Search Results (1,228)

Biological N₂ fixation (BNF) and ratoon rice growth are biological processes that mediate N and C cycling in rice paddy ecosystems, but their contributions to paddy soil fertility have rarely been evaluated in a quantitative and unified manner. In this study, we analyzed the contribution of BNF and ratoon rice growth to soil N fertility at six rice paddy sites in four prefectures of Japan, combining 2-year field monitoring and simulation using the DNDC-Rice biogeochemistry model. Across the sites and years, ratoon rice was found to accumulate up to 30 kg N ha⁻¹ without fertilization and irrigation after main rice harvest. BNF was not measured but estimated to be 33–63 kg N ha⁻¹ yr⁻¹ at the six sites, by applying a newly built BNF model after calibration against a literature dataset. Based on the simulations using DNDC-Rice under typical local management strategies, we estimated the following contributions of BNF and ratoon rice to soil N fertility, with variations based on the climate, soil properties, and management, as follows: (a) BNF and ratoon rice contributed 4–33% and 3–23% of the N supply from soil during the main rice season, respectively. (b) While BNF contributed 3–29% of the main rice N uptake, that from ratoon rice was much lower (6% or less), presumably because the decomposition of ratoon rice residue induced N immobilization during the main rice season. (c) Although the major part of N gain by BNF was being lost via denitrification and N leaching, BNF was contributing up to 6.6% of the organic N pool at the 0–30 cm soil layer. Ratoon rice was working to save N loss by reducing N leaching, consequently contributing up to 3.3% of the soil N pool. These findings provide quantitative insights into what roles BNF and ratoon rice play in paddy soil fertility. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

In the field of smart agriculture, soil property data at the field scale drives the precision decision-making of agricultural inputs such as seeds and chemical fertilizers. However, soil texture has significant spatial variability at the field scale, and traditional remote sensing monitoring methods have certain data intermittency, which limits small-scale prediction research. In this study, based on the Google Earth Engine platform, soil synthetic images were generated according to different time intervals using mean compositing and median compositing modes, image bands were extracted, and spectral indices were introduced; combined with the random forest algorithm, the effects of different compositing time windows, compositing modes, and compositing data types on prediction accuracy were evaluated; and three partitioning strategies based on crop growth, soil synthetic image brightness, and soil type were adopted to conduct local partitioning regression of soil texture. The results show that: (1) The use of mean compositing of multi-year May images from 2021 to 2024 can improve prediction accuracy. When this method is combined with the “band reflectance + spectral indices” dataset, compared with other compositing methods, the R² of clay particles, silt particles, and sand particles can be increased by 8.89%, 9.50%, and 2.48% on average. (2) Compared with using only image band data, the introduction of spectral indices can significantly improve the prediction accuracy of soil texture at the field scale, and the R² of clay particles, silt particles, and sand particles is increased by 4.58%, 3.43%, and 4.59% on average, respectively. (3) Global regression is superior to local partitioning regression; however, the local partitioning regression strategy based on soil type has good accuracy performance. Under the optimal compositing method, the average R² of soil particles of each size fraction is only 1.08% lower than that of global regression, which has great application potential. This study innovatively constructs a comprehensive strategy of “moisture spectral indices + specific compositing time window + specific compositing mode + soil type partitioning”, providing a new paradigm for soil texture prediction at the field scale in Northeastern China, and lays the foundation for data-driven water and fertilizer decision-making. Full article

