Effective methods of forest breeding and varietal seed production for forest reclamation of arid territories of European Russia

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Abstract

Background. Of particular relevance is the problem of creating a new generation of viable protective forest plantations from a reliable assortment on a breeding and genetic basis from seed material of the highest breeding conditions.

Purpose. Goal. Selection of promising tree species for the arid region (European territory of Russia) based on effective methods to increase the stability of plantations for forest breeding and seed production.

Materials and methods. Genetic assessment of forest seed plantations was carried out using analysis of variance, the coefficients of repeatability and heritability, and the proportion of influence of genetic and environmental factors on phenotypes. Genotypes were isolated using an inventory at the level of a competitive test based on a set of positive signs and the degree of their value for each species under study. The ranking of taxation indicators was carried out in order to select promising clones and families for high-yielding forest seed plantations with signs of intensive growth. Field and laboratory methods were used in the research: phenological, morphological, physiological and biochemical.

Results. It has been established that by reintroducing into the culture the selection of age-old reproducers with good vitality and resistance to adverse environmental factors of individuals, it is possible to significantly increase the stability of plantations for various purposes. The good preservation and taxation indicators of the studied rocks can be associated with the formation of an intrazonal ecological situation in the plantations. The high efficiency of the selection of hybrid forms is confirmed, since all the studied plants had a satisfactory condition and a high percentage of preservation.

Conclusions. The developed scheme for increasing the stability of ZLN (protective forest plantations) will create a valuable breeding fund for agroforestry development, the main breeds of which include Robinia pseudoacacia L., Quercus robur L., Pyrus communis subsp. pyraster (L.) Ehrh., Ulmus pumila L., Fraxinus excelsior L., Fraxinus pennsylvanica Marshall., Pinus nigra subsp. pallasiana (Lamb.), Pinus silvestris L. The effect of planting on the fruiting of clonal offspring is unreliable, the effect of the queen bee is reliable at the level of 1-5% significance in relation to plus trees on the plantation. The species composition of the plantations of the Novoanninsky population is classified as promising and can claim the status of a cultural variety.

About the authors

Sergey N. Kryuchkov

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Author for correspondence.
Email: kryuchkovs@vfanc.ru
ORCID iD: 0000-0001-8338-6460
SPIN-code: 5356-4194
ResearcherId: IAM-5063-2023

Dr. Sc. (Agriculture), Chief Researcher of the Laboratory of Breeding, Seed and Nursery Production

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

Alexander I. Belyaev

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Email: director@vfanc.ru
ORCID iD: 0000-0001-8077-7052
SPIN-code: 8733-7587
Scopus Author ID: 57206259735

Dr. Sc. (Agriculture), Professor, Honored Worker of Agriculture of the Russian Federation, Director

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

Anna M. Pugacheva

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Email: pugachevaa@vfanc.ru
ORCID iD: 0000-0003-0852-8056
SPIN-code: 6857-8236
Scopus Author ID: 57194047579
ResearcherId: T-5482-2017

Cand. Sc. (Agriculture), Scientific Secretary

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

Alexandra S. Solomentseva

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Email: alexis2425@mail.ru
ORCID iD: 0000-0002-5857-1004
SPIN-code: 6832-7471
Scopus Author ID: 57220036834
ResearcherId: W-4142-2018

Cand. Sc. (Agriculture), Senior Researcher of the Laboratory of Breeding, Seed and Nursery Production

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

Sergey A. Egorov

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Email: egorov-sa@vfanc.ru
ORCID iD: 0000-0001-8234-7355
SPIN-code: 8284-0790

Junior Researcher of the Laboratory of Breeding, Seed and Nursery Production, Postgraduate Student

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

Almagul’ K. Romanenko

Federal Scientific Center of Agroecology, Complex Melioration, and Protective Afforestation RAS

Email: romanenko-ak@vfanc.ru
ORCID iD: 0000-0002-6705-6135
SPIN-code: 9028-2920

Junior Researcher of the Laboratory of Breeding, Seed and Nursery Production, Postgraduate Student

 

Russian Federation, 97, Universitetsky pr., Volgograd, 400062, Russian Federation

