中国主要土壤类型的土壤容重传递函数研究

由于我国大多数土壤数据库缺失或部分缺失土壤容重数据,利用土壤其他属性来预测土壤容重具有重要意义。我国土壤类型多样,已有的土壤容重传递函数在不同土壤类型中的适用性也值得进一步探讨。本文基于我国现有的土壤数据库,对已有的两种土壤容重传递函数在不同土壤类型(土纲)中的预测精度与适用性进行评估,最后通过SPSS进行回归分析建立我国主要土壤类型最适宜的容重传递函数。研究结果表明,已有的两种土壤容重传递函数应用于部分土壤类型时预测精度不高,但基于土壤系统分类的数据分组后建立的容重传递函数能够明显提高预测精度。新建的容重传递函数对有机土、铁铝土、潜育土、均腐土、富铁土、淋溶土、雏形土、新成土和变性土土壤容重的预测精度较高,但人为土、盐成土和干旱土土壤容重传递函数的预测精度整体偏低,在应用时需要慎重。在利用土壤容重传递函数时一定要注意研究区及适用范围。

Pedotransfer Functions for Prediction of Soil Bulk Density for Major Types of Soils in China

Soil bulk density,which can be measured through several labor-intensive procedures,is often missing from most soil databases. However,it is an essential parameter in calculation in many cases and models,and it is feasible to derive soil bulk density from some other attributes of the soil. As China has a huge variety of soil types,whether the existing pedotransfer functions(PTFs)are still applicable to the various soils calls for in-depth analysis. The soil data involved in this study were cited from the Second National Soil Survey of China,from the database for the Chinese Soil Taxonomy and from other publications, covering all the major types of soils in China. The data gathered during the Second National Soil Survey were converted into the Chinese Soil Taxonomy system by means of approximate reference between the two systems using a WebGIS-based inquiry system. After screening of the data in quality and depleting some abnormal values,a total of 2 441 complete soil datasets were obtained,covering all the major types of soils in China. In this paper,2 published PTFs were evaluated and compared in prediction accuracy and applicability for different types of soils,and a PTF,the most adaptable to the major types of soils in China,was developed through regression analysis using SPSS. In addition,exploratory stepwise regression models were proposed and the parameters of the nonlinear model that used only the OM variable were revised based on taxonomical partitioning of the data. Results show that the two existing models varied in performance and were not high in prediction accuracy when used for some types of soils. The bulk density MPE values acquired with the polynomial model of Histosols,Halosols,Gleyosols,Isohumosols and Cambosols were negative(-0.06~-0.01),while those of Anthrosols,Ferralosols,Aridosols,Ferrosols,Argosols,Primosols and Vertosols were positive(0.00~0.02),indicating that the model overestimated BDs of the soils in the former group,and underestimated those in the latter group. The MPE values obtained with the nonlinear model of Aridosols,Halosols,Isohumosols and Ferrosols were negative(-0.02~-0.01),while those of Histosols, Anthrosols,Ferralosols,Gleyosols,Argosols,Cambosols,Primosols and Vertosols were positive (0.00~0.10),indicating the model overestimated BDs of the soils in the former group,and underestimated those of the soils in the latter group. Comprehensive comparison of the scattergraphs of MPE,RMSPE,R2 and measured and predicted BDs indicates that the two models did not vary much in prediction accuracy when used for soils of the same soil order. The PTF developed after the data of the soil taxonomy were partitioned significantly improved the model's performance. The new PTF was pretty high in prediction accuracy when used for Histosols,Ferralosols,Gleyosols,Isohumosols,Ferrosols,Argosols,Cambosols,Primosols and Vertosols,but it tended to be low in accuracy when used for Anthrosols,Halosols and Aridosols. So the model should be used with care. Besides,in using PTFs,it is essential to pay special attention to area and scope they are applied to.