Electron transfer (ET) is a foundational biogeochemical process in paddy soils, distinctively molded by alternating anaerobic-aerobic conditions from flooding-drainage cycles. Despite extensive research on heavy metal(loid) (denoted as “HM”, e.g., As, Cd, Cr, Hg) dynamics in paddies, ET has not been systematically synthesized as a unifying regulatory mechanism, and the trade-offs of ET-based mitigation strategies remain unclear. These critical gaps have drastically controlled HMs’ mobility, which further modulates bioavailability and subsequent accumulation in rice (Oryza sativa L., a staple sustaining half the global population), posing substantial food safety risks. Alongside progress in electroactive microorganism (EAM) research, extracellular electron transfer (EET) mechanism delineation, and soil electrochemical monitoring, ET’s role in orchestrating paddy soil HM dynamics has garnered unparalleled attention. This review explicitly focuses on the linkage between ET processes and HM biogeochemistry in paddy ecosystems: (1) elucidates core ET mechanisms in paddy soils (microbial EET, Fe/Mn/S redox cycling, organic matter-mediated electron shuttling, rice root-associated electron exchange) and their acclimation to flooded conditions; (2) systematically unravels how ET drives HM valence transformation (e.g., As(V) to As(III), Cr(VI) to Cr(III)), speciation shifts (e.g., exchangeable Cd to oxide-bound Cd), and mobility changes; (3) expounds on ET-regulated HM bioavailability by modulating soil retention capacity and iron plaque formation; (4) synopsizes ET-modulated HM accumulation pathways in rice (root uptake, xylem/phloem translocation, grain sequestration); (5) evaluates key factors (water management, fertilization, straw return) impacting ET efficiency and associated HM risks. Ultimately, we put forward future avenues for ET-based mitigation strategies to uphold rice safety and paddy soil sustainability. Full article

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

Availability of irrigation water during growing seasons in the Republic of South Africa (RSA) remains a significant concern. Persistent droughts and unpredictable rainfall patterns attributed to climate change, coupled with an increasing population, have exacerbated irrigation water scarcity. Globally, treated wastewater has been utilised as an irrigation water source; however, despite global advances in the usage of treated wastewater, its suitability for irrigation in RSA remains a contentious issue. Considering this uncertainty, this review article aims to unravel the South African scenario on the suitability of treated wastewater for irrigation purposes and highlights the potential environmental impacts and public health risks. The review synthesised literature in the last two decades (2000–present) using Web of Science, ScienceDirect, ResearchGate, and Google Scholar databases. Findings reveal that treated wastewater can serve as a viable irrigation source in the country, enhancing various soil parameters, including nutritional pool, organic carbon, and fertility status. However, elevated levels of salts, heavy metals, and microplastics in treated wastewater resulting from insufficient treatment of wastewater processes may present significant challenges. These contaminants might induce saline conditions and increase heavy metals and microplastics in soil systems and water bodies, thereby posing a threat to public health and potentially causing ecological risks. Based on the reviewed literature, irrigation with treated wastewater should be implemented on a localised and pilot basis. This review aims to influence policy-making decisions regarding wastewater treatment plant structure and management. Stricter monitoring and compliance policies, revision of irrigation water standards to include emerging contaminants such as microplastics, and intensive investment in wastewater treatment plants in the country are recommended. With improved policies, management, and treatment efficiency, treated wastewater can be a dependable, sustainable, and practical irrigation water source in the country with minimal public health risks. Full article

Improving nutrient use efficiency and minimizing environmental pollution from excessive fertilization require appropriate nutrient management supported by continuous monitoring of soil nutrient levels during crop growth. As only a few real-time sensors for the measurement of soil nutrients are available, this study evaluated the potential of electrical conductivity (EC) sensors, which reflect the ionic concentrations of the soil solution, for real-time estimation of plant-available nutrient levels. Nitrogen and potassium were sequentially supplied to achieve cumulative application rates of 25–300% of the nutrient uptake-based fertilization rate. The relationship between cumulative fertilization rate and accumulated sensor-based EC increase was described using linear, polynomial, and nonlinear saturation models. Sensor EC increased linearly from 25 to 125% of the nutrient uptake-based fertilization rate, while higher application rates were better explained by the nonlinear saturation equation. Sensor-based EC showed strong correlation with soil ammonium nitrogen (NH₄⁺-N), indicating that the sensor effectively reflected nutrient dynamics. In open-field pepper soil, fertigation-induced increases in sensor EC followed the patterns predicted by both the linear and nonlinear saturation models established in the laboratory. These results demonstrate that EC sensors can be used for real-time monitoring of soil nutrient levels and may contribute to efficient nutrient management in open-field cultivation. Full article