References

  1. Antwi, E. K., Boakye-Danquah, J., Owusu-Banahene, W., et al. (2022). Global review of cumulative effects assessments of disturbances on forest ecosystems. Journal of Environmental Management, 317, 115277. https://doi.org/10.1016/j.jenvman.2022.115277
  2. Alfaro, R. I., Fady, B., Vendramin, G. G., et al. (2014). The role of forest genetic resources in responding to biotic and abiotic factors in the context of anthropogenic climate change. Forest Ecology and Management, 333, 76-87. https://doi.org/10.1016/j.foreco.2014.04.006 EDN: https://elibrary.ru/uopkzz
  3. O’Neill, A. G., Stoehr, M., Jaquish, B. (2014). Quantifying safe seed transfer distance and impacts of tree breeding on adaptation. Forest Ecology and Management, 328, 122-130. https://doi.org/10.1016/j.foreco.2014.05.039
  4. Callaway, R. M., Ridenour, W. M., Laboski, T., et al. (2005). Natural Selection for Resistance to the Allelopathic Effects of Invasive Plants. Journal of Ecology, 93(3), 576–583.
  5. Calleja-Rodriguez, A., Li, Z., Hallingbäck, H. R., et al. (2019). Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design. Journal of Theoretical Biology, 462, 283-292. https://doi.org/10.1016/j.jtbi.2018.11.007
  6. Crossa, J., Pérez-Rodríguez, P., Cuevas, J., et al. (2017). Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science, 22(11), 961-975. https://doi.org/10.1016/j.tplants.2017.08.011
  7. Du, Y., Mi, X., Liu, X., et al. (2009). Seed dispersal phenology and dispersal syndromes in a subtropical broad-leaved forest of China. Forest Ecology and Management, 258(7), 1147-1152. https://doi.org/10.1016/j.foreco.2009.06.004
  8. Dungey, H. S., Dash, J. P., Pont, D., et al. (2018). Phenotyping Whole Forests Will Help to Track Genetic Performance. Trends in Plant Science, 23(10), 854-864. https://doi.org/10.1016/j.tplants.2018.08.005
  9. Faucon, M.-P., Houben, D., Lambers, H. (2017). Plant Functional Traits: Soil and Ecosystem Services. Trends in Plant Science, 22(5), 385-394. https://doi.org/10.1016/j.tplants.2017.01.005 EDN: https://elibrary.ru/oybaih
  10. Graudal, L., Dawson, I. K., Hale, I., et al. (2022). Systems approach’ plant breeding illustrated by trees. Trends in Plant Science, 27(2), 158-165. https://doi.org/10.1016/j.tplants.2021.09.009 EDN: https://elibrary.ru/leggpy
  11. Geburek, T., & Myking, T. (2018). Evolutionary consequences of historic anthropogenic impacts on forest trees in Europe. Forest Ecology and Management, 422, 23-32. https://doi.org/10.1016/j.foreco.2018.03.055 EDN: https://elibrary.ru/ygcmfn
  12. Gibson, C., & Warren, A. (2020). Keeping time with trees: Climate change, forest resources, and experimental relations with the future. Geoforum, 108, 325-337. https://doi.org/10.1016/j.geoforum.2019.02.017
  13. Harfouche, A. L., Jacobson, D. A., Kainer, D., et al. (2019). Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends in Biotechnology, 37(11), 1217-1235. https://doi.org/10.1016/j.tibtech.2019.05.007 EDN: https://elibrary.ru/cqzlnj
  14. Heidari, P., Rezaei, M., Sahebi, M., et al. (2019). Phenotypic variability of Pyrus boissieriana Buhse: Implications for conservation and breeding. Scientia Horticulturae, 247, 1-8. https://doi.org/10.1016/j.scienta.2018.11.075
  15. Hlásny, T., Augustynczik, A. L. D., & Dobor, L. (2021). Time matters: Resilience of a post-disturbance forest landscape. Science of The Total Environment, 799, 149377. https://doi.org/10.1016/j.scitotenv.2021.149377 EDN: https://elibrary.ru/ayjkhk
  16. He, T., & Li, C. (2020). Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. The Crop Journal, 8(5), 688-700. https://doi.org/10.1016/j.cj.2020.04.005 EDN: https://elibrary.ru/zfyynz
