Search Results (233)

This study is Part II of a five-year (2018–2022) field trial in western Portugal evaluating the effects of three microbial biofertilizers—Mycoshell^® (Glomus spp. + humic/fulvic acids), Kiplant iNmass^® (Azospirillum brasilense, Bacillus megaterium, Saccharomyces cerevisiae), and Kiplant All-Grip^® (Bacillus megaterium, Pseudomonas spp.)—applied at different dosages alongside two mineral fertilizer regimes, T100 (full dose) and T70 (70% of T100, alone or combined with biofertilizers), on the physiological performance of ‘Gala Redlum’ apple trees. Part I had shown that Myc4 (Mycoshell^®, 4 tablets/tree), iNM6, and iNM12 (Kiplant iNmass^®, 6 and L ha⁻¹, respectively) consistently enhanced fruit growth, yield, and selected quality traits. While Part I showed clear agronomic gains, Part II demonstrates that these improvements occurred without significant alterations in seasonal photosynthetic performance, canopy reflectance, or chlorophyll fluorescence parameters over five years, highlighting the contrast between observed yield improvements and physiological stability. Seasonal monitoring of physiological traits—including specific leaf area (SLA), chlorophyll content index (CCI), gas exchange (A_n, g_s, E, C_i), spectral indices (NDVI, OSAVI, SIPI, GM2), and chlorophyll fluorescence (OJIP). It is clear that physiological values remained largely stable across biofertilizer treatments and years. Importantly, this stability was maintained even under a 30% reduction in mineral fertilizer (T70), indicating that specific microbial biofertilizers can sustain physiological resilience under reduced nutrient inputs, thereby providing a physiological basis for the yield-enhancing effects observed and supporting their integration into fertilizer reduction strategies in Mediterranean orchards. Full article

Background/Objectives: Traditional semen analysis techniques frequently result in incorrect male infertility diagnoses, despite advancements in assisted reproductive technology (ART). Reduced fertilization potential, decreased embryo development, and lower pregnancy success rates are associated with elevated DNA Fragmentation Index (DFI), which has been proposed as a diagnostic indicator of sperm DNA integrity. Improving reproductive outcomes requires incorporating DFI into predictive models due to its diagnostic importance. Methods: In this study, semen samples were stratified into low and high DFI groups across two datasets: the “Reference” dataset (162 samples) containing sperm motility (A, B, and C), total sperm count, and morphology percentage, and the “ORP” dataset (37 samples) with the same features plus oxidation-reduction potential (ORP). We trained and evaluated four machine learning (ML) models—Logistic Regression, Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), and Random Forest (RF)- using three feature subsets and three preprocessing techniques (Robust Scaling, Min-Max Scaling, and Standard Scaling). Results: Feature subset selection had a significant impact on model performance, with the full feature set (X_all) yielding the best results, and the combination of Robust and MinMax scaling forming the most effective preprocessing pipeline. Conclusions: ORP proved to be a critical feature, enhancing model generalization and prediction performance. These findings suggest that data enrichment, particularly with ORP, could enable the development of ML frameworks that improve prognostic precision and patient outcomes in ART. Full article