Soil fertility assessment is fundamental for improving agricultural productivity and promoting sustainable land management. This study proposes an integrated methodological framework that combines Sentinel-2 satellite imagery, spatial analysis techniques, and field-based soil data to evaluate soil fertility in Arabica coffee plantations in the Lonya Grande district, Peruvian Amazon. The framework involves three analytical phases: (i) spatial interpolation of soil macronutrients using Inverse Distance Weighting (IDW), (ii) local modeling through Geographically Weighted Regression (GWR), and (iii) spectral correlation analysis between field-measured soil properties and Sentinel-2 reflectance bands. The SWIR2 (Band 12) data were identified as the most sensitive predictor of soil moisture-related properties, with the strongest relationship observed for soil saturation (R² = 0.40). Field validation revealed pronounced spatial heterogeneity, particularly for macronutrients such as nitrogen, phosphorus, and potassium. The study also found that soils exhibited moderately acidic pH values (5.1–6.8), favorable for coffee cultivation. Despite adequate water retention, nutrient deficiencies highlight the need for site-specific soil management strategies. Overall, spatial analysis confirmed consistent relationships between remote sensing data and soil parameters, demonstrating the feasibility and cost-effectiveness of this approach under data-limited tropical conditions. The proposed framework offers a scalable basis for regional soil fertility monitoring, and future research should incorporate machine learning and expanded sampling networks to further enhance predictive performance. Full article

Soil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment essential for sustainable agriculture. Visible and near-infrared (Vis–NIR) spectroscopy offers a non-destructive and cost-effective alternative to conventional laboratory analyses, allowing for the simultaneous estimation of multiple soil properties from a single spectrum. This study aimed to predict SOC content using machine learning techniques applied to Vis–NIR spectra of 860 soil samples collected in the Sierra Nevada de Santa Marta, Colombia. The spectra (400–2500 nm) were acquired using a NIR spectrophotometer, and the soil organic carbon (SOC) content was quantified using a wet oxidation method that employs dichromate in an acidic medium. A hybrid modeling framework combining Random Forest (RF) with support vector regression (SVR) and XGBoost was implemented. Spectral pretreatments (Savitzky–Golay first derivative, MSC, and SNV) were compared, and spectral bands were selected every 10 nm. The 30 most relevant wavelengths were identified using RF importance analysis. Data were divided into training (80%) and test (20%) subsets using stratified random sampling, and five-fold cross-validation was applied for parameter optimization and overfitting control. The RF–XGBoost (R² = 0.86) and RF–SVR (R² = 0.85) models outperformed the individual RF and SVR models (R² < 0.7). The proposed hybrid approach, optimized through features, and advanced spectral preprocessing demonstrate a robust and scalable framework for rapid prediction of SOC and sustainable soil monitoring. Full article

The Lower Liaohe River Plain (LLRP) is a core grain production base in Northeast China. Monitoring the dynamics and changing rates of soil organic carbon (SOC) in cultivated lands is essential for regulating soil fertility, safeguarding food production, and maintaining the regional carbon balance. Based on soil survey data from three periods, 1980, 2008, and 2019, this study investigated the spatiotemporal dynamics of SOC content and its changing rate (SOC_r) using geospatial analysis. Results showed that SOC content declined significantly from 11.19 g kg⁻¹ to 10.47 g kg⁻¹ during 1980–2008, then recovered slightly to 10.58 g kg⁻¹ in 2019. Moreover, SOC_r varied temporally in the period of 2008–2019, exhibiting a positive mean rate of 0.01 g kg⁻¹ yr⁻¹, which was significantly higher than that of the period of 1980–2008 (−0.03 g kg⁻¹ yr⁻¹). A significant negative correlation was examined between the initial SOC content and SOC_r, showing an identification of the SOC equilibrium point (SOC_ep). The SOC_ep in the period of 2008–2019 was 9.69% higher than that in the period of 1980–2008. These findings provide a scientific basis for formulating regional policies and optimizing spatially differentiated management strategies to improve cropland SOC in the study area. Full article

