No-tillage with straw mulching (NTSM) is one of the effective conservation tillage practices in China. However, continuous NTSM practice can lead to soil compaction. Therefore, it is of great significance to explore the effects of different tillage practices following short-term NTSM on soil physical properties, which is important for optimizing tillage practices, developing rotational tillage systems and improving soil health. An experiment was conducted with a randomized block design with three replicates of three tillage treatments, viz. deep plowing (DP), rotary tillage following subsoiling (RTFS) and subsoil tillage (SST), following two consecutive years of NTSM from 2019 to 2022 in typical Mollisols of northeastern China. Soil water content (SWC), bulk density (SBD), temperature, penetration resistance (SPR), physical properties, were tested in the 5-, 15-, 25- and 35-cm soil layers in the plant row of each experiment plot at the three-leaf (V3, 21 May), six-leaf (V6, 27 June), milk ripe (R3, 2 August) and full ripe (R6, 20 September) stages of the maize growth cycle in 2022. The results indicated that SWC in the 25- and 35-cm soil layers was significantly higher than in the 5- and 15-cm soil layer under DP treatment at all growth stages (P < 0.05). SBD under DP treatment was on average 5.9% and 9.3% lower than under RTFS and SST treatments in 25–35 soil layers (except for the R3 stage) (P < 0.05). SPR under DP treatment was on average 18.6% and 33.8% lower than under RTFS and SST treatments in the 15- and 25-cm soil layers (P < 0.05). DP treatment was the effective tillage practice after 2 years of NTSM operation for improving soil physical properties.
Prolonged flooding creates strongly reductive paddy soils that limit nutrient availability and increase greenhouse gas emissions. To address this issue, air and oxygen micro-nano bubbles were evaluated as a low-input amendment in a pot experiment. The oxygen bubbles remodeled soil structure (increased micropores 2.4 times) and raised soil ORP to 374 mV. These changes expanded the abundances of CNP genes, reduced total reducing substances by 22.4%, improved nutrient availability (increased alkali-hydrolyzable nitrogen, available phosphorus and potassium to 216, 72.2 and 284 mg·kg−1, respectively), and lowered the global warming potential by about 26%. Correlation and path analyses indicated that micropore expansion, rather than greater connectivity, plus moderate re-oxidation drove microbial functional gains, nutrient release and methane oxidation. This study offers valuable insights into the multidimensional connection of soil structure, nutrients and soil ecology and it offers a chemical-free route for the green remediation of waterlogged fields.
Agricultural socialized services (ASS) are essential for connecting smallholders with modern agriculture, enhancing agriculture productivity and driving sustainable green production. Therefore, these services constitute a fundamental pillar of contemporary agricultural frameworks. This study empirically analyzed how ASS drive agricultural green production under China’s dual-carbon goals (carbon peaking and carbon neutrality) using an extended regression model with micro-macro panel data. This empirical analysis provided four key findings: (1) ASS exert a positive effect on agricultural green production on the household level; (2) rural household resource endowments serves as a positive moderator of this relationship; (3) ASS exert non-linear impacts on agricultural green production, with distinct threshold effects observed at critical service provision levels and (4) ASS exhibit dual heterogeneity-regional disparities and scale-dependent responsiveness. These findings highlight the dual environmental-rural development benefits of the services, prompting the three-pillar policy framework: (1) farmer endowment empowerment policy system establishment, (2) adoption of endowment-sensitive differentiation strategy, and (3) multi-actor carbon governance. This study contributes to sustainable agriculture theory while offering actionable strategies to advance green agricultural development and enhance the integration of agricultural socialization services with smallholder operations.
