دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52823 Research Paper Effect of long-term cattle grazing on root distribution and morphological characteristics of Brachiaria decumbens (Case study: tropical pasture, Malaysia) Effect of cattle grazing on root characteristics Ajorlo Majid Assistant Professor, Faculty of Natural Resources, University of Zabol, Iran Ebrahimian Mahboubeh PhD Candidate, Faculty of Forestry, University Putra, Malaysia Abdullah Ramdzani Associate Professor, Faculty of Environmental Studies, University Putra, Malaysia 23 10 2014 67 3 333 344 28 04 2013 25 08 2013 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52823.html

Root morphological and distribution responses of signal grass (Brachiaria decumbens) to long-term cattle grazing were examined in a tropical pasture in Malaysia. The treatments were no grazing by cattle and grazing at a moderate stocking density (2.7 animal unit/ha) under rotational grazing for 33 years. The method consists of taking soil core using a soil corer to a depth of 30 cm and extracting roots from cores by hand-washing and subsequent measuring of root morphological characteristics including length, surface area, average diameter and volume using WinRhizo Root Scanner. Root length density, mass density, surface area density, and volume density were calculated as indicators of root distribution pattern in the soil volume. Data were analyzed using repeated measure analysis of variance and independent t-test. Root diameter, length and length density were affected neither by grazing treatment nor the interaction between them (P > 0.05). Root diameters in the middle (10–20 cm) and lower (20–30 cm) soil layers of grazed site were 50% and 72% greater than that in the ungrazed site. Root volume, surface area and their densities were not affected (P > 0.05) by grazing and the interaction between grazing and soil depth. Mean root mass and mass density were affected (P < 0.05) by moderate grazing and soil depth, and the interaction between them. Mean root mass in all soil depths in grazed site was greater than that in the ungrazed site. Long-term rotational moderate grazing has no negative impact on root variables of signal grass in tropical pasture.

 root diameter Root length root mass root surface area root volume tropical pasture [1] Arredondo, J.T. and Johnson, D.A. (1998). Clipping effects on root architecture and morphology of three range grasses. Journal of Range Management, 51, 207-214. [2] Arredondo, J.T. and Johnson, D.A. (1999). Root architecture and biomass allocation of three range grasses in response to non-uniform supply of nutrients and shoot defoliation. New Phytologist, 143, 373-385. [3] Arredondo, J.T. and Johnson, D.A. (2009). Root responses to short-lived pulses of soil nutrients and shoot defoliation in seedlings of three rangeland grasses. Rangeland Ecology and Management, 62, 470-479. [4] Beyrouty, C.A., West, C.P. and Gbur, E.E. (1990). Root development of bermudagrass and tall fescue as affected by cutting interval and growth regulators. Plant and Soil, 127, 23-30. [5] Bilotta, G.S., Brazier, R.E. and Haygarth, P.M. (2007). The impacts of grazing animals on the quality of soils, vegetation, and surface waters in intensively managed grasslands. Advances in Agronomy, 94, 237-280. [6] Chen, Y., Lee, P., Lee, G., Mariko, S. and Oikawa, T. (2006). Simulating root responses to grazing of a Mongolian grassland ecosystem. Plant Ecology, 183, 265-275. [7] Dawson, L.A., Grayston, S.J. and Paterson, E. (2000). Effects of grazing on the roots and rhizosphere of grasses. 61-84. In: Grassland Ecophysiology and Grazing Ecology, CABI Publishing, New York. [8] Delgado, G.H.J., Aviless, L.R. and Vera, J.K. (2004). Root density in Panicum maximum cv. Tanzania monoculture and in a mixture with Leucaena leucocephala with different densities in Mexico. 20th International Grassland Congress, Dublin, Ireland, p.129. [9] Derner, J.D. and Briske, D.D. (1999). Does a tradeoff exist between morphological and physiological root plasticity? A comparison of grass growth forms. Acta Oecology, 20, 519-526. [10] Deutsch, E.S., Bork, E.W. and Willms, W.D. (2010). Soil moisture and plant growth responses to litter and defoliation impacts in parkland grasslands. Agriculture, Ecosystem and Environment, 135, 1-9. [11] Engel, R.K., Nichols, J.T., Dodd J.L. and Brummer, J.E. (1998). Root and shoot responses of sand bluestem to defoliation. Journal of Range Management, 51, 42-46. [12] Greenwood, K.L. and Hutchinson, K.J. (1998). Root characteristics of temperate pasture in New South Wales after grazing at three stocking rates for 30 years. Grass and Forage Science, 53, 120-128. [13] Hendrickson, J. and Olson, B. (2006). Understanding plant response to grazing. 32-39. In: Launchbaugh, K. (ed.), Targeted grazing: A natural approach to vegetation management and landscape enhancement. American Sheep Industry Association, Denver, Colorado. [14] Lodge, G.M. and Murphy, S.R. (2006). Root depth of native and sown perennial grass-based pastures, North-West Slopes, New South Wales. 1. Estimates from cores and effects of grazing treatments. Australian Journal of Experimental Agriculture, 45, 337-345. [15] Milchunas, D.G. and Lauenroth, W.K. (1993). Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecololgy Monograph, 63, 327-366. [16] Miles, J.W., Maass, M.L. and Do Valle, C.B. (1996). Brachiaria: Biology, Agronomy, and improvement. CIAT publication. No. 259, Colombia. [17] Mousel, E.M., Schacht, W.H., Zanner, C.W. and Moser, L.E. (2005). Effects of summer grazing strategies on organic reserves and root characteristics of big bluestem. Crop Science, 45, 2008-2014. [18] Oliveira, M.R.G., Van Noordwijk, M. and Gaze, S.R. (2000). Auger sampling, ingrowth cores and pinboard methods. In Smit, A.L. et al. (ed.) Root methods. Springer-Verlag, Berlin. [19] Piccolo, M.C. and Augusti, K.C. (2004). Root systems in tropical pasture restoration treatments inRondonia, Brazil. 20th International Grassland Congress, Dublin, Ireland, p. 187. [20] Richards, J.H. (1984). Root growth response to defoliation in two Agropyron bunchgrasses: Field observations with an improved periscope. Oecologia, 64, 21-25. [21] Van der Maarel, E. and Titlyanova, A. (1989). Above-ground and below-ground biomass relations in steppes under different grazing intensities. Oikos: Journal of Ecology, 56, 364-370.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52824 Research Paper Assessment of desertification potential using IMDPA model In Derakhte Senged water shed Abrisham Elham alsadat Ph.D. Combating Desertification, University of Tehran, Iran Feiznia Sadat Professor, Faculty of Natural Resources, University of Tehran, Iran The 23 10 2014 67 3 345 358 19 09 2012 31 01 2014 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52824.html

The Derakhte Senged area is located in south of Neishaboor town at Khorasan Razavi province. In this research, desertification intensity of Derakht senged area was evaluated using IMDPA, one of the newest method to Assess desertification potential in arid and semi arid regions of Iran. To evaluate desertification intensity, based on the primary research in this area, 4 criteria were selected including vegetation cover, soil, climate, geology and geomorphology. Each criterion was assessed based on the selected indices which result in qualitative mapping of each criterion cased on geometric mean of the indices. Then, sensitive map of region was extracted using geometric mean of all criteria. By laminate of thematic databases layers and using geometric mean of main criteria intensity map was obtained. The results showed that 0.37 % of study area categorized in low class, 80% was medium and 19.63% involved high class of desertification. Analysis of desertification criteria in this region showed that among study criteria, vegetation cover criteria is a major problem with a geometric average of 3.05 which shows high class, while geology and geomorphology with a weighted average of 1.8, has moderate effect in desertification. Also, climate and soil criteria with a geometric average of 2.04 and 2.21 show medium class desertification.

