Impact of agricultural inputs on soil organisms—a review
Bibliographic record
Abstract
External agricultural inputs such as mineral fertilisers, organic amendments, microbial inoculants, and pesticides are applied with the ultimate goal of maximising productivity and economic returns, while side effects on soil organisms are often neglected. We have summarised the current understanding of how agricultural inputs affect the amounts, activity, and diversity of soil organisms. Mineral fertilisers have limited direct effects, but their application can enhance soil biological activity via increases in system productivity, crop residue return, and soil organic matter. Another important indirect effect especially of N fertilisation is soil acidification, with considerable negative effects on soil organisms. Organic amendments such as manure, compost, biosolids, and humic substances provide a direct source of C for soil organisms as well as an indirect C source via increased plant growth and plant residue returns. Non-target effects of microbial inoculants appear to be small and transient. Among the pesticides, few significant effects of herbicides on soil organisms have been documented, whereas negative effects of insecticides and fungicides are more common. Copper fungicides are among the most toxic and most persistent fungicides, and their application warrants strict regulation. Quality control of organic waste products such as municipal composts and biosolids is likewise mandatory to avoid accumulation of elements that are toxic to soil organisms.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".