Waterlogging assessment in wheat remains constrained by the inability of duration-based or state-based indicators to represent the cumulative environmental exposure experienced by roots and its translation into crop responses. This study asked whether dynamic root-zone water conditions can be converted into a biologically informative cumulative exposure measure and whether such a measure can unify physiological and yield responses across waterlogging scenarios. Controlled experiments spanning wheat cultivars, growth stages, and exposure durations were used to derive a soil water-based waterlogging index (WI) from time-resolved root-zone soil moisture and to evaluate its relationships with leaf Soil Plant Analysis Development (SPAD) and yield. Yield declined with increasing waterlogging duration but varied substantially across growth stages, demonstrating the limited resolution of event duration alone. WI integrated the magnitude and persistence of excess soil water, showed a consistent negative association with SPAD, and captured a nonlinear yield response characterized by a transition from relatively limited effects at lower cumulative exposure to increasingly pronounced yield loss at higher exposure levels. Cultivar-specific transition ranges further indicated that yield responses can be expressed as distinct exposure boundaries on a common WI scale. These findings establish an exposure–response framework that recasts wheat waterlogging from an event-duration problem into a cumulative process linking dynamic root-zone conditions, plant functional status, and yield-risk transitions, providing a quantitative basis for resolving cultivar-specific waterlogging response boundaries beyond conventional duration-based assessment.

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The rapid expansion of artificial intelligence (AI) in crop growth analysis has produced fragmented advances across multimodal sensing, phenotypic perception, spatiotemporal prediction, and decision support. The central challenge is no longer the availability of AI applications, but understanding how heterogeneous observations can be transformed into reliable crop-state information and agronomically meaningful decisions, and under what conditions such intelligence remains transferable across crops, environments, and management regimes. This review addresses this problem through an integrated framework that traces the evolution of crop growth AI from data and perception to prediction, decision, and agricultural value, linking sensing modalities, model architectures, biological processes, and deployment contexts. Comparative synthesis shows that increasing model complexity does not necessarily improve agricultural value, because predictive performance is constrained by data quality, environmental representativeness, crop–environment–management interactions, and distribution shifts between development and deployment. Mechanism- and knowledge-guided learning, multimodal integration, and edge–cloud computing can improve generalization, interpretability, or deployability in specific settings, but their benefits remain context-dependent and cannot be inferred from within-dataset accuracy alone. These findings reveal a fundamental boundary between statistical prediction and reliable agricultural intelligence, particularly under limited labels, heterogeneous environments, uncertain observations, and resource-constrained deployment. Future research should therefore prioritize interoperable multimodal data infrastructures, mechanism-guided and uncertainty-aware learning, cross-environment validation, and scalable human–AI decision systems rather than isolated algorithmic advances. By integrating technological evolution with biological constraints and decision requirements, this review reframes crop growth AI as a transition from task-specific pattern recognition to context-aware decision intelligence and defines the conditions that govern its reliability, transferability, and agricultural value.

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The rapid transition toward Agriculture 4.0 has exposed a fundamental question for autonomous agricultural machinery: how can heterogeneous environmental information be transformed into reliable, adaptive, and scalable machine autonomy under complex and dynamic field conditions? This review addresses this question by systematically synthesizing the technological foundations, application domains, performance advantages, and adoption constraints of autonomous agricultural machinery and by developing an integrated framework organized around observation, heterogeneity, and infrastructure. Rather than classifying technologies solely by machine type, the review examines the coupling among multimodal perception, AI-enabled decision-making, autonomous navigation and control, precision execution, and multi-machine coordination, with particular attention to the challenges of environmental uncertainty, real-time decision-making, interoperability, and system-level scalability. The analysis further identifies a progressive transition from isolated task automation toward data-driven, adaptive, and networked agricultural autonomy, while emphasizing that improvements in individual components do not necessarily translate into system-level performance without compatible computational, communication, mechanical, and institutional infrastructure. On this basis, future research priorities are synthesized around robust multimodal perception, adaptive intelligence, interoperable multi-machine coordination, lifecycle sustainability, and accessible deployment models. The proposed framework reframes autonomous agricultural machinery as an interconnected physical–digital infrastructure in which observation characterizes agricultural states, heterogeneity governs adaptive decision-making, and infrastructure enables reliable execution and coordination, providing a conceptual basis for understanding and advancing scalable agricultural autonomy.

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Intelligent irrigation is increasingly positioned as a means of reconciling agricultural productivity with growing constraints on water availability, yet its effectiveness depends on how heterogeneous field observations are transformed into reliable and context-specific irrigation decisions. This review addresses the central question of how sensing, communication, analytics, decision-making, and control can be integrated into a coherent intelligent irrigation framework. We synthesize recent advances in multi-source sensing, Internet of Things (IoT) networks, data analytics and artificial intelligence (AI), automated control, and human–machine interaction, and examine their roles across the irrigation decision–control chain. Particular attention is given to the representation of spatial and temporal heterogeneity, data quality and uncertainty, model generalization, and the compatibility of intelligent decisions with hydraulic and agronomic constraints. Evidence from diverse application contexts is further assessed in terms of water productivity, crop performance, resource use, economic feasibility, and environmental outcomes, while technological, socioeconomic, and institutional barriers to large-scale deployment are critically examined. On this basis, we propose a conceptual progression from observation and heterogeneity representation to adaptive decision-making and infrastructure-enabled control, highlighting multi-source data fusion, hybrid process–AI modeling, edge–cloud collaboration, digital twins, and context-specific adaptive control as key directions for advancing system intelligence. The review reframes intelligent irrigation from a collection of digital technologies into an integrated observation–decision–control framework, providing a basis for evaluating not only what intelligent irrigation can achieve, but also where, when, and under what conditions it can deliver reliable and scalable water-management outcomes.