Excessive nitrogen fertilization can degrade soil quality by inducing nutrient leaching, disrupting the microbial balance, and impairing plant reproductive growth, ultimately reducing crop yields. Optimizing nitrogen application rates and integrating humic acid fertilizers are promising strategies for enhancing soil fertility and improving agricultural productivity. The experimental design included four nitrogen application rates (N0:0 kg ha⁻¹, N1:120 kg ha⁻¹, N2:150 kg ha⁻¹, and N3:180 kg ha⁻¹) with and without humic acid (H: 1500 kg ha⁻¹). Key findings revealed that: (1) The combined application of humic acid (1500 kg ha⁻¹) and medium nitrogen (150 kg ha⁻¹) significantly increased the contents of soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) by an average of 21.7% (p < 0.05), 90.5% (p < 0.01), 59.4% (p < 0.05), and 11.3% (p < 0.05), respectively (two-year mean), with significant interactive effects between nitrogen and humic acid on nutrient accumulation; (2) humic acid supplementation significantly increased soil bacterial abundance and diversity: under the combined treatment of medium nitrogen (150 kg ha⁻¹) and humic acid, the bacterial Ace index (indicating species richness) and Shannon index (indicating community diversity) increased by an average of 0.76% and 0.30%, respectively, compared with the single medium nitrogen treatment (p < 0.05), promoting a more balanced microbial community; and (3) quinoa yields improved by 24.62–66.83% with humic acid application, with the highest yield increase observed under the moderate nitrogen rate (150 kg ha⁻¹) in combination with humic acid. These results demonstrate that integrating humic acid with optimized nitrogen fertilization (150 kg ha⁻¹ N + 1500 kg ha⁻¹ HA) can effectively improve soil nutrients and enhance quinoa productivity. The increases in soil total nitrogen (TN, p < 0.01), available phosphorus (AP, p < 0.05), bacterial Shannon index (p < 0.05), and quinoa yield (p < 0.01) under this combined treatment were all significantly higher than those under single nitrogen fertilization or humic acid application, confirming the synergistic effect of the two fertilizers. Full article

Soil heavy metal contamination in agricultural land has emerged as a critical environmental issue, threatening both food security and ecological sustainability. However, the contamination characteristics and associated potential ecological risks under different land use types remain poorly understood. This study presents a systematic comparison of heavy-metal pollution between three distinct agricultural land use systems (suburban vegetable fields, paddy fields, and maize fields) using an integrated approach that combines spatial analysis, pollution indices, and receptor modeling. Dehui City, a major grain-producing region in Northeast China, was selected as the study region, in which 73 topsoil samples were systematically collected. The concentrations and spatial distributions of heavy metals (Cd, Cr, Cu, Hg, Ni, Pb, Zn, and As) were analyzed. Source apportionment of soil heavy metals was performed using principal component analysis (PCA) and positive matrix factorization (PMF), while pollution assessment employed the geo-accumulation index (I_geo), Nemerow integrated pollution index (NIPI), and potential ecological risk index (PERI). The results showed that the mean concentrations of all heavy metals exceeded the soil background values for Jilin Province. The enrichment factors for Hg, Pb, and Cu were 3.51, 1.32, and 1.31, respectively, while all metals remained below the risk screening values (GB 15618-2018, China) for agricultural soils. Land use-specific patterns in heavy-metal accumulation were evident. Suburban vegetable fields showed elevated levels of Ni, As, and Cr, paddy fields showed elevated levels of Cd, Hg, and As, and maize fields showed elevated levels of Hg and Pb. Source apportionment revealed that agricultural fertilization, traffic emissions, industrial and coal-combustion activities, and natural sources were the main contributors. Notably, industrial and coal-combustion sources accounted for 77.7% of Hg in maize fields, while agricultural fertilization contributed 67.7% of Cd in suburban vegetable fields. The I_geo results indicated that 65.75% of the sampling sites exhibited slight or higher pollution levels for Hg. However, the NIPI results showed that 97.26% of the sampling sites remained at a safe level (NIPI < 0.7). The PERI results revealed a moderate ecological risk across the study area, with the risk levels following the order: maize fields > paddy fields > vegetable fields. Although agricultural soils generally met the safety standards, Hg-dominated ecological risks warrant priority attention and mitigation measures. Full article

Nitrate in groundwater (GW) poses a public-health concern in semi-urban northeastern Saudi Arabia, where households rely on untreated wells. We measured nitrate in 45 wells spanning treated/untreated commercial stations, private domestic wells, and agricultural wells, and linked contamination severity to age-specific risks using the Nitrate Pollution Index (NPI), Chronic Daily Intake (CDI), and Hazard Quotient (HQ). Nitrate ranged from 12 to 380 mg·L⁻¹ (35% > 50 mg·L⁻¹ World Health Organization (WHO) guideline), with untreated private and agricultural wells most affected. Based on NPI, 65% of wells were “clean”, while 18% showed significant to very significant pollution. Infants and children had the highest exposure: CDI frequently exceeded the oral reference dose (1.6 mg·kg⁻¹·d⁻¹), and HQ > 1 occurred in 56% (infants) and 51% (children) of samples from untreated sources. Treated stations consistently achieved lower nitrate and HQ < 1. Sensitivity analysis identified nitrate concentration as the dominant risk driver, followed by ingestion rate, with body weight mitigating the dose. The findings suggest that monitoring based solely on compliance may underestimate risks in sensitive age groups, thereby advocating for immediate actions such as fertilizer management, septic system upgrades, extension of treatment to vulnerable households, and community monitoring. The integrated NPI–CDI–HQ framework provides a replicable methodology for associating groundwater contamination with demographic-specific health risks in arid, water-stressed regions. Full article

