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Origin, distribution, and characteristics of Archaeological Dark Earth soils—a review

Asare, Michael O., Afriyie, Jerry Owusu, Hejcman, Michal·2020
DOI 10.5194/soil-2020-51
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Abstract

Abstract. Archaeological Dark Earth (ADE) is a layer of anthrosol (syn. anthroposol) visually characterized by dark color mainly due to homogenous charcoal inclusion, and substantial enrichment by nutrients in comparison to surrounding soils. ADE is distributed from the tropics (Amazonian Terra preta, African ADE), moderate climatic zones (European ADE) up to the Arctic (kitchen middens). Although ADE soils have been studied also in other regions of the world, they have no special regional names. All types of ADE developed as a result of deliberate and/or unintentional deposition of domestic/occupational wastes, charred residues, bones, shells, and biomass ashes from prehistoric up to recent times. ADEs have optimum C : N ratio for effective mineralization, stable organic matter content, reduced acidity, higher CEC and C, N, P, Ca, Mn, Cu, Zn, Mn, Mg, Fe, Sr, and Ba content in comparison to surrounding soils. The unclear remains the level of ADEs enrichment by these elements as enrichment factors for different elements are based on different analytical approaches from plants-available up to total contents in the soil. Although generally highly productive, comparison of herbage production and crop yields between ADEs and natural soils are still rare. The distribution and persistence of anthropogenic activities leading to the formation of ADEs indicate that they are subject to the continual formation.

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  • ADEs are intermediate in phosphorus levels in Fig. 2, although phosphorus levels within the actual ADE soils themselves are much higher.

    “ADEs are intermediate in phosphorus levels in Fig. 2, although phosphorus levels within the actual ADE soils themselves are much higher (Schmidt & Heckenberger, 2009; Asare, Afriyie & Hejcman, 2020).”

    From Predicting the geographic distribution of ancient amazonian archaeological sites with machine learning (2023) · cites this paper as Asare, Afriyie & Hejcman, 2020

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Authors
Asare, M. O., Afriyie, J. O., & Hejcman, M.
ORCID
Asare, Michael O.
Publisher
Copernicus GmbH
Published
2020-09-30
DOI
10.5194/soil-2020-51
Cited by
1
References
1
Type
Preprint
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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