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Enregistrement W4283367393 · doi:10.1186/s13750-022-00275-x

Existing evidence on the impacts of within-field farmland management practices on the flux of greenhouse gases from arable cropland in temperate regions: a systematic map

2022· article· en· W4283367393 sur OpenAlex

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Notice bibliographique

RevueEnvironmental Evidence · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAgriculture Sustainability and Environmental Impact
Établissements canadiensCarleton University
Organismes subventionnairesNatural Environment Research CouncilSight Research UK
Mots-clésArable landEnvironmental scienceTemperate climateGreenhouse gasAgroforestryFlux (metallurgy)Field (mathematics)GreenhouseTemperate forestGeographyForestryEnvironmental resource managementEcologyAgricultureAgronomy

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Reducing the emissions of greenhouse gases (GHGs) is vital for mitigating climate change and meeting commitments to international agreements such as the COP 21 Paris Agreement of 2015. Agriculture is reported to account for approximately 11 percent of total global GHG emissions such that: the agricultural sector has an important role to play in meeting climate change mitigation objectives. However, there is currently little consensus on how farm management and interventions, along with interactions with in-field variability, such as soil type, affect the production and assimilation of GHGs in arable crop lands. Practical recommendations for farmers are often vague or generalised, and models (e.g. on the amount of nitrogen fertiliser applied) are used despite limited understanding of the influence of local conditions, such as the importance of soil type. Here, we report the findings of a systematic map of the evidence relating to the impact on GHG flux from the in-field management of arable land in temperate regions. METHODS: We searched for, collated and catalogued research relating to the effects of in-field arable farming practices in temperate systems on GHG emissions. Results from 6 bibliographic databases, a web-based search engine and organisational websites were combined with evidence from stakeholders. Duplicates were removed and the results were then screened for relevance at title and abstract, and full-text levels according to a predefined set of eligibility criteria (following consistency checking). Relevant studies were then coded and their meta-data extracted and used to populate a systematic map database describing each study's settings, methods and measured outcomes. RESULTS: The mapping process identified 538 relevant studies from 351 articles. Nearly all of these (96%) were found from traditional research papers, with 42% from European countries and nearly half (203 studies) lasting for 12 months or less. Over half of all studies (55%) investigated multiple interventions with chemical fertiliser (n = 100), tillage (n = 70), and organic fertiliser (n = 30) the most frequently studied single intervention types. When combining individually studied and multiple interventions, the top three intervention types most frequently studied were: chemical fertiliser (n = 312); organic fertiliser (n = 176) and tillage (n = 158). Nitrous oxide was the most commonly studied outcome, with over double the number of studies compared to carbon dioxide, the next most studied outcome. Sandy loam and silty loam were the most commonly studied soils but there was a good distribution of studies across other types. However, studies predominately focused on humid sub-tropical (Cfa) and temperate oceanic (Cfb) climates, with hot summer Mediterranean (CSa) and warm summer Mediterranean (Csb) climate zones less represented. CONCLUSIONS: The mapping process identified clusters of research for chemical and organic fertiliser especially in relation to nitrous oxide emissions and for both carbon dioxide and nitrous dioxide in relation to tillage. Therefore, there is potential for further synthesis for these interventions. The spread of research across soil textures and in the humid sub-tropical and temperate oceanic climates may enable further synthesis to provide tailored in-field advice for farmers and provide an evidence base to inform subsidies policy. However, smaller amounts of research relating to biochar, cover crops, crop rotation, and nitrogen inhibitors highlight gaps where further research would be beneficial.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,125
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,268
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle