How effective are on-farm mitigation measures for delivering an improved water environment? A systematic map
Bibliographic record
Abstract
Abstract Background Agricultural activities are estimated to contribute 70% of nitrates, 28% of phosphates and 76% of sediments measured in UK rivers. Catchments dominated by agriculture also have elevated levels of pesticides and bacterial pathogens. European member states have a policy commitment to tackle this pollution through the water framework directive. Here we report on the results of a systematic map to investigate and describe the nature and coverage of research pertaining to the effectiveness of 6 on-farm mitigation measures, slurry storage, cover/catch crops, woodland creation; controlled trafficking, subsoiling and vegetated buffer strips for delivering an improved water environment in terms of a reduction in nitrogen (N), phosphorus (P), sediment, pesticides and faecal indicator organisms (FIOs) or pathogens from faecal material. Methods Research evidence for the effectiveness of the 6 on-farm mitigation measures for delivering an improved water environment (as detailed above) was collated using English language search terms for temperate farming systems in Europe, Canada, New Zealand and northern states of the United States of America. Searches for literature were made from online publication databases, search engines, specialist websites and bibliographies of topic specific reviews. Recognised experts, authors and practitioners were also contacted to identify unpublished literature. Articles were screened for relevance at title, abstract and full text using predefined inclusion criteria set out in an a priori published protocol. All relevant articles were mapped in a searchable database using pre-defined coding and critically appraised for relevance and reliability. Articles reporting the same study were removed. All full text studies without confounding factors were identified and coded for in a separate searchable database. Results A total of 718 articles were included in the database. Buffer strips were the most commonly studied intervention followed by cover crops and slurry storage. Little evidence was found for woodland creation and sub-soiling. No studies were found for controlled trafficking on grassland. Nitrogen was most frequently measured, followed by P, sediment, pesticides and FIOs or pathogens from faecal material. Conclusions The majority of the evidence collated in this map investigated the effectiveness of buffer strips and cover crops for improving water quality. This evidence was predominantly focussed on reducing N pollution. An evidence gap exists for the impact of cover/catch crops in reducing leaching of pesticides, FIOs and pathogens, and for organic forms of N and P. There was limited research investigating the effectiveness of buffer strips for reducing leaching of organic forms of N or P, or for pesticides that are currently authorised for use/commonly used in UK agriculture. Further, long term studies across different seasons with controls, pre and post water quality measurements and multiple sampling points from both field and rivers would improve the evidence base. Evidence gaps exist for woodland creation, subsoiling and controlled trafficking on grassland.
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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 | 0.038 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.064 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".