To address the inherent problems of high labor costs and poor efficiency of current visual diagnosis methods for tea leaf diseases, this study proposes a GDE-YOLO-based real-time detection method for tea leaf diseases identification in complex tea plantation environments. The proposed architecture integrates three key enhancements: (1) combination of the neck network with a global attention mechanism (GAM), (2) optimization of the C2f module through a diverse branch block (DBB), and (3) replacement of a complete intersection over union loss function with an efficient intersection over union loss function, collectively improving recognition accuracy and speed. Experimental validation demonstrates that GDE-YOLO achieved 91.7% precision (3.1% higher than YOLOv8n) across different tea plantation scenarios and disease types, with specific improvements of 0.7% for tea anthracnose and 12.4% for tea white spot detection. Also, the enhanced model attained 80 FPS real-time performance. The deployment test on the NVIDIA Jetson Orin Nano edge device showed that GDE-YOLO could achieve precise diseases identification with confidence threshold > 0.8 and inference speed maintaining 18 FPS, satisfying edge computing requirements of accuracy and real-time performance. This research provides critical technical foundations for vision-guided precision sprayers in tea plantations, while promoting the practical implementation of machine vision in intelligent agricultural management.
Although the chemical and biochemical pathways of phosphorus (P) mobilization following vermicompost application are relatively well-documented, the microbial processes that drive the release of sparingly-soluble P remain insufficiently explored. Consequently, this study examined key microbial mechanisms that enhance soil-P bioavailability in response to vermicompost application. First, vermicompost-derived microorganisms can mobilize inorganic P by releasing solubilizing compounds such as protons, siderophores, and carboxylates. Second, vermicompost-derived microorganisms mobilize organic P by exuding phosphatases. Resident soil microorganisms can also contribute to these processes when they are stimulated by the changed soil properties following vermicompost application, including pH and the concentrations of mineral nitrogen (N) and organic carbon (C). Additionally, microbial cell lysis can increase dissolved P concentrations in the soil solution, particularly as microbial biomass expands with vermicompost-derived organic matter inputs. Moreover, the study further explored how microbial mineralization of organic P is facilitated by carboxylates and the C:N ratio in vermicompost. Interactions between vermicompost-derived microorganisms and resident soil microbial communities are crucial, as competition or cooperation between them can significantly affect inorganic-P solubilization and organic-P mineralization. The rapid advancement of multi-omics technologies and the development of synthetic microbial community approaches provide opportunities to identify and optimize efficient P-solubilizing microorganisms. Future research should focus on elucidating microbial interactions and long-term effects of vermicompost-derived microorganisms on soil-P cycling, particularly in agricultural systems. This will provide a foundation for developing tailored, highly efficient P-solubilizing microbial inoculants, effectively translating theoretical insights into practical applications for sustainable agriculture.
Rapid development of the pig industry in China has led to numerous challenges in managing livestock manure and slurry. Field application of slurry has proven to be an economical, effective and environmentally beneficial approach to sustainable resource recycling globally. However, China remains largely dependent on fertilizer inputs from mineral sources, with limited adoption of slurry application practices. A common challenge with slurry field application is its typically higher emission of ammonia and greenhouse gases (GHGs) compared to mineral fertilizers. This work investigated selected treatments with specific ratios of pig slurry and mineral fertilizers aimed at reducing the use of mineral fertilizers and emission following basal fertilizer application and topdressing after maize planting specifically, the ratios of pig slurry included 30%, 50% and 100%. The methods of fertilizer application involved a comparison of acidified versus non-acidified pig slurry for field application, as well as a comparison between sprinkler and drip irrigation. The results showed that replacing 30% of mineral fertilizers with pig slurry (RC30) reduced total GHG emission by 62% and NH3 emission by 60.4% compared to a full slurry substitution during field application. Meanwhile, the RC30 group recorded the lowest total NH3 emission, totaling 5.08 kg·ha−1, among all treatments using pig slurry. The acidification of pig slurry significantly reduced NH3emission, decreasing them by 42.1% compared to the direct application of untreated pig slurry. Drip irrigation proved to be more effective in reducing total GHG emission compared to sprinkler irrigation. Drip irrigation reduced NH3emission by 38.9%–42.6%, N2O emission by 12.4%–18.6%, and GHG emission by 21.5%–34.7%. In summary, this study demonstrated that replacing 30% of mineral fertilizers with pre-acidified pig slurry, combined with drip irrigation, reduced GHG and NH3 emission.
