Abstract: The coKriging method of selecting effective variables is helpful to improve the spatial prediction accuracy of soil available potassium in county-scale rubber plantation, which is of significance in precision fertilization management of rubber plantation. In this study, we analyzed the spatial variability characteristics of soil available potassium in 0-20 cm layer in the rubber plantation of Baisha County, Hainan Province, by geostatistics. The significant characteristic variables were screened by correlation analysis, and the spatial interpolation precisions of coKriging (COK) of different variables were compared. The results showed that the average soil available potassium content in the study area was 44.65 g·kg^-1, generally at a state of shortage. The variable coefficient was 52.6%, which was a moderate intensity of variation. The nugget effect was 12.5%, with a strong spatial autocorrelation. The organic matter and elevation were closely related to soil available potassium content. The COK spatial interpolation prediction precisions of the three covariates of organic matter (COK₁), elevation (COK₂), and organic matter+elevation (COK₃) were all higher than ordinary Kriging (OK), and the fitting precision of the cross-validation model was COK₁>COK₃>COK₂>OK. The fitting precision was not proportional to the number of covariates selected. Selecting more correlated covariates was more conducive to reflecting the spatial heterogeneity of soil properties. The soil available potassium content was higher in the northwest and lower in the central and eastern regions, which provided a theoretical basis for the further development of soil potassium management in rubber plantations.

Key words: soil available potassium, coKriging, covariate, spatial variation