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پیشبینی زیتوده اندامهای هوایی و زیرزمینی گونههای قابل چرا در اثر چرای دام با استفاده از سیستم استنتاج فازی- عصبی (ANFIS) | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 2، دوره 28، شماره 3، مهر 1400، صفحه 395-409 اصل مقاله (778.88 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2021.125006 | ||
| نویسندگان | ||
| ژیلا قربانی* 1؛ کیومرث سفیدی2؛ مهشید سوری3؛ مهدی معمری4 | ||
| 1دانش‌آموخته کارشناسی ارشد مرتع‌داری، دانشگاه محقق اردبیلی، ایران | ||
| 2دانشیار، گروه جنگل‌داری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، ایران | ||
| 3استادیار پژوهشی، بخش تحقیقات مرتع، مؤسسه تحقیقات جنگل‌ها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| 4استادیار، دانشگاه محقق اردبیلی، ایران | ||
| چکیده | ||
| آگاهی از میزان و نحوه اثرپذیری پوشش گیاهی از اختلال چرای دام می‌تواند راهکاری برای تدوین راهبرد‌های مدیریت اکوسیستم‌های مرتعی در جهت رسیدن به پایداری و تولید مستمر در این اکوسیستم‌ها باشد. در این تحقیق به بررسی تولید اندامهای هوایی و زیرزمینی گونههای قابل چرای دام در مراتع جنوب‌شرقی سبلان تحت تأثیر شدتهای مختلف چرایی و فاصله از روستا به‌عنوان کانون بحران پرداخته شد. به‌علاوه اینکه توسعه و ارزیابی مدل استنتاج فازی- عصبی (انفیس) به‌منظور پیشبینی تولید اندامهای هوایی و زیرزمینی گونههای خوشخوراک و مقایسه نتایج آن با مدل رگرسیونی ارائه گردید. برای ارزیابی مدلهای رگرسیونی و انفیس از مجذور میانگین مربعات خطا (RMSE) و ضریب همبستگی (R2) استفاده شد. نتایج نشان داد که شدتهای مختلف چرا، فاصله از روستا و اثرهای متقابل آنها اثر معنیداری در سطح احتمال یک درصد بر تولید اندامهای هوایی و زیرزمینی گونههای خوشخوراک دارند. همچنین، با افزایش شدت چرا، تولید اندامهای هوایی و زیرزمینی این گونهها کاهش یافت. نتایج بخش انفیس نشان داد که در شدت چرای کم و فاصله حدود 400 متر، بیشترین مقدار تولید گونههای خوشخوراک ملاحظه میگردد. کمترین مقدار تولید این گونهها نیز فواصل نزدیک به روستا (200 متری) پیشبینی شده است. بیشترین میزان زیتوده ریشه در فاصله 600 متری و کمترین میزان زیتوده ریشه نیز مربوط به فاصله 200 متری بود. به‌علاوه، مدل ANFIS با دقت بالاتر (98/0R2= و 95/0R2=) و خطای کمتر (9792/0RMSE= و 6168/1RMSE=) نسبت به مدل کم دقتتر رگرسیونی (92/0R2= و 77/0R2=) که خطای بیشتری نیز داشت (2835/2RMSE= و 8954/3RMSE=)، به‌ترتیب تولید اندامهای هوایی و زیرزمینی را پیشبینی نمود. | ||
| کلیدواژه‌ها | ||
| شدت چرا؛ هوش محاسباتی؛ مدل‌سازی؛ تولید | ||
| عنوان مقاله [English] | ||
| Predicting the biomass of aerial and underground parts of palatability species caused by grazing using Adaptive Neuro-Fuzzy Inference System (ANFIS) | ||
| نویسندگان [English] | ||
| zhila ghorbani1؛ Kiumars Sefidi2؛ Mahshid Souri3؛ Mehdi Moameri4 | ||
| 1Former M.Sc. Student in Range Management, University of Mohaghegh Ardabili, Iran | ||
| 2Associate Professor, Department of Forest Management, University of Mohaghegh Ardabili, Iran | ||
| 3Research Assistant Professor, Range Research department, Research institute of forest and rangeland, Tehran, Iran | ||
| 4Assistant Professor of University of Mohaghegh Ardabili, Iran | ||
| چکیده [English] | ||
| Awareness of the extent and impact of vegetation from livestock grazing disorders can be a solution to develop rangeland ecosystem management strategies to achieve sustainability and continuous production in these ecosystems. In this study, the production of aerial and underground organs of grazable livestock species in the southeastern rangelands of Sabalan under the influence of different grazing intensities and distance from the village as the focus of the crisis was investigated. In addition, the development and evaluation of ANFIS model was presented in order to predict the production of aerial and underground organs of food species and compare the results with the regression model. For evaluation of regression and ANFIS models the Root Mean Square Error (RMSE) and