Comparison Study on the Spatial Estimation of Ji Wheat 22 Yield on the Precision Scale

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Abstract:

Under the framework of soil-wheat system, the sampling area was selected in the Lingxian country of Shandong province, based on the DGPS localization data, a total of 849 sampling sites of Ji wheat 22 yield which was cultivated by Shandong Academy of Agricultural Science were involved in the study, combination with grid sampling and random sampling. the spatial estimation comparison study of wheat yield was developed on the precision scale, with the support of Kriging and Inverse Distance Weighted (IDW) method. The main objective of the study analyzed the spatial estimation on basis of sampling sites with the help of two methods. The results showed that IDW method is more accurate on basis of the sampling sites data than Kriging method, under less than 40m interval between sites for Ji wheat 22 yield, though the major advantage of kriging in comparison with IDW is estimating weights for locations based not only on the distance between separated locations but also on the global semivariogram model fitted to the collected data. The results also indicated that the estimation accuracy depended on the threshold of sampling numbers, if the numbers are less than the threshold, the sampling density is in contact with the spatial estimation accuracy, if the numbers are more than the threshold, the density of the sampling numbers is improved, but the estimation results are not proportional to the density.

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1259-1264

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August 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Haining, R. (2004). Spatial Data Analysis Theory and Practice. London, University of Combridge Press.

Google Scholar

[2] (2010). Handbook of applied Spatial Analysis Sofware tools, Methods and Applications, Springer.

Google Scholar

[3] Richard Webster, M. A. O. (2007). Geostatistical for Environmental Scientists (Second Edition).

Google Scholar

[4] Kevin Johnston, J. M. V. H., Konstantin Krivoruchko, and Neil Lucas (2001). Using ArcGIS Geostatistical Analyst.

Google Scholar

[5] Sandy Dall'erba. Distribution of regional income and regional funds in Europe 1989–1999: An exploratory spatial data analysis, Ann Reg Sci, 2005, 39: 121–148.

DOI: 10.1007/s00168-004-0199-4

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