Soil salinity is an essential constraint on sustainable crop production, particularly in arid and semi-arid regions, due to its effects on soil fertility. This study presents a data-driven approach for mapping soil salinity by integrating field-based electrical conductivity (EC) measurements with remote sensing and geospatial analysis in the district of Mandi Baha Uddin, Pakistan. Eleven georeferenced soil samples were collected and analyzed for EC (range: 0.59–1.06 dS/m), serving as training data for model calibration. Using Landsat 8 Surface Reflectance imagery within Google Earth Engine, spectral indices Normalized Difference Salinity Index (NDSI), Salinity Index (SI), and Brightness Index (BI) were extracted. Among various modeling approaches, a linear regression model was applied to these indices, revealing NDSI as the most significant predictor (coefficient = 12.938), while SI and BI show negligible contribution. The model achieved moderate accuracy (R² = 0.566, RMSE = 0.085 dS/m). A Random Forest approach yielded higher training accuracy (R² = 0.841) but suffered from overfitting during cross-validation, indicating limited sample size constraints. The regression equation (EC = 12.938 × NDSI + 5.864) was applied in GEE to generate the EC prediction map. The resulting 30 m resolution EC map was classified into salinity categories and validated through independent field observations. This framework highlights the effectiveness of using freely available satellite data and cloud-based platforms like GEE for cost-effective soil salinity monitoring. The study provides a transferable methodology for precision agriculture, enabling informed land management and crop planning in salinity-affected regions. Full article

As the most extensive terrestrial ecosystem, grassland exhibits substantial ecological functions and scientific research significance. Conducting a scientific assessment of the soil fertility of grasslands is of paramount importance for attaining sustainable grassland management, especially for the Tibetan Plateau, which has the most vulnerable ecosystem. This study endeavors to evaluate the soil fertility and spatial differentiation patterns of the natural grasslands in the Tibetan Plateau. Initially, we developed a Soil Fertility Evaluation Index (SFEI) for natural grasslands by integrating three representative soil indicators (total nitrogen, soil organic matter, and bulk density) and a vegetation indicator (fractional vegetation cover). The selection of these indicators followed the Minimum Data Set (MDS) principle, ensuring both ecological relevance and consistent data availability across all sampling plots in the Tibetan Plateau. Subsequently, validation based on field sampling data showed an overall accuracy of 69.89%. Moreover, the evaluation result revealed a clear eastward-increasing gradient in soil fertility, with low fertility in the western regions (e.g., Ngari and Nagqu) and medium-to-high fertility in the central and eastern regions (e.g., Lhasa, Yushu, and Golog), consistent with regional hydrothermal patterns. The proposed method offers a novel and practical framework for assessing soil fertility of natural grassland in the Tibetan Plateau, with significant implications for differentiated grassland management and ecological restoration. Full article

