Crop-specific and microbially-mediated impacts of alternative agricultural amendments on plant performance
Bibliographic record
Abstract
Abstract Rising fertilizer costs and dwindling freshwater supplies are driving interest in alternative agricultural amendments such as biosolids and reclaimed water. While these inputs can promote crop growth, they also contain persistent contaminants like PFAS, which may affect plants directly or indirectly via changes to plant-associated microbiomes. The extent and mechanisms of these effects remain poorly understood, particularly across crops with differing functional traits. We conducted two greenhouse experiments to evaluate the direct and microbially mediated effects of biosolid and reclaimed water amendments spiked with increasing contaminant concentrations on lettuce ( Lactuca sativa ), radish ( Raphanus sativus ), and green pea ( Pisum sativum ). Peas exhibited pronounced negative responses to biosolids, including reduced germination, survival, and biomass, which scaled with contaminant concentration and coincided with visible pathogen infection. Lettuce and radish showed minimal impact, suggesting that plant functional traits—such as symbiotic capacity and nutrient-dependent immune regulation—may mediate susceptibility to contaminant-induced stress. Soil microbiome diversity and composition were altered in a crop- and amendment-specific manner, but microbiomes shaped by prior exposure had limited effects on pea performance when transferred to new plants in the absence of ongoing stressors. These findings suggest that current soil conditions and active plant–microbiome interactions may outweigh legacy microbiome effects. Overall, our study provides a novel framework for evaluating the risks and benefits of wastewater-derived amendments, emphasizing the importance of functional trait variation in predicting plant responses to contaminants and guiding sustainable agricultural 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".