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《Journal of Earth Environment》 2018-03
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A study on the pedo-transfer functions and influencing factors for prediction of soil bulk density for limestone soil in karst area of south China

LI Ying;LIU Xiuming;WANG Shijie;ZHOU Dequan;LUO Hui;State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences;School of Geography and Environmental Science, Guizhou Normal University;Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences;  
Background, aim, and scope Soil bulk density(BD) is an important physical property of soils and is a basic parameter in many soil mass-volume conversion models. The conventional soil BD determination method is a time-consuming, labor-intensive, expensive and tedious, so most soil databases in our country are lack of soil BD data. The lack of bulk density data has seriously affected the estimation of soil carbon storage in the karst area of south China. It is urgent to construct the bulk density pedo-transfer function models(PTFs) using the available data. Materials and methods Based on the data of limestone soil in the south karst distribution province, this paper mainly covers 15 soil properties except for the bulk density of four limestone sub-classes. Firstly, the applicability test of published soil bulk density transfer function was carried out. The function model with relatively higher prediction ability was optimized by 1st Opt software to improve its accuracy. In order to further improve the accuracy of the prediction results of bulk density of karst limestone soil, soil types were classified by limestone soil sub-class, and the regression analysis was carried out by SPSS software to establish a new function model. Results The results show that:(1) The published PTFs at home and abroad have poor applicability to non-zonal limestone soil in karst area of south China, and need to be optimized or rebuilt to predict the new bulk density function.(2) The prediction accuracy of the optimized models Shiri et al(2017)~*, Han et al(2016)-a~* and Han et al(2016)-c~* has been significantly improved. However, compared with the newly established PTFs that based on limestone soil sub-class, the precision is not as good as the latter. Therefore, PTFs based on limestone soil subclasses are more suitable for predicting soil bulk density of limestone in this study area.(3) There are differences in the factors affecting the prediction of bulk density of different limestone soil subclasses. Soil organic matter content is the key factor for predicting limestone bulk density, which is highly correlated with BD in all sub-classes of limestone soils. Discussion The application of soil bulk density pedotransfer function has not only the limitation of geographical area but also the limitation of soil type, and the finer soil type classification, the higher prediction accuracy of bulk density pedo-transfer function. Correlation analysis and bulk density prediction model proved that soil organic matter content(OM) has an important influence on the bulk density prediction of limestone soil sub-class. Due to the difference of soil-forming conditions and soil-forming process between different types of limestone soil, there are great differences in soil properties, resulting in different correlations between soil bulk density and other soil properties. Therefore, it is necessary to classify different soil subtypes when predicting limestone soil bulk density to improve the prediction accuracy of the models. Conclusions This paper discusses the prediction model of karst limestone soil bulk density and its influencing factors. Established bulk density pedotransfer function models applicable to different limestone soil subclasses in the karst area of the south China, and provides convenience for later soil-related work. Recommendations and perspectives We suggest that in the future, the study of soil bulk density forecasting models in karst area should take full account of the regional differences and soil type factors. Meanwhile, due to the limitation of the data, the spatial distribution of soil bulk density is not taken into account. This is one of the factors to be considered in the future to further accurately establish soil bulk density prediction models.
【Fund】: 国家重点研发计划项目(2016YFC0502301);; 贵州省高层次创新型人才培养计划“十”层次人才项目(黔科合平台人才[2016]5648)~~
【CateGory Index】: S152.5
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