| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,005 |
| تعداد مقالات | 33,631 |
| تعداد مشاهده مقاله | 44,479,787 |
| تعداد دریافت فایل اصل مقاله | 30,136,298 |
تعیین مؤثرترین عوامل اقلیمی و خاکشناسی بر وضعیت پوشش گیاهی مراتع استان اصفهان | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 7، دوره 27، شماره 3 - شماره پیاپی 80، مهر 1399، صفحه 463-484 اصل مقاله (1.22 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2020.126906.1871 | ||
| نویسندگان | ||
| لیلا بخشنده مهر1؛ محمد رضا یزدانی* 2؛ رضا جعفری3؛ سعید سلطانی4 | ||
| 1دانشجوی دکتری بیابان‌زدایی، گروه بیابان‌زدایی، دانشکده کویر‌شناسی، دانشگاه سمنان. | ||
| 2دانشیار گروه بیابان‌زدایی، دانشکده کویر‌شناسی، دانشگاه سمنان. | ||
| 3دانشیار گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان | ||
| 4استاد گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان | ||
| چکیده | ||
| پژوهش بر روی کنترل‌‌گرهای محیطی در جوامع گیاهی، یکی از زمینه‌های تحقیقاتی برای بسیاری از اکولوژیست‌‌هاست. شناسایی عوامل مؤثر بر پوشش گیاهی مناطق خشک، اولین گام در جهت شناسایی عوامل مخرب و موانع رشد و گسترش پوشش گیاهیست. در این پژوهش با استفاده از روش مدلسازی معادلات ساختاری و تکنیک حداقل مربعات جزئی (PLS-SEM) متغیرهای اقلیمی و خاکشناسی مؤثر بر پوشش گیاهی درمنه‌زارهای استپی استان اصفهان شناسایی و مدلسازی شد. طبق نتایج، درصد رس خاک، حداکثر ارتفاع هرزآب بر روی خاک، درجه حرارت و خشکی محیط از عوامل خاکشناسی و اقلیمی مؤثر بر میزان تولید و پوشش گیاهی در مکان‌های مورد مطالعه می‌باشند. ضمن اینکه، نقش عوامل اقلیمی نسبت به عوامل خاکشناسی در پراکنش پوشش گیاهی منطقه بیشتر است. مدل ارائه شده در این پژوهش، دارای دقت مناسب و قابلیت انعطاف‌پذیری بالا برای مدلسازی پدیده‌های اکولوژیک می‌باشد. | ||
| کلیدواژه‌ها | ||
| پوشش گیاهی؛ حداقل مربعات جزئی؛ مدلسازی معادلات ساختاری؛ روایی؛ پایایی | ||
| عنوان مقاله [English] | ||
| Determining the most effective climatic and pedological factors on the vegetation status in rangeland of Isfahan Province | ||
| نویسندگان [English] | ||
| Leila Bakhshandehmehr1؛ Mohammadreza Yazdani2؛ Reza Jafari3؛ Saeid Soltani4 | ||
| 1Phd Candidate of desertification Combating, Department of Desert Studies, Semnan University | ||
| 2Associate Professor at, Department of Desert Studies, Semnan University | ||
| 3Associate Professor at Department of Natural Resources, Isfahan University of Technology | ||
| 4Professor at Department of Natural Resources, Isfahan University of Technology | ||
| چکیده [English] | ||
| Research on the environmental controllers in plant communities is one of the research fields for many ecologists. Identifying the factors affecting the vegetation cover in the arid regions is the first step to recognize the destructive factors, which inhibit the growth and development of vegetation. In the current study, using the structural equation modeling method and Partial Least Square – Structural Equation Modeling (PLS-SEM), climatic and pedological variables that affecting the vegetation cover in steppe rangelands of Zayandehrood basin of Isfahan province were identified and modeled. According to the results, soil clay content, maximum runoff height on the soil, temperature, and dryness of the environment are the most important variables affecting the quantity and quality of vegetation in the rangelands of the Zayandehrud basin. Besides, the role of climatic factors is more than soil factors in the distribution of vegetation in the region. The model presented in this research has good accuracy and high flexibility for modeling ecological phenomena. | ||
| کلیدواژه‌ها [English] | ||
| Vegetation cover, Partial least square, Structutal equation modeling, Validity, Reliability | ||
| مراجع | ||
|
