Predicting soil map using Jenny equation

Paper Details

Research Paper 01/12/2013
Views (515) Download (23)
current_issue_feature_image
publication_file

Predicting soil map using Jenny equation

Z. Alijani, F. Sarmadian
J. Bio. Env. Sci.3( 12), 125-133, December 2013.
Certificate: JBES 2013 [Generate Certificate]

Abstract

Today, with rapid advancement of technology, many methods have been developed to soil mapping that now we know them as digital soil mapping (DSM). Each of these methods is based on mapping rules and specific characterizations of region that can distinguishe the different soils. Soil forming factors that control the direction and speed of soil formation have been expressed in Jenny’s equation. These are climate, organism, topography, parent material and time. These factors do not act in isolation but always together which set limits to the operation as a whole. The aim of this study is predicting the soil map using this equation. So, the factors in the Jenny equation converted as a data layer in GIS and then used to predict the soil map using ENVI (4.7) software. To estimate the correct selection of soil forming factors as data layer, other parameters derived of DEM were selected and then were used to predict the soil map. Results showed that the highest accuracy of predicted soil map is when the soil forming factors are used.

VIEWS 40

Badla D, Martl CM, Aznar J, Leon J. 2013. Influence of slope and parent rock on soil genesis and classification in semiarid mountainous environments. Geoderma 193-194, 13-21. http://dx.doi.org/10.1016/j.geoderma.2009.05.006

Behrens Th, Zhu A, Schmidt K, Scholten Th. 2010. Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155, 175-185. http://dx.doi.org/10.1016/j.geoderma.2011.07.031

Bouma J, Stoorvogel JJ, Quiroz R, Staal S, Herrero M, Immerzeel W, Roetter RP, Van den Bosch H, Sterk G, Rabbinge R, Chater S. 2007. Ecoregional research for development. Advances in Agronomy 93, 257–311. http://dx.doi.org/10.1016/S0065-2113(06)93005-3

Bui EN, Loughhead A, Corner R. 1999. Extracting soil- landscape rules from previous soil surveys. Australian Journal of Soil Research 37, 495-508.

Cambul AH, Rossiter DG, Stoorvogel JJ. 2013. A methodology for digital soil mapping in poorly-accessible areas. Geoderma 192, 341-353.

Carre F, McBratney AB. 2005. Digital terron mapping. Geoderma 128, 340-353. http://dx.doi.org/10.1016/j.geoderma.2005.04.012

Carre F, McBratney AB, Mayr Th, Montanarella L. 2007. Digital Soil Assessments: Beyond DSM. Geoderma 142, 69-79. http://dx.doi.org/10.1016/j.geoderma.2007.08.015

Cook SE, Corner RJ, Grealish G, Gessler PE, Chartres CJ. 1996., A Rule-based System to Map Soil Properties. Soil Science Society of America 60, 1893-1900. http://dx.doi.org/10.1016/j.geoderma.1997.10.016

Hengl T, Heuvelink GBM, Stein A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120, 75–93. http://dx.doi.org/10.2136/sssaj1996.036159950060 00060039x

Jenny H. 1941. Factors of Soil Formation, A System of Quantitative Pedology. McGraw- Hill, New York.

Kravchenko AN, Robertson GP, Hao X, Bullock DG. 2006. Management practice effects on surface total carbon: differences in spatial variability patterns. Agronomy Journal 98, 1559–1568. http://dx.doi.org/10.1016/S0166-2481(06)31001-X

Lagacherie P, McBratney AB. 2007. Chapter 1. Spatial Soil Information Systems and Spatial Soil Inference Systems: perspectives for digital soil mapping. In Lagacherie, P., McBratney, A.B., Voltz, M. (eds.), Digital Soil mapping: An Initial Perspective. Developments in Soil Science 31. Elsevier, Amsterdam, 250 p. http://dx.doi.org/10.1016/S0166-2481(06)31001-X

Lagacherie P, McBratney AB, Voltz M. 2007. Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science, 31. Elsevier, Amsterdam.

