Modeling Soil Organic Carbon Pool Weight Associated with Soil Physico-chemical Properties within Glandrood Forest in Northern Iran

Document Type : Research Paper

Authors

1 Ph.D, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University

2 Associate Professor, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University

3 Assistant Professor, Agriculture and Natural Resources Science university of Sari

Abstract

Soil organic carbon (SOC) storage estimation with high accuracy is very important due to having such prominent role corresponding to climate change and global warming reduction. Therefore, the current study was carried out in the beech mixed-stands forest of reserved compartment in Glandrood forests, in the northern parts of Iran. Stepwise regression method was used in order to present the SOC pool weight modeling considering the prominent soil physic-chemical properties at 3 different soil depths (0-10, 10-30, and 30-50 cm). The results of ANOVAs showed that exchangeable calcium content, lime and clay percentage (P < 0.05) and also carbon content, soil organic matter, nitrogen and C/N ratio (P < 0.01) were significantly different among the three soil depths. Moreover, on average, the total soil carbon stored considered as SOC pool weight in the ecosystem equaled 543.87 ± 22.07 t.C ha-1. According to the stepwise regression analysis C/N ratio was the effective content as explanatory variable to predict SOC pool (R2adj = 0.44; SEE = 4.8). In the following models, P, N and clay percentage, respectively, were the effective contents which improved significantly SOC pool prediction (R2adj = 0.64-0.83; SEE = 3.8-2.6). C/N ratio, P, N and clay were included 46.3, 22.41, 14.71 and 9.44 percent respectively with respect to the variations of SOC pool. Regression analysis indicated that introducing electrical conductivity, pH, and Mg as input variables in the following steps did not ameliorate the precision of SOC pool prediction clearly (R2adj = 0.85-0.89; SEE = 2.5-2.1). Furthermore, during the modeling and according to the collinearity diagnostic test, the result indicated that the maximum variance inflation factor of the models was less than 10 (VIF < 10), validating them for application.

