Soil organic matter as a headline indicator of soil...

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Soil organic matter as a headline indicator of soil health Appendix to Report to Defra – SP0546 • Appendix Tables A to E and A1 & A2 • Project references and bibliography • Sub-ordinate Indicators of Soil Organic Carbon National Soil Resources Institute Cranfield University Silsoe Bedfordshire MK45 4DT February 2007.

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Table A Changes in topsoil SOC in Soilscapes under arable and grassland Arable & rotational grass Permanent grass

Changes in number of vulnerable sites and in rate of change in SOC

Soil type in Defra Soil Management Guidance

Saltmarsh soils Insufficient sites (1) Shallow very acid peaty soils over rock Shallow lime-rich soils over chalk or limestone

No loss Little change in number of sites below threshold (82)

Chalk and limestone soils Low loss rate Increase in number of sites below threshold (31)

Sand dune soils Freely draining lime-rich loamy soils Low loss rate

Increase in number of sites below threshold (35)

Chalk and limestone soils Insufficient sites (6)

Freely draining slightly acid loamy soils No loss Small increase in number of sites below threshold (138)

Medium soils Low loss rate Increase in number of sites below threshold (144)

Freely draining slightly acid but base-rich soils

Low loss rate Increase in number of sites below threshold (27)

Medium soils Low loss rate Little change in number of sites below threshold (12)

Slightly acid loamy and clayey soils with impeded drainage

No loss Little change in number of sites below threshold (48)

Medium soils Low loss rate Increase in number of sites below threshold (68)

Lime-rich loamy and clayey soils with impeded drainage

Low loss rate Little change in number of sites below threshold (24)

Medium soils Low loss rate Small increase in number of sites below threshold (27)

Freely draining slightly acid sandy soils No loss Little change in number of sites below threshold (26)

Sandy and light silty soils Low gain rate Little change in number of sites below threshold (10)

Freely draining sandy Breckland soils Insufficient sites (2) (Sandy and light silty soils) Key to Table A & B Grey squares = no NSI sites (n) = number of re-sampled sites

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Arable & rotational grass Permanent grass



Freely draining floodplain soils Insufficient sites (4) (Medium soils with some light silty soils) No loss No vulnerable sites (12)

Freely draining acid loamy soils over rock Low loss rate Little change in number of sites below threshold (29)

Medium soils

Low loss rate No vulnerable sites (46)

Freely draining very acid sandy and loamy soils

Insufficient sites (2) (Sandy and light silty soils) Insufficient sites (1)

Naturally wet very acid sandy and loamy soils

No loss Increase in number of sites below threshold (17)

Sandy and light silty soils Low loss rate Little change in number of sites below threshold (10)

Very acid loamy upland soils with a wet peaty surface

Insufficient sites (7)

Slowly permeable seasonally wet acid loamy and clayey soils


Heavy soils Low loss rate Little change in number of sites below threshold (133)

Slowly permeable seasonally wet slightly acid but base-rich loamy and clayey soils

Low loss rate Increase in number of sites below threshold (166)

Heavy soils Low loss rate Increase in number of sites below threshold (146)

Slowly permeable wet very acid upland soils with a peaty surface

Insufficient sites (4) (Peaty and heavy soils) Low loss rate Little change in number of sites below threshold (15)

Loamy and clayey floodplain soils with naturally high groundwater


Medium soils with some heavy soils Low loss rate Little change in number of sites below threshold (34)

Loamy and clayey soils of coastal flats with naturally high groundwater

No loss (37)

Medium soils with some heavy soils Low loss rate No vulnerable sites (24)

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Arable & rotational grass Permanent grass



Loamy soils with naturally high groundwater

Insufficient sites (8) (Medium soils) Insufficient sites (7)

Loamy and sandy soils with naturally high groundwater and a peaty surface

High loss rate Little change in number of sites below threshold (22)

Medium, sandy and light silty soils Insufficient sites (7)

Restored soils mostly from quarry and opencast spoil

Insufficient sites (1) (Heavy soils) Insufficient sites (2)

Blanket bog peat soils Raised bog peat soils Fen peat soils Insufficient sites (7) (Peaty soils) Insufficient sites (6)

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Table B. Changes in topsoil SOC in Soilscapes under extensive management Lowland

heath Upland heath

Rough grazing

Upland grazing

Scrub Bog Coniferous woodland

Deciduous woodland

Saltmarsh soils Shallow very acid peaty soils over rock (3) (1) Low gain

rate (11) (1) (1)

Shallow lime-rich soils over chalk or limestone (3) (2) (6) Sand dune soils (1) Freely draining lime-rich loamy soils (5) Freely draining slightly acid loamy soils (2) (2) Low gain

rate (20) 4 8 (1) Low gain rate

(12) Low loss rate (24)

Freely draining slightly acid but base-rich soils (2) (3) (2) 4 Slightly acid loamy and clayey soils with impeded drainage

(1) (2) (2) (3) Low gain rate (13)

Lime-rich loamy and clayey soils with impeded drainage (1) (1) (3) Freely draining slightly acid sandy soils (1) (1) (5) (1) (5) (2) Freely draining sandy Breckland soils (4) (1) Freely draining floodplain soils (1) Freely draining acid loamy soils over rock (5) (7) Low loss

rate (16) (2) Low loss rate

(11) No loss or gain (10)

Freely draining very acid sandy and loamy soils (1) (5) (2) (3) (1) (6) (3) Naturally wet very acid sandy and loamy soils (3) (6) (2) Very acid loamy upland soils with a wet peaty surface Low loss

rate (14) Low gain rate (11)

