Impact of sampling density on efficiency of soil salinity mas (A case study: Karkaj research station, university of Tabriz)

Document Type : Complete scientific research article

Authors

1 Soil Science Depart., faculty of Agriculture, University of Tabriz

2 Soil Science Department, Faculty of Agriculture, University of tabriz

3 Soil Science Department, Faculty of Agriculture, University of Tabriz

Abstract

Background and objectives: Soil surveying and mapping emphasize in various aims at different soil science aspects for applicants. Paying attention for scale and reconciliation of estimated with real data should be considered in all studies related to remote sensing and soil spatial variability. Although estimating of spatial variability of various soil parameters and soil surveying were studied many times, unfortunately the map accuracy and efficiency were not assessed in many cases. Salinization is one of the most important problems in arid and semi-arid regions. Therefore, it is essential to be studied the spatial variability of soil salinity in east Azerbaijan province because of its climatic condition. As well as easy readily of salinity measuring, it was used to assess the map efficiency.
Materials and Methods: This study was carried out in the part of Karkaj Agriculture Research Station belongs to the university of Tabriz in an area of about 4.2 ha. Sampling density is one of the parameters which influences not only on observation accuracy but also on map efficiency. Accordingly, two kinds of high and low sampling density including grids of 25 m and 50 m were designated, respectively. The numbers of 106 samples were taken at surface soil which its salinity was then measured in the laboratory. Statistical analysis, mean comparison, F-test and t-test performed by using MSTATC software.GS+ software was also used for Geostatistical analysis. Two methods of Jacknife and direct evaluating were applied for validity of models in order to testing maps accuracy. Soil salinity maps created with integrating the kriging geostatistics procedure and GIS. The map efficiency is being evaluated using four aspects: (1) map scale and texture, (2) map legend, (3) base map quality, and (4) ground truth, as well as may be interpreted by average size delineation, density of delineation, index of maximum reduction, effective scale number, maximum location accuracy and minimum legible delineation. Therefore, above indices were determined for two created maps according to sampling density.
Results: The results revealed that the provided map at low sampling density is closed to the optimum condition compared with high sampling density. In despite of various number of polygons in both soil salinity maps, statistical analysis showed that there is no significant differences (pConclusion: It can be finally reported that reducing the number of soil samples may not decrease the optimum density of delineations nor has distinct impact on soil salinity map. Thus, to exploit more number of samples is not economic in all cases. On the other hand, using low density maps can be recommended to save money and time.

