Simulation of Organic Matter Variability on a Sugarbeet Field Using the Computer Based Geostatistical Methods

Computer based geostatistical methods can offer effective data analysis possibilities for agricultural areas by using vectorial data and their objective informations. These methods will help to detect the spatial changes on different locations of the large agricultural lands, which will lead to effective fertilization for optimal yield with reduced environmental pollution. In this study, topsoil (0-20 cm) and subsoil (20-40 cm) samples were taken from a sugar beet field by 20 x 20 m grids. Plant samples were also collected from the same plots. Some physical and chemical analyses for these samples were made by routine methods. According to derived variation coefficients, topsoil organic matter (OM) distribution was more than subsoil OM distribution. The highest C.V. value of 17.79% was found for topsoil OM. The data were analyzed comparatively according to kriging methods which are also used widely in geostatistic. Several interpolation methods (Ordinary,Simple and Universal) and semivariogram models (Spherical, Exponential and Gaussian) were tested in order to choose the suitable methods. Average standard deviations of values estimated by simple kriging interpolation method were less than average standard deviations (topsoil OM ± 0.48, N ± 0.37, subsoil OM ± 0.18) of measured values. The most suitable interpolation method was simple kriging method and exponantial semivariogram model for topsoil, whereas the best optimal interpolation method was simple kriging method and spherical semivariogram model for subsoil. The results also showed that these computer based geostatistical methods should be tested and calibrated for different experimental conditions and semivariogram models.

Authors:

• M. Rüstü Karaman
• Tekin Susam
• Fatih Er
• Servet Yaprak
• Osman Karkacıer

Keywords:

• Geostatistic
• kriging
• organic matter
• sugarbeet.

References:

[1] D.C., Reicosky and M.J. Lindstrom. "Impact of fall tillage on short-term
carbon dioxide flux," p. 177-187. In: R.Lal, J. Kimble, E. Levine, and
B.A. Stewards (Eds.) Soils and Global Change. Lewis Publisher,
Chelsea, Michigan, 1995.
[2] H. DeCourt, P.L. Darius and J.D. Baerdemaeker. "The spatial variability
of topsoil fertility in two Belgian Fields," Computers and Electronics in
Agr., Vol: 14, 1996, pp 179-196.
[3] E.J. Sadler, W.J. Busscher, P.J. Bauer and D.J. Karlen. "Spatial scale
requirements for precision farming: A case study in the southeastern,".
Agronomy Journal, Vol: 90, 1998, pp. 191-197.
[4] C. Okano, A. Nishimune, M. Fukuhara, K. Okamoto and M. Hayasaka,
"Sugarbeet nutritional diagnosis using remote sensing," In T. Ando et
al., (ed) Plant nutrition for sustainable food production and environment.
Kluwer Acad. Publishing, Tokyo, 1997.
[5] S.M. Eltaib, M.S.amin, m.M. Hanafi, A.R.Shariff and A. Wayayok
"Spatial variability of N, P and K in rice field in Sawah sempadan,
Malaysia" Songklanakarin J. Sci. Technol., Vol:24, 2002, pp. 321-328.
[6] B.B. Trangmar, R.S. Yost and G. Uehara, "Aplication of Geostatistics to
spatial Studies of Soil Properties," Advances in Agronomy, Vol.38,
1985, pp. 45-93.
[7] D. Mallants, P.M. Binayak, J. Diederik and J. Feyen, "Spatial Variability
of Hydraulic Properties in a Multi-Layered Soil Profile," Soil Science,
Vol:161, 1996, pp.167-180.
[8] E.H. Ishaaks and R.M. Sriwastawa, "An Introduction to Applied
Jeostatistics," Oxford University Press. New York, 1989.
[9] R. Webster, "Quantitative Spatial Analysis of Soils in The Field,"
Advances in Soil Science, Vol:3, 1985, pp. 65-69.
[10] R. Sunila and K.Kollo, A comparison of geostatistics and fuzzy
application for digital elevation model. The International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences,
Vol: 34
[11] R. Webster and M. Oliver, Geostatistics for environmental Scientists.
John Wiley & Sons, U.K., 2001.
[12] ESRI, "Geostatiscial Analysis Module Hand Book," ESRI Inc.
Publication, The Netherland, 2005.
[13] StatMost, "Dataxiom Software Inc. User-s Guide: StatMost," 5th Ed.
Dataxiom Soft. Inc., LA., USA, 1995.
[14] S.R. Olsen, C.V. Cole, F.S. Watanable. and L.A. Dean, "Estimation of
available phosphorus in soils by extraction with sodium bicarbonate,"
Agricultural Handbook, U.S. Soil Dept. 939, Washington D.C., 1954.
[15] L.A. Richards, "Diagnosis and improvement of saline and alkaline
soils," USDA Agricultural Handbook, 60, Washington, D.C., 1954
[16] L.E. Allison and C.D. Moodie, "Carbonate, In: Methods of Soil
Analysis," Part 2., Agronomy J., Vol:9, 1965, pp. 1379-1400.
[17] M.L. Jackson, "Soil Chemical Analysis. Prentica-Hall Inc., Englewood
Cliffs", New Jersey, USA, 1958.
[18] A. Walkley, "A critical examination of a rapid method for determining
organic carbon in soils: effect of variations in digestion conditions and
inorganic soil constituents," Soil Sci. Vol:63, 1947, pp. 251-263.
[19] H.D. Chapman and F.P. Pratt. Methods of Analysis for Soils, Plants and
Waters, Univ. of California Div. Agr. Sci. USA, 1961.
[20] Kerry, R. and Oliver, M.A., 2007. Determining the effect of skewed data
on the variogram. II. Outliers. Computers and Geosciences (Accepted for
Publ.).
[21] M.R. Karaman, T. Susam, S. Yaprak and and F. Er. "Computer based
geostatistical strategies in assessing of spatial variability of agricultural
phosphorus on a sugarbeet field," IACSIT International Association of
Computer Sci. and Information Technology, Proceedings of ICIME,
2009, pp. 201-205, Malaysia.