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Precision agriculture (PA) is evolving from a set of site-specific technologies into an integrated management paradigm for addressing agricultural heterogeneity under resource and climate constraints. This review addresses a central question: how can heterogeneous agricultural observations be transformed into reliable, scalable, and actionable management decisions? Through a structured synthesis of research on sensor networks, unmanned aerial vehicle (UAV) and satellite remote sensing, artificial intelligence (AI), and big data analytics, we examine their roles across soil management, crop monitoring, irrigation, and production management, and assess the technological and institutional conditions governing implementation. The synthesis indicates that the principal value of PA lies not in individual technologies but in coupling multiscale observation, heterogeneity-aware analysis, decision support, and field-level actuation to improve the alignment of resource inputs with spatially and temporally variable crop and soil requirements. Its broader application remains constrained by investment costs, data interoperability and governance, model transferability, digital infrastructure, and uneven technical capacity. Accordingly, we propose an observation–heterogeneity–infrastructure framework that links sensing and remote observation with AI-enabled interpretation and interoperable implementation systems, providing a conceptual basis for understanding PA as an adaptive agricultural management architecture rather than a technology portfolio. This framework integrates technological, operational, and institutional dimensions and offers a basis for evaluating the scalability, robustness, and sustainability of precision agriculture across heterogeneous production contexts.

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Precision seeding and harvesting are increasingly evolving from equipment-based automation toward data-driven, closed-loop field operations, yet the integration of sensing, intelligent decision-making, and adaptive control across the two critical stages remains fragmented. This review establishes an integrated framework of multi-source perception–intelligent decision-making–adaptive control–real-time feedback to systematically synthesize recent advances in precision seeding and harvesting. For precision seeding, we examine multi-source sensing, variable-rate seeding, and machine vision- and deep learning-based monitoring of seeding quality, with emphasis on how heterogeneous field information is transformed into real-time seeding decisions and control actions. For precision harvesting, we evaluate crop maturity detection, yield mapping, and adaptive regulation of harvesting parameters, highlighting the role of multi-sensor fusion and edge computing in enabling responsive harvesting operations. Across both processes, we identify data heterogeneity, interoperability among machinery and sensing systems, model generalizability, and the balance between technological complexity and economic benefits as major barriers to large-scale deployment. Based on these findings, we propose a transition pathway from isolated precision operations toward agronomy–machinery integration, digital-twin-enabled process optimization, and autonomous field systems. By unifying precision seeding and harvesting within a common perception–decision–control framework, this review clarifies the technological logic and key bottlenecks underlying intelligent field operations and provides a systems-level basis for advancing precision agriculture from component-level automation toward autonomous, adaptive production.

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Agricultural intelligence and precision agriculture are rapidly evolving toward data-driven, autonomous, and climate-adaptive production systems under increasing pressures from climate change, resource constraints, and demand for sustainable food supply. This paper discusses four key research frontiers: multimodal sensor fusion and artificial intelligence for high-throughput crop phenotyping, swarm robotics enabling coordinated field operations and autonomous agricultural decision-making, blockchain- and IoT-enabled end-to-end traceability systems for ensuring transparency and reliability in agri-food supply chains, and climate-smart decision support systems for adaptive precision management under increasing climate uncertainty. These developments collectively address critical challenges in integrating heterogeneous data streams, improving system autonomy, and enhancing robustness under environmental variability. The synthesis further reveals a paradigm shift from isolated intelligent tools toward fully integrated, system-level agricultural intelligence with proactive and adaptive capabilities. This study establishes a structured analytical framework for next-generation precision agriculture and identifies key technological convergence pathways toward resilient, efficient, and sustainable agricultural systems.

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Agricultural carbon emissions, dominated by non-CO₂ gases such as methane (CH₄) and nitrous oxide (N₂O), pose a key constraint to achieving climate mitigation targets amid intensifying pressures from climate change and food system transformation. This paper discusses recent advances in agricultural carbon mitigation research across four interlinked frontiers: (i) AI- and big data-driven precision quantification and optimization of carbon emissions for improving accounting accuracy and decision support; (ii) gene editing and synthetic biology approaches enabling low-carbon redesign of crop and livestock production systems at the molecular level; (iii) life cycle assessment-based integration of food systems and circular agriculture for system-level carbon footprint reduction and synergistic mitigation; and (iv) the stability and climate resilience of agricultural carbon sinks under increasing frequency of extreme climatic events. These developments indicate a shift from emission-reduction-focused strategies toward integrated, multi-scale, and system-oriented mitigation frameworks that couple technological innovation with ecosystem resilience. This synthesis provides a structured knowledge framework for advancing agriculture-oriented carbon neutrality pathways and identifying key leverage points for coordinated mitigation across molecular, system, and regional scales.

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Crop rotation optimization requires resolving how rotation patterns can be designed to jointly regulate soil microbial diversity, environmental responses, and agronomic performance under heterogeneous and dynamic conditions. This review develops an AI-enabled framework for crop rotation optimization by integrating rotation-pattern optimization, soil microbial community intelligence, and environmental response prediction and decision support. We first synthesize the ecological mechanisms through which crop rotation reshapes soil microbial communities and identify the key biological and environmental variables that constrain rotation performance. We then critically examine the application of machine learning, deep learning, and optimization algorithms to rotation-pattern design, microbial community analysis, and prediction of crop–soil–environment interactions, with emphasis on how heterogeneous data can be transformed into actionable rotation decisions. Representative applications further demonstrate the potential of data-driven approaches to identify high-dimensional rotation–microbiome–environment relationships and support adaptive management. Key challenges remain in integrating multimodal and longitudinal datasets, improving model interpretability and cross-region generalizability, and translating algorithmic predictions into robust field-scale decisions. Overall, this review establishes an AI-driven crop rotation–microbiome–environment optimization framework that shifts crop rotation research from experience-based pattern selection toward data-informed, adaptive, and mechanism-aware decision-making, providing a conceptual foundation for precision management of soil biodiversity and sustainable agroecosystems.

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The spatiotemporal dynamics of water resources are increasingly governed by the coupled effects of climate change and human activities, creating substantial challenges for accurate monitoring, prediction, and equitable management. This paper discusses four major research frontiers in big data-driven water resource science: multi-scale predictive modeling using deep learning and multi-source data fusion, multimodal intelligent sensing for resilient urban water systems, data-driven assessment of water allocation equity through geospatial analytics, and long-term monitoring and causal attribution of water storage dynamics using remote sensing and causal inference. By integrating advances in artificial intelligence, geospatial technologies, and data analytics, the study proposes a unified research framework linking intelligent sensing, predictive simulation, system assessment, and adaptive governance across multiple spatial and temporal scales. This synthesis highlights the transition from data-rich observation to mechanism-informed, equity-oriented water resource management, providing an integrated theoretical framework to advance big data-driven research on the spatiotemporal dynamics of water resources.

