Visual Soil Evaluation
Realizing Potential Crop Production with Minimum
Environmental Impact
Visual Soil Evaluation
Realizing ­Potential Crop Production with ­Minimum
­Environmental Impact
Edited by
Bruce C. Ball
SRUC, Edinburgh, UK
and
Lars J. Munkholm
Aarhus University, Tjele, Denmark
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Library of Congress Cataloging-in-Publication Data
Visual soil evaluation : realizing potential crop production with minimum
e­ nvironmental impact / editors, Bruce C. Ball (SRUC, Edinburgh, UK) and Lars
J. Munkholm (Aarhus University, Tjele, Denmark).
pages cm
Includes bibliographical references and index.
ISBN 978-1-78064-470-7 (hardback : alk. paper) -- ISBN 978-1-78064-745-6
(pbk. : alk. paper) 1. Crops and soils. 2. Soils--Analysis. 3. Soils--Quality. 4. Soil
structure. 5. Soil chemistry. I. Ball, Bruce C., editor. II. Munkholm, Lars J., editor.
S596.7.V57 2015
631.4--dc23
2015020427
ISBN-13: 978 1 78064 470 7 (hbk)
978 1 78064 745 6 (pbk)
Commissioning editor: Ward Cooper
Associate editor: Alexandra Lainsbury
Production editor: Tim Kapp
Typeset by SPi, Pondicherry, India
Printed and bound by Gutenberg Press Ltd, Tarxien, Malta
Contents
List of Contributors
ix
Preface
xi
1 Describing soil structures, rooting and biological activity
and recognizing tillage effects, damage and recovery
in clayey and sandy soils
Anne Weill and Lars J. Munkholm
1
1.1 Evaluation of soil structure2
1.1.1 Evaluation of the structure of clayey soils
2
1.1.2 Evaluation of the structure of sandy soils
4
1.1.3 Observation of the structure in the entire 0.6 or 1 m of the profile
7
1.1.4 Soils with natural structural limitation
9
1.2 Observation of roots: density, deformation, concentration in cracks
or between layers10
1.2.1 Root development in clayey soils
10
1.2.2 Root development in sandy soils
11
1.3 Other criteria for recognizing compaction11
1.3.1 Evaluation of soil aeration using soil colour
11
1.3.2 Evaluation of biological activity
11
1.4 Conclusions
13
2 Assessing structural quality for crop performance
and for agronomy (VESS, VSA, SOILpak, Profil cultural, SubVESS)
Tom Batey, Rachel M.L. Guimarães, Joséphine Peigné and Hubert Boizard
15
2.1 Introduction
15
2.2 Visual Evaluation of Soil Structure (VESS) for topsoil16
2.3 Visual Soil Assessment (VSA) for topsoil17
2.4 SOILpak method for topsoil and subsoil19
2.4.1 Validation and future development
20
2.5 ‘Le profil cultural’ or agronomic profile method20
2.5.1 Le profil cultural – evaluation and limitations
24
v
vi
Contents
2.6 The numeric visual evaluation of subsoil structure (SubVESS)
2.7 Recommendations
2.8 Conclusions
3 Reduction of yield gaps and improvement of ecological
function through local-to-global applications of visual soil assessment
David C. McKenzie, Mansonia A. Pulido Moncada and Bruce C. Ball
24
25
28
31
3.1 Introduction
31
3.2 Yield Gap Analysis
33
3.3 Soil structure assessment Using VSE
35
3.4 Soil structure – its Relationship with soil water status and
hydrological cycles36
3.5 Land management frameworks related to soil productivity, yield gap
assessment and ecological function37
3.5.1 Frameworks for agricultural land management linked with VSE
techniques at field scale
39
3.5.2 Packages for land management at the landscape scale with potential
to be more effective if inter-linked with VSE techniques
41
3.5.3 A possible new and broad conceptual approach for yield gap reduction
and ecological improvement based on VSE techniques
41
3.6 Relating visually assessed soil conditions to crop growth and selection
of soil management inputs44
3.7 Training of practitioners44
3.8 Conclusions
45
4 Visual evaluation of grassland and arable management
impacts on soil quality
Lars J. Munkholm and Nicholas M. Holden
49
4.1
4.2
Introduction
49
Evaluation of arable management impact49
4.2.1 Biological factors
51
4.2.2 Mechanical factors
52
4.3 Evaluation of grassland management impact54
4.3.1 Biological factors
56
4.3.2 Mechanical impacts
58
4.3.3 Drainage/water status
58
4.3.4 Management intensity
58
4.4 Aspects Requiring Further Development 59
4.4.1 Assessment of pores
59
4.4.2 Taking account of soil layering
59
4.4.3 Extraction and separation of soil blocks for assessment
60
4.4.4 Faunal activity
60
4.4.5 Need for specific methods or interpretations for grassland soils
61
4.5 Conclusions
62
5 Choosing and Evaluating Soil Improvements by Subsoiling
and Compaction Control
Richard J. Godwin and Gordon Spoor
66
5.1 Introduction
66
5.2 Identification of compaction problems and alleviation requirements68
Contents
5.3
5.4
5.5
5.6
5.7
5.8
vii
Basic action of soil loosening and mole drainage equipment69
5.3.1 Narrow tine disturbance and critical depth
69
5.3.2 Winged tine disturbance
69
5.3.3 Leg disturbance for subsoiling vs moling
72
Soil disturbance with multiple tine arrangements73
Draught forces and power requirements74
Implement selection, adjustment and in-field evaluation76
5.6.1 Implement selection
76
5.6.2 Implement adjustment
77
5.6.3 In-field evaluation
78
Minimizing and alleviating recompaction78
5.7.1 Reduced weight and inflation pressure
79
5.7.2 Controlled traffic farming
80
Conclusions
82
6 Valuing the Neglected: Lessons and Methods from an Organic,
Anthropic Soil System in the Outer Hebrides
Mary Norton Scherbatskoy, Anthony C. Edwards and Berwyn L. Williams
86
6.1
6.2
Introduction
86
Background
88
6.2.1 Geology, slope and rainfall
88
6.2.2 Physical structure
88
6.2.3 Microbiological processes
90
6.2.4 Cultivation
90
6.2.5 Crofting: an agricultural and social system
90
6.2.6 Maintaining soil fertility within a mixed system
91
6.2.7 Current situation
91
6.3 Tools for visual evaluation91
6.3.1 Methods 91
6.3.2 Blackland Index 95
6.3.3 Blackland Vegetation Scoring (BVS) 97
6.3.4 von Post Humification Scale 97
6.3.5 Evaluation 99
6.4 Return to use99
6.5 Conclusion
100
7 Evaluating land quality for carbon storage, greenhouse gas
emissions and nutrient leaching
Joanna M. Cloy, Bruce C. Ball and T. Graham Shepherd
103
7.1 Introduction
103
7.2 Soil properties regulating carbon storage, greenhouse gas emissions
and nutrient leaching and their relationship with soil structure103
7.2.1 Soil carbon storage and soil structure
104
7.2.2 Soil greenhouse gas exchange and soil structure
105
7.2.3 Soil nutrient leaching and soil structure
111
7.3 Estimation of soil C storage, GHG emissions and nutrient leaching
using visual techniques112
7.3.1 Soil C storage
112
7.3.2 GHG emissions
114
7.3.3 Nutrient leaching
117
7.4 Future directions118
7.5 Conclusions
119
viii
Contents
8 Soil structure under adverse weather/climate conditions
Rachel M.L. Guimarães, Owen Fenton, Brian W. Murphy and Cássio A. Tormena
8.1 Introduction
8.2 Climate Change
8.3 Soil Structure under Intensive Rainfall
8.3.1 Erosion and soil quality screening toolkit
8.4 Wet Weather Conditions and Soil Compaction
8.5 Periods of Droughts
8.6 Extreme Temperature
8.7 The Further Role of VSE
8.8 Conclusion
9 The expanding discipline and role of Visual Soil Evaluation
Bruce C. Ball and Lars J. Munkholm
122
122
123
125
126
130
133
134
135
136
142
9.1 Introduction
142
9.2 The scale and scope of VSE and the relationship with crop yield142
9.3 Improving and harmonizing VSE methods143
9.4 Expanding the role of VSE
145
9.4.1 Sustainability, environmental conservation and climate change
145
9.4.2 Soil monitoring and resilience
146
9.4.3 Improvement of arable and grassland soils
148
9.4.4 Improvement of marginal and urban soils
149
9.4.5 Soil science
151
9.5 Conclusions
152
Index155
List of Contributors
Bruce C. Ball, SRUC Crop and Soil Systems Research Group, West Mains Road, Edinburgh, EH9 3JG,
UK. bruce.ball@sruc.ac.uk
Tom Batey, 125 Blenheim Place, Aberdeen, AB25 2DL, UK. tombeth33@gmail.com
Hubert Boizard, INRA, UPR1158 Agro-Impact, Estrées-Mons, BP 50136, 80203 Péronne, France.
hubert.boizard@mons.inra.fr
Joanna M. Cloy, SRUC Crop and Soil Systems Research Group, West Mains Road, Edinburgh, EH9
3JG, UK. joanna.cloy@sruc.ac.uk
Anthony C. Edwards, SRUC Crop and Soil Systems Research Group, Craibstone, Aberdeen, AB51
6FA, UK. Tony.Edwards@sruc.ac.uk
Owen Fenton, Teagasc Environment Research Centre, Johnstown Castle, Co. Wexford, Ireland.
owen.fenton@teagasc.ie
Richard J. Godwin, Harper Adams University, Newport, Shropshire, TF10 8NB, UK. dickjillgodwin@
waitrose.com
Rachel M. L. Guimarães, Department of Agronomy, Federal University of Technology-Paraná, Via
do Conhecimento, km 1 – 85503-390, Pato Branco, PR, Brasil. rachelguimaraes@utfpr.edu.br
Nicholas M. Holden, UCD School of Biosystems Engineering, University College Dublin, Belfield,
Dublin 4, Ireland. nick.holden@ucd.ie
David C. McKenzie, Soil Management Designs, Orange, New South Wales 2800, Australia. david.
mckenzie@soilmgt.com.au
Lars J. Munkholm, Department of Agroecology, Aarhus University, Blichers Allé 20, P.O. Box 50,
8830 Tjele, Denmark. lars.munkholm@agro.au.dk
Brian W. Murphy, Office of Environment and Heritage, Cowra, New South Wales 2794, Australia.
brian.murphy@environment.nsw.gov.au
Mary Norton Scherbatskoy, Blackland Centre, 5 Scotvein, Grimsay, North Uist, Western Isles,
HS6 5JA, UK. mns@uistwool.co.uk
Joséphine Peigné, ISARA Lyon, 23 rue jean Baldassini, 69364 Lyon cedex 07, France. jpeigne@
isara.fr
Mansonia A. Pulido Moncada, Institute of Edaphology, Faculty of Agronomy, Universidad Central
de Venezuela, Av. Universidad vía El Limón, Maracay, 2101, Aragua, Venezuela. mansoniapulido­@
gmail.com
T. Graham Shepherd, BioAgriNomics Ltd, 6 Parata Street, Palmerston North 4410, New Zealand.
gshepherd@BioAgriNomics.com
Gordon Spoor, Model Farm, Maulden, Bedfordshire, MK45 2BQ, UK. g.spoor.t21@btinternet.com
ix
x
List of Contributors
Cássio A. Tormena, Department of Agronomy, State University of Maringá. Av. Colombo, 5790 87020-900, Maringá, PR, Brasil. catormena@uem.br
Anne Weill, Centre of expertise and technology transfer in organic agriculture and local food
­systems, 475, rue Notre-Dame Est, Victoriaville, Québec, G6P 4B3, ­Canada. weill.anne@cegepvicto.ca
Berwyn L. Williams, formerly Macaulay Land Use Research Institute (now James Hutton Institute),
Aberdeen, AB21 9YA, UK. berwyn.williams@btinternet.com
Preface
This book describes the main methods for Visual Soil Evaluation (VSE) of soil structure and soil-­
related properties. It includes clear visual images of the variation of soil quality and how these relate
to soil productivity and environmental sustainability. Such images raise awareness and provide a
measure of the soil degradation that is a looming threat to the viability of world agriculture.
­Emphasis is given to recognizing, protecting and restoring soil quality as these are of vital importance for tackling problems of food insecurity, global change and environmental degradation. We
show how these aims can be achieved with Visual Soil Evaluation by describing tools that can readily
be used by land users and environmental authorities to assess crop performance, soil improvement
and soil productivity. Visual Soil Evaluation is also placed in the context of future sustainable intensification of agriculture including factors of soil loss, resilience, climate change, scarcity of water and
other resources, nutrient retention and increased risk of degradation. This book is relevant not only
to students, lecturers, scientists and advisors working directly with soils but also to policy makers,
food security experts, environmentalists and engineers who have an interest in soils and sustainable
agricultural production. Last, but not least, we hope that these simple VSE techniques will be used
extensively in years to come as a tool to link soil specialists and non-specialists together with the
­mutual aim of developing sustainable soil management to advance global food security and improve
the environment.
This book developed mainly from the activities of members of the ‘Visual Soil Examination and
Evaluation’ working group within the International Soil Tillage Research Organisation. The editors
thank all the authors for their valued contributions, summarizing their extensive knowledge and
experience. The editors are also grateful for the support from the publishers.
Bruce C. Ball
Lars J. Munkholm
xi
1 Describing Soil Structures, Rooting
and Biological Activity and Recognizing
Tillage Effects, Damage and Recovery
in Clayey and Sandy Soils
Anne Weill1* and Lars J. Munkholm2
Center of Expertise and Technology Transfer in Organic Agriculture and
Local Food Systems (Centre d’expertise et de transfert en agriculture
biologique et de proximité – CETAB+), Cégep de Victoriaville, Québec,
Canada; 2Department of Agroecology – Soil Physics and Hydropedology,
Aarhus University, Tjele, Denmark
1
Soil compaction and erosion have emerged as
major threats to global agriculture as they negatively affect plant production and have detrimental impacts on the environment. Soil compaction
is responsible for decreased crop yield and quality, emissions of greenhouse gases and increased
water runoff (Hamza and Anderson, 2005; Ball
et al., 2008). Unless severe, it is often unrecognized because plant growth can appear normal,
especially when mineral fertilizers are used liberally. The major cropping factors affecting soil
compaction are the weight of machinery, poor
timing of field operations with respect to soil
water content and intensification of crop production. Soil erosion is responsible for losses of soil
particles, nutrients and agrochemicals resulting
in decreased soil fertility as well as eutrophication of rivers and lakes (Rasouli et al., 2014). Site
characteristics (rainfall quantity and intensity,
slope and soil texture) have strong effects on
soil erosion; in addition, important cropping
factors related to soil erosion are crop rotation,
percentage soil cover and management practices
affecting soil structure and compaction (Pimentel
et al., 1995; Morgan, 2005). Erosion deposits are
mostly silt and fine sand with little structure and
porosity and thus resemble soil damaged by compaction. Because compaction plays a central role
in soil degradation and yield losses, it has to be
properly diagnosed in the field. This can be done
by observing soil structure, root development,
aeration and evidence of biological activity.
This chapter will therefore focus on describing and illustrating important soil structural
features associated with compaction and anaerobic conditions. It will cover the evaluation of
soil structure and compaction status for both
clayey and sandy soils. Since tillage is often responsible for the creation of a number of anthropic layers, each having a different structure,
the identification of the different soil layers will
be explained. The use of other indicators of soil
compaction such as root development (density,
deformation, concentration in cracks or between
layers), aeration (soil colour) and biological activity (soil macroporosity of biological origin, rapidity of residue turnover, presence of earthworms)
will also be covered.
*E-mail: weill.anne@cegepvicto.ca
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
1
2
A. Weill and L.J. Munkholm
A quick, preliminary evaluation of soil structure can be done using a spadeful of soil, allowing
rapid verification of soil structure over the entire
field. Since agricultural practices can often affect
soil conditions to a depth of 30–50 cm, and sometimes more, soil condition may have to be investigated to such depths, depending on the situation.
Different tools can be used to assess soil
structural quality, either using spade methods
(e.g. the visual evaluation of soil structural quality, VESS, Ball et al., 2007; Guimarães et al., 2011),
visual soil assessment (VSA, Shepherd et al., 2008;
Shepherd, 2009), or profile methods (e.g. Cultural
Profile, Manichon,1987; or the SoilPAK method,
Mckenzie, 2001). These tools are described by
Batey et al., Chapter 2, this volume.
Some helpful information for soil compaction
diagnosis should also be collected by checking
soil maps and interviewing farmers. The following information should be gathered:
•
•
The origin and characteristic of the soil;
The field situation; for example, surface and
sub-surface drainage situation, crop rotation,
yield variation in the field, size of the equipment for manure spreading and timing for
spreading, harvesting strategy, tillage and
number of passes, depth of tillage, etc.
For the purposes of this chapter a soil is considered
to be in good condition if it has good structure,
is well aerated and contains a sufficient amount
of organic matter in the A horizon to be capable
of supporting microbial activity and optimum
plant growth.
1.1
Evaluation of Soil Structure
Soil structure is best evaluated considering soil
texture because the criteria for assessing structure depend on the clay content. The pressure
exerted on the soil by machinery forces aggregates to stick to each other and to form clods.
Texture is important because the clods resulting
from compacted clayey soil are often hard and
difficult to break down, while clods resulting from
compacted sandy soils are fairly easy to break.
Although the relationship between soil characteristics and clay content lies on a continuous
spectrum, the evaluation of soil structure will
only be described here for two main, discrete
groups labelled as follows: clayey soils (more
than 25–30% clay) and sandy soils (less than
25–30% clay). Soil having 20–30% clay content
will sometimes behave more like a clayey soil
and sometime more like a sandy soil, depending
on clay type and the organic matter content.
1.1.1 Evaluation of the structure
of clayey soils
The structure of clayey soils can mostly be evaluated by observing the shape of aggregates and
clods. When describing structure, soil horizonation needs to be taken into account because organic matter content, root density, aeration and
biological activity tend to be much higher in the
A horizon and these foster aggregation. This section aims at describing typical good and typical
poor structure for clayey soils for both topsoil
(A horizon) and subsoil layers (B and C horizon).
The structure of naturally recovered clay soil is
also described.
1.1.1.1 Soil structure of clayey soils
in good condition
topsoil (a horizon). Aggregates of a well-­
structured clayey topsoil are small, in the
1–10 mm range, and well separated (Fig. 1.1a).
They can be observed in some grasslands, some
non-­cultivated soils and in some areas that are
not trafficked (permanent beds, controlled traffic
systems). They are also common in intensively
tilled top layers of recently cultivated soils.
If the compaction pressure is light enough,
the clods that are formed have a rough surface
because the aggregates that constitute them
keep their individual shapes (Fig. 1.1b). They
are porous because of the space between the aggregates (not always visible with the naked eye)
and the biological activity which creates pores.
In a non-compacted soil it should be very
easy to separate the aggregates in the clod by
simply squeezing the clod in the fist. However, to
do this the clod must be fairly moist. Clay becomes very hard when it dries, which can give a
false impression of being highly compacted.