Keywords

References

1.       اسحاقی راد، ج.، زاهدی امیری، ق.ا.، مروی مهاجر، م. ر.، متاجی، ا.، 1388. ارتباط بین پوشش­های رستنی با خصوصیات فیزیکی و شیمیایی خاک در جوامع راش (مطالعه موردی: جنگل آموزشی پژوهشی حیرود کنار نوشهر). فصلنامه جنگل و صنوبر ایران، جلد 17، شماره 2، 187-174.
2.       اسماعیل زاده،ا.، حسینی،م.،1386. رابطه بین گروه­های اکولوژیک گیاهی با شاخص­های تنوع زیستی گیاهی در ذخیره گاه سرخدار افراتخته. محیط­شناسی، سال سی و سوم، شماره 43، صفحه 30-21.
3.       باوکر، هزمر، علیرضا جباری، جرالد لیبرمن و هاشم محلوجی، 1384. آمار مهندسی، مرکز نشر دانشگاهی، ص 852.
4.       بی نام ، 1387. طرح جنگلداری سری سه گلندرود (تجدید نظر دوم)، اداره کل منابع طبیعی و آبخیزداری استان مازندران – نوشهر.
5.       روحی مقدم، ع.، حسینی، س.م.، ابراهیمی، ع.، رحمانی، ا.، طبری، م.، مهدوی، ر 1390. بررسی برخی از ویژگی­های خاک در جنگل­کاری­های خالص و آمیخته بلندمازو. مجله پژوهش­های خاک، 25 (1): 48-39.
6.       متاجی، ا.، زاهدی امیری، ق. ا.، عصری، ی.، 1388. آنالیز پوشش گیاهی بر اساس جوامع و ارتباط آن با شرایط فیزیکی و شیمیایی خاک در جنگل­های طبیعی. فصلنامه جنگل و صنوبر ایران، جلد 17، شماره 1، 98-85.
7.       واحدی ع.ا.، 1391. ارتباط بین تنوع زیستی گیاهی و میزان ترسیب کربن در جنگل­های طبیعی شمال ایران (مطالعه موردی جنگل گلندرود نور). رساله دکتری جنگلداری. دانشگاه آزاد اسلامی علوم و تحقیقات تهران، 127 صفحه.
8.       ورامش، س.، 1388. مقایسه میزان ترسیب کربن گونه­های پهن برگ و سوزنی برگ در جنگل شهری (مطالعهموردی: پارک چیتگر تهران). پایان نامه کارشناسی ارشد، دانشکده منابع طبیعی دانشگاه تربیت مدرس، 132صفحه.
9.      Barnes, B.V., Zak, D.R., Denton, S.R., and Spurr, S.H., 1998. Forest ecology, John Wiley and Sons. INC., 773 pp.
10.  Carlyle, J., 1993. Organic carbon in forested sandy soils: properties, processes, and the impact of forest management. New Zealand Journal Forest Science 23, 390–402.
11.  DeGryze, S., Six, J., Paustian, K., Morris, S.J., Paul, E.A., Merckx, R., 2004. Soil organic carbon pool changes following land-use conversions. Global Change Biology 10, 1120–1132.
12.  Grigal, D.F., Vance, E.D., 2000. Influence of soil organic matter on forest  productivity. New Zealand Journal of Forestry Science 30, 169–205.
13.  Hollingsworth, T.N., Schuur, E.A.G., Schuur, F.S., Walker, M.D., 2008. Plant Community Composition as a Predictor of Regional Soil Carbon Storage in Alaskan Boreal Black Spruce Ecosystems. Ecosystems,  DOI: 10.1007/s10021-008-9147-y.
14.  Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D.W., Minkkinen, K., Byrne, K.A., 2007. How strongly can forest management influence soil carbon sequestration? Geoderma, 137 , 253–268.
15.  Jime´nez, J.J., Lal, R., 2006. Mechanisms of C sequestration in soils of Latin America. Crit. Rev. Plant Sci. 25, 337–365.
16.  Jime´nez, J.J., Lal, R., Leblanc, H.A., Russo, R.O., 2007. Soil organic carbon pool under native tree plantations in the Caribbean lowlands of Costa Rica, Forest Ecology and Management,  241, 134–144.
17.  Johnson, D., Knoepp, J., Swank, W., Shan, J., Morris, L., van Lear, D., Kapeluck, P., 2002. Effects of forest management on soil carbon: results of some long-term resampling studies. Environmental Pollution 116, 201–208.
18.  Kirby, K.R & C., Potvin, 2007. Variation in carbon storage among tree species: Implications for the management of a small-scale carbon sink project. Forest Ecology and Management, 246, 208–221.
19.  Kirschbaum, M.U.F., 1995. The temperature-dependence of soil organic-matter decomposition, and the effect of global warming on soil organic-C storage. Soil Biology & Biochemistry 27, 753–760.
20.  Kooch, Y., Hosseini, S.M., Zaccone, C., Jalilvand, H., Hojjati, S.M., 2012. Soil organic carbon sequestration as affected by afforestation: the Darab Kola forest (north of Iran) case study. Journal of Environmental Monitoring
21.  Lal, R., 2005. Forest soils and carbon sequestration. Forest Ecology and Management 220, 242–258.
22.  Langley, J.A., Chapman, S.K. & Hungate, B.A. (2006). Ectomycorrhizal colonization slows root decomposition: the postmortem fungal legacy. Ecol. Lett., 9, 955–959.
23.  MacDicken, K.G., 1997. A guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects . Winrock International Institute for Agricultural Development, Forest Carbon Monitoring Program. PP;  91.
24.  Meentemeyer, V., Berg, B., 1986. Regional variation in rate of mass loss of Pinus sylvestris needle litter in Swedish pine forests as influenced by climate and litter quality. Scand. J. For. Res. (1), 167–180.
25.  Nave, L.E., Vance, E.D., Swanston, C.W., Curtis, P.S., 2010. Harvest impacts on soil carbon storage in temperate forests. Forest Ecology and Management, 259, 857–866.
26.  Paul, K.I., Polglase, P.J., Richards, G.P., 2003. Predicted change in soil carbon following afforestation or reforestation, and analysis of controlling factors by linking a c accounting model (CAMFor) to models of forest growth (3PG), litter decomposition (GENDEC) and soil C turnover (RothC). Forest Ecology and Management 177, 485–501.
27.  Peichl, M. & M.A., Arain, 2006. Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agricultural and Forest Meteorology 140: 51–63.
28.  Rillig, M.C. & Mummey, D.L. (2006). Mycorrhizas and soil structure. New Phytol., 171, 41–53.
29.  Rodrıguez-Loinaz, G., Onaindia, M., Amezaga, I., Mijangos, I., Garbisu, C., 2008. Relationship between vegetation diversity and soil functional diversity in native mixed-oak forests. Soil Biology & Biochemistry, 40, 49–60.
30.  Rouhi-Moghaddam, E., Hosseini, S.M., Ebrahimi, E., Tabari, M., Rahmani, A., 2008. Comparison of growth, nutrition and soil properties of pure stands of Quercus castaneifolia and mixed with Zelkova carpinifolia in the Hyrcanian forests of Iran.  Forest Ecology and Management, 255, 1149–1160
31.  Rubio, A., Gavilلn, R.G., Montes, F., Gutiérrez-Girَn, A., Daz-Pines, E., T & E.,  Mezquida, 2011. Biodiversity measures applied to stand-level management: Can they really be useful? Ecological Indicators 11 (2011) 545–556.
32.  Silver, W.L. & Miya, R.K. (2001). Global patterns in root decomposition: comparisons of climate and litter quality effects.Oecologia, 129, 407–419.
33.  Stefan, K., Frust, A., Hacker, R., Bartels, U., 1997. Forest foliar condition in Europe. Technical Report. EC and UN/ECE, Brussels, Geneva, p. 207.
 