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Optimizing crop rotation through data-driven approaches has emerged as a promising strategy for improving soil structure, enhancing soil fertility, and promoting sustainable agricultural production. This review systematically synthesizes recent advances in the application of big data to crop rotation optimization, with emphasis on multi-source data integration, soil property modeling, intelligent rotation planning, yield prediction, and decision-support systems. Recent studies demonstrate that the integration of remote sensing, sensor networks, machine learning, and spatial analysis enables dynamic assessment of soil conditions, improves rotation design under heterogeneous environments, and enhances resource-use efficiency and agroecosystem sustainability. The review further examines key challenges, including data heterogeneity, limited model transferability, interoperability of heterogeneous datasets, and data governance, and discusses emerging opportunities arising from artificial intelligence, digital twins, and cross-regional agricultural data platforms. By integrating advances in big data analytics with crop rotation management, this review establishes a comprehensive framework for data-driven rotation optimization and identifies future research priorities for intelligent soil management and sustainable agricultural intensification.

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Water supply–demand relationships are increasingly shaped by the combined effects of climate change, socio-economic development, and growing resource constraints, requiring a transition from conventional resource allocation toward integrated systems-based analyses. This review synthesizes four major research frontiers in contemporary water resources science: resilience and robustness of water systems under extreme hydrological events, multi-objective coordination within the water–energy–food nexus, intelligent regulation of urban water systems through socio-ecological-technological coupling, and regional water resource optimization based on nature-based solutions and ecosystem services. By integrating interdisciplinary perspectives and multi-scale analytical frameworks, this study reveals the emerging shift from single-sector management toward resilience-oriented, coupled human–natural system governance, highlighting the growing convergence of engineering, ecological, and socio-economic approaches in addressing water supply–demand challenges. The proposed synthesis provides an integrated theoretical framework for advancing research on water supply–demand relationships and identifies key directions for developing resilient and sustainable water governance under global environmental change.

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The increasing frequency of drought, salinity, and temperature extremes under global climate change poses a major constraint on sweet potato productivity and stability, primarily through complex, multi-scale disruptions in stress-response regulatory networks. Improving stress resilience and environmental adaptation in sweet potato, therefore, requires more precise identification of functional genetic determinants and more efficient integration of multi-source biological and environmental data. In this context, artificial intelligence (AI) offers new opportunities to address limitations of conventional breeding approaches, particularly in disentangling complex genotype–phenotype–environment interactions. This review synthesizes recent advances in AI-enabled stress-resilience improvement, focusing on deep learning-based stress-resistance gene discovery, optimization of adaptive trait prediction, and AI-guided selection of gene-editing targets. It further highlights integrative frameworks that couple machine learning models with gene-editing technologies to enhance the accuracy of target identification under multi-stress conditions. In addition, emerging AI-driven breeding strategies that simulate plant performance across heterogeneous environmental scenarios are discussed, emphasizing their potential to accelerate data-informed cultivar development. Key challenges remain in model generalizability, biological interpretability, and multi-environment validation. This study provides a consolidated perspective on the role of AI in decoding stress-response complexity and supports the development of next-generation intelligent breeding systems for climate-resilient sweet potato improvement.

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Ecological agriculture is increasingly recognized as a critical paradigm for addressing the coupled challenges of food security, resource constraints, environmental degradation, and climate change; however, its transition toward scalable, system-level implementation remains constrained by fragmented knowledge across technological, biological, and socio-ecological domains. This study synthesizes four interconnected frontier directions in ecological agriculture research: (i) AI- and big data–enabled intelligent agroecosystems, which integrate sensing, modeling, and decision-support systems to enhance whole-chain efficiency and sustainability; (ii) soil microbiome engineering and soil health management, leveraging multi-omics approaches to elucidate microbial community structure–function relationships and optimize low-input, environmentally friendly farming practices; (iii) quantification and value realization of agroecosystem services, focusing on integrated assessment frameworks and emerging incentive mechanisms such as ecological compensation and carbon markets; and (iv) climate-resilient agricultural systems, encompassing stress-tolerant crop improvement, water-efficient management, biochar-based carbon sequestration, and remote sensing–driven prediction for adaptation and mitigation. These advances highlight the convergence of digital technologies, ecological processes, and policy instruments in reshaping agricultural systems.

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Drought stress is a major constraint on crop productivity under climate change, yet the genetic improvement of drought tolerance remains challenging due to the polygenic nature of stress responses and limitations of conventional breeding. Gene-editing technologies, particularly CRISPR/Cas-based systems, provide a precise and efficient approach for dissecting and engineering complex drought-response networks. This review synthesizes recent progress in gene editing for improving crop drought tolerance from a mechanistic and translational perspective. It highlights key regulatory modules targeted by genome editing, including transcription factor networks (e.g., DREB and NAC families), phytohormone signaling pathways such as abscisic acid-mediated responses, osmotic adjustment processes, and reactive oxygen species scavenging systems, all of which collectively determine plant drought adaptability. Recent applications in major cereal crops, including rice, wheat, and maize, demonstrate that multiplex genome editing can effectively modulate stress-responsive traits and improve yield stability under water-limited conditions. The review further discusses key bottlenecks limiting field deployment, including transformation efficiency, off-target effects, regulatory uncertainty, and challenges in pyramiding polygenic traits. Emerging gene editing platforms, such as base editing and prime editing, are highlighted as promising tools for enabling precise, programmable, and trait-specific improvement of drought resilience. This review provides an integrated framework linking molecular mechanisms, editing strategies, and crop-level outcomes, offering a conceptual and technological foundation for accelerating the development of climate-resilient crop varieties.

