Contrasting effects of food waste and its biochar on soil properties and lettuce growth in a microplastic-contaminated soil
Bibliographic record
Abstract
Abstract The incorporation of organic amendments, such as food waste (FW) and biochar, into soil is an established agronomic practice known for enhancing soil fertility and improving overall soil health. However, the individual and combined effects of FW and biochar on soil properties in microplastic (MP)-contaminated soil–plant systems remain poorly understood. To address this knowledge gap, we conducted a field experiment to investigate the individual and combined effects of polystyrene MPs, FW, and FW-derived biochar on soil properties and lettuce growth. Soil chemical properties were unaffected by the addition of MPs. However, the application of FW and biochar increased the soil pH, with the highest pH (8.2) observed in the combined treatment of biochar and MPs. Despite the presence of MPs, FW application resulted in notable increases in soil electrical conductivity (EC; 2.04 dS m−1), available nitrogen (NO3−–N: 325.5 mg kg−1, NH4+–N: 105.2 mg kg−1), available phosphorus (88.4 mg kg−1), and total exchangeable cations (18.6 cmol(+) kg−1). However, these values decreased after lettuce cultivation. In soil cultivated with lettuce, the coexistence of MPs and biochar reduced soil Fluorescein diacetate hydrolase enzyme activity by 46.2% and urease activity by 94.0%. FW addition doubled acid phosphatase activity, whereas FW and its coexistence with MPs decreased alpha diversity. The relative abundance of Actinobacteria decreased with MP application, whereas that of Acidobacteria and Actinobacteria decreased with FW treatment. Gemmatimonadetes and Nitrospirae decreased in soil treated with FW and biochar. The highest relative abundances of Firmicutes and Proteobacteria were observed in the FW-added soils, and Planctomycetes were the highest in the biochar-added soils. FW application negatively affected lettuce growth. Overall, the coexistence of MPs with FW or biochar had limited effects on soil properties and lettuce growth, with FW and biochar serving as the primary factors in modifying soil–plant systems. Future studies should investigate the effects of different MPs and their interactions with organic soil amendments on soil properties and crop growth under different management practices. Graphical Abstract
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".