 Criteria Derakhte Senged desertification severity IMDPA model index [1] Abrisham, E. (2004). Assessment and mapping of desertification using MICD, ICD, FAO-UNEP (Case study, fakhraba, mehriz). MSc. Thesis, University of Tehran, 204p. [2] Abrisham, E. (2011). Assessment of desertification potential using IMDPA model in Derakhte Senged water shed. PH.D. Seminar, University of Tehran, 70p. [3] Ahmadi, H. (2006). Iranian model of desertification potential assessment in (East of Esfahan). Faculty of natural resources university of Tehran, 178p. [4] Ahmadi, H. (2009).Geomorphology. 2ed Edition, University of Tehran press, 180p. [5] Bahreini, F., Pahlavanravi, A., Moghaddamnia, A. and Rahi, Gh. (2012). Spatial prioritization of land degradation using IMDPA model with emphasis on wind erosion and climate (Case study: Bordekhun Region of Boushehr). Journal of Water and Soil, 26, 897-907. [6] Danfeng, S. (2006). Agricultural causes of desertification risk in Minqin,China. Journal of Environmental Management, 79, 348-356. [7] Esfandiari, M. and Hakimzadeh Ardakani, M.A. (2011). Evaluation of active desertification with emphasis on the soil degradation by IMDPA model (case study: Abadeh-Tashk, Fars). Journal of Range and Desert Reseac, 17, 624-631. [8] Khosravi, H. (2004). Assessment of desertification using MEDALUS (Case study: Kashan). MSc. thesis, Tehran University, 140 p. [9] Kharin, N. (1985). A methodological principles of desertification processes assessment and mapping, arid lands of Turkmenistan taken as example, Ashkhabad. Australian journal of basic and applied sciences, 2, 157-164. [10] Ladisa, G., Todorovic, M. and Trisorio_liuzzi, G. (2002). Characterization of area sensitive to desertification in southern Italy. Eco-compatible solutions for Aquatic Environmental, 87-99. [11] Lavado Conntador, J.F. and Schnabel, S. (2008). Mapping sensitivity to land degradation Extremadura. SW Spain, 1, 25-41. [12] Mesbah zade, T. (2007). Assessment and mapping of desertification using IMDPA, base on geology criteria (case study, Aboozobeid abad). MSc. Thesis, Tehran University, 154p. [13] Mohamad Ghasemi, S. (2007). Investigation of criteria and indicators of desertification, based on soil and water to assess and mapping desertification (Case study, Zabol). MSc. Thesis, Tehran University, 163 p. [14] Nateghi, S., Zehtabian, G. H. and Ahmadi, H. (2009). Evaluation of desertification intensity in Sagzi plain using IMDPA model. Journal of Range and Watershed Management, 62, 419-430. [15] Rafii Emam, A. (2003). Evaluation of desertification intensity in Varamin plain, base on Soil and water criteria. MSc. Thesis, Tehran University, 152 p. [16] Raisi, A. (2008). Evaluation of effective factors on desertification, using IMDPA model in Kahir Konarak area. MSc. Thesis, Tehran University, 180 p. [17] Reynolds J. F. (2009). Cutting through the confusion Desertification an old problem viewed through the lens of a new framework, the dry lands development paradigm. Dry lands, Deserts & Desertification, 45, 65-78. [18] TabaTabaii zade, M. (2010). Compare of desertification potential using IMDPA and MICD, base on wind erosion criteria (case study: Fakhrabad plain). MSc. Thesis, Tehran University, 160 p. [19] Zehtabian, Gh., Khosravi, H. and Ghodsi, M. (2007). Evaluation of desertification intensity in Kashan area, using IMDPA model. Desert journal, 54, 67-78. [20] Zolfaghari, F., Shahriari, A., Fakhireh, A., Rashki, R. and Noori, S. (2011). Assessment of desertification potential using IMDPA model in Sistan plain. Watershed Management Research, Pajouhesh and Sazandegi, 91, 97-107.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52826 Research Paper An optimized semi- automatic method for geomorphometric classification of Lut Yardangs using artificial neural etwork ehsani amir houshang Associate Professor, Environment Faculty, University of Tehran, Iran Foroutan Marzieh Ph.D Candidate, Geography, Calgary University, Canada 23 10 2014 67 3 359 380 09 01 2013 14 11 2014 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52826.html

In this study the land surface in western half of hyper-arid Lut desert, in south east of Iran, which is covered by Yardangs, a worldwide typical landform for Aeolian erosion, were classified by Self Organizing Maps (SOM) method. In the first step by using Digital Elevation Model with 10 m resolution and Matlab software, 22 morphometric parameters were calculated based on derivative of the surface elevation with first, second and third orders. In the second step most affective parameters for classification and the optimum number of classes were found through utilizing Optimum Index Factor and Davies Bouldin Index. Finally SOM classification was performed on seven morphometric parameters to result in seven classes. The results showed that most appropriate parameters in classification of area are plan curvature, rotor, hypsometric Integral, total accumulation curvature, slope steepness, extreme curvature and mean curvature. The study area were divided to seven classes including saddle valley, Concave ellipsoid, Gentle slope corridor, shoulder with concave slope, shoulder with convex slope, ridge, corridor channels. Sensitivity analysis results revealed that the most sensitive parameters are rotor, mean curvature and hypsometric Integral. Also the results of Jeffreys-Matusita Distance illustrated that parameter pair hypsometric integral / extreme curvature has the most ability in separation of classes in this area. Comparison of the separated classes with the landforms on aerial photographs confirms our classification results.

 D-B index DEM Lut Desert OIF quantitative geomorphology Self Organizing Maps (SOM) [1] Bue, B.D. and Stepinski, T.F. (2006). Automated classification of landforms on Mars. Computers & Geosciences, 32, 604-661. [2] Dikau, R. (1989). The application of a digital relief model to landform analysis in geomorphology. In: Raper, J. (Ed.), Three Dimensional Applications in Geographical Information Systems. Taylor & Francis, London, pp. 51-77. [3] Darvishzadeh., A. (1991). Geology of Iran, Amirkabir Press, Tehran, Iran. [4] Davies, D.L. and Bouldin, D.W. (1979). A cluster separation measure. IEEE Trans. Patt. Anal. Machine Intelligence, 1, 224-227. [5] Ehsani, A.H. and Quiel, F. (2008). Geomorphometric feature analysis using morphometric parameterization and artificial neural networks. Geomorphology, 99, 1-12. [6] Ehsani, A.H. and Quiel, F. (2008). Application of self organizing map and SRTM data to characterize yardangs in the Lut desert, Iran. Remote Sensing of Environment, 112, 3284-3294. [7] Ehsani, A.H. and Quiel, F. (2009). Self-organizing maps for multi-scale morphometric feature identification using shuttle radar topography mission data. Geocarto International, 24, 335-355. [8] Ehsani, A.H. et al. (2010). Effect of SRTM resolution on morphometric feature identification using neural network-self organizing map. Geoinformatica, 14, 405-424. [9] Evans, I.S. (1972). General geomorphology, derivatives of altitude and descriptive statistics. In R.J. Chorley (Ed.), Spatial Analysis in Geomorphology (pp. 17-90). London: Methuen & Co. Ltd. [10] Florinsky, I.V. (1998). Accuracy of local topographic variables derived from digital elevation models. International Journal of Geographical Information Science, 12, 47-61. [11] Florinsky, I.V. (1998). Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in Physical Geography, 22, 33-60. [12] Florinsky, I.V. (2002). Errors of signal processing in digital terrain modelling. International Journal of Geographical Information Science, 16, 475-501. [13] Florinsky, I.V. (2009). Computation of the third-order partial derivatives from a digital elevation model. International Journal of Geographical Information Science, 23, 2: 213-231. [14] Frankel, K.L. and Dolan, J.F. (2007). Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data. Journal of Geophysical Research-Earth Surface, 112, F02025. [15] Grebby, S (2010). Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data. Remote Sensing of Environment, 114, 713-724. [16] Hengel. T. and Router, H. (2008). Geomorphometry, Concepts, Software, Applications. Elsevier. [17] Ji, C.Y. (2000). Land-use classification of remotely sensed data using Kohonen Self- Organizing Feature Map neural networks. Photogrammetric Engineering and Remote Sensing, 66, 1451-1460. [18] Kohonen, T. (2001). Self Organizing Maps. 3rd Ed. Springer, New York. [19] Prima, O.D.A., Echigo, A., Yokoyama, R. and Yoshida, T. (2006). Supervised landform classification of Northeast Honshu from DEM-derived the maticmaps. Geomorphology, 78, 373-386. [20] Saux, E., et al.( 2004). A New Approach for a Topographic Feature-Based Characterization of Digital Elevation Data. GIS’04, 73-81. [21] Shary, P.A., Sharaya, L.S. and Mitusov, A.V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107, 1-32. [22] Wood, J. (1996). The Geomorphological Characterization of Digital Elevation Models. Ph.D. Thesis, Department of Geography, University of Leicester, UK. [23] Zevenbergen, L.W. and Thorne, C.R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12, 47-56.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52827 Research Paper Mineral element values of 8 rangeland species in highland pastures of Taleghan Arzani Hossein Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran. Motamedi(torkan) javad Assistant Professor, Faculty of Natural Resources, University of Urmia, Urmia, Iran Hamidian Mahmood Graduate Student, Faculty of Natural Resources, University of Tehran, Karaj, Iran Arzani Zahra University of Farhanghian, Hakim Ferdosy, Karaj, Iran Akhshi Majid Faculty of Natural Resources, University of Tehran, Karaj, Iran 23 10 2014 67 3 381 391 11 03 2013 06 06 2014 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52827.html

