Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility (${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$) measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$ ranging from 6.45 × 10⁻⁸ m³/kg to 319.23 × 10⁻⁸ m³/kg and ${\mathsf{\chi }}_{\mathrm{f}\mathrm{d}}$ from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$ (r = 0.51–0.53) and Mn and Fe with ${\mathsf{\chi }}_{\mathrm{f}\mathrm{d}}$ (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$. Regression analysis confirms soil is considered to be non-polluted below ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$ of $26\times {10}^{-8} {\mathrm{m}}^3/\mathrm{k}\mathrm{g}$ and polluted above this threshold, with all contamination factors of metals showing a linear correlation with ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$ (R = 0.72), indicating that a significant relationship between ${\mathsf{\chi }}_{\mathrm{l}\mathrm{f}}$ and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response.

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model’s parameters for paddy field soils was further validated through a comparative analysis of the simulation test’s cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p < 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p < 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m², a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model’s contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields.

To elucidate the vibrational characteristics of power chassis in paddy fields, we examined the Yanmar VPG6G rice transplanter across diverse terrains, including paddy fields, dry land, and concrete roads. Vibrational acceleration measurements, taken in longitudinal, transverse, and vertical orientations at key chassis locations, revealed noteworthy findings. The Mizuta power chassis exhibited its lowest root-mean-square (RMS) vibrational acceleration on concrete, while the highest was observed on paddy fields. The acceleration power spectra predominantly peaked between 1~14 Hz, with peak values amplifying as speed increased. Additionally, pendant orientation frequencies exceeded those of longitudinal and lateral directions. Both front and rear wheels mirrored the vibrational accelerations of the rear axle, but dynamic load coefficients for the front wheels consistently surpassed the rear, particularly at elevated speeds. This research not only enhances our understanding of terrain-induced vibrations and the intricate dynamics between terrain and tires but also lays the groundwork for designing optimized vibration-damping solutions tailored to prevalent road conditions.