Abstract: 【Objective】The objective of this study was to explore the spatial-temporal variability of soil organic matter (SOM) and its influencing factors, and to provide a firm scientific foundation for soil fertility assessment and management. 【Method】SOM content in arable layer in 1980, 1990 and 2000 in Daxing County of Beijing was obtained by collecting data in the Second National Soil Survey (SNSS) and recent measures. The general statistics and geostatistics methods were used to analyze the data. 【Result】The average SOM content at different times were 9.64 g·kg-1, 12.76 g·kg-1 and 12.69 g·kg-1, respectively. The semivariance analysis showed that their spatial correlation distances at different times were 80.5 km, 59.2 km and 49.0 km, respectively, showing a descending trend. Kriging method was applied to estimate the unobserved points and as a result SOM content contour maps were derived, and the map indicated a decreasing trend from north to south across the county for SOM content. The SOM content had been classified into six categories (bellow 6, 6 to10, 10 to 12, 12 to 15, 15 to 20 and above 20 g·kg-1) according to the standards set by SNSS. The SOM changed slowly from low to high category based on the accumulation of SOM in the low category from 1980 to 2000. During the last 20 years, the area with the highest and the second highest category of SOM content changed from 0 to 8.0% and 14.3% of the total area. The area with the third category for SOM content increased from 23.0% to 37.1% of the total area, respectively. The area with the forth category for SOM content decreased little, about 2.19% of the total area. The area with the fifth category for SOM content decreased sharply, about 50.4% of the total area. 【Conclusion】SOM content increasing trend might be attribute to the widespread practices of mulching and organic manure application. With the yield improvement, some regions have exhibited SOM deficit and attention should be paid to the fertilization of SOM.

Key words: Arable layer, Soil organic matter, Geostatistics, Spatio-temporal variability, Affected factor