Distributed collaborative scheduling of multi-region integrated energy systems (MR-IESs) has emerged as a key research paradigm for enhancing multi-energy coupling efficiency, facilitating large-scale renewable energy integration, and supporting the transition toward the Energy Internet under carbon neutrality objectives. However, the deep coupling of heterogeneous energy networks—including electricity, heating, gas, and energy storage—together with the increasing uncertainty of renewable energy generation, poses significant challenges to conventional centralized scheduling approaches in terms of computational scalability, communication efficiency, privacy preservation, and cross-regional coordination. This review presents a comprehensive synthesis of recent advances in distributed collaborative scheduling for MR-IESs and proposes a unified analytical framework encompassing system architecture, multi-energy coupling modeling, collaborative scheduling mechanisms, distributed optimization algorithms, uncertainty management, and engineering implementation. Classical distributed optimization methods, including Lagrangian dual decomposition, the alternating direction method of multipliers (ADMM), and analytical target cascading (ATC), are systematically reviewed alongside emerging artificial intelligence-driven scheduling paradigms based on multi-agent reinforcement learning, federated learning, and generative artificial intelligence. Furthermore, stochastic optimization, robust optimization, distributionally robust optimization, and scenario generation techniques are comparatively analyzed with respect to modeling capability, convergence characteristics, communication overhead, privacy preservation, and scalability under renewable energy uncertainty. Building upon this synthesis, the review identifies several critical research challenges, including dynamic multi-energy coupling, cross-regional coordination mechanisms, multi-timescale distributed optimization, and communication–computation co-design. Finally, future research directions are outlined, highlighting physics-informed intelligent optimization, coordinated source–grid–load–storage–carbon scheduling, autonomous multi-timescale collaborative control, and industrial-scale distributed scheduling platforms. This review provides a systematic research roadmap and methodological foundation for the development of next-generation distributed collaborative scheduling strategies for MR-IESs and the sustainable evolution of the Energy Internet.

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Space-based solar power (SBSP) is increasingly regarded as a transformative solution for achieving sustainable, large-scale clean energy supply under global carbon neutrality initiatives, owing to its ability to continuously harvest solar energy without the temporal and geographical constraints of terrestrial photovoltaic systems. Recent advances in heavy-lift launch vehicles, on-orbit manufacturing and autonomous assembly, together with significant progress in microwave- and laser-based wireless power transmission, have accelerated the evolution of SBSP from conceptual design toward engineering realization. Meanwhile, system architectures have progressively evolved from monolithic rigid platforms to modular, flexible, and distributed configurations, offering improved scalability while introducing greater complexity in system integration and operation. This review presents a systematic synthesis of recent advances in SBSP from a systems engineering perspective by establishing a unified analytical framework that integrates system architectures, wireless power transmission technologies, key enabling technologies, and engineering implementation strategies. Representative SBSP architectures are comparatively evaluated with respect to structural characteristics, mass-to-power ratio, multiphysics coupling, deployment complexity, and operational reliability, while microwave- and laser-based power transmission technologies are critically assessed in terms of transmission efficiency, atmospheric effects, beam control, safety, scalability, and engineering applicability. The review further identifies the principal barriers to large-scale SBSP deployment, including the stability of ultra-large space structures, multiphysics-coupled beam pointing and energy conversion, high-voltage power management and thermal control, launch and in-orbit assembly costs, and overall system economics. Finally, future research priorities are proposed, emphasizing integrated multiphysics design, intelligent autonomous assembly, high-efficiency wireless power transmission, and phased engineering deployment guided by technology maturity. By bridging fundamental technologies with engineering implementation, this review provides a systematic research roadmap and decision framework for advancing next-generation SBSP systems from laboratory-scale validation to practical large-scale deployment.

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Hydrogen is increasingly recognized as a cornerstone of future low-carbon energy systems owing to its pivotal role in renewable energy storage, sector coupling, and deep decarbonization. Among emerging hydrogen production pathways, seawater electrolysis has attracted considerable attention as a sustainable alternative to freshwater-dependent processes, particularly in regions facing growing freshwater constraints. However, the complex chemical composition of seawater introduces substantial challenges, including electrode corrosion, competing chlorine evolution, catalyst deactivation, interfacial fouling, and reduced energy efficiency, which collectively limit large-scale implementation. This review provides a comprehensive synthesis of recent advances in seawater hydrogen production by establishing a unified analytical framework that integrates direct seawater electrolysis, desalination-assisted electrolysis, and indirect solar-driven routes, including photocatalytic and photoelectrochemical hydrogen production. These technological pathways are comparatively evaluated with respect to reaction mechanisms, catalyst and electrode design, electrolyzer configurations, system integration, energy efficiency, technology maturity, and engineering deployment potential. The review further elucidates the complementarities and trade-offs among different hydrogen production strategies, highlighting their respective advantages, limitations, and application scenarios from both scientific and engineering perspectives. Key challenges are identified, including insufficient understanding of corrosion and interfacial reaction mechanisms, inadequate standardized protocols for performance, durability, and techno-economic assessment, and the lack of long-term validation under realistic marine operating conditions. Finally, future research priorities are proposed, emphasizing highly selective and corrosion-resistant electrocatalysts, integrated renewable-powered hydrogen production systems, artificial intelligence-enabled materials discovery, digital optimization, and scalable offshore engineering deployment. By bridging fundamental electrochemical science with systems engineering, this review provides a systematic research roadmap and decision framework for accelerating the development and commercialization of next-generation seawater hydrogen production technologies.

