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利用X射线粉末衍射图谱聚类分析和成分分析法评估非洲土壤中的矿质营养关系。

Mineral-nutrient relationships in African soils assessed using cluster analysis of X-ray powder diffraction patterns and compositional methods.

作者信息

Butler Benjamin M, Palarea-Albaladejo Javier, Shepherd Keith D, Nyambura Kamau M, Towett Erick K, Sila Andrew M, Hillier Stephen

机构信息

The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK.

Biomathematics & Statistics Scotland, JCMB, The King's Buildings, Edinburgh EH9 3FD, UK.

出版信息

Geoderma. 2020 Oct 1;375:114474. doi: 10.1016/j.geoderma.2020.114474.

Abstract

Soil mineral compositions are often complex and spatially diverse, with each mineral exhibiting characteristic chemical properties that determine the intrinsic total concentration of soil nutrients and their phyto-availability. Defining soil mineral-nutrient relationships is therefore important for understanding the inherent fertility of soils for sustainable nutrient management, and data-driven approaches such as cluster analysis allow for these relations to be assessed in new detail. Here the fuzzy-c-means clustering algorithm was applied to an X-ray powder diffraction (XRPD) dataset of 935 soils from sub-Saharan Africa, with each diffractogram representing a digital signature of a soil's mineralogy. Nine mineralogically distinct clusters were objectively selected from the soil mineralogy continuum by retaining samples exceeding the quantile of the membership coefficients in each cluster, yielding a dataset of 239 soils. As such, samples within each cluster represented mineralogically similar soils from different agro-ecological environments of sub-Saharan Africa. Mineral quantification based on the mean diffractogram of each cluster illustrated substantial mineralogical diversity between the nine groups with respect to quartz, K-feldspar, plagioclase, Fe/Al/Ti-(hydr)oxides, phyllosilicates (1:1 and 2:1), ferromagnesians, and calcite. Mineral-nutrient relationships were defined using the clustered XRPD patterns and corresponding measurements of total and/or extractable (Mehlich-3) nutrient concentrations (B, Mg, K, Ca, Mn, Fe, Ni, Cu and Zn) in combination with log-ratio compositional data analysis. Fe/Al/Ti/Mn-(hydr)oxides and feldspars were found to be the primary control of total nutrient concentrations, whereas 2:1 phyllosilicates were the main source of all extractable nutrients except for Fe and Zn. Kaolin minerals were the most abundant phyllosilicate group within the dataset but did not represent a nutrient source, which reflects the lack of nutrients within their chemical composition and their low cation exchange capacity. Results highlight how the mineral composition controls the total nutrient reserves and their phyto-availability in soils of sub-Saharan Africa. The typical characterisation of soils and their parent material based on the clay particle size fraction (i.e. texture) and/or the overall silica component (i.e. acid and basic rock types) alone may therefore mask the intricacies of mineral contributions to soil nutrient concentrations.

摘要

土壤矿物质组成通常复杂且在空间上具有多样性,每种矿物质都具有独特的化学性质,这些性质决定了土壤养分的固有总浓度及其对植物的有效性。因此,定义土壤矿物质与养分的关系对于理解土壤的固有肥力以实现可持续养分管理至关重要,而诸如聚类分析等数据驱动方法能够以新的详细程度评估这些关系。在此,模糊c均值聚类算法被应用于来自撒哈拉以南非洲的935份土壤的X射线粉末衍射(XRPD)数据集,每份衍射图谱代表一种土壤矿物学的数字签名。通过保留每个聚类中隶属系数超过分位数的样本,从土壤矿物学连续体中客观地选择了9个矿物学上不同的聚类,从而得到一个包含239份土壤的数据集。这样,每个聚类中的样本代表了来自撒哈拉以南非洲不同农业生态环境的矿物学相似的土壤。基于每个聚类的平均衍射图谱进行的矿物定量表明,这9个组在石英、钾长石、斜长石、铁/铝/钛(氢)氧化物、层状硅酸盐(1:1和2:1)、铁镁矿物和方解石方面存在显著的矿物学差异。利用聚类的XRPD模式以及总养分和/或可提取(Mehlich-3)养分浓度(硼、镁、钾、钙、锰、铁、镍、铜和锌)的相应测量值,并结合对数比成分数据分析,定义了矿物质与养分的关系。发现铁/铝/钛/锰(氢)氧化物和长石是总养分浓度的主要控制因素,而2:1层状硅酸盐是除铁和锌之外所有可提取养分的主要来源。高岭土矿物是数据集中最丰富的层状硅酸盐组,但不是养分来源,这反映了其化学成分中缺乏养分以及其低阳离子交换容量。结果突出了矿物组成如何控制撒哈拉以南非洲土壤中的总养分储备及其对植物的有效性。因此,仅基于粘粒粒径级分(即质地)和/或整体硅成分(即酸性和碱性岩石类型)对土壤及其母质进行典型表征,可能会掩盖矿物对土壤养分浓度贡献的复杂性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d27d/7386901/4d790104e191/gr1.jpg

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