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Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe

The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, Stipa bungeana, Artemisia capillaris, and Lespedeza davurica, data were o...

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Autores principales: Huang, Xiaojuan, Liu, Yongjie, Wang, Niya, Li, Lan, Hu, An, Wang, Zhen, Chang, Shenghua, Chen, Xianjiang, Hou, Fujiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600926/
https://www.ncbi.nlm.nih.gov/pubmed/33028041
http://dx.doi.org/10.3390/plants9101314
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author Huang, Xiaojuan
Liu, Yongjie
Wang, Niya
Li, Lan
Hu, An
Wang, Zhen
Chang, Shenghua
Chen, Xianjiang
Hou, Fujiang
author_facet Huang, Xiaojuan
Liu, Yongjie
Wang, Niya
Li, Lan
Hu, An
Wang, Zhen
Chang, Shenghua
Chen, Xianjiang
Hou, Fujiang
author_sort Huang, Xiaojuan
collection PubMed
description The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, Stipa bungeana, Artemisia capillaris, and Lespedeza davurica, data were observed from 165 quadrats during the peak plant growing season, and the product of plant height (PH) and canopy diameter (PC) were calculated for each species. AGB of population were predicted for the same species and other species through using 2/3 of the measured data, and the optimal predictive equation was linear in terms of determination coefficient. The other 1/3 of the data, which was measured from no grazing paddocks or rotational grazing paddocks, was substituted into the predictive equations for validation. Results showed that PC of one dominant species could be used to predict AGB of the same species or other species well. The predicted and measured values were significantly correlative, and most of the predictive accuracy was above 80%, and not affected by managements of grassland, including rotational grazing or no grazing. A combination of 3 to 6 representative species was used to predict AGB of the community, and the predictive equations with PC of six species as an independent variable were the most optimal because explaining 83.5% variation of AGB. The predictive methods cost 1/15, 1/9, and 1/51 of time, labor, and capital as much as the destructive sample method (quadrat sampling method), respectively, and thus improved the efficiency of field study and protecting the fragile study areas, especially the long-term study sites in grassland.
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spelling pubmed-76009262020-11-01 Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe Huang, Xiaojuan Liu, Yongjie Wang, Niya Li, Lan Hu, An Wang, Zhen Chang, Shenghua Chen, Xianjiang Hou, Fujiang Plants (Basel) Article The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, Stipa bungeana, Artemisia capillaris, and Lespedeza davurica, data were observed from 165 quadrats during the peak plant growing season, and the product of plant height (PH) and canopy diameter (PC) were calculated for each species. AGB of population were predicted for the same species and other species through using 2/3 of the measured data, and the optimal predictive equation was linear in terms of determination coefficient. The other 1/3 of the data, which was measured from no grazing paddocks or rotational grazing paddocks, was substituted into the predictive equations for validation. Results showed that PC of one dominant species could be used to predict AGB of the same species or other species well. The predicted and measured values were significantly correlative, and most of the predictive accuracy was above 80%, and not affected by managements of grassland, including rotational grazing or no grazing. A combination of 3 to 6 representative species was used to predict AGB of the community, and the predictive equations with PC of six species as an independent variable were the most optimal because explaining 83.5% variation of AGB. The predictive methods cost 1/15, 1/9, and 1/51 of time, labor, and capital as much as the destructive sample method (quadrat sampling method), respectively, and thus improved the efficiency of field study and protecting the fragile study areas, especially the long-term study sites in grassland. MDPI 2020-10-05 /pmc/articles/PMC7600926/ /pubmed/33028041 http://dx.doi.org/10.3390/plants9101314 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Xiaojuan
Liu, Yongjie
Wang, Niya
Li, Lan
Hu, An
Wang, Zhen
Chang, Shenghua
Chen, Xianjiang
Hou, Fujiang
Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title_full Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title_fullStr Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title_full_unstemmed Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title_short Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe
title_sort growth indicators of main species predict aboveground biomass of population and community on a typical steppe
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600926/
https://www.ncbi.nlm.nih.gov/pubmed/33028041
http://dx.doi.org/10.3390/plants9101314
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