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Climate change is intensifying uncertainty in agricultural production, creating an urgent need for da-ta-driven approaches that enhance system resilience and adaptive decision-making. This review systemati-cally synthesizes recent advances in data-driven agriculture by integrating four major technological do-mains: artificial intelligence and machine learning for climate risk prediction and adaptive management, blockchain-enabled resilient agricultural supply chains, remote sensing–Internet of Things fusion for crop monitoring and carbon assessment, and genomics-driven precision breeding for climate adaptation. The review critically examines the principles, applications, and complementary strengths of these technologies, highlighting how multisource data integration, intelligent analytics, and digital connectivity enable more accurate risk assessment, resource optimization, and adaptive crop management under changing climatic conditions. It also identifies key challenges, including data interoperability, model generalization, scalabil-ity, and cross-platform integration, which currently limit large-scale implementation. By establishing an integrated analytical framework linking digital technologies with climate adaptation, this review provides a systematic perspective for advancing climate-resilient, data-driven agricultural systems and accelerating the digital transformation of sustainable agriculture.

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Genotype-by-environment (G×E) interactions represent a fundamental constraint in stabilizing wheat yield under increasing climate variability, yet their quantitative characterization and translation into adaptive variety deployment strategies remain insufficiently resolved. Artificial intelligence (AI) has emerged as a powerful paradigm for integrating heterogeneous agricultural data and capturing nonlinear G×E relationships, enabling a shift from empirical assessment toward predictive and decision-oriented crop management. This review synthesizes recent advances in AI-driven wheat adaptability evaluation and precision planting strategies, with emphasis on the integration of multi-source datasets, including meteorological, soil, remote sensing, and phenotypic information, through machine learning and deep learning frameworks. We further examine how AI models are employed to quantify genotype performance, predict yield stability, and assess stress tolerance across diverse environments, and how these outputs are translated into variety recommendation and site-specific planting optimization systems. Despite substantial progress, key limitations persist in data standardization, cross-regional model transferability, and the interpretability of black-box models, which hinder large-scale operational deployment. Emerging directions including multimodal data fusion, transfer learning across environments, and explainable AI (XAI) are discussed as critical enablers for improving model robustness and transparency. By consolidating current methodologies into a unified analytical framework linking G×E modeling with adaptive decision-making, this review provides a conceptual basis for advancing predictive breeding systems and precision wheat production under climate uncertainty.

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Land use change is a dominant driver of ecosystem structure and functional reorganization, reshaping the spatial–temporal patterns and interaction networks of ecosystem service supply and thereby influencing regional sustainability. Recent decades have seen intensified land system transformation driven by urbanization, population growth, and climate change; however, the underlying mechanisms governing ecosystem service trade-offs, synergies, and regime shifts across multifunctional landscapes remain insufficiently understood. This review synthesizes recent advances in four interlinked domains: (1) trade-off and synergy mechanisms of ecosystem services in multifunctional landscape systems; (2) resilience dynamics and adaptive governance of coupled social–ecological systems under land use transitions; (3) integration of nature-based solutions (NbS) into land management for enhancing ecosystem multifunctionality; and (4) emerging methodological frameworks enabled by remote sensing, big data analytics, and artificial intelligence for ecosystem service quantification and prediction. Current evidence highlights a paradigm shift from static assessment toward dynamic, process-based, and decision-oriented modeling of ecosystem services under land system change.

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Potato starch content is a key quality trait determining its value in food processing and industrial applications, regulated by the coordinated action of multiple gene networks. Genome-wide association study (GWAS), a high-throughput approach for dissecting the genetic basis of complex quantitative traits, has become a powerful tool for functional gene discovery in crops. In this study, phenotypic data and whole-genome resequencing information from various Solanum tuberosum cultivars were integrated to identify significant genetic loci and candidate genes associated with starch synthesis using GWAS. The analysis revealed several loci significantly correlated with starch content, with most single-nucleotide polymorphisms (SNPs) located in genomic regions involved in carbon metabolism, sugar transport, and starch biosynthetic pathways. Further functional annotation and bioinformatic analyses identified several key candidate genes, including those encoding ADP-glucose pyrophosphorylase, starch synthase, and transporter proteins, which may play crucial roles in the regulation of starch biosynthesis. These findings provide a theoretical foundation for elucidating the molecular mechanisms underlying starch accumulation in potatoes and offer valuable genetic resources and molecular targets for marker-assisted breeding of high-starch-content cultivars.

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Against the backdrop of evolving global risks, building a resilient agricultural system has become a core strategic objective for ensuring food security and sustainable agricultural development. As a critical component enabling agricultural systems to respond to shocks, recover rapidly, and adapt over the long term, resource allocation mechanisms must transition from traditional static paradigms to intelligent, systematic, and multi-scale coupled approaches. This paper systematically reviews the theoretical foundations and driving forces behind current resilient agricultural resource allocation and highlights four key research directions with both frontier relevance and practical value: (1) precision dynamic resource allocation mechanisms based on digital twin and artificial intelligence technologies; (2) cross-scale synergistic allocation models empowered by ecosystem services; (3) blockchain- and distributed ledger–enabled mechanisms for agricultural supply chain resource sharing and trust; and (4) adaptive labor resource allocation pathways from a socio-ecological-technical systems perspective. Through in-depth analysis of technological logic, system interaction mechanisms, and governance structures, this paper aims to provide a systematic framework for the theoretical development and policy design of resilient agricultural resource allocation mechanisms in the future.

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With the increasing demand for high-quality raw materials in the food processing and agricultural industries, optimizing potato starch quality has become a key factor in enhancing product value and meeting diverse market needs. This study systematically explores methods for improving potato starch quality through the integration of big data and phenotypic analysis. Focusing on key phenotypic traits such as starch granule size, gelatinization characteristics, viscosity, and gel strength, the research integrates multidimensional data from genomics, transcriptomics, and environmental factors to reveal the complex regulatory mechanisms underlying genotype–environment interactions in starch quality formation. By applying big data analytics and machine learning algorithms, a multidimensional predictive model for starch quality was developed, enabling effective fusion of high-throughput phenotypic and genomic data to support data-driven decision-making in targeted breeding. Moreover, the application of artificial intelligence (AI) in phenotypic feature recognition and model optimization significantly improved analytical efficiency and predictive accuracy. Practical case studies demonstrated the potential of big data and AI technologies in starch quality improvement, and future research directions were discussed, including data standardization, cross-environment adaptability modeling, and the development of intelligent decision-support systems. This study aims to establish a data-driven, systematic research framework to provide theoretical and technical support for precise phenotypic analysis and molecular breeding of potato starch quality, thereby promoting agricultural intelligence and the sustainable development of the food industry.