Excessive nitrogen (N) fertilization in rice systems has caused soil degradation and reduced N use efficiency. Green manure, especially Astragalus sinicus (Chinese milk vetch), provides a sustainable alternative, but the microbial and functional gene mechanisms underlying its interaction with reduced N input remain unclear. In this study, a field experiment was conducted at Dingdian Village, Natong Town, Long’an County, Nanning City, Guangxi Province, China (107°51′21″ E, 23°00′41″ N) during the 2018–2019 rice growing seasons. Four treatments were established: conventional N fertilization (N100), 20% N reduction (N80), green manure plus full N (GMN100), and green manure plus 20% N reduction (GMN80). Soil physicochemical traits, microbial community composition, and carbon-cycling functional genes were analyzed using high-throughput sequencing and metagenomic profiling. Compared with N100, GMN80 significantly increased soil organic matter (by 21.3%), microbial biomass carbon (by 32.6%), and available phosphorus (by 17.8%). The Shannon index rose from 4.18 to 4.63, while Proteobacteria and Actinobacteria increased by 9.5% and 7.2%, respectively. Functional genes encoding glycoside hydrolases (GH5, GH9) and carbohydrate esterases (CE1, CE10) were enriched by 25–40%, with upregulation of carbon fixation (rbcL) and methane metabolism (mcrA) genes. Integrating A. sinicus with moderate N reduction improves soil fertility, stimulates microbial diversity, and enhances carbon turnover efficiency, offering a practical pathway toward sustainable low-carbon rice production. Full article

In the context of global climate change and the transition to a low-carbon economy, utilizing crop straw as a resource is a key strategy for green transformation. Taking Guangdong province as a case, this study investigates the carbon reduction effects of integrated straw utilization and their spatiotemporal evolution, based on crop yield data from 2019 to 2023 across various municipalities. Different from one-way straw utilization for carbon reduction, this work analyzes the carbon reduction effects of five co-existing pathways to utilize straw as fertilizer, feed, energy, substrate, and raw material. The Theil index, slope value, and exploratory spatial data analysis (ESDA) method are employed to form an analytical framework for the spatiotemporal evolution of carbon reductions by straw utilization. Over this five-year period, the overall and off-field straw utilization steadily increased, and a 6.2% increase in straw utilization was achieved to realize a 19.8% rise in carbon reduction. In 2023, the carbon reduction from straw utilization was chiefly contributed by fertilization, subsequently followed by feed, energy, substrate, and raw material. Over 90% of the carbon reduction contributions came from four major crops, namely rice, peanuts, sugarcane, and potatoes. Carbon reduction across different areas in Guangdong showed positive spatial correlation, with high–high (HH) and low–low (LL) clusters being the primary local autocorrelation patterns. Model applications confirm that incentive policies and industrial development largely facilitate the integrated straw utilization in Guangdong. However, further increases in straw utilization will not necessarily ensure proportional carbon reduction. The regional heterogeneity and coordinated clustering development should be considered to strengthen carbon-reduction intensity. In particular, policies should be tailored to crop straw recovery and utilization, inter-regional straw allocation, and preferentially support straw utilization for energy and as a substrate. Full article

Water and nitrogen are fundamental factors for maintaining yield stability and achieving efficient resource utilization in wheat–maize rotation systems. Based on 131 publications indexed in the Web of Science Core Collection from 2010 to 2025, this review systematically synthesizes current knowledge on how irrigation, nitrogen application, and soil management jointly regulate water–nitrogen migration and transformation processes during wheat and maize growth. The results indicate that irrigation practices influence nitrogen transformation and availability by altering the temporal and spatial distribution of soil moisture; optimized nitrogen application strategies align nitrogen release with crop demand at critical growth stages; and the use of soil amendments improves soil physicochemical and biological conditions, thereby enhancing water retention and nitrogen stability. These three management measures exhibit strong complementarity and synergistic effects. Integrating irrigation, fertilization, and soil management can not only improve wheat and maize yields but also harmonize resource use efficiency with ecological sustainability. This review highlights the potential and pathways of integrated management practices for enhancing water and nitrogen use efficiency and ensuring food security, providing theoretical support and practical guidance for developing efficient and sustainable region-specific water–nitrogen management systems. Full article