Mc Bratney A, Mendonça Santos ML, Minasny B. 2003. On digital soil mapping. Geoderma 117, 3–52.

McBratney AB, Odeh IOA, Bishop TFA, Dunbar MS, Shatar TM. 2000. An overview of pedometric techniques for use in soil survey. Geoderma 97, 293–327.

McBratney A, Mendonça Santos ML, Minasny B. 2003. On digital soil mapping. Geoderma 117, 3–52. http://dx.doi.org/10.1016/S0016-7061(03)00223-4

McKenzie NJ, Ryan PJ. 1999. Spatial prediction of soil properties using environmental correlation. Geoderma 89, 67–94.

Mora-Vallejo A, Claessens L, Stoorvogel J, Heuvelink GBM. 2008. Small scale digital soil mapping in Southeastern Kenya. Catena 76, 44-53. http://dx.doi.org/10.1016/j.catena.2008.09.008

Remondo J, Soto J, Alberto G, Ramon Diaz de Teran J, Cendrero A. 2005. Human impact on geomorphic processes and hazards in mountain areas in northern Spain. Geomorphology 66, 69-84. http://dx.doi.org/10.1016/j.geomorph.2004.09.009

Rubio A, Escudero A. 2005. Effect of climate and physiography on occurrence and intensity of decarbonation in Mediterranean forest soils of Spain. Geoderma 125, 309-319. http://dx.doi.org/10.1016/j.geoderma.2004.09.005

Schmidt K, Berens TH, Scholten TH. 2008. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma 146, 138-146. http://dx.doi.org/10.1016/j.geoderma.2009.05.006

Scull P, Franklin J, Chadwick OA, McArthur D. 2003. Predictive soil mapping: a review. Physical Geography 27, 171-197. http://dx.doi.org/10.1191/0309133303pp366ra

Shaw JN, West LT, Bosch DD, Truman CC, Leigh DS.  2004. Parent material influence on soil distribution and genesis in a Paleudult and Kandiudult complex, southeastern USA, Elsevier, Catena 57, 157-174. http://dx.doi.org/10.1016/j.catena.2003.10.016

Soil Survey Staff. 2010. Keys to Soil Taxonomy, United states. Department of Agriculture. 11nd ed. Natural resources, conservation service.

Stoorvogel JJ, Antle JM. 2001. Regional land use analysis: the development of operational tools. Agricultural Systems 70, 623–640. http://dx.doi.org/10.1016/S0308-521X(01)00062-2

Stoorvogel JJ, Kempen B, Heuvelink GBM, Bruin S. 2009. Implementation and evaluation of existing knowledge for digital soil mapping in Senegal. Geoderma 149, 161-170. http://dx.doi.org/10.1016/j.geoderma.2008.11.039

Udomsri S. 2006. Application of computer assisted geopedology to predictive soil mapping and its use in assessing soil erosion prone areas: a case study of Doi Ang Khang, Ang Khang Royal Agricultural Station, Thailand. MSC. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands.

USDA. 1984. Procedures for collecting soil samples and methods of analysis for soil survey. Soil Survey Investigations Report No. 1. USDA, Soil Conservation Service, Washington DC.

USDA. 2007. National Soil Survey Handbook, title 430-VI. U.S. Department of Agriculture, Natural Resources Conservation Service, Washington DC.

Van Reeuwijk LP. 1997. Introduction to Physico-Chemical Aspects of Soil Formation ITC, Enschede.

Vergari F, Della Seta M, Del Monte M, Barbieri M, 2012. Badlands denudation “hot spots”: The role of parent material properties on geomorphic processes in 20-years monitored sites of Southern Tuscany (Italy). Catena- 01757; No of Pages 11.

Ziadat FM. 2005. Analyzing digital terrain attributes to predict soil attributes for a relatively large area. Soil Science Society of America 69, 1590– 1599. http://dx.doi.org/10.2136/sssaj2003.0264

Zinck JA. 1986/87. Physiography and Soils. Soil Science Division. Soil survey courses Subject matter: K6 ITC, Enschede, The Netherlands.