Anderson, R. G. and Goulden, M. L., 2011. Relationships between climate, vegetation and energy exchange across a montane gradient, 116: 81-94. -Azarnivand, H., Jafari, M., Moghaddam, M. R., Jalili, A.and Zare chahouki, M. A., 2003. The effects of soil characteristics and elevation on distribution of two Artemisia species (Case study: Vard avard, Garmsar and semnan rangelands, 56 (1 -2): 93 - 100. -Bagnouls, F. and Gaussen, H., 1957. Les climats biologiques et leur classification. Annales de géographie, 355:193-220. -Blake, G. R. and Hartge, K. H., 1986. Bulk density. 363-375: In A. Klute (Eds.), Methods of soil analysis: Part 1—physical and mineralogical methods. Soil Science Society of America, American Society of Agronomy, Madison, USA. https://doi.org/10.2136/sssabookser5.1.2ed.c13. -Chang, W. Y. B., 1981. Path analysis and factors affecting primary productivity. Journal of Freshwater Ecology. 1(1):113-120. -Chepil, W. S., 1958. Soil conditions that influence wind erosion. U.S. Dept. of Agriculture, Washington DC, USA. -Chin, W. W., 1998. The partial least squares approach for structural equation modeling. 295-336: In G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research Lawrence Erlbaum Associates Publishers, Mahxah, NJ, US. -Christian, R. R., 2009. Concepts of ecosystem, level and scale. 504-505: In A. Bodini & K. Stefan (Eds.), Ecology Eolss Publishers, Oxford, UK. -Churkina, G. and Running, S. W., 1998. Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Journal of Ecosystems 1(2):206-215. -Cook, W. and Stubbendieck, J., 1986. Range research: Basic problems and techniques. Society for Range Management, Colorado, USA. -De Martonne, E., 1941. Une nouvelle function climatologique : L'indice d'aridite. Journal of Meteorologie, 2:449-459. -Eisenhauer, N., Bowker, M. A., Grace, J. B. and Powell, J. R., 2015. From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology. Journal of Pedobiologia, 58(2): 65-72. -Fick, S. E. and Hijmans, R. J., 2017a. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International. Journal of Climatology, 37(12): 4302-4315. -Fick, S. E. and Hijmans, R. J., 2017b. WorldClim Version2. (Gridded climate data). Retrieved 12 September 2017, from The U.S. Government's Global Hunger and food Security Initiative http://worldclim.org/version2. -Fölster, H., Dezzeo, N. and Priess, J. A., 2001. Soil–vegetation relationship in base-deficient premontane moist forest–savanna mosaics of the Venezuelan Guayana. Journal of Geoderma, 104(1): 95-113. -Fornell, C. and Larcker, D. F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. -Gaitán, J. J., Oliva, G. E., Bran, D. E., Maestre, F. T., Aguiar, M. R., Jobbágy, E. G. and Massara, V., 2014. Vegetation structure is as important as climate for explaining ecosystem function across Patagonian rangelands. Journal of Ecology, 102(6):1419-1428. -Gama-Rodrigues, A. C., Sales, M. V. S., Silva, P. S. D., Comerford, N. B., Cropper, W. P. and Gama-Rodrigues, E. F., 2014. An exploratory analysis of phosphorus transformations in tropical soils using structural equation modeling. Biogeochemistry. Journal of Biogeochemistry, 118(1-3): 453-469. -Gao, N., Zhou, J., Zhang, X., Cai, W., Guan, T., Jiang, L. and Zheng, Y., 2017. Correlation between vegetation and environment at different levels in an arid, mountainous region of China. Journal of Ecology and Evolution, 7(14):5482-5492. -George, D. and Mallery, P., 2003. SPSS for windows step by step answers to selected exercises: A simple guide and reference (4th ed.). Allyn & Bacon, Boston, USA. -Grace, J. B., 2006. Structural equation modeling and natural systems. Cambridge University Press, New York, USA. -Hair Jr, J. F., Sarstedt, M., Hopkins, L. and Kuppelwieser, V. G., 2014. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2):106-121. -Hazen, A., 1893. Some physical properties of sands and gravels, with special refrence to their use in filteration. Retrieved from Boston. -Henseler, J., Hubona, G. and Ray, P. A., 2016. Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1): 2-20. -Henseler, J., Ringle, C. M. and Sinkovics, R. R., 2009. The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319): Emerald Group Publishing. -Houze, R. A., 2012. Orographic effects on precipitating clouds, 50, RG1001. -Huete, A., 2016. Vegetation's responses to climate variability. Journal of Nuture, 531, 181. -Hulland, J., 1999. Use of partial least squares (PLS) in strategic management research: a review of four recent studies Strategic Management Journal, 20(2):195-204. -Ichii, K., Kawabata, A. and Yamaguchi, Y., 2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. 