引用本文:于冬雪,贾小旭,黄来明,邵明安,王 娇.黄土区不同土层土壤容重空间变异与模拟[J].土壤学报,2019,56(1):55-64.
YU Dongxue,JIA Xiaoxu,HUANG Laiming,SHAO Mingan,WANG Jiao.Spatial Variation of Soil Bulk Density in Different Soil Layers in the Loess Area and Simulation[J].Acta Pedologica Sinica,2019,56(1):55-64
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黄土区不同土层土壤容重空间变异与模拟
于冬雪, 贾小旭, 黄来明, 邵明安, 王 娇
中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室
摘要:
容重(ρb)是土壤最基本物理性质之一,是衡量土壤质量和生产力的重要指标,也是土壤碳氮贮量估算的重要参数。为探明黄土区不同土层ρb的分布特征并建立预测模型,在黄土区布设243个样点,获取0~10、10~20和20~40 cm土壤ρb及环境因子,采用经典统计学与地统计学方法,分析了不同土层ρb的空间变异特征,并利用逐步回归和传递函数方程对ρb的空间分布进行了模拟。结果表明:黄土区不同土层ρb均为中等程度变异,ρb随土层深度的增加而增大。不同土层农地ρb最大,其次为林地和草地。0~10、10~20和20~40 cm土壤ρb半方差函数最佳拟合模型分别为指数模型、指数模型和球状模型,变程为22~780 km。粉粒含量、坡度、海拔、多年平均降水量、气温、干燥度和土地利用是影响区域尺度黄土区ρb空间分布的重要因素,基于相关因子建立的传递函数模型可以解释0~40 cm深度ρb变异的38%~52%,且预测效果优于逐步回归方程,可用于田间条件下ρb空间分布特征的预测。
关键词:  黄土区  容重  空间变异  土地利用  传递函数
DOI:10.11766/trxb201802040086
分类号:
基金项目:国家自然科学基金项目(41501233,41530854)
Spatial Variation of Soil Bulk Density in Different Soil Layers in the Loess Area and Simulation
YU Dongxue, JIA Xiaoxu, HUANG Laiming, SHAO Mingan, WANG Jiao
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Abstract:
【Objective】Soil bulk density (ρb) is one of the most important soil physical properties and can be used to characterize soil quality and soil productivity and as a basic parameter to assess soil carbon and nitrogen storage. Furthermore, ρb has a significant effect on transport of soil water and solutes. However, large-scaled soil databases do not encompass much ρb data, owing to the time- and labor-consuming methods to acquire such data, especially the data of deep soil layers in the field. This study was conducted to (1) explore characteristics of regional spatial variation of ρb relative to soil layer across the loess area, (2) determine effects of soil texture, topography, climate and land use on ρb and (3) compare the stepwise regression method with the pedotransfer function method in simulation of spatial variation of ρb.【Method】A total of 243 sampling sites were set based on a grid sampling scheme (40 km×40 km) in the loess area, and ρb of the soils at the depth of 0~10, 10~20 and 20~40 cm of the soil profile and relevant environmental variables, were collected separately, at each sampling site. Spatial variation of ρb was analyzed with the classical statistic method and geostatistical method, respectively. Stepwise regression equation and pedotransfer function equation was used to simulate spatial distribution of soil ρb, separately.【Result】Results show that ρb varied moderately within a soil layer, and generally increased with soil depth in the profile loess area. ρb variability in the 0~40 cm soil layer was moderate according to the coefficient of variation. In general, cropland was the highest in mean ρb, and followed by forestland and grassland. Semivariance of soil ρb of the 0~10, 10~20 and 20~40 cm soil layer can be best fitted by the exponential model, the exponential model and the spherical model, respectively. Soil ρb of the 0~10 cm soil layer exhibited strong spatial dependence and those of the 10~20 and 20~40 cm soil layer did moderate ones. The optimal interval between sampling sites was 5.6~11.2, 70.9~141.7 and 195.4~390.8 km for the 0~10, 10~20 and 20~40 cm soil layer, respectively. Silt content, land use, elevation and slope gradient were the key factors affecting soil ρb in the 0~10 cm layer; silt content, elevation, multi-year mean annual air temperature, aridity and land use were in the 10~20 cm soil layer; and silt content, elevation, land use, multi-year mean annual precipitation, slope gradient and aridity, were in the 20~40 cm soil layer. The pedotransfer function equation explained 38%~52% the variation of ρb, while the stepwise regression equation did only 34%~39%.【Conclusion】Spatial distribution of soil ρb varies significantly with soil depth and vegetation type across the loess area, and is affected jointly by soil texture, topography, climate and land use at the regional scale. The pedostransfer function equation is recommended for modeling and predicting spatial distributions of ρb, particularly for soil layers below 40 cm in the loess area of China.
Key words:  Loess area  Bulk density  Spatial variation  Land use  Pedotransfer function