High loss rate (26)




(2) (2) (5) (7) (8)


(1) (7) (1) (3) (2) No loss or gain (12)

Low loss rate (19)


(1) High loss rate (12)

High loss rate (33)

High loss rate (25)

(4) High loss rate (16)


(5) (1)


(1)

Loamy soils with naturally high groundwater (1) Key to Table A & B Grey squares = no NSI sites (n) = number of re-sampled sites

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Lowland

heath Upland heath

Rough grazing

Upland grazing

Scrub Bog Coniferous woodland

Deciduous woodland


(2) (2)

Restored soils mostly from quarry and opencast spoil (1) Blanket bog peat soils (11) (5) Low loss

rate (20) High loss

rate (14) (4)

Raised bog peat soils (1) Fen peat soils (2) (2)

Appendix tables for SP0546 7

Table C Guidance in Defra Soil Management Guidance and Soilscape land cover

Arable & rotational grass Soil type in Defra

Soil Management Guidance

Soilscape Land cover

Specific guidance references to SOC

Saltmarsh soils Natural vegetation Shallow very acid peaty soils over rock Open moor Shallow lime-rich soils over chalk or limestone Chalk and

limestone soils Arable and grassland

Avoid deeper ploughing or cultivation into the rock beneath to prevent dilution of the topsoil

Sand dune soils Natural vegetation Freely draining lime-rich loamy soils Chalk and

limestone soils Arable with grassland at higher altitude

Avoid deeper ploughing or cultivation into the rock beneath to prevent dilution of the topsoil

Freely draining slightly acid loamy soils Medium soils Arable and grassland

Freely draining slightly acid but base-rich soils Medium soils Arable and grassland

Slightly acid loamy and clayey soils with impeded drainage

Medium soils Arable and grassland

Lime-rich loamy and clayey soils with impeded drainage

Medium soils Arable some grassland

Freely draining slightly acid sandy soils Sandy and light silty soils

Arable

Aim to increase the OM content and improve the stability of the topsoil by retaining crop residues, by applying manures or by using grass breaks or green manures

Freely draining sandy Breckland soils (Sandy and light silty soils) Arable forestry and

heath


Freely draining floodplain soils (Medium soils with some light silty soils)

Grassland some arable

Freely draining acid loamy soils over rock Medium soils Grassland and rough grazing

Freely draining very acid sandy and loamy soils (Sandy and light silty soils)

Heath and forestry



Arable & rotational grass Soil type in Defra

Soil Management Guidance

Soilscape Land cover

Specific guidance references to SOC

Naturally wet very acid sandy and loamy soils Sandy and light silty soils

Arable and horticulture some wet lowland heath


Very acid loamy upland soils with a wet peaty surface Moorland rough grazing forestry and grassland


Heavy soils Grassland with some arable and forestry


Heavy soils Grassland and arable some woodland


(Peaty and heavy soils)

Moorland rough grazing and forestry


Medium soils with some heavy soils

Grassland some arable


Medium soils with some heavy soils

Arable some grassland

Loamy soils with naturally high groundwater (Medium soils) Arable grassland and woodland


Medium, sandy and light silty soils Mostly arable

Restored soils mostly from quarry and opencast spoil (Heavy soils) Grassland arable and trees

Blanket bog peat soils Moorland rough grazing and forestry

Raised bog peat soils Bog; grassland and some arable

Fen peat soils (Peaty soils) Arable and horticulture

To avoid wind erosion of fenland soils


Table D Results from field study of sites in 2004

Site Grid reference

Rainfall (mm)

SOC1 (at various dates between 1958 and 1983)

SOC2 (in 1994)

SOC3 (in 2004)

Clay % Silt %

Rate of change per year from SOC1 to SOC2 (%)



1 TL344621 561 2.2 2.2 3.1 43 31 0.00 0.07 0.05 2 TM369638 618 1.8 2.1 1.7 25.3 12.7 0.01 -0.03 0.00 5 TM196536 614 1.2 1.6 1.9 27 23 0.02 0.03 0.04 6 SE890697 770 2.4 2.3 2.5 25 59 -0.01 0.02 0.01 7 SE890695 770 2.8 2.8 2.5 27 57 0.00 -0.03 -0.02 8 SP945464 604 1.9 2.3 2.0 45.1 35.9 0.02 -0.03 0.00 10 SP313092 730 2.3 2.5 2.6 54 33 0.01 0.01 0.01 11 SP315091 730 1.9 2.4 2.4 56 35 0.02 0.00 0.02 16 TL243455 568 1.6 1.9 1.7 29 36 0.02 -0.02 0.01 19 SK676603 630 4.4 3.2 2.8 35.2 49.3 -0.05 -0.04 -0.06 20 SK684642 625 1.0 1.5 1.8 21.6 31.7 0.02 0.03 0.03 21 SK690638 625 1.3 1.3 2.3 20.1 38.2 0.00 0.09 0.04 22 SP905169 678 3.3 2.1 2.5 56 37 -0.03 0.04 -0.02 23 SP947154 705 2.1 2.1 2.5 30 57 0.00 0.04 0.01 24 SP943157 705 2.6 1.9 2.2 36 51 -0.02 0.03 -0.01 25 TL425601 565 1.6 2.2 2.3 9 6 0.02 0.01 0.02 28 SP368620 891 1.2 1.8 2.0 36.2 26.2 0.03 0.02 0.03 29 SP391609 780 3.8 7.1 9.9 64.7 27.6 0.14 0.26 0.26 30 SP378688 840 1.2 2.8 2.0 14.3 19.5 0.07 -0.07 0.03 31 SU928046 773 1.7 2.0 1.6 19 65 0.01 -0.04 0.00 32 SU906034 782 1.1 1.2 22 61 0.00 0.01 0.00 34 SU890061 782 2.1 2.3 2.5 20 62 0.01 0.02 0.01 35 TL461791 529 2.2 2.1 2.4 34.2 34.8 0.00 0.03 0.01 36 TL235806 570 2.0 2.7 2.1 61.8 34.2 0.03 -0.05 0.00 37 TF059063 574 1.4 1.5 1.6 20 36 0.01 0.01 0.01 38 TF071082 580 1.2 1.4 1.4 16 42 0.01 0.00 0.01 39 TL164808 600 2.4 2.8 2.3 45.5 32.6 0.02 -0.05 0.00 40 SP163552 630 3.9 3.4 4.9 56 26 -0.03 0.14 0.06

