Search Results (726)

This perspective examines the interactions among microplastics (MPs), emerging contaminants (ECs), climate change, and extreme hydrological events. Droughts, water scarcity, heatwaves, intense rainfall, and floods can alter the occurrence, mobilisation, transport, fate, and risks of MPs and associated ECs across water, soil, and groundwater systems. The analysis focuses on the context-dependent potential of MPs to act as vectors of ECs, soil–water interactions, and the potential influence of MPs on greenhouse gas (GHG) emissions. When these stressors co-occur, their combined effects may be additive, synergistic, or antagonistic. Accordingly, the “perfect storm” framing is used here to describe the potential for mutually reinforcing and cascading risks rather than to imply that synergy occurs universally. An integrated perspective therefore helps anticipate worst-case scenarios and move beyond the fragmented assessment of individual stressors. This paper discusses sustainable mitigation and adaptation strategies, including advanced wastewater treatment, water reuse, climate-resilient water management, infrastructure adapted to increasing hydrological variability, climate-smart agriculture, and safer alternatives to conventional plastics and chemicals. Effective implementation combines technological innovation with monitoring, governance, policy action, and public awareness. By framing the microplastics–contaminants–water–soil–climate nexus as an interconnected sustainability challenge, this work aims to support environmental resilience and progress toward the Sustainable Development Goals. Full article

High-Standard Farmland Construction (HSFC) is widely expected to support both food security and low-carbon agriculture in China, yet prevailing assessments treat agricultural carbon performance (ACP) as a single aggregate, obscuring the structural asymmetry between emission mitigation and carbon sequestration. This study examines whether HSFC simultaneously reduces agricultural emissions and enhances carbon sequestration. Using a city-level panel of the Yangtze River Economic Belt (2007–2022), this study treats HSFC as a quasi-experiment and decomposes ACP into carbon sequestration performance (CSP) and carbon mitigation performance (CMP). The results show that HSFC robustly improves overall ACP and CSP, while its effect on CMP is statistically insignificant. Further decomposition indicates that HSFC promotes ACP through both efficiency change and technological change, though these gains are concentrated primarily in the sequestration dimension. Mechanism analysis shows that farmland scale management improves both efficiency and technology adoption, whereas agricultural socialized services promote both channels with a particularly pronounced effect on technological progress; neither channel, however, delivers a strong mitigation effect. In addition, the CSP-enhancing effect is robust across mechanization levels and in major grain-producing areas, whereas it is statistically absent in the upstream reaches characterized by fragmented terrain, and no significant CMP effect emerges in any subgroup. These findings provide new evidence on the climate consequences of farmland policies and offer implications for better aligning food security with agricultural decarbonization. Full article

The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human–machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise—advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems. Full article

The rapid expansion of solar photovoltaics has intensified land-use competition between renewable energy and agriculture, particularly in regions where food security and clean-energy transitions must progress together. Semi-transparent photovoltaic technologies may provide a land-use-compatible pathway for potential agrivoltaic applications by allowing partial light transmission while generating electricity. This study evaluates Dye-Sensitized Solar Cells (DSSCs) as a semi-transparent photovoltaic option and investigates the use of DSSC-generated electricity to power Direct Air Capture (DAC). The scope is limited to photovoltaic-system performance and economics and does not include Photosynthetically Active Radiation (PAR) transmission, crop growth, crop yield, or microclimate analysis. A techno-economic assessment was performed using the System Advisor Model for two contrasting regions, Karachi, Pakistan, and Davis, USA. DSSC and monocrystalline silicon systems were compared over the same land area. Due to their lower installed capacity, the DSSC systems generated less annual electricity. However, their lower assumed capital expenditure resulted in a Levelised Cost of Electricity (LCOE) that was 10% lower in Davis, at US Dollars (USD) 0.29/kWh, and 0.7% lower in Karachi, at USD 0.25/kWh. Based on literature-reported DAC energy requirements, the DSSC systems could support annual Carbon Dioxide (CO₂) removal of 331–9707 tonnes in Karachi and 334–9783 tonnes in Davis, depending on the selected DAC pathway. These results indicate that semi-transparent DSSCs may provide a lower-capital, land-use-compatible photovoltaic pathway combined with renewable-electricity-driven carbon removal. Their suitability for practical agrivoltaic deployment requires future PAR-transmission measurements and crop-specific experimental validation. Full article