To achieve the desired level of animal performance providing animal food supplies containing mineral elements is essential. For this purpose, mineral elements of 8 important rangeland species, including Ferula ovina, Prangus uloptera, Medicago sativa, Lotus goeblia, Trifolium montanum, Melilotus officinalis, Sanguisorba minor and Stachys inflate, were measured at different growth stages in the summer pastures of Taleghan. Therefore, at each growth stage 3 samples were taken and for each sample at least five vegetable bases were removed from different vegetation types. Data were analyzed using one-way ANOVA and to see the sources of variation within groups the Duncan test was utilized. Moreover, T-test was used to compare the values of mentioned elements with the critical level to provide the daily requirements of an animal unit in the maintenance mode. The results show that the values of minerals at the early stages of growth are more than those at the final stages of development (seeding stage). Moreover, the mentioned values in each stage of the growth are not uniform among the studied species. In general, the studied species are in good condition in terms of the calcium, iron, copper, cobalt and manganese values for meeting the daily needs of animals. However, they are not in optimal condition in terms of the highly consumed elements such as sodium and magnesium values at different growth stages, especially at later stages of growth, for meeting the daily needs of animals. This indicates that the desirability of grassland forage at different times of grazing is not identical regarding the mineral elements. It is normal that depending on the year’s conditions the values of plant mineral elements may change slightly; however, due to the cost of determining mineral elements, the results can be used to estimate the daily animal needs in different years.

 mineral elements daily animal need critical level growth stage pastures of Taleghan [1] AOAC (2000). Official methods of analysis, 17th Ed., Association of Official Analytical Chemists (Animal Feed, chapter 4, p.54): Arlington: AOAC International. [2] Arzani, H. (2009). Forage quality and daily requirement of grazing animal, University of Tehran press, 350pp. [3] Arzani, H., Yousefi, SH., Jafari, M. and Farahpour, M. (2005). Model for determination of range suitability for sheep grazing using GIS (Case study: Taleghan region in Tehran province). Journal of Environmental Studies, 31, 59-68. [4] Arzani, H., Kaboli, H., Nikkhah, A. and Jalili, A. (2004). Introduction of important forage quality index, Journal of Natural Resources, 57 (4): 777-789. [5] Baghestani meybodi, N., Arzani, H., Shokat fadaii, M., Nikkhah, A. and Baghestani meybodi, M.A. (2004). Study of changes in soluble carbohydrates reserves in important rangeland spieces in Nir region (Yazd Province), Natural Resources Journal, 57 (2): 779-811. [6] Brekken, A. and Steinnes, E. (2004). Seasonal concentrations of cadmium and zinc in native pasture plants: consequences for grazing animals, Science of the Total Environment, 181-195. [7] Ebn Abbasi, E. and Saeedi, K. (2009). Quantitative study of some micro elements of three important range species in different phonological stages in Saral, Kurdistan Province, Journal of Rangeland, 3 (1): 78-89. [8] Eghbali, N. (2007). Determination forage quality of range plants in north of Fars Province, M.Sc. thesis of range management, Faculty of Natural Resources. University of Tehran, 120pp. [9] Garcia-Ciudad, A., Ruano-Ramos, A., Vhquez de Aldana, B.R. and Garcia-Criado, B. (1996). Interannual variations of nutrient concentrations in botanical fractions from extensively managed grasslands, Institute de Recursos Nahwalesy Agrobiologia, CSIC, Apdo 257, E-37071 Salamanca, Spain. [10] Holechek, J.L., Pieper, R.D. and Herbel, C.H. (2004). Range management principles and practices, Prentice Hall, Englewood Cliff, 587pp. [11] Jafari, A. and Navid Shad, B. (2007). Principal of animal nutrient, Hagh Shenas press, 420pp. [12] Mathis, C.P. and Sawyer, J.E. (2004). New Mexico forage mineral survey, Proceedings, Western Section, American Society of Animal Science, Vol. 55, 35pp. [13] Motamedi, J. (2011). A model of estimating short-term and long-term grazing capacity for animal and rangeland forage equilibrium, Ph.D., thesis of Range Management, Faculty of Natural Resources. University of Tehran, 352pp. [14] Neville, F.S. (2010). Mineral Nutrition of Livestock, 4th Edition, 354pp. [15] Pulina, G. and Bencini, R. (2004). Dairy sheep nutrition, CABI publishing, London, UK., 222pp. [16] Ram´ırez, R.G., González-Rodr´ıguez, H. and Haenlein, G.F.W. (2005). Mineral content of browse species from Baja California Sur, Mexico, Small Ruminant Research, 57,1-10. [17] Ram´ırez, R.G., Haenleinb, G.F.W. and NuÂnÄez-GonzaÂlez, M.A. (2001). Seasonal variation of macro and trace mineral contents in 14 browse species that grow in northeastern Mexico, Small Ruminant Research, 39, 152-159. [18] Ranjbari, A.R., Rasti, M., Yazdi, K.R. and Sadegian, M. (2001). Investigation effect of two kind supplemental mineral on weight and parts of quarry lamb grazing in pasture, 3th conference of feeding animal and brids, pp: 92-100. [19] Safari, J., Mushic, D.E., Kifaro, G.C., Mtenga, L.A. and Eik, L.O. (2010). Seasonal variation in chemical composition of native forages, grazing behaviour and some blood metabolites of Small East African goats in a semi-arid area of Tanzania, Animal Feed Science and Technology,25,128-135. [20] Sanjay, K. Uniyal, Anjali Awa Sthi and Gopal S. Rawat, (2005). Biomass availability and forage quality of Eurotia ceratoides Mey in the rangelands of Changthang, eastern Ladakh, Current Science, 89 (1): 201-204. [21] Shadnoush, G.H. (2006). Mineral determination of some range plants for grazing sheep in semi-arid areas of Chaharmahal and Bakhtiari Province, Journal of Rangeland & Desert Reserchers of Iran, 13 (4): 285-295. [22] Shemaa, M., Saeedi, H. and Nikpour Tehrani, K. (2003). Principal feeding of animal and brids. University of Tehran press, Vol. 1, 269pp. [23] Sofi Siyavash, R. (1990). Animal nutrient. Aamidi press, 235pp. [24] Varmaghani, S., Moosavi, M.A. and Jafari, H. (2005). Determination of minerals in range plants of Ilam Province, Research & Construction, 73, 103-109. [25] Zafar, I.K., Muhammad, A.K. and Ahmad, F.A. (2010). Seasonal assessment of selenium as a hazardous element in pasture and animal system: A case study of Kajli sheep in Sargodha, Pakistan, Journal of Hazardous Materials, 179: 1111-1114.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52829 Research Paper Spatial variability of rainfall erosivity indices using geostatistics in Khouzestan Province Erosivity INdices Evaluation Eslami Hossein Department of Watershed management, Science and Research Branch, Islamic Azad University, Tehran, Iran Salajagheh Ali Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran Khalighi sigaroudi Shahram Associate Professor., Faculty of Natural Resources, University of Tehran, Karaj, Iran Ahmadi hasan Professor, Department of Watershed management, Science and Research Branch, Islamic Azad University, Tehran, Iran Ayoubi Shamsollah Associate Professor, Department of Soil Science, College of Agriculture, Isfahan University of Technology, 84156-83111, Isfahan, Iran 23 10 2014 67 3 393 406 12 07 2010 31 05 2011 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52829.html

Rainfall erosivity is the ability of rainfall to detach the soil particles. This study was conducted to evaluate spatial variability of rainfall erosivity indices in Khouzestan Province. The point data of indices (EI30, AIm, KE>1 and Onchev indices) in 74 stations were used to generate spatial erosivity maps through deterministic and geostatistical interpolation methods (Radial Basis Functions, Inverse Distance Weighted, Kriging and Cokriging). Results indicate that cokriging have least error and most correlation with determining coefficient of 0.89, 0.89, 0.48 and 0.49 for EI30, AIm, KE>1 and Onchev indices. Based on the correlation relationships between the basins specific sediment yield (in basins dominating the sedimentation stations) and mean indices of EI30, AIm, KE>1 and Onchev, EI30 index with correlation coefficient of 0.98 (P<0.01) is selected as the appropriate rainfall erosivity index. Based on the prepared map on the basis of Cokriging method with secondary variable of maximum mean monthly rainfall, the east and northeastern regions presented the highest values of EI30 index, while the southern and western regions showed the lowest values of EI30 index. The annual rainfall erosivity (EI30) ranged from 404 to 3064 Mj.mm.ha-1.h-1.y-1.