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Underground hydrogen storage (UHS) and in-situ hydrogen generation (ISG-H₂) are complementary technologies for large-scale hydrogen storage and low-carbon energy systems. Advances in geological characterization, reactive transport modeling, multiphase flow simulation, and subsurface engineering have improved the understanding of hydrogen behavior in salt caverns, depleted oil and gas reservoirs, and saline aquifers, supporting the transition from laboratory studies to field applications. In parallel, thermochemical, catalytic, and microbiologically mediated hydrogen generation has emerged as a promising approach for localized hydrogen production. This review presents an integrated framework covering geological media evaluation, hydrogen generation mechanisms, storage–production interactions, reactive transport, and engineering implementation. Representative geological formations are compared in terms of storage capacity, injectivity, containment integrity, geochemical reactivity, operational flexibility, and technology maturity, while the synergy between UHS and ISG-H₂ is critically assessed. Key challenges, including coupled thermo-hydro-chemo-biological processes, hydrogen loss, wellbore integrity, reactive transport, and long-term monitoring, are highlighted. Future research should emphasize integrated subsurface hydrogen systems, intelligent reservoir characterization, digital twins, advanced monitoring, and standardized techno-economic and life-cycle assessment to accelerate commercial deployment.

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Underground thermal energy storage (UTES) has emerged as a cornerstone technology for enabling large-scale renewable energy integration, improving energy system flexibility, and supporting the decarbonization of heating and cooling sectors. Its long-term performance is fundamentally governed by the interactions among geological media, reservoir structures, and coupled thermal, hydraulic, mechanical, chemical, and biological processes operating across multiple spatial and temporal scales. Recent advances in aquifer thermal energy storage (ATES), borehole thermal energy storage (BTES), and cavern- and mine-based thermal energy storage (CTES/MTES) have substantially expanded their engineering applicability through improvements in storage efficiency, temperature adaptability, system integration, and subsurface monitoring technologies. This review presents a comprehensive synthesis of recent developments by establishing a unified analytical framework that integrates geological characterization, storage mechanisms, multiphysics coupling, engineering implementation, and performance evaluation. Representative UTES technologies are comparatively assessed with respect to geological suitability, thermal recovery efficiency, operational flexibility, geotechnical stability, environmental impacts, and technology maturity, while the governing thermo–hydro–mechanical–chemical–biological (THMCB) coupling processes controlling heat transport, reactive evolution, and reservoir behavior are critically examined. The review further identifies the principal scientific and engineering challenges limiting large-scale deployment, including thermal short-circuiting, mineral precipitation and clogging, geomechanical instability, microbial activity, geological uncertainty, long-term monitoring, and the absence of standardized techno-economic and life-cycle assessment frameworks. Finally, future research priorities are proposed, emphasizing physics-informed digital modeling, artificial intelligence-enabled predictive analysis, digital twin-assisted operation, integrated multi-energy systems, uncertainty quantification, and geoscience-informed optimization for next-generation UTES deployment. By bridging fundamental geoscientific processes with engineering implementation, this review provides a systematic research roadmap and decision framework for advancing the scientific understanding, technological innovation, and large-scale application of underground thermal energy storage.

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Intelligent and digital oilfields have emerged as a transformative paradigm for improving hydrocarbon recovery, enhancing operational safety, and supporting the low-carbon transition of the petroleum industry under increasingly complex reservoir conditions and stringent environmental requirements. Rapid advances in the Internet of Things, artificial intelligence, big data analytics, cloud computing, edge intelligence, and digital twin technologies have fundamentally reshaped oilfield development by enabling integrated sensing, real-time data fusion, intelligent prediction, and autonomous decision-making throughout the exploration, production, and management lifecycle. This review presents a comprehensive synthesis of recent developments by establishing a unified analytical framework that integrates system architecture, key enabling technologies, representative application scenarios, and engineering implementation strategies for intelligent and digital oilfields. Representative applications—including intelligent exploration and drilling, reservoir characterization and production optimization, predictive equipment maintenance, safety management, and low-carbon operation—are comparatively evaluated with respect to technical maturity, operational effectiveness, scalability, and industrial applicability. Particular attention is given to the convergence of physics-based modeling and data-driven artificial intelligence, highlighting physics-informed intelligent systems as a promising paradigm for enhancing prediction accuracy, model interpretability, decision robustness, and engineering reliability. The review further identifies the principal barriers to large-scale deployment, including heterogeneous data quality, limited interoperability and data governance, insufficient model generalization and explainability, inadequate integration of mechanistic and artificial intelligence models, cybersecurity risks, and organizational constraints. Finally, future research priorities are proposed, emphasizing autonomous decision-making, foundation models for subsurface intelligence, digital twin-enabled closed-loop optimization, cloud–edge collaborative computing, and low-carbon intelligent operations. By bridging petroleum engineering with digital intelligence and systems engineering, this review provides a systematic research roadmap and decision framework for advancing next-generation intelligent and digital oilfields from technological innovation to large-scale industrial deployment.