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Maize–peanut intercropping systems, row ratio configurations, agronomic traits, and economic benefits have become important research topics in the optimization of intercropping agriculture and efficient resource utilization. Based on field positioning experiments, this study systematically constructed a dataset of agronomic traits and economic benefits under different row ratio configurations in a maize–peanut intercropping system, including monocropping patterns and multiple intercropping treatments. The dataset contains growth indicators such as maize plant height, ear height, stem diameter, root number, and aboveground dry matter accumulation, as well as key agronomic traits of peanut, including main stem height, lateral branch length, and branch number. In addition, yield data and comprehensive economic benefit assessments for all treatments were integrated into the dataset. Unified field management practices, standardized observation procedures, and replicated experimental designs were adopted to ensure data standardization, reproducibility, and cross-treatment comparability. The results demonstrated that different row ratio configurations significantly affected population structure, interspecific growth competition, and economic output characteristics of maize and peanut, among which the 8:8 row ratio treatment exhibited superior performance in terms of crop synergistic growth and overall economic benefits. This dataset can provide high-quality baseline data for studies on resource competition mechanisms, interspecific synergistic effects, and agricultural economic evaluation models in intercropping systems, while also offering reliable data support and scientific references for regional optimization of intercropping patterns, precision agricultural decision-making, and the development of sustainable agricultural production systems, thereby possessing important theoretical value and practical significance for promoting efficient ecological agriculture.

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Dryland farming technology plays a vital role in global agricultural production, particularly in ensuring food security and promoting sustainable development in arid and semi-arid regions. With the intensification of climate change and the increasing scarcity of water resources, research and dissemination of efficient water-saving and yield-stabilizing technologies for dryland farming have become increasingly urgent. This paper systematically reviews the research background and developmental significance of dryland farming, defines its fundamental connotations and core characteristics, and analyzes its current development status globally and in China. At the technical level, it highlights typical water-saving and yield-enhancing practices such as mulching cultivation, rainwater harvesting and utilization, and soil water- and nutrient-retaining agents, elucidating their mechanisms and practical effectiveness. In addition, it summarizes the crucial role of drought-tolerant crop breeding, tillage system optimization, and integrated management measures in enhancing the stability and sustainability of dryland agriculture. On this basis, the challenges in technology promotion are discussed, including limited regional adaptability, low adoption rates among farmers, and insufficient technological integration. Finally, future research and application directions are proposed, emphasizing the need for interdisciplinary integration, intelligent and precise applications, as well as regionalized and localized practices. This paper aims to provide systematic references and strategic insights for both the theoretical research and practical promotion of dryland farming technology.

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Against the backdrop of rapid digital technology advancement, cultural digitization is emerging as a strategic pathway to promote cultural inheritance, innovation, and the vitality of intercivilizational exchange. This paper systematically explores four key research directions in the current frontier of cultural digitization: the integration of embodied intelligence and immersive cultural experiences; the revitalization and re-creation mechanisms of cultural heritage driven by generative artificial intelligence; the synergistic application of blockchain and digital twin technologies in cultural heritage preservation and traceability; and the ethical implications of cultural data and issues of social equity. It highlights the profound impacts of emerging technologies on cultural experience modes, content generation, asset rights confirmation, and digital governance, revealing a complex landscape of both opportunities and challenges. Through interdisciplinary perspectives and diverse case studies, this study aims to provide theoretical support and practical insights for advancing systematic research and governance frameworks in cultural digitization, fostering a deeper integration of technological rationality and cultural values.

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Legume–cereal rotation systems represent a vital pathway toward green, efficient, and resilient agriculture, with their ecological advantages derived from functional complementarity among species, nutrient redistribution, and coordinated regulation across soil–plant–microbial interfaces. Recent studies have made notable progress in understanding rhizosphere interaction networks, the biogeochemical processes underlying biological nitrogen fixation, microbial community succession and metabolic responses, and soil quality enhancement driven by carbon–nitrogen coupling, while advances in molecular ecology, high-throughput omics, precision monitoring, and system modeling have further deepened insights into rotation-based ecological processes. At the management level, strategies involving crop-combination optimization, planting-regime regulation, strengthened nitrogen-substitution effects, enhanced carbon-sequestration potential, and assessment of ecosystem-service values have gradually formed a practical and scalable framework. However, current research remains constrained by limited quantitative characterization of multi-factor interactions, insufficient long-term and cross-regional datasets, and uncertainties that affect the predictive accuracy of models. This study systematically reviews the research trajectory of legume–cereal rotation systems, synthesizes their core ecological mechanisms and sustainable management pathways, and highlights future priorities—including cross-scale process coupling, integration of mechanisms with models, and the development of intelligent decision-support systems—to sustain long-term agricultural productivity and ensure food security.

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Under the backdrop of growing global energy demand and the gradual depletion of conventional oil and gas resources, the efficient development of tight oil faces significant challenges due to the complex coupling of geological and engineering parameters. This study focuses on a typical fractured horizontal well in the Damin Tun Sag of the Liaohe Oilfield, aiming to elucidate the relative control of geological factors (matrix and fracture permeability, effective thickness) and fracturing parameters (main fracture conductivity, stage spacing, cluster spacing, fracture half-length, etc.) on well productivity and their dynamic evolution over time. A refined dual-porosity numerical model combined with single-factor sensitivity analysis and normalized impact factor ranking was employed, with model accuracy validated through history matching. Results reveal that productivity evolution can be divided into three stages: early high production dominated by fracture network fluid supply, mid-term rapid decline driven by matrix transition, and late gradual decline controlled by remote matrix. Geological factors, initially weak, become increasingly significant in the mid to late production stages. Main fracture conductivity consistently serves as the primary controlling factor, followed by stage and cluster spacing. The dominant controls shift from engineering parameters to geological properties as production progresses, reflecting the intrinsic evolution of the oil supply mechanism. This study clarifies the multifactor coupled control mechanisms and provides theoretical and quantitative support for optimizing fracturing design and integrated geological-engineering development, promoting a more refined and dynamic approach to tight oil exploitation.

