The choice of an optimal interpolation technique for estimating soil properties at unsampled locations is an important issue in site-specific management. The objective of this study was to evaluate inverse distance (InvD) weighting, ordinary kriging (KO), and lognormal ordinary kriging (KO(log)) to determine the optimal interpolation method for mapping soil properties. Relationships between statistical properties of the data and performance of the methods were analyzed using soil test P and K data from 30 agricultural fields. For InvD weighting, we used powers of 1, 2, 3, and 4. The numbers of the closest neighboring points ranged from 5 to 30 for the three methods. The results suggest that KO(log) can improve estimation precision compared with KO for lognormally distributed data. The criteria helpful in deciding whether KO(log) is applicable for the given data set were the Kolmogorov-Smirnov goodness-of-fit statistic, coefficient of variation, skewness, kurtosis, and the size of the data set. Careful choice of the exponent value for InvD weighting and of the number of the closest neighbors for both InvD weighting and kriging (KO or KO(log)) significantly improved the estimation accuracy (P ≤ 0.05). However, no a priori decision could be made about the optimal exponent and the number of the closest neighbors based on the statistical properties of the data. For the majority of the data sets, kriging with the optimal number of the neighboring points, a carefully selected variogram model, and appropriate log-transformation of the data performed better than InvD weighting. Correlation coefficients between experimental data and estimated results of kriging were higher than those of InvD for 57 out of a total of 60 data sets, kriging mean absolute errors were lower for 44 data sets, and kriging mean errors were lower than those of InvD weighting for 31 data sets.
|Original language||English (US)|
|Number of pages||8|
|State||Published - Jan 1 1999|
ASJC Scopus subject areas
- Agronomy and Crop Science