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Intelligent drilling and geosteering are reshaping modern drilling operations by enabling more accurate subsurface characterization, adaptive trajectory control, and automated decision-making in complex geological settings such as deep and heterogeneous reservoirs. Recent advances in downhole sensing, artificial intelligence, digital twins, and automated control systems have driven a shift from experience-based operations to data-driven closed-loop drilling. This review summarizes key developments using a unified perception–decision–execution framework. Perception technologies (e.g., measurement-while-drilling and multi-source data fusion) are reviewed for real-time capability and accuracy. Decision methods, including physics-based models, data-driven approaches, and digital twin systems, are discussed with emphasis on uncertainty handling and prediction improvement. Execution technologies, such as rotary steerable systems and automated control, are analyzed for stability and adaptability. The integration of these components is shown to improve drilling efficiency, reduce geological uncertainty, and enhance well placement. Remaining challenges include data quality, real-time communication, model generalization, system integration, and cost constraints. Future directions focus on intelligent subsurface models, edge computing, physics-informed digital twins, and fully closed-loop autonomous drilling systems. This review provides a structured overview and practical reference for advancing intelligent drilling toward industrial applications.

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Accurate, efficient, and robust reservoir decision-making remains a major challenge as reservoir development becomes increasingly constrained by geological complexity, operational uncertainty, and computational demands. Recent advances in surrogate modeling, mechanism–data fusion, and reinforcement learning have accelerated the transition from conventional simulation-based workflows to intelligent reservoir decision-making. This review systematically synthesizes the theoretical foundations, methodological advances, and engineering applications of intelligent reservoir decision-making, with particular emphasis on mechanism–data fusion modeling and reinforcement learning–driven optimization. Surrogate and deep learning models substantially improve computational efficiency for high-dimensional reservoir prediction, while physics-informed learning enhances predictive accuracy, physical consistency, and cross-scenario generalization by integrating governing reservoir physics with data-driven models. Reinforcement learning further provides a unified framework for sequential optimization of production control, enhanced oil recovery, and integrated CCUS–EOR operations under dynamic uncertainty. The complementary strengths of physics-based and data-driven approaches indicate that their deep integration is emerging as the dominant paradigm for next-generation reservoir management. Remaining challenges include trustworthy model development, robust decision-making under uncertainty, scalable learning from sparse data, and large-scale field deployment. This review establishes an integrated analytical framework linking mechanism–data fusion with reinforcement learning, providing a systematic perspective for developing trustworthy, scalable, and intelligent reservoir decision-making systems.

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Shale oil enrichment mechanisms and sweet-spot prediction in transitional lacustrine basins remain poorly understood because of the complex interactions among depositional environment, lithofacies evolution, and reservoir heterogeneity. Using the Paleogene Hetaoyuan Formation in the Biyang and Nanyang sags of the Nanxiang Basin as case studies, this study investigates the differential enrichment mechanisms of shale oil in saline–freshwater transitional lacustrine systems and establishes a unified genetic framework for shale oil accumulation. A multi-scale integrated workflow combining core observations, X-ray diffraction (XRD), petrographic and fluorescence thin-section analysis, field-emission scanning electron microscopy (FE-SEM), laser confocal microscopy, and comprehensive logging, seismic, and geochemical interpretation was employed to characterize lithofacies, reservoir architecture, hydrocarbon occurrence, and sweet-spot attributes. The results reveal two distinct shale oil enrichment patterns controlled by contrasting basin evolution. The Biyang Sag, developed in a closed saline deep-lake setting, is dominated by matrix-controlled enrichment within dolomitic-calcareous and felsic laminated shales, characterized by relatively high organic matter abundance, oil-bearing capacity, brittleness, and vertically continuous sweet-spot intervals. In contrast, the Nanyang Sag, formed in an open freshwater lacustrine environment, exhibits an interlayer-controlled enrichment pattern associated with alternating felsic–argillaceous shales and sandstone interbeds, where reservoir quality and hydrocarbon accumulation are strongly governed by interlayer development, resulting in vertically discrete sweet spots. Basin evolution, jointly regulated by boundary fault activity and paleowater-depth variations, controlled depositional environments and lithofacies architecture, whereas organic matter supply and preservation conditions determined source rock quality and hydrocarbon enrichment. These coupled processes define a differential genetic framework linking basin evolution, depositional environment, lithofacies assemblage, reservoir quality, and shale oil enrichment. This study establishes a unified genetic framework and multi-parameter sweet-spot evaluation methodology for transitional lacustrine shale oil systems, providing a transferable conceptual model for differentiated exploration and resource evaluation in continental lacustrine basins.