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Tight oil in China features ultra-low permeability and complex pore structures, posing challenges for predicting productivity in hydraulically fractured horizontal wells. Existing models often rely on single-flow assumptions, failing to capture multi-flow-regime transitions and reservoir dynamics over the production life cycle. This study introduces a multiregional coupled seepage model dividing the flow into hydraulic fractures, stimulated reservoir volume, and far-field matrix, incorporating threshold pressure gradients and stress sensitivity, solved via Laplace transform. Validation with a well from G Oilfield shows strong agreement with actual production, capturing typical production phases. Sensitivity analysis reveals stress sensitivity dominates early production decline, while threshold pressure gradient limits long-term stability. The model aids in precise productivity evaluation, fracturing design, and production strategy for tight oil reservoirs, supporting improved economic development of unconventional resources.

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To address the micro-scale effects triggered by the complex pore-throat structures of tight oil reservoirs and the ambiguous fluid seepage mechanisms resulting from the coexistence of multiple media after volume fracturing, this study aims to construct a production prediction model capable of accurately characterizing the dynamic evolution of multi-scale media. Based on the trilinear flow theory framework, a mathematical productivity prediction model for fractured horizontal wells in tight oil reservoirs was established by integrating the matrix threshold pressure gradient, high-velocity non-linear flow (Forchheimer flow regime) in primary hydraulic fractures, and multi-scale dynamic stress sensitivity effects across the matrix, secondary fractures, and primary fractures. The model was solved efficiently using a non-linear iterative coupling algorithm. Validation against field production data from the study area demonstrates a high degree of consistency, confirming the reliability of the model in evaluating production dynamics within complex fracture networks. Sensitivity analysis reveals a differential effect of stress sensitivity across multi-scale media: the stress sensitivity of secondary fractures exerts a decisive influence on productivity, serving as the core physical bottleneck constraining long-term stable production in tight oil. Additionally, the high-velocity non-linear effects in primary hydraulic fractures significantly inhibit the initial production rate, while the matrix threshold pressure gradient has a relatively limited marginal impact on overall yield under well-developed fracture network conditions. These findings quantitatively elucidate the contribution weights of the dynamic evolution of physical properties across different scales of media, providing essential theoretical support and a decision-making basis for the optimization of fracturing treatments and differentiated proppant placement designs in tight oil reservoirs.

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Due to reservoir heterogeneity and the complexity of development technologies, tight oil reservoirs exhibit complex single-well production dynamics and multi-solution issues in reserve evaluation, posing significant challenges in reservoir engineering. Focusing on single tight oil wells with multistage fractured long horizontal sections in the Xi 233 well block of the Ordos Basin, this study systematically investigates the evolution of flow regimes and the dynamic characteristics of reserve changes. By integrating the normalized material balance time method with log-log diagnostic curve techniques, the characteristic signals of boundary-dominated flow were accurately identified, effectively addressing the subjective errors associated with the selection of the decline exponent in the traditional Arps decline method. For new wells with insufficient initial production data, an analog prediction model based on regional “seed well” big data was constructed, enabling rapid reserve estimation using key indicators such as the cumulative oil production in the first year. Empirical results demonstrate that the correlation coefficient of the analog model exceeds 0.88, and the accuracy of reserve prediction using the material balance time method is significantly improved during the pseudo-steady-state flow phase. The research reveals that the productivity of a single tight oil well is jointly controlled by the quality of the geological “sweet spot” and the intensity of the fracturing engineering, with the initial flowback period of 2–3 months being the primary cause of early production decline fluctuations. The “classified evaluation and staged evolution” differentiated reserve assessment system proposed in this paper provides a scientific basis for the accurate evaluation of single-well reserves and the optimization of dynamic development plans in tight oil reservoirs, offering substantial engineering application value and promotion prospects.

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As tight oil serves as a critical direction for increasing reserves and production of unconventional oil and gas resources in China, its high costs and complex economics have become key constraints for large-scale development. Focusing on the optimization of design parameters and the collaborative evaluation of economic benefits for tight oil horizontal wells in the Longhupao Block of the Daqing Oilfield, this study constructs a techno-economic integrated evaluation model encompassing casing programs, platform scale, and horizontal section length based on extensive field measurement and supervision data. Utilizing methods such as least squares regression, nonlinear fitting, and incremental analysis, the coupling relationship between cost and production, as well as the threshold characteristics of economies of scale, is revealed. The results indicate that simplifying the casing program significantly improves drilling and completion efficiency while reducing the cost per foot. Furthermore, a clear boundary for economies of scale exists in platform “factory” operations; once the number of wells per platform exceeds a critical value, the increase in marginal costs offsets the scale benefits. Additionally, the relationship between horizontal section length and production is significantly nonlinear, with an optimal interval existing for comprehensive economic benefits. Consequently, an optimized development mode for tight oil horizontal wells characterized by “large platforms, simplified structures, and moderate horizontal sections” is proposed. This study provides scientific and quantitative decision support for the selection of design parameters and economic evaluation of unconventional horizontal wells, demonstrating significant value for widespread application.

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Tight oil in the Paleogene–Neogene Chaixi area of the Qaidam Basin is a strategic but complex exploration frontier. This study integrates geochemical analysis, thermal simulation, and seismic restoration to evaluate its accumulation mechanisms and resource potential. Results show that high-quality source rocks in the Ganchaigou Formations, dominated by lacustrine humic organic matter, exhibit continuous hydrocarbon generation from low-mature to mature stages. A “source-reservoir integration” model is established, driven by the spatial coupling of semi-deep lacustrine source rocks and shore–shallow lacustrine reservoirs. Structural analysis reveals that paleo-slope belts and source depression centers are primary controls for enrichment. Specifically, the Zhahaquan, Nanyishan–Xiaoliangshan, and Qigequan–Yuejin slope belts exhibit optimal accumulation conditions and high resource potential, marking them as priority targets for large-scale production. These findings refine the theoretical framework for tight oil in continental saline lacustrine basins and provide a vital reference for similar exploration globally.

