Spatial variability of some nutrient constituents of an Alfisol from loess II. Geostatistical analysis

Abstract

Spatial variability of NO3, K, Mg and organic C of a loess field was studied by using a geostatistical concept, known as theory of regionalized variables. Fifty measurements were made at the nodes of a 30 m × 30 m grid for each of 0‐30, 30‐60 and 60‐90 cm depths. Semivariograms determined from the data showed that NO3 observations were spatially independent, and hence could be analyzed only by classical methods. Semivariograms of K, Mg and C showed these parameters to be correlated over space for a separating distance between two observations well exceeding 150 m. Their semivariograms were then used in an interpolation method called kriging, which takes into account the correlation between adjacent samples while estimating the interpolated (kriged) value without bias and with minimum variance. Means and estimation variances calculated by punctual kriging were compared to those obtained by classical theory assuming random sampling (i.e., no interdependence between observations). We obtained 1.4 to 3 fold gains in efficiency over that estimated by classical theory. One can, therefore, be sure that the estimation variance of the mean obtained in classical manner will overestimate the real variance unless the sample sites are so far apart that they are spatially independent. The kriged estimates were used to draw contour maps of the properties. Usefulness of such maps and the kriging technique as a whole is discussed to provide better options for management decisions. Finally, a method for determining sample sizes (i. e., number of observations), and hence sample spacing, is developed, taking account of spatial dependence. By this method, sample sizes can be chosen to achieve any desired precision. The sampling effort determined this way in less, and can be very less when based on block or universal kriging, than would have been judged necessary using the classical approach.

The spatial variability of mineral nitrogen content in topsoil and subsoil

“…Also other authors observed lognormal N-NO 3¯ distributions (e.g. Meirvenne and Hofman 1989) but normal distribution was reported, too (Dahiya et al 1985, López-Granados et al 2002.…”

“…Ilsemann et al (2001) did not detect a range at three sites and they conclude that minimum distance between sampling positions was obviously too large to observe effects of small scale variability. Also Dahiya et al (1985) reported no spatial correlation of soil N-NO 3 . Meirvenne and Hofman (1989) observed ranges 9.5, 23 and 34 m for their data.…”

“…Ilsemann et al (2001) observed CV of late-fall N-NO 3¯ in 0-90 cm within 1-ha area of fields after cereals in three sites to range from 31 to 36%, Meirvenne and Hofman (1989) 21-31% in a 1 ha field after potatoes in autumn and spring, Dahiya et al (1985) between 19-23% in spring, Delin and Lindén (2002) 12, 32 and 36% in spring and 14, 28 and 47% after harvest of winter wheat in three years, respectively. Higher values were found by López-Granados et al (2002) under Mediterranean climate.…”

Spatial Variability Patterns of Wheat Growth and Soil Properties in a Small Field as Affected by Tillage Intensity

“…A comparison of the spatial variability of soil nutrients on two scales, 0.25 and 3.3 ha, showed that nitrate had the shortest range ( < 5 m) , soil organic carbon the longest (>180m), and phosphate an intermediate range (Cahn et al, 1994). In some studies, nitrate exhibited no spatial dependence for distances larger than the minimum sampling distance, which was 30 m in an Alfisol field for cereals and sugarbeet (Dahiya et al, 1985) or 0.4 m in grassland (White et al, 1987). Spatial variability of soil and crop parameters is influenced by time and by soil management.…”

Spatial variability of total soil nitrogen and sulphur content at two conventionally managed fields

“…Differences in this characteristic were compared between individual plots, as well as within a plot; they were related to the possibility of exact identification of the plot and its parts and further usage of these data for specific measures, locally applied (Dahiya et al 1985, Brodský et al 2001, Baxter et al 2003.…”