 Cokriging Geostatistics interpolation rainfall erosivity [1] Arnoldus, H.M.J. (1980). An approximation of the rainfall factor in the universal soil loss equation. In: de Boodt, M., Gabriels, D. (Eds.), Assessment of erosion, Wiley, New York, 127-132. [2] Capolongo, D., Diodato, N., Mannaerts, C.M., Piccarreta, M. and Strobl, R.O. (2008). Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (southern Italy). Journal of Hydrology, 356, 119-130. [3] Ceglar, A., Crepinsek, Z., Zupanc, V. and Kajfez-Bogataj, L. (2008). A comparative Study of rainfall erosivity for eastern and western Slovenia. Acta Agriculturae Slovenia, 91(2), 331-341. [4] Goovaerts, P. (1999). Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena, 34, 227-242. [5] Hadley, R.F., Lal, R., Onstad, C.A., Walling, D.E. and Yair, A. (1985). Recent developments in erosion and sediment yield studies. UNESCO, Paris, 127p. [6] Hakimkhani, Sh., Mahdian, M.H., Arab Khedri, M. and Ghorbanpour, D. (2005). Investagating rain erosivity using Modified Fournier for Iran. Third National Conference of Erosion and Sediment, Tehran, 281-288. [7] Hasani Pak, A. (1998). Geostatistics. Tehran University Press, 314p. [8] Hoghoughi, M. (1995). Khouzestan and its development capacities, Water and Development Journal, 4,17-25. [9] Hosini Zare, N. and Saadati, N. (2005). Estimating of erosion and sediment using of sedimentology data and computation of suspended load in Khouzestan Province rivers catchments. Third National Conference of Erosion and Sediment, Tehran, 273-280. [10] Hoyos, N., Waylen, P.R. and Jaramillo, A. (2005). Seasonal and spatial patterns of erosivity in a tropical watershed of the Colombian Andes. Journal of Hydrology, 314, 177–191. [11] Hudson, N. (1971). Soil conservation. Billing & Sons Ltd, Great Britain, 320p. [12] Isaaks, E.H. and Serivastava, R.M. (1989). An introduction to applied geostatistics. Oxford University Press, 561p. [13] Lal, R. (1976). Soil erosion problems on Alfisols in western Nigeria. Effects of rainfall characteristics. Geoderma, 16, 389-401. [14] Lal, R. and Elliot, W. (1994). Erodibility and erosivity. In Lal, R. (ed), Soil erosion research methods. Soil and Water Conservation Society, Ankeny, 181-208. [15] Mohammadi, J. (1998). Rain erosivity map providing for Iran using Fournier Index and Kriging method. Agricultural Science and Natural Resources Journal, 3&4, 35-44. [16] Morgan, R.P.C. (1986). Soil erosion and conservation. Logman Group Limited, Hong-London, 210p. [17] Onchev, N.G. (1985). Universal index for calculating rainfall erosivity. In: El-Swafify, S.A., Moldenhauer, W.C and Lo, A. (eds), Soil Erosion and Conservation, Soil Conservation Society of America, Ankeny, IO, 242-431. [18] Renard, k. and Freimund, J.R. (1994). Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of Hydrology, 157, 287-306. [19] Salles, C. and Poesen, J. (2000). Rain properties controlling soil splash detachment. Hydrological Process, 14, 271-282. [20] Salako, F.K., Ghuman, B.S. and Lal, R. (1995). Rainfall erosivity in south-central Nigeria. Soil Technology, 7, 279-296. [21] Shesh Angosht, S., Alimohammadi, A. and Soltani, M.J. (2005). Geostaistics models evaluation in GIS for erosivity map providing in Latian watershed. Geographical Information System Conference, National Cartographic Center of Iran, 113-123. [22] Silva, A.M. (2004). Rainfall erosivity map for Brazil. Catena, 57, 251-259. [23] Taj Ali Pour, Z., Mahdian, M.H., Pazira, A. and Heidarizadeh, M. (2009). Spatial variations investigation of erosivity index in Daryacheh Namak watershed. 11th Soil Science Conference of Iran, Gorgan, 43-47. [24] Wang, G., Gertner, G., Singh, V., Shinkareva, S., Parysow, P. and Anderson, A. (2002). Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall–runoff erosivity R factor. Ecological Modelling, 153 , 143–155. [25] Wischmeier, W.H. and Smith, D.D. (1978). Predicting rainfall erosion losses: a guide to conservation planning. USDA, Agriculture Handbook No. 537. Government printing office, Washington, DC, 58p.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52830 Research Paper Quantity assessment of water erosion intensity using regional model of erosion and sediment yield (Case study: Nir watershed, Ardebil) Esmali Abazar Assistance Professor, Faculty of Agriculture, University of Mohaghegh Ardebili, Iran Ahmadi Hasan Professor, Sciences & Research Branch, Islamic Azad University, Tehran, Iran Tahmoures Mohammad PhD Candidate, Faculty of Natural Resources, University of Tehran, Karaj, Iran 23 10 2014 67 3 407 417 07 11 2009 14 03 2011 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52830.html

The aim of this research is to describe the development of a methodology based on present knowledge and available data for evaluation of water erosion behavior and risk as well as modeling and estimation of soil erosion, which is compatible for other similar areas of Iran. Accordingly, the conducted research was based on four major types of water erosions including: sheet, rill, channel and riverbank which have considerable role on sediment yields of Baleghli Chay Watershed, Ardebil Province, were separately and spatially studied. In order to determine the inter-effects of effective factors, the study was conducted using stepwise multivariate statistical tests. For each erosion type, an individual model was then presented. In the next step, after determining of relations between sediment yield and environmental factors (fixed & variable) through statistical analyses and selecting of effective factors on erosion and sediment yields, was created an empirical structure for modeling erosion and sediment yields based on MPSIAC erosion model. In formulation of the new model, were used of eight effective factors on erosion in the area. These factors are susceptibility of geological formation, soil erodibility, rainfall erosivity, runoff erosivity, topography, hydrographic drainage, Normalized Difference of Vegetation Index (NDVI) and field conditions of erosion features. In the presented model, with summation of the scores of mentioned eight factors, obtains the M value, which can estimate the amount of erosion and sediment yields of the area, using exponential formula between sediment yield and M values. In addition, in order to obtain the confidence of presented model, it was used in "Nir" catchment for evaluation the precision. The results showed 11 percent difference. With accepting of this error value, the water erosion hazard map of the area was provided and presented using new model.

 Ardebil hazard map modeling Nir Water erosion [1] Ahmadi, H. (1999). Applied Geomorphology. Vol. 1 (Water Erosion), 3rd Edition. University of Tehran Press, 688 p. (In Persian). [2] Esmali, A. (2007). Modeling and mapping erosion risk prediction (Case study: Baleghli Chay, Ardebil). PhD Thesis, 250 p. (In Persian). [3] Feyznia, S. (1995). Rocks resistance to erosion in different climates of Iran. Journal of Natural Resources, 47(1), 95-116 (In Persian). [4] Ghodoosi, J. (2003). Morphological Modeling and gully erosion hazard. PhD Thesis, University of Tehran, 224 p. (In Persian). [5] Barthes, B. and Roose, E. (2002). Aggregate stability as an indicator of soil susceptibility to runoff and erosion, Catena, 47, 133-149. [6] Bissonnais, Y.L., Monitor, C., Jamagne, M., Daroussin, J. and King, D. (2001). Mapping erosion risk for cultivated soil in France, Catena, 46, 207-220. [7] Bou Kheir, R., Cerdan, O. and Abdallah, C. (2006). Regional soil erosion risk mapping in Lebanon. Journal of Geomorphology, 82, 347-359. [8] Hughes, A.O. and Prosser, I.P. (2003). Gully and riverbank erosion mapping for the Murray- Darling basin, CSIRO land and water, Canberra, Technical report 3/03. [9] Miller, S.N., Semmens, D.J., Goodrich, D.C., Hernandez, M., Miller, R.C., Kepner, W.G. and Guertin, D.P. (2007). The Automated Geospatial Watershed Assessment tool. Environmental Modelling & Software, 22, 365-377. [10] Morgan, R.P.C. (2001). A simple approach to soil loss prediction, a revised Morgan–Morgan–Finney model. Catena ,44, 305-322. [11] Najmoddini, N. (2003). Assessment of Erosion and Sediment Yield Processes, Using RS & GIS, A case study in Rose Chai sub-catchment of Orumieh basin ,W. Azarbaijan, Iran. [12] Stoching, M.A. and Elwell, H.A. (1976). Rainfall erosivity over Rhodesia. Transactions of the Institute of British Geographers New Series, 1, 231-245.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52835 Research Paper Selection of the best empirical formula to estimate time of concentration in urban watersheds (Case study: Mahdasht town) shahbazi ali PhD. Candidate, Faculty of Natural Resources, University of Tehran, Karaj, Iran khaliqi sygarodi Shahram Assistant Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran Malekian Arash Assistant Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran Salajegheh Ali Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran In 23 10 2014 67 3 419 435 04 08 2013 12 02 2014 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52835.html

