Assessment of Cambodian forest concession management planning based on criteria and indicators of Montreal Process : a case study of CFC Company
Notice bibliographique
Résumé
Forest fragmentation results because the spatial scales of resource extraction do not match the scales of natural disturbance that shaped the evolution of the landscape. Tropical deforestation was still proceeding at 14.2 million hectares per annum in the 1990s, and only 5.5% of all forest in developing countries was under formal management plans in the year 2000. The Cambodian tropical forests contain partly continuous tropical forest in the world. However, it has suffered serious deforestation 0.6% annually since the last 30 years because of road-building, logging, mining, mismanagement-decision, and agricultural raising expansion. This paper aims at scaling and forecasting the problem in Cambodian forest concession management to fulfill the gape of sustainable forest management decision. To scale and forecast problems of forest management, we used data from CFC company in Cambodia to compare the modern forest management Montreal criteria. The work responds to the need to assess progress toward sustainable forest management as established by the Montreal Process of Criterion 2 and its Indicators. The focus is on a single criterion (commonly referred to as indicator 10 to 14), which addresses the "maintenance of the productive capacity of forest ecosystems" to compare with data of 25-year strategic sustainable forest concession management level. There were 33 subindicators of Criterion 2 in Montreal and only 52% equivalent to 17 sub-indicators were fulfilled. We found that 3 indicators (48%) of management indicators were not in the plan yet. We suggested that growing stock of plantation and non-timber forest products have made the forest management into terrible condition because the forest area in CFC Company was mainly studied only on the timber production. The CFC Company is not alone in facing the challenge of sustainable renewable resource management. The results of this study on assessment of forest concession in Cambodia are applicable for tropical forest management. The assessment was more accurate using a forest concession planning and the Montreal criteria and indicators. Because the sustainable forest management remedies are based on specific knowledge of criteria and indicators, our results may be useful for the future establishment and management of sustainable yields at tropical forests.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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 ».