MétaCan
Menu
Back to cohort
Record W4410592658 · doi:10.1016/j.fcr.2025.109992

Balancing agricultural benefits and environmental risks: Trace element contamination from prolonged straw incorporation

2025· article· en· W4410592658 on OpenAlexaff
Weiting Ding, Qi Wang, Vilim Filipović, Jinbo Li, Zhiming Qi, Liangjie Sun, Yeru Wu, Hailong He

Bibliographic record

VenueField Crops Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Manitoba
FundersHigh-end Foreign Experts Recruitment Plan of ChinaMinistry of Science and Technology of the People's Republic of China
KeywordsContaminationStrawEnvironmental scienceTrace elementAgricultureTRACE (psycholinguistics)AgronomyEnvironmental chemistryChemistryBiologyEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.038
GPT teacher head0.320
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueField Crops ResearchSame topicHeavy metals in environmentFrench-language works237,207