High-standard farmland construction is a major land use policy aimed at improving farmland quality and strengthening the foundation of agricultural production. However, whether farmland improvement policies reshape the direction of agricultural technological change remains insufficiently understood. This study examines the impact of China’s high-standard farmland construction on land-enhancing technological change. Using provincial panel data from China, we construct two land-related biased technological progress indices—the capital–land and labor–land technological bias indices—based on a normalized factor-augmenting CES production framework, and further explore the underlying mechanisms through scale and price effects. The results show that high-standard farmland construction makes agricultural technological progress more inclined toward improving land factor efficiency, indicating a clear land-enhancing characteristic. Mechanism analysis reveals that the policy affects technological bias through both scale and price channels. In the capital–land relationship, both channels contribute to land-enhancing technological change, suggesting that capital inputs associated with farmland construction mainly function as land-supporting investments that improve land productivity rather than simply replacing land. In the labor–land relationship, the scale effect plays a dominant role, indicating that improved farmland conditions reduce dependence on traditional labor inputs and strengthen the productive role of land. Regional heterogeneity analysis shows that the land-enhancing effect is particularly evident in central China, while differences in agricultural development conditions and factor allocation structures lead to heterogeneous regional responses. This study extends the understanding of land use policies by revealing their role in shaping the direction of agricultural technological change. The findings highlight high-standard farmland construction as an important pathway for promoting sustainable land management and long-term food security. Full article

Due to the highly seasonal nature of agricultural production, extensive field trials are required when conducting research and development of agricultural machinery and studying crops. Field trials are not only time-consuming and labor-intensive but also costly; however, the discrete element method (DEM) has found widespread application in agricultural production due to its time- and labor-saving advantages and superior visualization capability. The DEM is extensively used in soil tillage, crop seeding, grain harvesting, and cleaning and separation. This paper provides a comprehensive overview of research progress on discrete elements in the agricultural sector, categorizes methods for directly measuring and inversely calibrating discrete element parameters of agricultural materials, and summarizes the methods for obtaining various parameters and their applicable ranges. classifies crops into stems, leaves, fruits, and whole plants; and summarizes the development and evolution of crop and soil modeling. It comprehensively summarizes the development and significance of multiphysics coupling simulation technologies integrating the Discrete Element Method (DEM) with Computational Fluid Dynamics (CFD), Multibody Dynamics (MBD), and the Finite Element Method (FEM). Finally, this paper analyzes existing challenges and proposed solutions for applying the Discrete Element Method in the agricultural sector and outlines its future prospects. The aim is to provide guidance for the in-depth application of the Discrete Element Method in agricultural equipment research and development (R&D), thereby helping to enhance the efficiency of agricultural mechanization and sustainable food production capacity. Full article

Driven by the escalating global agricultural workforce shortage and the urgent need to meet the “Zero Hunger” mandate, the automation of harvest–transport workflows has emerged as a cornerstone of Agriculture 4.0. This paper highlights the latest research progress in multi-robot collaborative logistics scheduling across highly unstructured farming environments, underpinned by cutting-edge spatial perception and digital twin frameworks. Initially, we summarize the technological leap from conventional 2D geometric mapping to multi-modal semantic 3D reconstruction—fusing light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, and spatial data—to enable high-fidelity forward-looking predictions. The discussion then transitions to algorithmic advancements, emphasizing the shift from traditional centralized operations research to decentralized, data-driven approaches such as Multi-Agent Reinforcement Learning (MARL). We also explore micro-kinematic predictive control mechanisms and the growing integration of ecological sustainability metrics into routing models. To demonstrate practical engineering progress, multi-agent implementations are analyzed across three typical spatial settings: high-throughput continuous relays in open fields, global navigation satellite system (GNSS)-denied discrete routing in dense orchards, and close-proximity human–robot collaboration (HRC) in smart greenhouses. Finally, we identify the remaining barriers to the large-scale commercialization of Agricultural Cyber-Physical Systems (ACPS), such as the “Sim-to-Real” gap restricted by edge-computing capacities, unclosed economic loops, and HRC ethical dilemmas, offering a forward-looking roadmap for next-generation resilient agricultural networks. Full article

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural population aging on food prices and its spatial spillover effects. This study derives the following findings based on empirical research: (1) Rural population aging exhibits a significant inverted U-shaped relationship with food prices, with an inflection point at approximately 19.03%. Before reaching this point, rural population aging helps facilitate food prices. When the inflection point is passed, rural population aging adversely impacts food prices. This effect is significant in western regions but not in eastern and central regions. (2) Farmland transfer and agricultural technological progress significantly influence this relationship, causing the curve to reverse into a U-shaped pattern, which implies a gradual future increase in food prices. (3) Local rural population aging has a significant U-shaped spillover effect on food prices in neighboring provinces. These findings indicate that China’s rural population aging presents a complex dynamic for food price fluctuations. To address the current changes in the population and capital structure and ensure food security, the government will need to formulate forward-looking policies, further improve socialized agricultural services, and systematically optimize food production models. Full article

