Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.

The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be iden...

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Autores principales:	Paul Simfukwe, Paul W Hill, Bridget A Emmett, Davey L Jones
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a5890ef1fbbd44318010b74f105ae9ed
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spelling	oai:doaj.org-article:a5890ef1fbbd44318010b74f105ae9ed2021-12-02T20:13:35ZIdentification and predictability of soil quality indicators from conventional soil and vegetation classifications.1932-620310.1371/journal.pone.0248665https://doaj.org/article/a5890ef1fbbd44318010b74f105ae9ed2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0248665https://doaj.org/toc/1932-6203The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be identified in soils of Great Britain, (2) whether conventional soil classification or aggregate vegetation classes (AVCs) could predict SQIs and (3) to what extent do soil types and/ or AVCs act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQI which were named as; soil organic matter (SOM), dissolved organic matter (DOM), soluble N, reduced N, microbial biomass, DOM humification (DOMH). SOM was identified as the most important SQI in the discrimination of both soil types and AVCs. Soil attributes constituting highly to the SOM factor were, microbial quotient and bulk density. The SOM indicator discriminated three soil type groupings and four aggregate vegetation class groupings. Among the soil types, only the peat soils were discriminated from other groups while among the AVCs only the heath and bog classes were isolated from others. However, the peat soil and heath and bog AVC were the only groups that were distinctly discriminated from other groups. All other groups heavily overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types. We conclude that conventionally classified soil types cannot predict the SQIs defined from large areas with differing climatic and edaphic factors. Localised areas with similar climatic and topoedaphic factors may hold promise for the definition of SQI that may predict the soil types or AVCs.Paul SimfukwePaul W HillBridget A EmmettDavey L JonesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0248665 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paul Simfukwe Paul W Hill Bridget A Emmett Davey L Jones Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
description	The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be identified in soils of Great Britain, (2) whether conventional soil classification or aggregate vegetation classes (AVCs) could predict SQIs and (3) to what extent do soil types and/ or AVCs act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQI which were named as; soil organic matter (SOM), dissolved organic matter (DOM), soluble N, reduced N, microbial biomass, DOM humification (DOMH). SOM was identified as the most important SQI in the discrimination of both soil types and AVCs. Soil attributes constituting highly to the SOM factor were, microbial quotient and bulk density. The SOM indicator discriminated three soil type groupings and four aggregate vegetation class groupings. Among the soil types, only the peat soils were discriminated from other groups while among the AVCs only the heath and bog classes were isolated from others. However, the peat soil and heath and bog AVC were the only groups that were distinctly discriminated from other groups. All other groups heavily overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types. We conclude that conventionally classified soil types cannot predict the SQIs defined from large areas with differing climatic and edaphic factors. Localised areas with similar climatic and topoedaphic factors may hold promise for the definition of SQI that may predict the soil types or AVCs.
format	article
author	Paul Simfukwe Paul W Hill Bridget A Emmett Davey L Jones
author_facet	Paul Simfukwe Paul W Hill Bridget A Emmett Davey L Jones
author_sort	Paul Simfukwe
title	Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
title_short	Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
title_full	Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
title_fullStr	Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
title_full_unstemmed	Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
title_sort	identification and predictability of soil quality indicators from conventional soil and vegetation classifications.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a5890ef1fbbd44318010b74f105ae9ed
work_keys_str_mv	AT paulsimfukwe identificationandpredictabilityofsoilqualityindicatorsfromconventionalsoilandvegetationclassifications AT paulwhill identificationandpredictabilityofsoilqualityindicatorsfromconventionalsoilandvegetationclassifications AT bridgetaemmett identificationandpredictabilityofsoilqualityindicatorsfromconventionalsoilandvegetationclassifications AT daveyljones identificationandpredictabilityofsoilqualityindicatorsfromconventionalsoilandvegetationclassifications
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Identification and predictability of soil quality indicators from conventional soil and vegetation classifications.

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