引用本文:杜佩颖,张海涛,郭 龙,杨顺华,章 清,田 雪.平原丘陵过渡区土壤有机质空间变异及其影响因素[J].土壤学报,2018,55(5):1286-1295.
DU Peiying,ZHANG Haitao,GUO Long,YANG Shunhua,ZHANG Qing,TIAN Xue.Variation of Soil Organic Matter in Transition Zones and Its Influencing Factors[J].Acta Pedologica Sinica,2018,55(5):1286-1295
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平原丘陵过渡区土壤有机质空间变异及其影响因素
杜佩颖,张海涛,郭 龙,杨顺华,章 清,田 雪
华中农业大学资源与环境学院,华中农业大学资源与环境学院,华中农业大学资源与环境学院,华中农业大学资源与环境学院,华中农业大学资源与环境学院,华中农业大学资源与环境学院
摘要:
研究土壤有机质(SOM)在平原丘陵过渡区域的空间变异规律及其影响因素对指导农业生产实践具有重要意义。选取平原丘陵过渡区域(江汉平原与鄂西山区)作为研究区,采集500个土壤表层(0~20 cm)样本,利用相关分析和逐步回归分析从14个影响因素中选取与土壤有机质密切相关的7个变量作为解释变量:高程、坡度、坡向、有效铁、容重、砾石度、黏粒含量。利用普通克里格(OK),回归克里格(RK)和地理加权回归克里格(GWRK)方法对研究区土壤有机质含量进行预测,并用平均误差(ME)、平均绝对误差(MAE)、均方根误差(RMSE)、相关系数(r)和不精确度(IP)作为验证指标来检验模型的预测精度。结果表明,GWRK插值结果最优,局部空间回归模型可以更好地表明过渡区域SOM的空间变异规律。且GWR模型的系数空间分布图可以反映环境变量在不同地理位置对SOM的空间非平稳性的影响程度,为探讨SOM在不同地形条件下的主导影响因子提供了依据,同时也为精确模拟过渡地带土壤有机质空间制图提供了重要的参考方法。
关键词:  土壤有机质  平原丘陵过渡区  空间变异  地理加权回归克里格  空间非平稳性
DOI:10.11766/trxb201702080009
分类号:
基金项目:国家自然科学基金项目(41371227)
Variation of Soil Organic Matter in Transition Zones and Its Influencing Factors
DU Peiying,ZHANG Haitao,GUO Long,YANG Shunhua,ZHANG Qing and TIAN Xue
Hua Zhong Agriculture University,Hua Zhong Agriculture University,College of Resource and Environment, Huazhong Agricultural University,College of Resource and Environment, Huazhong Agricultural University,College of Resource and Environment, Huazhong Agricultural University,College of Resource and Environment, Huazhong Agricultural University
Abstract:
The research on rules of spatial variation of Soil Organic Matter in plain-hill transitional zones and their affecting factors is of great practical significance to guiding agriculture. This study was oriented to explore variation of soil organic matter (SOM) and its influencing factors in a transitional zone between the Jianghan Plain and the mountainous area in West Hubei) in China. A total of 500 soil samples from the surface soil layer (0~20cm) were collected and analyzed for SOM content. By means of correlation analysis and stepwise regression analysis, out of the 14 affecting factors, seven variables, including elevation, slope, aspect, available iron, soil bulk density, soil gravel degree, clay content that were closely related to SOM, were selected as explanatory variables. OK and GWRK methods were used to interpolate the SOM data into the study area, and ME, MAE, RMSE, r, IP were used as validation indices for determination of prediction accuracy of the models. Results show that GWRK was higher OK and RK in prediction accuracy, which indicates that the local spatial regression model can be used to explain spatial variation of the SOM in the transitional region.The coefficient spatial distribution maps of the GWR model can be used to reflect degree of environmental variables affecting SOM spatial nonstationarity relative to geographical location, which may serve as a basis for exploring dominant affecting factors of SOM relative to terrain condition, and also as an important reference for plotting accurate SOM spatial distribution maps of transitional zones.
Key words:  Soil organic matter  Transition zone  Spatial variability  Geographically Weighted Regression Kriging (GWRK)  Spatially Non-stationary