Document Type : Research Paper

Authors

Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran

Abstract

Soil organic carbon (OC) is one of the most important soil properties, especially from an environmental point of view. For this reason, OC modeling and estimating has been highly considered. In modeling, application of pedotransfer functions to estimate soil properties from the other ones have an important place in soil science. Unfortunately, not much attention has been paid to the valuable data that are obtained with the least cost and time in the soil profile description. The aim of this study was to determine the importance of data that obtained from soil profile description to estimate the soil organic carbon in Dehgolan region in Kordestan Province. For this purpose, 30 pedons were excavated and described. Soil samples were collected from different horizons and soil properties such as texture, pH, EC, CCE and gypsum were determined. Modeling was performed in three scenario including laboratory data, data of soil profile description and application of laboratory and soil profile description data simultaneously. The results showed that based on laboratory data, soil organic carbon has a significant relationship with silt and CCE properties with a coefficient of determination about 25% (R2 = 0.25); While, the two soil profile description data of soil color (chroma) and genetic horizon with coefficients of determination about 65% (R2 = 0.65). With compilation of laboratory and soil profile description data the coefficient of determination was also obtained 65%. This level of accuracy clearly shows the value and importance of data related to the soil profile description data.

Keywords

[1] Abbasian, A., Delavar, M.A., Golchin, A. and Beheshti Al-Agha, A. (2012). Comparison of Accuracy of Regression Transfer Functions and Artificial Neural Network in Estimating Organic Carbon of Histosol Soils in Shahrekord, National Soil Conference, Sustainable Agriculture, Malayer.
 
[2] Aitkenhead, M., Coull, M., Towers, W., Hudson, G. and Black, H.I.J. (2013). Prediction of soil characteristics and colour using data from the National Soils Inventory of Scotland. Geoderma. 200–201: 99–107.
 
[3] Asensio, S., A. Mateu, H. Moreno-Ramon, J.A. Balasch, and X. Lio. (2013). Statistical relationships between soil colour and soil attributes in semiarid areas. Biosystems Engineering. 116: 120-129.
 
[4] Bagheri Bodaghabadi  M., Mosleh Z. Ebrahimi F. (2021). The Importance of Soil Profile Information in Determining Relationships Between Different Soil Properties, Case Study: Estimation of Soil Organic Carbon. 17th Iranian Soil Science Congress, Karaj, Iran.
 
[5] Bagheri Bodaghabadi, M. (2018). Is it necessarily a normally distributed data for kriging? A case study: soil salinity map of Ghahab area, central Iran. Desert. 23-2: 284-293.
 
[6] Bezabih, B., Aticho, A., Mossisa, T. and Dume, B.( 2016). The effect of land management practices on soil physical and chemical properties in Gojeb Sub-river Basin of Dedo District, Southwest Ethiopia. Journal of Soil Science and Environmental Management. 7:154-165.
 
[7] Costa, J.J.F., Giasson, E., da Silva, E.B., Coblinski, J.A. and Tiecher, T. (2020). Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon. Computers and Electronics in Agriculture. 177: 105710.
 
[8] Dadgar, M., Mahmoudi, S., Mahdian, M., Masih Abadi, M., Sokooti Oskouie, R. (2014). Estimating soil organic carbon using pedotransfer functions in Damavand Rangelands. , 21(3), 409-415. doi: 10.22092/ijrdr.2014.12114.
 
[9] Devore, J.L. (2011). Probability and Statistics for Engineering and the Sciences (8th ed.). Boston, MA: Cengage Learning. pp. 508–510. ISBN 978-0-538-73352-6.
 
[10] Fahmideh, S., Davari, M., Mosaddeghi, M., Sharifi, Z. (2020). Performance evaluation of reflectance spectroscopy for estimation of soil organic carbon content in Zrebar lake watershed, Kurdistan province. Journal of Water and Soil Conservation, 26(6), 59-78. doi: 10.22069/jwsc.2019.16387.3171
 
[11] Franzmeier, D.P. (1988). Relation of organic carbon content to texture of Indiana soils. Soil and Atmospheric Sciences. 98: 1-10.
 
[12] Ghaemi, M., Astaraei, A., Sanaeinejad, S. (2011). Assessment of Spatial Variations and Estimating Soil Organic Carbon by Using Pedotransfer Functions and Feasibility Study of SOC by Remote Sensing in Arid and Semi Arid Area (A Case Study in Neyshaboor Area, Iran). Iranian Journal of Field Crops Research, 9(2), 294-300. doi: 10.22067/gsc.v9i2.11007
 
[13] Kasel, S., Singh, S., Sanders, G. J. and Bennett, L. T. (2011). Species-specific effects of native trees on soil organic carbon in biodiverse plantings across north-central Victoria. Geoderma. 161: 95–106.
 
[14] Lindbo, D.L., Rabenhorst, M.C. and Rhoton, F.E. (1998). Soil color, organic carbon, and hydromorphy relationships in sandy epipedons, Quantifying Soil Hydromorphol. SSSA Spec. Publ. 54. 96e105.
 
[15] Matinfar, H., Mahmodzadeh, H. and Fariabi, A. (2018). Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models. Iranian Journal of Remote Sensing & GIS, 10(2), 15-32.
 
[16] Munsell Soil Color Charts. (2000). Munsell Soil Color Charts (revised). Munsell Color.
 
[17] Schoeneberger, P.J, Wysocki, D.A, Benham, E.C, and Soil Survey Staff. (2012). Field book for describing and sampling soils, Version 3.0. Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE. 300p.
 
[18] Shangshi, L., Haihua, Sh., Songchao, C., Xia, Z., Asim, B., Xiaolin, J., Zhou, Sh., and Jingyun, F. (2019). Estimating forest soil organic carbon content using  vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma. 348: 37-44.
 
[19] Soil Survey Staff. (2014a). Keys to Soil Taxonomy. 12th. ed. USDA-Natural Resources Conservation Service, Washington, DC.
 
[20] Soil Survey Staff. (2014b). Kellogg Soil Survey Laboratory Methods Manual. Soil Survey Investigations Report No. 42, Version 5.0. R. Burt and Soil Survey Staff (ed.). U.S. Department of Agriculture, Natural Resources Conservation Service.
 
[21] Viscarra Rossel, R.A., Behrens, T., Ben-Dor, E., Brown, D.J., Demattê, J.A.M., Shepherd, K.D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B.G., Bartholomeus, H.M., Bayer, A.D., Bernoux, M., Böttcher, K., Brodský, L., Du, C.W., Chappell, A., Fouad, Y., Genot, V., Gomez, C., Grunwald, S., Gubler, A., Guerrero, C., Hedley, C.B., Knadel, M., Morrás, H.J.M., Nocita, M., Ramirez-Lopez, L., Roudier, P., Campos, E.M.R., Sanborn, P., Sellitto, V.M., Sudduth, K.A., Rawlins, B.G., Walter, C., Winowiecki, L.A., Hong, S.Y. and Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews. 155: 198–230.
 
[22] Viscarra Rossel, R.A., Cattle, S.R., Ortega, A. and Fouad, Y. (2009). In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy. Geoderma. 150: 253–266.
 
[23] Vodyanitskii, Y.N. and Savichev, A.T. (2017). The influence of organic matter on soil color using the regression equations of optical parameters in the system CIE- Lab. Annals of Agrarian Science. 15: 380–385