When examining a spadeful of healthy soil,
it is often possible to see an excellent structure
with aggregates well separated from each other
Describing Soil Structures, Rooting and Biological Activity
3
Fig. 1.1. Aggregates and clods in well-structured clayey soils. (a) Topsoil: small round aggregates, 1–5 mm
in size, coming from a healthy A horizon. (b) Topsoil: very rough and porous clod coming from a very
biologically active soil. The aggregates should detach from each other when the clod is squeezed.
(c) Subsoil: small non-porous, angular, 2–10 mm aggregates. (d) Subsoil: lamellar structure, usually
found in soil that contain less clay and more silt.
in the seedbed layer because of the effect of harrowing. Below the seedbed, the clods are rough
and easy to break (Fig. 1.2).
subsoil (b and c horizons).
In a well-structured
subsoil the aggregates are small (2–10 mm) and
can either be rounded (Fig 1.1a) or more angular in shape (Fig. 1.1c). They can be fairly massive and non-porous. Soils that are rich in silt
sometimes have a lamellar structure (Fig. 1.1d).
The thickness of the lamellae can be 2–10 mm.
1.1.1.2 Soil structure of compacted
clayey soils
As the pressure exerted on the soil (topsoil or
subsoil) by machinery increases, the aggregates
are more and more tightly pressed together and
stick to each other more and more strongly. They
form clods that are increasingly more difficult to
break apart, more massive, less porous and
smoother.
When examining a shovel full of compacted
soil, the soil must be gently broken into pieces
that can fit into a hand (Fig. 1.3a) (Ball et al.,
2007). When it is possible to break up the clods
with pressure, the result will be a mixture of
small and large aggregates (Fig. 1.3b). The more
compact the soil, the smaller will be the proportion of small aggregates.
When compaction is severe the aggregates
fuse to each other and lose their individual shape
in the clod (massive structure) (Fig. 1.4a), which
cannot be broken down in the hand.
4
A. Weill and L.J. Munkholm
and ­angular edges. However, full recovery of
structure in the A horizon such as that shown
in Fig. 1.4b will only occur if roots and other
biological activity develop in the soil.
1.1.2 Evaluation of the structure
of sandy soils
The structure of sandy soils tends to be weaker
than that of clayey soils because of their lower
clay content and is more dependent on organic matter level and biological activity. In
the topsoil it is also affected by tillage intensity.
Visual assessment of sandy soil structure can
be challenging and often needs to be complemented with observations of root development (see section below). This section aims at
describing typical good and typical poor structure for sandy soils for both the topsoil and
subsoil layers.
Fig. 1.2. Healthy clay soil with mostly aggregates
in the top part (seedbed) and rough and porous
clods in the bottom part (below seedbed).
1.1.2.1 Soil structure of sandy soils
in good condition
topsoil (a horizon).
1.1.1.3 Effect of texture on the identification
of compaction of clayey soils
When the soil is moist, but not waterlogged, the
strength of clods of compacted soils increases
with clay content (Barzegar et al., 1994; Barzegar
et al., 1995); as a result soils with a low clay
content can be broken down much more easily
even when the soil is quite compact. As the clay
content of a soil decreases, the situation will resemble more and more that of a sandy soil as
­described in the next section. Very wet, compacted clayey soils may have a plastic consistency, which results in clods being easily deformed by pressure.
1.1.1.4 Natural recovery of clayey
soils after compaction
In clayey soils, the cycles of shrinking/swelling and freezing/thawing will fracture the soil
by cracking. The clods (Fig. 1.4a) will crack
into two pieces, then four and so on. Aggregates formed in this way often have flat sides
As for the clayey soils, aggregates of well-structured sandy topsoils are small
and rounded, in the 1–10 mm range (Fig. 1.5a).
Such structure can be seen in soils that have a
lot of organic matter, roots and biological activity. These are mostly grassland, non-cultivated
soils and some cultivated soils with crops having
a very dense rooting system and excellent biological activity. Small and rounded aggregates
can also commonly be seen in recently tilled topsoil layers – particularly in seedbeds. They may
be formed by the breaking up of larger aggregates during tillage and do not necessarily indicate a good stable structure. If the soil has been
too intensively tilled the structure may easily
collapse.
The lack of clay, unless organic matter
content is high, causes aggregates of sandy
soil to have a low resistance to compaction
and they are easily crushed or compressed.
After aggregate compression, the soil can appear massive whether it is very compact or
not. The resulting clods have a smooth surface
and are usually easy to break (Fig. 1.5b).
When a clod is squeezed it usually crumbles
easily into pieces that do not correspond to the
Describing Soil Structures, Rooting and Biological Activity
5
Fig. 1.3. Separating a spadeful of compacted soil into pieces. (a) Shovel full of compact soil after
breaking it into smaller pieces (clods) (VESS method, Ball et al., 2007). (b) Mixture of various sized
aggregates, 5 mm (centre) to 4 cm (left and right) resulting from breaking the clod.
Fig. 1.4. Structure of a very compacted clay soil and of a compacted restructured clay soil. (a) Severely
compacted clayey soil where aggregates have disappeared. (b) Restructuration of compacted clayey soil
due to cycles of shrinking/swelling and freezing/thawing.
shape of the original aggregates because even
light pressure can destroy the original granular structure.
When examining a spadeful of healthy
sandy soil, aggregates often appear well separated from each other in the seedbed layer because of frequent tillage and root growth.
However, the aggregation effect of tillage may
disappear over the season as the soil settles
­because of weathering and compaction. Below
the seedbed, the usually massive soil can be
broken by squeezing a handful of soil into clods,
which are smooth and always easy to break in a
moist state.
In Fig. 1.6, tillage has loosened the soil in
the upper layer allowing roots to develop and
contribute to the formation of a very good structure. Careful examination is required to assess
the state of the soil below the seedbed layer in
case it needs to be loosened.
6
A. Weill and L.J. Munkholm
Fig. 1.5. Good soil structure (a) and marginally adequate soil structure (b). (a) Small round aggregates,
1–5 mm in size, from a healthy A horizon – note the abundance of roots. (b) Aggregates compressed
into clods by pressure. In this case, the structure may or may not be adequate for plant growth.
subsoil (b or c horizon – from 10–20 cm to
60–90 cm).
Below the tilled layer, even when
not compacted, sandy soils often have a massive or
amorphous structure (bottom part of Fig. 1.6).
Such structure results from the low organic matter
level and biological activity in these layers as well
as the naturally weak abiotic soil-forming factors.
1.1.2.2
Soil structure of compacted
sandy soils
Most tilled sandy soils have some degree of compaction due to:
•
•
Excess tillage which destroys the >1 mm
­ uring
aggregates: the soil can then collapse d
rain and become compact without any applied pressure (Fig. 1.7);
Pressure in the soil exerted by machinery
(Fig. 1.8a).
In both cases aggregates are destroyed and the
soil appears massive.
Assessing compaction by observations has
two key aspects:
1. The structure in thick layers (3–10 cm): this
can be observed by examination using spade
methods like VESS (Ball et al., 2007). Each layer
­impedes the vertical development of roots (Fig. 1.8).
2. The development of roots restricted to the
upper tilled layer (Fig. 1.8b): when sandy soils are
very compact, the grains of sand are interlocked
and cannot be displaced by the growing roots
(Batey, 2000). As a consequence, the roots do
Fig. 1.6. Well-structured tilled layer (0–10 cm)
above the line and massive structure below the line.
not penetrate the soil and remain in the upper
tilled layer. This topic is covered further in the section on roots.
Although compaction is easier to deal with in
sandy soils, its effect on plant growth can be more
severe than in some clayey soils. This is because
the cracks often present in clay soils allow at least
some roots to grow deeper, whereas root penetration in sandy soils can be completely blocked.
Describing Soil Structures, Rooting and Biological Activity
7
Fig. 1.7. Aspects of the structure a few weeks after an aggressive (left) and a gentle (right) tillage.
(b)
Fig. 1.8. (a) Spadeful of compacted sandy soil having well defined horizontal layers within 0–20 cm
depth. (b) Tilled layer (0–20 cm) with a fairly loose structure and an abundance of root growth over a very
compact layer (in the rectangle) that cannot be penetrated by roots.
1.1.2.3
Natural recovery of sandy soils
after compaction
The low clay content in sandy soils results in low
effectiveness of cycles of wetting/drying and
freezing/thawing for improving the soil structure. Tillage loosens sandy soil very easily and
can start the recovery process by allowing roots
to develop and biological activity to increase.
1.1.3 Observation of the structure in the
entire 0.6 or 1 m of the profile
Observing the structure of a soil down to a
depth of 0.6–1m is important, particularly
where anthropic subsoil damage is suspected
(Fig. 1.9). Such observation will allow diagnosis of most of the structural problems of agricultural soils and can be done using the SOILpak and SubVESS methods (McKenzie, 2001;
Ball et al., 2015). There is usually a significant
variation in soil structure with depth. The different layers of the soil profile must be identified
not only as a function of pedological horizons
but also as a function of the tillage they received and the compaction they have suffered.
The situation will vary ­depending on the tillage system in use. When possible, it may be
helpful to compare with the same soil nearby in
natural condition, for example, under forest or
long-term grass.
8
A. Weill and L.J. Munkholm
(a)
(a)
(b)
(b)
(c)
(c)
(d)
Fig. 1.9. The different layers of the profile of a tilled
(mouldboard plough) poorly drained clay soil where
structure varies with depth. The non-affected layer
below 50 cm is not visible. (a) Seedbed layer with a
good structure; in this case, it is hard to distinguish
from the deeper tilled layer. (b) Deeper tilled layer
with a good structure. (c) Very compact transition
layer (bottom not visible on the picture); in this
situation the main cause of compaction was the
poor drainage, which resulted in cultural operations
often done in moist conditions.
1.1.3.1 Identification of the different layers
in conventional tillage systems
In these soils tillage is by mouldboard plough,
chisel plough, heavy discs or similar machines to
a depth of 15–25 cm. After seedbed preparation,
the soil profile can often be divided into three or
four main layers (two of them in the tilled layer
and two below the tilled layer (for layers a–c, see
Fig. 1.9 and for layers a–d, see Fig. 1.10)):
a. The seedbed layer (2.5–10 cm thick, part of
horizon A): this layer is generally harrowed before seeding in order to make the structure very
fine for a good soil-to-seed contact.
b. The deeper tilled layer (10–20 cm thick, part
of horizon A): this is the lower part of the tilled
layer just below the harrowed layer. It is normally rather loose with well-defined aggregates
unless the agricultural operations just before
seeding were done when the soil was too moist,
in which case it may be compacted.
Fig. 1.10. Variation of structure with depth;
example of a naturally well-structured clay soil that
has been compacted over the previous years and
also just before seeding (note that the thickness of
the layers is not representative on this picture).
(a) Seedbed layer with a good structure: porous
aggregates 1–5 mm. (b) Very compact deeper
tilled layer: massive clods 10–20 cm with crop
­residue visible on the photo (these residues were
at the bottom of what was the plough layer).
(c) Very compact transition layer: massive clods
10–20 cm. Here, the structure of this layer is fairly
similar to that of the layer above but this is not
always the case. (d) Improvement of structure with
depth. Layer not affected by agricultural activity
with a good structure. Aggregates 2–10 mm.
c. The transition layer: this layer is just below
the tilled layer (Peigné et al., 2013), whether tillage is shallow (harrow only – see next section) or
deep (mouldboard plough or chisel plough). It is
generally compacted by agricultural machinery
(mostly traffic with heavy equipment) but not
regularly tilled unless subsoiled. The thickness
of the transition layer varies with the type of
Describing Soil Structures, Rooting and Biological Activity
tillage, the soil moisture content during operations and the weight of the machinery (tractors,
manure spreaders, harvesters). It can be shallow when only harrows are used or deeper
when a mouldboard plough, a chisel plough or
heavy discs are used. The transition layer can
start in the lower part of the A horizon (where
this is deeper than the tilled layer) and depends
on the depth of compaction, it can extend into
the B horizon and, exceptionally, into the C horizon. The structure of the transition layer gradually changes with depth into a layer that is not
affected by anthropic activity.
d. The non-affected layer: this is not affected by
compaction and is subsoil in natural condition.
Depending on the soil type the structure can
range from excellent to very massive.
The situation described above can be more complicated if the texture varies with depth. In such
situations, it may be necessary to seek more
layers (e.g. using SubVESS, Ball et al., 2015) as a
function of both the agricultural ­activity and
the pedogenetic horizons. On the other hand,
when the soil has a good structure, it is more
usual to see only two layers, that is, the tilled
layer and the layer below.
An easy way to see the variation of soil
structure with depth while observing the soil
profile in situ is to extract some soil from each
identified layer with a spade, place the soil from
the different layers on a plastic or cardboard
surface (Weill, 2009) and gently separate the
soil into clods as described in the VESS method
(Ball et al., 2007) (Fig. 1.10). This allows the
easy comparison of the aggregates or clods between layers. In Fig. 1.10, the soil was compacted during spring tillage, which was readily
detected in the deeper tilled layer.
1.1.3.2 Identification of the different
layers in minimum tilled and
no-tilled systems
Under minimum tillage, where tillage for seedbed preparation is to 2.5–10 cm depth only, the
transition layer is just below the seedbed. A deeper
tilled layer may only exist in the soil profile in the
form of an old plough layer.
Under no-till, where tillage is only due to
the seeding coulters, the surface layer is often
the most compact and any compaction is likely
9
to gradually decrease with depth. A tillage pan
related to previous tillage practices may sometimes be present.
1.1.4 Soils with natural structural
limitation
Some soils are naturally massive and limit plant
growth even when they have not been compacted by anthropic activities. In order to separate
natural compaction from machinery-induced
compaction, it may be useful to compare agricultural soil with a nearby soil under natural
vegetation (forest or grassland). Some examples
are given below.
1.1.4.1 Clayey soils with a naturally
massive structure
Some soils (e.g. some gleysols) may have a naturally massive structure that does not result from
machinery-induced compaction. Recognizing this
structure is important because it is much more
difficult, and sometimes impossible, to improve
it. Some experience is needed to identify such
natural massive structure.
Collecting information on the soil origin
and farming practices as described in the introduction is very helpful in this situation.
The depth and thickness of the massive layer
can give a clue. Machinery-induced compaction
is expected to be found mostly in a transition layer
5–30 cm thick below tillage depth. Only in extreme situations, that can easily be identified, will
a compacted transition layer be very thick (>30 cm).
An example would result from a farmer spreading
slurry in multiple passes using big tankers in
early spring when the soil is generally too wet.
Layers with a naturally massive structure can be
much thicker than 30 cm. In this case, it is very
helpful to compare with soils in their natural condition (under forest or long-term grass).
1.1.4.2 Cemented layers
Chemically cemented layers in sandy soils (e.g.
iron pans or indurated layers) are usually thin
(3–10 cm) and very hard. They are easy to diagnose because of their different colour (reddish
because of the iron) and their hardness.
10
A. Weill and L.J. Munkholm
1.1.4.3
Soils of glacial origin (tills)
Some tills may have a natural massive structure
because they have been compressed by the weight
of ice for long periods. They can belong to different soil orders depending on their internal drainage. In some situations, the massive structure is
easy to diagnose because the clods are difficult to
break when squeezed in one hand. In other situations, the clod breaks down into aggregates
that easily detach themselves from each other
(giving an impression of good structure) even
though the soil is too massive for roots to develop. In this case, aggregates may be too strongly
interlocked, leaving no space for roots to grow. It
is therefore important to observe root growth in
order to make a diagnosis with these types of soils.
1.2 Observation of Roots: Density,
Deformation, Concentration in
Cracks or Between Layers
Roots are probably the best indicator of soil compaction. Whilst overall root density is very hard to
evaluate because it is impossible to see the full extent of root development when observing a limited
part of the soil profile, root deformations and localized concentrations of growth are easy to see. Roots
have different patterns of development in compacted clayey soils and in compacted sandy soils.
1.2.1 Root development in clayey soils
Roots in non-compacted clay soil can explore a
large volume of soil. Their density decreases with
depth at a fairly uniform rate. They are not deformed and grow within clods or aggregates as
well as between clods or aggregates (Fig. 1.11a).
Some important characteristics of roots
that develop in compacted clayey soils relate to
their growth in cracks. Thus in compacted clayey
soils, roots can either be seen concentrated in
cracks with none in the clods (Fig. 1.11b and c),
or as flattened roots with secondary roots developing only in the plane of the crack (Fig. 1.11c).
Other characteristics that are sometimes visible with roots growing in compacted clayey soils
Fig. 1.11. Roots that develop in the clods as well as between the clods in a well structured clayey soil (a);
only around a clod in a compacted clayey soil (b); or in cracks in a compacted clayey soil (c).
Describing Soil Structures, Rooting and Biological Activity
are thickened roots, enlarged tips or a tap root that
stops abruptly and splits into a few secondary roots.
An abrupt change in root density between
two adjacent soil layers can also be an indicator
of compaction, but is not always easy to observe
in clayey soils.
1.2.2
1.3
11
Other Criteria for Recognizing
Compaction
The other criteria for recognizing soil compaction are signs of restricted soil aeration and associated waterlogging, and evidence of restricted
soil biological activity.
Root development
in sandy soils
In sandy soils, an abrupt change in root density
between two adjacent soil layers is an important
indicator of a compacted layer. Roots can be very
numerous in the upper soil layer (usually the
tilled layer) and very scarce or even absent in the
underlying layer (Fig. 1.12). Such a change is
usually more pronounced than in clayey soils.
When the roots are blocked by a compacted
layer, they develop horizontally (Fig. 1.12). In
the presence of a compacted structure in layers,
they develop between the horizontal layers. As
in clayey soils, tap roots can also stop abruptly at
the transition layer and split into several secondary roots, be thickened or have enlarged tips.
1.3.1 Evaluation of soil aeration
using soil colour
Aerobic soil tends to have a brownish-red colour, while anaerobic soils tend to have a bluish
and/or grey colour. Reduction of iron due to
lack of oxygen is responsible for the colour
change. In topsoils, lack of oxygen is mostly
related to compaction. Signs of reduction are
often clearly visible around decomposing organic matter because of the increased respiratory demand for oxygen (Fig. 1.13a). Bluish
colours are commonly observed at the bottom
of a plough layer where most residues are concentrated and where compaction is often
present. In addition, a perched water table
sometimes forms above the compacted layer,
which further increases the reductive conditions (Fig. 1.13b).
In subsoils, fluctuation of the water table is
often responsible for the lack of oxygen and
therefore the bluish/grey colour. Such reduction conditions can be increased by compaction.
When the soil is very anaerobic it also smells
bad, like rotten eggs, due to the production of
reduced sulfur gases. Evaluation of soil aeration
status is integrated as part of many visual
evaluation methods such as VSA (Shepherd,
2009), VESS and SubVESS (Ball et al., 2007;
Ball et al., 2015).
