Balancing agricultural benefits and environmental risks: Trace element contamination from prolonged straw incorporation
Bibliographic record
Abstract
Context Straw return (SR) is an effective resource recycling practice that improves soil properties by providing essential nutrients and organic matter, while also reducing agricultural waste. However, emerging evidence indicates that SR enhances the bioavailability of metals (e.g., Cd and Hg) in soils and crops, potentially threatening food safety and sustainable agriculture. Nevertheless, this conclusion has not been tested globally. Method We conducted a global-scale meta-analysis to examine the effects of SR on major trace elements (TEs), including heavy metals in cropland, using 1306 paired datasets. Results Our findings show that SR increases the concentration of total Cd (T-Cd) (3.47 %), T-Hg (8.21 %), T-Cu (2.00 %), T-Pb (3.28 %), and T-Cr (1.10 %) in soil; T-Hg (24.78 %), T-Pb (6.50 %), T-Ni (25.35 %), and T-Fe (25.48 %) in straw; and T-Zn (7.06 %) and T-Mn (8.16 %) in grain. Further analysis reveals that SR significantly increases TEs concentration in the topsoil. SR exhibits a time-accumulative effect on TEs concentrations, with a significant increase observed for durations exceeding 5 years ( p < 0.05). TEs contamination is more severe in paddy fields than in upland and upland-paddy rotation systems. The random forest model demonstrates that high temperatures, heavy rainfall, low initial soil bulk density, low nitrogen input, and prolonged SR increase the risk of TEs pollution. Furthermore, SR promotes TEs accumulation in the soil and their migration to crop straw and grains through three mechanisms: the dissolution effect (lowered pH increasing metal ion solubility), the complexation effect (DOM complexation), and the physiological effect (enhanced root growth promoting metal adsorption and accumulation). Conclusions Overall, our research highlights TEs pollution resulting from SR, elucidates its driving factors and mechanisms, and provides theoretical guidance for implementing safe SR practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".