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This article in SSSAJ

  1. Vol. 73 No. 5, p. 1676-1681
     
    Received: June 10, 2008


    * Corresponding author(s): wchf1680@sina.com
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doi:10.2136/sssaj2008.0205

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

  1. Chunfa Wu *ab,
  2. Jiaping Wuc,
  3. Yongming Luoa,
  4. Limin Zhangd and
  5. Stephen D. DeGloriae
  1. a Current address: Key Lab. of Soil Environ. and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
    b Yantai Institute of Coastal Zone Research for Sustainable Development, Chinese Academy of Sciences, Yantai, 264003, China
    c Dep. Natural Resources and Environ., Zhejiang Univ., Hangzhou 310029, China
    d Haining Agricultural Extension Service, Haining 314400, China
    e Dep. Crop and Soil Sciences, Cornell Univ., Ithaca, NY 14853

Abstract

Accurate measurement of soil total N (TN) content in agricultural fields is important to guide reasonable application of nitrogenous fertilizer. Estimation of soil TN content with limited in situ data at an acceptable level of accuracy is important because laboratory measurement of N is a time- and labor-consuming procedure. This study was conducted to evaluate cokriging of soil TN with predicted soil organic matter (SOM) content as auxiliary data. The SOM content was predicted by cokriging with a digital number (DN) of Band 1 of Landsat Enhanced Thematic Mapper (ETM) imagery. Soil TN content was estimated by using 88 soil samples for prediction and 43 soil samples for validation in a study area of 367 km2 in Haining City, China. Field-measured soil TN content ranged from 0.47 to 2.48 g kg−1, with a mean of 1.25 g kg−1 Soil TN content of all 131 soil samples including samples for prediction and validation was highly correlated with measured (r = 0.81, p < 0.01) and predicted (r = 0.81, p < 0.01) SOM content in paddy fields. Then, the predicted SOM content was used as auxiliary variable for the prediction of soil TN content. By using the 43 samples for validation, we had a mean error (ME) of 0.03 g kg−1 and a root mean square error (RMSE) of 0.31 g kg−1 for kriging, and a mean error of 0.00 g kg−1 and a root mean square error of 0.25 g kg−1 for cokriging, respectively. Our results indicate cokriging with predicted SOM content data was superior to kriging. In addition, predicted data of the auxiliary variable have the potential to be useful for cokriging when the predicted auxiliary data have high prediction accuracy.

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