Please use this identifier to cite or link to this item: https://elar.usfeu.ru/handle/123456789/10569
Title: The introduction of an additive modeling method responsive to temperature and precipitation variables, and its applications to estimate the forest stand biomass of PICEA SPP. Of Eurasia
Authors: Shobairi, S. O. R.
Usoltsev, V. A.
Tsepordey, I. S.
Issue Date: 2021
Publisher: Corvinus University of Budapest
Citation: Shobairi, S. O. R. The introduction of an additive modeling method responsive to temperature and precipitation variables, and its applications to estimate the forest stand biomass of PICEA SPP. Of Eurasia / S. O. R. Shobairi, V. A. Usoltsev, I. S. Tsepordey. – 2021. – Vol. 2. – Iss. 19. – P. 1107-1122.
Abstract: This article was the first attempt of modeling changes in the additive component composition of 900 spruce stands’ (genus Picea sp.) biomass (t/ha), according to the trans-Eurasian hydrothermal gradients of Eurasia using the database compiled on the structure of harvest biomass. This study found that the biomass of all components increases with higher mean January temperature, regardless of mean annual precipitation, but any increase in precipitation leads to a decrease in biomass of all the components. For the first time, trans-Eurasian patterns of changes in the biomass of all components of spruce stands in the climate gradients were obtained due to the authors’ first database on the biomass of forest-forming species in Eurasia. In contrast to previously published regional and transcontinental weakly adequate or unreliable regularities that are not additive in the biomass component composition and depersonalized in the species composition, in the proposed additive models for spruce, the contribution of climate variables to the explanation of biomass variability was 9%. The importance of selecting the correct structure of the developed model is shown. © 2021, ALÖKI Kft., Budapest, Hungary.
Keywords: ADDITIVE BIOMASS EQUATIONS
BIOLOGICAL PRODUCTIVITY
CLIMATE CHANGE
JANUARY MEAN TEMPERATURE
MEAN ANNUAL PRECIPITATION
REGRESSION MODELS
URI: https://elar.usfeu.ru/handle/123456789/10569
DOI: 10.15666/aeer/1902_11071122
SCOPUS: 2-s2.0-85104339434
WoS: WOS:000637972000017
RSCI: 44839745
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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