The Current State of Forest Breeding in the Russian Federation: The Trend of Recent Decades
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".