In order to decrease the risks associated with the management of urban watersheds, the use of proper methods is an essential task to estimate the runoff with a high degree of confidence. Time of concentration is one of factors that impacts on peak discharge and runoff volume. The objective of this study is to select the best method among the empirical formulas for estimating the time of concentration. In this study, for determination of actual time of concentration, the field method based on measuring the travel time by using floating-object method was employed. To select the best empirical formula of the time of concentration, the statistical criteria including percentage Relative Error (RE), Root Mean square error (RMSE), Average percentage Relative Error (RME), Nash - Sutcliffe criteria (NS) and determination coefficient were used. Then, differences among the estimations obtained from empirical equations were compared with the actual values. The results of this study based on comparison of the relative error in each interval showed that in the reach No. 2, empirical formulas of California, Chow, Carter and Federal Aviation, with percentage error of 2.7, 2.9, 4.4 and 4.4 have showed the best estimation, respectively. The equation proposed by Kirby with percentage error of 1 in the reach No. 3, the equation of Ventura with percentage error 8.5 in the reach No. 9 and the equation of rational hydrograph with percentage error 4.8 in the reach No. 10 have showed the best estimates. Therefore, it is recommended to use the empirical formula that has the lowest percentage of error for areas with features similar to the studied reaches. In general, the results show that only rational hydrograph method in all of the reaches has the lowest error and then provides the most proper estimates compared than others.

 empirical formula rational hydrograph time of concentration travel time Urban Watershed [1] Abustan, I., Sulaiman, A.H., Abdul Wahid, N. and Baharudin, F. )2008(. Determination of Rainfall-Runoff Characteristics in An Urban, (Case study: Sungai Kerayong Catchment, Kuala Lumpur). 11th International Conference on Urban Drainage. [2] Alizadeh, A. )2006(. Principles of applied hydrology. 20ed Edition, University of Mashhad Press, 807p. [3] Arabi, M. and Govindaraju, R.S. and Hantush, M.M. )2007(. A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practice. Journal of Hydrology, 333, 459-471. [4] Azadnia, F., Rostami, N. and Kamali Moghaddam, R. )2009(. Comparison of empirical equations for estimating time of concentration of the basin Meimeh Ilam. Iranian Water Research Journal, 3(4), 1-8. [5] Chin, D.A. )2000(. Water-resources engineering. Prentice Hall, New Jersey, 750p. [6] Dastorani, M.T., Abdullah Wand, A., Talabi, A. and Moghadamnia, A.R. ) 2014(. Evaluate the use of empirical equations for estimating the focus time navigating time streams. Journal Research and development, 103(93), 1-8. In press. [7] Dongquan, Z., Jining, C., Haozheng, W., Qingyuan, T., Shangbing, C. and Zheng, S. )2009(. GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach, (Case study in Macau). Environ Earth Sci, 59, 465- 472. [8] Eslamian, S. and Mehrabi, A. )2005(. Empirical relations for estimating the time of concentration mountainous watersheds. Journal of Natural Resources and Agricultural Sciences, 12, 36-45. [9] Fang, X., Thompson, D.B., Cleveland, T.G., Pradhan, P. and Malla, R. )2008(. Time of concentration estimated using watershed parameters determined by automated and manual methods. Journal of Irrigation and Drainage Engineering, 134(2), 202-211. [10] Green, J. and Nelson, E. )2002(. Calculation tim of Concentration for hydrologic design and analysis using geographic information system vector objects. Journal of Hydro informatics, 4, 75-81. [11] Jamshidi, S. (2011). Compared to the estimated time of concentration using empirical relations (Case study: Watershed basin Srfyrvz Abad). 2th Geoscience Conference, 9p. [12] Li, M.H., and Chibber, P. (2008). Overland flow time of concentration on very flat terrains. Transportation Research Record 2060. Transportation Research Board, Washington, DC, 133-140p. [13] Mahdavi, M. (2011). Applied hydrology. 7ed Edition, Thesis. University of Tehran Press, 439p. [14] McCuen, R. (1984). Eestimating urban time of concentration. Hydraulic Engineering ASCE, 100, 633-638. [15] Mobaraki, J. (2006). Accuracy in the estimation of empirical relationships time to peak concentration Hydrograph (Case study: Tehran Province). MSc. Thesis. University of Tehran, 151p. [16] Moghadamnia, A.R. (1997). A comparative study on the time-delay and time to reach peak flood hydrograph based on experimental methods and analysis of two regional climate. MSc. Thesis. University of Tarbiat Modarres, 163p. [17] Motamedvaziri, B. (2004). Evaluation of some empirical relations to estimate the time of concentration (Case study: Karaj watershed). MSc. Thesis University of Tehran, 125p. [18] Najafi, A. (2009). Factors of flood basin catchment Esfahan- Sirjan the factor analysis. Journal of Geography and Environmental Planning, 4, 101-118. [19] Pavlovic, S.B. and Moglen, G.E. (2008). Discretization issues in travel time calculation. J. Hydrol. Eng., 13(2), 71-79. [20] Shafai Bajestan, M. (2005). Principles and application of physical models – hydraulic, 1ed Edition, University of Martyr Chamran Press, 292p. [21] Sharifi, S. and Hosseini, S.M. (2011) .Methodology for Identifying the Best Equations for Estimating the Time of Concentration of Watersheds in a Particular Region. Journal of Irrigation and Drainage Engineering, 137(11), 712-719. [22] Sepahvand, A., Taei Semirom, M., Myrnya, Kh. and Moradi, H.R. (2011). Evaluate the model sensitivity to the variability of soil moisture. Journal of Soil and Water, 25(2), 338-346. [23] Sourisseau, S.A., Basser, S.F. and Perie, T. (2007). Calibration, validation and sensitivity analysis of an ecosystem model applied to artificial streams. Water Research, 42, 1167-1181. [24] Wong, T.S.W. (2005). Assessment of time of concentration formulas for overland flow. J. Irrig. Drain Eng., 131(4), 383-387. [25] Wong, T.S.W. (2009). Evaluation of kinematic wave time of concentration formulas for overland flow. Journal of Hydraulic Engineering, 14(7), 739-744.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52836 Research Paper Effects of pre-cooling treatment, osmotic potential and water stress on seedling growth of some population in two savory species (Satureja bachtiarica and S. sahendica) in laboratory and greenhouse conditions shafie Mysam MSc Student Plant Breeding, Islamic Azad University Boroujerd, Iran Alizadeh Mohammad Ali Assistant Professor , Research Institute of Forests and Range country, Iran Jafari Ali Ashraf Professor, Research Institute of Forests and Range country, Chairman of the gene bank, Iran 23 10 2014 67 3 437 450 19 02 2013 18 09 2013 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52836.html

In order to study, the effect of pre-cooling treatment, osmotic potential and water stress on some population of two species of Satureja bachtiarica and Satureja sahendica was studied by a factorial experiment based on completely randomized design with three replications in laboratory and greenhouse conditions during one year in the gene bank of Institute of forests and range lands by the years of 2010-2011. In laboratory, treatment were including: cold and osmotic potential making of polyethylenglycol (PEG) 6000 Da in 5 concentrations (0, -0.3, -0.6, -0.9, -1.2 Mgapascal) compared with control. In greenhouse, treatment were including: cold and drought stress in five levels of field capacity (100,80,60,40,20) compared with control. Result showed that seed germination and seed emergence were decreased as rate of 33% and 6% in order of laboratory and greenhouse condition with increasing of osmotic potential and water stress while the ratio of root to shoot and ratio of dry weight to fresh weight was increased. In both experimental conditions, the most of the seed characteristics were higher with pre-cooling than other treatments. In response to osmotic potential in germinator, and dry stress in greenhouse, Bijar 2 population from Sahandy savory and piranshar from bachtiary savory showed more tolerant to dry stress due to have more vigor index and ratio of dry weight to fresh weight and they introduced as superior population.Comparing of the population showed that population of the takestan of Sahandy savory had lower tolerance.

