Anthropogenic drivers of soil microbial communities and impacts on soil biological functions in agroecosystems
Bibliographic record
Abstract
Anthropogenic interventions play a key role in promoting positive feedback of soil–plant–environment interactions, but systematic reports on how anthropogenic activities influence soil physiochemical, microorganism-induced properties and soil health are still limited. Here, we assessed the impact of anthropogenic interventions, including crop diversification in rotations, soil physical disturbance, synthetic chemical inputs, and biofertilizer use on soil microbial community structure and function, and the consequential effects on agroecosystem productivity and environmental sustainability. Summarizing the results of over 160 medium- to long‐term experiments from various soil-climatic zones across the globe in this review illustrated that (1) increasing crop diversification in rotations could bring positive impacts on soil microorganisms and soil health, especially including legume crops in rotations. (2) However, monocultures such as continuous wheat cropping could negatively impact soil health by enhancing activities of host specific pathogens. (3) Physical agronomic practices such as tillage can alter soil microbial communities by shifting microclimate conditions. (4) Mineral nitrogen fertilizer use, a leading nutrient input, may have exceeded the planetary boundary of N cycling, and is causing soil acidification and decreasing microbial biomass. (5) Synthetic chemicals, essential for disease management (pesticides) and yield sustainability (fertilization) in conventional agroecosystems are often toxic to non-target soil microorganisms, while bio-fungicides and biofertilizers—a more sustainable approach—carry significant risks to trigger succession of the native soil microbial community, thus impacting soil health. The key is to establish a rational balance between anthropogenic activities for agroecosystem productivity and potential negative influences on the soil microbial community and long-term soil health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".