As a vital component of the forestry economy, the enhancement and development of tea-related socialized services contribute significantly to the diversification of forestry-based economic activities. This study established a theoretical framework to examine the multidimensional effects of tea farmer adoption of such services. These findings indicate that the household head age, health, number of family agricultural laborers, productive tea garden area, government technical training, green production demonstration zone and engagement in cooperatives have significant positive impacts on the adoption of socialized services, whereas factors such as tea garden slope, government penalties and family social status exert inhibitory effects. Also, the determinants influencing adoption vary across different service links. Adoption of socialized services by tea farmers also demonstrates multiple beneficial outcomes, including the inhibition of land outflow, increase the quantity of green technology adoption, higher income levels, and enhance subjective well-being (SWB), with the magnitude of these effects differing across various production links. Additionally, the impact of adopting socialized services varies between individual groups and village groups, particularly in terms of household income, land transfer and green technology adoption. Finally, the adoption of socialized services influences tea farmer SWB through pathways involving total income, tea income, green technology adoption, and leisure time.
Mulched drip irrigation with appropriate strategies is recommended for effectively reclaiming saline soils in the Hetao Irrigation District of China. This study investigated soil water-salt dynamics and crop growth under different irrigation strategies through soil box experiments, scenario simulation experiments and field validation experiments, with particular focus on two drip irrigation strategies: single 30-mm irrigation and split irrigation (15 mm on Days 1 and 15). Hydrus-2D was used to simulate the distribution of water and salt. The results demonstrated that soil water content (SWC) fluctuated under mulched drip irrigation, with higher amounts near the drip emitter. The highest SWCs with the single and split irrigations were 0.22 and 0.19 cm3·cm–3, respectively. The split irrigation strategy better maintained SWC within the 0−25 cm depth range. Simulation experiments further revealed that increasing irrigation amounts to 40 or 50 mm effectively sustained soil water content, while producing salt leaching effects largely comparable to the 30-mm irrigation. The lower soil salinity (0.53 g·kg–1) was recorded with split irrigation. However, no significant differences in salinity were observed between the tested mulched drip-irrigation strategies within the 0−15 cm soil depth. Field validation experiments demonstrated that split irrigation resulted in significantly greater plant height, stem diameter and leaf area index compared to the single irrigation at about 30 days after sowing. It was concluded that with a limited 30-mm irrigation, split drip irrigation effectively delays soil water depletion, performs better than a single drip irrigation by enhancing overall soil water content and promoting desalination, and thereby facilitating improved crop growth.
This study investigated the effectiveness of manure-based slurry film (MSF) technology and validated its dual benefits in reducing mineral fertilizer input and enhancing fertilizer use efficiency through field experiments. The results showed that MSF maintained silage maize yield while reducing mineral fertilizer input by 30%, and achieved a significant yield increase under 15% reduction in fertilizer input compared to standard polyethylene film. An investigation of soil microorganisms demonstrated that MSF changed the microbial community structure, promoting the activation and use efficiency of nutrients such as nitrogen, phosphorus and potassium, while increasing soil organic matter content. Life cycle assessment was performed with SimaPro 9.5 revealing that the environmental impact of MSF is significantly greater than that of polyethylene film across various environmental assessment factors. The high environmental impact of MSF production stems from its energy and water consumption, necessitating a focus on process simplification while maintaining high yield. This study provides new insights into the development of cleaner technologies, examines diversified uses of cow manure, emphasizes the role of MSF in reducing mineral fertilizer application, and highlights potential environmental risks.