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Tight sandstone reservoirs commonly experience rapid porosity loss during early diagenesis, followed by localized pore enhancement during later burial, forming a characteristic diagenetic evolution pathway of early densification–late-stage modification that fundamentally controls reservoir quality and tight oil accumulation. Using the Chang 8 Member in the Huanqing area of the Ordos Basin as a case study, this study investigates the evolutionary processes and controlling mechanisms of this diagenetic system through an integrated analysis of petrographic observations, scanning electron microscopy, cathodoluminescence, fluid inclusion analysis, three-dimensional lithofacies modeling, and diagenetic facies evolution. The results show that the reservoir underwent an initial densification stage dominated by mechanical compaction and early cementation, followed by late-stage pore enhancement driven by dissolution and microfracture development associated with organic matter thermal evolution, acidic fluid migration, and tectonic activity. These processes generated a spatially heterogeneous reservoir architecture characterized by strong directional and zonal variations in pore development. The evolution of the early densification–late-stage modification system exerts a fundamental control on pore structure evolution, reservoir–fluid coupling, and hydrocarbon enrichment, with high-quality tight oil reservoirs preferentially occurring where late-stage dissolution and microfracture systems are favorably superimposed. This study establishes a process-based diagenetic evolution framework that links early reservoir densification to late-stage pore modification and tight oil enrichment, providing a transferable genetic model for reservoir quality prediction and sweet-spot evaluation in tight sandstone systems.

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The genetic relationship between slope-break zones and gravity-sliding structures and their integrated control on hydrocarbon trap formation remains poorly constrained in foreland basin successions. Using the fourth and fifth members of the Upper Triassic Xujiahe Formation in the central–western Sichuan Basin as a case study, this study investigates the evolutionary mechanisms of slope-break zones and gravity-sliding structures and their coupling effects on sedimentation, reservoir architecture, and hydrocarbon accumulation. An integrated workflow combining seismic interpretation, drilling, core analyses, and granular-flow numerical simulations was employed to characterize structural evolution and trap development. The results demonstrate that fault-controlled slope-break zones promoted the development of gravity-sliding structural systems characterized by frontal compression and rear extension, which exerted first-order controls on stratigraphic thickness, sandbody architecture, reservoir quality, and trap configuration. Two distinct hydrocarbon trap types were identified. The first comprises synsedimentary anticlinal sandbodies developed in gentle-slope settings through extensional deformation, forming laterally sourced and laterally trapped reservoirs adjacent to organic-rich mudstones. The second consists of gravity-sliding-induced synsedimentary anticlines developed along steep slope-break zones, where lateral and vertical sealing by mudstones facilitated hydrocarbon migration and accumulation within source-bearing intervals. These findings demonstrate that the coupled evolution of slope-break zones and gravity-sliding structures constitutes a fundamental control on hydrocarbon trap development in the Xujiahe Formation. The proposed coupling framework and associated trap models provide a new genetic basis for predicting structural–stratigraphic traps in foreland basins and analogous sedimentary systems.

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Deep learning (DL) and reinforcement learning (RL) are rapidly transforming intelligent irrigation by enabling accurate environmental perception, predictive modeling, and adaptive irrigation decision-making. Despite substantial progress, a comprehensive understanding of their methodological characteristics, application scope, and remaining challenges is still lacking, limiting their broader deployment in precision water management. This review systematically synthesizes recent advances in DL- and RL-based intelligent irrigation, covering crop water requirement prediction, soil moisture and evapotranspiration estimation, irrigation scheduling, and autonomous control. DL has significantly improved feature extraction and predictive performance by integrating multisource data from meteorological observations, soil monitoring, remote sensing, and Internet of Things (IoT) sensing networks, whereas RL has demonstrated unique advantages in sequential decision-making, long-term policy optimization, and adaptive control under dynamic environmental conditions. Their integration with crop growth models, remote sensing, and intelligent sensing technologies has further expanded the capability of intelligent irrigation systems. However, several critical challenges remain, including limited data availability and quality, inadequate model generalization and cross-regional transferability, difficulties in reward design under environmental uncertainty, insufficient model interpretability, and barriers to large-scale field deployment. This review provides a comparative assessment of DL and RL from the perspectives of methodological frameworks, application scenarios, and performance characteristics, and identifies key research priorities, including interpretable and trustworthy artificial intelligence, mechanism–data fusion, multi-agent collaborative decision-making, and water–energy–food nexus optimization. By integrating these perspectives into a unified analytical framework, this review establishes a roadmap for the next generation of intelligent irrigation systems and highlights future directions toward scalable, robust, and sustainable precision water management.