Induced pluripotent stem cells have revolutionized biomedical research—yet the vast majority of life on Earth remains beyond their reach. Non-model species lack the annotated genomes, validated reagents, and species-specific culture infrastructure that make iPSC technology routine in humans and mice, and this infrastructure deficit, compounded by genuine biological differences in pluripotency network architecture across taxa, is what has kept the field narrow. The deep conservation of the core pluripotency network across vertebrates suggests that reprogramming may, in principle, be achievable across a far broader range of species than currently demonstrated—though the extent to which this holds across more divergent taxa remains to be established. This review consolidates current progress and future potential of iPSC technology across five domains: technical reprogramming challenges and advances; conservation applications including genetic rescue, in vitro gametogenesis, and de-extinction; medical applications within a one medicine framework; agricultural applications spanning disease resistance, climate resilience, and cultured meat; and species-specific iPSC-derived systems in ecotoxicology. Throughout, we distinguish what has been demonstrated from what remains aspirational and identify the priorities that will determine whether the iPSC revolution can be extended—rigorously and at scale—beyond model organism research. Full article

River sediments act not only as important sinks for heavy metal pollution in watersheds, but also as potential secondary sources under changing environmental conditions. Heavy metals can enter river systems through industrial wastewater discharge, agricultural non-point runoff, urban stormwater and sewage inputs, mining and smelting activities, and atmospheric deposition. During adsorption onto suspended particles, sedimentation, and resuspension, metals such as Cd, Pb, Cr, Cu, Zn, Ni, As, and Hg progressively accumulate in sediments. Because heavy metals are persistent, non-degradable, and bioaccumulative, contaminated sediments can record historical watershed pollution while also releasing metals back into overlying water under hydrodynamic disturbance, pH and redox fluctuations, organic matter mineralization, benthic bioturbation, and dredging activities, thereby threatening aquatic ecosystem stability and human health. Using a global methodological framework with particular emphasis on Chinese river basins, this review systematically summarizes key issues in the study of heavy metal pollution in river sediments, including spatial–temporal distribution and operationally defined fractionation, pollution levels and ecological risk assessment, source apportionment, and remediation and management technologies. Current evidence indicates that heavy metal contamination in river sediments exhibits pronounced spatial heterogeneity and watershed-specific characteristics. Its distribution is jointly controlled by geological background, land use patterns, source input intensity, hydrodynamic conditions, sediment particle size composition, and organic matter content. Methodologically, the field has evolved from single total concentration monitoring and exceedance-based evaluation toward integrated assessment systems that combine total concentrations, operationally defined fractionation, bioavailability, ecological risk, health risk, and source contribution. The joint use of BCR sequential extraction, the geoaccumulation index (Igeo), the pollution load index (PLI), the potential ecological risk index (RI), the risk assessment code (RAC), sediment quality guidelines (SQGs), receptor models, isotope tracing, and machine learning has substantially improved pollution identification, risk zoning, and source apportionment. Overall, research on heavy metal pollution in river sediments has shifted from descriptive judgments of whether contamination exists toward mechanistic and management-oriented questions concerning pollution sources, risk evolution, and remediation strategies. However, important gaps remain in compound pollution transformation mechanisms, regional background values and evaluation benchmarks, uncertainty in model parameters, long-term dynamic monitoring, and engineering-scale verification of remediation technologies. Future studies should strengthen multi-media, multi-scale, and long-term monitoring and further integrate fractionation analysis, toxicological effects, source apportionment models, and remediation technologies to provide a scientific basis for watershed ecological security and precision management of contaminated sediments. Full article

