Search Results (1,438)

In Northeast China’s black soil region, we examined how different straw return strategies—no return (CK), direct straw incorporation (SD), and biochar application (BC)—interact with three nitrogen levels (N0, N60, N100) to affect soil properties, enzyme activities, and the chitin-degrading ChiA functional gene. Both SD and BC significantly increased soil nutrients (total carbon, ammonium nitrogen, available potassium, and soil organic matter) and reduced bulk density. They also enhanced the activities of key carbon—and nitrogen-cycling enzymes, including nitrate reductase and N-acetylglucosaminidase. Straw return method and nitrogen rate both influenced ChiA abundance, but straw management was the primary driver of ChiA community structure according to principal component analysis. Actinobacteria and Proteobacteria dominated the bacterial phyla. Correlation analysis identified bulk density, total nitrogen, C/N ratio, available potassium, soil organic matter, and NAG activity as key factors shaping the ChiA community. Both straw return strategies improved soil fertility and chitin degradation potential. Among them, direct straw return combined with conventional nitrogen application (SD N100) showed the most balanced performance in sustaining soil health and agricultural productivity in the Mollisol region. Full article

Fertilizer management plays a critical role in regulating both yield and aroma quality in fragrant rice. However, the combined effects of functional fertilizers on these traits across different varieties and ecological conditions remain poorly understood. In this study, field experiments were conducted at two sites (Fumin and Dali) using two japonica fragrant rice varieties (Yunjing 37 and Liuxiangzi 1) under four fertilization treatments: T1 (conventional fertilization); T2 (compound fertilizer + silicon fertilizer); T3 (compound fertilizer + magnesium ammonium phosphate + amino acid-chelated calcium); and T4 (compound fertilizer + bio-organic fertilizer + zinc + amino acid water-soluble fertilizer). Compared with T1, silicon application (T2) significantly increased grain yield by 8.58–15.08%, primarily through synergistic increases in effective panicles and grains per panicle. Treatments T3 and T4 significantly enhanced grain 2-acetyl-1-pyrroline (2-AP) content by 18.32–32.67% and increased the diversity of volatile compounds. Correlation analysis revealed that 2-AP content was positively correlated with ketones (r = 0.373, p < 0.05) and alcohols (r = 0.363, p < 0.05), and negatively correlated with aldehydes and esters. Multifactor ANOVA showed no significant variety × treatment interaction for yield or 2-AP content (p > 0.05), indicating consistent responses across varieties. These results provide preliminary evidence that silicon fertilizer serves as an effective strategy for yield improvement, while combined application of calcium, magnesium, and amino acids enhances aroma quality by promoting the accumulation of 2-APm ketones, and alcohols. However, because treatments T3 and T4 contained multiple components, the individual contributions of Ca, Mg, or amino acids cannot be isolated. Multi-year trials are required to confirm the stability of these effects, featuring a differentiated fertilization strategy—silicon for yield and medium/trace elements for aroma—applicable across varieties, with site-specific variety selection further optimizing performance. Full article

Long-term straw return combined with optimized fertilization represents a effective strategy to enhance soil quality and crop productivity in cold regions, yet its integrated effects on the photosynthesis–soil–yield continuum of spring maize remain unclear, particularly under conditions of low accumulated temperature and slow straw decomposition. Based on a 9-year field experiment (2017–2025) conducted in the cold spring maize zone of Northeast China, this study investigated five treatments: CK, CF, CK + S, CF + S, and OPT + S. Photosynthetic parameters at four growth stages, soil nutrients, and grain yield were systematically measured. The results showed that OPT + S achieved the highest grain yield (15,016.11 kg·ha⁻¹ in 2025) and maintained superior photosynthetic performance throughout the growing season, with a photosynthetic decline rate of only 37.21% during the grain-filling stage–significantly lower than that of CK (48.20%). Soil available phosphorus (AP) and available potassium (AK) were significantly increased under straw return treatments (CK + S, CF + S, OPT + S). Correlation and stepwise regression analyses identified AP and net photosynthetic rate at the jointing stage (PnJ) as the key drivers of yield, jointly explaining 84% of yield variation. These findings demonstrate that the OPT + S treatment optimizes the coupling among early-stage photosynthesis, soil nutrient availability, and grain yield, providing a practical and high-yielding nutrient management strategy for spring maize in cold regions. Full article

