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Tytuł artykułu

Analysis of Bioresource Collections on Climatic Rhythms and Phenological Processes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A comprehensive assessment of dendrological resources for the formation of forest reclamation complexes is a process of qualitative and quantitative expansion of economically important plants and a scientifically based choice of the best possible option. The development of methods for improving the bioresources of degraded landscapes is aimed at the adaptive organization of land use in forestry and water management, recreation, urban planning, landscaping of settlements based on a comprehensive assessment of dendrological resources and increasing their biodiversity. The Federal Research Center of Agroecology of the Russian Academy of Sciences for a long period of its existence has created a network of experimental stations (cadastre No. 34:34:000000:122, 34:34:060061:10; Nizhnevolzhskaya station for the selection of tree species; 34:36:000014:178; West Siberian AGLOS; 22:23:010003:0014; Volga AGLOS; No. 63:23:0908001:0002) with dendrological collections located in different soil and climatic zones (Altai Krai, Samara and Volgograd regions) – typical areas of the arid zone with stable and moderate (in some years acute) summer moisture deficiency.
Słowa kluczowe
Twórcy
  • Federal Research Centre of Agroecology, Complex Melioration, and Forest Reclamations RAS, Volgograd, Russian Federation
  • Federal Research Centre of Agroecology, Complex Melioration, and Forest Reclamations RAS, Volgograd, Russian Federation
  • Federal Research Centre of Agroecology, Complex Melioration, and Forest Reclamations RAS, Volgograd, Russian Federation
  • Federal Research Centre of Agroecology, Complex Melioration, and Forest Reclamations RAS, Volgograd, Russian Federation
Bibliografia
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  • 2. Belitskaya M. 2018. Ecologically adaptive receptions control the number of pests in the ecosystems of transformed at the forest reclamation. World Ecology Journal, 8(2), 1–10. https://doi.org/https://doi.org/10.25726/NM.2018.2.2.001
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  • 4. Dolgikh А. 2018. Monitoring of introduction resources of the Kulunda arboretum and allocation of valuable gene pool for protective afforestation. World Ecology Journal, 8(1), 29–42. https://doi.org/https://doi.org/10.25726/NM.2018.1.1.003
  • 5. Fadyushin S.G., Vereshchagina E.A., Kayak A.B. 2019. Diagnosis of Internet-dependent Human Behavior in the Information Aspect. In Proceedings of the International Science and Technology Conference “FarEastСon” (ISCFEC 2019) (pp. 168–170). Atlantis Press. https://doi.org/https://doi.org/10.2991/iscfec-19.2019.45
  • 6. Fadyushin S., Vereshchagina E., Rudnichenko A. 2019. Entropy As an Assessment Factor of the Current State of Vessel When Approaching an Object of Maneuver. IOP Conference Series: Earth and Environmental Science, 272, 22120. https://doi.org/10.1088/1755–1315/272/2/022120
  • 7. Fialkovsk, I., Lutskiy D., Alekseev A., Blinov A. 2021. Modelling the effect of temperature on the stability of bromide and carbonate complexes of europium, gadolinium, terbium and determination of their thermodynamic functions. In: Materials Science Forum, Vol. 1031 MSF. https://doi.org/10.4028/www.scientific.net/MSF.1031.103
  • 8. Fomina T.I. 2017. Comprehensive approach to study seasonal development of introduced plants on the example of rare species of genus Campanula. Rastitel’nyj Mir Aziatskoj Rossii, 4(28), 59–65
  • 9. Germanovich A.A. 2020. Algorithm and analysis of the construction of the geometry of the movement of vehicles. International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 1473–1478. doi:10.30534/ijatcse/2020/86922020
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  • 11. Hereford J., Schmitt J., Ackerly D.D. 2017. The seasonal climate niche predicts phenology and distribution of an ephemeral annual plant, Mollugo verticillata. Journal of Ecology, 105, 1323–1334. doi: 10.1111/1365–2745.12739.
  • 12. Kizatova M.Y., Medvedkov Y.B., Shevtsov A.A., Drannikov A.V. and Tlevlessova D.A. 2017 Experimental-Statistical Analysis and Multifactorial Process Optimization of the Crust from Melon Pulp Separation Process. Journal of Engineering and Applied Sciences, 12(7), 1762–1771.