Rice (Oryza sativa L.) is one of the most important crops globally. However, its production faces significant challenges due to climate change, reduced arable land, and increased demand. In this context, the present study conducted a systematic literature review (SLR) on technological advances in rice production in Latin America. Recognized scientific databases were consulted, and rigorous inclusion and exclusion criteria were applied to synthesize current knowledge on the subject. The results show that the main innovations include genetically improving varieties with greater resistance to biotic and abiotic stresses; implementing advanced water management techniques, such as intermittent irrigation; and applying biofertilizers and organic amendments to improve soil fertility. Additionally, precision agriculture tools, such as remote sensing and artificial intelligence-based modeling, have optimized crop monitoring and input efficiency. Brazil, Mexico, and Colombia are the main generators of rice production technologies in the region. Despite the progress made, challenges remain regarding the adoption of these innovations by producers, highlighting the need for comprehensive policies to facilitate technology transfer. This review establishes a foundation for researchers and policymakers interested in the sustainable development of rice production in Latin America. Full article

Exosomes are nanoscale extracellular vesicles that mediate intercellular communication by transporting microRNAs, proteins, and lipids. Generated through Endosomal Sorting Complex Required for Transport (ESCRT)-dependent mechanisms or ESCRT-independent pathways, exosomes are released when multivesicular bodies fuse with the plasma membrane. The ESCRT-dependent pathway involves sequential protein complexes (ESCRT-0, I, II, III) that recognize and sort ubiquitinated cargo, induce membrane budding, and facilitate vesicle scission. In contrast, the ESCRT-independent pathway relies on membrane lipids such as ceramide and proteins like tetraspanins (CD9, CD63, CD81) to promote vesicle formation without ESCRT machinery. Furthermore, post-translational modifications, including ubiquitination, sumoylation, and phosphorylation, further serve as molecular switches, modulating the affinity of ESCRT complexes or cargo proteins for membrane domains and affecting ILV formation rates. In reproductive medicine, exosomes regulate oocyte maturation, embryo–endometrial crosstalk, placental development, and maternal–fetal communication. Altered exosomal signaling contributes to obstetric complications, including preeclampsia, gestational diabetes mellitus, and preterm birth, whereas distinct exosomal miRNA signatures serve as potential diagnostic biomarkers. In gynecology, dysregulated exosomes are implicated in endometriosis, polycystic ovary syndrome, premature ovarian insufficiency, and gynecological malignancies. In contrast, mesenchymal stem cell-derived exosomes show therapeutic promise in restoring ovarian function and enhancing fertility outcomes. The distinctive molecular profiles of circulating exosomes enable minimally invasive diagnosis, while their biocompatibility and ability to cross biological barriers position them as vehicles for targeted drug delivery. Characterization of accessible data provides non-invasive opportunities for disease monitoring. However, clinical translation faces challenges, including standardization of isolation protocols, establishment of reference ranges for biomarkers, and optimization of therapeutic dosing. This review summarizes exosome biogenesis, characterization methods, physiological functions, and clinical applications in obstetrics and gynecology, with an emphasis on their diagnostic and therapeutic potential. Future directions include large-scale biomarker validation studies, engineering approaches to enhance exosome targeting, and integration with precision medicine platforms to advance personalized reproductive healthcare. Full article

Heavy metal contamination of agricultural soils represents a critical environmental and agronomic challenge, particularly in regions exposed to intensive land use and transport-related emissions. This study presents a detailed assessment of soil contamination in the Dargov cadastral area (Eastern Slovakia), where elevated concentrations of Cu, Zn, Pb, Ni, As, Cd, and Cr were detected through multi-depth sampling near the I/19 first-class road. Analytical results confirmed exceedances of Slovak regulatory thresholds (Decree No. 59/2013), with persistent contamination observed even in the deepest sampling interval (20–40 cm), indicating vertical migration and long-term accumulation. Concentrations of Pb (85–210 mg·kg⁻¹), Cd (2.1–5.4 mg·kg⁻¹), Zn (120–340 mg·kg⁻¹), and Ni (45–95 mg·kg⁻¹) exceeded Slovak regulatory thresholds. The highest values were consistently detected in the 0–10 cm layer and within 3 m of the I/19 road, with a gradual decline at greater depths and distances. Nevertheless, Cd and Ni remained above permissible limits even in the deepest sampling interval (20–40 cm), confirming vertical migration and long-term persistence of contamination. The spatial distribution of contaminants suggests a dominant influence of road traffic, with implications for crop safety, soil fertility, and rural land management. Based on the findings, the study proposes context-sensitive remediation strategies, including phytoremediation and chemical immobilization, and emphasizes the need for integrated monitoring systems and land-use planning to mitigate risks. The case study contributes to the broader discourse on sustainable soil management in Central European agricultural landscapes affected by diffuse pollution. Full article