Figures in bold are “vulnerable” by 1980 baseline 10th percentile


Table E Land use and management of field sites Site Land use and management comment between initial

sampling and 1994 Land use and management comment post 1994

1 14 years winter wheat 1 year oil seed rape (OSR) 3 years winter beans

2 years OSR 6 years winter wheat 1 year set aside Reduced tillage using power harrow, rotavator

2 Ploughed land in 1964 and 1993 No Records

5 1 year sugar beet (SB) 10 winter wheat 1 linseed 8 years no records at start of study period

1 year set aside 1 year winter barley 2 OSR 6 winter wheat 1 year spring beans Reduced tillage – Simba Solo

6 No Records 3 OSR 6 wheat 1 potatoes

7 No Records 3 OSR 6 wheat 1 potatoes

8 “Arable for century” 1992 wheat 1993 peas

8 wheat 2 barley 2 OSR Plough double press & drill

10 14 barley 5 wheat 2 peas 1 OSR

OSR then 6 grass 1 wheat 1 Triticale 2 setaside 1 wheat Ploughed

11 Wheat 1975 OSR then 6 grass 1 wheat 1 Triticale 2 setaside 1 wheat Ploughed

16 1 cultivated 3 sprouts 1 spring barley 1 potatoes/peas 7 wheat 1 linseed Mainly 2 cereals followed by break

6 wheat 2 barley 3 OSR 2 or 3 cereal then break (OSR)

19 2 year OSR 4 years winter wheat 1 beans 1 barley 18 years no records

7 wheat 2 set aside 2 OSR 2 cereal then break and set aside Min till & ploughing.

20 4 barley 7 winter wheat 3 grass (conseq) 1 potatoes 1 OSR 8 years no records at beginning of recording Two or three cereals and a break crop

3 barley 4 OSR 5 wheat Min tillage for last 8 years, straw incorporated, chicken manure

21 4 barley 6 winter wheat 3 OSR 3 grass (conseq) 10 years no record at begin of recording

2 winter barley 7 winter wheat 3 OSR Mostly min tillage

22 12 winter wheat 5 winter barley

6 wheat 1 OSR


1 OSR 1 peas 2 beans 19 years no records at beginning of recording 4 to 6 six cereals then break crop

1 beans 3 years no records at beginning of recording Never been grass; min tillage – Simba Solo

23 13 spring barley (then to wwht) 6 sainfoin 17 winter wheat 1 OSR 1 oats Straw ploughed in to 1956 then spread and burned to 1992

1 wheat then 6 ? 1 wheat 3 barley Always cereal, mostly plough in straw

24 13 spring barley (then to wwht) 2 sainfoin 21 winter wheat 1 beans Straw ploughed in to 1956 then spread and burned to 1992

1 wheat then 6 years no records at beginning of recording 1 wheat 3 barley Always cereal, mostly plough in straw

25 Sugar beet 1958 8 winter barley 1 spring barley 1 OSR 1 potatoes Mostly ploughed, disced and power harrowed

28 14 wheat 4 OSR 2 beans 2 set aside 2 years no records at beginning of recording Mainly break following 2 cereals

No Records

29 Ley grass 1970 to 1974 Permanent grass to 1994

Permanent grass Heavy poaching by horses

30 1 potatoes 4 winter wheat 8 temporary grass 12 years no records at beginning of recording Mucked when cultivated

1 ley then 6 maize then 4 grass

31 Prior to 1960 long ley then 21 years no records then 1 wheat 2 maize 2 oats 4 barley 2 ley for seed

5 winter barley 2 ley for seed 4 oats ploughed and drilled

32 No Records

34 No Records 3 wheat 2 peas 3 lettuce 2 red clover (most recent) Plough, power harrow and drill – organic conversion started 2002.

35 7 years no records at beginning of recording then 1 potatoes 10 wheat 2 grass 5 barley 3 OSR 1 beans 2 or 3 years cereals followed by break

8 winter wheat 1 set aside 2 OSR 1 peas Wheat alternately ploughed & Simba

36 No Records 2 wheat 1 beans 1 barley 1 OSR then 2 wheat Deep disc harrow, flat lift and drill

37 14 winter wheat 1 potatoes 4 sugar beet 1 spring barley

6 winter wheat 1 set aside 2 winter beans 1 OSR


1 spring beans Regular break crop after 2 or 3 cereals

1 barley 1 oats

38 No Records No Records

39 1 grass 11 winter wheat 5 barley 3 beans 2 OSR Regular break crop after 2 or 3 cereals

7 wheat 2 barley 2 OSR 1 beans Ploughed this year but normally 10cm cultivation Usually 3 cereals then break

40 6 ley grass then 8 wheat 3 beans 3 cereals then break crop

6 wheat 1 OSR 1 setaside then 3 willow coppice Ploughed power harrowed drilled.

