Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (22): 4716-4725.doi: 10.3864/j.issn.0578-1752.2013.22.009

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Comparison of Spatial Prediction of Soil Properties Under Different Sampling Sizes

 CHAI  Xu-Rong-1, HUANG  Yuan-Fang-2   

  1. 1.College of Urban and Environmental Science, Shanxi Normal University, Linfen 041000, Shanxi
    2.College of Resources and Environmental Science, China Agricultural University, Beijing 100094
  • Received:2012-06-14 Online:2013-11-15 Published:2013-06-07

Abstract: 【Objective】The objective of this study was to investigate the impact of the sample size on the accuracy of the spatial prediction of soil properties.【Method】Soil organic matter(SOM), mass water content of soil, soil available potassium and available manganese were used as research objects, the methods of moments (MoM) and the residual maximum likelihood (REML) were used to compute the variogram of soil properties. Predictions based on the MoM and REML variograms were compared under different sampling sizes.【Result】Whether the MoM or REML was used, there was a significantly improved prediction accuracy for each soil variables with the increase of the sampling size from 50 to 70. As the number of samples gradually increased from 70 to 150, the prediction accuracy was not significantly improved. Predictions based on REML variograms were not more accurate than those from MoM variograms with the inreasion of sampling size from 50 to 150.【Conclusion】The number of samples has a significant impact on the accuracy of prediction of soil properties. When the number of samples is less than 70, the predictions are not reliable regardless of which variogram is used.

Key words: sampling sizes , variogram , method of moments , residual maximum likelihood

[1]史文娇, 岳天祥, 石晓丽,宋伟.土壤连续属性空间插值方法及其精度的研究进展.自然资源学报, 2012, 27(1): 163-175.

Shi W J, Yue T X, Shi X L, Song W. Research progress in soil property interpolators and their accuracy. Journal of Nature Resources, 2012, 27(1): 163-175. (in Chinese )

[2]赵其国.土壤科学发展的战略思考.土壤, 2009, 41 (5):  681-688.

Zhao Q G.  Strategic Thinking of Soil Science in China. Soils, 2009, 41(5): 681-688. (in Chinese )

[3]史舟,Lark R M.土壤学的新分支——计量土壤学的形成与发展.土壤学报, 2007, 44(5):919-624.

Shi Z, Lark R M. A new broach of soil science—pedometrics, its origin and development. Acta Pedologica Sinica, 2007, 44(5): 919-624. (in Chinese )

[4]史文娇, 刘纪远, 杜正平, 岳天祥.基于地学信息的土壤属性高精度曲面建模.地理学报, 2011, 66(11): 1576-1581.

Shi W J, Liu J Y, Du Z P, Yue T X.High accuracy surface modeling of soil properties based on geographic information.Acta Geoggrphica Sinica, 2011, 66(11): 1576-1581. (in Chinese )

[5]雷宏军, 李保国, 白由路, 黄元仿, 吕贻忠, 李贵桐, 李科江.集约农作条件下土壤有机碳动态模拟及其在黄淮海平原区的应用.中国农业科学, 2005, 38(5): 956-964.

Lei H J, Li B G, Bai Y L, Huang Y F, Lü Y Z, Li G T, Li K J. Modeling and applications of soil organic matter in intensive cropping in china’s huang-huai-hai plain.Scientia Agricultura Sinica, 2005, 38(5): 956-964.(in Chinese )

[6]Jourenl A G, Huijbregts C J. Mining Geostatistics. London: Academic Press, 1978.

[7]Webster R, Oliver M. Geostatistics for Environmental Scientists. Chichester: John Wiley and Sons, Ltd, 2001.

[8]Kerry R, Oliver M A. Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood. Geoderma, 2007, 140(4): 383-396.

[9]张仁铎. 空间变异理论及应用. 北京: 科学出版社, 2005.

Zhang R D. Geostatistics in Environmental Science and Its Opplication. Beijing: Science Press, 2005. (in Chinese )

[10]王政权. 地统计学及在生态学中的应用. 北京: 科学出版社, 1999.

Wang Z Q. Geostatistics and Application in Ecological Science. Beijing: Science Press, 1999. (in Chinese)

[11]Goovaerts P. Geostatistics for Natural Resources Evaluation. New York: Oxford University Press, 1997.

[12]Pardo-Iguzquiza E. MLREML: A computer program for the inference of spatial covariance parameters by maximum likelihood and restricted maximum likelihood. Computers & Geosciences, 1997, 23(2): 153-162.

[13]Pardo-Iguzquiza E. MLREML4: A program for the inference of the power variogram model by maximum likelihood and restricted maximum likelihood. Computers & Geosciences, 1998, 24(6): 537-543.

[14]Lark R M, Cullis B R, Welham S J. On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E-BLUP) with REML. European Journal of Soil Science, 2006, 57(6): 787-799.

[15]Payne R W. The Guide to GenStat Release 9 — Part 2: Statistics. Oxford: VSN International, 2006.

[16]Minasny B, McBratney A B. Spatial prediction of soil properties using EBLUP with the Matern covariance function. Geoderma, 2007, 140(4): 324-336.

[17]Minasny B, McBratney A B. The Matern function as a general model for soil variograms. Geoderma, 2005, 128(3/4): 192-207.

[18]Kerry R, Oliver M A. Determining the effect of asymmetric data on the variogram. I. Underlying asymmetry. Computers & Geosciences, 2007, 33(10): 1212-1232.
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