  • 13. Korshunov G.I., Eremeeva A.M., Drebenstedt C. 2021. Justification of the use of a vegetal additive to diesel fuel as a method of protecting underground personnel of coal mines from the impact of harmful emissions of diesel-hydraulic locomotives. Journal of Mining Institute, 247(1), 39–47. https://doi.org/10.31897/PMI.2021.1.5
  • 14. Kupchishin A., Voronova N., Shmygaleva T., Kupchishin A. 2018. Computer simulation of vacancy clusters distribution by depth in molybdenum irradiated by alpha particles. Key Engineering Materials, 781 KEM, 3–7. https://doi.org/10.4028/www.scientific.net/KEM.781.3
  • 15. Mendoza I., Peres C.A., Morellato L.P.C. 2016. Continental-scale patterns and climatic drivers of fruiting phenology: a quantitative neotropical review. Global and Planetary Change. 2016;148:227–241. doi: 10.1016/j.gloplacha.2016.12.001.
  • 16. Munson S.M., Long A.L. 2017. Climate drives shifts in grass reproductive phenology across the western USA. New Phytologist. 2017;213:1945–1955. doi:10.1111/nph.14327.
  • 17. Panchen Z.A., Primack R.B., Aniśko T., Lyons R.E. 2012. Herbarium specimens, photographs, and field observations show Philadelphia area plants are responding to climate change. American Journal of Botany. 2012;99:751–756. doi: 10.3732/ajb.1100198.
  • 18. Park I.W., Schwartz M.D. 2015. Long-term herbarium records reveal temperature-dependent changes in flowering phenology in the Southeastern USA. International Journal of Biometeorology. 2015;59:347–355.
  • 19. Pearse W.D., Davis C.C., Inouye D.W., Primack R.B., Davies T.J. 2017. A statistical estimator for determining the limits of contemporary and historic phenology. Nature Ecology & Evolution. 2017;1:1876–1882. doi: 10.1038/s41559–017–0350–0.
  • 20. Rodnyansky D.V., Abramov R.A., Repin M.L., Nekrasova E.A. 2019. Estimation of innovative clusters efficiency based on information management and basic models of data envelopment analysis. International Journal of Supply Chain Management, 8(5), 929–9360.
  • 21. Semenyutina A. and Klimov A. 2018. Analysis of bioresources of the gene pool of Robinia, Gleditsia for forest meliorative complexes on the basis of studying adaptation to stress factors. World Ecology Journal, 8(2), 33–45. https://doi.org/https://doi.org/10.25726/NM.2018.2.2.004
  • 22. Semenyutina A., Svintsov I., Huzhahmetova A. and Semenyutina V. 2018. Regulation of increase of biodiversity of woody plants in protective forest plantings of the Volga region. World Ecology Journal, 8(2), 46–59. https://doi.org/https://doi.org/10.25726/NM.2018.2.2.005
  • 23. Sergeevich F.I., Evgenievna L.T., Sergeevich L.D., Alexandrovich, A.A. 2022. Determination of the parameters of thermodynamic stability constants of bromide complexes of rare earth metals for modeling the optimal regimes of hydrometallurgical extraction. Arab Journal of Basic and Applied Sciences, 29(1). https://doi.org/10.1080/25765299.2021.2015897
  • 24. Shmygaleva T.A., Kupchishin A.I., Kupchishin A.A., Shafii C.A. 2019. Computer simulation of the energy spectra of {PKA} in materials irradiated by protons in the framework of the Cascade-Probabilistic method. {IOP} Conference Series: Materials Science and Engineering, 510, 12024. https://doi.org/10.1088/1757–899x/510/1/012024
  • 25. Tereshkin A. 2018. Specificity of optimization of recreational potential Forest park (on the example of the green zone of Saratov). World Ecology Journal, 8(2), 60–70. https://doi.org/https://doi.org/10.25726/NM.2018.2.2.006
  • 26. Van Proosdij A.S.J., Sosef M.S.M., Wieringa J.J., Raes N. 2016. Minimum required number of specimen records to develop accurate species distribution models. Ecography. 2016;39:542–552. doi:10.1111/ecog.01509.
  • 27. Voronova N.A., Kupchishin A.I., Kupchishin A.A., Kuatbayeva A.A., Shmygaleva T.A. 2018. Computer Modeling of Depth Distribution of Vacancy Nanoclusters in Ion-Irradiated Materials. Key Engineering Materials, 769, 358–363. https://doi.org/10.4028/www.scientific.net/kem.769.358
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-27b61cc2-bb93-4ed8-98d6-7e5e07c4b9c5
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