 cold Field capacity germination osmotic potential PEG6000 Satureja sahendica Satureja bachtiarica [1] Abdul - baki, A.A. and Anderson, J.D. (1975). Vigour determination in soybean seed by multiple criteria. Crop aci, 630-633. [2] Alizadeh, M.A. and Isvand, H.R. (2004). Evaluation and the study of germination potential, speed of germination and vigour index of the seeds of two species of medicinal plant (Eruca sativa Lam.,Anthemis altissima L) under coold room anry storage condition. Iranian Journal of Medicinal and Aromatic plant Research, 20(3), 301-307. [3] Allen, S.G., Dobrenz, A.K., Schonhorst, M.H. and Stoner, J.E. (1985). Heretability of Nacl tolerance in germination of alfalfa seed. Agron, J., 77, 99-101. [4] Baba Khanlo, P., Mirza, M., Sefidkon, F., Ahmadi, L. and Asgari, F. (1998). Evaluation of the constituent composition of Essential oil in Ziziphora clinopodioides, Zataria mutaria multiflora, Z.tenuior. Iranian Journal of Medicinal and Aromat [19] Zic Plants, 2, 22-120. [5] Chuanren, D., Bochu, W., Wanqian, L., Jin, C., Jie, L., Haan, z. (2004). Effect of chemical and physical factors to imporove the germination rate of Echinacea anqustifolia seed‌. Colloids and surfaces biointer faces, 37, 101-105. [6] Emmerich, W.E. and Hardgree, S.P. (1990). Polyethylenglycol solution contact effection seed germination. Agron .J., 82, 1103-1107. [7] Faker Baher, Z., Rezai, M.B., Mirza, M.M. and Abbaszadeh, B. (2001). Evaluation of Qualitive and Quantitive changing of Essential Oil of Satureja. Iranian Journal of Medicinal and Aromatic Plants,[p1]  11, 36-51. [8] Goicochea, N., Antolin, M.C. and Sanchez, D.M. (1997). Gas exchange is related to hormone balancein mycorrhizal or nitrogen fixing alfalfa subjected to drought. Physiologia planturum, 100, 989-997. [9] Hossienpour Ghazveni, A. (2010). Evaluation germination and vigour problem in four ecotype in laboratory and greenhouse Msc Thesis. Azad university –unit of Takestan, 1-102. [10] Jamzad, Z. (2009). Thymus and Savory of Iran, Pub. Research Istitute of Forest and Rangeland, 81-132. [11] Kochaky, A., Bromand, J. and Rezazadeh, Z. (2005). Evaluation of seed germination response to osmotic and matrix potential of polyethylenglycol and sodium coloride in different temperature, J. agronomical research of Iran, 3, 207-217. [12] Lebaschi, M., Sharif Ashorabadi, H. and Mazaheri, D. (2003). Effect of dry stress on changing of hypersin. J. of Pagohesh and Sazandeghi, 58(1), 44-52. [13] Lekh, R. and Khairwal, I.S. (1993). Evaluation of pearl millet hybrids and their parents for germ inability and field emergence. Indian Jour. Plant Physiol, 125-127. [14] Lowlor, D.w. (1970). Absorption of polyethylenglycols by plants and Their effecte on plant growth, New phytol, 69. [15] Maguire, J.D. (1962). Speed of germination: Aid in selection and evaluation for seedling vigour, Crop science, 2, 176-177. [16] Michel, B.E. and Kaufman, M.R. (1973). The osmotic potential of polyethylenglycol 6000 plant physiology, 51. [17] Omidbighy, R. (1997). The obtained strategy of production and technology of plant medicine No of Issue (1, 2 and 3). Publication of Ghods. [18] Sarmadnia, Gh.H. (1996). Translation of seed technology. Published by Jahad university of Mashhad. [19] Sedighi, M. and Porkermani, M. (1983). Un-uniform figure in arid zone. Translation- Publication of Ghods. [20] Sefidkon, F., Jamzad, Z. and Barzandeh, M. (2004). Essential oil of Satureja bachtiarica as a full source of Karvakrol. Iranian Journal of Medicinal and Aromatic Plants, 20(4), 439-425. [21] Waterman, p.g. and mole, s. (1994). Analysis of phenol and secondary metabolites. black well scientific publications. oxford, new york, 44-66.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52837 Research Paper The changes in monthly and seasonal values of carbon emission in different grazing intensities (Case study: Ghoosheh, Semnan) Sadeghipour Ahmad Assistant Professor, Desert studies Faculty, Semnan University, Iran kamali nadia Ph.D. candidate of range management, University of Tehran, Iran Kamali Paria Ph.D. student of range management, University of Gorgan, Iran Joneidi Hamed Assistant Professor, Natural resources Faculty, University of Kurdistan, Iran This 23 10 2014 67 3 451 458 04 05 2013 14 11 2013 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52837.html

This study investigates monthly and seasonal variations of carbon emission from the soil at different grazing intensities. Three areas of exclosure, low and high grazing intensities were selected in Ghoosheh region of Semnan province. Carbon emission was measured monthly, in each treatment applying alkali traps (CSC) during a year. Emission and grazing data were analyzed, using a factorial experiment in a completely randomized design with four replications. To investigate the relation of soil moisture and air temperature with carbon emissions in each area, Pearson correlation was used. Results showed that the emission levels under different grazing intensities had significant difference. The highest emission occurred in high grazing intensity, in August (3.34 g C m-2 day-1) and lowest in February (0.033 g C m-2 day-1) in exclosure. The seasonal distribution of emission showed the highest amount, in summer, autumn, winter and spring respectively. There was also a negative correlation between carbon emissions and soil moisture.

 Alkali trap Exclosure carbon emission Ghoosheh Grazing Semnan [1] Ball, B.C., Scott, A. and Parker, J.P. (1999). Field N2O, CO2 and CH4 fluxes in relation to tillage, compaction and soil quality in Scotland. Soil and Tillage Res, 53, 29-39. [2] Batjes, N.H. (1996). Total carbon and nitrogen in the soils of the worlds. European journal of soil science, 47, 151-163 [3] Bond-Lamberty, B., Wang, C. and Gower, S. (2004). A global relationship between the hetero-trophic and autotrophic components of soil respiration? Global Change Biology, 10(10):1756-1766. [4] Eamus, D., Hutley, L.B.A. and O’Grady, P. (2001). Daily and seasonal patterns of carbon and water fluxes above a north Australian savanna. Tree Physiology, 21, 977-988. [5] FAO (2001). FAO Stat. Agricultural Production Databases. Food and Agriculture Organization of the United Nations, Rome, Italy. [6] Forest rangeland and watershed management organization (2002). National action programme of combating desertification. Rural Development Publications. 357p. [7] Frank, A.B. (2003). Evapotranspiration from northern semiarid grasslands. Agron. J., 95, 1504-1510. [8] Gupta, R.D., Sanjay, A. and Sumberia, N.M. (2010). Soil physical variability in relation to soil erodibility under different land uses in foothills of Siwaliks in N-W India. Tropical Ecology, 51(2):183-197. [9] Harvell, A.D., Wienhoil, B.J. and Black, A.L. (2002). Tillage nitrogen and cropping system effect on carbon sequestration. SSSA J., 66, 906-912. [10] Hill, M.J., Braaten, R. and Mekeon, G.M. (2003). A scenario calculator for effect of grazing land management on carbon stock in Australian rangelands. Environmental modeling and software, 18, 627-644. [11] Hirotaa, M., Tanga, Y., Hub, Q., Kat, T., Hiratad, S., Moe, W., Caob, G. and Marikoe, S. (2005). The potential importance of grazing to the fluxes of carbon dioxide and methane in an alpine wetland on the Qinghai-Tibetan Plateau. Atmospheric Environment, 39, 5255-5259. [12] Huang, C.H. and Kronrad, G.D. (2001). The Cost of Sequestration Carbon on Private Forest Lands. Forest Policy and Economics, 2, 133-142. [13] Jabro, J.D., Sainju, U., Stevens, W.B. and Evans, R.G. (2008). Carbon dioxide flux as affected by tillage and irrigation in soil converted from perennial forages to annual crops. Journal of Environmental Management, 88,1478-1484. [14] Lal, R. and Kimble, J.M. (2000). Pedogenic carbonates and the global carbon cycle. In: Lal, R., Kimble, J.M., Eswaran, H. and Stewat, B.A. (eds), Global climate change and pedogenic carbonates, CRC press, Boca Raton, 1-14. [15] Lal, R. (2004). Soil carbon sequestration to mitigate climate change, Geoderma, 123, 1-22. [16] Li, G. and Sun, S. (2011). Plant clipping may cause overestimation of soil respiration in a Tibetan alpine meadow, southwest China. Ecol. Res, 26, 497-504. [17] Luo, Y. and Zhou, X. (2006). Soil Respiration and the Environment. 320pp. [18] Morris, J. and Jensen, A. (1998). The carbon balance of grazed and non-grazed Spartina anglica salt marshes at Skallingen, Denmark. Journal of Ecology, 86, 229-242. [19] Peichla, M., Cartonc, O. and Kiely, G. (2012). Management and climate effects on carbon dioxide and energy exchanges in a maritime grassland. Agriculture, Ecosystems and Environment,158, 132-146. [20] Sadeghipour, A. (2012). The study of carbon sequestration and it's distribution in different land uses (Case study: Shahriar). Ph.D. thesis of range management, University of Tehran. 153pp. [21] Sainju, U.M., Jabro, J.D. and Stevens, W.B. (2008) . Soil carbon dioxide emission and carbon content as affected by irrigation, tillage, cropping system, and nitrogen fertilization . Plant and environment intraction, 37, 98-106. [22] Schmitt, M., Bahn, M., Wohlfahrt, G., Tappeiner, U. and Cernusca, A. (2010). Land use affects the net ecosystem CO2 exchange and its components in mountain grasslands. Biogeosciences, 7, 2297-2309. [23] Schwining, S. and Sala, O.E. (2004). Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia, 141, 211-220. [24] Sherrod, L.A., Peterson, G.A., Westfall, D.G. and Ahuja, A.R. (2003). Cropping intensity enhances soil organic carbon and nitrogen in a no till agro ecosystem. Soil Sci. Soc. Am. J., 67,1533-1543. [25] Tan, Z. and lal, R. (2005). Carbon sequestration potential estimates with changes in land use and tillage practice in ohio, USA. Agric. Ecosys. and Environ, 12, 113-12. [26] Valentini, R., Dore, S., Marchi, G., Mollicone, D., Panfyorov, M., Rebmann, C., Kolle, O. and Schulze, E.D. (2000). Carbon and water exchanges of two contrasting central Siberia landscape types: regenerating forest and bog. Funct. Ecol, 14, 87-96. [27] Vickers, D., Thomas, C.K., Pettijohn, C., Martin, J. and Law, B.E. (2011). Five years of carbon fluxes and inherent water-use efficiency at two semi-arid pine forests with different disturbance histories. International Meteorological Institute in Stonkholm 3 December 2011. [28] Wohlfahrt, G. and et al. (2008). Biotic, abiotic, and management controls on the net ecosystem CO2 exchange of European mountain grassland ecosystems. Ecosystems, 11, 1338-1351. [29] Zeeman, M.J., Hiller, R., Gilgen, A.K., Michna, P., Pluss, P., Buchmann, N. and Eugster, W. (2010). Management and climate impacts on net CO2 fluxes and carbon budgets of three grasslands along an elevational gradient in Switzerland. Agric. Forest Meteorol, 150, 519-530.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52838 Research Paper Comparisons of different models for landslide susceptibility mapping in Zangvan watershed, Ilam province Karimi Haji Associate Professor, Faculty of Agriculture, University of Ilam, Iran Naderi Fathollah Geomorphologist, Teacher of Ilam Payam Noor University, Iran Naseri Behrooz Staff Member of Islamic Azad University Ilam Branch, Iran Salajeqeh Ali Professor, Faculty of Natural Resources, University of Tehran, Iran 23 10 2014 67 3 459 485 11 08 2009 09 11 2011 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52838.html

