Volume 5, Issue 18 (2-2017)                   2017, 5(18): 73-83 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Vahedi A A. Modeling Soil Organic Carbon Sequestration in Relation to Plant Biodiversity in the Natural Mixed-Beech Forests . Iranian Journal of Applied Ecology 2017; 5 (18) :73-83
URL: http://ijae.iut.ac.ir/article-1-814-en.html
Res. Edu. and Extension Organization (AREEO), Tehran, Iran.
Abstract:   (8846 Views)

Having the richest plants biodiversity, Hyrcanian natural mixed-beech forests contribute to the huge carbon pool in the different soil layers. This research aims to develop modeling soil carbon sequestration in terms of the plant biodiversity indices to manage soil carbon stock with respect to trend of sustainability, fertility, carbon cycle, and planning to face with climate change in local/ regional scales. After measuring plants biodiversity indices and soil carbon factor over the field operations, simple and multiple linear regressions as well as curve estimation regression were applied in the process of modeling. According to Adj.R2, SEE and AIC, simple and multiple linear regressions had no considerable accuracy (AICmin = +151.74). Analysis of non-linear models showed that model S including index of species dominance belonging to herbal coverage was the best predictor with the least error and highest certainty (AICmin= -171.23) to estimate soil carbon pool in the studied forests. In the following, the results showed that although the log-transformed models with increasing the parameters and adding the correlated explanatory variables were valid (VIF < 10), the accuracy of the estimates was less than the optimal model.

Full-Text [PDF 270 kb]   (2224 Downloads)    
Type of Study: Research | Subject: General

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Iranian Journal of Applied Ecology

Designed & Developed by : Yektaweb