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Smart irrigation systems integrating the Internet of Things (IoT), advanced sensing technologies, and intelligent decision-making have become a key pathway toward precision water management and sustainable agriculture. This review synthesizes recent advances in multi-source sensing, sensor networks, and low-power communication, edge–cloud collaborative computing, intelligent decision-making models, and automated irrigation control. The strengths and limitations of representative sensing technologies, communication architectures, and decision-support approaches are critically compared in terms of sensing accuracy, reliability, energy efficiency, and application suitability. Current evidence shows that, despite significant progress in precise irrigation and real-time regulation, major challenges persist in heterogeneous data fusion, model generalization and interpretability, system robustness, operational costs, and large-scale deployment. Regional differences in climate, soil properties, and crop requirements further complicate system optimization and transferability. Future development should focus on multi-source information integration, hybrid mechanistic–data-driven modeling, edge intelligence, digital twins, and system-level optimization for water conservation and sustainable agricultural production. This review provides an integrated framework for understanding the evolution of smart irrigation technologies and highlights critical pathways toward scalable, intelligent, and resource-efficient irrigation systems.

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Climate change–driven drought, heat, salinity, and combined stresses pose major challenges to crop productivity and food security. Multi-omics technologies, high-throughput phenotyping, and artificial intelligence (AI) have emerged as powerful tools for elucidating stress-response mechanisms and accelerating crop resilience breeding. This review synthesizes recent advances in genomics, transcriptomics, proteomics, metabolomics, phenomics, and AI-driven modeling, and critically evaluates their roles in decoding genotype–phenotype–environment interactions and predicting complex adaptive traits. Evidence suggests that the convergence of multi-omics, phenomics, and AI substantially enhances the precision, scalability, and predictive power of resilience-oriented breeding. Nevertheless, challenges related to data heterogeneity, standardization, model interpretability, causal inference, and field-level implementation continue to constrain practical deployment. Future research should prioritize causal and explainable AI, multi-scale data integration, digital phenotyping, and closed-loop breeding systems linking gene discovery with breeding decisions. This review highlights the emerging convergence of multi-omics and AI as a transformative paradigm for climate-resilient crop breeding and provides a strategic framework for developing next-generation intelligent breeding systems.

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Soil microbiomes play a central role in regulating agricultural ecosystem functioning by linking soil biogeochemical processes with crop productivity; however, traditional agroecosystems increasingly face constraints in resource efficiency, environmental sustainability, and system stability under intensifying global change and agricultural practices. Recent advances in multi-omics technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics, have enabled a shift from reductionist analyses of individual microorganisms toward integrated, system-level investigations of microbial communities and their interactions with plants and the environment. Current evidence indicates that soil microbiomes regulate ecosystem productivity, stability, and resilience through the mediation of carbon, nitrogen, and phosphorus cycling, the modulation of soil structure formation, and the coordination of plant nutrient acquisition, stress adaptation, and immune responses. In addition, microbial interaction networks and plant–microbe regulatory feedbacks jointly determine functional stability and environmental responsiveness at the ecosystem scale. Despite these advances, major challenges remain in moving from correlation-based observations to causal mechanistic understanding, integrating laboratory and field-scale evidence, and improving predictive modeling of dynamic microbial networks under environmental variability. This review synthesizes recent progress in soil microbiome research enabled by multi-omics approaches, critically evaluates their functional mechanisms and ecosystem-level roles, and compares emerging regulatory strategies across scales and application contexts. By integrating current knowledge, this study establishes a conceptual framework for soil microbiome–driven regulation of agricultural ecosystems and highlights key pathways toward mechanistic understanding and predictive management of microbiome functions for sustainable agriculture.

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The crop rhizosphere microbiome is a key determinant of plant productivity, nutrient acquisition, stress adaptation, and soil ecosystem functioning, making it a central focus of sustainable agriculture research. Recent advances in metagenomics, metatranscriptomics, metabolomics, multi-omics integration, and systems biology have substantially improved understanding of rhizosphere microbial assembly, community succession, and functional regulation, shifting the field from descriptive characterization toward mechanism-based interpretation and targeted microbiome manipulation. Current evidence demonstrates that rhizosphere microbiomes regulate nutrient cycling, plant immunity, and tolerance to biotic and abiotic stresses through complex plant–microbe and microbe–microbe interactions, supporting the development of functional microbial inoculants, synthetic microbial communities, microbiome-assisted breeding, and ecological crop management. Despite these advances, major challenges remain in establishing causal relationships between microbial composition and ecosystem functions, improving the stability and environmental adaptability of microbiome-based interventions, integrating multi-omics datasets across biological scales, and translating laboratory discoveries into reproducible field applications. This review synthesizes recent progress in the assembly mechanisms, functional regulation, and agricultural applications of the crop rhizosphere microbiome, critically compares the strengths and limitations of current research paradigms and microbiome engineering strategies, and identifies key scientific challenges and future research priorities. By integrating microbial ecology, multi-omics technologies, and agricultural applications within a unified conceptual framework, this review provides a mechanistic perspective for advancing predictive microbiome management and the rational design of sustainable crop production systems.

