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. [ABSTRACT FROM AUTHOR]