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 75% 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.
Highlights
Minerals are the major component of most soils
A total of 10 elements are examined to understand how nutrient and micro-nutrient concentrations relate to the mineralogy of African soils
A broad spread of the X-ray powder diffraction (XRPD) data when plotted in principal component space was observed, reflecting the soil mineralogy continuum (Fig. 2)
Summary
Minerals are the major component of most soils. Through direct inheritance from the parent material and subsequent alteration by chemical weathering, the soil mineral composition can be spatially diverse - reflecting the many soil forming factors (Jenny, 1994). To maintain the current level of cereal selfsufficiency of approximately 80% by 2050, nearly complete closure of the gap between current farm yields and water-limited yield potential is needed, which is in the range of 20% to 50% (Van Ittersum et al, 2016) Meeting these demands for food production will require an increase in nutrient inputs combined with a better understanding of the intrinsic soil mineral reserves of plant nutrients. Identifying and quantifying soil minerals from XRPD data is a time and labour intensive process, requiring each diffractogram to be manually inspected and analysed in combination with mineral databases [e.g. the Powder Diffraction File; ICDD, 2019] and specialised computer software Such workflows become inconvenient when processing large numbers of samples, and the recent acquisition of highthroughput XRPD datasets containing thousands of soil diffractograms has promoted application of alternative, data-driven, approaches to soil XRPD data for the first time (Butler et al, 2018; Butler et al, 2019; Hillier and Butler, 2018). In combining soil XRPD patterns and nutrient concentrations with cluster analysis and compositional methods, we generate new understanding that can contribute to a move towards more sustainable and mineralogically tailored land management practices
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