China has emerged as one of the most influential actors in South–South scientific cooperation, progressively integrating agricultural innovation, technology transfer, and sustainable development into its international engagement strategy, with tropical agriculture positioned as a strategic domain in its relations with Latin America. However, despite China’s growing role as a driver of agricultural research collaboration, the specific mechanisms through which its institutions transfer knowledge and strengthen local scientific capacities in the Latin American region remain insufficiently studied, particularly in the case of Ecuador. This study adopted a qualitative scoping review methodology following PRISMA-ScR guidelines, screening 9199 records across Scopus, Web of Science, and SciELO, of which 61 documents were retained for thematic analysis. Results show that Chinese cooperation has evolved through three distinct phases: an initial phase centered on agricultural investment and resource-oriented cooperation (2000–2010); a second phase (2010–2018) characterized by the initial institutionalization of scientific and technological cooperation through bilateral agreements, joint action plans, researcher training, and institutional exchanges; and a third phase (2018–present), marked by the deliberate integration of science, technology, and innovation as central pillars of China’s engagement with Latin America, including long-term collaborative research, joint laboratories, scientific networks, and initiatives led by institutions such as the Chinese Academy of Tropical Agricultural Sciences (CATAS). In Ecuador, CATAS’s partnership with the National Institute of Agricultural Research (INIAP) illustrates China’s capacity to build joint research platforms, though sustained impact depends on long-term institutionalization, continuous financing, and transparent governance over genetic resources. The findings underscore China’s expanding support in shaping South–South agricultural cooperation and innovation, and the conditions required to translate this support force into durable, mutually beneficial scientific outcomes. From a public policy perspective, China and Ecuador could strengthen their international agricultural cooperation framework through long-term mechanisms supporting strategic scientific partnerships between the two countries, including multi-year joint research programs, dedicated funding instruments, researcher mobility, and institutional mechanisms for transparent governance of genetic resources and jointly generated intellectual property. Full article

This paper provides a comprehensive review of deep learning in agricultural visual perception. Climate change and land degradation demand a shift from experience-driven to data-driven intelligent agriculture. Traditional manual inspections remain subjective and unscalable. Computer vision offers a non-invasive solution for precision crop and livestock management, while challenges also exist. Its application in actual agricultural scenarios faces specific challenges such as severe occlusion, drastic changes in lighting, and non-rigid deformation of biological targets. To systematically summarize how these perception bottlenecks are being resolved, this review explores deep learning architectures featuring spatial extraction and spatio-temporal modeling. This comprehensive review first constructs a progressive analytical framework from low-level data augmentation and static spatial cognition to high-level dynamic spatio-temporal reasoning, and then systematically classifies existing literature. In-depth analysis reveals that these three core challenges can be computationally resolved using three unified technologies. Domain drift and label scarcity, rather than baseline accuracy, are the main obstacles to practical deployment. We further identified four priority research directions, i.e., architectural efficiency, data-level annotation efficiency, deployment-level privacy and simulation, and trust-level security, to guide future research. Full article

The deep integration of digital technologies with agricultural production is reshaping how rural land is used and offers new opportunities for narrowing the urban–rural gap in land use efficiency. Building on theoretical analysis, this study uses panel data for 700 Chinese counties from 2010 to 2024, treats the Digital Agriculture Innovation and Application Base pilot program as a quasi-natural experiment, and applies a double machine learning model to examine the effect of smart agriculture construction on the urban–rural gap in land use efficiency. The results show the following: (1) Smart agriculture construction significantly narrows the urban–rural gap in land use efficiency, with channel evidence pointing to agricultural technological progress, improved capital allocation, and agricultural industrial upgrading. (2) The policy effect is statistically significant only in formerly poverty-stricken counties, counties with higher land-transfer rates, and counties with stronger digital foundations, while Fisher permutation tests do not establish statistically significant cross-group differences. (3) Decomposition results show that the program raises rural land use efficiency and the rural marginal returns to capital and labor while leaving the corresponding urban indicators statistically unchanged, so the observed convergence is driven primarily by rural catch-up rather than urban decline. From a factor allocation perspective, this study clarifies how smart agriculture construction can rebalance urban–rural land resource use while identifying the statistical limits of the heterogeneity and channel evidence. Full article

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production. Full article

Modified atmosphere packaging (MAP) is a preservation and packaging technology used to extend the shelf life of foods and agricultural products, maintain quality stability, and ensure food safety. To systematically review and summarize the current research landscape, technical challenges, and development trends in the MAP field, this study selected 1568 publications related to MAP from the Web of Science Core Collection (WOSCC) database during 2016–2025 and conducted bibliometric and visualization analysis. The results indicate that research activity in the MAP field has remained robust over the past decade, mainly focusing on four research areas: atmosphere regulation and packaging system design; microbial ecology, safety, and spoilage control; physicochemical deterioration and quality regulation; functional packaging materials and integrated preservation technologies. Countries such as China, Italy, and Spain have demonstrated outstanding performance in terms of publication output and academic influence in the MAP field, forming a solid research foundation in the MAP of perishable foods such as fruit and vegetables, meat products, and aquatic products. Research in the MAP field has gradually shifted from the verification of application effects toward system design and mechanistic analysis. Active packaging, intelligent packaging, bio-based materials, natural functional ingredients, volatile organic compounds, microbial community succession, and quality deterioration mechanisms have gradually become research hotspots, indicating a trend toward precision, sustainability, and functionality. These findings may provide references for future research topic selection, innovative packaging system design, and the development of novel food preservation technologies in the MAP field for foods and agricultural products. Full article