Keywords

References

1.Ahmadauli, Kh., Nikmeht, S., and Liagat, A.M. 2008. A Comparison between Kriging
and Cokriging methods in estimating soil salinity and pH (Case study: Boukan region). IWRJ. 2: 3. 55-63. (In Persian)
2.Ayoubi, Sh., and Khormali, F. 2009. Spatial variability of soil Surface nutrients using principal component analysis and geostatistics: A case study of Appaipally Village, Andhra Pradesh, India. JWSS - Isfahan University of Technology. 12: 46. 609-622. (In Persian)
3.Bameri, A., Khormali, F., Kiani, F., and Dehghani, A.A. 2012. Spatial variability of soil organic carbon on different slope positions of loess hillslopes in Toshan area, Golestan Province. JWSC. 19: 2. 43-62. (In Persian)
4.Bower, C.A., and Wilcox, L.V. 1965. Soluble salts, P 933-951. In: C.A. Black (Ed.),
Methods of soil analysis, Soil Sci. Soc. Amer.
5.Brevik, E.C., and Fenton, T.E. 2002. Influence of soil water, clay, temperature, and carbonate minerals on soil electrical conductivity readings taken with an EM-38. Soil Surv. Horiz.
43: 1. 9-13.
6.Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., and Konopka, A.E. 1994. Field-scale variability of soil properties in central Iowa soils. SSSAJ. 58: 1501-1511.
7.Daempanah, R., Haghnia, Gh., Alizadeh, A., and Karimi, A. 2011. Mapping salinity and sodicity of surface soil by remote sensing and geostatistic methods in south side of MahValat County. JSW. 25: 3. 498-508. (In Persian)
8.Farifte, J., Farshad, A., and George, R.J. 2005. Assessing salt–affected soils using remote sensing, solute modeling and geophysics. Geoderma. 130: 3-4. 191-206.
9.Forbes, T.R., Rossiter, D., and Van Wambeke, A. 1982. Guidelines for evaluating the adequacy of soil resource inventories. SMSS Tech. Monogr. 4. Cornell University Department of Agronomy, Ithaca, NY.
10.Golizadeh, A.Gh., Momeni, A., Bahrami, H.A., and Banaei, M.H. 2001. Investigation of geopedologic method and common pedology in Iran to increase of map unit purity and reduction of pedologic studying charges. ISRJ. 15: 3. 13-30. (In Persian)
11.Hasanipak, A.A. 2013. Geostatistic. Tehran University Press, 314p. (In Persian) 
12.Hengl, T., and Husnjak, S. 2006. Evaluating adequacy and usability of soil maps in Croatia. Soil Sci. Soc. Am. J. 70: 3. 920-929.
13.Herrero, J., Ba, A.A., and Aragues, R. 2003. Soil salinity and its distribution determined by soil sampling and electromagnetic techniques. Soil Use Manage. 19: 2. 119-126.
14.Kaffka, S.R., Lesch, S.M., Bali, K.M., and Corwin, D.L. 2005. Site-specific management in salt-affected sugar beet fields using electromagnetic induction. Comput Electron Agr.
46: 329-350.
15.Kalkali, M., Karimi, A., Haghnia, G., and Esfandiarpour, I. 2014. Comparison of Geostatistical and Conventional Mapping Methods in Determining the Variation of Selected Soil Properties (Case study: Jiroft, Kerman Province). JWS. 28: 2. 353-364.
16.Keskin, S.G., Khalilian, A., Han, Y.J., and Dodd, R.B. 2011. Variable depth tillage based
on geo-referenced soil compaction data in coastal plain region of South Carolina. IJAST.
1: 2. 22-32.
17.Khaksaran, D., Waismoradi, A., Moradi, S., and Rahmati, H. 2013. Spatial and temporal changes in soil salinity with geostatistics: A case study in Urmia Plain. IJACS. 5: 3. 285-291.
18.Krige, D.G. 1951. A statistical approach to some mine valuation allied problems of the Witwatersrand. Master's thesis of the University of Witwatersrand. 272p.
19.Lu, G.Y., and Wong, D.W. 2008. An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci. 34: 9. 1044-1055.
20.Mohammadi, J. 2006. Pedometry (Spatial statistics). Pelk Press, 453p. (In Persian)
21.Outeiro, L., Aspero, F., and Ubeda, X. 2008. Geostatistical methods to study spatial variability of soil cations after a prescribed fire and rainfall. Catena.74: 3. 310-320.
22.Sokouti, R., Mahdian, M., Mahmoodi, Sh., and Ghahremani, A. 2007. Comparing the applicability of some geostatistic methods to predict the variability of soil salinity, a case study of Uromieh plain. Pajauhsh and Sazandegi. 74: 90-98. (In Persian)
23.Taghizadeh-Mehrjardi, R., Sarmadian, F., Omid, M., Savaghebi, Gh., Rousta, M.J., and Rahimian, M.H. 2013. Mapping of soil salinity using geostatistic and electromagnetic induction methods in Ardakan. IJSR. 26: 4. 369-380.
24.Rossiter, D.G. 2000. Methodology for Soil Resource Inventories. Lecture notes, 2nd Revised Version. Soil Science Division, International Institute for Aerospace Survey and Earth Sciences (ITC). Enschede, the Netherlands, 132p.
25.Tajgardan, T., Ayoubi, Sh., Shataii, Sh., and Khormali, F. 2009. Mapping soil surface salinity using remote sensing data of ETM+ (Case study: North of AghGhala, Golestan Province). JWSC. 16: 2. 1-18. (In Persian)
26.Vink, A.P.A. 1975. Land use in advancing agriculture. Springer –Verlag, Berlin, 394p.
Journal of Water and Soil Conservation
Volume 23, Issue 2
June 2016
Pages 239-251

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