Distinguishing the susceptible areas to landslide using different landslide susceptibility mapping (LSM) models is one of the primitive and basic works to reduce probable damages and reduce risk. The main purpose of this research is the efficiency evaluation of four methods including Information value (WINF), Valuing area accumulation (Wa), Analytical Hierarchy Process (AHP), Kopta-Joshi proposed method (LNRF) for LSM in Zangvan watershed, Ilam province. At first, all the effective factors in landslide occurrence were inspected. By analyzing the parameters, nine factors including slope, aspect, elevation, precipitation, distance from road, distance from fault, distance from drainage, land use and lithology were distinguished as the effective factors in landslides occurrence in the studied area. After preparing the information of these nine factors in GIS environment, the location of landslides were determined using areal photographs and satellite images and LSM performed by the above four methods. Finally, the landslide index was used for evaluation the ability of appropriate LSM model. Based on this Index, the information value method classified more 52 percent of occurred landslides in very high danger class. Therefore, this method is more efficient and proposed as the best LSM method in the Zangvan watershed because of compatibility of landslides with high danger classes and ability of differentiation of danger classes.

 GIS Landslide Index Mapping model Zangvan Ilam [1] Ahmadi, H., Mohammadkhan, Sh., Feiznia, S. and Ghoddousi, J. (2005). A modeling of mass movement hazard, case study: Taleghan drainage catchment. Journal of the Iranian Natural Rec., 58, 1: 3-14. [2] Brabb, E., Pampegan, E.H. and Bonilla, M.G. (1972). landslide susceptibility in Sanmateo County, California, U.S Geological Survey Miscellaneous field studies map Mf-360. [3] Fanyu liu, Z. (2007). Study on Landslide Susceptibility Mapping Based GIS and with Bivariate Statistics- a Case Study in Longnan Area Highway 212, Science paper online. [4] Faraji Sabokbal, H. (2005). Location of trade servicing units using AHP. Journal of the Geography Rec., 51, 32-37. [5] Gupta, R.P. and Joshi, B.C. (1990). Landslide hazard zoning using the GIS approach- A case study from the Ramganga catchment, Himalayas, Engineering Geology, 28(1): 119-131. [6] Haqhshenas, A. (1995). Landslide hazard mapping and associated with sediment yield in Taleghan watershed. Ms. Thesis. Tarbeyat Modares University, 187 pp. [7] Kolarostaqi, A. (2002). Study effective factors in occurrence landslides and mapping landslide hazard in the Shirin rood watershed, Ms. Thesis. Tehran University, 141 pp. [8] Khullar, V.K., Sharam, R.P. and Paramanik, K. (2000). A GIS approach in the landslide zone of Lawngthlia in southern Mizoran. Landslide, proceeding of the 8th international symposium on landslides, 3, 1461-1472. [9] Komac, M. (2006). A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in per alpine Slovenia.Geomorphology,24,17-28. [10] Naderi, F. and Karimi, H. (2011). Efficiency assessment of two Information Value and Gopta-Joshi methods in landslide hazard mapping in the Talkhab watershed of Ilam. Pajouhesh & Sazandagi, 92. [11] Nikandish, N. (1997). Theory for landslide movement in Iran. Jehade Keshavarzi Journal, 155, 84-95. [12] Pachauri, A.k. and PANT, M. (1992). Landslide Hazard Mapping based on Geological attributes, Eng, Geological, 32, 81-100. [13] Sayarpoor, M. (1992). Landslide hazard potential mapping in south Ghalghal, Ardabil province. Ms. Thesis. Tehran University, 173 pp. [14] Sefidgari, R. (2002). Efficiency methods Landslide hazard mapping in Damavand watershed. Ms. Thesis. Tehran University, 159 pp. [15] Shadfar, S., Norozi, A.A., Ghoddousi, J. and Ghauomeyan, J. (2005). Landslide hazard mapping in Lakertashan watershed. Journal of the Water & Soil conservation, 1, 1-10. [16] Van Westen, C.J and Soeters, R. (1998). Workshop on GIS in Landslide Hazard Mapping, the Kakani Area (Nepal): Theory. Version 1, ITC, 14 p. [17] Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA, 72, 1-12.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52839 Research Paper Investigation of morphological changes and nebkha formation in Capparis deciduas and C. spinosa in arid lands Mosleh arany Asghar Associated Professor, Natural Resources and Desert Studies Faculty, Aridland and Desert Research Institute (ADRI), Yazd University, Plant Ecology, Iran Azimzadeh Hamid Associated Professor, Natural Resources and Desert Studies Faculty, Aridland and Desert Research Institute (ADRI), Yazd University, Soil Sciences, Iran Ekhtesasi Mohammad Reza Professor of Natural Resources and Desert Studies Faculty, Yazd University, Wind Erosion Morphodynamics, Iran imantalab Neda MSc Graduated, Yazd University Aridland Forestry Management, Iran Dolati Ali Assistant Professor, Yazd University, Mathematics Department, Statistics, Iran 23 10 2014 67 3 475 485 06 07 2011 14 11 2013 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52839.html

This study aimed to investigate morphological changes of Capparis deciduas and C. spinosa and their effects on nebkha formation. In order to examine the morphological changes of these plants, nine C. spinosa nebkhas of varying ages were randomly selected and size and number of adventitious roots, number of shoot and buds were counted. Results showed that the dead and green parts of C. spinosa in nebkha were significantly more than control plants. These showed that nebkha in C. spinosa stimulated growth by increasing bud and shoot at first and then there was a steady mortality when the volume of nebkhas increased. In C. deciduas, nebkha caused stimulation of adventitious roots in buried shoots. Increasing nebkha volume did not increased the number of shoots and buds in C. decidua. Control plants did not produce adventitious roots. Formation of nebkha in C. decidua happened in two ways. In the first, C. decidua plants in middle age produced root stock in base of stems. Root stocks continued to grow, trap sand, and form nebkha. Root stock in nebkha produced adventitious root. In consecutive years root stocks continued to grow, trap sand, and form larger nebkha. In contrast, growth of main stems stopped and they were finally dried. C. decidua also formed nebkha like C. spinosa in the second way. It is concluded that C. decidua stimulated better adaptive morphological changes compared to C. spinosa in facing nebkh formation.