1.3.2 Evaluation of biological activity
Biological activity can be visually observed using
three indicators:
Fig. 1.12. Root development restricted to the tilled
layer in a compacted sandy soil.
1. The density of visible macropores of biological
origin;
2. The rapidity of turnover of plant residue;
3. The presence of earthworms.
12
A. Weill and L.J. Munkholm
(a)
1.3.2.1 Macroporosity of biological origin
and earthworms
Macroporosity of biological origin is a very good
indicator of biological activity and therefore of
soil health (Fig. 1.14). Munkholm (2000) proposed a grid for estimating the number of macropores of biological origin in the soil. Macroporosity
was classified as fine (0.5–2.0 mm pores) and large
(>2.0 mm pores). An average frequency of fine
macropores is 1–5 cm–2 and an average frequency
of large macropores is 1–5 dm–2. An estimation of
the density of earthworm burrows may yield information on the activity of earthworms, especially anecic species (Lamandé et al., 2011).
1.3.2.2 The rapidity of turnover
of plant residue
(b)
Fig. 1.13. (a) Bluish colour related to compaction
of the tilled layer and the presence of residues.
(b) Water seeping from a perched water table due
to compaction below the tilled layer – the bluish/
grey colour is visible just below the zone where the
water is seeping out.
When a soil has a good structure and is well aerated,
buried plant residues should decompose rather rapidly. In a biologically active soil, residues can disappear in 1 year. Where decomposition is restricted
and slow, plant material will remain intact and
tough for a long time (Fig. 1.15). The colour is either
yellow/bright (i.e. very slow decomposition) or black
(i.e. anaerobic decomposition) (Munkholm, 2000).
The latter is normally associated with bluish soil colours and a bad smell as described above.
The rate of decomposition varies with climate, soil texture, residue type and tillage, so it is
not possible to give general figures for a normal
rate of decomposition. Such figures should be
defined for each region. According to Preuschen
(1994), for a healthy soil, applied plant material
should decompose to a large extent within 3–4
weeks during the summer time under northern
Fig. 1.14. High and low soil porosity of biological origin. (a) Very high number of fine and large macropores.
(b) High number of large macropores (>2 mm). (c) Low number of pores (<1–5 dm–2). (d) No pores, but
some cracks.
Describing Soil Structures, Rooting and Biological Activity
13
Fig. 1.14. Continued.
grown continuously for several years, it is much
more difficult and sometimes impossible to evaluate the age of the residues.
1.4
Fig. 1.15. Yellow old residues at the bottom of a
plough layer in a clay soil indicating a slow
biological activity.
European conditions. Straw and stubble incorporated immediately after harvest should be friable by the following spring.
It is important to know whether residues are
1, 2 or 3 years old. When crops alternate from
year to year, it is usually easy to evaluate the age
of the residues simply by recognizing the type of
residue and checking when that particular crop
was grown in the rotation. When the same crop is
Conclusions
Observations of soil structure, rooting, evidence
of aeration (colour) and evidence of biological activity provide an efficient way to identify soil damage problems. When such observations are made
for each of the different tillage layers it is often possible to find the cause of poor plant growth and to
target zones requiring loosening. The visual evaluation of soil also enables identification of tillage-related problems such as inadequate seedbed
preparation or uneven seeding depth, which result
in irregular emergence. Visual evaluation can also
be used to identify inadequate drainage, which
may increase the risk of compaction and restrict
plant growth. Visual evaluation of the soil is also a
means to understanding natural soil limitations.
It is an essential technique for any agronomist trying to diagnose poor crop yield and trying to specify the target soil conditions for good yields.
Acknowledgements
The work was partly carried out within the
context of OptiPlant project supported by the
Danish Ministry of Food, Agriculture and Fisheries and the Center of Expertise and Technology Transfer in Organic Agriculture and Local
Food Systems (CETAB+) with the financial support of the Natural Sciences and Engineering
Research Council of Canada (NSERC).
14
A. Weill and L.J. Munkholm
References
Ball, B.C., Batey, T. and Munkholm, L.J. (2007) Field assessment of soil structural quality – a development
of the Peerlkamp test. Soil Use Management 23, 329–337.
Ball, B.C., Crichton, I. and Horgan, G.W. (2008) Dynamics of upward and downward N2O and CO2 fluxes in
ploughed or no-tilled soils in relation to water-filled pore space, compaction and crop presence. Soil
and Tillage Research 101, 20–30.
Ball, B.C., Batey, T., Munkholm, L J., Guimarães, R.M.L., Boizard, H., McKenzie, D.C., Peigné, J., Tormena, C.A.
and Hargreaves, P. (2015) The numeric visual evaluation of subsoil structure (SubVESS) under agricultural production. Soil and Tillage Research 148, 85–96.
Barzegar, A.R., Murray, R.S., Churchman, G.J. and Rengasamy, P. (1994) The strength of remoulded soils
as affected by exchangeable cations and dispersible clay. Australian Journal of Soil Research 32,
185–199.
Barzegar, A.R., Oades, J.M., Rengasamy, P. and Murray, R.S. (1995) Tensile strength of dry, remoulded
soils as affected by properties of the clay function. Geoderma 65, 93–108.
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Environmental Analysis: Physical Methods. Marcel Dekker, Inc., New York, pp. 595–627.
Guimarães, R.M.L., Ball, B.C. and Tormena, C.A. (2011) Improvements in visual evaluation of soil structure.
Soil Use and Management 27, 395–403.
Hamza, M.A. and Anderson, W.K. (2005) Soil compaction in cropping systems: a review of the nature,
causes and possible solutions. Soil and Tillage Research 82, 121–145.
Lamandé, M., Labouriau, R., Holmstrup, M., Torp, S.B., Greve, M.H. et al. (2011) Density of macropores as
related to soil and earthworm community parameters in cultivated grasslands. Georderma 162, 319–326.
Manichon, H. (1987) Observation morphologique de l’état structural et mise en évidence d’effets de compactage des horizons travaillés. In: Monnier, G. and Goss, M.J. (eds) Soil Compaction and Regeneration.
Balkema, Rotterdam, the Netherlands, pp. 39–52.
McKenzie, D.C. (2001) Rapid assessment of soil compaction damage. I. The SOILpak score, a semi-quantitative
measure of soil structural form. Australian Journal of Soil Research 39, 117–125.
Morgan, R.P.C. (2005) Soil Erosion and Conservation, 3rd edn. Blackwell Publishing, Malden, Massachussetts.
Munkholm, L. (2000) The spade analysis – A modification of the qualitative spade diagnosis for scientific
use. Ministry of food, agriculture and fisheries. Danish Institute of Agricultural Sciences. DIAS Report
Plant Production 28.
Peigné, J., Vian, J.F., Cannavacciuolo, M., Lefevre, V., Gautronneau, Y. and Boizard, H. (2013) Assessment
of soil structure in the transition layer between topsoil and subsoil using the profil cultural method. Soil
and Tillage Reasearch 127, 13–25.
Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D. et al. (1995) Environmental and economic
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Preuschen, G. (1994) Anleitung zur Spatendiagnose: Die Kontrolle der Bodenfruchtbarkeit. Stiftung Ökologie
und Landbau, Bad Dürkheim, 46 pp.
Rasouli, S., Whalen, J. K. and Madramootoo, C.A. (2014) Review: reducing residual soil nitrogen losses
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practices, policies and perspectives. Canadian Journal of Soil Science 94, 109–127.
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Shepherd, T.G., Stagnari, F., Pisante, M. and Benites, J. (2008) Visual Soil Assessment – Field Guide for
Annual Crops. FAO, Rome.
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2 Assessing Structural Quality for Crop
Performance and for Agronomy (VESS,
VSA, SOILpak, Profil Cultural, SubVESS)
Tom Batey,1 Rachel M.L. Guimarães,2* Joséphine Peigné3 and Hubert Boizard4
1
Plant and Soil Science, University of Aberdeen, Aberdeen, Scotland, UK;
2
­Agronomy Department, Federal University of Technology – Paraná, Brazil;
3
ISARA Lyon, Lyon, France; 4INRA, UPR1158 AgroImpact, Péronne, France
2.1 Introduction
The physical properties of a soil are a key factor
in evaluating land quality whether for agronomy or for other purposes. Soil structure is an
important component of the physical properties.
Its qualities are transient and may be altered by
both anthropic and natural events. For example,
cultivation, the passage of harvester and trailer
wheels, grazing when the soil is wet and intense
rainfall can all alter soil structure. Methods to
assess the quality of soil structure are therefore
important tools in the management of soils.
Such methods should be cost-effective, widely
available and the results easily interpreted.
Many measurements related to structure
can be made such as strength, penetration resistance, bulk density, porosity, hydraulic conductivity; specific tests for structure stability
such as wet sieving can also be carried out. However, these tests are relatively time consuming
and assess only part of a complex property.
An advantage of visual evaluation methods
is that they assess the overall condition of the soil
in three dimensions. In the 1950s and 1960s
Peerlkamp, Boekel and others in the Netherlands
developed a rapid method to assess the structural
quality of the entire topsoil; it was based on a
visual and tactile examination by hand of a block
of soil dug out from the surface using a spade
(e.g. Peerlkamp, 1959). Other field methods have
since been developed, as exemplified during workshops held in 2005 in France (Boizard et al., 2007),
in Denmark in 2011 (Munkholm et al., 2013)
and in Brazil 2014 (Guimarães et al., Soil and Tillage Research, special issue, projected publication
date 2016). Visual and tactile methods to evaluate soil structure have ‘come of age’ to be part of
mainstream soil science; they are no longer
regarded as fringe subjects supported only by
eccentric enthusiasts.
In this chapter we describe five of the most
commonly used methods that are supported
by good online training material or printed material readily available. The methods presented
cover a range of conditions and input levels from
quick ‘look see’ spade methods usable by farmers
to detailed profile descriptions for research. The
methods can be used at any time of year and at
any time during the cropping cycle. However,
after harvest is a particularly good time as this is
when any structural damage made during tillage or harvest is most evident and while roots of
the recent crop are still visible. Furthermore, it is
a good time for making decisions regarding soil
management. When conducting the assessment,
*E-mail: rachelguimaraes@utfpr.edu.br
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
15
16
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
the soil should not be too dry or too wet; soil
water content just below field capacity is the optimum. None of the tests is suitable for use on
soils with a high content of sand or stones.
Methods are described in Sections 2.2–2.6.
•
•
•
•
•
2.2 Visual Evaluation of Soil Structure
(VESS) for Topsoil (Ball et al., 2007);
2.3 Visual Soil Assessment (VSA) for Topsoil (Shepherd, 2000);
2.4 SOILpak Method for Topsoil and Subsoil
(McKenzie, 1998);
2.5 Le Profil Cultural or Agronomic Profile
Method (Peigné et al., 2013);
2.6 The Numeric Visual Evaluation of Subsoil Structure (SubVESS) (Ball et al., 2015).
With training and with the use of visual charts,
VESS, VSA, SOILpak and SubVESS can be used by
farmers, their advisors, consultants and agronomists. All five methods can be used by soil scientists
to characterize experimental sites and to assess the
effects of treatments involving soil management.
For some methods, online videos are available.
For each method we will discuss its origin,
provide a description and also its application and
limitations. A comprehensive evaluation of soil
structure in the framework of an overall soil
quality rating which incorporates both VESS and
VSA should be consulted (Mueller et al., 2013).
2.2 Visual Evaluation of Soil
­Structure (VESS) for topsoil
At a field meeting held at Péronne (France) in
2005, the Peerlkamp (1959) method was the
only one that provided enough data to enable
statistical validation of the results to be carried
out (Boizard et al., 2007). Since then, the original method (Peerlkamp, 1959) has been further developed and renamed as VESS (Ball et al.,
2007; Guimarães et al., 2011) to provide five
categories from 1 = best to 5 = worst structure;
intermediate values can be assigned. The only
equipment required is a flat-bladed spade c.200
× 180 mm. Each test takes between 5 and 15 min
to perform dependent on the compactness,
moisture content and stone content of the soil
(Ball et al., 2007). The method involves gently
­breaking down the soil to reveal the major structural units and layers of contrasting structure.
Each layer is then compared with a colour chart
and accompanying descriptions and allocated to
one of five categories (Fig. 2.1). It is therefore
possible to make a sufficient number of assessments within each land unit to provide a robust
average score as discussed by Newell-Price et al.
(2013) and Shepherd (2009). However, in dry
and hard soils the tests take much longer (Giarola
et al., 2013). A flowchart and description of the
categories for use in the field is available to download (original chart, Ball et al., 2007; updated
chart, Guimarães et al., 2011 and Ball et al.,
2012 Fig. 2.1, search under VESS).
The results of VESS tests have been compared with other physical measurements. For
example, with bulk density; Newell-Price et al.
(2013) found a significant relationship between
mid-topsoil bulk density and Peerlkamp ‘St’ score
in 300 grassland fields in England and Wales.
They also found a relationship (although weak)
between maximum penetration resistance and
visual scores. In arable soils in Scotland, a strong
correlation was also found between VESS and
tensile strength (Guimarães et al., 2011).
In Canada (Munkholm et al., 2013), crop
yield correlated significantly with VESS scores
and the results showed that a diverse rotation
was required for good structural development
under no-tillage.
Many studies have used VESS to evaluate
soil structure under different scenarios and conditions; for example, tillage systems (Mueller et al.,
2009; Giarola et al., 2010; Garbout et al., 2013;
Giarola et al., 2013; Guimarães et al., 2013; Pulido
Moncada et al., 2014b), contrasting textures
(Guimarães et al., 2013; Pulido Moncada et al.,
2014a, b), crop rotations (Mueller et al., 2009;
­Askari et al., 2013), agricultural traffic (Mueller
et al., 2009; Guimarães et al., 2013), land use
(Pulido Moncada et al., 2014a, b) and grassland
management (Cui et al., 2014; Pulido Moncada
et al., 2014b) (see Munkholm and Holden,
Chapter 4, this volume, for more information on
visual evaluation of management impact).
A strength of the VESS method is its ability
to allow quick and easy identification of layers
with structural differences in temperate regions
(Ball et al., 2007; Guimarães et al., 2013) and in
subtropical regions (Guimarães et al., 2011;
­Giarola et al., 2013). In Brazil, VESS tests were used
to show the beneficial effects of subsoiling and
biological loosening on compacted no-tilled soils
Assessing Structural Quality for Crop Performance and for Agronomy
Structure
quality
Sq1
Friable
Size and
appearance of
aggregates
Mostly < 6 mm after
crumbling
Visible porosity
and Roots
Appearance after
break-up: various
soils
Appearance after breakup: same soil different
tillage
Distinguishing
feature
Highly porous
The action of breaking the
block is enough to reveal
them. Large aggregates
are composed of smaller
ones, held by roots.
Roots throughout
the soil
Aggregates
readily crumble
with fingers
Appearance and description of natural
or reduced fragment
of ~ 1.5 cm diameter
17
0
1
2
3
4
Fine aggregates
5
Aggregates
easy to break
with one hand
Sq3
Firm
Most
aggregates
break with one
hand
Sq4
Compact
Requires
considerable
effort to break
aggregates
with one hand
Sq5
Very compact
Difficult to
break up
A mixture of porous,
rounded aggregates
from 2mm - 7 cm.
No clods present
Most aggregates
are porous
Aggregates when
obtained are rounded,
very fragile, crumble very
easily and are highly
porous.
Roots throughout
the soil
High aggregate
porosity
A mixture of porous
aggregates from
2mm - 10 cm; less
than 30% are <1 cm.
Some angular, nonporous aggregates
(clods) may be
present
Macropores and
cracks present.
Mostly large > 10 cm
and sub-angular nonporous;
horizontal/platy also
possible; less than
30% are <7 cm
Few macropores
and cracks
Mostly large > 10 cm,
very few < 7 cm,
angular and nonporous
Porosity and roots
both within
aggregates.
All roots are
clustered in
macropores and
around aggregates
Very low porosity.
Macropores may
be present. May
contain anaerobic
zones.
Few roots, if any,
and restricted to
cracks
Low aggregate
porosity
Aggregate fragments are
fairly easy to obtain. They
have few visible pores
and are rounded. Roots
usually grow through the
aggregates.
10
Aggregate fragments are
easy to obtain when soil is
wet, in cube shapes which
are very sharp-edged and
show cracks internally.
Distinct
macropores
15
Aggregate fragments are
easy to obtain when soil is
wet, although
considerable force may be
needed. No pores or
cracks are visible usually.
Grey-blue colour
cm
Sq2
Intact
Fig. 2.1. VESS flow chart and description of the categories. (From Guimarães et al., 2011.)
(Giarola et al., 2013). Although VESS works well
under clayey tropical soils, some extra considerations need to be taken into account. For example,
soil moisture content can have a major effect when
assessing soil structures, as can the increased pore
formation resulting from the employment of
no-tillage systems and crop rotations, with visible
pores being found even in compacted aggregates.
VESS has been shown to be useful in identifying changes in land use. The method has been
used to identify the depth and thickness of compact layers so that deep tillage can be targeted.
Fig. 2.2 shows the changes in use from native
forest to pasture and pasture to sugarcane. The
decrease in quality can be easily seen. Soils in
Sq4 and 5 (such as under sugarcane) are structures requiring remediation usually by tillage.
2.3 Visual Soil Assessment (VSA)
for Topsoil
This method was developed in New Zealand to
provide a simple, standardized test that can be
used to assess and monitor soil quality and plant
performance quickly and cheaply (Shepherd
and Janssen, 2000; Shepherd, 2009). The test
provides an objective way to numerically score
soil physical quality by combining indices of
structure, porosity, rooting, smell, colour and
surface soil and crop condition in the field. Interpretation of the visual indicators is independent
of soil type enabling the method to be used anywhere. VSA focuses on the interrelationships
between soil condition, plant performance, farm
production and farm management practices;
scores are closely correlated to measured indicators of soil quality and farm production.
The equipment needed includes a spade to
dig out a 20-cm cube of soil, a plastic basin (45 ×
35 × 25 cm3), one hard board (c.26 × 26 × 1.8 cm3)
and a heavy duty plastic bag (c.75 × 50 cm2). The
results are assessed using a VSA Field Guide and
recorded on a scorecard. The soil assessment at
one spot takes about 25 min and plant indicators
a further 15–20 min.
Any depth of topsoil can be sampled, as
long as the equivalent of a 20-cm cube of soil is
extracted. If, for example, the top 100 mm of
18
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
Fig. 2.2. Soil samples processed showing the differences among the land uses (native forest, pasture
and sugarcane, from left to right) in Brazil with VESS scores of 1.5, 3.0 and 4.0, respectively, from left to
right. (Photographs taken by Maurício Cherubin.)
the soil is compacted, to assess its condition, dig
out two 200 × 200 × 100-mm samples with a
spade.
VSA is based on key visual soil indicators
(10 for pasture and 11 for cropping) that are
diagnostic of soil quality including soil structure.