correlation coefficient (R2) were used. The results showed that different grazing intensities, distance from village and interaction between them were significant effect on the production of aerial and underground organs of palatability species at (p≤0.01). Also, with increasing grazing intensity, the production of aerial and underground organs of these species decreased. The results of ANFIS section showed that in low grazing intensity and distance of about 400 meters, the highest amount of production of palatable species is observed. The lowest production of these species is predicted to be close to the village (200 meters). Moreover, the highest amount of underground biomass at farther distances (600 meter) and lowest amount of that was observed at 200 meter. In addition, ANFIS model with higher accuracy (R2 = 0.98 and R2 = 0.95) and lower error (RMSE = 0.9792 and RMSE = 1.168) than less accurate regression model (R2 = 0.92 and R2 0.77) which also had more errors (RMSE = 2.2835 and RMSE = 3.8954), predicted the production of aerial and underground organs, respectively. | ||
| کلیدواژه‌ها [English] | ||
| Grazing intensity, artificial intelligence, modelling, production | ||
| مراجع | ||
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Aghajanlou, F. and Mousavi, A., 2007. Investigating the effects of conservation area on the quantity and quality changes of rangeland plant cover. Journal of Iran Natural Resources, 4 (59): 981-986. Ahmadali, V., Ghorbani, A., Azimi, M., Asghari, A., Teimorzade, A. and Badrzade, M., 2015. Investigating the flour, Chrotip and change of diversity and uniformity of species under the effect of different grazing distances from the villages in Sabalan Mountain. Journal of Taxonomy and Biosystematics, 7 (2): 69-84. Akbarlou, M., Sheidai Karkaj, E. and Ehsani, M., 2012. The effect of grazing intensities on aerial biomass, underground biomass and specifications of 3 main species of gramine in mountain ranges. Journal of Rangeland, 6 (3): 186-197. Badripour, H., 1996. The effect of distance from water point on condition and properties of vegetation cover, M.Sc. thesis on the field of rangeland management, Faculty of Natural Resources, Tehran University, 150p. Bahrami, B., Dianati Tilaki, G. A., Beigi, S. K., Janizadeh, S. and 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. Journal of Operational Researches about Soil, 1 (1): 94-106. Blüthgen, N., Dormann, C. F., Prati, D., Klaus, V. H., 2012. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Journal of Basic and Applied Ecology, 13 (2): 207-220. Ghorbani, A., Sharifi, J., Kavianpour, H., Malekpour, B. and Mirzaei, F., 2008. Investigating the ecological specifications of Festuca ovina L. in southeast of Sabalan mountain. Journal of Forest and Desert, 20 (2): 379-396. Ghorbani, A., Sharifi, J., Kavianpoor, H., Malekpoor, B. and Mirzaei Aghche Gheshlagh, F., 2013. Investigation on ecological characteristics of Festuca ovina L. in south-eastern rangelands of Sabalan. Iranian Journal of Range and Desert Research, 20 (2): 379-396. Ghorbani, Z., Sefidi, K., Keivan Behjou, F. and Moammeri M., 2018. The effect of different grazing intensities on the above and underground biomass of Festuca ovina and Agropyron libanticum at southeast ranges of Sabalan Mountain. Journal of rangelands, 3 (2): 496-505. Hoseini, S. M., Mosaedi, A. and Naseri, K., 2012. Features of Fuzzy logic and its application in water and soil resources managements. Abstracts of the 3th national Symposium of water resources management. Iran, 8-9 September: 1-12. Hutchings, M. J., and John, E. A., 2003. Distribution of roots in soil, and root foraging activity. In .de Kroon, H., Visser, E.J.W. (Ends), Ecological Studies, Berlin, 33- 60. James, C. D., 2003. Response of vertebrates to fenceline contrasts in grazing intensity in semi-arid woodlands of eastern Australia. Journal of Austral Ecology, 28 (2):137-151. Jnkanlvyy, M., Sepehri, A. and Hosseini, S.A., 2012 .The effect of grazing on underground organs of plants in arid and semi-arid rangelands (case study area Incheh Brown), Abstracts of the Fifth National Conference on pasture and rangeland in Iran, 379p. Khoshnevisan, B., Rafiee, S.H., Omid, M. and Mousazadeh, H., 2014. Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Journal of Information Processing in Agriculture, 5 (1):14-22. Krueger, E., Prior, S. A., Kurtener, D., Rogers, H. H. and Runion, G. B., 2011. Characterizing root distribution with adaptive neuro-fuzzy analysis. Journal of International Agrophysics, 25: 93-96. Mesdaghi, M., 2010. Vegetation description and analysis. Mashhad University Jihad Publications, 228 p. Millchunas, D. G. and Lauenroth, W. K., 1993. Quantitative effects of grazing and soils over a global range of environments. Journal of Ecology Monographs, 6 (3): 327- 366. Moghimi, S., Parvizi, Y., Mahdian, M.H. and Masihabadi, M.H., 2015. Comparison of applying multi- linear regression analysis and artificial neural network methods for simulating topographic factors effect on soil organic carbon. Journal of Watershed Engineering and Management, 6 (4):312-322. Naderlou, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Yaser Torabi, M. and Alimardani, F., 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement, 45 (3):1406-1413. Pentos, K. and Pieczarka, K., 2017. Applying an artificial neural network approach to the analysis of tractive properties in changing soil conditions. Journal of Soil and Tillage Research, 16 (5):113-120. Qaddoum, K., Hines, E. and Illiescu, D., 2011. Adaptive neuro-fuzzy modeling for crop yield prediction. Recent Researches in Artificial Intelligence, Knowledge Engineering and Data Bases, 21 (2): 199-204. Sajikumar, N. and Thandaveswara, B. S., 1999. A nonlinear rainfall–runoff model using artificial neural networks. Journal of Hydrology, 21 (6), 32-55. Salar, M. R., Esehaghbeygi, A. and Hemmat, A., 2013. Soil loosening characteristics of a dual bent blade subsurface tillage implement. Journal of Soil and Tillage Research, 13 (4): 17-24. Schlesinger, W. H., Reynolds, J. F., Cunningham, G. L., Huennke, L. F., Jarrel, W. M., Virginia, R. A. and Withford, W. G., 1990. Biological feedback in global desertification. Journal of Science, 24 (7):1043- 1048. Sedghi, R. and Abbaspour-Gilandeh, Y., 2014. Prediction of soil fragmentation at tillage operation using ANFIS. Journal of agricultural machinery, 4 (2):387-398. Tahmasebi, P., Ebrahimi, A. and Faal, M., 2011. Investigating the regression models in predicting the production of range plants. Journal of Rangeland, 5 (2):137-146. Tamartash, R., 2012. Investigating the relation of plant specifications with topography parameter in yield units of mountain ranges of VAZ Mazandaran. Iranian Journal of Range and Desert Research, 19 (3): 469-481. Wang, L., Good, S. P. and Caylor, K. K., 2014. Global synthesis of vegetation control on evapotranspiration partitioning. Geophysics Research Letter, 41. Doi: 10.1002/2014GL061439. Xia, L., Jia, S., Huang, S., Wang, H., Zhu, Y., Mu, Y., Kan, L., Zheng, W., Wu, D., Li, X. et al., 2010. The fused/surf complex controls the fate of drosophila germ line stem cells by generating a gradient BMP response. Cell, 143, 9781-990. 10.1016/j.cell.2010.11.022. Zare kia, S., Arzani, H., Jafari, M. and Zare, N., 2015. The effect of grazing yield on the plant cover specification and soil of stepped regions of Saveh ranges. Iranian Journal of Range and Desert Research, 22 (4):745-756.
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