Efficient and clean separation of residual plastic mulch film is the primary bottleneck hindering its resource-oriented reutilization. Currently, the field faces critical technical challenges, most notably the elusive motion mechanisms of flexible materials and the inherent difficulty of film–impurity separation. To address these issues, this study investigates a drum-type mulch-film impurity-removal unit by modeling the throw-off motion mechanism of the material stream, followed by comprehensive multiphysics simulation and optimization. First, to overcome the simulation hurdles typical of flexible materials, “Meta-particles” and the “Bonding V2” contact model were implemented on the EDEM platform to establish a discrete element method (DEM) framework. The resulting analysis revealed a non-linear transport trajectory and morphological evolution within the drum flow field, characterized by a “wall-adhering–slipping–throwing” sequence. These findings were further quantified through MATLAB-based numerical calculations to determine collision frequency and axial residence behavior. Second, ANSYS modal analysis verified the dynamic stability of the frame structure, confirming that the operating frequency (2.37 Hz) remains well below the first natural frequency (6.77 Hz). Furthermore, Box–Behnken response surface methodology (RSM) was employed to elucidate the coupled effects of key process parameters. The results demonstrated that separation efficiency and impurity-removal mass are predominantly governed by the quadratic terms of the inclination angle and rotational speed, respectively. After multi-objective optimization and engineering refinement, the optimal operating parameters were established: a film length of 220 mm, an inclination angle of 3°, and a drum rotational speed of 25 r/min. Bench tests indicated that, under these optimal conditions, the impurity-removal rate stabilized between 71.5% and 72.4%, satisfying the design requirement (≥70%). By elucidating the drum’s throw-off screening mechanism, this study achieves a coordinated improvement in both impurity-removal mass and separation efficiency, resolving long-standing engineering uncertainties regarding film–impurity trajectories and providing a theoretical foundation for the clean treatment of waste mulch film.

Pursuing the so-defined biorefinery approach, residual biomass, such as agro-industrial wastes, should first be exploited for the extraction and production of high-value-added products and then processed for energy valorisation through anaerobic digestion (AD). However, the treatments applied to achieve the first goal could impact biogas yield. This problem can be solved by co-digesting the treated biomass with others. In this study, Brewery’ Spent Grain (by itself, a good biogas producer) was treated with an ionic liquid (IL) composed of triethylamine and sulfuric acid [TEA][HSO4] for lignin removal. The residual biomass (pulp, BSGp) was then used for biogas production. The tests revealed a marked reduction in the total quantity of biomethane (per unit of volatile solid—VS). In detail, 6.82 × 10−4 Nm3CH4/gVS of biomethane was produced with BSGp, against 1.31 × 10−3 Nm3CH4/gVS with BSG. The lack of organic nitrogen after the IL-based treatment prevented biogas production, resulting in a shorter production period. To compensate for the nitrogen deficiency and restore the optimal C/N ratio, BSGp was mixed with Lemna minor (LM), an aquatic weed with a high nitrogen content. By itself, LM cannot be considered a good biogas producer as proven in this study. However, the co-digestion of LM with BSGp extended the production period and kept the daily production close to that registered in test made with the sole BSGp, thus achieving a total biomethane production equal to 1.83 × 10−3 Nm3CH4/gVS, even higher than the one registered with untreated BSG.

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Selenium (Se) is an essential trace element for humans, and agricultural soils are a major source of dietary Se. Therefore, identifying the key environmental drivers of Se in farmland is crucial for evaluating the resource base for Se-rich agriculture and improving human health. Although soil Se distribution and its controlling factors have been widely investigated, quantitative assessments of soil Se in small-scale farmland systems under humid monsoon conditions remain limited. Sampling sites were designed to represent different geological types, soil types, and topography, and 314 farmland topsoil (0–20 cm) samples were collected. Total Se was determined after complete HNO3–HClO4 wet digestion and quantified by HG–AFS (AFS–830), with certified reference materials showing recoveries of 95.3–101.2%. The spatial patterns were mapped using ordinary kriging. Geographically weighted regression (GWR) and Geodetector were used to explore the impact of environmental factors (geological type, precipitation, etc.) on soil Se from both local and overall perspectives. The findings reveal a mean total soil Se of 1.76 mg/kg (95% CI: 1.540–1.974), and 91.40% (n = 287) of soil samples were classified as Se-rich (0.4–3 mg/kg). Organic matter (OM), elevation, slope, and the topographic wetness index (TWI) exhibited non-stationary spatial relationships with Se. The spatial variation trend of precipitation corresponds with the local R2 values between Se and elevation, indicating that precipitation may strengthen the association between elevation and Se distribution. Geological type and rainfall were identified as key driving factors affecting soil Se content within the study area, particularly through their interactions with OM. Overall, the synergistic effects of geological type, precipitation, and OM are responsible for the accumulation of Se in the agricultural soils of Xin’an Town.