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Heavy metal contamination in farmland soils poses a major challenge to agricultural sustainability because its environmental behavior, bioavailability, and ecological risks are governed by complex interactions among soil properties, crop uptake, and rhizosphere microbial processes. Consequently, remediation strategies have shifted from contaminant removal toward risk control and safe land utilization. Recent advances have substantially improved understanding of heavy metal migration, transformation, and risk regulation, while a wide range of remediation technologies, including chemical immobilization, soil washing, phytoremediation, microbial remediation, and engineering approaches, have been developed. However, individual technologies often suffer from limited long-term stability, economic feasibility, and field applicability. Increasing evidence indicates that integrated remediation combining multiple technologies with agronomic management practices, such as soil amendment, water and nutrient regulation, and the cultivation of low-accumulation crop cultivars, provides a more effective pathway for reducing contaminant bioavailability and ensuring sustainable agricultural production. Nevertheless, the absence of standardized performance evaluation systems, insufficient long-term field validation, and limited understanding of multi-contaminant interactions and dynamic environmental processes continue to impede large-scale implementation. This review synthesizes current knowledge of heavy metal sources, environmental behavior, and risk regulation in farmland soils, critically evaluates the mechanisms, applicability, and limitations of major remediation technologies, and elucidates the synergistic roles of integrated remediation and agronomic management. It further highlights emerging directions in functional materials, biotechnology, intelligent monitoring, and risk-based management, providing a unified conceptual framework for advancing precision remediation and the sustainable utilization of contaminated farmland.

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Plant growth-promoting rhizobacteria (PGPR) are central regulators of plant–soil–microbiome interactions, influencing crop productivity, nutrient acquisition, stress adaptation, and rhizosphere health. Recent advances have shifted PGPR research from empirical screening of individual strains toward a mechanistic understanding of microbial functions, ecological interactions, and system-level regulation. Current evidence indicates that PGPR enhance plant performance through complementary direct and indirect mechanisms, including biological nitrogen fixation, nutrient mobilization, phytohormone regulation, ACC deaminase activity, pathogen suppression, induced systemic resistance, and niche competition. However, the effectiveness of these mechanisms is strongly influenced by environmental conditions, host genotype, and microbial community interactions, resulting in inconsistent field performance and limited reproducibility. Emerging approaches integrating multi-omics, synthetic biology, advanced formulation technologies, and artificial intelligence are enabling a transition from strain-based applications to predictive microbiome engineering and precision microbial management. This review synthesizes current understanding of PGPR functional mechanisms, ecological interactions, and agricultural applications, critically evaluates the factors governing field efficacy and the limitations of existing application strategies, and highlights future research priorities for developing robust and scalable microbiome-based solutions. By integrating mechanistic insights with emerging engineering approaches, this review provides a conceptual framework for advancing precision PGPR design and accelerating the sustainable deployment of microbiome technologies in crop production.

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Intercropping and relay intercropping regulate soil health through coordinated plant–soil–microbe interactions, offering an effective strategy to enhance resource use efficiency, ecosystem functioning, and the sustainability of agricultural production. Recent advances integrating high-throughput sequencing, multi-omics technologies, and process-based modeling have substantially improved understanding of how these cropping systems modify soil physicochemical properties, nutrient cycling, microbial community assembly, and ecosystem functions. Current evidence indicates that intercropping-driven changes in rhizosphere processes and microbial interactions enhance soil nutrient availability, improve soil structure, and strengthen system resilience; however, the magnitude and direction of these responses vary considerably with crop combinations, environmental conditions, and management practices. Consequently, the mechanistic basis, scale dependence, and generalizability of plant–soil–microbe interactions remain incompletely understood, while the lack of standardized evaluation frameworks and long-term, multi-scale field validation continues to limit cross-study comparison and practical application. This review synthesizes current knowledge of the mechanisms by which intercropping and relay intercropping regulate soil health and microbial ecological processes, critically evaluates the consistency and limitations of existing evidence across different cropping systems, and identifies key research priorities in multi-scale process coupling, microbiome-mediated regulation, and predictive modeling. By integrating ecological mechanisms with emerging analytical approaches, this review establishes a unified conceptual framework for advancing mechanism-based design and precision management of sustainable intercropping systems.