Fertilization is widely used in managed Moso bamboo (Phyllostachys edulis (Carrière) J.Houz.) forests in subtropical China, but its short-term effects on soil properties and microbiomes remain uncertain. In this study, we conducted a field experiment with four treatments: no fertilization (NF), compound fertilizer (CF), sheep manure organic fertilizer plus compound fertilizer (SOCF), and a biochar-based sheep manure organic fertilizer combined with compound fertilizer (BSOCF). Surface soils samples were collected approximately one year after the initial application, and soil properties were measured together with bacterial and fungal communities using high-throughput sequencing. Results showed that fertilization mainly affected soil chemical properties rather than overall microbial community structure. Compared with CF, BSOCF significantly increased soil pH (4.56 ± 0.10 vs. 4.30 ± 0.05) and resulted in the highest available phosphorus (AP, 6.38 ± 1.10 mg kg⁻¹) and available potassium (AK, 128.16 ± 17.56 mg kg⁻¹) contents. Microbial responses were comparatively limited. Bacterial richness remained stable, fungal alpha diversity showed only a weak increasing trend, and both beta diversity and phylum-level composition changed little among treatments. Variation in treatment-enriched taxa was associated mainly with soil pH and nutrient availability. Overall, the results indicate that biochar-based sheep manure organic fertilizer can improv soil fertility and partially alleviated soil acidity, while causing only limited short-term shifts in the overall microbial community structure. These findings suggest that BSOCF may be a suitable fertilization strategy for enhancing nutrient availability in Moso bamboo forests with relatively low short-term disturbance to soil microbial assemblages. Full article

Tomato seedlings are highly sensitive to nutrient deficiencies, and rapid identification of nitrogen (N), phosphorus (P), and potassium (K) stress is essential for precision fertilization. In this study, a novel hierarchical classification framework integrating visible and near-infrared (Vis-NIR) spectroscopy and machine learning was developed for fine-grained identification of nutrient stress in tomato seedlings. A total of 2814 leaf spectra were collected, and multiple preprocessing methods were systematically evaluated. Feature wavelength selection was conducted using the successive projection algorithm (SPA) and Random Frog to reduce redundancy and enhance model performance. Four machine learning models were implemented within a three-stage classification strategy to identify stress occurrence, nutrient type, and deficiency severity across three gradients (50%, 70%, and 100%). Results indicated that multiplicative scatter correction (MSC) achieved the best preprocessing performance. The MSC-SPA-XGBoost model yielded the highest overall classification accuracy of 92.74% across the complete 10-class stress categorization on an independent test set. Bootstrap analysis further confirmed model robustness, with a 95% confidence interval of [0.9024, 0.9436]. Compared with traditional vegetation indices (which achieved a maximum validation accuracy of only 75.73%), the proposed method showed superior discriminative capability for multi-class nutrient stress. These findings demonstrate that Vis-NIR spectroscopy combined with feature-driven machine learning provides a rapid and reliable approach for precision nutrient management in tomato cultivation. Full article

