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To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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13 pages, 1115 KB

Open AccessArticle

A Clue for the Hen and Egg Question: The Simultaneous Formation of Uracil and Amino Acids Under Simulated Hadean Conditions

by Christian Seitz, Denis Schuldeis, Konstantin Vogel, Wolfgang Eisenreich and Claudia Huber

Life 2026, 16(4), 624; https://doi.org/10.3390/life16040624 (registering DOI) - 8 Apr 2026

Abstract

The origin of life is commonly discussed within two competing conceptual frameworks: the metabolism-first and information-first hypotheses. While each emphasizes a different defining property of early life, modern biochemistry reveals a fundamental interdependence between metabolic processes and genetic information transfer, leading to a persistent chicken-and-egg problem. In this study, we investigate a prebiotically plausible reaction system that enables the concurrent formation of molecular precursors associated with both frameworks. Under simulated Hadean hydrothermal conditions, acetylene, ammonia, cyanide, and carbon monoxide were reacted in aqueous solution in the presence of transition metal sulfides. Using gas chromatography–mass spectrometry combined with stable isotope labeling, we demonstrate the simultaneous formation of the nucleobase uracil and the amino acids alanine and aspartic acid. Isotopic incorporation patterns allow reconstruction of the underlying reaction pathways and confirm the contribution of all starting materials to product formation. While amino acids are produced continuously over the observed period in significantly higher yields than uracil, uracil formation exhibits a pronounced time-dependent maximum after three days. Variations in pH, reaction time, and metal sulfide catalysts modulate product yields but do not prevent the parallel emergence of both molecular classes. These findings support a scenario in which proto-metabolic chemistry and molecular precursors of genetic information could have arisen simultaneously within a shared geochemical setting. The results provide experimental support for a coupled origin of metabolism and transcriptional building blocks, offering a potential resolution to the dichotomy between metabolism-first and information-first models of early life. Full article

(This article belongs to the Special Issue Chemical Evolutionary Pathways to Origins of Life)

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17 pages, 357 KB

Open AccessArticle

Revealing Risk Preferences Through AI Prompting Effort

by Brian A. Toney, Gregory G. Lubiani and Albert A. Okunade

J. Risk Financial Manag. 2026, 19(4), 269; https://doi.org/10.3390/jrfm19040269 (registering DOI) - 8 Apr 2026

Abstract

This paper analyzes “prompt engineering” through the economic lens of self-insurance against the risk of errors from noisy AI systems. To formalize this approach, we model an agent under cognitive load, allocating effort between working unassisted and prompting an AI assistant. The theoretical model demonstrates that an agent’s optimal prompting effort is driven by the agent’s attitude toward risk. Specifically, the model proves that risk-averse agents rationally “over-invest” in prompting effort, while risk-seeking agents “under-invest” relative to the risk-neutral benchmark. This outcome stems from the covariance between the marginal utility of performance and the marginal product of prompting. This alignment is positive for risk-averse agents, effectively boosting the AI’s perceived productivity. The novel implication is that prompting effort is an economically meaningful behavior that can be informative about an individual’s underlying attitude toward downside AI risk. These results offer a new perspective for understanding heterogeneity in AI adoption and oversight. They also suggest that, under comparable task conditions and controlling for prompting ability, observed prompting effort may be informative about attitudes toward downside AI risk. The framework therefore provides a risk-management perspective for understanding heterogeneity in AI governance in high-stakes settings such as healthcare and finance. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Journal of Risk and Financial Management, 2nd Edition)

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22 pages, 2073 KB

Open AccessArticle

TVAE-GAN: A Generative Model for Providing Early Warnings to High-Risk Students in Basic Education and Its Explanation

by Chao Duan, Yiqing Wang, Wenlong Zhang, Zhongtao Yu, Yu Pei, Mingyan Zhang and Qionghao Huang

Information 2026, 17(4), 356; https://doi.org/10.3390/info17040356 (registering DOI) - 8 Apr 2026

