Outlier identification of soil phosphorus and its implication for spatial structure modeling

Outlier identification of soil phosphorus and its implication for spatial structure modeling Outliers are classified as global outliers and spatial outliers. Up to now, there is little information about the outliers especially the spatial outliers and their influence on the spatial structure modeling of soil properties. A total of 537 soil samples were collected based on a 30 × 30 m grid in a permanent dairy farm in southeast Ireland. Graphic methods of histogram and box plot combined with Moran’s I were applied to detect the outliers of soil phosphorus (P). Sixteen outliers (5 global outliers and 11 spatial outliers) of soil P were found in the study area. Compared to the raw data, the data with global outliers excluded always had the larger global Moran’s I value indicating a stronger spatial autocorrelation. Clear spatial clusters (High–High and Low–Low clusters) were observed based on local Moran’s I. The High–High spatial clusters were located around the main farm yard and near the traffic route due to more intensive management by farmers. The Low–Low spatial clusters were mainly close to the river. For these areas, P fertilizer or slurry should be applied for healthy grass growth. The dataset with outliers excluded had a reliable semi-variogram model with a low nugget/sill ratio (32.4 %), which was closed to its corresponding transformed data (30.5 %). The cross-validation results revealed that the dataset without outliers had the strongest linear regression model (r = 0.768), indicating that the outliers played an important role in the spatial structure modeling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Outlier identification of soil phosphorus and its implication for spatial structure modeling

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-015-9411-z
Publisher site
See Article on Publisher Site

References

  • Variability in contamination by heavy metals: sampling implications
    Andronikov, SV; Davidson, DA; Spiers, RB
  • Local indicators of spatial association-LISA
    Anselin, L

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