The Current State of Forest Breeding in the Russian Federation: The Trend of Recent Decades
Notice bibliographique
Résumé
The work is devoted to the study of the trends existing in forest breeding in Russia over the recent years, their comparison with similar achievements in foreign countries with close climatic conditions, and the assessment of the prospects for the development of this scientific and production direction in our country, based on the obtained results. The official data of State inventories over the last 25 years and national scientific publications were used. A number of foreign literature sources were also considered for comparison in addition to Russian sources. Quantitative indices of the following processes were studied: selection of plus trees; creation of clone archives, provenance trial and population-ecological plantations; allocation of forest genetic reserves and plus stands; organization of temporary and permanent forest seed plots; and creation of mother plantations, forest seed orchards and progeny field tests of plus trees. Materials on the development or degradation of forest genetic resources in Russia were analyzed by years. The analysis has shown that in Russia there is a regression of the state forest genetic and breeding complex. Over the past 25 years, there has been an average 50 % decline in individual components, with fluctuations in various indices ranging from 7 to 940 %. A comparison of the development of the unified forest genetic complex in our country with its development in a number of foreign countries (Canada, Norway, Sweden, and Finland) revealed our lag in almost all indices by several times. In particular, the selection intensity of plus trees in the countries of Northern Europe (Norway, Sweden, and Finland) is 21.0–61.7 times higher than in Russia. The provision with forest seed orchards in the Russian Federation is 2.7–12.0 times lower than in Norway and Finland. At the same time forest seed orchards of the more progressive, second order represent a large share in the Nordic countries. For instance, in Canada there are more than 30 % of them. In the Russian Federation, such plantations are practically absent and are not listed in official documents. The analysis has shown that it is time to develop a new long-term program of genetic and breeding improvement of forest tree species in order to preserve sustainable reforestation of Russian forests and their valuable gene pool, as well as to identify those responsible for its implementation. For citation: Tsarev A.P., Laur N.V., Tsarev V.A., Tsareva R.P. The Current State of Forest Breeding in the Russian Federation: The Trend of Recent Decades. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 6, pp. 38–55. DOI: 10.37482/0536-1036-2021-6-38-55
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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 | 0,002 | 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 | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».