The structure score is based on a drop-shatter
test. A 20-cm cube of soil is removed and dropped
a maximum of three times from a height of 1.0 m
onto a firm base in a plastic basin. The number
of times the sample is dropped and the height it
is dropped from depend on the texture of the soil,
and the degree to which the soil breaks up. The
aggregates are sorted according to their size
(Fig. 2.3) and given a score between 2 (mainly
fine aggregates with few clods) to 0 (mainly large
clods). If the clod distribution in the sample appears to be between those shown in the photographs (Fig. 2.3), an intermediate score is given,
e.g. 0.5 or 1.5. Each of the 10 soil (and 11 plant)
indicators is assessed on a scale of 0, 0.5, 1.0,
1.5 or 2.0 by comparison with three photographs
per indicator in the Field Guide.
The indicators vary according to the type of
land use. For example, soil indicators for cropping include soil texture, soil structure, soil porosity, mottles, soil colour, earthworm numbers,
soil smell, potential rooting depth, surface cover
and surface crusting and soil erosion. A weighting of 1 to 3 is given for each indicator and the
products are added to give an overall index of
soil quality (SQI) where a total score of less than
20 is poor, 20–37 is moderate and over 37 is good.
A reference soil sample is taken from under a
fence or scrub cover nearby, where it is in a relatively undisturbed condition. The soil scorecard
is complemented by a plant ‘performance’ scorecard that links plant response to soil condition.
The visual soil quality index is sufficiently sensitive to provide an early warning indication of
any change or decline in soil quality from a baseline reference point of place and time. Management advice can be tailored according to this
information.
VSA was trialled at 36 sites throughout
New Zealand (Shepherd et al., 2001) and in 10
countries throughout the world. It was tested
­ arent
at 91 sites on 40 soil types from different p
materials, climates, topographies, and under
different land uses including livestock farming,
cropping, orchards and forestry in 10 regions
of the North Island of New Zealand (Shepherd
et al., 2002; Shepherd, 2003). VSA scores are
significantly correlated to corresponding lab-­
based methods (Shepherd, 2003). VSA structure scores are strongly correlated to dry
aggregate-­size distribution (r2 = 0.91), saturated
hydraulic conductivity (r2 = 0.86) and air permeability (r2 = 0.80), and moderately correlated
to macroporosity (r2 = 0.69) and dry bulk density (r2 = 0.64). VSA colour scores are strongly
related to total carbon (r2 = 0.80). VSA index
scores are also closely related to crop yield, pasture dry matter production, biomass cover and
pasture utilization in New Zealand (Shepherd
and Park, 2003).
Assessing Structural Quality for Crop Performance and for Agronomy
19
Fig. 2.3. Visual scoring of soil structure where the aggregates are sorted according to their size. Good
condition VS = 2, soil dominated by friable fine aggregates with no significant clodding. Moderate
condition VS = 1, soil contains significant proportions (50%) of both coarse firm clods and friable, fine
aggregates. Poor condition VS = 0, soil dominated by extremely coarse, very firm angular or sub-angular
clods with very few finer aggregates. (From Shepherd, 2000.)
2.4
SOILpak Method for Topsoil
and Subsoil
The SOILpak scoring procedure was designed originally to assess soil compaction status under irrigated cotton on black and grey Vertisols in NSW,
Australia (McKenzie, 1998). It has been further
developed and an online series (search under
‘SOILpak’) is available for dryland grain producers and vegetable growers; it is now also applicable to a wide range of soil types (apart from
sands) and cropping systems (Anderson et al.,
1999). The method forms part of a comprehensive Decision Support System for the assessment
and management of soil quality in NSW. It can be
used postharvest to assess compaction, as an aid
to yield map interpretation or to assess land quality and land capability and possible management
options in developments when new crops are to
be introduced. Several properties are assessed
and their scores combined into a single value.
SOILpak structural assessment considers
three basic aspects of soil structure: structural
form, structural stability in water and structural
resilience (Kay, 1990). The key factor that links
the separate components of the procedure is crop
root growth. The SOILpak scoring procedure
is linked to aeration and strength limitations
(­
McKenzie and McBratney, 2001). Structural
stability in water is assessed in the field using the
aggregate stability in water (ASWAT) procedure
(Field et al., 1997).
The target audience for SOILpak assessment is mainly crop management consultants
and leading farmers who require fast and inexpensive techniques, both diagnostic and prognostic, as aids to management of their soils.
A pit (3 m × 0.8 m × 1.4 m deep) is dug using a mechanical digger, oriented at right angles
to the main direction of machinery movement.
Some landholders consider digging the trenches
to be excessively disruptive to their operations,
they prefer inspections via 0.4-m-deep mini-pits
hand-dug with a spade. Where controlled traffic
and raised bed farming is being examined, the
assessment zones are both under the main wheel
track, and under a bed not adjacent to a main
wheel track. Where the traffic patterns have
been random, the scoring should be carried out
under both the worst (under wheel) and best
(­inter-wheel) sections of the trimmed pit faces.
Comprehensive details are available online.
The upper 90 cm of the root zone is assessed
after the pit face has been picked back to remove
20
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
soil disturbed by the excavator. The target depths
of assessment are: 0–10 cm (topsoil), 10–30 cm
(sub-surface), 30–60 cm (upper subsoil) and
60–90 cm (mid subsoil). A 7-cm cube of soil is
dug out from the centre of each of these depth
intervals and its structure evaluated. The SOILpak score is on a scale ranging from 0.0 (worst)
to 2.0 (best) (Fig. 2.4).
The test takes c.10 min for a ‘rapid’ inspection; about 90 min for a ‘detailed’ inspection with
paint impregnation and replication.
The most important factors in a moist soil
are the width of primary clods produced by
moderate hand pressure, their ease of breakage
and the behaviour of fresh roots. Also taken
into account is clod shape, the presence of clods
within clods (related to soil friability) and internal porosity of the smallest observable clods.
Override factors include the positive influence
of inter-­connected pores from the soil surface
and the negative effect of thin smeared layers.
The worst condition with a score of less
than 0.5 (‘terrible’) consists of blocky-platy clods
mostly >50 mm wide, it is difficult for a blade to
penetrate the soil face and few or no fresh roots
are found. By contrast, with an ‘excellent’ score
of between 1.5 and 2.0, the primary clods would
be mostly <5 mm wide and rounded, the profile
face would readily separate into porous component clods and there would be prolific growth of
fresh roots throughout.
Beginners can start with a basic three-point
(0, 1, 2) scale, with a little experience a five-point
(0.0, 0.5, 1.0, 1.5, 2.0) scale can be used,
whereas experienced operators use a 21-point
(0.1, 0.2, 0.3, . . . 1.9, 2.0) scale.
The presence of macropores can be determined by pouring a mix of one part white emulsion paint and seven parts water onto the soil in
the field. The macropores can be then highlighted
by excavating the soil, after a period of 2 h.
SOILpak score
0.0
Interpretation:
0.5
Terrible
1.0
Moderate
Recommendation: Soil loosening Soil loosening
is essential for is desirable
healthy
root growth
1.5
2.0
Where a thin smeared layer (often associated with anaerobism) is found, the SOILpak
score is downgraded to 0.5, if the score is above
0.5 (McKenzie, 2001a). The smeared layers –
usually horizontal and created by the impact of
plough tines under moist conditions – may only
be a few millimetres thick (typically at a depth of
50 mm), but they can have a major influence on
vertical gas flow and root growth.
SOILpak training material is available in
the form of loose-leaf manuals (McKenzie,
1998; Anderson et al., 1999), a summary brochure with colour illustrations (McKenzie et al.,
2003) and as an internet guide (http://www.agric.
nsw.gov.au/reader/soil-management-guides).
A video/DVD is also available that includes a
­description of how to carry out the SOILpak
scoring procedure.
The SOILpak scoring procedure works well
on Vertisols where controlled traffic farming is
practised and yield maps are available. However,
it can be useful under any farming system on
clay-rich or loam soil; it becomes more difficult
to use as the sand content of the soil increases.
As with VESS, the application of ‘moderate’
hand pressure to a soil sample prior to assessment of aggregate/clod size is a poorly defined
concept, particularly in situations where the soil
is dry and extra pressure needs to be exerted.
2.4.1 Validation and future development
McKenzie (2001b) showed that visual–tactile
methods of assessing soil conditions were at
least as good as those using a range of standard
physical tests. Special procedures are needed
for sandy soils possibly by incorporating the
use of penetrometers and water repellence
tests (P.S. Blackwell, Western Australia, 2013,
personal communication). Investigations are
also in hand to improve the clod/aggregate assessment, for example, by dropping a moist
sample onto a wooden board similar to the VSA
soil structure assessment (Shepherd, 2000).
Excellent
Soil loosening
is not required
Fig. 2.4. Relationship between SOILpak ‘structural
form’ scores and recommendations for treatment.
(From McKenzie, 2001a.)
2.5
‘Le Profil Cultural’ or Agronomic
Profile Method
In France, agronomists have used the ‘profil
­cultural’ method to examine the soil s­ tructure
Assessing Structural Quality for Crop Performance and for Agronomy
on the vertical face of a pit (Roger-­Estrade et al.,
2004; Peigné et al., 2013). It was developed by
Manichon (1982) and adapted for use in agriculture by Gautronneau and Manichon (1987)
to help understand the effects on soil structure
of tillage and of compaction caused by the passage of agricultural ­machinery.
L3
L1
21
The whole profile is examined in a trench
dug 1–1.5 m deep (depending on the maximum
depth of rooting), 60 cm wide and 2–4 m long
depending on the pattern of traffic. The examination is based on vertical and horizontal partitioning of zones and horizons of contrasting
structure (Fig. 2.5). The topsoil horizons, which
L3
L2
H1
H5
H6
P1
P2
20 cm
Fig. 2.5. Two-way partition of the soil profile – vertical then lateral – which provides the framework
for the description. The first horizon is called H1 and corresponds to loosening operations such as
seedbed preparation. Secondary tillage operations correspond to H2, H3 or H4 horizons according
to the number of horizons observed (not shown in the figure). The H5 horizon is the primary tilled
horizon, which can either be created by a mouldboard plough, a disc tiller or loosened with a chisel
or a deep loosener in a no-tillage system. H6 (and H7, if present) correspond to old tilled horizons:
an old ploughed layer dating back to when farmers ploughed very deeply, or a recently ploughed layer
from before the adoption of no-tillage. L1 corresponds to the part of the profile located under wheel
tracks visible on the soil surface, L2 corresponds to the part located under wheels not visible on the
soil surface (seedbed preparations) and L3 corresponds to the part untouched by the wheels. (From
Peigné et al., 2013.)
22
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
are distinguished by the working depth of the
successive tillage tools, are identified first (Fig. 2.5).
Pedological horizons are also identified throughout the profile. Zones of different structure are
then identified and marked across the pit face
based on the location of the wheel tracks created
by machinery operations.
To begin the test, before describing topsoil
structure, the structural stability of the soil surface (called H0) is estimated by looking for crusting, weeds and estimating crop density, stone
content and number of earthworm casts.
To describe topsoil structure, boundaries
are defined on the face of the trench by the intersection of the horizons and the location of the
vertical zones, usually wheel tracks. For each
soil layer, the different types of structure are
identified by exerting lateral pressure with a
knife and removing pieces of soil c.4 cm across
by a levering action. The identification is made
by using different features: presence of visible
macropores; the roughness of the broken surface (smooth, rough…) and cohesion (Boizard
et al., 2002). Morphological units with homogeneous soil structure are then identified and a
photograph is taken or drawings are made. After
each layer has been identified, any dense lumps
within each morphological unit are broken apart
in order to characterize more accurately their
­internal state and the way the fragments are
­assembled together (Fig. 2.6).
The soil structure is then described using
two criteria:
1. The spatial arrangement of voids, cracks, organic residues and dense lumps is described for
each soil morphological unit. According to the
way the clods are assembled, three structural
states are defined: o for an open structure, b for a
blocky structure and c for a continuous structure as defined by Roger-Estrade et al. (2004).
Clods are lumps created by climate, tillage and/
or compaction.
2. The clods >4 cm are separated into three
classes according to the proportion of visible
porosity (Fig. 2.7). Loose structure is termed
gamma clods (Γ); compacted structure, without
any visible structural porosity, is termed delta
clods (Δ); and soil structure that exhibits cracks
due to weathering is termed phi clods (Φ).
Fig. 2.6. Assessing the properties of a lump of soil. (Photo: Joséphine Peigné.)
Assessing Structural Quality for Crop Performance and for Agronomy
23
2 cm
Γ : High structural porosity
and rough surface
2 cm
Δ : No visible macropores,
high cohesion and aspect of
the breaking surface smooth
2 cm
Φ : Result of Δ and the action of
climatic conditions
Fig. 2.7. Internal state of the clods/aggregates. (From Peigné et al., 2013.)
The strength of the ‘profil cultural’ method is
that it takes into account the spatial variation of
overall structure and not just of the individual
units. Diagnosis can be carried out by taking into
account the location of wheel tracks (Fig. 2.5).
For example, a compacted soil structure noted as
‘cΔ’ in section L1/H5 would imply a compacted
structure due to wheels. It can also help us to
understand the dynamics of the structural conditions and diagnose/predict the effect on soil/
plant functioning. The changes with time depend on cropping system, biological activity and
climate. Soil tillage can fragment Δ clods into
finer soil, which often evolve into Γ clods (fragmented), or inversely wheels can compact fine
soil and create Δ clods. Roots and, above all,
24
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
c­ limate can modify a Δ clod into a Φ over time, or
fauna can transform a Δ clod into Δ0 by burrowing. Δ0 corresponds to an aged Δ state with
earthworm burrows in evidence.
The next step, if required, is subsoil examination, which can complement the description of
the topsoil. The objective is to evaluate the inherent capacity of the soil to support root growth
and therefore to determine its potential for cropping. The subsoil volume represents the agronomical potential of the soil.
In the subsoil we first delimit and describe
each pedological layer (P1, P2, see Fig. 2.5). The
delimitation is based on soil texture or colour differences. Then, the properties of each layer are
described: texture, pedological structure (crumbly,
platy . . .), hydromorphy, and soil moisture, presence of roots and traces of activity of soil fauna.
We particularly focus on earthworm burrows
visible on the face of the soil profile by observing
and counting: empty and shiny burrows, or partially filled with earthworm casts, presence of
aestivation chambers and roots following burrows. We also describe the presence, density and
shape of roots and note their maximum depth,
which indicates their capability to explore the
subsoil. Root shape such as bent or thickened roots
can indicate the presence of compacted areas
(­Peigné et al., 2013). These visual evaluations of
subsoil characteristics help to identify key properties such as the water-holding capacity of the
profile. For instance, texture, soil structure and
the proportion of stones can also give indications as to the potential water infiltration and
potential soil water retention. Number and shape
of earthworm burrows and roots in the subsoil
provide information on the ability of roots and
earthworms to exploit subsoil.
2.5.1
Le profil cultural – evaluation
and limitations
This method enables an assessment of spatial variability of the soil structure due to tillage and seeding and of the likely evolution of recent anthropic
effects. A comprehensive picture of the impact of
tractors and other machinery on soil structure is
obtained. Main conclusions given by le profil cultural relate to the effect of tillage and wheels on
soil compaction, effects of natural agents such as
climate and living organisms on soil porosity,
and soil structural impacts on rooting.
It also permits the assessment of both topsoil and subsoil and their interaction through
the transition layer between both, wholly in topsoil or wholly in subsoil (Peigné et al., 2013). It
has been used for more than 40 years in France
in research and also in rural development.
Soil structure evaluation with le profil cultural is validated by several measurements, for
instance comparison of the internal state of clods
with soil bulk density (Roger-Estrade et al., 2004).
The main limitations of the method are:
(i) it is very destructive and thus not easily
undertaken in the field; (ii) good soil expertise
and training are required; and (iii) it has been
designed for ploughed land, so there is a need to
improve it to include formation of cracks and
macropores before use on no-tilled or grassland
soils (Roger Estrade et al., 2004; Boizard et al.,
2013; Peigné et al., 2013).
2.6 The Numeric Visual Evaluation
of Subsoil Structure (SubVESS)
The subsoil provides an important store of plant
available water and allows water and air to permeate. Subsoil structure tends to be stable and
soil organic matter neither features in its development nor in its stability as it does in the topsoil. The physical effects of wetting and drying,
freezing and thawing are the principal agencies
influencing the formation of structure in the
subsoil but are not involved in its degradation.
Compaction of the subsoil by tractors and
harvest machinery, which are becoming heavier
and being used more frequently in unsuitable
conditions, is considered to be one of the major
threats to future crop productivity (Jones et al.,
2003; Van den Akker et al., 2003; Hartemink,
2008). Poor management in no-till can cause
subsoil compaction and, as a result, it is common to find no-tillage systems being subjected
to subsoil loosening. However, conventional
­subsoiling can fragment the pore network (see
­Godwin and Spoor, Chapter 5, this volume) and
stimulate the release of carbon thus negating
one of the benefits of no-till (Reicosky, 1997).
Assessment of subsoil structure that allows
regular monitoring of compaction is thus a key
tool in the development of no-tillage systems.
Assessing Structural Quality for Crop Performance and for Agronomy
It is also particularly important to loosen any
compacted layers in the subsoil before changing
to a no-till system. A no-tillage management system commonly requires a minimum of 10 years
of non-disturbance to mature.
The numeric visual assessment of VESS for
evaluation of compaction in topsoil layers is well
established (Guimarães et al., 2011) and this
principle was extended to develop a method for
the numeric evaluation of structure in the subsoil (Ball et al., 2015).
Subsoil examination therefore begins below
spade depth. Although sample trenches may be
dug by hand, it is recommended that a trench
some 40–60 cm wide should be dug using a
mechanical digger. The length can vary but
should be 2 m or more and be orientated across
the direction of the principal tillage or method of
harvest to cut through any potentially compacted
layers. The depth of evaluation needs to take into
account the role of the subsoil as a store of water
to meet the peak demand for transpiration by crop
plants, but for safety reasons the depth should not
exceed 1.4 m. In humid c­ limates, a depth of 50
cm may be enough to meet peak summer water
deficits, whereas in a drier climate, a depth of over
1.2 m may be required (in the absence of irrigation). A critical zone to examine is that just beneath the topsoil – the anthropic ‘transition layer’
as discussed above and by Peigné et al. (2013).
Criteria used in the SubVESS assessment are
based on those described by Batey and McKenzie
(2006) and Batey (2000). Key criteria are depth
of root penetration, hardness, porosity, strength,
the presence of cracks (incipient or actual), the
presence of macropores (faunal or physical) and,
to a lesser extent, colour. A flowchart to assess
subsoil structural quality is shown in Fig. 2.8.
To assess the soil quality, first the layers of soil
are identified using a knife or trowel pushed into
the face of the soil profile to locate layers of different strength. Mottling may also help to distinguish
layers. After finding and marking the boundaries
between layers, factors (a) to (e) are assessed in
turn for each layer. The knife is used to extract
­individual aggregates or slices of soil in order to
assess strength, porosity and shape and size of
aggregates with the help of the chart (Fig. 2.8).
