How effective are on-farm mitigation measures for delivering an improved water environment? A systematic map
Notice bibliographique
Résumé
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 Recognised experts, authors and practitioners were also contacted to identify unpublished literature.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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,038 | 0,135 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,064 | 0,041 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».