  17. İçgen, Y., Kaya, Z., Çengel, B., et al. (2006). Potential impact of forest management and tree improvement on genetic diversity of Turkish red pine (Pinus brutia Ten.) plantations in Turkey. Forest Ecology and Management, 225(1-3), 328-336. https://doi.org/10.1016/j.foreco.2006.01.009
  18. Kang, K.-S., El-Kassaby, Y. A., Han, S.-U., et al. (2005). Genetic gain and diversity under different thinning scenarios in a breeding seed orchard of Quercus accutissima. Forest Ecology and Management, 212(1-3), 405-410. https://doi.org/10.1016/j.foreco.2005.03.063
  19. Khazipova, A., Chafizov, A., & Komissarov, A. (2020). Choice of Melioration Facies Regimes Using Catenary-facies Models of Watersheds of the Forest-steppe Zone of the Republic of Bashkortostan. Asian Journal of Water, Environment and Pollution, 17(1), 19-26. https://doi.org/10.3233/AJW200002 EDN: https://elibrary.ru/xkdfkd
  20. Kim, I. S., Lee, K. M., Shim, D., et al. (2020). Plus Tree Selection of Quercus salicina Blume and Q. glauca Thunb. and Its Implications in Evergreen Oaks Breeding in Korea. Forests, 11(735). https://doi.org/10.3390/F11070735 EDN: https://elibrary.ru/wslдап
  21. Kollmann, J., & Bañuelos, M. J. (2004). Latitudinal Trends in Growth and Phenology of the Invasive Alien Plant Impatiens glandulifera (Balsaminaceae). Diversity and Distributions, 10(5-6), 377-385. https://doi.org/10.1111/j.1366-9516.2004.00126.x
  22. Kroll, A. J., Johnston, J. D., Stokely, T. D., et al. (2020). From the ground up: Managing young forests for a range of ecosystem outcomes. Forest Ecology and Management, 464, 118055. https://doi.org/10.1016/j.foreco.2020.118055 EDN: https://elibrary.ru/mxnycl
  23. Kryuchkov, S. N., Vdovenko, A. V., Vorobyova, O. M., et al. (2021). Technology of growing planting material of woody species in arid conditions of Southern Russia. Volgograd State Pedagogical University, 108 p.
  24. Kumar, A., Kumar, P., Sharma, A., et al. (2022). Scientific insights to existing know-how, breeding, genetics, and biotechnological interventions pave the way for the adoption of high-value underutilized super fruit Sea buckthorn (Hippophae rhamnoides L.). South African Journal of Botany, 145, 348-359. https://doi.org/10.1016/j.sajb.2021.11.045
  25. Lantz, V., McMonagle, G., Hennigar, C., et al. (2022). Forest succession, management and the economy under a changing climate: Coupling economic and forest management models to assess impacts and adaptation options. Forest Policy and Economics, 142, 102781. https://doi.org/10.1016/j.forpol.2022.102781
  26. Lelli, С., Bruun, H. H., Chiarucci, A., et al. (2019). Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation. Forest Ecology and Management, 432, 707-717. https://doi.org/10.1016/j.foreco.2018.09.057
  27. Liu, J. (2022). From afforestation to forest landscape restoration in DPRK: Gaps and challenges. Trees, Forests and People, 8, 100243. https://doi.org/10.1016/j.tfp.2022.100243 EDN: https://elibrary.ru/iutpaw
  28. MacLachlan, I. R., Wang, T., Hamann, A., et al. (2017). Selective breeding of lodgepole pine increases growth and maintains climatic adaptation. Forest Ecology and Management, 391, 404-416. https://doi.org/10.1016/j.foreco.2017.02.008
  29. Mölder, A., Sennhenn-Reulen, H., & Fischer, C. (2019). Success factors for high-quality oak forest (Quercus robur, Q. petraea) regeneration. Forest Ecosystems, 6(49). https://doi.org/10.1186/s40663-019-0206-y
  30. Orton, T. J. (2020). Horticultural Plant Breeding. Academic Press, pp. 97-112. https://doi.org/10.1016/B978-0-12-815396-3.00006-8
