Collection and consumption of non-wood forest products in Europe
Notice bibliographique
Résumé
Abstract Many non-wood forest products (NWFPs) such as mushrooms and berries are collected and consumed in Europe; but both national statistical and scientific data on this topic are reported only for a limited number of countries, products and case-study areas. Without an adequate quantitative basis, their importance as source of food and income, their links to recreation and cultural heritage, are all under-valued in forest-focused and forest-related policies. In this study, we aimed to address this gap by assessing the consumption and collection of NWFPs through a statistically representative survey in 28 European countries with over 17 000 respondents. Our results show that 90 per cent of households consume NWFPs and about one-quarter collects them. The consumption and collection rates, as well as the number of collected products and their contribution to household income, increase from the west to east of Europe. The vast majority of collected products are consumed fresh. Households with higher income consume a more diverse range of NWFPs, especially in Western Europe. The relation between income and collection is more ambiguous, but there is some indication that the collection rate is higher than average among higher-income households in North and Western Europe and among lower-income households in Eastern and South-Eastern Europe. Households for which NWFP collection is the main income source are predominantly located in Eastern Europe, and they focus their activities on few key products. Our results also identify recreational, hobby and professional collectors whose characteristics vary across socio-economic variables and geographical gradient. Recreational collectors in Western and Southern Europe collect 8 kg of NWFPs from five different products, while recreational collectors in Central-Eastern and North-Baltic Europe collect about four times more from 10 different products. Hobby collectors collect ~100 kg of NWFP per year and professional collectors half a ton, where both groups focus on 8–12 different products. Professional collectors are predominantly located in Eastern and South-Eastern Europe. We end the study by pointing to future research directions and with a series of policy recommendations on how NWFPs could be addressed along the geographical, income and urban–rural gradient with respect to their role in forest recreation as a food and income source.
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».