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:: Volume 9, Issue 19 (12-2021) ::
PEC 2021, 9(19): 63-78 Back to browse issues page
Predicting the plant diversity under the effect of different grazing intensities at Sabalan southeast rangelands
Zhila Ghorbani * , Kiomarsi Sefid , Farshad Keivan Behjou , Zeynab Jafarian , Mahshid Souri
Sari Agricultural Sciences and Natural Resources University , engzhghorbani@gmail.com
Abstract:   (2192 Views)
      One of the most important issue at natural resources management is plant diversity conservation and detecting the different effects of usage methods on the its quality and quantity. This research was done with the goal of investigating the disaster center (village) at evaluation of rangeland damage with the accent of plant cover combination and diversity in Sabalan mountain southeast rangelands. Plant cover sampling was performed at three villages named Alvares, Aldashin and Asbmarz with the grazing intensity low, med and high using 1 m2 plots in the route of 600 m transect. After the detection of plant combination from the analysis of variance and compare means, the diversity and unique indexes were extracted. Then, development and evaluation of regression and ANFIS models to predict of plant diversity was done and comparing the results was performed. RMSE and R2 indexes were used for evaluation of regression and ANFIS models. The results showed that with the increment of grazing intensity, the plant diversity decreased and the max plant diversity was happened at distance of 400 m plus min plant diversity was occurred at distance of 200 m near the village. Moreover, the prediction part results showed that ANFIS model predicted the plant diversity with higher accuracy (R2=0.91) and lower error (RMSE=0.601) relative to the regression model with lower accuracy (R2=0.83) and higher error (RMSE=1.230). Consequently, the ANFIS approach can predict the relations parameters to rangelands, accurately.
Keywords: rangeland damage, plant diversity, correlation coefficient, ANFIS model
Full-Text [PDF 814 kb]   (465 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/01/29 | Accepted: 2021/09/24 | Published: 2022/03/16
References
1. Bahrami, B., Dianati Tilaki, G.A., Beigi, S.K., Janizadeh, S., Moetamedi, J. 2013. Evaluation of Artificial Neural Network (ANN), Adative Neuro-Fuzzy Inference System (ANFIS) and Regression Models in Prediction of Particulate Organic Matter-Carbon (POM-C) in the Rangelands Kharabe Sanji of Urmia. Operational Researches about Soil, 1(1): 94-106.
2. Goharnejad, A., Zare, A., Tahmasebi, P., Asadi, E., Ebrahimi, A. 2015. A grazing capacity model with fuzzy inference system in semi-steppe rangelands. Environment and Natural Resources J, 13(2): 1-13.
3. Jacovides, C. P. 1997. Reply to comment on Statistical procedures for the evaluation of evapotranspiration models. Agricultural Water Management, 3: 95-97
4. Jang, J.S.R. 1993. ANFIS: Adaptive Network Based Fuzzy Inference System, IEEE transactions on Systems. Man Cyber, 23(3): 665-683.
5. Khosravi, Kh., Panahi, M., Tien Bui, D. 2018. Spatial prediction of ground water spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrology and Earth Systems Science, 22: 4771-4792. https://doi.org/10.5194/hess-22-4771-2018
6. Parvizi, Y. 2010. Mapping of spatial variability of soil organic carbon and predicting the influences of physical and management factors by use of multivariate analysis and artificial neural networks. Ph.D. Thesis. 338 pages.
7. Sajikumar, N., Thandaveswara, B. S. 1999. A nonlinear rainfall–runoff model using artificial neural networks. Journal of Hydrology, 21(6): 32-55.
8. Yousefi, S., Pourghasemi, H. R., Avand, M. T., Janizadeh, S., Tavangar, Sh, Santosh, M. 2021. Assessment of land degradation using machine-learning techniques: A case study of declining rangelands. Land Degradation and Development, 32: 1452-1466.
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Ghorbani Z, Sefid K, Keivan Behjou F, Jafarian Z, Souri M. Predicting the plant diversity under the effect of different grazing intensities at Sabalan southeast rangelands. PEC 2021; 9 (19) :63-78
URL: http://pec.gonbad.ac.ir/article-1-763-en.html
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Volume 9, Issue 19 (12-2021) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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