The most frequently occurring score is taken as
that for each layer and then combined to give an
overall profile score, for example, a soil scoring
mostly 4 (a) to (e) for a transition layer at 25–40 cm
25
and 3 (a) to (e) for the looser layer below is reported as Ssq4 (25–40 cm)/Ssq3 (40–100 cm).
In practice, use of the SubVESS is usually accompanied by topsoil VESS made adjacent to the pit.
When possible, the area of interest should
be compared with one nearby that has not been
previously cropped. This helps to identify the
depth to which the soil has been altered through
management practices, for example, when the
strength of the layer is equal to that of the soil at
the same depth under native forest or uncropped
land, it confirms the maximum depth of the anthropic influence.
The method was found to be able to identify
limiting transition layers in both well-drained
and imperfectly drained soils in experiments on
compaction under arable and grassland production. It has been used to identify differences in
subsoil structural quality within fields associated with field traffic levels in Brazil, Denmark
and Scotland (Ball et al., 2015).
The assessment of subsoil quality by visual
scoring within soil pits has enabled the identification of anthropic layers due to compaction that
limit the agronomic potential of a soil in several
countries (Ball et al., 2015). These layers were
mostly in the zone immediately below the topsoil,
the ‘transition layer’. Structure, porosity and
root pattern were the most important diagnostic
­ uality of
criteria. Distinguishing whether the q
these layers reflects the natural soil composition
or degradation by land management is helped by
comparison of the test soils with reference soils
under forest or long-term grassland. The score of
structural quality derived was used to judge the
requirement for amelioration by soil management, mainly by subsoiling.
2.7
Recommendations
A comparison of the five methods of soil evaluation is provided in Table 2.1.
VESS and VSA are now well established in
many countries and in different climates as good
indicators of topsoil structural quality. Several authors report a good correlation between VSA and
Peerlkamp tests (either as per the original method
(‘St’ values) or as VESS scores) (Shepherd, 2000;
Newell-Price et al., 2013). In one study, the VSA
index was strongly dependent on texture, whereas
26
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
Subsoil Visual Evaluation of Structure, SubVESS
Produced by: Bruce Ball; Rachel M. L. Guimarães; Tom Batey and Lars Munkholm
Subsoil structure quality, Ssq, is a rating of the agronomic quality of soil. Use of this rating allows identification of problem soil layers
caused by compaction or waterlogging that may need improvement. Work through steps 1) to 10), using the flowchart overleaf.
Typical profile
1) Dig profiles to 1-1.4m depth located across the
direction of travel of cultivators and tractors.
Consider locating profiles on ‘high yielding areas’.
Typical surface
2) Remove soil from any surfaces compacted or smeared
during digging the pit using a spade or a knife.
Typical fragment
Ssq1
3) Observe the soil below the topsoil , the transition layer,
and to the expected rooting depth (~ 30 cm to 1.4 m depth).
4) Aim to record information on the score sheet.
5) Identify layers of contrasting colour and hardness.
Look for hard layers e.g. the transition layer that may be
compacted or platy, by prodding with the point of a knife
or a pen. Usually there are only one or two layers.
6) Mark the layers with a knife or by inserting plastic tags
and measure their depths.
Ssq2
Fragment extraction
Ssq3
7) Using the flowchart overleaf, give a score for each heading.
starting with mottling, then strength (already assessed with the
knife), then porosity, roots and aggregates. When observing
strength and small pores, use a knife to extract fragments about
10 cm long, 10 cm wide and 2-3 cm thick. To assess the strength
of a fragment, hold the ends in either hand and snap like a twig.
Look for small pores on the broken surfaces.
Ssq4
8) Use the individual assessments to reach the final score
e.g. Strength 3b, Porosity 3c, Roots 3d, Aggregates 3e = Ssq3
9) After scoring each layer give the overall score as the sequence
of layers and depths e.g. Ssq4 25-45cm/Ssq3 45-90cm.
Ssq5
10) Repeat in another location if the pit is wide enough.
11) For a complete assessment of soil quality, that includes the
topsoil, measure VESS in undisturbed soil nearby.
For further information, contact: bruce.ball@sruc.ac.uk; rachelguimaraes@utfpr.edu.br;
tombeth33@gmail.com; lars.munkholm@agro.au.dk
Subsoil structural quality (Ssq) assessment of a soil layer
a) Mottling
b) Strength
c) Porosity
d) Roots
1c Many small pores
(< 2mm) throughout,
includes loose sand
1a-3a No mottling
or many diffuse
(faint) mottles
4a-5a Welldefined rustcoloured zones
around pores or
blocked channels
e) Aggregates
Ssq Subsoil quality
1e Rounded friable
aggregates
Ssq1 Friable with high
2e Uniform, small
scale roughness
due to sub-angular
aggregates
Ssq2 Firm with slightly
porosity and fissures. Good
drainage and aeration.
1b-2b Easily
fragmented
with fingers
2c As for 1c, but
occasional less
porous zones
3b Difficult to
penetrate
with knife
and slices
keep their
shapes after
breakage
3c Visible porosity
mostly outside
aggregates as cracks,
isolated pores and
earthworm holes,
acting as bypass
pores
3d Roots
mainly in
cracks
and
worm
channels
3e Large-scale
angular roughness
with angular
aggregates
4b-5b
Fragments
are difficult
to extract
and are
angular
wedges
4c Very few small
pores and cracks
visible on broken
surfaces
4d Roots
can be
distorted
4e Dense with a
mixture of angular
aggregates and
poorly visible
structure. Knife
marks visible.
Includes single grain
structures
Ssq4 Compact or large
5d No
roots
5e Smooth
unbroken face very
dense. No visible
structure.
Fragments tough
(clay). Knife marks
visible
Ssq5 Massive or
structureless. Dense
structural units with
smooth, unbroken faces,
possibly laminated. If poor
drainage, colour mostly
grey, with very few
well-defined mottles.
(< 5/100 cm2)
5c No pores or few,
blocked channels
Photo by Anne Weill, Quebec
1d-2d
Roots
growing
throughout
SubVESS Flowchart
less porosity and fissures
than Ssq1, but with only a
small effect on rooting. If
present, mottling due to
anaerobism is minor.
Ssq3 Some compaction
as either natural or manmade pans among angular
or weak-grained structures.
If present, mottling due to
anaerobism is faint.
scale structures. Large
aggregates, possibly
prismatic, laminated or
single grained. If poor
drainage, grey colours,
mottles few and
well-defined.
Fig. 2.8. SubVESS flowchart and description of the categories. (From Ball et al., 2015.)
Table 2.1. Comparison of the five methods of visual soil evaluation.
What
Main indicators
Scoring
For whom
VESS
Evaluation of
topsoil structure
quality with
spade
Size, shape, strength and colour
of aggregates
Visible porosity and roots
From 1 (best) to
5 (worst
structure)
Farmers,
Quick and easy identification Soil moisture can
advisors and
of soil structure and layers
influence soil
researchers Scores for a quick
structure evaluation
understanding
No information below
Widely applicable
30 cm depth
VSA
Evaluation of
Soil texture, soil structure
topsoil structure
(visual evaluation of soil
quality with
aggregates after drop test),
spade (and
soil porosity, mottles,
drop test) and
soil colour, earthworm
interrelationships
numbers, soil smell, roots,
with farm
surface cover and crusting
production
SOILpak
Profil
cultural
Limitations
Drop test is an objective
indicator of structure
Link with farm production
Scorecards for a quick
understanding
Applicable to cropping and
grassland
Soil moisture and
texture can
influence soil
structure
evaluation
No information below
topsoil depth.
Time taken for the
complete methods
Evaluation of
Structural form: clod size
A simple scale from Advisors and
topsoil and
and shape, resistance to
0 (terrible) to
progressive
subsoil qualities,
deformation, clods within clods
2 (excellent)
farmers
rooting depth
(friability) and internal porosity
For experts,
in a pit
of the smallest observable clods
a 21-point scale
Structural stability in water
Roots
Rapid for simple test
(10 min) for a quick
understanding
Evaluation of the
whole soil, link
with rooting
Time taken for
experts (90 min)
High soil disturbance
due to the pit
Difficult to use it in
sandy soil
Evaluation of
Description of morphological
No scoring,
topsoil and
units (intersection of soil layers
description
subsoil qualities,
created by tools and wheel
summarized
rooting depth
tracks)
on a sheet
in a pit
Spatial arrangement of soil
fragments; classification
of lumps: porous (Γ), compacted
(Δ) and with cracks (Φ)
Roots, colour and soil texture
Evaluation of the whole
profile link with rooting
Understanding of the effects
of tools and wheels on
soil compaction
Long and detailed
(minimum 60 min)
Significant expertise
is required
High soil disturbance
due to the pit
No score
Mainly confined to
tilled soils
Mottles, colour, strength, roots,
porosity and structure
From 1 (friable
structure) to
5 (massive
structure)
Advisors and
researchers
Farmers,
Quick and easy identification High soil disturbance
advisors and
of soil structure layers
due to the pit
­researchers Scores for a rapid
understanding
Link with rooting
Complementary to VESS
27
SubVESS Evaluation of
subsoil quality
and rooting
depth in a pit
Index of soil quality Farmers,
(SQI) from 1 to
advisors and
20 (poor), from 20
researchers
to 37 (moderate)
and over
37 (good)
The SQI is given
on a scorecard
Same scorecard for
plant production
Advantages
Assessing Structural Quality for Crop Performance and for Agronomy
Methods
28
T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard
using the VESS method, the relationship with
texture was not significant (Giarola et al., 2013).
Both VESS and VSA assess the properties of
the layer being sampled as a whole. Where the topsoil structure has more than one distinct layer,
such as under grassland or minimum till, the layers
may be assessed separately (Newell-Price, 2013).
With VESS it is also possible to observe where a
compacted layer is located within the soil profile, allowing specific amelioration practices to be implemented. This also proves useful for detecting layers
within the topsoil, good as a guide to select the
depths of sampling for detailed measurements
and for pre and post-tillage assessments.
Being based on a suite of indicators, VSA is
comprehensive, but some of the sub-tests may
not be necessary for every field. It is more time
consuming to sample the upper subsoil and impractical to assess the structural properties of
the lower subsoil. VSA is good as an integral part
of an overall, holistic soil and plant assessment
and as an objective structural test.
One weakness of both the VESS and VSA
methods is that they give a snapshot at one specific time. Changes over time can be followed by
repeating measurements on a yearly (or longer)
basis and by taking a photograph of the loosened soil blocks as a record of the structural condition. The stability of structure can be tested
using methods such as that used in the SOILpak
method (McKenzie, 1998). For soils rich in clay,
visual methods seem to be currently the only reliable means to detect potentially harmful damage (Mueller et al., 2013).
SubVESS has been developed for use where
compaction has occurred below topsoil depth, as
under many systems of arable production particularly where heavy machinery is used for crop
harvest under wet conditions. In such circumstances, SubVESS can be carried out to complement
VESS for a more complete visual soil assessment.
SOILpak and le profil cultural can be used
where a detailed assessment of compaction is
r­equired, for example, in land under intensive
­arable production. The strength of the profil cultural method is that it records the spatial variation of the units of structure and is well suited
for research use. A comprehensive picture of the
impact of tractors and other machinery on soil
structure is obtained. It can evaluate the effects
of natural agencies such as climate and living
organisms on soil porosity and the impact of
structure on rooting. SOILpak is specifically designed for agricultural consultancy and can be
used in ‘rapid’ and ‘detailed’ modes. Both the
‘detailed’ SOILpak and profil cultural methods
are time consuming and significant expertise is
required.
Methods of visual assessment of structure
can be combined with data from soil surveys and
the experience of land users to provide a comprehensive guide to long-term management of
the soil. Those which include an examination
and evaluation of the subsoil to the full depth of
rooting can be used both to assess the inherent
quality of the land and also to determine whether
this capability has been adversely modified by
the anthropic effects of tillage, crop management
and crop harvest.
2.8
Conclusions
Visual and tactile methods that provide a numeric assessment of structure directly in the field
have been used on a range of soils and in many
countries. They have been developed as an aid to
the management of soils, with particular stress
on the need to assess compaction and to determine any need for subsoil loosening. Because the
methods are able to combine both inherent and
anthropic physical properties, they can also be
used or modified to assess other degradative processes such as desertification, surface dispersion,
hardsetting phenomena or erosion.
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Morphological characterisation of soil structure in tilled fields: from a diagnosis method to the modelling of structural changes over time. Soil and Tillage Research 79, 33–49.
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Flat to Rolling Country. Horizons mw and Landcare Research, Palmerston North, New Zealand.
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Flat to Rolling Country, 2nd edn. Horizons Regional Council, Palmerston North, New Zealand.
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(2001) Visual Soil Assessment of soil quality – trial by workshops. In: Currie, L.D. and Loganathan, P.
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3 Reduction of Yield Gaps and
Improvement of Ecological Function
through Local-to-Global Applications
of Visual Soil Assessment
David C. McKenzie,1* Mansonia A. Pulido Moncada2
and Bruce C. Ball3
1
Soil Management Designs, Orange, Australia; 2Universidad Central
de Venezuela, Maracay, Venezuela; 3Scotland’s Rural College, Edinburgh, UK
3.1
Introduction
Although global hunger was reduced in the decade up to 2014, about one in every nine people in
the world still had insufficient food for an active
and healthy life (FAO et al., 2014). An estimated
25% increase in 2015 population to approximately 9.1 billion people in 2050 will aggravate
the shortages of food. This means that the
world’s farmers will be expected to boost their
outputs, possibly by as much as 60% by 2050
(Fischer et al., 2014), and maintain those improvements indefinitely into the future in our
pursuit of ‘food security’.
Food security is defined by the Food and
Agriculture Organization of the United Nations
(FAO et al., 2014) as: ‘A situation that exists
when all people, at all times, have physical, social and economic access to sufficient, safe and
nutritious food that meets their dietary needs
and food preferences for an active and healthy
life’. Based on this definition, four food security
dimensions can be identified: food availability,
economic and physical access to food, food utilization and stability over time.
Unfortunately, farmers will face a broad
range of formidable challenges when attempting
to provide global food security:
•
•
•
•
Soil constraints – naturally occurring and
as a consequence of land degradation – are
­associated with an inability of land to provide the required functions for land managers, for example, suboptimal crop growth
that leads to a serious gap between actual
yield and potential yield under the prevailing weather conditions. In many parts of
the world, significant areas of agricultural
land become less productive each year because of land degradation.
Less fresh water (unpolluted) will be available for irrigation of crops; a loss of capacity
of major groundwater systems is anticipated
in some regions.
Impedance of soil functions because of pollution by nitrogen (N) fertilizers and waste
disposal for which soil is a filter and buffer.
The Earth is getting warmer. This is increasing evapotranspiration demands in crop
and pasture production systems, at a time
*E-mail: david.mckenzie@soilmgt.com.au
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
31
32
•
•
•
•
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
when rainfall is becoming more extreme
and variable. Temperature increases make
it more difficult to store unwanted atmospheric carbon in soil, possibly because of
accelerated rates of mineralization of soil
organic matter.
Increasing affluence in previously poor
countries with very large human populations is quickly increasing the demand for
products such as meat that require more
production inputs than basic food stuffs
such as grains.
To prevent further loss of biodiversity from
natural ecosystems, widespread clearing of
native forest and grassland for new farmland is likely to become unachievable.
The limited availability of land for food and
fibre production is aggravated by dedication
of land to biofuel farming.
There is likely to be a reduction in rate of
supply of easily accessible liquid fuels derived
from crude oil, and mineral reserves, which
are critical for modern forms of agriculture.
Associated with this is the geopolitical unrest related to disputed access to resources,
including water.
The timing and severity of probable future
emergencies – for example, shortages of crude
oil-derived products such as diesel fuel; failure by
some countries to curb greenhouse gas emissions that have been linked to global warming –
is impossible to predict accurately. However,
possible ‘disaster response scenarios’ can be
‘mapped out’ by government agencies to assess
the likelihood of success of various land management responses which are available to teams
of professionals that include soil scientists.
The need for major improvements to soil
management practices – in response to an inability to produce enough food and fibre without
further damage to the natural environment –
will be very challenging both on low-income
farms in developing countries, and in nations
with what are currently considered to be ‘modern’ or ‘industrial’ forms of agriculture that use
relatively large inputs of oil/gas-derived inputs
such as N fertilizer, pesticides and liquid fuels.
However, it can be argued that industrial farming is more vulnerable to input restriction than
low-income farming where survival can occur
with fewer inputs (Cribb, 2010).
To deal with the massive challenge of providing food security for planet Earth, a sensible
sequence is to conserve areas of good land quality while also focusing on restoring areas with
land degradation problems that have already
been cleared of natural vegetation for agriculture, encourage land users to promote restoration of soil condition, and then intensify crop
production and grazing management so that use
of all inputs is optimized. Land degradation, described by Eswaran et al. (2001) as ‘a decline in
land quality caused by human activities’ has
been a matter of concern for many centuries
and it is a major, increasing problem. Recent
data (Osman, 2013) have shown that 38% of
the areas used by humans (agricultural areas,
permanent pasture and forests) on the Earth
can be considered as degraded. In Africa, South
America, Asia and Europe, the percentages of
agricultural areas that are degraded are 65, 45,
38 and 25%, respectively. A major aspect of land
degradation is soil degradation, which has been
defined by FAO (2015) as ‘a change in the soil
health status resulting in a diminished capacity
of the ecosystem to provide goods and services
for its beneficiaries’.
Mueller et al. (2012) have called for an increasing focus on ‘sustainable intensification’.
Fischer et al. (2014) strongly support the intensification of cropping as the means to deliver
higher yield and feed a hungry world. Intensification can occur sustainably if based on scientifically determined ‘best practice’ management
that improves input use efficiency and soil quality. The biggest positive environmental consequence of crop intensification will be the reduced
pressure to clear new land for cropping. Major
new technological advances may emerge that
dramatically ease adverse global pressures on
soil-related processes (Haff, 2014), but our planning at the moment will have to focus on the use
of soil assessment and management systems
that currently exist to reduce soil degradation
and allow attainment of crop yields that are
close to genetic potential.
This chapter explores options to assess
local, national and global potential of soil so that
soil modification strategies for reducing yield
gaps and improving ecological function can be
developed. Yield gap refers to the difference between the potential yield and the average actual
crop yield produced by farmers (Lobell et al., 2009).
Reduction of Yield Gaps and Improvement of Ecological Function
Our main emphasis is on soil structure and soil
water status, with a focus on simple but effective
measurement techniques that are based on
visual–tactile assessment of soil condition, that
is, visual soil examination and evaluation (VSE);
these issues often are overlooked in yield gap
assessment.
It is recognized that ‘one size does not fit all’
when selecting soil assessment/management
strategies. An immense range of factors – edaphic,
climatic, economic, sociological – are relevant
locally and nationally, so global generalizations
need to be considered with caution and wisdom.
Approaches associated with the ‘yield gap framework’ are highlighted in this discussion, but the
possible roles of other systems of land management analysis are also considered.