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Optimizing the coordination between sowing date and nitrogen topdressing is essential for improving yield stability and nitrogen use efficiency in winter wheat, yet the mechanisms underlying their interactive regulation of canopy development and biomass production remain insufficiently understood. A two-factor field experiment was conducted to quantify the independent and interactive effects of sowing date and nitrogen topdressing timing on crop phenology, canopy development, dry matter accumulation, and yield formation. Delayed sowing reshaped crop growth dynamics by shortening the vegetative growth period, thereby altering canopy development, population establishment, and biomass accumulation patterns. Nitrogen topdressing timing regulated canopy functional longevity and assimilate production, with its effectiveness strongly dependent on sowing date. Their interaction significantly influenced canopy development, dry matter accumulation, and final grain yield, indicating that the response to nitrogen management is governed by crop developmental status rather than nitrogen supply alone. These results demonstrate that synchronizing sowing date with nitrogen topdressing optimizes the temporal coordination between canopy development and biomass allocation, thereby enhancing yield formation and nitrogen use efficiency. This study provides a mechanistic framework for integrating sowing date and nitrogen management to support precision agronomic practices and sustainable winter wheat production.

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Potato productivity and water use efficiency (WUE) in semi-arid regions are strongly influenced by the timing of drought stress, yet the stage-specific mechanisms governing yield formation remain insufficiently understood. A field experiment was conducted to evaluate the effects of drought imposed at the seedling, tuber initiation, tuber bulking, and starch accumulation stages on crop growth, biomass partitioning, yield formation, and WUE. Potato responses exhibited pronounced stage dependence. Early-season drought triggered compensatory growth after rewatering, partially restoring biomass production and improving WUE without significant yield loss. In contrast, drought during tuber initiation and tuber bulking represented the critical water-sensitive period, markedly suppressing canopy development, reducing dry matter allocation to tubers, and consequently causing the greatest yield penalties. Drought during the starch accumulation stage had comparatively minor effects on yield, indicating greater tolerance to late-season water deficits. These results demonstrate that the impacts of drought on potato productivity are governed by growth stage-dependent shifts in biomass allocation and sink development rather than water deficit alone. Optimizing irrigation according to stage-specific water demand—allowing moderate water deficits during early growth, maintaining adequate water supply during tuber initiation and bulking, and applying deficit irrigation during late growth—can improve the coordination between yield formation and water use efficiency. This study establishes a physiological basis for stage-specific deficit irrigation and provides a mechanistic framework for precision water management in potato production under water-limited conditions.

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Nitrogen management and cover cropping are key strategies for improving maize productivity and nitrogen use efficiency, yet their interactive effects on photosynthetic regulation and yield formation remain insufficiently understood. A two-factor field experiment was conducted to investigate how different cover crops and nitrogen supply levels jointly regulate maize photosynthetic performance, canopy development, and yield formation. The results showed that cover crops significantly enhanced photosynthetic capacity and canopy light-use efficiency under low nitrogen conditions, thereby promoting biomass accumulation and kernel set. Under moderate to high nitrogen supply, cover crops improved photosystem II efficiency, delayed leaf senescence, and sustained grain filling, resulting in greater yield potential. Significant interactions between cover cropping and nitrogen supply indicated that yield improvement was primarily driven by the coordinated enhancement of leaf photosynthetic efficiency, canopy architecture, and nitrogen use efficiency rather than by increased nitrogen input alone. These findings demonstrate that optimizing cover crop–nitrogen management synchronizes photosynthetic carbon assimilation with nitrogen utilization, providing a physiological basis for improving maize productivity and resource-use efficiency under sustainable cropping systems.

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Water-saving irrigation systems that simultaneously improve crop productivity and utilize agricultural residues remain a major challenge for sustainable wheat production. This study evaluated the performance of a novel straw-composite pipe subsurface irrigation system through a field experiment comparing straw-composite pipe subsurface irrigation, conventional subsurface drip irrigation, surface drip irrigation, and a rainfed control. Crop growth, yield formation, water use efficiency, and economic performance were systematically assessed to elucidate the agronomic advantages of the proposed irrigation strategy. Straw-composite pipe subsurface irrigation significantly enhanced plant growth during the critical reproductive period, increased spike number and grain weight, and consequently improved grain yield compared with conventional irrigation systems. The localized subsurface water supply effectively reduced non-productive water loss, resulting in higher water use efficiency while improving economic returns. These benefits were associated with the combined effects of improved soil physical conditions provided by biodegradable straw-composite materials and more efficient root-zone water distribution. The results demonstrate that straw-composite pipe subsurface irrigation integrates water-saving irrigation with straw resource utilization, providing an effective strategy for enhancing wheat productivity, water-use efficiency, and the sustainability of irrigated cropping systems.