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The Daanzhai section of the Lower Jurassic Ziliujing Formation in the central Sichuan Basin hosts substantial tight oil resources; however, strong reservoir heterogeneity and low resource abundance result in significant production variability among individual wells, severely limiting large-scale efficient development. This study aims to identify the main controlling factors and physical mechanisms of tight oil accumulation by analyzing the geological characteristics of typical high-yield wells. Integrating production data from over a thousand wells with microscopic rock physics experiments, multi-scale fracture characterization, and two-dimensional filling physical simulation, the control effects of source-reservoir configuration, sedimentary facies belts, and formation pressure systems on well productivity were quantitatively evaluated. Results indicate that high production in the Daanzhai tight oil is governed by a “facies-reservoir-driving” trinity coupling mechanism: the high-energy littoral bioclastic shoal facies provide the foundation for quality reservoir development; the multi-scale fracture network significantly improves fluid conductivity in ultra-low permeability matrix and enhances hydrocarbon charging efficiency by regulating the pressure gradient at the source-reservoir interface; additionally, the elastic energy release from associated gas within the system is a key driving force for sustaining long-term stable well production. The study concludes that effective exploration in the Daanzhai section should focus on the spatial superposition of closely coupled source-reservoir zones, high-energy facies belts, and fracture networks forming the “physical sweet spots.” The proposed “ternary coupling” accumulation model advances the theory of low-abundance continental tight oil accumulation and provides important theoretical guidance and practical reference for oil and gas exploration in the Sichuan Basin and analogous continental lacustrine basins.

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Calcium, as an essential macronutrient for plants, plays an irreplaceable role in maintaining cell wall stability, mediating calcium signaling, regulating growth and development, and enhancing stress adaptation; its uptake and utilization efficiency directly influence crop yield formation and quality safety, making it a key scientific issue in nutrient-efficient utilization and genetic improvement. Focusing on improving crop calcium uptake and use efficiency, this study systematically reviews the physiological functions of calcium in plants, its transmembrane transport pathways, and its signaling regulatory mechanisms, and further introduces artificial intelligence (AI) to construct a new analytical framework for genotype × environment (G×E) interactions. Methodologically, this review evaluates the applications of machine learning, deep learning, and big data analytics in multi-environment phenotyping, calcium-use efficiency prediction, and elite genotype identification, with emphasis on integrative strategies that combine multi-omics datasets (genomic, transcriptomic, phenomic, and environmental data) with advanced AI models. The findings indicate that AI-enabled G×E modeling can effectively identify key regulatory factors underlying calcium uptake and utilization, enhance the accuracy of predicting high-efficiency calcium-use genotypes under complex environments, and provide technical support for precision breeding of calcium-efficient and stress-resilient crop varieties. Overall, the study concludes that the deep integration of AI and calcium nutrition biology holds strong potential to overcome bottlenecks in conventional breeding related to trait dissection and selection efficiency, offering new theoretical foundations and methodological pathways for intelligent, targeted molecular breeding aimed at improving calcium-use efficiency, while simultaneously posing new challenges regarding data quality, model interpretability, and cross-environment generalization.

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Peanut (Arachis hypogaea L.) is a globally important oilseed crop, and its oil content, as a key trait determining yield and economic value, has long been a central focus of genetic research and breeding improvement. In recent years, with the advancement of genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping, significant progress has been achieved in elucidating the genetic basis of peanut oil content. GWAS identifies single-nucleotide polymorphism (SNP) markers closely associated with oil content by analyzing large-scale genotype and phenotype data from natural populations, while QTL mapping reveals key genomic regions controlling oil content through the construction of genetic populations and genome scanning. The integration of GWAS and QTL mapping enhances the resolution of gene discovery for oil content and provides strong support for marker-assisted selection (MAS). This paper reviews the current applications of GWAS and QTL mapping in peanut oil content research, elaborating on their technical principles, research progress, and combined strategies. By integrating multi-omics data and genomic big data analysis, key regulatory networks in lipid metabolism pathways have been uncovered. Furthermore, the challenges of current research are discussed, along with prospects for the genetic improvement of oil content in peanuts. Overall, this study aims to provide a theoretical basis and technical guidance for enhancing peanut oil content and achieving precision breeding through a comprehensive analysis of GWAS and QTL mapping applications.

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Addressing the development bottlenecks caused by the strong heterogeneity of the Permian Lucaogou Formation mixed tight oil reservoirs in the Santanghu Basin, it is of significant strategic importance to research key fracturing technologies to enhance the development efficiency of unconventional oil and gas resources. This study aims to overcome the challenges of effective fracture network control under complex mixed lithological conditions and resolve technical issues such as high treatment pressure and rapid production decline. Based on the “geology-engineering integration” philosophy, a characterization model covering seven key properties was constructed through a comprehensive analysis of lithology, physical properties, oil-bearing capacity, and geomechanical parameters. A technical pressure-controlled drainage and production scheme was developed, integrating pre-pad acid-assisted pressure reduction, sand-plug rounding processes, and large-displacement volume fracturing, followed by field pilot tests. The results demonstrate that this technical system effectively reduces breakdown pressure, significantly activates natural fracture networks, and creates a large-scale stimulated reservoir volume. Simultaneously, stable long-term production is achieved by leveraging the spontaneous imbibition mechanism. The research indicates that the proposed integrated volume fracturing technology for mixed tight oil horizontal wells not only remarkably improves the recovery efficiency of complex regional reserves but also provides theoretical support and an engineering demonstration for the efficient development of similar continental mixed unconventional resources, offering extensive potential for widespread application.

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Tight oil in continental saline lake basins is a key frontier in unconventional hydrocarbon exploration, featuring complex depositional settings and diverse accumulation mechanisms. This study reviews tight oil accumulation in the lower Xinguozui Formation, Qianjiang Sag, highlighting the critical control of salt rhythms. Integrating core analysis, pore structure characterization, well logging, and biomarker data, it reveals a source-reservoir integration model where reducing saline conditions enhances hydrocarbon generation, and late-stage salt mineral infill creates pore heterogeneity. Fourth-order salt rhythm cycles act as fundamental accumulation units, with spatial alternation between hydrocarbon generation during salinization and storage during freshening, aided by overpressure-driven near-source charging. Tight oil sweet spots align with salinization-freshening transition surfaces, showing regular cyclic distribution. The “salt rhythm” theory advances understanding of accumulation processes and guides precise exploration in similar basins.