Background/Objectives: Endometriosis is a chronic condition affecting women of reproductive age. Its symptoms have a negative impact on the quality of life and fertility of many women in the population. The aim of this literature review was to examine the use of phototherapy, heating and acupuncture in the treatment of endometriosis. Methods: A structured review of the available literature using the PubMed, Embase and Scopus databases, including studies from the last 10 years and with full free access, was applied. The literature search was conducted using the keywords: “endometriosis”, “phototherapy”, “heating” and “acupuncture”. Results: Phototherapy, including photothermal (PTT) and photodynamic therapy (PDT), demonstrated promising results in preclinical animal models, suggesting a potential for reducing endometrial lesions, primarily through mechanisms involving apoptosis, necrosis, and oxidative stress. However, most studies were limited to animal models. Thermal interventions, including magnetic hyperthermia and perioperative heating strategies, were associated with pain reduction, although improper use may lead to adverse effects such as erythema. Acupuncture showed effectiveness in reducing pain and improving quality of life, although its effects may be temporary and supported mainly by small-scale studies and case reports. Conclusions: Studies available in the literature demonstrate the effectiveness of the phototherapy effect, utilizing the mechanism of apoptosis or necrosis, in eliminating endometrial tissue and reducing pain. Acupuncture, derived from Traditional Chinese Medicine, also reduces pain. Non-pharmacological interventions may provide supportive benefits in the management of endometriosis, particularly in pain reduction and lesion control. Full article

The declining productivity, fertilizer inefficiencies, and rising energy cum production costs are the key issues in crop production, especially in semi-arid regions with alkaline soils. Integration of crop management strategies needs to be adopted to address these issues within the water–energy–food nexus (WEFN). For this purpose, a case study was conducted in semi-arid region of central Punjab, Pakistan, to evaluate the interactive effects of irrigation water source [canal water (CW) and tubewell water (TW)], phosphorus fertilizer source [diammonium phosphate (DAP) vs. phosphoric acid_25% (PA)], and fertilizer application levels [100% and 80% of recommended dose of fertilizer (RDF)] on maize productivity, energy efficiency and economic performance. The experiment comprises eight treatments under raised bed planting (RBP) and one control treatment under ridge-furrow sowing (RFS). Each treatment had three replicates, and the experiment was laid out under a randomized complete block design (RCBD). Maize growth, yield, water productivity, energy efficiency, and economic performance were analyzed using field measurements, energy equivalents, and partial budget analysis. The T1 (RBP+CW+PA+100%RDF) produced the highest maize yield, and it varied from 6.36 to 7.90 t ha⁻¹ under other treatments. CW significantly showed better water productivity (1.14–1.37 kg m⁻³) than that under TW (1.13–1.31 kg m⁻³); however, total energy input was higher under TW-based treatments (29,269–41,033 MJ t ha⁻¹) than that under CW-based treatments (24,129–29,681 MJ ha⁻¹). This results in lower energy productivity under TW-based treatments compared with CW-based treatments (0.17–0.23 kg MJ⁻¹ vs. 0.25–0.31 kg MJ⁻¹, respectively). Moreover, T2 (RBP+CW+PA+80%RDF) produced the highest energy use efficiency (0.59). Economic analysis revealed that production costs were nearly 15–17% higher under TW-based treatments, mainly due to the cost associated with groundwater pumping, and it reduced net profit to USD 1134–1385 ha⁻¹. Better net profits were achieved by CW-based treatments (USD 1244–1593 ha⁻¹), while those produced by BCR ranged from 3.11 to 3.69, with the highest value under T2 (RBP+CW+PA+80%RDF). Overall, irrigation water source emerged as the dominant driver of WEFN performance, while phosphoric acid significantly improved phosphorus availability, energy productivity, and economic returns, particularly under reduced fertilizer input. This study evidenced better maize productivity, less energy consumption, and improved farm profitability in semi-arid irrigated systems through the integration of canal water irrigation with optimized phosphorus management. Full article