SP0546 Appendix

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PROJECT REFERENCES AND BIBLIOGRAPHY Defra funded projects since c.1995 CC0242 – Modelling soil carbon fluxes and land use change for the National Carbon Dioxide

Inventory CC0308 – Evaluating organic matter dynamics in cultivated mineral topsoils – the use of historical

analytical data CC0375 – The development of a soil properties database for England and Wales for climate

change impact studies GA01054 – UK Emissions by sources and removals by sinks due to land use, land use change

and forestry (LUUCF) LE0203 – Evaluating organic matter dynamics in cultivated organic topsoils – the use of historical

analytical data OC9403 – The impact of changing farming practice on sustainability of soils in England and

Wales SP0203 – To improve model of lime loss from arable and grassland soils SP0306 – Critical Levels of Soil Organic Matter SP0310 – Indicative SOC management regimes SP0501 – Effects of farm manure additions on soil quality and fertility SP0506 – Comparison of National Soil Inventory Datasets SP0510 – The effect of crop rotation and rotational position on soil structure and structural

resilience SP0511 – UK soil database for modelling soil carbon fluxes and land use for the national carbon

dioxide inventory SP0515 – Comparison of soil data from different sources (review of soil monitoring schemes in

UK SP0521 – Changes in organic carbon content of non-agricultural soils SP0523 – Development of economically and environmentally sustainable methods of carbon

sequestration SP0533 – Initial assessment of projected trends of SOC in English arable soils SP0538 – The impact of climate change on soil functions SP0544 – Development of performance criteria for soil monitoring schemes SP0545 – Spatial analysis of change in organic carbon and pH using re-sampled National Soil

Inventory data across the whole of England and Wales. SP0546 – Soil organic matter as a headline indicator of soil health Aitken, M.N., Evans, B. & Lewis, J.G. (1998). Effect of applying paper mill sludge to arable land

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SUBORDINATE INDICATORS OF SOC When considering which soil properties convey a measure of health or a degree of quality or of general soil fertility, it is customary to compartmentalise them into the three divisions of physical, chemical and biological attributes. The list has remained fairly comprehensive since early attempts to define measures of quality or fertility and a consensus list is given in Table A1. This covers those examined by many authors since c.1990. Soil organic matter itself is often cited (for example Ashad & Coen 1992) but is not included, though the ways in which it influences the other indicators is commented on. Also absent from the list, but commonly cited, are measures of soil salinity and sodicity. These are rarely found, very localised problems in British soils. Table A1. Soil properties most consistently cited as being useful as indicators of soil health and/or quality since c.1990. Type of property

Indicator References Description Interaction with SOM

Depth of soil 1, 4, 8, 13, 27,

Depth of soil layer available as a rooting medium.

Water holding capacity

1, 4, 8, 13, 27, 32, 34, 42, 43,

Influenced by both texture and structure of soil, the ability of soil to hold sufficient water for plant growth and can determine crop yields.

SOM can influence the water holding capacity of soil through its effects on soil structure as well as being a medium for moisture retention itself.

Hydraulic permeability and infiltration rate

1, 4, 8, 13, 27, 42, 43, 45,

The ability of a soil surface to accept rainfall and the rate at which water can be transmitted through soil. Impermeable soils are prone to water-logging and erosive surface run-off.

SOM can influence a soil’s permeability by its effect on structure and structural stability.

Structural stability

1, 4, 8, 17, 19, 32, 39, 42, 43, 45,

Both the size distribution and stability of aggregate structural units in topsoil, can influence the workability of soil and its value as a rooting medium.

The amount of SOM in a topsoil can greatly effect the size of aggregates formed and their degree of stability in water.

Physical

Bulk density and soil strength

1, 8, 13, 26, 27, 28, 32, 42, 43, 44, 45,

Both texture and structure influence the strength of soil, and its degree of penetrability to roots and tillage implements.

SOM can reduce bulk density, but both raise and lower soil strength depending upon its concentration and interaction with structure

Chemical Cation exchange capacity (CEC)

1, 4, 8, 32, 36, 45,

A measure of the amount of plant nutrient cations a soil can retain by electrostatic charge.

Soils CEC is governed by charged surfaces on clay and SOM

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particles. These also affect the soils structure.

Soil pH and base saturation

1, 4, 8, 13, 24, 27, 32, 34, 36, 42, 43, 45,

The pH and ionic environment of plant roots directly effects how well different species grow.

Plant available nutrients

4, 8, 13, 24, 26, 27, 28, 32, 34, 36, 41, 42, 43, 45,

A measure of the amount of nutrients a soil can supply (cf with retention by CEC above).

SOM is a source of plant nutrients via mineralisation processes.

Soil organic carbon fractions

17, 19, 24, 26, 28, 31, 34, 39, 41, 42, 43,

A specifically defined sub-division of the total soil organic matter. The “light fraction” and “active carbon” fractions are such sub-division fractions.

A measure of the more labile organic matter in the soil, open to biological transformation, these fractions are more sensitive to change than total SOM .

Microbial population indicators

2, 4, 5, 8, 11, 12, 13, 17, 21, 24, 26, 27, 28, 29, 30, 34, 39, 41,

Either indicators of total amount of microbial growth i.e. biomass. Or measures of abundance of particular species according to function. Relates to nutrient supply of soil.

Amount and quality of SOM can effect and limit the growth of microbial populations.

Microbial community indicators

2, 4, 5, 8, 19, 23, 29, 34, 41,

Measures of bio-diversity or range of functional groups of organisms present in a soil. Relates to supply of particular nutrients or environmental regulation in soil.

