Sustainable farmland management is vital for global food security and for mitigating environmental degradation and climate change. While individual practices such as crop rotation and no-tillage are well-documented, this review synthesizes current evidence to illuminate the critical synergistic effects of integrating four key strategies: crop rotation, conservation tillage, organic amendments, and soil microbiome management. Crop rotation enhances nutrient cycling and disrupts pest cycles, while conservation tillage preserves soil structure, reduces erosion, and promotes carbon sequestration. Organic amendments replenish soil organic matter and stimulate biological activity, and a healthy soil microbiome boosts plant resilience to stress and enhances nutrient acquisition through key functional groups like arbuscular mycorrhizal fungi (AMFs). Critically, the integration of these practices yields amplified benefits that far exceed their individual contributions. Integrated management systems not only significantly increase crop yields (by up to 15–30%) and soil organic carbon but also deliver profound global ecosystem services, with a potential to sequester 2.17 billion tons of CO₂ and reduce soil erosion by 2.41 billion tons annually. Despite challenges such as initial yield variability, leveraging these synergies through precision agriculture represents the future direction for the field. This review concludes that a holistic, systems-level approach is essential for building regenerative and climate-resilient agroecosystems.

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties.

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. We first delineate the fundamental physicochemical criteria for efficient photoanodes, including suitable band alignment, visible-light absorption, charge carrier mobility, and electrochemical stability. Conventional strategies such as nanostructuring, elemental doping, and surface/interface engineering are critically evaluated. We then discuss the integration of ML techniques—ranging from high-throughput density functional theory (DFT)-based screening to experimental data-driven modeling—for accelerating the identification of promising oxides (e.g., BiVO₄, Fe₂O₃, WO₃) and optimizing key parameters such as dopant selection, morphology, and catalyst interfaces. Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. ML-assisted performance prediction and tandem device design are also addressed. Finally, current challenges in data standardization, model generalizability, and experimental validation are outlined, and future perspectives are proposed for integrating ML with automated platforms and physics-informed modeling to facilitate scalable PEC material development for clean energy applications.

The sustainable management of farmland soils is fundamental to addressing the intertwined challenges of food security, environmental degradation, and climate change. This review synthesizes current knowledge on key management practices—crop rotation, no-tillage agriculture, organic amendments (specifically farmyard manure), and soil microbiome regulation—and their synergistic effects on soil health and crop productivity. Crop rotation disrupts pest and disease cycles, enhances nutrient cycling, and stabilizes yields. No-tillage improves soil physical properties, promotes carbon sequestration, and supports more diverse and resilient microbial communities. Organic amendments enrich soil organic matter, stimulate microbial-mediated nutrient cycling, and improve soil fertility over the long term. Targeted management of soil microbiomes further boosts plant stress resistance, nutrient acquisition, and disease suppression, offering powerful avenues for ecosystem resilience. Critically, the integration of these practices amplifies their individual benefits. Systems that combine rotation with no-tillage, or organic amendments with conservation practices, demonstrate superior performance in enhancing soil structure, nutrient dynamics, biological diversity, and carbon storage. Precision agriculture technologies and microbiome-based interventions are poised to refine these integrated systems further, enabling site-specific optimization. Despite technical and operational challenges—such as early-stage yield variability and management complexities—synergistic soil health management offers a clear pathway toward regenerative, climate-resilient agriculture. Future research must focus on understanding microbial functional dynamics, advancing real-time soil health monitoring, and developing holistic, scalable strategies that align productivity goals with ecological stewardship. An integrated, ecosystem-based approach to farmland management is essential to achieve sustainable agricultural development and global carbon neutrality targets.

Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing (RS) and GPS—where GIS serves as the core platform for managing, analyzing, and communicating spatial data throughout the EIA process. GIS plays a crucial role at each stage of EIA, from baseline data collection to spatial analysis, ecological sensitivity mapping, impact prediction, scenario simulation, and landscape connectivity assessment. These capabilities support alternatives analysis, risk communication, and decision-making in EIA. This paper synthesizes thematic evidence and presents case studies to illustrate the synergies between GIS, remote sensing, GeoAI, and multisource data fusion. It highlights operational workflows and key deliverables for EIA applications, including urban expansion, transport corridors, and protected-area management. We identify persistent challenges in data quality and standardization, interoperability, model uncertainty, and policy gaps. To address them, we propose a minimum geospatial dataset with clear metadata standards, interpretable GeoAI paired with formal sensitivity analysis, IoT–GIS pipelines for real-time monitoring and adaptive management, and the systematic inclusion of cumulative effects and climate scenarios. By linking GIS methods to typical decision points and reporting standards in EIA, this review clarifies where GIS adds value, how to quantify and communicate uncertainty, and how to align analytical outputs with regulatory requirements and stakeholder expectations. The study offers a practical framework and implementation checklist for standardized, transparent, and reproducible EIA processes, contributing to evidence-based ecological governance.

Climate change is reshaping how forests balance carbon uptake and water loss. This review aims to clarify how climate change alters forest carbon–water coupling. Using water-use efficiency (WUE) as a unifying lens, we synthesize mechanisms from leaves to ecosystems and evaluate evidence from studies screened in 2000–2025 spanning eddy covariance, tree-ring isotopes, remote sensing and models. Globally, tree-ring data indicate ~40% intrinsic WUE increases since 1901, yet ecosystem-scale gains are usually <20% after accounting for mesophyll conductance. Under drought, heat and high vapor-pressure deficit, photosynthesis declines more than evapotranspiration, producing partial carbon–water decoupling and lower WUE_e. Responses vary with hydraulic traits, forest type/age and site water balance, with notable tropical data gaps. We identify when WUE gains translate into true resilience: stomatal regulation and canopy structure jointly maintain GPP, prevent hydraulic failure and ensure post-event recovery. Management options include thinning, species/provenance choice, mixed stands and adaptive rotations to balance carbon storage with water yield. Key uncertainties stem from sparse long-term observations, tropical satellite biases and models that overestimate WUE or underplay extremes. We recommend integrating multi-source, multi-scale data with interpretable hybrid models, expanding tropical networks and strengthening MRV frameworks to support risk-aware, climate-smart forestry.