Spatial and Temporal Characteristics of Soil Nutrients in Taihu Region of China.

Soil nutrients are closely related to crop growth and productivity formation. Based on the nutrient contents of organic matter, total nitrogen, available phosphorus and potassium from soil samples of 2342 and 2614 in the years of 1980 and 2004 in Taihu region of Jiangsu province in China, the reasonable sampling numbers for spatial interpolation in different soil nutrient indices were calculated. Then, the interpolation effects of four different methods as Inverse Distance Weighted (IDW), Global Polynomial (GP), Ordinary Kriging (OK) and Simple Kriging (SK) for four soil nutrient indices were compared with the platform of ArcGIS 9.2, and the spatial-temporal distribution characteristics of the main soil nutrient indices in Taihu Region were analyzed with the best interpolation method. The results showed that comparing with the soil nutrient data in 1980, the mean contents of soil organic matter, total nitrogen, available phosphorus and potassium in 2004 increased 15.85%, 79.55%, 195.72% and 10.37%, respectively. At 95% confidence level and 5% relative error, the soil sampling numbers were satisfactory for interpolating analysis of four soil nutrient indices. SK is the best spatial interpolating method for soil organic matter and total nitrogen contents, while for available phosphorus and potassium contents, OK is the best spatial interpolating method. For spatial distribution, the contents of soil organic matter, total nitrogen and available potassium in different years showed moderate variability, and the available phosphorus content showed strong variability. The contents of soil organic matter and available potassium in different years had strong and moderate spatial correlation, respectively. The spatial correlation of total nitrogen content was strong in 1980, but moderate in 2004. The spatial correlation of available phosphorus content was weak in 1980, but moderate in 2004. These results can provide a basic soil database for prediction of regional crop productivity with a growth simulation model.
Key Words: Spatial and temporal variability; soil nutrient; geo-statistics; Taihu region