A pot experiment was carried out to evaluate the effects of composts, vermicomposts, and mineral fertilization on maize (Zea mays L.) growth, grain quality, soil chemical properties, and nematode communities. Eight treatments were tested, including organic amendments combined with mineral nitrogen, exclusive mineral fertilization, and an unfertilized control. Soil chemical properties, including pH, salinity, nitrogen compounds, and macro- and microelements, varied notably across treatments. Nematode community analysis revealed distinct patterns among treatments: Shannon diversity was moderate and relatively stable across most treatments, but a statistically significant reduction was recorded in treatment 7. In contrast, the Plant Parasitic Index (PPI) varied significantly, reflecting differences in community maturity and parasitic pressure. Bacterivores and fungivores indicated active nutrient cycling, while omnivores and predators reflected soil food web stability. Fertilization treatments significantly affected maize grain development. The highest thousand-kernel weight (TKW) was recorded in treatment 6 (+8.9% vs. control) and treatment 4 (+7.4% vs. control). The kernel number per cob was greatest in treatments 4 and 5 (+38% and +32%), with corresponding increases in grain mass per cob (+48% and +40%). The mean cob core weight ranged from 20.1 g in the control treatment to 30.2 g in treatment 1. The greatest increases compared to the control were observed in treatments 1 and 5, amounting to 50.2% and 44.8%, respectively. Overall, fertilization influenced grain quality, soil chemistry, and nematode communities, highlighting the importance of integrating organic and mineral amendments for sustainable crop production. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article

The low fertility of plinthosols is a major constraint on agricultural production, largely due to the presence of plinthite, which restricts the availability of water and nutrients. This study aimed to simulate the growth and yield of grain maize on a loosened plinthosol amended with termite mound (from Macrotermes falciger) material in the Lubumbashi region. A 660-hectare perimeter was established, subdivided into ten maize blocks (B1–B10) and a control block (B0), which received the same management practices as the other blocks except for subsoiling and termite mound amendment. The APSIM model was used for simulations. The leaf area index (LAI) was estimated from Sentinel-2 imagery via Google Earth Engine, using the Simple Ratio (SR) spectral index, and integrated into APSIM alongside agro-environmental variables. Model performance was assessed using cross-validation (2/3 calibration, 1/3 validation) based on the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE). Results revealed a temporal LAI dynamic consistent with maize phenology. Simulated LAI matched observations closely (R² = 0.85 − 0.93; NSE = 0.50 − 0.77; RMSE = 0.29 − 0.40 m² m⁻²). Maize grain yield was also well predicted (R² = 0.91; NSE > 0.80; RMSE < 0.50 t ha⁻¹). Simulated yields reproduced the observed contrast between treated and control blocks: 10.4 t ha⁻¹ (B4, 2023–2024) versus 4.1 t ha⁻¹ (B0). These findings highlight the usefulness of combining remote sensing and biophysical modeling to optimize soil management and improve crop productivity under limiting conditions. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

Winter wheat covers approximately 2.21 × 10⁸ ha globally, making it the most widely cultivated cereal crop in the world. In recent years, integrated water and fertilizer management has significantly improved winter wheat yield and nitrogen use efficiency; however, quantitative assessments of nitrogen cycling under different fertilizer forms in such high-yield systems remain limited. From 2022 to 2024, a two-year field experiment was conducted in drip-irrigated winter wheat fields in northern China. Four nitrogen fertilizer forms were applied: nitrate nitrogen fertilizer (NON), ammonium nitrogen fertilizer (NHN), amide nitrogen fertilizer (CON), and urea ammonium nitrate fertilizer (UAN), along with an unfertilized control (CK). Compared with NON, NHN, and CON, UAN reduced cumulative N₂O emissions by 10.40–15.64% and NH₃ volatilization by 2.04–9.33% (p < 0.05). It also increased the leaf area index and biomass accumulation at maturity, as well as grain yield (3.70–10.28%), nitrogen harvest index (4.58–12.88%), and nitrogen use efficiency (12.14–39.25%) (p < 0.05). Furthermore, UAN significantly decreased the net nitrogen surplus (24.18–45.70%) and nitrogen balance values (25.64–55.82%) (p < 0.05). Correlation analysis indicated that the reduction in nitrogen balance was primarily attributed to lower N₂O emissions and improved nitrogen use efficiency (p < 0.05). In conclusion, the application of urea ammonium nitrate under integrated water–fertilizer management achieved higher yield, greater efficiency, and environmentally sustainable production in drip-irrigated winter wheat systems in northern China. Full article