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Terrestrial tight oil reservoirs under atmospheric pressure commonly face challenges such as insufficient formation energy, high startup pressure gradients, and complex seepage mechanisms. Efficient development of these reservoirs is critical for advancing unconventional oil and gas exploitation and ensuring national energy security. This study addresses key scientific and engineering issues observed in the early development of the Lucao Formation tight oil reservoirs in the Jimusar Sag, including rapid pressure depletion and limited single-well recovery. Aiming to clarify the pressure evolution patterns and main controlling factors of estimated ultimate recovery (EUR) in atmospheric tight oil reservoirs, an integrated evaluation framework was established by combining multi-stage volumetric fracturing and monitoring data from horizontal wells, long-term production dynamics, and pressure buildup test analyses, incorporating non-Darcy flow theory and production decline analysis. Results indicate that the Lucao Formation tight reservoirs operate under a pronounced atmospheric pressure regime, with reservoir pressure exhibiting nonlinear, staged depletion during production. This behavior fundamentally reflects an energy imbalance between insufficient matrix recharge and rapid fluid loss through the induced fracture network. Moreover, well productivity evolution follows a characteristic pattern of high initial decline followed by a long-tail stable production phase, demonstrating the critical role of volumetric fracturing in reshaping the flow field and reducing startup pressure gradients. These findings provide theoretical support and engineering guidance for optimizing development strategies and enhancing recovery efficiency in the Jimusar Sag and similar terrestrial tight oil reservoirs.

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Against the backdrop of rapid digitalization and intelligent transformation, the deep coupling between information and geographic space is driving information geography to emerge as a critical frontier at the intersection of geography and information science. This study systematically reviews the origin and development of information geography, clarifies its fundamental concepts and core connotations, and reveals the interactions among information flows, spatial structures, and social behaviors. Building on existing research, it proposes a theoretical framework encompassing four dimensions: cognitive foundation, spatial structure, information behavior, and technological support. By integrating key technologies such as geographic information systems (GIS), remote sensing, big data mining, and artificial intelligence, the paper summarizes methodological innovations in data acquisition, processing, and analysis. It further highlights recent advances in applications, including the spatial evolution of information dissemination, digital city construction, socio-economic spatial analysis, and the integration of virtual and physical spaces, demonstrating the practical value of information geography in urban governance, ecological monitoring, and regional coordinated development. Meanwhile, it addresses critical challenges such as insufficient interdisciplinary collaboration mechanisms, inadequate data privacy protection, and limitations in algorithm interpretability and generalization, offering potential strategies to overcome these issues. Finally, it outlines future research priorities and development trends, aiming to provide insights and references for advancing theoretical refinement and technological innovation in this field.

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Photosynthesis is the core process of plant growth and biomass accumulation, directly determining the yield potential of crops. This paper systematically explores the relationship between photosynthetic efficiency and crop yield to provide a scientific basis for improving agricultural productivity. First, the fundamental principles of photosynthesis are explained, and the concept and measurement methods of photosynthetic efficiency are defined. Second, the key mechanisms through which photosynthetic efficiency affects crop yield are analyzed, including light energy capture efficiency, carbon assimilation rate, and the structural and functional regulation of photosynthetic organs. Third, major strategies for improving photosynthetic efficiency are reviewed, such as genetic improvement (e.g., genetic engineering and variety breeding), precision agricultural management (e.g., optimizing light conditions and fertilization techniques), and environmental regulation measures (e.g., greenhouse and shading technologies). Through typical case studies, the practical effects of high photosynthetic efficiency varieties in enhancing yield and resource use efficiency are further revealed. Finally, future research directions are proposed, emphasizing the critical role of photosynthetic efficiency enhancement technologies in addressing global food security and achieving sustainable agricultural development. This study provides both theoretical support and practical guidance for a deeper understanding of the relationship between photosynthetic efficiency and crop yield.

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The widespread application of artificial intelligence technologies in higher education is profoundly reshaping traditional teaching models and learning modes, and investigating college students’ acceptance of artificial intelligence tools and the associated mechanisms underlying their learning behavior responses is of substantial theoretical and practical significance for advancing the digital transformation of higher education. This study aims to elucidate the mechanisms through which the adoption of artificial intelligence tools influences college students’ learning engagement and learning outcomes. Grounded in the technology acceptance model, a conceptual framework incorporating perceived usefulness, perceived ease of use, learning engagement, and learning outcomes was constructed. Questionnaire survey data were collected from 812 undergraduate students across three universities, and empirical analyses were conducted using reliability tests, exploratory factor analysis, and hierarchical regression analysis. The results indicate that both perceived usefulness and perceived ease of use exert significant positive effects on learning engagement, and that learning engagement mediates the relationships between perceived usefulness, perceived ease of use, and learning outcomes. In addition, significant differences were observed in artificial intelligence tool usage behaviors across students from different disciplinary backgrounds and academic years. These findings suggest that enhancing students’ perceptions of the usefulness and ease of use of artificial intelligence tools constitutes a critical pathway for strengthening learning engagement and improving learning outcomes.

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Against the backdrop of the deep embedding of digital technologies in educational settings, university students’ knowledge acquisition behaviors are increasingly mediated by algorithm-centered digital platforms, including search engines, social media, video applications, and online education systems. Traditional teacher-led and linearly structured learning pathways are being reconfigured by platform-driven recommendation mechanisms. Drawing on questionnaire survey data and in-depth interviews with undergraduate students at a comprehensive university, this study systematically examines how algorithmic recommendations shape students’ knowledge selection, the construction of learning pathways, and their judgments of knowledge authority. The findings indicate that platform recommendation mechanisms enhance the immediacy and fragmentation of knowledge acquisition, fostering tendencies toward “cognitive echo chambers” and “path dependence,” which in turn constrain knowledge diversity and critical thinking. At the same time, students’ trust in content from informal platforms has increased markedly, leading to a partial erosion of traditional academic authority. This study contributes in three main respects: first, it introduces a perspective from the sociology of algorithms to construct an analytical framework of interactions among technology, knowledge, and power; second, it centers on learners’ subjective experiences to reveal how algorithmic logics reshape cognitive structures and learning behaviors at the micro level; and third, it proposes the concept of a “platformized learning ecology,” offering theoretical support and practical implications for information literacy education and curricular reform in higher education. Overall, this research extends sociological understandings of learning mechanisms in the digital era and provides empirical evidence and strategic insights for educational governance amid the digital transformation of universities.

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