Abstract

The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students’ online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students’ emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment. Full article

(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)

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31 pages, 1438 KB

Open AccessReview

A Conceptual Decision-Support Agent-Based Framework for Evacuation Planning Under Compound Hazards

by Omar Bustami, Francesco Rouhana and Amvrossios Bagtzoglou

Sustainability 2026, 18(8), 3658; https://doi.org/10.3390/su18083658 (registering DOI) - 8 Apr 2026

Abstract

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

(This article belongs to the Special Issue Sustainable Disaster Management and Community Resilience)

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36 pages, 5989 KB

Open AccessArticle

Hierarchical Structure of the Entrepreneurial Career Competency Instrument: Evidence from Frequentist and Bayesian Bifactor Structural Equation Modelling

by Pieter Schaap and Melodi Botha

Adm. Sci. 2026, 16(4), 180; https://doi.org/10.3390/admsci16040180 (registering DOI) - 8 Apr 2026

Abstract

Robust measurement of entrepreneurial competencies (ECs) is crucial for entrepreneurship education, yet their internal structure remains theoretically contested and empirically underexamined. This study examined whether the four-factor Entrepreneurial Career Competency Instrument (ECCI) exhibits a hierarchical (bifactor) structure among South African entrepreneurs. Using two non-probability samples (N = 1305; N = 280), we analysed competing models, including a bifactor exploratory structural equation model (ESEM). The selected 56-item bifactor ESEM solution was examined for conceptual replicability in the smaller sample using Bayesian structural equation modelling (BSEM) with informative priors and sensitivity analyses to address small-sample uncertainty. Our findings revealed a theoretically supported hierarchical structure with a strong general factor and distinct specific factors: entrepreneurial career mindset, innovativeness, motivation, and implementation, enhancing the interpretation of scores. This study guides ECCI usage by suggesting total scores for broad assessments and domain scores for diagnostic feedback. Methodologically, the findings demonstrate that combining frequentist and Bayesian approaches across samples strengthened structural validity and provided insights into evaluating imprecise responses to self-report measures and addressing sampling constraints. Overall, this work contributes a robust structural model of the ECCI and enriches the EC literature, serving as a framework for refining, testing and applying attribute-based EC measures in diverse contexts. Full article

(This article belongs to the Section Organizational Behavior)

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26 pages, 6011 KB

Open AccessArticle

CFADet: A Contextual and Frequency-Aware Detector for Citrus Buds in Complex Orchards Enabling Early Yield Estimation

by Qizong Lu, Lina Yang, Haoyan Yang, Yujian Yuan, Qinghua Lai and Jisen Zhang

Horticulturae 2026, 12(4), 459; https://doi.org/10.3390/horticulturae12040459 (registering DOI) - 8 Apr 2026

Abstract

Citrus trees exhibit severe alternate bearing, resulting in significant annual yield fluctuations and posing substantial challenges to orchard management planning. Accurate citrus bud counting provides an effective solution by supplying essential data for tree-level and orchard-level yield prediction. However, citrus buds are extremely small (5–10 mm in diameter) and are frequently occluded by leaves during the flowering stage, which makes precise detection highly challenging in complex orchard environments. To address these challenges, this paper proposes a Contextual and Frequency-Aware Detector (CFADet) for robust citrus bud detection. Specifically, an Enhanced Feature Fusion (EFF) module is introduced in the neck to refine multi-scale feature aggregation and strengthen information flow for small targets. A Contextual Boundary Enhancement Module (CBEM) is designed to capture surrounding contextual cues and enhance boundary representation through dimensional interaction and max-pooling operations. To suppress background interference, a Frequency-Aware Module (FAM) is developed to adaptively recalibrate frequency components in the amplitude spectrum, thereby enhancing target features while reducing background noise. In addition, Spatial-to-Depth Convolution (SPDConv) is employed to reconstruct the backbone to preserve fine-grained bud features while reducing model parameters. Experimental results show that CFADet achieves 81.1% precision, 80.9% recall, 81.0% F1-score, and 87.8% mAP, with stable real-time performance on mobile devices in practical orchard scenarios. This study presents a preliminary investigation into robust citrus bud detection in real-world orchard environments and provides a promising technical foundation for intelligent orchard monitoring and early yield estimation, while further validation on larger and more diverse datasets is still required. Full article