The accumulation and spread of agricultural environmental pollutants pose a serious threat to the ecological environment and crop growth. Accurately predicting changes in pollutant concentrations is of great significance for achieving sustainable agricultural development. In response to the challenges of predicting pollutant concentrations in agricultural environments, this paper proposes a novel hybrid deep learning model. The variational mode decomposition algorithm is used to process raw data, reducing nonlinearity and enhancing feature distinguishability. A double-layer attention mechanism based on sample entropy evaluates sub-sequences and focuses on key regions, further improving the predictive performance of the model. Finally, a long short-term memory neural network is used to obtain prediction results. In time series prediction experiments involving multiple pollutants, the proposed method demonstrated the needed stability and accuracy. Experimental results indicate that, compared to existing methods, this approach achieves a minimum improvement of 4.8% in mean absolute error and 23.5% in mean absolute percentage error for predicting concentrations of three pollutants. Also, the root mean square error of predictions is reduced by up to 29.1%. This study provides reliable technical support for agricultural environmental pollutant monitoring. With mean absolute errors of 5.92, 6.85, and 2.38 for CO, non-methane hydrocarbons and NO2 predictions respectively, it accurately predicts pollutant variation risks. In the future, it can be deployed on mobile robot platforms to achieve automatic monitoring and early warning, thereby promoting the development of smart agriculture.
Tomato is a globally important economic crop whose growth and development are influenced by various environmental factors. As an emerging physical regulation method, high-voltage electrostatic fields (HVEF), has recently gained attention in plant growth modulation. This study investigates the effects of positive (+HVEF) and negative HVEF (−HVEF) on the phenotypic characteristics, microstructure and mechanical properties of tomato seedlings. Fourier-transform infrared spectroscopy, inductively coupled plasma analysis and electrical characterization techniques, were used to systematically examine changes in functional group distribution and ion profiles within plant tissues. The results show that +HVEF treatment for 25 d significantly enhanced the mechanical strength and photosynthetic performance of tomato seedlings. These improvements were associated with the modulation of polar functional groups and magnesium ion distribution, optimizing the electrophysiological responses of the plant. Electrical measurements further demonstrated that +HVEF altered membrane potentials and ion channel activities, thereby impacting overall physiologic functions and enhanced membrane hyperpolarization, stimulated key photosynthetic enzyme activities and promoted biomass accumulation in tomato seedlings. This study, through a multiscale analysis, elucidates the synergistic regulation of plant electrophysiology and photosynthesis under electric field exposure. These findings provide a theoretical foundation for the application of electric field technologies in agriculture and offer a basis for integrating equivalent circuit models with plant sensors.
Agrophotovoltaics (APV) integrates solar photovoltaic systems with agricultural production, offering a sustainable solution to meet growing energy and food demands. This review explores the transformative role of artificial intelligence (AI) and machine learning (ML) in optimizing APV performance. Key applications include energy yield forecasting, predictive maintenance, fault detection and crop-energy balancing strategies. Advanced algorithms, such as neural networks, decision trees and reinforcement learning, deliver high prediction accuracy and significant operational improvements. Reported studies show forecasting accuracies up to R2 of 0.96 for simulated irradiance datasets, representing model prediction performance rather than actual plant energy conversion efficiency. Likewise, AI-driven co-optimization of light distribution and irrigation improved leafy-vegetable yields by 10%–18% in experimental APV plots in India and France. The integration of Internet of Things sensors with ML models enables real-time environmental monitoring and dynamic resource management. This review presents the integrated synthesis of AI/ML applications specific to APV systems. It proposes a taxonomy of AI/ML use-cases, a research roadmap, and a quantitative synthesis linking model performance metrics with agronomic and energy outcomes. The study also identifies research gaps in data interoperability, environmental variability, model interpretability, and socioeconomic adoption. Addressing these challenges through interdisciplinary research and policy frameworks can accelerate intelligent APV deployment, advancing renewable energy, sustainable agriculture and climate resilience.