Excessive basal and tillering nitrogen (BTN) application in rice production causes substantial N losses and environmental costs, yet practical soil fertility thresholds to guide safe BTN reduction (RBTN) have not been established. This study combined meta-analysis with field experiments to determine these thresholds and elucidate underlying mechanisms. The meta-analysis showed that the BTN yield response was negatively correlated with soil total nitrogen (STN) and soil organic matter (SOM), with no significant RBTN yield penalty above 2.0 g kg⁻¹ STN or 30 g kg⁻¹ SOM. Field experiments in soils exceeding these thresholds demonstrated that zero BTN with optimized panicle N (90 kg ha⁻¹ for indica; 120 kg ha⁻¹ for japonica) increased indica yield by 3.6% while reducing N input by 62%, and maintained japonica yield with 50% less N. Nitrogen recovery efficiency rose from 35.5% to 86.1% in indica and from 42.0% to 55.8% in japonica. These improvements were physiologically underpinned by sufficient indigenous soil N supply for productive tiller formation and improved synchrony between panicle N application and crop demand. Collectively, these findings establish quantifiable soil fertility thresholds for safe early N reduction and demonstrate that BTN can be substantially reduced or even omitted in high-fertility soils without compromising yield—offering a simplified, efficient N management strategy for intensive rice production systems. The divergent compensatory pathways between indica and japonica rice further highlight the need for cultivar-specific calibration in future applications. Full article

Accurate estimation of soil total nitrogen (TN) and soil organic matter (SOM) is important for sustainable soil fertility assessment and precision nutrient management. Visible–near-infrared hyperspectral sensing provides a rapid and non-destructive solution, but its inversion accuracy is strongly affected by spectral preprocessing, especially under small-sample conditions. To reduce dependence on a manually selected preprocessing operator, this study proposes a homologous preprocessing representation fusion framework based on greedy concatenation (HPRF–GC). The framework constructs multiple homologous spectral views from the same raw spectrum, selects informative views through cross-validation-guided greedy forward selection, and concatenates the selected views before random forest or support vector regression. A self-built in situ hyperspectral dataset was collected from two representative black calcareous Mollisol farms in Heilongjiang Province, China, including 200 composite samples measured with a GaiaField Pro V10 imager at 5 m height under midday illumination using white reference calibration. On this dataset, HPRF–GC reduced RMSE by 3.61% for TN–RF, 9.94% for TN–SVR, 0.87% for SOM–RF, and 7.15% for SOM–SVR compared with the strongest single-preprocessing baseline, while introducing only a modest training-time overhead. On the public LUCAS 2015 dataset, HPRF–GC achieved competitive TN prediction performance, with an $R^2$ of 0.890 and an RMSE of 1.191 under RF. These results indicate that HPRF–GC provides a lightweight, interpretable and reproducible strategy for reducing preprocessing selection sensitivity in small-sample soil hyperspectral inversion. Full article

The ecological restoration of degraded sandy land in the Yarlung Zangbo River Valley is constrained by the metabolic functions of soil microorganisms. This study investigates the dynamic mechanisms of microbial elemental use efficiency in walnut plantations, with a focus on seasonal variations in soil chemical stoichiometry, extracellular enzyme activity, and microbial nutrient efficiency in rhizosphere and bulk soils. This paper explores the effects of conventional organic fertilizer (CF) and organic–inorganic compound fertilizer (OIF) on microbial nutrient use strategies and their seasonal dynamics. The results showed significant seasonal fluctuations in soil active nutrients and microbial biomass, while the total nutrient content remained stable. OIF enhanced microbial chemical stoichiometric homeostasis but simultaneously triggered a “carbon–phosphorus metabolic trade-off”, leading to a restraint of microbial carbon use efficiency (CUE) during the growing season. Microbial elemental use efficiency (EUE) exhibited clear seasonal differentiation: CUE was higher in summer, promoting biomass accumulation, whereas NUE and PUE increased in winter and spring, reflecting a nutrient conservation strategy. The EUE pathways were decoupled between rhizosphere and non-rhizosphere microenvironments. The rhizosphere was more directly driven by soil chemical stoichiometry and microbial biomass, while the non-rhizosphere was influenced by nutrient limitation states, represented by vector characteristics. This study provides insights into the seasonal adaptability and microenvironmental heterogeneity of microbial metabolism during the restoration of cold sandy land. It is suggested that future ecological management should focus on N-P balanced fertilization and consider the differential responses between rhizosphere and non-rhizosphere zones to enhance ecosystem productivity and soil carbon, nitrogen, and phosphorus sequestration potential. Full article

