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首页> 外文期刊>Surveys in Geophysics: An International Review Journal of Geophysics and Planetary Sciences >Prediction of Bulk Density of Soils in the Loess Plateau Region of China
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Prediction of Bulk Density of Soils in the Loess Plateau Region of China

机译：黄土高原地区土壤容重的预测。

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摘要

Soil bulk density (BD) is a key soil physical property that may affect the transport of water and solutes and is essential to estimate soil carbonutrients reserves. However, BD data are often lacking in soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km~2) at which we collected undisturbed and disturbed soil samples from two soil layers (0–5 and 20–25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson’s correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.
机译：土壤容重（BD）是可能影响水和溶质运输的关键土壤物理性质，对于估算土壤碳/养分储量至关重要。但是，由于直接测量BD的挑战，土壤数据库中通常缺乏BD数据，这被认为是劳动密集型，耗时且昂贵的，特别是对于诸如中国黄土高原地区的深层土壤的下层而言。我们确定了区域尺度上与BD密切相关的因素，并通过测量中国黄土高原748个选定地点（620,000 km〜2）的BD以及潜在的土壤和环境因素，为BD开发了强大的pedotransfer函数（PTF）。 ），我们从两个土壤层（0–5和20–25 cm）收集未受干扰的土壤样本。区域BD值呈正态分布，并显示出微弱的空间变化（CV = 12％）。皮尔森的相关性和逐步多元线性回归分析确定了淤泥含量，坡度梯度（SG），土壤有机碳含量（SOC），粘土含量，坡度纵横比（SA）和海拔高度是与BD密切相关的因素，并解释了25.8。分别是BD变化的6.3％，5.8％，1.4％，0.3％和0.3％。基于这些紧密相关的变量，使用多元线性回归为BD开发了一个合理的鲁棒PTF，其在当前研究中与人工神经网络方法的效果相同。包含地形因素可以显着提高BD PTF的预测能力，其中SG是重要的输入变量，可以用来代替SA和海拔高度而不会影响其BD预测能力。因此，仅具有四个输入变量（粘土，淤泥，SOC，SG）的已开发PTF，包括它们的常见转换和交互项，就可以以合理的准确性预测BD，因此可用于中国黄土高原的大多数应用。在开发用于BD预测的PTF时，应更加注意地形的作用。需要对用于世界其他黄土地区的已开发PTF进行测试。

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