(This article belongs to the Section Fruit Production Systems)

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20 pages, 425 KB

Open AccessArticle

Associations Between Heavy Episodic Drinking and Perceived Social Isolation in U.S. Young Adults by Sexual Orientation

by Derek Sean Falk

Youth 2026, 6(2), 43; https://doi.org/10.3390/youth6020043 (registering DOI) - 8 Apr 2026

Abstract

Heavy episodic drinking (HED) is prevalent in young adulthood, yet its relationship with psychosocial well-being remains complex. This study examines the association between HED and perceived social isolation among young adults and tests whether this relationship varies by sexual orientation. Using pooled, nationally representative data from the 2022 and 2024 Health Information National Trends Survey (HINTS), this study analyzed adults aged 18–29 (N = 723). Perceived social isolation was measured using the PROMIS Social Isolation Short Form. Weighted multivariable linear regression models assessed interactions between sexual orientation and HED occasions (0 vs. 1+), adjusting for sociodemographic variables and psychological distress. 45.5% reported HED. Lesbian/gay (B = 5.62, SE = 0.58, p < 0.001) and bisexual (B = 1.66, SE = 0.34, p < 0.001) young adults reported higher isolation than straight peers; HED was inversely associated with isolation (B = −1.71, SE = 0.20, p < 0.001). A significant interaction indicated that among lesbian/gay young adults, heavy drinking was associated with lower perceived isolation (B = −5.77, SE = 0.98, p < 0.001). Interventions should account for the social meanings of alcohol use to avoid unintentionally increasing isolation among sexual minoritized populations. Full article

(This article belongs to the Special Issue Alcohol Use in Young People)

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31 pages, 3254 KB

Open AccessArticle

Working Memory, Attention Control, and Vocabulary Retention in AI (ChatGPT)-Assisted Foreign Language Learning: A Structural Cognitive Modelling Approach

by Mohammad Hamad Al-khresheh, Mayez Almayez and Shatha F. Alruwaili

J. Intell. 2026, 14(4), 62; https://doi.org/10.3390/jintelligence14040062 - 8 Apr 2026

Abstract

This study examined how working memory, attention control, and frequency of ChatGPT-4 use are structurally associated with vocabulary retention in foreign language learning. A quantitative cross-sectional survey design was employed, with data collected from 1002 EFL learners via stratified random sampling. Validated self-report instruments measured working memory, attention control, frequency of ChatGPT use, and vocabulary retention (immediate recall, delayed retention, semantic integration, and productive use). Structural equation modelling was used to test the proposed model. The results showed that working memory was strongly associated with attention control and exerted a direct effect on vocabulary retention across all dimensions. Attention control explained a substantial share of the relationship between working memory and retention, indicating that regulatory allocation of attention, rather than memory capacity alone, governs whether lexical information is stabilised during ChatGPT-assisted learning. The frequency of ChatGPT use conditioned these cognitive pathways by strengthening links between working memory and attention control, and between attention control and vocabulary retention, at higher levels of engagement. Frequency did not predict retention independently, indicating that repeated use supports learning only to the extent that it reinforces cognitive regulation rather than increasing exposure. Vocabulary learning with AI relies more on cognitive regulation and engagement than exposure. Full article

(This article belongs to the Section Studies on Cognitive Processes)