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Improving phosphorus use efficiency while sustaining crop productivity is a major challenge in wheat–maize rotation systems, particularly in phosphorus-limited regions. Based on a long-term field experiment in northern Shaanxi, this study evaluated the effects of single-season phosphorus application on yield formation, dry matter partitioning, phosphorus uptake and use efficiency, and soil phosphorus dynamics. Single-season phosphorus application significantly increased biomass accumulation and grain yield by promoting dry matter translocation to grains and enhancing phosphorus acquisition and utilization. Appropriate phosphorus application rates also maintained adequate soil available phosphorus while reducing fertilizer inputs and potential environmental risks. The integrated agronomic and soil responses indicate that the yield advantage of single-season phosphorus application is primarily associated with improved phosphorus cycling and more efficient coordination between crop phosphorus demand and soil nutrient supply. These findings demonstrate that single-season phosphorus application is an effective strategy for simultaneously enhancing crop productivity, phosphorus use efficiency, and fertilizer sustainability, providing a physiological and agronomic basis for optimizing phosphorus management in wheat–maize rotation systems.

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Optimizing the interaction between irrigation and straw return is essential for improving maize productivity and water use efficiency, yet their synergistic effects under contrasting hydrological conditions remain insufficiently understood. Based on a long-term split-plot field experiment in Northeast China, this study evaluated the interactive effects of irrigation regimes and straw return rates on spring maize growth, yield formation, water use efficiency, and their underlying regulatory mechanisms. Appropriate irrigation significantly promoted canopy development and biomass accumulation, whereas straw return enhanced soil water retention and improved the soil environment, thereby strengthening crop growth and productivity. Their interaction markedly increased yield and water use efficiency compared with either practice alone, primarily through coordinated improvements in soil water availability, biomass production, and yield component development. Moreover, the optimal irrigation–straw return combination varied with hydrological conditions, indicating that adaptive management is required to maximize resource-use efficiency and crop productivity. These findings demonstrate that integrating irrigation with straw return provides an effective strategy for synchronizing soil water conservation with crop growth, offering a mechanistic basis for developing climate-resilient and water-efficient maize production systems.

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Agricultural water management is undergoing a fundamental transition from engineering-oriented water conservation to intelligent, low-carbon, and system-level optimization in response to climate change, growing water scarcity, and decarbonization goals. This review synthesizes recent advances in intelligent agricultural water management, focusing on multi-source sensing and remote sensing, crop–soil–water process modeling, precision irrigation and automated control, and multiscale digital twins spanning field, irrigation district, and watershed systems. Particular attention is given to the mechanisms by which these technologies improve water use efficiency, optimize irrigation energy consumption, strengthen climate resilience, and reduce agricultural carbon emissions. From an integrated water–energy–carbon perspective, the review comparatively evaluates intelligent irrigation, digital twin technologies, and renewable energy-powered irrigation systems with respect to resource-use efficiency, operational resilience, and decarbonization potential, while highlighting the trade-offs and rebound effects associated with water-saving and carbon mitigation strategies. Critical barriers to large-scale implementation are identified, including fragmented data infrastructures, limited cross-scale integration, insufficient real-time decision support, the interpretability and robustness of artificial intelligence models, inadequate incorporation of low-carbon objectives into water allocation and operation, and socioeconomic constraints affecting technology adoption. Future research priorities are proposed in four key areas: water–energy–carbon–food nexus optimization, physics-informed artificial intelligence, climate risk-informed operational decision-making, and digital twin-enabled adaptive governance. By integrating technological innovation, system reconfiguration, and governance coordination into a unified analytical framework, this review provides a conceptual foundation for developing resilient, low-carbon, and intelligent agricultural water management systems.

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Agricultural irrigation is transitioning from technology-oriented management to digitally enabled, governance-driven systems capable of addressing increasing water scarcity, climate uncertainty, and sustainability challenges. This review systematically synthesizes recent advances in intelligent irrigation technologies, including multi-source sensing, digital twins, and data-driven decision support, while evaluating their capabilities and application boundaries in improving water allocation, operational efficiency, and adaptive management. To integrate fragmented research, a four-layer analytical framework comprising data acquisition, process understanding, decision optimization, and governance coordination is proposed, providing a system-level perspective that links digital technologies with irrigation governance. The review further identifies critical scientific challenges, including limited model generalization, the trade-off between algorithm robustness and engineering applicability, insufficient cross-scale water allocation and decision coordination, and weak integration of institutional arrangements with digital technologies. These challenges reveal a fundamental paradigm shift from technology-centered optimization toward reliable, adaptive, and governance-oriented irrigation systems. Future research priorities are highlighted in trustworthy data infrastructures, physics-informed artificial intelligence, cross-scale collaborative optimization, and digitally enabled governance frameworks. By conceptualizing agricultural irrigation as an integrated socio–technical–ecological system, this review establishes a unified analytical framework that connects technological innovation, governance coordination, and system resilience, providing a theoretical foundation for the next generation of intelligent and sustainable irrigation systems.

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