We emphasize the importance of VSE training, and ongoing support, for ‘soil management
knowledge brokers’, that is soil scientists who
work closely with landholders to overcome their
food production and environmental impact
challenges.
3.2
Yield Gap Analysis
Potential yield is defined as ‘the yield of a crop
cultivar when grown with water and nutrients
non-limiting and biotic stress effectively controlled’ (van Ittersum et al., 2013). Potential
yield is location specific because of the climate
(i.e. solar radiation and temperature), but in
theory is not dependent on soil properties assuming that the required water and nutrients
can be added through management. This, of
course, is not practical or cost effective in cases
where major soil constraints, such as salinity or
physical barriers to root proliferation, are difficult to overcome (van Ittersum et al., 2013). An
associated term is ‘locally attainable yield’. This
is defined as ‘the maximum yield achievable by
resource-endowed farmers in their most productive fields’ (Tittonell and Giller, 2013), or
the ceiling yield under farmer management.
Yield maps – either hand-drawn by experienced
landholders or produced via the use of yield
sensors and GPS equipment on harvesting machines – highlight the spatial and temporal
variability of crop yield and profitability across
entire farms.
33
Fischer et al. (2014) have noted that to ensure that real food price increase does not exceed
c.30% over the record lows of 2000–2006,
staple crop production must increase by 60% between 2010 and 2050 because of population
and per capita income growth. It therefore follows that, after accounting for likely minor increases in crop area, farm yield must increase by
a rate of 1.1% per annum, relative to 2010. This
is the minimum rate required, and a higher target of 1.3% per annum is recommended to offset
risk. Unfortunately, the current average global
rates of progress for farm yield in wheat, rice and
soybean are each only 1.0% per annum (Fischer
et al., 2014).
Progress in actual farm yield (FY) is influenced by two components (Fischer et al., 2014):
(i) increase in potential yield (PY) – or waterlimited potential yield (PYw), depending on which
is appropriate; and (ii) closing the yield gap between FY and PY (or PYw). Progress in PY averages between 0.6 and 1.1% per annum for most
crops, and progress is currently largely attributed to plant breeding. The yield gap is being
narrowed with a rate of change in the range
0.2% per annum to 0.8% per annum. However,
yield gaps often are much greater than these
­incremental changes, and exceed 100% of farm
yield in many developing countries. The most
feasible and fastest way to lift global farm yield
will be to close large yield gaps. A broad range of
factors needs to be considered when determining reasons for large yield gaps (Table 3.1). Lal
(2013) identifies yield gaps for crops in different
countries, concluding that the lower the national/regional farmers’ yield and the more degraded the soil, the larger the yield gap.
Where major differences exist between FY
and PY at food production sites around the world,
there is lack of information about how much of
the loss is caused by soil constraints. French and
Schultz (1984) have provided a framework that
helps to overcome this problem. Figure 3.1 – an
adaptation of this concept by Bowman and Scott
(2009) – shows the relationship between grain
yield of wheat and estimated water use (April–
October rainfall in a southern hemisphere
‘Mediterranean’ climate). It indicates a benchmark water use efficiency of about 20 kg ha–1
grain for every millimetre of water transpired by
the crop (beyond the 110 mm water needed before plants will produce grain). The water limited
34
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
Table 3.1. Factors that contribute to yield losses in farmers’ fields. (Adapted from Lobell et al., 2009.)
Yield decline issues
Factors requiring attention by farm managers
Climate and landform
limitations
Lack of water (drought stress)
Too much water (flooding / waterlogging)
Extreme heat
Extreme cold (frost damage)
Wind damage (lodging of mature crops)
Poor crop establishment caused by sealing, crusting, hardsetting
Aggravation of drought stress by poor structure (compaction and/or sodicity
causing excessive runoff and large evaporative losses)
Aggravation of waterlogging stress by slow drainage associated with poor
structure (compaction, sodicity)
Poor water holding capacity caused by rockiness
Inadequate water entry associated with water repellence
Nutrient deficiencies and imbalances
pH extremes (acidity, alkalinity)
Salinity/boron toxicity
Lack of soil carbon to provide food for beneficial soil organisms
Removal or thinning of the topsoil layer (where the highest soil organic matter
accumulation occurs) by erosion
Weed pressure
Insect damage
Diseases (head, stem, foliar, root)
Inappropriate selection of crop species/varieties
Inferior seed quality
Grain loss caused by faulty planting and/or harvesting equipment
Lack of capital to improve soil conditions and farm infrastructure/machinery
Risk aversion
Poor access to professional advice from reputable soil scientists and agronomists
Soil related
Agronomic
Farmer constraints
6000
Grain yield (kg/ha)
B
4000
E
D A
2000
C
A
B
B
0
100
200
300
400
500
Water use (mm)
Fig. 3.1. An adaptation by Bowman and Scott (2009) of the French and Schultz (1984) relationship
between grain yield of wheat and estimated water use (April–October rainfall in a southern hemisphere
‘Mediterranean’ climate). The blue line shows the water limited potential yield. Experimental data (blue
dots) show improved yield and water use efficiency (blue arrows) with earlier time of sowing (A),
increased nitrogen (B) or phosphorus fertilizer (C), improved weed control (D) or multiple improvements
in agronomic management (E).
Reduction of Yield Gaps and Improvement of Ecological Function
potential yield in Fig. 3.1 is shown in relation to
FY, and the ability of management inputs to narrow the yield gaps (i.e. provide ‘more crop per
drop of water’) is shown.
Surprisingly, soil structural condition and
its impact on soil water intake and storage was
not mentioned by French and Schultz (1984)
and Bowman and Scott (2009). Fischer et al.
(2014) discuss, in general terms, the importance of improving the management of soil
physical, chemical and biological fertility
when narrowing yield gaps, but do not offer
detailed advice about how this can be achieved.
However, Oliver and Robertson (2013) have
shown how a crop yield map for a 4500-ha
rain-fed farm in Western Australia can be converted to a ‘yield gap’ map that guided subsequent soil descriptions and soil sampling;
site-specific soil management inputs based on
this information were then provided for the
farmer. This accords with the suggestion of
Lal (2013) that we need to optimize soil conditions that support favourable crop growth
even under harsh climatic conditions. Such a
strategy enhances soil/ecosystem resilience
through improved soil quality.
Nhamo et al. (2014) consider water and
nutrition to be the two main factors that limit
yield in agricultural environments. Other agronomic factors influencing crop productivity are
pests and diseases, cultivar choice and crop
management. Precipitation and soil moisture
storage are very important factors in rain-fed
agriculture but in tropical regions characterized by strongly weathered soils, such as Senegal,
Vietnam and central Brazil, the main causes of
yield gaps have been identified by Affholder
et al. (2013) as:
•
•
•
Senegal: poor soil fertility and weed infestation, both related to low purchasing power
of farmers, and water runoff.
Vietnam: weed infestation, soil fertility and
soil compaction, all related to rice cultivation and the removal or burning of crop
residues, and overgrazing by buffaloes during the dry period.
Central Brazil: aluminium toxicity in soils,
weeds and soil waterlogging.
According to Tittonell and Giller (2013), land in
sub-Saharan African countries is regarded as
not being limiting, and it is considered that the
35
area cultivated, rather than the yield per unit
area, is of more importance to food security; nutrient supply tends to be a more important
yield-limiting factor than water. In contrast,
physical degradation processes such as soil erosion, soil sealing and soil compaction (which originate from soil structure degradation) are the
main factors affecting soil functions and limiting
yield in intensive arable areas of tropical land (low
evolution state) in Venezuela (Pulido Moncada
et al., 2014b).
Under less restricting environmental conditions in temperate climates, sometimes the
yield gap is low because yields are very high,
whilst soil structure ratings indicate suboptimum conditions. This is often due to excessive
inputs of agrochemicals (often associated with
poor nitrogen use efficiency) and overexploitation of water resources, which are both threats
to the environment (Foley et al., 2011). VSE
offers the potential to identity this situation
through its linkage with greenhouse gas emissions and nutrient loss (Shepherd, 2009 and
Cloy et al., Chapter 7, this volume). Other key
components constraining yields in Europe are
subsoil compaction and the increased use of
minimum tillage (Knight et al., 2012) and the
variation in soil physical qualities associated
with extreme weather (see Guimarães et al.,
Chapter 8, this volume).
3.3
Soil Structure Assessment
Using VSE
This section examines the potential for VSE
techniques to act as a key component of measurement packages that will allow land managers
to assess and then overcome soil quality problems that are restricting food production and
impeding ecological functions both locally and
globally.
We consider complete systems of management in which VSE is an integral part; VSE
techniques mainly describe soil structure and
porosity required for adequate moisture storage
and flow, root development and nutrient uptake.
Existing land management frameworks into
­
which VSE procedures can be inserted also are
reviewed.
VSE is chosen to describe soil conditions
­because this is a simple and credible means of
36
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
informing potential users of the relevant soil
science information. The expense and slowness
of many soil assessment schemes often is a barrier to adoption. Complex soil science terminology associated with some techniques also
limits acceptance and application by farmers.
One of the beauties of VSE (Fig. 3.2) is that it is
an immediate, efficient and inclusive system
that ‘lets the soil tell its story’ (T. Batey, Aberdeen, 2014, personal communication) and allows practitioners to ‘connect with the memory
of a soil’ (R.M.L. Guimarães, Maringa, 2014,
personal communication). VSE can be applied
to any form of agricultural system – it is not
­restricted to high-input mechanized farms in
developed countries. The feasibility and reliability of VSE for soil quality assessment has been
demonstrated through the development of relationships between VSE and physical and hydraulic properties such as saturated hydraulic
conductivity (Pulido Moncada et al., 2014a), limiting soil water ranges for root growth (McKenzie
and McBratney, 2001) and other soil physical features (Mueller et al., 2009; Guimarães
et al., 2013).
3.4
Soil Structure – Its Relationship
with Soil Water Status and
Hydrological Cycles
The critical importance of soil water for crop
production and its association with yield gap reduction is highlighted in Table 3.1 and Fig. 3.1.
The following objectives need to be considered
by farmers when managing water in soil to
maximize their production of food:
•
•
•
Reduce runoff, which provides an associated improvement in water entry into the
soil profile through the soil surface.
Maximize water storage within the root
zone.
Maximize the least limiting water range
(LLWR) so that root growth restriction
caused by waterlogging (when the soil is
moist) and excessive hardness (when the
soil is dry) is minimized; the LLWR is determined by soil structure and high values
of LLWR occur in soils of good structure
and high porosity (da Silva et al., 1994;
Guimarães et al., 2013).
Fig. 3.2. A compacted layer and impeded plant roots highlighted via visual soil assessment under
rain-fed wheat in north-western New South Wales; pit trimming techniques illustrated by Trouse (1978)
were used. (Photo: Adam Kay.)
Reduction of Yield Gaps and Improvement of Ecological Function
•
Minimize water losses through deep drainage, but allow enough of a leaching fraction where subsoil salinity is a concern.
Figure 3.3 shows the soil–water interrelationships that exist at both local and global scales. Soil
structure – particularly soil structural form and
its internal porous system (Kay, 1990 and Batey
et al., Chapter 2, this volume) – is, together with
soil texture, the principal soil factor influencing
the entry, storage and drainage of soil water. VSE
techniques provide a convenient means to quantify soil structural form in the field and to indirectly assess the presence or absence of water
movement and/or storage restrictions.
The profound influence of soil structural
form on the water holding capacity of loam and
clay soils is shown in Fig. 3.4. The extra water is
stored in the new soil porosity that becomes
available as a soil is loosened and becomes well
structured. Also, the dominant type of aggregate in the arable layer has been directly associated with a change of the conductivity of soil
pores. Pulido Moncada (2014) has shown that
sharper aggregates (abundant in degraded soils)
result in lower conductivity than rounded aggregates (abundant in soils that are not degraded). This is consistent with the observation
by Alvarez et al. (2012) that the arrangement of
the porous system is related to the morphology,
stability and roughness of the aggregates. These
three morphological characteristics vary according to the contrasting management practices
37
applied to agricultural soils, and hence are
promising indicators of soil degradation.
Examples of how VSE techniques have been
used to assess various components of the soil
hydrological cycle are shown in Table 3.2. Optimization of soil water status through improvement
of soil structure will help to reduce yield gaps. The
strong relationship between the Muencheberg
Soil Quality Rating (M-SQR score) by Mueller et al.
(2013) and cereal grain yield, under contrasting N fertilizer regimes, is presented in Fig. 3.5.
Crop growth models – such as PLANTGRO
(Hackett and Vanclay, 1998) and AquaCrop
­(Ardakanian and Walter, 2011) – can provide
an illustration of the impact on plant growth of
changes in soil physical and hydraulic factors
and other soil constraints for a broad range of climatic conditions and crop types. VSE techniques
are likely to have an important role to play in provision of data for these types of simulations.
3.5
Land Management Frameworks
Related to Soil Productivity,
Yield Gap Assessment
and Ecological Function
Although soil structure assessment using VSE
procedures is an extremely important issue in
yield gap assessment and ecological function
analysis, it cannot be used in isolation. As indicated in Table 3.1, many other soil, agronomic,
Fig. 3.3. Water movement in and out of soil at the farm (local) scale.
38
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
Self-mulching
Compacted
Clay
Well structured
Compacted
Clay
loam
Loam
Compacted
Coarse
50
Sandy
loam
Fine
100
Well structured
150
Compacted
Well structured
200
Sand
Plant available water in 1 metre of soil (mm)
250
Soil texture class
Fig. 3.4. The influence of soil structural form on the amount of water that can be held in a soil.
(From Anderson et al., 2007; based on Moore et al., 1998.)
Table 3.2. VSE systems with the capacity to assess key components of the soil hydrological cycle.
Soil water component, and the associated VSE
correlation studies
Relevant VSE system
Water entry and movement
(Pulido Moncada et al., 2014a, c)
Le profil cultural (Roger-Estrade et al., 2004)
Visual soil assessment, VSA (Shepherd, 2009)
Visual evaluation of soil structure, VESS
(Ball et al., 2007)
Profile water storage (McKenzie et al., 2008)
SOILpak score (McKenzie, 2013)
Deep drainage/subsoil waterlogging
Subsoil visual evaluation of soil structure
­(SubVESS) (Ball et al., 2015)
Soil suitability for water extraction by plant roots,
SOILpak score (McKenzie, 2013)
i.e. non-limiting water range (NLWR) and partially Muencheberg Soil Quality Rating, M-SQR
limiting water range (PLWR) (McKenzie and
(Mueller et al., 2013)
McBratney, 2001)
SubVESS (Ball et al., 2015)
Le profil cultural (Roger-Estrade et al., 2004)
climate/landscape and sociological factors need
to be considered as well.
Three land management frameworks are
described below. Two have a food/fibre production emphasis; the third also has an ecological process focus. What they all have in
common is a need to measure and interpret
soil structural quality prior to decision making
about future management. The overall aim
is similar to that proposed by Mueller et al.
(2014) of finding a common basis for soil
productivity evaluation, as required by a global community of land users to allow achievement of high productivity in the context of a
sustainable multifunctional use of landscapes.
Potential clearly exists to refine and perhaps
Reduction of Yield Gaps and Improvement of Ecological Function
(a)
7
y = 0.07 x
n=76, r 2=0.78***
SE=0.8
6
(b)
14
12
5
Grain yield [t ha–1]
Grain yield [t ha–1]
39
4
3
2
10
8
6
4
1
0
y = 1.64+0.00096x2
n=122, r 2=0.66***
SE=1.34
2
0
20
40
60
80
0
100
Soil Quality [M-SQR score]
0
20
40
60
80
100
Soil Quality [M-SQR score]
Dedelow
Neu Rosenthal
Plotnikovo
Muencheberg
Elora
Luancheng
Libbenichen
Shebanzevo
Tai-Han
Lietzen
Vyatkino
Gu Yuan
Seelow
Ordinskoje
Xilin River
Sydowswiese
Ust-Kamenka
Palmerston North
Fig. 3.5. Overall soil quality score (M-SQR) vs yield of small grain cereals for sites from Germany,
Canada, Russia, China and New Zealand. (a) Sites where annual nitrogen fertilizer addition was
<100 kg ha−1; (b) Sites where annual nitrogen fertilizer addition was >100 kg ha−1. (Data from Mueller
et al., 2013.)
combine the contrasting approaches to land
management – in particular, improved linkage
with VSE techniques.
3.5.1 Frameworks for agricultural land
management linked with VSE
techniques at field scale
3.5.1.1
SOILpak
The Australian cotton industry has benefited
greatly from systematic assessment and management of soil structure in both the topsoil
and subsoil. Their SOILpak decision support
system (McKenzie, 1998; McKenzie, 2013 and
Batey et al., Chapter 2, this volume) provides
target specifications for a reference state, that
is, the ‘ideal’ soil for cotton production. It is
based on an appreciation of Liebig’s ‘Law of the
Minimum’. Where limitations to cotton growth
are identified, soil amelioration strategies with
potential to improve depth of rooting and non-­
limiting water range are considered, thereby
improving the chances of producing yields
close to the genetic potential for the crops being
grown under the prevailing climatic conditions. Soil sampling ­locations are strongly influenced by yield/­profitability map information.
Objectives that can be achieved through successful management of soil structure under
cotton crops include: substantial water entry
and storage without development of severe waterlogging, avoidance of excessive soil hardness
for root extension and function, and creation of
suitable habitat for soil biota. Development of
relationships between VSE structure assessment scores and cotton lint yield in Australia
has become difficult because zones with severe
compaction are difficult to find for experimental work since the successful implementation of
GPS-guided controlled traffic farming became
widely adopted. Also, yield decline in moderately compacted soil used for cotton production
in Australia tends to be masked by the use of
40
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
relatively large N fertilizer applications and
shortening of irrigation intervals (Rochester
and Filmer, 2007).
The rapidly assessed ‘SOILpak score’ (a
measure of soil structural form; Kay, 1990) is
linked to soil degradation thresholds (McKenzie
and McBratney, 2001) and the numerical results
can be mapped easily with red–amber–green ‘traffic light’ colour coding. Valuable alternatives to
the ‘SOILpak score’ procedure used in other parts
of the world for assessing soil structural form are
described in Chapter 2. The aggregate stability in
water (ASWAT) score used with ‘SOILpak’ (Field
et al., 1997) is a test for soil structural stability in
water that can also be mapped easily with colour
coding.
ASWAT and ‘SOILpak score’ maps showing
both lateral and vertical changes in soil quality
can be related to maps of ‘soil amelioration requirements’ (e.g. loosening of compacted layers,
either mechanically or via shrink–swell cycles;
gypsum application) and ‘cost of repair of soil
constraints’, which can then be linked to crop
yield maps and farm profitability maps.
Soil structural resilience is a measure of the
ability of soil to regain a desirable soil structural
form through swelling and shrinkage induced
by wetting and drying cycles, and is vital for
maintaining yield under extreme weather conditions and can be assessed by observing soil
shrinkage patterns when the soil is dry. This is
particularly relevant in dry climates. In wetter
climates, it can also be assessed from the rate of
recovery of visual structure scores after damage
by extreme weather (see Ball and Munkholm,
Chapter 9, this volume).
