Volume 5, Issue 1 (2017)                   ECOPERSIA 2017, 5(1): 1699-1709 | Back to browse issues page

XML Print


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

Parvizi Y, Heshmati M, Gheituri M. Intelligent Approaches to Analysing the Importance of Land Use Management in Soil Carbon Stock in a Semiarid Ecosystem, West of Iran. ECOPERSIA 2017; 5 (1) :1699-1709
URL: http://ecopersia.modares.ac.ir/article-24-970-en.html
1- Assistant Professor, Department of Soil Conservation and Watershed Management, Agriculture and Natural Resource Research Center of Kermanshah, AREEO, Kermanshah, Iran
2- Assistant Professor, Department of Soil Conservation and Watershed Management, Agriculture and Natural Resource Research Center of Kermanshah, AREEO, Kermanshah, Iran, Kermanshah
Abstract:   (5649 Views)
The effects of different climatic, soil, geometric, and management factors on soil organic carbon (SOC) degradation and sequestration potential was evaluated in the semi-arid zone of Mereg watershed, west of Iran. Two nonparametric methods, viz. Classification and Regression Tree (CART) and feed forward back propagation Artificial Neural Network (ANN) were compared with parametric Multivariate Linear Regression (MLR) in estimation of SOC content. Soil sampling was conducted using randomized systematic method in work unit map by overlying soil, aspect and slope maps. Results indicated that linear models had higher prediction errors. The CART with all variables (physical and management) and the ANN with 31-2-1 topology carried the highest predictive capability, explaining 81% and 76% of SOC variability, respectively. ANN models overestimated SOC content and showed a higher capability to detect the effects of management factors on SOC variations. In all the methods, management factors dominantly controlled SOC stock sequestration or degradation in different land use.
Full-Text [PDF 447 kb]   (3094 Downloads)    
Article Type: مقاله Ø§Ø³ØªØ®Ø±Ø§Ø Ø´Ø¯Ù‡ از پایان نامه | Subject: Environment
Received: 2016/10/5 | Accepted: 2016/12/12 | Published: 2017/03/1

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

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