Quality of SOM is important in controlling the diversity of communities and the ability of functional groups to operate effectively.

Biological (microbial)

Microbial activity 2, 4, 5, 10, 11, 12, 13, 15, 16, 21, 24, 26, 27, 29, 30, 32, 34, 37, 41,

Measures of decomposition and respiration rates, carbon and nitrogen turnover rates and enzyme activity. The level of activity as well as size and diversity of populations affects a soil’s ability to supply plant nutrients.

Microbial populations respond to new (quantity) and more degradable (quality) organic matter by increasing activity for a period. Changes in activity can relate to SOM turnover.

Biological (soil fauna)

Keystone species or ecosystem engineers

3, 4, 8, 9, 11, 22, 27, 44,

A measure of the abundance or biomass of certain micro, meso or macro-fauna species that demonstrate an unambiguous contribution to

Abundance may be linked to concentration of SOM as a food source.

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soil quality or health. Diversity at the taxonomic group level.

3, 9, 19, 23,

A measure of the abundance or biomass of classes of organisms to provide a simple indication of the complexity of a soil community.

As above.

Diversity at the species level

3, 9, 23, A measure of the species richness of the soil.

Quality of SOM may have an impact on the number of species it can support.

The centrality of SOM as an indicator related to a suite of other sub-ordinate ones is schematically represented in Figure A1 and this clearly shows the wide scope of SOM effects on both soil processes and measurable organic pools, as well as some of the constraints placed upon normal system functioning (Elliott (1997).

Figure A1. The interrelationship between soil organic matter, other chemical and physical indicators and soil biota. (After Swift & Woomer (1993)). The ability for soil organic matter concentrations to influence a whole range of other indicators of soil health and thereby warrant the term “headline indicator” is shown by a recent study where structural degradation in a clay loam in a semi-arid area was partially reversed merely by growing different crops. The study by Gülser (2004) measured a range of 11 chemical and physical indicators over a period of three years growth of a range of pasture and cover-crop species. Depending upon species, all crops but in particular grass species (brome grass caused the greatest increase), variably improved the range of indicators with respect to bare ground. These increases in structural stability, porosity, infiltration rate and concomitant decreases in bulk

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density and penetration resistance, could all be attributed to a rise in the SOM concentration as there were no differential manurial or tillage treatments.

Biological indicators Central to many interpretations of what may be useful indicators of health or quality is the idea that the biological functions of the soil microbiota and to a lesser degree the soil fauna, are of particular importance due to their role in the self-remediation properties of soil (Filip 2002). Through their continued activity and function, many of the other indicators can be rectified, and hence a wider range of more specific microbial functional indicators has been examined than the classes of indicator listed in Table A1.

Microbiological indicators Filip (2002) reviewed such a range of functional indicators which included microbial biomass as well as abundance of copiotrophic and oligotrophic bacteria, actinomycetes, fungi, proteolytic spore forming bacteria, cellulose decomposers, N2 fixing bacteria and pseudomonads. In addition, specific microbial activities were also used as indicators, covering, respiration, ammonification, de-nitrification, de-hydrogenase activity and humification activity. He concluded that total biomass, the abundance of N2 fixing bacteria, and respiration, de-hyrogenase and humification activities, may be the most sensitive and useful parameters. Their interaction with organic matter is strong, being integral in both the mineralisation and synthesis of SOM, even though they were used in relation to a toxicity stress (from heavy metals) placed upon the soil.

Microbial population The size of microbial populations sustained have often been used as indicators of the biological status of soil and Turco et al. (1994) found three measurements of population size, biomass by chloroform fumigation, plate counts of colony forming units and the amount of ATP in the soil to be broadly comparable and able to differentiate between soils types. Further precision and subdivision can be attained by measuring the amounts of chitin or ergosterol in the soil to measure just the bacteria or fungal components respectively, or if a measure of just the living cells is required the phospholipid content can be measured. Little direct relationship is drawn between SOM concentration and biomass levels, though Powlson et al. (1987) have demonstrated that microbial biomass can be a more sensitive indicator of change in response to organic matter additions before changes in SOM itself are discernible. The direct relationship between the size of the active microbial biomass and SOM levels has also been found when comparing fields with different land use treatments (Jordan et al. 1995) where SOM concentrations were reliably indicated by the extractable phospholipid levels. Sparling (1997) discussed further the pros and cons of using microbial biomass indicators of both SOM and general soil processes. Although potentially useful, they could be misleading as well, due to interactions with soil texture, profile location and seasonal variability. He found more promise in the use of a microbial quotient, which is the amount of microbial-C / SOC, and gives some idea of the trend in decomposition processes similar to the respiratory quotient discussed below. This quotient has been used to monitor the restoration of soil health in reclaimed mine soils in New Zealand and was also favoured to indicate trends in the growth or otherwise of microbial biomass in response to changes in cropping practice between rotations and monocultures, by Anderson (2003). Besides being related to SOM concentrations as an energy resource, microbial biomass constitutes a component of SOM and can play a particular role in influencing other indicators, such as the binding role that fungal hyphae play in structural stabilisation (Pankhurst 1994) and their interaction with vegetation type. As such, the relative abundance of fungi in soil as compared with bacteria was found to be more influential in the stability of soil aggregates (63 % of intra-aggregate SOC compared to 37% from bacteria) when Simpson et al. (2004) compared them from differential tillage treatments. They measured the difference in source of SOC, by

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measuring the relative amounts of fungal or bacterial amino-sugars extracted from the soil, a ratio which could therefore possibly serve as an indicator for structural stability. The role of fungi in creating aggregate structure and conferring stability, has also been discovered by Young & Crawford (2004), this time measured by ergosterol concentrations in the soil. Although not directly linked to structural indicators Bending et al. (2004) also noted that one particular group of fungi, the Arbuscular Mycorrhizal Fungi were an adept indicator of management differences between conventional cereals and organic rotational systems.