Australian cotton growers and their advisors do not use SOILpak in isolation. It is complemented by a series of companion manuals that
include NUTRIpak, WATERpak and WEEDpak,
in conjunction with ‘Best Management Practice’
(BMP) guidelines (Cotton Australia, 2015). Australian cotton growers produce yields two and a
half times the global average and have produced
the world’s highest yields for 20 years running.
Better management of water (including soil
water and its interactions with soil structure)
has been responsible for 50% of the yield increases
seen in Australia, with 50% attributed to plant
breeding (Cotton Australia, 2014). Much of the
area under cotton has subsoil sodicity challenges
and is very prone to serious soil compaction by
heavy farm machinery. Surprisingly, the comprehensive approach used by the Australian cotton industry has not been replicated by other
nearby cropping industries. However, the Cotton
SOILpak concept has been modified for use by
producers of other crops, for example, dryland
wheat (Anderson et al., 1999) and vegetables
(Anderson et al., 2007), and many grain producers in eastern Australia have improved their soil
management through knowledge transfer from
the cotton industry.
3.5.1.2 Muencheberg Soil Quality
Rating (M-SQR)
The M-SQR approach is an indicator-based system for the overall quantification of soil quality
on a global scale, but also applicable locally. This
system relies heavily on structural assessments
(Mueller et al., 2013). Besides indicators of soil
structure, it contains further crop yield-­relevant
‘basic’ indicators of site and climate, namely,
rooting depth, profile available water, wetness,
texture, slope and relief. It includes ‘hazard’
­indicators specific to the site such as contamination, acidification, salinization, drought and
flooding. The basic indicators are weighted
­according to their likely influence on potential
yield and are added together to give a score. The
cumulative basic indicator soil score is combined with active hazard indicators to give a
final rating number of the overall soil quality
between 0 and 100 that allows soils to be compared at all scales. The rating procedure can be
done on the basis of a field manual (­Mueller
et al., 2007). Overall rating scores of M-SQR are
significantly correlated with grain crop yields at
different input levels (see Fig. 3.5). Abdollahi
et al. (2015) confirmed that the M-SQR soil
quality index is able to reflect the crop yield potential of a soil, and thus the provisioning function of that soil.
M-SQR rating examples for agricultural research sites on different continents show that
the potential yield under normal agronomic
conditions is largely affected by water availability and drainage (Mueller et al., 2013) in cereal
growing areas. VESS and visual soil assessment
(VSA) (Shepherd, 2009) scores can be used in
the production of the ‘basic’ M-SQR score.
The M-SQR approach has potential as a global reference soil quality rating system, meeting
Reduction of Yield Gaps and Improvement of Ecological Function
the requirements of an assessment framework
of the land productivity function as defined by
Mueller et al. (2010). It will form a central element of ‘impact assessment’ procedures (Helming
et al., 2011) defining optimum land use and
potential yields. The M-SQR system is limited in
its use by farmers and advisors because it is relatively labour intensive and requires specialist
knowledge, but it may be possible to produce an
abbreviated version for a more rapid assessment
of soil structure (Mueller et al., 2013).
3.5.2 Packages for land management
at the landscape scale with potential
to be more effective if interlinked
with VSE techniques
3.5.2.1 Landscape Function Analysis (LFA)
for assessment of ecological function
The ‘Landscape Function Analysis’ (LFA) system
(Tongway and Ludwig, 2011) is based on a conceptual framework composed of landscape components and processes (Fig. 3.6) that define how
materials – particularly water – flow into, around
and out of both natural and agricultural landscapes. It is particularly useful for evaluation of
semi-arid landscapes where severe and prolonged
droughts are interspersed with periods of intense
and heavy rainfall. For example, on rangelands
that fringe the deserts of inland Australia, severe
damage to topsoil structure and erosion losses
following overgrazing by sheep and a variety of
introduced pests (e.g. rabbits) has created degradation problems that can be evaluated via LFA
prior to selection and implementation of improved
land management practices.
LFA is part of a five-step procedure (Fig. 3.7)
for restoring damaged landscapes that, if assessed
trends in responses to applied technologies are not
satisfactory, includes an adaptive learning loop
to help achieve success by adjusting restoration
technologies. A procedure exists (Fig. 3.8) for
description of surface conditions as part of LFA
(Tongway and Hindley, 2004). The ASWAT test
for soil stability from the SOILpak system has
been suggested as a possible replacement for the
‘slake test’ (Tongway and Ludwig, 2011). There
may also be potential for the ‘surface coherence’
indicator to be replaced by a topsoil structure
41
evaluation system such as visual evaluation of
soil structure VESS (Ball et al., 2007).
LFA deals with an ecological function subsection of this chapter; the other systems that we
discuss focus mainly on yield gap closure. LFA
has been successfully applied to a broad range of
drought-constrained land management situations
including rangeland management, mine site rehabilitation and farmland restoration (Tongway
and Ludwig, 2011).
3.5.3 A possible new and broad
conceptual approach for yield gap
reduction and ecological
improvement based on VSE
techniques
Significant progress could likely be made towards widespread narrowing of yield gaps and
enhancement of ecological function by adapting
and/or merging existing systems of soil analysis
and interpretation. For example, the linking of
relevant components of the SOILpak system –
with its emphasis on VSE assessment of existing
soil structure – with LFA in farming areas would
allow introduction of subsoil information to
LFA’s excellent ecological framework and proven
ability to deal with topsoil assessment and management. This would allow targeted interventions to improve degraded soils and intensify
food production on the best quality soils.
For progress to occur when tackling global ecological improvement via soil, the challenges have to be expressed in terms that can
be understood easily by politicians and senior
bureaucrats. The observed reduction in global
hunger over recent years has been linked to the
inclusion of ‘food security and nutrition’ in the
formulation and implementation of policies in
different countries. The united commitment of
government, policy makers and civil society to
improve food security is the crucial factor for
this outcome (FAO, 2015). Associated with
‘food security’ is the concept of ‘soil security’ put
forward by McBratney et al. (2014). It is proposed as a unifying replacement for soil quality,
health and protection frameworks. The soil security concept includes capability, condition, capital, connectivity and codification of soil entities
and encompasses the social, economic and
42
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
Storms: rain & wind
Trigger
events
(inputs of rainwater)
Runoff
Transfer
processes
(infiltration)
Physical feedbacks
(patches)
(storage)
Reserves
Biological feedbacks
(nutrients)
Threshold responses
(growth) (reproduction)
Losses
Offtakes
Pulses
Feedbacks
Gains
Fig. 3.6. A conceptual framework (Tongway and Ludwig, 2011) depicting how functional landscapes,
when triggered by events such as rainfall, respond in space and time with processes that transfer water
by runoff and storage in soil reserves, which then initiate pulses of plant growth that are gained or lost by
the system. The landscape under consideration is shown to be sitting on a fulcrum to represent the fact
that, in the long term, internal gains of biomass and external losses fluctuate over time and space but are
dynamically balanced. (From Restoring Disturbed Landscapes by David J. Tongway and John A. Ludwig.
Copyright © 2011 by the authors. Reproduced by permission of Island Press, Washington, DC.)
biophysical sciences. It is argued that soil has
the same existential status as the global environmental sustainability challenges of food security, water security, energy sustainability, climate
stability, biodiversity and ecosystem service
­delivery – and therefore should be recognized
and highlighted similarly. VSE methodology –
with its simple, flexible and holistic approach –
could prove to be valuable as one of the crucial
procedures for the assessment of soil capability,
condition, capital and connectivity within the
proposed ‘soil security’ framework.
A broad and easily understood approach is
required that integrates aspects of soil care within
food security in its wider context based on community participation (Ball, 2015). The widely
read National Geographic magazine has popularized the challenges we face to produce enough
food globally to avert severe famine. Foley (2014),
in his article entitled ‘Five-step plan to feed the
world’, has proposed the following action plan:
1. Freeze agriculture’s environmental footprint;
2. Grow more on farms we’ve got;
3. Use farm inputs more efficiently;
4. Shift diets;
5. Reduce waste.
Big reductions in human reproduction rates by
non-coercive means, changes in diets and consumption habits and increasing the awareness
of consumers of the food system would greatly
reduce the need for farmers and their advisors to
boost food production, but it is unlikely that such
objectives will be pursued by current world leaders. Sections 3.3 and 3.4 of this chapter suggest
that VSE procedures will have an important role
to play when pursuing success with Foley’s steps
1, 2 and 3.
Reduction of Yield Gaps and Improvement of Ecological Function
43
1
Laws
Treaties
Regulations
Agreements
Aspirations
Set goals
2
Define and analyse
the problem
ADAPTIVE
LEARNING
3
LOOP
Design solutions to
the problem
4
Select and apply
technologies
Adjust
technologies?
5
Monitor and
evaluate trends
TRENDS
NOT OK
TRENDS OK
NO
GOALS
YES
ACHIEVED?
SUCCESSFUL
LANDSCAPE
RESTORATION
Fig. 3.7. Tongway and Ludwig’s (2011) five-step adaptive procedure for restoring landscapes. (From
Restoring Disturbed Landscapes by David J. Tongway and John A. Ludwig. Copyright © 2011 by the
authors. Reproduced by permission of Island Press, Washington, DC.)
3.6
Relating Visually Assessed
Soil Conditions to Crop
Growth and Selection of Soil
Management Inputs
Where VSE techniques reveal soil restricting
crop yields, it is important to interpret the
quality scores via comparison with crop
growth thresholds, then respond by considering soil management options for soil improvement from as broad a range of sources
as possible.
Impressive components already exist for
i­nclusion in land management packages that
are used at the field scale. They include:
•
•
‘Location-specific tillage’: reduced or zero tillage where soil structure already is excellent,
loosening of soil where compaction is a problem, increasing surface clay content (through
the use of soil inversion implements such as
mouldboard ploughs) in duplex soil suffering
from hardsetting and/or water repellence.
Compaction prevention through the use of
controlled traffic farming (see Godwin and
44
D.C. McKenzie, M.A. Pulido Moncada and B.C. Ball
Indicator
1. Soil cover
2. Perennial plant cover
Stability
Index
3a. Litter cover
3b. Litter origin and decomposition
4. Cryptogam cover
Infiltration
Index
5. Crust broken-ness
6. Erosion type and severity
7. Deposited materials
Nutrient
cycling
Index
8. Surface roughness
9. Surface coherence
10. Slake test
11. Soil texture
Fig. 3.8. LFA soil-surface condition indicators (Tongway and Ludwig, 2011) used to calculate indices of
the potential of a site to resist erosion (stability), retain and store water (infiltration), and cycle nutrients to
enhance plant growth (nutrient cycling). VSE techniques for assessment of soil structural form and
stability have a particularly strong relevance to Indicators 9 and 10. (From Restoring Disturbed
­Landscapes by David J. Tongway and John A. Ludwig. Copyright © 2011 by the authors. Reproduced by
permission of Island Press, Washington, DC.)
Spoor, Chapter 5, this volume) and innovative grazing strategies.
Addition of soil ameliorants such as gypsum,
lime and organic matter where appropriate.
Maximization of the value of soil biological
processes (e.g. free-living N fixers, mycorrhizae to improve access by roots to immobilize nutrients such as phosphorus), which
are strongly influenced by soil structure
and the supply of organic matter.
Permaculture/agroforestry to provide as diverse a range of annual and perennial plant
species (and associated root systems and
soil microorganisms) as possible on farms.
Drip irrigation can greatly improve efficiency
of use of inputs such as irrigation water and
fertilizer.
Terracing to minimize erosion hazards and
increase soil depth in steep areas.
Raised beds to reduce the risk of waterlogging damage.
regarding suitability of management practices
for end users − particularly nutrient and water
management (Mueller et al., 2012). A high proportion of the yield gap might be overcome
through ‘proper agronomic management even
when fertilizers are not applied’, for example, use
of organic production techniques. Where high investment, for example, inputs of fertilizer and labour, is needed to achieve a productivity response
of the degraded soil, a ‘poverty trap’ may occur
for the farmers (Tittonell and Giller, 2013).
New technologies to increase crop yields
such as genetic engineering or other gene manipulation have an important role to play in ‘sustainable intensification’, although Sinclair and
Rufty (2012) have noted that their performance
still is limited by soil water and/or N supply.
The provision of location-specific land management packages for closure of yield gaps (to achieve
food security), and reduction of environmental
impact from agriculture, calls for an open mind
Limitations associated with the human component of soil management must be understood clearly. Challenges include education
of soil s­cientists and their clients, professional
•
•
•
•
•
•
3.7
Training of Practitioners
Reduction of Yield Gaps and Improvement of Ecological Function
­ccreditation and ‘political will’ from governa
ment leaders locally, nationally and internationally. Bouma (2014) has noted that acceptance
and implementation by land users of measures
to combat soil degradation and to preserve and
improve soil quality are key to sustainable production. Current structures for grant winning
still restrict the opportunities for soil scientists to
engage with farmers at a grassroots level. There
is a need to conduct integrated research both at
the farm and advisory levels where the farmer,
researcher and advisor work together in a triangular relationship (Le Gal et al., 2011). A
major research programme on sustainable agriculture in the Netherlands has shown that to ensure implementation, much attention should be
paid to interaction with stakeholders before any
project starts. What do they really think and feel?
How can we encourage farmer-led research and
development? Exchange of knowledge may not
be a straightforward process and may involve
considerable effort and understanding, otherwise we run the risk of using ‘technology without wisdom’ (Lal, 2009). Most environmental
problems are land related, and consultants with
thorough training in soil science are particularly
suitable to act as ‘knowledge brokers’. They need
to be richly rewarded to make this type of work
the ‘profession of choice’ amongst talented
young people.
3.8
Conclusions
The world faces daunting food security and ecological management problems over the upcoming decades. Soil (and the visual evaluation of
soil in particular) is likely to receive higher status
in a crude oil and freshwater diminishing world
45
with what may be a less technologically dependent agriculture.
We have shown that there is a variety of land
management and agronomic frameworks (some
with an agricultural production focus, others
with a focus on ecological processes) that can be
usefully linked to the results of rapid soil structure
assessment and associated procedures. They have
the potential to provide farmers and their advisors with a practical and flexible means of narrowing yield gaps and enhancing beneficial ecological processes. Soil structure often is overlooked
in yield gap evaluations, despite the existence of
frameworks such as SOILpak and M-SQR that include structure assessment for crop production.
Although SOILpak has been successful, there is
potential for further impro­vement via inter-connection with ecologically f­ocused systems such
as ‘Landscape Function Analysis’.
Several techniques for visual soil examination and evaluation (VSE) are available that have
fundamental importance for the ‘soil assessment
and management packages’ because of their correlation with soil structure and associated hydrological processes. These need to be applied with
urgency to develop a large accredited workforce of
land management ‘knowledge brokers’ with thorough training in VSE and management in order
to overcome the soil-related barriers to productivity outlined in the introduction to this chapter.
Pragmatic new systems that evolve for soil
assessment and management therefore will have
to be interlinked with proposed new global networks – associated, for example, with the new
‘soil security’ framework described in Section 3.5 –
so that land management initiatives involving
farmers, soil scientists and agronomists are part
of local, national and global political processes
that provide the required financial incentives and
stability necessary for success.
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4 Visual Evaluation of Grassland
and Arable Management Impacts
on Soil Quality
Lars J. Munkholm1* and Nicholas M. Holden2
Aarhus University, Tjele, Denmark; 2University College Dublin, Dublin, Ireland
1
4.1
Introduction
Soil management has a profound influence on
soil quality1 through land use, crop rotation,
manure spreading, fertilization, irrigation, liming, tillage and traffic. Management effects on
soil quality are in many cases complex interactions, and therefore extensive research has
been carried out to describe, quantify and understand these effects. Visual soil evaluation (VSE)
is one of the tools developed over the last century
to specifically evaluate management impact on
soil quality.
During the early days of modern farming
there was a focus on soil nutrients and mineral
fertilizer; however by the mid-20th century
Görbing realized that factors like soil compaction,
crusting or drainage also caused poor growth.
Görbing and others recognized that there was a
need to supplement assessment of chemical properties with visual assessment of soil structure,
root growth and biological activity. His visual assessment spade method (Görbing, 1947) has since
been refined by Preuschen (1983), Beste (1999),
Munkholm (2000) and Ball and Douglas (2003).
quantitative visual methods for soil
Other semi-­
evaluation include the Peerlkamp method (Peerlkamp, 1959) ‘le profil cultural’ (Gautronneau and
Manichon, 1987), visual soil assessment (VSA)
(Shepherd, 2000; Shepherd, 2009) and SOILpak
(McKenzie, 2001a). There is now a range of
methods being used to assess management impacts that are also used for soil research as highlighted in Soil and Tillage Research (Munkholm
et al., 2013a). In some cases visual methods are
used as complementary tools to quantitative
methods and to assess overall soil structural
quality and biological activity.
This chapter will focus on the evidence
on how useful visual assessment methods are to
better understand the impact of land management
on soil quality, focusing on arable and grassland
systems by reviewing the available evidence in
the literature. The extension of visual methods
to land classification will also be considered.
4.2 Evaluation of Arable
Management Impact
The source references (Table 4.1) showed that a
large range of management practices has been
evaluated, covering many methods, countries
and soil types. Visual methods have been used in
14 countries in Europe, in North America, South
America, Asia and Oceania, but most studies
were from Northern Europe, Brazil, Australia
and New Zealand. Few studies have been carried
*E-mail: lars.munkholm@agro.au.dk
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
49
50
L.J. Munkholm and N.M. Holden
Table 4.1. Published papers where visual methods have been applied to evaluate management impact
on arable soils.
Paper
Country
Visual method(s) Soil
Boekel (1963)
NL
Peerlkamp
Boekel (1982)
NL
Munkholm
(2000)
Munkholm et al.
(2001a)
Munkholm et al.
(2001b)
DK
McKenzie
(2001b)
Boizard et al.
(2002)
Shepherd et al.
(2002)
Ball and Douglas
(2003)
AU
Boizard et al.
(2007)
FR
Ball et al. (2007)
DK, UK
Giarola et al.
(2009)
BR
Mueller et al.
(2009)
CA, CN, DE
Giarola et al.
(2010)
BR
Niero et al.
(2010)
Müller et al.
(2012)
Askari et al.
(2013)
Boizard et al.
(2013)
Garbout et al.
(2013)
Giarola et al.
(2013)
BR
Guimarães et al.