Potato nitrogen (N) demand varies with developmental stage rather than remaining uniformly high throughout the season. This review re-examines the “nitrate-first, ammonium-later” strategy by separating total N amount, N-supply timing, N form, and soil N transformation. Current evidence suggests that nitrate is better aligned with pre-tuber initiation because it supports stolon development and tuber set under non-excessive N supply, whereas ammonium-containing nutrition may benefit tuber bulking only when NH₄⁺ persists in the root zone and soil chemical constraints are controlled. Field responses attributed to N form are often shaped by crop N status, genotype × environment × management interactions, nitrification–denitrification dynamics, water regime, soil texture, fertilizer placement, and cultivar. We therefore interpret the strategy as a conditional, stage-oriented framework rather than a universal fertilizer prescription. Integrating NNI-/CNDC-based diagnosis, root-zone monitoring, enhanced-efficiency fertilizers, and soil-process evidence can improve synchronization between N supply and potato demand, supporting yield formation, N-use efficiency, and reduced environmental risk. Full article

Optimizing fertilizer management is crucial for increasing crop yields while reducing environmental impact. However, traditional methods rely on extensive field trials, which are costly and limit their scalability. To overcome these limitations, this study developed data-driven yield prediction models (YPM) for wheat, rice, and maize by integrating multiple feature selection and machine learning algorithms with explainable ensemble learning, namely stacking regression (SR) and voting mean (VM). The optimal YPM was subsequently combined with the hybrid optimization strategy to construct an intelligent fertilization decision model (IFDM), and the economic–environmental benefits were subsequently evaluated. The best-performing models were SHAP-SR for wheat and rice and GBM-SR for maize, achieving R² values of 0.79, 0.69, and 0.67, and RMSEs of 681.69, 725.35, and 1091.49 kg ha⁻¹, respectively. Based on the IFDM, the recommended application ranges for nitrogen (N), phosphorus (P₂O₅), and potassium (K₂O) were as follows: for wheat, 122.1–256.3, 45.4–98.2, and 30.6–60.7 kg ha⁻¹; for rice, 170.8–261.2, 55.1–91.4, and 40.6–98.5 kg ha⁻¹; and for maize, 157.5–293.4, 84.2–156.4, and 30.1–62.7 kg ha⁻¹. Simulation-based evaluation suggested that adopting these recommendations could potentially increase average yields by 9.2–12.4% and enhance economic–environmental benefits by 32.86–97.73% across the three crops. This study indicates that coupling interpretable ensemble learning with a hybrid optimization strategy can support efficient decision-making for field-scale fertilization and provides a data-driven and cost-effective approach for precision fertilization, with potential applicability to arid agricultural regions under similar agro-ecological conditions. Full article

Cadmium (Cd) and lead (Pb) co-contamination in construction and demolition waste landfill soils presents a significant challenge to ecosystem health, necessitating effective remediation strategies. This study investigated a synergistic approach combining a composite amendment (compost, superphosphate, desulfurized gypsum) with seven plant species to elucidate the interactions driving metal immobilization and phytoextraction. The amendment significantly altered soil properties: it reduced total Cd while increasing its bioavailability, and enhanced soil fertility (e.g., elevated organic matter and total nitrogen). Plant responses varied: Solanum americanum Mill. and Tagetes patula L. exhibited high Cd phytoextraction capacity, whereas Lolium perenne L. sequestered Cd/Pb primarily in roots. The bacterial community shifted from an oligotrophic, stress-tolerant state (e.g., Sphingomonas-dominated) in contaminated soil to a copiotrophic, functionally active state (e.g., Streptomyces-enriched) in amended soil. Community structure was strongly correlated with available Cd, pH, and nutrient levels. Key microbial biomarkers were specifically enriched in different plant rhizospheres. In contrast, the fungal community exhibited minimal responsiveness. These findings demonstrate that remediation efficiency is governed by an integrated “amendment–plant–microbe” framework: amendments regulate metal bioavailability, plants execute extraction or stabilization, and the restructured microbiome supports nutrient cycling and plant health. This integrated remediation strategy directly supports the Sustainable Development Goals of the 2030 Agenda, especially on environmentally sound management of chemicals and wastes and land degradation neutrality. This mechanistic understanding underscores the necessity of combined biological and chemical strategies for sustainable remediation of co-contaminated soils, ultimately enabling ecological reclamation and safe recycling of such urban marginal lands into productive uses. Full article