The eastern honeybee (Apis cerana) is a keystone pollinator for native ecosystems and agricultural crops in China. However, its distribution faces significant uncertainty due to accelerating climate change. To quantify these risks and inform management strategies, we employed an ensemble species distribution model (Biomod2) integrating ten algorithms to project the suitable habitat of the eastern honeybee under current and future (2060s and 2100s) climate scenarios (SSP126, SSP245, and SSP585). The ensemble model achieved an excellent predictive performance (AUC > 0.9, TSS > 0.8). The current suitable habitat spans approximately 1.47 million km2, primarily south of the Yangtze River. Biomod2 simulation indicates that the precipitation of the wettest month and mean diurnal temperature range are the dominant environmental stressors influencing the shift in the spatial distributions of the eastern honeybee. Comparisons between current and future climate scenarios reveal a distinct trend of spatial range contraction in southern China and a northwestward shift of the habitat centroid. The most severe impact is projected under the SSP585 scenario, with a potential net habitat loss of 42.25% by 2100. We propose a dynamic conservation strategy that prioritizes the protection of southern climate refugia while managing habitat connectivity to facilitate the species’ migration, thereby safeguarding agricultural resilience.

This study calibrated discrete element parameters for organic fertilizer (OF) and compost fertilizer (CF) to support spreading equipment design. Using the Hertz–Mindlin with JKR model, DEM simulations were integrated with physical angle of repose measurements. Parameters were systematically optimized via Plackett–Burman screening, steepest ascent, and Box–Behnken response surface methodology. Results indicated distinct moisture-sensitive behaviors: OF exhibited monotonic increases in dynamic friction coefficient (0.223–0.362) and JKR surface energy (0.064–0.166 J/m2), whereas CF showed nonlinear friction trends with surface energy rising from 0.209 to 0.326 J/m2. A predictive model directly linking moisture content to DEM parameters was established using the cylinder-lifting method. Validation confirmed model accuracy, with angle of repose errors of 2.57% (OF) and 4.05% (CF). Simulated spreading widths closely matched field data, showing relative errors below 8%. The calibrated DEM framework provides a reliable basis for optimizing organic manure spreader performance.

Biochar, produced by pyrolyzing biomass under limited oxygen, can improve soil quality while supporting long-term carbon sequestration. This study compared two wheat-straw biochars (BC)made at 450 °C (BC1) and 600 °C (BC2), with a commercial hardwood biochar produced at 1280 °C (BC3) using lettuce in a sandy, nutrient-poor soil under a carbon capture, utilization, and storage (CCUS) perspective. Higher pyrolysis temperature increased fixed carbon, ash, and alkalinity and reduced volatile matter, indicating greater carbon stability (BC2 > BC1). Germination tests showed good compatibility, with BC1 performing best, likely because moderate temperatures retain more labile organic fractions. In growth-chamber trials (0.75% w/w), biochar boosted lettuce biomass and root development mainly when combined with mineral fertilization, with BC2 (25% and 59%, respectively) and BC3 (18% and 52%, respectively) yielding the strongest gains; unfertilized plants responded little, confirming that biochar is mainly a soil conditioner rather than a nutrient source. Biochar also stimulated soil enzymes linked to C, N, and P cycling and improved leaf chlorophyll, nitrogen status, and antioxidant capacity under fertilization. The nutrient profiles differed by biochar: BC1 increased K and nitrate, while BC2/BC3 lowered nitrate and BC3 enhanced Ca, Mg, and P uptake. Overall, agronomic outcomes depend on feedstock and pyrolysis temperature: mid-temperature biochars enhance productivity and soil biological activity, whereas high-temperature biochars maximize carbon permanence.