Microbial community The functional diversity of the soil microbial community has only recently been open to adequate methods of discernment, and these are still only just finding specific uses. The measure of specific phospholipid profiles and fatty acid profiles in soils are such developing methods to discern between groups of bacteria, and the use of BIOLOG redox technology enables functional groups based on specific nutrient usage to be evaluated (Turco et al. 1994; Pankhurst 1997; Bending et al. 2004). Functional diversity as measured by the Shano recordson Index derived from BIOLOG proved of some use in differentiating crop rotations of under organic management systems, as did the same index applied to a range of enzyme functions. However, the differentiation was not the same, though both distinguished conventional cereals and their use requires more research before being consistent enough as a monitoring tool. Such measures are likely to relate more to the quality of SOM more than amounts available. A more highly developed use of the identification of diverse strains of bacteria in microbial communities as an indicator of soil health is in the area of toxicity problems. An example would be the identification of heavy metal tolerant strains of bacteria in communities in response to contamination by soil amendments (Elliott 1997). Pankhurst (1997) discusses the sheer size of estimates of the diversity of various groups of organisms in the soil (from 3 000 earthworm species up to 1 500 000 fungal species) in relation to reasons why little progress has so far been made in cataloguing them or measuring their diversity in any meaningful way. However, new methods such as the functional techniques mentioned above and DNA and RNA profiling are being developed, and several diversity indices have been used to describe communities of soil organisms. In relation to soil health, Pankhurst discusses mycorhizae and phosphorus uptake, plant pest and diseases, protozoa and nutrient cycling and earthworms and soil structure, but concludes that for practical purposes there are still too many significant gaps in our knowledge to be able to apply the undoubted potential that measures of diversity seem to offer.

Microbial activity Measures of microbial metabolic activity have often been favoured as indicators as they are likely to respond to changes in the soil environment and be the first indication of stress experienced by biological organisms. As such the respiratory quotient (qCO2) of the soil has long been thought to have potential as an indicator of change. This is the ratio of the respiration of a community to its size (basal respiratory C / biomass C), and has been found sensitive to changes induced by temperature, cropping pattern (Turco et al. 1994). It is suggested that it may be a useful indicator of stability, becoming progressively lower as perturbed soils age in a new ecological succession, though as Sparling (1997) points out a high respiratory quotient can be an indicator of both beneficial and detrimental processes to the long term “health” of the soil. Anderson (2003) associated a lower qCO2 with the more efficient SOM dynamics of long-term crop rotations compared with monocultures and as such considered that it may be useful indicator of changes in cropping practice. Bending et al. (2004) however, failed to find consistent evidence of this when comparing organic rotations with continuous cereal management, though found it to distinguish certain rotations from others and would consider it a useful indicator when used in conjunction with other biochemical and microbial indicators.

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The level of respiration itself is not favoured as an indicator as it is recognised that though it may indicate microbial activity, this activity itself can be detrimental to soil “health” as well as beneficial. A rather more promising use of total soil respiration could be in relation to the total inputs of organic material to a soil and Parkin et al. (1996) published data that correlated this ratio (C inputs / daily respired C) with the total soil organic carbon content. Effectively this is a measure of how much carbon inputs exceed the decomposition rate and so contribute to a net increment in SOM. The use of enzyme activity is considered here under the heading “microbial activity” as the main source of soil enzymes is microbial. Certainly not all though, as some originate in higher plants and soil fauna or are introduced to the soil with organic material. They can also exist in extra-cellular form associated with soil particles, abiontic enzymes, or indeed inside biological organisms, biotic. Their role in the general breakdown of organic residues in soil has made them a possible indicator of at least biological health of soil. Early work saw promise in dehydrogenase in particular due to its universal role in the oxygenation of organic matter, and further studies focused on suits of enzymes associated with nutrient element cycles: amylase, cellulase, lipase, glucosidases & invertase for the C cycle; proeases, amidases, urease & deaminases for the N cycle; phosphatases for the P cycle; and arylsulfatase for the S cycle (Dick 1994). One advantage they have is that changes to land management can be detected in their activity levels far in advance of changes in SOM, and they do seem to return to previously stable levels of activity once perturbations have subsided (elevated activity therefore signalling perturbation). However, the knowledge of how activity relates to other indicators and soil functions is still insufficient for their undoubted potential to be fully realised in any practical way at present. One system that seems promising though is that of phosphatase which Jordan et al. (1995) found correlated very strongly with SOM concentration across grass and cereal sites, increasing to a peak of activity around 4.5 % SOM in prairie soils in the USA. Another promising approach was that of Liu et al. (2002) who adapted a compound index system of Mausbach & Seybold (1998) to substitute enzyme activities for key elements in their framework. They found that two in particular, β-glucosidase and L-asparginase substituted for organic carbon and nitrate nitrogen most effectively. One reason why enzyme systems hold promise, especially as they relate to SOM dynamics in soil, is that they respond to change much more quickly than SOM or even fractions of SOM and related chemical and physical indicators. This was a strong reason for their inclusion in a suite of indicators specifically targeted at local conditions in Western Australia by Milton et al. (2002), and they indicate in Figure A2 how three key enzyme systems (phosphatase, arylsulphatase and β-glucosidase) react over only 2-4 years compared with 6 years for LF SOM in the sodosols under investigation. The time of statistically significant response to a treatment was calculated by using “power analysis” (Zar 1999) to determine the differences between treatments that had been applied for known lengths of time.