Long-term fertilization regulates soil microbial communities and is essential for black soil health and sustainable productivity, yet its key drivers remain unclear. Using a 39-year field experiment, we evaluated the effects of four fertilization regimes: no fertilizer (CK), chemical fertilizer (NPK), organic fertilizer (M), and combined organic-inorganic fertilizer (MNPK). Soil properties and bacterial communities were analyzed using Illumina MiSeq sequencing, quantitative real-time PCR (qRT-PCR), and multivariate analyses. Proteobacteria, Actinobacteriota, Acidobacteriota, Chloroflexi, and Gemmatimonadota dominated (>80% of the community), and all treatments significantly altered their relative abundances. Compared with CK, NPK reduced soil pH by 8.3% and bacterial abundance by 29.7%, increased soil organic matter (SOM) by 22.9%, and decreased community evenness. MNPK reduced pH by only 2.0%, increased SOM by 53.8% and bacterial abundance by 38.9%, and improved community evenness, mitigating acidification while maintaining high diversity. M increased pH by 2.3%, SOM by 73.3%, and bacterial abundance by 71.8%. Soil pH, available phosphorus, and SOM were the main drivers of community structure. Overall, MNPK showed the strongest synergistic effects on soil fertility and microbial stability, making it an optimal strategy for sustainable black soil management. Full article

Rapid and cost-effective estimation of soil total nitrogen (TN) is essential for soil fertility assessment and nutrient management. However, the performance of laboratory visible–near-infrared (Vis–NIR) models is shaped not only by preprocessing and modeling strategy but also by sample preparation and the soil’s compositional background. In this study, TN prediction was evaluated using 376 topsoil samples from two contrasting datasets: Mollisols from the black-soil region of Northeast China and Ultisols from Qiyang County, Hunan Province, southern China. Spectra acquired over 350–2500 nm for three particle-size fractions were preprocessed using Savitzky–Golay smoothing combined with standard normal variate (SNV), first-derivative, or second-derivative transformations, and modeled using partial least squares regression (PLSR), support vector regression (SVR), and extreme gradient boosting (XGBoost). Model development used a 5 × 5 nested cross-validation followed by evaluation on a sample-grouped held-out test set. Among all combinations, XGBoost with first-derivative preprocessing on the 0.25 mm fraction produced the best performance, with test R² values of 0.91 for Mollisol and 0.78 for Ultisol. Shapley additive explanations (SHAP) and principal component analysis (PCA) consistently identified informative spectral regions at 430–480 and 1330–1450 nm for Mollisol and at 585–635, 820–900, and 2180–2240 nm for Ultisol. Prediction errors were larger in the sampled Ultisol dataset and increased with DCB-extractable Fe and mineral backgrounds. A second-stage log-domain residual correction incorporating ancillary soil properties further reduced the Ultisol RMSE from 0.30 to 0.27 g kg⁻¹. These findings support the 0.25 mm, first-derivative, XGBoost workflow as a robust laboratory Vis–NIR approach for TN prediction and indicate that composition-aware residual correction can improve prediction in oxide- and mineral-rich soils. Full article