中国南方喀斯特地区石灰土容重传递函数模型及影响因素研究

土壤容重(BD)数据的缺失严重影响了我国南方喀斯特地区土壤碳储量的估算,亟待利用已有数据来构建容重传递函数模型(PTFs)。本文利用南方喀斯特分布省份的石灰土土壤普查数据,首次较为系统地研究了喀斯特地区石灰土的容重传递函数模型及影响因素。研究结果表明:(1)国内外已发表的容重PTFs对中国南方喀斯特地区非地带性石灰土的适用性较差,需要进行优化或重新建立新的容重函数预测模型;(2)优化后的模型Shiri et al(2017)~*、韩光中等(2016)-a~*和韩光中等(2016)-c~*的预测精度得到明显提高;(3)基于石灰土亚类建立的PTFs具有很高的拟合度和预测精度,比优化模型更加适宜于喀斯特石灰土的土壤容重预测;(4)不同石灰土亚类容重预测的影响因素存在差异,其中土壤有机质含量是石灰土容重预测的关键因素,与各亚类土壤的BD都有很高的相关性;(5)土壤容重传递函数模型的适用性不仅与研究区域有关,同时也与研究的土壤类型有关。建议在今后喀斯特地区土壤容重预测模型研究中充分考虑地域差异性和土壤类型因素。

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

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 1stOpt 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 sub-classes 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.