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18 pages, 5511 KB

Open AccessArticle

Exploring the Application of Large Language Models (LLMs) in Data Structure Instruction: An Empirical Analysis of Student Learning Outcomes in Computer Science

by Hongzhi Li, Lijun Xiao, Kezhong Lu, Dun Li, Zheqing Zhang and Qishou Xia

Information 2026, 17(4), 353; https://doi.org/10.3390/info17040353 - 8 Apr 2026

Abstract

Recent advancements in Large Language Models (LLMs), including ChatGPT, DeepSeek, and Claude, have facilitated their growing integration into computer science education, including data structure courses. Despite their widespread adoption, the association between sustained and informal LLM usage and students’ learning outcomes remains insufficiently understood. This study seeks to address this gap by empirically examining the association between LLM usage and undergraduate performance in data structure education. We conduct a twelve-week empirical study involving fifty-four undergraduate students, in which LLMs were made freely accessible but neither explicitly encouraged nor discouraged during coursework and assignments. Students’ LLM usage patterns are analyzed in relation to their academic performance across different task types. Findings reveal a significant negative association between extensive reliance on LLMs for cognitively demanding tasks and overall learning outcomes. Additionally, an inverse associative trend is observed between the frequency of LLM usage across some learning activities and academic performance. In contrast, the use of LLMs for supplementary purposes, including conceptual clarification and theoretical understanding, exhibits a notably positive association with final performance. These findings suggest a task-dependent associative relationship between LLM usage and learning outcomes: LLM usage for conceptual learning shows a positive association with the mastery of relevant knowledge when used as a supplementary learning tool, while excessive LLM usage shows a negative association with the development of fundamental analytical and problem-solving skills. This study highlights the importance of carefully integrating LLMs into data structure education to support learning while preserving students’ independent cognitive engagement. Full article

(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)

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20 pages, 3161 KB

Open AccessArticle

Research on the Core Pricing Mechanism of Shared Energy Storage for Wind Power Systems with Incentive Compatibility

by Zhenhu Liu, Weiqing Wang, Sizhe Yan and Haoyu Chang

Sustainability 2026, 18(8), 3649; https://doi.org/10.3390/su18083649 - 8 Apr 2026

Abstract

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article

(This article belongs to the Special Issue Achieving Carbon Neutrality: Recent Progress of Sustainable Energy Economics, Energy Policy and Energy Transition, 2nd Edition)

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32 pages, 823 KB

Open AccessArticle

A Hybrid Temporal Recommender System Based on Sliding-Window Weighted Popularity and Elite Evolutionary Discrete Particle Swarm Optimization

by Shanxian Lin, Yuichi Nagata and Haichuan Yang

Electronics 2026, 15(8), 1544; https://doi.org/10.3390/electronics15081544 - 8 Apr 2026

Abstract

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

(This article belongs to the Special Issue Evolutionary and Swarm Intelligence Approaches for Recommender Systems)

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14 pages, 1705 KB

Open AccessArticle

Baseline Body Composition Characteristics and Overall Survival in Young Women with Breast Cancer: Matched Case–Control Study Nested Within a Cohort

by Aynur Aktas, Diptasree Mukherjee, Danielle Boselli, Brandon N. VanderVeen, Lejla Hadzikadic-Gusic, Rebecca S. Greiner, Michelle L. Wallander, Declan Walsh and Kunal C. Kadakia

Tomography 2026, 12(4), 54; https://doi.org/10.3390/tomography12040054 - 8 Apr 2026

