Scientific evidence of sustainable plant disease protection strategies for oats in Sweden: a systematic map
Notice bibliographique
Résumé
Abstract Background Oat (Avena sativa L.) is an important cereal crop for livestock feed and human consumption. The largest oat-producing countries are located in the Northern Hemisphere with Sweden as the tenth largest producer. Oat production is challenged by different diseases that can lead to significant yield reductions and impaired grain quality. The use of efficient and sustainable plant protection management is of great economic and ecological importance. The systematic map in this study aims to provide a knowledge base inventory and to identify areas that need to be researched in the future in terms of plant disease management for more sustainable oat production. Methods Literature searches were conducted in both academic bibliographic databases and relevant online sources of grey literature. A time-span restriction of 40 years (1978–2018) was applied to the searches. English was used in all searches, and Swedish, Norwegian and Danish languages were used in the grey literature searches. The screening process, which followed a protocol with eligibility criteria, was conducted at three levels: title, abstract and full text. Metadata incorporating bibliographic information, study location, climatic zone, disease name, the common and scientific names of the disease-causing organism, pathogen type, intervention and management methods, diseased plant part, plant stage, and outcome were extracted from the studies and included in the systematic map. The systematic map findings are visualized in figures and tables and described. All included studies can be found in a searchable database. Review findings A total of 58 eligible articles, most (n = 51) from scientific journals published in English, were included in the systematic map. A majority of the studies were conducted in the Northern Hemisphere in temperate climatic zones, where most of the world’s oats are produced. The earliest article was published in 1980, followed by an oscillating temporal distribution of articles over the following years. By country, Canada had the highest number of articles, and by region, Europe had the highest number. Fungi were the most studied pathogen type, and a total of 16 different diseases were reported. Fusarium head blight (Fusarium spp.) and crown rust (Puccinia coronata) were the most studied diseases. In total, 17 different intervention management approaches for controlling the diseases were analyzed in the articles, with cultivar resistance and pesticide application as the most studied methods. Conclusion The map highlights the low quantity of available relevant field research on oat disease management. To our knowledge, this is the first systematic map of crop protection. This map provides a database of scientific literature that can be used to develop sustainable disease management strategies. The method used in this study has great potential and can also be used to benefit other crops. Research is often based on the availability of funding, and this map could be a useful tool for researchers and funding organizations to identify relevant research topics that need to be further studied. In addition, this systematic map offers a useful tool for field-based advisors in providing scientifically relevant crop protection strategies for farmers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,016 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,048 | 0,032 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».