Global review of meta-analyses reveals key data gaps in agricultural impact studies on biodiversity in croplands
Bibliographic record
Abstract
Abstract Aim Agriculture depends heavily on biodiversity, yet unsustainable management practices continue to affect a wide range of organisms and ecosystems at unprecedented levels worldwide. Addressing the global challenge of biodiversity loss requires access to consolidated knowledge across management practices, spatial levels, and taxonomic groups. Location Global Time period 1994 to 2022 Major taxa studied Animals, microorganisms, plants. Methods We conducted a comprehensive literature review synthesising data from all meta-analyses about the impacts of agricultural management practices on biodiversity in croplands, covering field, farm, and landscape levels. From 200 retained meta-analyses, we extracted 1,885 mean effect sizes (from 69,850 comparisons between a control and treatment) assessing the impact of management practices on biodiversity, alongside characterising over 9,000 primary papers. Results Seven high-income countries, notably the USA, China, and Brazil dominate agricultural impact studies with fertiliser use, phytosanitary interventions and crop diversification receiving widespread attention. The focus on individual practices overshadows research at the farm and landscape level. Taxonomically, Animalia, especially arthropods, are heavily studied while taxa such as annelids and plants receive comparatively less attention. Effect sizes are predominantly calculated from averaged abundance data. Significant gaps persist in terms of studies on the effects of agricultural interventions on specific taxonomic groups (e.g. annelids, mammals) and studies analysing functional traits. Main conclusions Our study highlights the importance of analysing the effects of combined practices to accurately reflect real-world farming contexts. While abundance metrics are common, reflecting several biodiversity facets and adopting a more balanced research approach across taxa are crucial for understanding biodiversity responses to agricultural changes and informing conservation strategies. Given the unbalanced evidence on impacts of agricultural practices on biodiversity, caution is required when utilising meta-analytical findings for informing public policies or integrating them into global assessment models like life-cycle assessments or global flux models.
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 | 0.077 | 0.209 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".