 Nebkha Capparis decidua morphological changes C. spinosa [1] Ardon, K., Tsoar, H. and Blumberg, D.G. (2009). Dynamics of nebkhas superimposed on a parabolic dune and their effect on the dune dynamics. Journal of Arid Environments, 73, 1014-1022. [2] Bing, L., Wenzhi, Z. and Rong, Y. (2008). Characteristics and spatial heterogeneity of Tamarix ramosissima nebkhas in desert-oasis ecotones. Acta Ecologica Sinica, 28(4), 1446-1455. [3] Cooke, R.U., Warren, A. and Goudie, A. (1993). Desert Geomorphology. UCL Press, London, 256 p. [4] Dech, J.P. and Maun, M.A. (2006). Adventitious root production and plastic resource allocation to biomass determine burial tolerance in woody plants from central Canadian coastal dunes. Annals of Botany, 98, 1095-1105. [5] Ekhtesasi, M.R., Ahmadi, H., Feyznia, S. and Busche, D. (2005). Wind erosion, Faces and Damages in Yazd-Ardakan Plain. Iranian Journal of Natural Resources, 57(4), 567-583. [6] Ekhtesasi, M.R. (2010). Plants suitable for sand dunes & sand sheets fixation in Iran (Major psammophytes in Iran). Yazd University pub., 246p. [7] Hermesh, R. (1972). A study of the ecology of the Athabasca sand dunes with emphasis on the phytogenic aspects of dune formation. M.Sc. thesis. University of Saskatchewan, Saskatoon, Sask. [8] Hesp, P. and McLachlan, A. (2000). Morphology, dynamics, ecology and fauna of Arctotheca populifolia and Gazania rigens nabkha dunes. Journal of Arid Environments, 44, 155-172. [9] Khalaf, F.F.I., Miska, R. and Al Douseri, A. (1995). Morphological characteristics of some nebkha deposits in the northern coastal plain of Kuwait, Arabia. Journal of Arid Environments, 58, 335-355. [10] Maun, M.A. (1998). Adaptations of plants to burial in coastal sand dunes. Canadian Journal of Botany, 76, 713-738. [11] Maghsoudi, M., Negahban, S., Bagheri Said-Shokeri, S. and Chezgheh, S. (1391). Comparative and analysis of nebkhas geomorphologic features four plant species in west of Lut (East of Shahdad-Takab Plain). Research in Natural Geography, 79, 55-77. [12] Mozafarian, V. (2000). Flora of Yazd. Yazd University pub. [13] Mozafarian, V. (2004). Trees and Shrubs of Iran, 1st edition, Farhang -e-Moaser pub. [14] Ekhtesasi, M.R. and Azimzadeh, H. (2002). Bandar-e-Jask (Sedij & Biahe) detailed technical document of combat desertification and sand dune fixation. Natural Resources and Watershed Management Service of Hormozgan province pub. [15] Perumal, J. (1994). Effects of burial in sand on dune plant communities and ecophysiology of component species. Ph.D. thesis, University of Western Ontario, London, Ont. [16] Pourkhosravani, M., Vali, A.A. and Moayeri, M. (2009). Study of relationships between plant morphology and nebkha morphometry characteristic of Reaumaria turcestana in khairabad desert in Sirjan. Physical Geography Research, 69, 99-113. [17] Sabeti, H. (1994). Trees and Shrubs of Iran, 5th edition, Yazd University pub, 187p. [18] Saghafi Khadem, F. (1999). Flora of Iran, No:30, Capparidaceae, Forest and Rangeland Research Institute pub. [19] Tengberg, A. and Chen, D. (1995). Nebkha dunes as indicators of wind erosion and land degradation in the Sahel zone of Burkina Faso. Journal of Arid Environments, 30, 265-282. [20] Pourkhorasani, M., Vali, A.A. and Movahedi, S. (2010). A comparative classification for nebkhas; Seidlitzia florida, Alhagi mannifera and Reaumaria turcestanica, based on vegetative performance of plants in Khair Abad desert in Sirjan. Geographic Space, 10(31),137-158. [21] Wolfe, F. (1932). Annual rings of Thuja accidentalis in relation to climatic conditions and movement of sand. Botanical Gazette (Chicago), 93, 328-335. [22] Yuan, T., Maun, M.A. and Hopkins, W.G. (1993). Effects of sand accretion ophotosyntesis, leaf-water potential and morphology of two dune grasses. Functional Ecology, 7, 676-682.
دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52840 Research Paper Forecasting of runoff and sediment using neural network and multi regression in Aghajari Marls Forecasting of Runoff and Sediment Using Neural Network and Multi Regression Vatakhah Mahadi Associate Professor, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Iran Saidian Hamzeh Former Ph.D. Student, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Iran 23 10 2014 67 3 487 499 03 11 2012 09 09 2014 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52840.html

Erosion and sediment movement phenomena are one of the most complex issues in management of rivers drainage areas that in water projects are very important. That its measurement wants high time and cost. Issue of surface runoff in river basin is a complex issue that human knowledge and understanding about its physical laws a viewpoint of some mathematical formulas is limited. In this study to investigate modeling runoff and sediment production in different land uses of Aaghajari formation deposits, part of Margha watershed in Izeh city with area 1609 hectares was selected. In this study, some soil physical and chemical characteristics such as percentage of sand very fine, sand, clay, silt, pH, electrical conductivity, moisture, calcium carbonate and soil salinity in different land uses of Aghajari formation were used. Then the rain simulator in 7 point and with three replicated in different intensities 0.75, 1 and 1.25 mm in minute in three land use range, residential areas and agricultural lands, were used the amount of runoff and sediment. And the same of number were sampled in 0-20 cm in soil layer. In totally, 126 times sampling runoff and sediment were done. And 189 soil experiments were done. In order to perform all statistical analysis were used 11.5 SPSS and EXCEL and MATLAB 2008 software. The results showed that multi regression analysis in conditions with high input and little output data shows more favorable results than neural network. And in high intensities owing to data homogeny, neural network operation than to low precipitation intensities is better. But in multi regression in high and low precipitation intensities showed acceptable operation. The average of relative error in three land uses in sediment production in precipitation intensity 0.75 mm in minute were in multi regression 7.2 percent and root mean square error 0.06. And in neural network in same precipitation intensity the average of relative error 146/9 percent and root mean square error 0.41 were. The average of relative error in three land uses in sediment production in precipitation intensity 1 mm in minute were in multi regression 8.5 percent and root mean square error 0.19. And in neural network in same precipitation intensity the average of relative error 96.36 percent and root mean square error 0.85 were. The average of relative error in three land uses in sediment production in precipitation intensity 1.25 mm in minute were in multi regression 1.8 percent and root mean square error 0.38. And in neural network in same precipitation intensity were the average of relative error 37/6 percent and root mean square error 0.73.

 Soil erosion formation Aghajari Artificial Neural Network Margha watershed [1] Abrahart, R.J. and White, S.M. (2001). Modelling sediment transfer inMalawe: comparing backpropagation neural network solutionagainst a multiple linear regressionbenchmark using small datasets. Physics and Chemistry of the Earth, 26, 19-24. [2] Alp, M and Kerem Cigizoglu, H. (2009). Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling and Software, 22, 2-13. [3] ASCE (2000). Artificial neural networks in hydrology: 1. Preliminaryconcepts. Journal of Hydrologic Engineering, 5, 115-123. [4] Asghari moghadam, A., Norani, V. and Nadiri, A. (2008): Modeling[p1]  of Tabriz plain rainfall using neutral networks. Agriculture Science Journal, 18(1), 1-15. (In Persian). [5] Barthes, B. and Roose, E. (2002).Aggregate Stability as an Indicator of Soil Susceptibility to Runoff and Erosion; Validation at Several Levels, Catena, 47, 133-149. 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دانشکده منابع طبیعی دانشگاه تهران Journal of Range and Watershed Managment 5044-2008 دانشکده منابع طبیعی دانشگاه تهران 109 10.22059/jrwm.2014.52841 unavailable Abstracts 23 10 2014 67 3 1 12 17 02 2015 17 02 2015 Copyright © 2014, دانشکده منابع طبیعی دانشگاه تهران. 2014 https://jrwm.ut.ac.ir/article_52841.html

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