Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland

BS Griffiths, P Hallett, T Daniell, C Hawes, G Squire, S Mitchell, S Caul, T Valentine, K Binnie, A Adeloye, R Rustum, I Bevison

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.
Original languageEnglish
Pages (from-to)491 - 503
JournalSoil Use and Management
Volume31
Issue number4
DOIs
Publication statusFirst published - 2015

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farm
sampling
soil property
soil
network analysis
water retention
soil carbon
bulk density
soil type
management practice
land use
crop
soil stability
effect

Bibliographical note

1023321

Keywords

  • Bacterial community structure
  • Field scale soil properties
  • Land management
  • Neural network analysis
  • Resilience
  • Resistance

Cite this

Griffiths, BS., Hallett, P., Daniell, T., Hawes, C., Squire, G., Mitchell, S., ... Bevison, I. (2015). Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland. Soil Use and Management, 31(4), 491 - 503. https://doi.org/10.1111/sum.12214
Griffiths, BS ; Hallett, P ; Daniell, T ; Hawes, C ; Squire, G ; Mitchell, S ; Caul, S ; Valentine, T ; Binnie, K ; Adeloye, A ; Rustum, R ; Bevison, I. / Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland. In: Soil Use and Management. 2015 ; Vol. 31, No. 4. pp. 491 - 503.
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Griffiths, BS, Hallett, P, Daniell, T, Hawes, C, Squire, G, Mitchell, S, Caul, S, Valentine, T, Binnie, K, Adeloye, A, Rustum, R & Bevison, I 2015, 'Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland', Soil Use and Management, vol. 31, no. 4, pp. 491 - 503. https://doi.org/10.1111/sum.12214

Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland. / Griffiths, BS; Hallett, P; Daniell, T; Hawes, C; Squire, G; Mitchell, S; Caul, S; Valentine, T; Binnie, K; Adeloye, A; Rustum, R; Bevison, I.

In: Soil Use and Management, Vol. 31, No. 4, 2015, p. 491 - 503.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland

AU - Griffiths, BS

AU - Hallett, P

AU - Daniell, T

AU - Hawes, C

AU - Squire, G

AU - Mitchell, S

AU - Caul, S

AU - Valentine, T

AU - Binnie, K

AU - Adeloye, A

AU - Rustum, R

AU - Bevison, I

N1 - 1023321

PY - 2015

Y1 - 2015

N2 - Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.

AB - Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.

KW - Bacterial community structure

KW - Field scale soil properties

KW - Land management

KW - Neural network analysis

KW - Resilience

KW - Resistance

U2 - 10.1111/sum.12214

DO - 10.1111/sum.12214

M3 - Article

VL - 31

SP - 491

EP - 503

JO - Soil Use and Management

JF - Soil Use and Management

SN - 0266-0032

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ER -