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Figure A2. Time-scales over which changes in various indicators can be effectively measured (reproduced from Milton et al. 2002) One of the more unusual applications of soil enzyme analysis is that for enzymes associated with the suppression of specific arable weeds species. This is has been found to be associated with certain bacterial isolates from the rhizosphere and effective on Setaria viridis and Convolvulus arvensis species (Kremer & Li 2003). It is an example of a very precise use of an assay as an indicator of a particular soil function (weed suppression). One of the most frequently measured indicators of microbial activity is that of mineralisable N, which can be considered an integrated indicator of the sum of microbial activity on SOM to supply a key plant nutrient. As such it is both an indicator of soil health in terms of biological activity, but also soil quality in terms of the quality of SOM available (Drinkwater et al. 1996). For this reason alone it is very important because it introduces the idea that the headline indicator of SOM may itself be sub-divided and that the more biologically active component, often termed the “active fraction”, may be a more sensitive indicator than the total SOM. Certainly in the case of mineralisable N this represents the active, or labile, fraction of total soil N, and on its own, or in ratio with the total N, is often a more sensitive indicator of soil management changes or environmental impacts. The relative merits of various ways of measuring this are discussed by Drinkwater et al. (1996).

Faunal indicators The consensus as to the value of soil fauna as an indicator of soil quality or health is rather mixed. A review by Linden et al. (1994) pointed out that a range of 15 measurements of faunal properties that could serve as indicators, using individuals, communities or populations as the focus or indeed processes. They reported work on nematodes that exemplified their value as ecological indicators, namely; they are small with short generation times, so they respond quickly to substrate changes; they can survive temporary dessication; populations are relatively stable in soil except when it is perturbed; changes in nematode populations often signify changes to the

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whole trophic structure; and functional and trophic groups of nematodes can be identified and separated easily. Earthworms are often considered as the obvious candidate for a faunal indicator of soil health or quality not least because they play a direct role in maintaining several other chemical and physical indicators (nutrient supply, structural stability and pore formation) and indirectly several more (microbial populations, rainfall infiltration and transport). They also interact intimately with SOM by being responsible for communition and incorporation of organic residues into the soil and mixing them into close association with physical particles and microbial organisms and the production of key carbohydrate compounds important in structural stabilisation. Indeed their abundance can be linked directly to amounts of organic material applied to soil (manure and straw) (Doube & Schmidt 1997), as well as the performance of surrogate indicators of plant performance such as yield and biomass. However, as Linden et al. (1994) point out there are also many problems with their use as an indicator, not least being the fact that although important to soil health, their activity is often not critical, and they are often a catalyst rather than cause for more important transformations. In addition, they are often present as a result of a soil’s condition but cannot be identified as the cause of it, and their absence from soils may be due to entirely natural barriers to their colonisation. Such a case may be the correlation between earthworm numbers and burrow numbers, and reduced penetration resistance and bulk density in some Hungarian soils subject to compaction alleviation measures by Birkas et al. (2004). Doube & Schmidt (2004) reviewed many conflicting case histories and concluded that “earthworm abundance cannot be used as a universal indicator of soil health because key agronomic factors which determine plant yield and soil conservation are not those which influence earthworm abundance.” Faunal indicators have to be used with care, and possibly their most useful role is as indicator organisms of toxicity, either through certain species being susceptible to an introduced toxin, or by bio-accumulation of the toxic element in the tissue of higher trophic level organisms. Micro-fauna are intimately linked to microbial indicators in that many nematodes and protozoa predate bacteria and fungi, and nematodes are possibly the most abundant soil fauna there is (Pankhurst 1994). In addition to their abundance the species of nematode present indicate the dominant microbial group in the soil, i.e. fungivores in undisturbed soils and bacteriovores in cultivated soils predominate. The predominance of fungi in undisturbed systems is also related to the use of SOM concentration as a coarse indicator of soil health, in that SOM increases under the slower decomposition processes that fungal dominance signifies. Further possibilities of using the diversity of nematode species as a qualitative indicator of soil health were discussed by Elliott (1997), chiefly in relation to crop species grown as well as tillage system. Meso-faunal groups such as mites and Collembola are useful indicators of soil disturbance, increasing in abundance and diversity with conservation management practices (Pankhurst 1994). Macro-fauna, earthworms in particular are also sensitive to physical soil disturbance and the species diversity varies with cultivation intensity, keystone species such as Lumbricids being greatly reduced under continuous arable tillage (Curry & Byrne 1997).