  31. Santos, M. P., Araujo, M. J., & Silva, P. H. M. (2021). Natural establishment of Pinus spp. around seed production areas and orchards. Forest Ecology and Management, 494, 119333. https://doi.org/10.1016/j.foreco.2021.119333
  32. Sampaio, T., Gonçalve, E., Patrício, M. S., et al. (2019). Seed origin drives differences in survival and growth traits of cork oak (Quercus suber L.) populations. Forest Ecology and Management, 448, 267-277. https://doi.org/10.1016/j.foreco.2019.05.001
  33. Sagra, J., Ferrandis, P., Plaza-Álvarez, P. A., et al. (2018). Regeneration of Pinus pinaster Aiton after prescribed fires: Response to burn timing and biogeographical seed provenance across a climatic gradient. Science of The Total Environment, 637–638, 1550-1558. https://doi.org/10.1016/j.scitotenv.2018.05.138
  34. Sedgley, M., & Griffin, A. R. (1989). Fruit- and Seed-production Management and Tree Breeding. Applied Botany and Crop Science, Sexual Reproduction of Tree Crops, pp. 229-290. https://doi.org/10.1016/B978-0-12-634470-7.50014-3
  35. Seidler, R. (2017). Patterns of Biodiversity Change in Anthropogenically Altered Forests. Reference Module in Life Sciences. https://doi.org/10.1016/B978-0-12-809633-8.02186-5
  36. Silva Ramos, C. A., Soares, T. L., Santos Barroso, N., et al. (2021). Influence of maturity stage on physical and chemical characteristics of fruit and physiological quality of seeds of Physalis angulata L. Scientia Horticulturae, 284, 110124. https://doi.org/10.1016/j.scienta.2021.110124
  37. Solomentseva, A. S., Kolmukidi, S. V., Lebed, N. I., et al. (2020). Tree-shrub species promising for protective afforestation and planting in the Volgograd region. IOP Conference Series: Earth and Environmental Science, 012056. https://doi.org/10.1088/1755-1315/579/1/012056 EDN: https://elibrary.ru/qzssrj
  38. Song, P., Wang, J., Guo, X., et al. (2021). High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. The Crop Journal, 9(3), 633-645. https://doi.org/10.1016/j.cj.2021.03.015 EDN: https://elibrary.ru/tbpdkq
  39. Spengler, R. N. (2020). Anthropogenic Seed Dispersal: Rethinking the Origins of Plant Domestication. Trends in Plant Science, 25(4), 340-348. https://doi.org/10.1016/j.tplants.2020.01.005 EDN: https://elibrary.ru/tszjbk
  40. Spalvins, A., Eglite, I., & Lace, I. (2020). Modelling of the forest melioration system at Latvia. A case study. 20th International Multidisciplinary Scientific GeoConference SGEM, 257-264. https://doi.org/10.5593/sgem2020/3.1/s12.034 EDN: https://elibrary.ru/rakvmb
  41. Tzfira, T., Zuker, A., & Altman, A. (1998). Forest-tree biotechnology: genetic transformation and its application to future forests. Trends in Biotechnology, 16(10), 439-446. https://doi.org/10.1016/S0167-7799(98)01223-2
  42. Urruth, L. M., Braun Bassi, J., & Chemello, D. (2022). Policies to encourage agroforestry in the Southern Atlantic Forest. Land Use Policy, 112, 105802. https://doi.org/10.1016/j.landusepol.2021.105802 EDN: https://elibrary.ru/iwukai
  43. Vivas, M., Wingfield, M. J., & Slippers, B. (2020). Maternal effects should be considered in the establishment of forestry plantations. Forest Ecology and Management, 460, 117909. https://doi.org/10.1016/j.foreco.2020.117909 EDN: https://elibrary.ru/nioumw
  44. Zhao, Y., Li, M., Deng, J., et al. (2021). Afforestation affects soil seed banks by altering soil properties and understory plants on the eastern Loess Plateau, China. Ecological Indicators, 126, 107670. https://doi.org/10.1016/j.ecolind.2021.107670

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