Tóth,T., S. Matsumoto, R. Mao and Y. Yin. 1994. Plant Cover as 
Predictor Variable of Salinity and Alkalinity in Abandoned Saline 
Soils of the Huang-Huai-Hai Plain, China. Agrokémia és Talajtan. 
43:175-195.

The correlation between the seminatural vegetation cover and soil 
properties is useful for predicting soil properties in the abandoned 
saline soils of the Huang-Huai-Hai Plain of China. For the mapping 
of soil properties we propose a basic study of the correlation and 
spatial distribution of the important soil properties and plant cover. 
Based on it, it is possible to select variables of plant cover and 
other easily available field measured properties as predicting 
variables of soil properties. These predicting variables can be 
used in regression equations and cokriging to improve the prediction 
of soil properties.
In an area of 100x220m, large blocks (n=55) of 20x20m size and 
medium blocks of 5x5m size have been used for prediction of soil 
pH and soil salt content.
The correlations existing between soil properties and easily 
measurable plant cover, surface elevation and penetration resistance 
showed that the main plants of the abandoned land, Phragmites 
communis and Imperata cylindrica have distinctive preference for 
the soil properties.
The best predicting variables of soil properties were artificial 
variables, factor scores derived from the measured plant cover, 
the cover percentage of the two most important plants, Phragmites 
c. and Imperata c. and soil penetration resistance.
The two block sizes showed comparable precision in the estimation 
of the soil properties, and the size of the large block was 
reasonable in the mapping of the fertility of the abandoned plots. 
Kriging was more precise in the larger, averaged blocks and 
regression was more precise in the medium blocks. Kriging and 
cokriging with plant factor scores, plant cover and penetration 
is more precise than regression analysis for the estimation of soil 
pH and salinity and should be used for estimating the soil 
properties always when there is large enough uniform plot to do it. 
When the blocks to be mapped are small and not contiguous, multiple 
regression with easily measurable properties can be used with 
similar precision.