Utility of semivariogram for spatial variation of soil nutrients and the robust analysis of semivariogram

ＩｎｔｒｏｄｕｃｔｉｏｎＧｅｏｓｔａｔｉｓｔｉｃｓｈａｓｐｒｏｖｅｎｕｓｅｆｕｌｆｏｒｃｈａｒａｃｔｅｒｉｚｉｎｇａｎｄｍａｐｐｉｎｇｔｈｅｓｐａｔｉａｌｖａｒｉａｔｉｏｎｏｆｓｏｉｌｐｒｏｐｅｒｔｉｅｓ (Ｗｅｂｓｔｅｒ,1985;Ｔｒａｎｇｍａｒ,1985;Ｇｏｏｖａｅｒｔｓ ,1999) .Ｍｏｓｔｐｒｅｖｉｏｕｓｇｅｏｓｔａｔｉｓｔｉｃａｌｓｔｕｄｉｅｓｆｏｃｕｓｅｄｏｎｔｈｅｄａｔａｏｆａｒｅｌａｔｉｖｅｌｙｓｍａｌｌｓｐａｔｉａｌｓｃａｌｅ(Ｃｈｉｅｎ ,1997) .Ｔｈｅａｐｐｌｉｃａｔｉｏｎｏｆｇｅｏｓｔａｔ…

Utility of semivariogram for spatial variation of soil nutrients and the robust analysis of semivariogram

The spatial variation of soil nutrients in topsoil (0-20 cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates using various reduced sample sizes was also analyzed. The semivariograms of available N, total N, available P, organic matter were best described by a spherical model, except for available K, which best fitted a complex structure of exponential model and linear with sill model. The ratio of nugget to total sample variance ranged from 34.4% to 68.4%, indicating the spatial correlation of tested soil nutrients on a large scale was moderately dependent. Among five soil nutrients, available nitrogen and available phosphorus had the shortest spatial correlation range (5 km and 5.5 km), available K had the longest range (25.5 km), whereas total nitrogen and organic matter had intermediate spatial correlation range (14.5 km and 8.5 km). The semivariograms of available N, total N, available P, and organic matter were insensitive to a 50%-60% reduction in original sampling density, while for available K, it is up to 70%. The estimated spatial distributions of total N by kriging, under various reduced sample sizes, all correlated significantly (P = 0.001) with those obtained from original data. The results showed that the semivariogram was a relatively robust tool when used in a large region and sufficient spatial variation information could be retained regardless of a higher deletion proportion of the original data. The original sample data could be reduced by kriging and the estimates showed no loss of spatial information, however, the results may be unreliable unless a clearly identified semivariogram model could be obtained. The results may provide useful information for determining the appropriate sampling densities for these scales of soil survey.