal role of a precise N-P-K ratio regulation in fostering sustainable ectomycorrhizal symbiosis, offering a novel paradigm for integrating nutrient management with mycorrhizal biotechnology to enhance forest restoration efficiency in arid ecosystems. Full article

Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation that integrates both spectral and texture features extracted from UAV-based multispectral imagery through the development of novel Spectral–Texture Fusion Indices (STFIs). Field data were collected under nitrogen gradient treatments across three critical growth stages: heading, early filling, and late filling. A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). The models—RFECV-RF, RFECV-XGBoost, SFS-SVR, and SFS-DNN—were evaluated using four feature configurations. The SFS-DNN model with STFIs achieved the highest prediction accuracy (R² = 0.874, RMSE = 2.621 mg/g). SHAP analysis revealed the significant contribution of STFIs to model predictions, underscoring the effectiveness of integrating spectral and texture information. The proposed STFI-based framework demonstrates strong generalization across phenological stages and offers a scalable, interpretable approach for UAV-based nitrogen monitoring in rice production systems. Full article

Biochar (BC) demonstrates considerable potential for reducing nitrogen emissions. However, it frequently exhibits a limited capacity for the adsorption of NO₃⁻-N, thereby reducing its effectiveness in mitigating N₂O emissions. Nano-biochar (NBC) is attracting attention due to its higher surface energy, but there is a lack of information on enhancing NO₃⁻-N adsorption and reducing N₂O emissions. Accordingly, this study conducted batch adsorption experiments for NO₃⁻-N and simulated N₂O emissions experiments. The NO₃⁻-N adsorption experiments included two treatments: bulk BC and NBC; the N₂O emissions experiments involved three treatments: a no-biochar control, BC, and NBC. N₂O emissions experiments were incorporated into the soil at mass ratios of 0.3%, 0.6%, 1%, and 3%. The results demonstrate that NBC exhibits nearly twice the NO₃⁻-N adsorption capacity compared to bulk biochar (BC), with adsorption behavior best described by a physical adsorption model. The enhanced adsorption performance was primarily attributed to NBC’s significantly increased specific surface area, pore volume, abundance of surface acidic functional groups, and higher aromaticity, which collectively strengthened multiple sorption mechanisms, including physical adsorption, electrostatic interactions, π–π interactions, and apparent ion exchange. In addition, NBC application (0.3–3%) reduced cumulative N₂O emissions by 11.60–54.77%, outperforming BC (9.16–32.65%). NBC treatments also increased soil NH₄⁺-N and NO₃⁻-N concentrations by 2.4–8.2% and 7.3–59.0%, respectively, indicating improved inorganic N retention. Overall, NBC demonstrated superior efficacy over bulk BC in mitigating N₂O emissions and conserving soil nitrogen, highlighting its promise as a sustainable amendment for integrated nutrient management and greenhouse gas reduction in soil. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article