34.  Zhu, B., Wang, X., Fang,  W., Piao, S., Shen, H., Zhao, S & C., Peng, 2010. Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. J Plant Res. 123:439–452.
Soil organic carbon (SOC) storage estimation with high accuracy is very important due to having such prominent role corresponding to climate change and global warming reduction. Therefore, the current study was carried out in the beech mixed-stands forest of reserved compartment in Glandrood forests, in the northern parts of Iran. Stepwise regression method was used in order to present the SOC pool weight modeling considering the prominent soil physic-chemical properties at 3 different soil depths (0-10, 10-30, and 30-50 cm). The results of ANOVAs showed that exchangeable calcium content, lime and clay percentage (P < 0.05) and also carbon content, soil organic matter, nitrogen and C/N ratio (P < 0.01) were significantly different among the three soil depths. Moreover, on average, the total soil carbon stored considered as SOC pool weight in the ecosystem equaled 543.87 ± 22.07 t.C ha-1. According to the stepwise regression analysis C/N ratio was the effective content as explanatory variable to predict SOC pool (R2adj = 0.44; SEE = 4.8). In the following models, P, N and clay percentage, respectively, were the effective contents which improved significantly SOC pool prediction (R2adj = 0.64-0.83; SEE = 3.8-2.6). C/N ratio, P, N and clay were included 46.3, 22.41, 14.71 and 9.44 percent respectively with respect to the variations of SOC pool. Regression analysis indicated that introducing electrical conductivity, pH, and Mg as input variables in the following steps did not ameliorate the precision of SOC pool prediction clearly (R2adj = 0.85-0.89; SEE = 2.5-2.1). Furthermore, during the modeling and according to the collinearity diagnostic test, the result indicated that the maximum variance inflation factor of the models was less than 10 (VIF < 10), validating them for application.
Iranian Journal of Soil Research
Volume 28, Issue 1
September 2019
Pages 53-62

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