Efficient and reliable estimation of rice phenological stages is crucial for improving yield prediction, optimizing irrigation, and guiding fertilization management. Spectral indices (SIs) derived from remote sensing have demonstrated strong potential for phenology detection. However, the suitability of specific spectral indices (SIs) for individual growth stages remains unclear due to data limitations. This study addresses this gap using a 7-year (2019–2025) daily in situ hyperspectral dataset that includes shortwave infrared (SWIR) bands. We evaluated various SIs to determine their effectiveness in identifying key phenological stages. The results demonstrate that no single index captures the entire cycle; instead, a multi-index approach is required. The SWIR-based Normalized Difference Vegetation Index (SNDVI) proved superior for detecting irrigation, transplanting, and flowering. The Green–Red Vegetation Index (GRVI) effectively tracked tillering and heading, while the Normalized Difference Vegetation Index (NDVI) and Hue identified the maximum tillering stage. For the ripening phase, the Normalized Difference Yellowness Index (NDYI) exhibited the highest accuracy in detecting maturity. Validation against Sentinel-2 simulations revealed strong correlations ($R^2>0.81$) for greenness-related indices (NDVI, GRVI, SNDVI, EVI), whereas colorimetric indices showed weaker agreement. These findings establish a robust, multi-index framework for high-frequency rice phenology monitoring. Full article

Background and Objectives: Ectopic pregnancy (EP) is a life-threatening medical and surgical condition. Tubal stump EPs and heterotopic pregnancies can occur after in vitro fertilization-embryo transfer (IVF-ET), even after salpingectomy. The purpose of this study is to investigate the risk factors, diagnosis, and treatment of tubal stump EPs after IVF-ET in patients with prior salpingectomy or adnexectomy. We also aim to evaluate the intrauterine pregnancy (IUP) outcome in cases of heterotopic pregnancy in this population. Materials and Methods: This systematic review (PROSPERO CRD42023352959) followed PRISMA guidelines. A literature search of MEDLINE^®, Scopus, Web of Science, and clinicaltrials.gov was conducted on 30 April 2024. We included studies on tubal stump EP after IVF-ET in patients with previous salpingectomy or adnexectomy and created a qualitative summary. Results: We included 40 studies reporting on 57 patients (58 EP episodes). Most patients (69.0%) had prior bilateral salpingectomy. Tubal rupture occurred in 69.6% of cases, with 69.0% of these cases reporting hemoperitoneum. Abdominal pain was the most frequent symptom (71.7%). Heterotopic pregnancy occurred in 60.0% of cases (82.7% singletons). The IUP outcome was delivery in 81.9% of cases, with 95.5% of singletons delivering at term, compared with 40.0% of twins. The surgical approach (laparoscopy vs. laparotomy) did not change the IUP outcome. Tubal stump excision (74.1%) was the most common treatment. Overall, the certainty of the evidence was judged as moderate to very low according to the GRADE-CERQual approach, mainly due to small sample sizes, observational designs, and heterogeneity among studies. Conclusions: This review, the first on this topic, provides key data for counselling patients with a tubal stump heterotopic pregnancy. Despite its rarity, close follow-up until 8–10 weeks is recommended for IVF-ET patients with positive β-hCG, monitoring for abdominal pain. Successful management (expectant, medical, or surgical) should be guided by β-hCG levels and ultrasound findings (e.g., absence of heartbeat). Medical treatment shows encouraging obstetric outcomes and warrants further research. Full article