23(18):3873 - 3878. -Jafari-Parizi, M., Afsharzadeh, S., Akafi, H. and Abbasi, S., 2014. Ecological study of Artemisia aucheri communities in three rangelands of Isfahan provine. Journal of Plant Ecosystem Conservation, 2(4):79-94. -Lam, T. Y. and Maguire, D. A., 2012. Structural equation modeling: Theory and Applications in Forest Management. International Journal of Forestry Research, 16 pages, https://doi.org/10.1155/2012/263953. -Langbein, W. B. and Schumm, S. A., 1958. Yield of sediment in relation to mean annual precipitaion Transactions, American Geophysical Union, 39(6): 1076-1084. -Le Bagousse-Pinguet, Y., Maalouf, J., Touzard, B. and Michalet, R., 2014. Importance, but not intensity of plant interactions relates to species diversity under the interplay of stress and disturbance. Journal of Oikos, 123(7):777-785. -Maddox, G. D. and Antonovics, J., 1983. Experimental ecological genetics in Plantago: A Structural equation approach to fitness components in P. Aristata and P. Patagonica. Journal of Ecology, 64(5):1092-1099. -Moradi, G. and Vacik, H., 2018. Relationship between vegetation types, soil and topography in southern forests of Iran. 29(6), 1635-1644. -Nunnally, J. C. and Bernstein, I. H., 1994. Psychometric theory (3rd ed.). McGraw-Hill, Michigan, USA. -Peng, Y., Mi, K., Qing, F. and Xue, D., 2016. Identification of the main factors determining landscape metrics in semi-arid agro-pastoral ecotone. Journal Ecosystem Health and Sustainability,124: 249-256. -Rabbi, S. M. F., Tighe, M., Delgado-Baquerizo, M., Cowie, A., Robertson, F., Dalal, R. and Baldock, J., 2015. Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia. Scientific Reports. 5, 17866. -Richards, L. A., 1954. Diagnosis and improvement of saline and alkali soils (Vol. 60). Agricultural Research Service, Soil and Water Conservation Research Branch Washington, USA. -Rousseeuw, P. J. and Van Zomeren, B. C., 1990. Unmasking multivariate outliers and leverage points. Journal of the American Statistical association, 85(411): 633-639. -SCS, 1972. Section 4: Hydrology. In V. Mockus (Ed.), National engineering handbook: USDA/NRCS, Washington DC. -Skaggs, T. H., Arya, L. M., Shouse, P. J. and Mohanty, B. P., 2001. Estimating particle-size distribution from Limited Soil Texture Data. Soil Science Society of America Journal, 65(4):1038-1044. -Suescún, D., Villegas, J. C., León, J. D., Flórez, C. P., García-Leoz, V. and Correa-Londoño, G. A., 2017. Vegetation cover and rainfall seasonality impact nutrient loss via runoff and erosion in the Colombian Andes. Regional Environmental Change, 17(3):827-839. -Tsai, C., Kim, K., Liou, Y., Lee, G. and Yu, C., 2018. Impacts of topography on airflow and precipitation in the Pyeomgchang area seen from multiple-droppler radar observations. Monthly Weather Review, 146:3401 - 3424. -Tyler, S. W. and Wheatcraft, S. W., 1990. Fractal processes in soil water retention. Journal of Water Resources Research. 26(5):1047-1054. -Walkley, A., 1935. An examination of methods for determining organic carbon and nitrogen in soils. (With One Text-figure.). The Journal of Agricultural Science, 25(4): 598-609. -Walsh, R. P. D. and Lawler, D. M., 1981. Rainfall seasonality: Description, spatial patterns and change through time. Journal of Weather, 36(7): 201-208. -Wetzels, M., Odekerken, G. and Van Oppen, C., 2009. Using PLS path modeling for assessing Hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly. 33(1):177-196. -Wold, H. O. A., 1982. Soft modeling: The basic design and some extensions. 1-54: In K. G. Joreskog & H. O. A. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction. North-Holland, Amsterdam, Netherlands. -Yang, Y., Zhu, Q., Peng, C., Wang, H., Xue, W., Lin, G. and Li, S., 2016. A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. Scientific Reports, 6: 24110. -Yuan, L., Chen, X., Wang, X., Xiong, Z. and Song, C., 2019. Spatial associations between NDVI and environmental factors in the Heihe River Basin. Journal of Water, 29(9):1548-1564. | ||
|
آمار تعداد مشاهده مقاله: 785 تعداد دریافت فایل اصل مقاله: 448 |
||