Abstract

Background/Objectives: Young women with breast cancer (aged ≤ 40 years) have distinct prognostic characteristics, yet little is known about how modifiable body composition factors influence outcomes in this age group. This study examined whether CT-derived body composition measures could identify thresholds that predict overall survival (OS). Methods: This was a single-center, 10-year, matched case–control study nested within a cohort, utilizing retrospectively collected data. Using an institutional database (2009–2018) and the initial cohort of 112 patients, we performed a subset analysis of patients with stage I–III breast cancer at diagnosis who had available pretreatment CT scans to estimate associations with body composition metrics and OS. The final analytic dataset included 89 individuals (49 survivors and 40 deceased). CT scans at the L3 level were analyzed using Slice-O-Matic software to quantify visceral (VAT), subcutaneous (SAT), intermuscular (IMAT), total adipose tissue (TAT), skeletal muscle density (SMD), skeletal muscle gauge (SMG), and skeletal muscle index (SMI). Cox proportional hazard models determined optimal cutpoints for OS. Multivariable models included adjustments for disease stage and hormone receptor status. Results: The median age was 35 (IQR, 32–38); 47% were White and 37% were Black. The majority (78%) were not Hispanic or Latina. Most (67%) were overweight/obese. Specific thresholds for IMAT index (>2.57), VAT (>31.38), and SMG (<2419.89) were associated with worse survival (all p < 0.05), while no cutpoints were identified for other variables. Conclusions: These findings show that muscle fat infiltration and reduced muscle quality have important prognostic value in young women with breast cancer. Exploratory cutpoints derived from routine staging CT scans may help inform risk stratification and generate hypotheses for targeted nutritional or exercise interventions, but require validation in larger, independent cohorts before clinical application. Full article

(This article belongs to the Section Cancer Imaging)

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25 pages, 4741 KB

Open AccessArticle

An Edge-Enabled Predictive Maintenance Approach Based on Anomaly-Driven Health Indicators for Industrial Production Systems

by Bouzidi Lamdjad and Adem Chaiter

Algorithms 2026, 19(4), 286; https://doi.org/10.3390/a19040286 - 8 Apr 2026

Abstract

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition))

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18 pages, 2928 KB

Open AccessArticle

Root-Zone Nitrogen Fertilization Increases Oilseed Rape Yield: Reprogramming Rhizosphere N-Cycling and Strengthening Soil–Plant Coupling

by Liang Cheng, Quanjie Shen and Yifan Wang

Plants 2026, 15(8), 1137; https://doi.org/10.3390/plants15081137 - 8 Apr 2026

Abstract

Root-zone nitrogen fertilization (RZF) can increase crop N uptake and yield, yet the underlying rhizosphere N-cycling functional mechanisms remain insufficiently resolved. In a field experiment with winter oilseed rape (Brassica napus L.), RZF was compared with conventional fertilization (CF) under the same N input rates, alongside a zero-N control (N0). Compared with CF, RZF significantly increased seed yield (by 0.44 t ha⁻¹) and aboveground N uptake (by 20.45 kg ha⁻¹), while simultaneously enriching rhizosphere mineral N pools (NH₄⁺–N and NO₃⁻–N by 54.50% and 56.02%, respectively). Shotgun metagenomics revealed that RZF reprogrammed rhizosphere N-cycling functional potential, characterized by enhanced nitrogen fixation, reduced nitrification and denitrification, and a tendency toward increased assimilatory nitrate reduction. These module-level shifts were supported by concordant changes in key functional genes, indicating greater genetic potential for N retention and assimilation (nifD, glnA, gltB, nasA, napB, nrfA) and reduced potential for nitrification- and denitrification-driven N losses (amoB/C, narI, nirK, norB). Taxonomic composition analysis showed enrichment of Bradyrhizobium and suppression of key nitrifier taxa (Nitrosospira and a Nitrososphaeraceae-affiliated taxon) under RZF. Rhizosphere pH exhibited the strongest Mantel correlation with multiple N-cycling modules, and rhizosphere available N (AN; sum of NH₄⁺–N and NO₃⁻–N) was positively associated with plant N traits and yield. Structural equation modeling supported a pathway in which a functional balance index (retention/assimilation vs. loss/oxidation) increased AN (0.22), and AN strongly promoted yield (0.90). Collectively, these results elucidate a rhizosphere-centered mechanism whereby localized N placement strengthens soil–plant N coupling and enhances crop productivity through reprogramming microbial N-cycling functional potentials, positioning rhizosphere N processes as a key mechanistic bridge for microbiome-informed optimization of root-zone fertilization. Full article

(This article belongs to the Topic Recent Advances in Soil Health Management)

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