Chemical indicators Apart from the headline indicator itself of SOM (or soil organic carbon (SOC) as an alternative) chemical indicators do not feature in discussion of soil health much, except as part of a suite of indicators mentioned in minimum datasets (see below). One sub-ordinate indicator that can be classed in this category however, which does receive rather more attention, is one of a range of related sub-divisions of SOM that can be measured. Possibly the most common of these is the so called light fraction carbon (LF-C) of more labile organic material in the soil with shorter turnover times. This fraction is more open to microbial and faunal activity and its fate can determine that of the total organic carbon fraction. As such it has been found to be a more sensitive indicator of change in response to land management changes than SOM. Studdert et

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al. (1997) found that whereas a 10% decline in SOM could be detected over 12 years of continuous cropping, about 95% of the decline in LF-C could be detected in only 4 years. The corollary of this was also true, in that 95% of the potential increase to former concentrations of LF-C could be achieved within 4 years of a return to pasture management. An alternative nomenclature for the labile fraction of SOM that is sometimes used is “particulate organic matter” (POM), which covers LF-C but would include more recalcitrant pieces of organic matter of plant origin. Being defined by their extraction and sampling procedures the two fractions cannot be equated, but include a lot of the same material. POM has also been found a more sensitive indicator of change in relation to tillage systems than total SOM (Elliott 1994). When measured as POM-N this fraction provided the best means of comparing treatment effects over 20 agricultural experiments in the USA and with a half-life of 10 to 20 years could be more useful than LFC because it is able to integrate effects over several seasons within cropping systems. Virtually the same as POM is the fraction known as “macro-organic matter” (MOM) (Bollinder et al. 1999), and this (both MOM-N & MOM-C) also has been shown to be closely allied to LF measures of SOM changes (LF-N & LF-C). In fact the study of Bolinder et al. (1999) found LF-N (1.58) and MOM-N (1.54) to be the most sensitive indicators to change followed closely by LF-C (1.48), MOM-C (1.34) and microbial biomass C (1.44), when measured on treatment plots as an index against the same measurements made on conventional plots (figures in parenthesis). The light-fraction of soil organic carbon can itself be sub-divided by appropriate physical fractionation procedures, and is readily separated into “free” and “intra-aggregate” carbon fractions, by the use of density filtration and centrifugation techniques and sonic-dispersion methods (Sohi 2001). Diffuse reflectance infrared spectroscopy (DRIFT) techniques indicated that the “free-carbon” fraction was less decomposed material (more equivalent to POM and MOM above) than the “intra-aggregate carbon” which showed more signs of being microbially transformed material. It is this intra-aggregate material, which Gaunt et al. (pers. comm.) have identified as the “active carbon fraction” and demonstrated it as being more indicative of changes that eventually show up in the total carbon (Figure A3 and Table A2, based on CC0375 and Kelly et al. 1997). Table A2 Turnover rates of carbon pools under different land use types (after Kelly et al. 1997) Carbon pool

Turnover rates ratio

Timescale Arable Permanent grassland

Semi-natural grassland

Deciduous woodland

Coniferous woodland

Active Months – years

10% 4% 4% 9% 6%

Slow 20 – 50 years

20% 63% 63% 36% 27%

Passive 400 – 2000 years

70% 33% 33% 55% 67%

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Figure A3 Proportions of SOC fractions in arable and grassland soils. Of the relatively few studies that pay any detailed attention to concentrations of nutrient elements, cation exchange capacity and soil reaction, that of Motta et al. (2002) reveals the general trend that these are useful only in so far as they relate to changes in SOM status. They studied indicators in response to tillage changes, and found that apart from SOM itself, extractable P and pH were the only other indicators that showed a differentiation that could be attributed to these changes. Similarly, in a study by Shukla et al. (2004) where 17 soil properties were assessed as indicators of soil health to differentiate between tillage and cropping treatments, total N was so closely correlated to SOC that it could be successfully dropped from the list of indicators (when this was reduced to 11), without losing any power in regression analyses to explain the variation in yield or biomass.

Physical indicators Total soil organic matter has long been recognised as a key factor in the stability of soil aggregates in water (Loveland & Webb 2003), which in turn is an important indicator of soil health. An unstable structure in the surface soil will quickly lead to slumped surfaces, reduced infiltration and the resulting erosion and compaction problems. Studdert et al. (1997) demonstrated a correlation between aggregate stability and SOM, though the nature of this differed between the organic matter under pasture and that under arable crops, possibly indicating that it is more closely allied to light fraction carbon (LFC) than total. Other work has indicated that it is not so much the presence of SOM per se. in aggregates that is the vital cohesive factor for their stability, but more specifically fungal hyphae making use of the SOM resource (Elliott 1997). The number of references in Table A1 recorded for each class of indicator reflects the overwhelming interest there is in biological indicators, but it is also interesting to note that many authors realise that the net impact of changes in biological health of the soil is realised in physical attributes. One such study was that of Liebig & Doran (1999) who compared a range of soil quality indicators between organically farmed and conventionally farmed sites on the same soils in paired comparisons. Many differences in biological and chemical indicators were found, but crucially, to their thinking, differences were also found for physical indicators, such as lower bulk density, increased water holding capacity and even depth of topsoil in the organically managed soil compared with the conventional. They attributes these to the net effect of increased organic matter and biological activity in the organically farmed sites, which led to these soils resisting erosion, crusting and compaction as well as reduced drought stress in the crop. Physical indicators featured most strongly in studies of reclaimed mine-soils by Shukla et al. (2004a), where the overwhelming damage to the soil has itself been physical. In this case, bulk density, water infiltration, aggregate size distribution and porosity, were the most influential indicators that explained the variation in soils at two reclaimed sites. Only at one site was SOC a

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directly influential indicator, but probably had a secondary influence on the single most dominant soil property of water stable aggregation. This indicator was chosen as being the single most commonly valuable one to monitor soil development and changes in quality for reclaimed soil that had undergone physical damage. This indicator proved equally powerful in other work by the same authors to differentiate the soil quality due to tillage, manurial and rotational factors (Shukla et al. 2004b). In these studies water stable aggregation coupled with SOC explained 35% and 33% of the variation in biomass and grain yields respectively, across the treatments. In the same study bulk density, available water capacity and aggregate size distribution and pH, were also important indicators in identifying the causes of yield and biomass differences.

Soil organic matter as a headline indicator of soil...

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