Document Type : Research Paper
Authors
Assistant Professor, Range Research Division , Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension, AREEO, Tehran, Iran
Abstract
Extended abstract
Background and objectives: Efforts for sustainable management of rangelands require evaluation and monitoring in different temporal and spatial scales. It is necessary to identify the most important climatic effects and how they affect vegetation factors in the field of optimal rangeland management. Sablan peak is the third highest peak in the country and is known as the livestock hub of Ardabil province. Considering the importance of Sablan rangelands in the economy and livelihood of the residents of the region, maintaining water and soil and other important services, it is necessary to organize a suitable management plan for its long-term use and to determine a model to predict the percentage of canopy cover and its biomass rate.
Materials and methods: For this purpose, basic and annual information on canopy percentage, biomass and condition, and climatic information such as rainfall and temperature are needed. Also, obtaining a model that can estimate the percentage of canopy cover and biomass with acceptable accuracy and precision without visiting in person; it is one of the priorities of rangeland management in the region. Based on this, in order to identify the most effective climate factors and determine the relevant model, Shabil Sabalan site in Ardabil province was evaluated and monitored for 5 years (2017-2021). Vegetative factors, including crown percentage of total cover and biomass rate, and functional factors, including rangeland condition and tendency, were evaluated during a 5-year period. Evaluations were carried out in a 50-hectare site named Shabil at the foot of Sablan Mountains in Lahrood section of Meshginshahr city with Festuca ovina-Bromus tomentellus-Onobrychis cornuta plant type. This site is a representative area for rangelands in the north of Sablan in terms of plant and functional traits. The key area of two hectares with a height of 2805 meters inside the site was considered for the implementation of the plan and evaluation. The average long-term rainfall of 30 years leading to the year of its study is 460 mm. To analyze the data, General Linear Model test was used in Minitab 16 software. Pearson correlation test and stepwise regression were used to check and determine the correlation of environmental and vegetation data and to identify the most effective factors and predict the model.
Results: The results of the evaluation of plant factors, the percentage of canopy coverage and biomass showed that the highest amount of coverage and biomass was obtained in one year after with the highest amount of rainfall. Therefore, the analysis of the variance of canopy percentage and biomass during the studied years was significant at the level of 1%. The results of the evaluation of the site-based or environmental factor of the rangeland condition also showed that the best amount of rangeland condition was obtained in years with more rainfall and more coverage and biomass. In examining the correlation between the studied indicators, the percentage of vegetation with autumn and winter rainfall showed a significant positive correlation at the level of 5%, but biomass was not correlated with any of the studied variables. The results of the step-by-step regression also showed that among the analyzed indicators, autumn and winter rainfall is the most effective factor in predicting the percentage of vegetation and total rainfall, and autumn and winter rainfall are also the most effective factors in predicting biomass at a probability level of 5% were in the region.
Conclusion: The three important indicators of canopy percentage, biomass and condition have performed well in showing the difference of different years, but the vegetation index has the highest correlation and due to the ease of vegetation evaluation compared to biomass evaluation, therefore the annual evaluation of canopy percentage Coverage as the most important factor can help in determining the prediction model. With the continuation of data collection, stronger and more accurate models can be obtained to estimate the canopy percentage and the amount of biomass
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