Rural households’ risky financial asset selection (RFAS) is the foundation of households’ diversified asset allocation, which in turn helps expand their sources of property income. However, rural households rarely participate in risky financial markets due to limited participation access and a lack of financial knowledge and market information. Digital Village Construction (DVC) has brought new opportunities for a change in this phenomenon. This study determines the impact of DVC on RFAS using data on 5593 rural households from the 2020 China Family Panel Studies and County Digital Village Index. The findings show that DVC significantly increases the likelihood, amount, and rate of rural households’ RFAS. However, the impact of DVC varies across its different dimensions. Specifically, the development of digital infrastructure, digital economy, and digital lifestyles each exerts a significant positive effect on RFAS, whereas digital governance does not show a statistically significant impact—likely due to the underdevelopment or inefficiencies of current digital governance platforms. Mechanism analyses reveal that DVC promotes rural households’ RFAS by improving farmers’ access to information and expanding their market participation opportunities, while rural households’ education expenditure and pension income uncertainties weaken this positive effect. Heterogeneity analyses suggest that the impact of DVC on rural households’ RFAS is more pronounced among young families, those with lower education levels, and high-income families. With the projected advancement of digital villages in China, this study offers crucial guidance for implementing policies, such as the Digital Village Construction Guidelines, by guiding rural households toward more rational and inclusive participation in risky financial markets.

The concentrations of nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), and pH in soil are critical markers of fertility that influence crop growth and yield. Traditional wet-chemical analyses are labor-intensive, time-consuming, and costly, thereby constraining timely soil information acquisition for precision agriculture. This study evaluates whether multi-output Gaussian process regression (MOGPR) can enhance the prediction accuracy of multiple soil nutrients by exploiting their intrinsic correlations, in comparison with single-output Gaussian process regression (SOGPR). Near-infrared (NIR) spectroscopy was applied to 622 typical black soil samples collected from the Farm 855 (45°43′ N, 131°35′ E), Heilongjiang Province, China. Corresponding MOGPR and SOGPR models were developed for systematic performance comparison. Results indicated that MOGPR significantly outperformed SOGPR for nutrients exhibiting moderate-to-strong intercorrelations (N, P, K, and OM), yielding R2 improvements of 0.070.28 and RPD increases of 16–40%, whereas only limited gains were observed for pH due to its weak correlations with other nutrients. These findings indicate that combining NIR spectroscopy with MOGPR offers significant potential for rapid, nondestructive assessment of multiple soil nutrients. This study further establishes a correlation-aware multi-output modeling framework that links shared spectral responses with an inter-nutrient dependency structure, providing methodological guidance for multi-nutrient soil prediction.

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through physical experiments, the mean average diameter, moisture content, density, Poisson’s ratio, and elastic modulus were measured. The coefficients of collision restitution, static friction, and rolling friction between the potato seed and the Q235 steel plate were determined as 0.576, 0.559, and 0.073, respectively. Using the actual repose angle of 22.89° as the response target, and combining the steepest ascent test with the Box–Behnken design, the non-cohesive contact parameters between potato seed particles were calibrated. The resulting coefficients of collision restitution, static friction, and rolling friction between particles were determined as 0.404, 0.412, and 0.0156, respectively. Finally, based on physical shear tests (maximum shear force 23.56 N), significant influencing factors were identified through Plackett–Burman screening as the bonding radius ratio r and the normal stiffness per unit area Kn. Using the steepest ascent test and the Box–Behnken response surface method, the key bonding parameters of the Bonding V2 model were calibrated as follows: r = 1.098, Kn = 8.597 × 107 N·mm−3, tangential stiffness per unit area Kt = 3.250 × 106 N·mm−3, critical compressive stress σn = 5.500 × 105 Pa, and shear strength τt = 3.000 × 104 Pa. The relative error between the simulated and actual maximum shear forces was 0.89%, which is small. The calibrated flexible model accurately represents the physical characteristics of potato seeds and provides a reliable reference for the design of mechanized potato seed cutting and sorting equipment.