(2013)
Purpose of study
BR
Soil organic matter effects
induced by differences in
manure application and
land use
Peerlkamp
Not specified
Land use, rotation and traffic
effects
Spade method
Luvisol; sandy
Rotation, tillage and
loam
­fertilization effects
Spade method
Luvisol, Phaoezem; Rotation and fertilization
sandy loam
effects
Spade method
Luvisol,
Tillage effects (chiselling
Phaoezem;
versus ploughing)
sandy loam
SoilPAK
Vertisol; clay
Traffic and loosening effects in
cotton production
‘Le profil cultural’ Luvisol; Silt loam
Rotation and traffic effects in
arable farming
Peerlkamp
Not specified
Land use and rotation effects
(organic farming vs arable)
Spade method
Podsol; loamy
Rotation effects in a ley–­
sand, sandy
arable organic rotation
loam
experiment
10 different
Luvisol; silt loam
Rotation and tillage effects
methods
assessed using different
methods
VESS
Luvisol, Podsol;
Rotation and tillage effects
sandy loam
assessed using revised
Peerlkamp method
VESS
Oxisol
Land use effects (forest,
crop–livestock, no-till
arable)
VESS, VSA,
Luvisol, Cambisol; Rotation, tillage and traffic
MSQ
loamy sand to
effects using different
silt loam
methods
VESS
Oxisol
Land use and tillage effects
(forest vs long-term no-till
arable)
VSA
Oxisol
Land use and tillage effects
(arable vs forest)
VESS
Oxisol; clay
Gypsum application effects
IE
VESS
DK
DK
FR
UK
UK
Clay, marine clay
FR
Sandy loam to silty Rotation and tillage effects
clay
‘Le profil cultural’ Luvisol, silt loam
Tillage and traffic effects
DK
VESS
BR
VESS, VSA
UK, BR
VESS
Luvisol, Phaoezem; Tillage effects
sandy loam
Oxisol; clay
Tillage effects
Oxisol, Cambisol,
Luvisol; clay,
sandy loam
Land use and traffic effects
on differently textured soils
Continued
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
51
Table 4.1. Continued.
Paper
Country
Visual method(s) Soil
Purpose of study
Mueller et al.
(2013)
CN, CA, DE,
DK, RU,
NZ, UK
CA
VESS, VSA
Large range
Land use and tillage effects
VESS
Luvisol; silt loam
Rotation and tillage effects
FR
‘Le profil cultural’ Fluvisol; sandy
loam
Abdollahi and
Munkholm
(2014)
Abdollahi et al.
(2015)
DK
VESS
Luvisol, sandy
loam
DK
VESS
Pulido Moncada
et al. (2014a)
VE
VESS, VSA,
SQSP
Pulido Moncada
et al. (2014b)
BE
VESS, VSA
Ball et al. (2015)
BR, DK, FR,
UK
SubVESS
Luvisol,
Phaoezem;
sandy loam
Oxisols, Ultisol
Alfisol; loam to
silty clay
Cambisol, Luvisol;
sandy loam and
silt loam
Luvisol, Gleysol,
Oxisol
Munkholm et al.
(2013b)
Peigné et al.
(2013)
Tillage effect on structure in
the transition layer between
topsoil and subsoil
Cover crop and tillage effects
Rotation, residue
management and tillage
effects
Land use and texture effects
Land use and texture effects
Land use and traffic effects on
subsoil structure
AU = Australia, BE = Belgium, BR = Brazil, CA = Canada, CN = China, DE = Germany, DK = Denmark, FR = France,
IE = Ireland, NL = Netherlands, NZ = New Zealand, RU = Russia, VE = Venezuela, UK = United Kingdom
out in North America and Asia and none in
­Africa. Most visual methods were developed using medium textured, temperate soils, but they
have been used on soils ranging from loamy
sand to clay and on soil types ranging from
humid temperate Podsols and Luvisols to semi-­
arid Phaozems and tropical Ultisols and Oxisols
(Table 4.1). Land use, rotation, cover crops, residues, fertilization, gypsum application, tillage
and traffic have been evaluated, but most studies have concentrated on land use, rotation, tillage and traffic. The spade methods have been
most frequently used. The pit or profile methods,
such as ‘le profil ­cultural,’ SOILpak and the recently proposed numeric visual evaluation of
subsoil structure (SubVESS) method have been
less commonly used, and are the only methods
used for subsoil evaluation (McKenzie, 2001b;
Peigné et al., 2013; Ball et al., 2015). Almost all
studies are comparative, where soils/treatments
have been evaluated at one specific time, but in
some cases treatments have been followed over
a longer time, for example, to provide knowledge of the dynamic effects of management on
soil quality (Boekel, 1982; Boizard et al., 2013).
Askari et al. (2013) used a cross-sectional survey
to examine the status of a range of soils under
similar management at a specific time. The
findings of the studies summarized in Table 4.1
will be considered in order to reveal biological and
mechanical factors.
4.2.1 Biological factors
Biological factors include land use, rotation, animal residues (mainly manure), crop cover and
crop residues. In most cases the studies using visual methods have compared conditions rather than
monitored changes over time. For instance the
difference in soil quality between native forest
and arable land under both temperate and tropical conditions has been assessed by Guimarães
et al. (2013). Under temperate conditions they
showed significantly better soil structural quality scores using visual evaluation of soil structure
(VESS) for native forest compared with nearby arable fields. This was supported by soil physical
52
L.J. Munkholm and N.M. Holden
measurements of penetration resistance and air
permeability. A similar trend was also found
under tropical conditions by Giarola et al. (2009,
2010) and Niero et al. (2010). A comparison of
the surveys presented by Askari et al. (2013) and
Cui et al. (2014) showed relatively little difference in soil structural quality between arable
and grassland sites in Ireland. Pulido Moncada
et al. (2014b) found that cereal monoculture
maintained better soil structural quality than
permanent grassland for a silt loam in Belgium.
This was partly attributed to soil compaction by
cattle trampling for the grassland soil.
The effect of rotations with different intensities of cropping has shown that mixed arable
rotations resulted in better soil quality scores
compared with long-term cereal monoculture
(Munkholm et al., 2001a; Munkholm et al.,
2013b). Likewise, Askari and Holden (2014) found
better soil structural quality under mixed rotations compared with cereal monoculture under
conventional tillage. For these studies quantitative field and laboratory measurements supported the visual results. Row crops like maize,
sugarbeet and potato have been found to limit
soil quality (Boekel, 1982; Mueller et al., 2009).
Boekel (1982) found a decrease in Peerlkamp
score (decreasing soil quality) with increased
frequency of potatoes or sugarbeet in the rotation. Ball and Douglas (2003) reported that soil
structural quality improved under ley and decreased under arable in some organic ley–arable
rotation trials in Scotland. These findings suggest that visual methods are useful and robust,
but they are not necessarily robust enough to
remove the need for conventional quantitative
methods. Abdollahi et al. (2015) found that a
combination of visual assessment (VESS) and
quantitative soil data was needed to differentiate between mixed and cereal dominated rotations. This approach was similar to that of the
Muencheberg Soil Quality Rating system (Mueller
et al., 2007b; Chapter 2).
A key part of arable management is to provide
nutrient and organic matter return through animal manure applications. Boekel (1963) reported
a positive effect of organic matter application
(farmyard manure, green manure, sewage sludge)
on Peerlkamp scores after 6 years of contrasting
treatment. Likewise Munkholm et al. (2001a)
showed a better soil structural quality for a long-­
term animal-manured and diversely cropped soil
compared with cereal cropped neighbouring
soil. As well as animal manures, cover crops and
crop residues are also returned to soil under arable management to maintain structural quality.
Abdollahi and Munkholm (2014) and Abdollahi
et al. (2015) found no difference in topsoil structural quality (VESS) for plots grown with and
without cover crop after 5 years of differentiated cropping as well as for 10 years of +/− straw
removal, but they did show that cover crops had
produced a network of continuous vertically
oriented macropores, which are important for
root, gas and water transport, a feature not reflected in the soil structural quality score estimated by VESS. Nevertheless, the profile-based
methods might have captured this difference in a
way that the spade-based method could not.
4.2.2 Mechanical factors
The most important mechanical factor associated with arable soils is the physical disruption
caused by tillage. This can be seen in terms of
rearrangement of particles, the shape and size
of aggregates and in the maintenance or loss of
pore space. Visual evaluation methods have been
used to assess tillage and traffic impacts (Table 4.1)
either for field experiments or on working farms.
In general visual methods are sensitive to tillage
intensity (e.g. mouldboard ploughing versus shallow tillage) and depth (e.g. Mueller et al., 2009;
Garbout et al., 2013; Peigné et al., 2013) and can
correlate with crop yield (Mueller et al., 2013;
Munkholm et al., 2013b; Abdollahi et al., 2015).
In some cases intensively tilled topsoil can be interpreted as having better structural quality than
under reduced or no-tillage (Munkholm et al.,
2013b; Abdollahi and Munkholm, 2014; Abdollahi et al., 2015) because the cultivated layer has
relatively small, loose aggregates. This effect of
intensive tillage may diminish during the cropping
season as the soil reconsolidates after tillage. On
the other hand, Askari et al. (2013) found that
minimum tillage fields were associated with better structural quality (using VESS) than conventional tillage fields in Ireland. Generally, the
development of a network of continuous macropores offsets any negative effect of increased bulk
density under minimum tillage (Ehlers et al., 1983;
Garbout et al., 2013), and some visual methods
have difficulty in taking account of this, especially
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
Worst
5.0
5.0
4 June 2010
4.5
VSSE score
carried out within a diverse rotation (Fig. 4.1).
A range of soil physical measurements suppor­
ted this finding.
While tillage can directly contribute to soil
compaction, it is also caused by other field operations such as spraying, fertilizing and harvesting. Modern heavy machinery will impact soil
structure both in the topsoil and deep into the
subsoil (Etana et al., 2013). Compaction is a global threat to soil quality (Hamza and Anderson,
2005), has been assessed by most visual methods
and stimulated the development of SOILpak to
specifically address the issue for cotton producers
(Daniells et al., 1996), as well as the more recent
SubVESS method (Ball et al., 2015). Soil compaction causes changes in soil strength, the shape
and size of aggregates and clods, porosity, colour
and the distribution of roots and water. When
performing a visual evaluation it is relevant to
assess these properties (Batey and McKenzie,
2006; Batey, 2009) and some or all of these criteria are incorporated into all visual methods.
The spatial scale of topsoil compaction
evaluated using visual methods ranges from a
few decimetres under and between wheel tracks
using point-based methods (e.g. Boekel, 1982;
McKenzie, 2001b; Mueller et al., 2009; Guimarães
et al., 2013; Mueller et al., 2013) to dynamic,
systematic assessment under cropping systems
at the field scale using profile-based methods
18 October 2010
4.0
4.0
3.5
3.5
a
3.0
a
2.5
2.0
1.5
a
b
b
ab
ab
bc
b
c
3.0
2.5
b
c
2.0
1.5
Best 1.0
1.0
MP NT
MP NT
Div. rotation Corn-Soy
Worst
4.5
VSSE score
the spade methods. Peigné et al. (2013) showed
that minimum tillage promotes a greater density
of vertical oriented cracks and biopores in the
transition layer between topsoil and subsoil,
supporting the earlier view that, for some applications, profile-based methods may offer advantages over point-based methods such as VESS
and VSA.
The effect of deep loosening has been examined using visual methods. Munkholm et al.
(2001a) showed that chiselling to 35 cm depth
effectively loosened the upper subsoil to disrupt a
platy structured, dense, root restricting plough
pan (transition layer). The detailed spade method
of Munkholm (2000) provided results on the influence of deep loosening on aggregate type and
size as well as on visible porosity and was a valuable supplement to quantitative soil measurements. Giarola et al. (2013) also found a positive
effect of mechanical subsoiling on soil structure
using VESS for Brazilian Oxisols, but the improved soil structure was not persistent after
2 years and the short-term improved soil structure was not reflected in greater crop yields.
Visual methods have also been useful for
assessing the combined effect of rotation and
tillage. In a long-term tillage and rotation trial,
Munkholm et al. (2013b) reported good soil
structural quality for mouldboard ploughing but
not for no-tillage – except when no-tillage was
53
MP NT
MP NT
MP NT
Cont. corn
Div. rotation Corn-Soy
Best
MP NT
Cont. corn
Fig. 4.1. Visual Evaluation of Soil Structure (VESS) Sq score for 0–20 cm depth at the beginning and the
end of the 2010 growing season in diverse rotations. Corn was grown in all treatments. Columns with the
same letter within each time of sampling are not significantly different at P < 0.05 level. MP, mouldboard
ploughed; NT, no-tillage; Div. = diverse and Cont. = continuous. (After Munkholm et al., 2013b.)
54
L.J. Munkholm and N.M. Holden
(Boizard et al., 2002; Boizard et al., 2007; B
­ oizard
et al., 2013). Severely compacted U-shaped zones
were found by Boizard et al. (2013) below heavy
wheel load tracks. At the system scale, Boizard
et al. (2013) reported that only one incidence of
wet sugarbeet harvest caused a >5 year reduction in topsoil structural porosity under reduced
tillage. The proportion of unfavourable structural conditions (platy and compact clods)
changed with the weather and traffic events.
Their conclusions from visual field assessment
were supported by detailed morphological analysis of thin sections. The profile methods facilitate visual assessment of subsoil layers more
readily than point-based methods (Boizard et al.,
2007) and have been extensively used to assess
subsoil compaction (Batey and McKenzie, 2006).
They are useful and sensitive tools to detect compaction damage in the upper subsoil (down to
c.40 cm depth) (McKenzie, 2001b; Boizard et al.,
2007; Peigné et al., 2013). Traffic-­induced subsoil compaction may extend much deeper than
that as illustrated in Fig. 4.2. The SubVESS profile method was developed specifically for visual
evaluation of man-made impact on subsoil quality down to the expected rooting depth (Ball
et al., 2015).
4.3
Evaluation of Grassland
Management Impact
In the agricultural context grassland is managed
as fields subject to sward maintenance, grazing,
forage conservation and nutrient management.
Grassland is not usually used to describe fields
sown to grass as part of arable rotations, with
the exception of situations as found in, for example,
Northern Europe where a short, grass-dominated
rotation with maize is used for intensive dairy
forage production.
Grasslands, whether natural, semi-natural
or agricultural, typically have good soil structure
associated with abundant roots, macrofaunal
activity and abundant cycled carbon (Elliot,
1986). Degradation can occur following vegetation removal by overgrazing (Cao et al., 2013),
shrub and woody plant removal from natural
grassland (Moges and Holden, 2009), animal
treading (Herbin et al., 2011) or machinery traffic (Vero et al., 2014). It is generally assumed
that there is limited subsoil compaction under
grasslands unless there has been significant civil
infrastructure development nearby or fields are
used in short rotation with maize for intensive
dairy farming.
The main impacts of grassland management on soil functioning are caused by: (i) nutrient
management (mineral fertilizer, slurry, manure
and dirty water); (ii) grazing impact on organic
matter/carbon cycling, particularly input of
aboveground organic matter to the soil; (iii) animal
trampling (pugging, poaching); and (iv) machinery traffic for nutrient management, reseeding
and forage conservation.
Visual methods for (i) soil nutrient management utilize plant response rather than direct
soil assessment, with a focus on leaf colour, relative growth and sometimes species composition
(e.g. Shepherd, 2009). There is also evidence
that nutrient status will be reflected in soil structural conditions (e.g. Holden, 1994). There have
been no reports of studies using visual methods
to assess (ii) grazing impact on organic matter,
but visual methods are used to evaluate grass
production in grazing systems (e.g. Teagasc,
2011) and there is an expectation that differences in grass management and utilization will
be reflected in the soil structure (Cui et al., 2014).
Perhaps the most commonly used visual method
relates to (iii) soil surface damage by animal
trampling. The most straightforward visual method
is ‘pugging score’ (Nie et al., 2001), which uses
around 40 quadrats per paddock, placed randomly on the ground and the scoring goes from:
Score 0, no pugged area to Score 5, 100% of the
grazed area pugged. The pugged area is defined
as surfaces where plastic or compressive deformation of the soil had occurred (Patto et al., 1978).
Consistent surface damage, particularly in hot
spots, is expected to be associated with evidence
of soil structural degradation deeper in the profile because there will be a knock-on effect on
grass growth, aeration, faunal activity and nutrient status. The effect of (iv) machinery traffic
might well be expected to be similar to that in
arable systems, with surface deformation causing wheel ruts, and possibly deep compaction.
The magnitude of this impact should be much
less than for arable systems because the traffic is
usually less (except when heavy slurry tankers are
used), and the continuous presence of grass at
the surface provides both protection and regeneration functions.
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
55
Fig. 4.2. Soil profile affected by heavy compaction (right) and a non-compacted reference from two depths
(upper c.20–37 cm; bottom c.49–72 cm). The photos were taken in a Danish field experiment on soil
compaction on a sandy loam. The heavy compaction soil had been trafficked wheel by wheel early spring
3 years in a row with a tractor + slurry tanker (max. wheel load c.8 Mg and inflation pressure 3 bar).
Of the visual methods used to assess grasslands only two, VSA (Shepherd, 2009) and the
Muencheberg Soil Quality Rating (M-SQR) (Mueller
et al., 2007a) were originally designed with grassland in mind, while soil quality scoring procedure (SQSP) (Ball and Douglas, 2003) and VESS
(Guimarães et al., 2011) were originally tested on
sites in short rotation with grass as a forage crop.
McKenzie (2013) noted that in Australia the
pasture sector has not adopted the use of visual methods (referring to SOILpak, Daniells and
Larsen, 1991), and the other common pit method
(‘le profil cultural’, Gautronneau and Manichon,
1987) also appears not to have been used for
grassland. It is difficult to make specific statements
about the value of visual methods for understanding and monitoring the effects of grassland
management because there have been very few
studies published (Table 4.2) and their use is less
common as part of routine assessment of experiments. The discussion that follows evaluates interpretation of visual assessment data under four
main headings: (i) biological factors, that is
biologically driven stimulus of the soil system;
(ii) mechanical factors; (iii) drainage/­water status; and (iv) the role of management intensity.
56
L.J. Munkholm and N.M. Holden
Table 4.2. Summary of papers using visual methods for assessing soil structure under grassland
management.
Paper
Country Visual method(s) Soil type
Askari and
Holden (2014)
IE
VESS
IE
Based on VESS
and VSA
Mariscal-Sancho ES
et al. (2011)
Askari et al.
(2015)
Cui et al. (2014)
Kerebel and
Holden (2013)
Mueller et al.
(2007b)
DE
Newell-Price
et al. (2013)
Sonneveld
et al. (2014)
UK
NL
SQSP +
earthworm
count
VSA
M-SQR
Peerlkamp
VSA
Peerlkamp
Based on VSA
Ball et al. (2012)
NZ
VESS
Pulido Moncada
et al. (2014a)
VE
VSA
VESS
SQSP
Purpose of study
Range of mineral soils, Soil Quality Index (SQI) based
on management intensity
no extremes of
independently verified by
texture. Same sample
VESS
sites for these studies
Prediction of soil structural
quality using vis-NIR
spectroscopy –
­ interpreted
by management intensity
Soil structural quality and
grassland management
intensity
Range of mineral soils
Classification of grassland into
classes used for the Hybrid
soil moisture deficit model
(Schulte et al., 2005)
Clay loam
Organic manure amendment
(eutric Gleysol)
impact
Fen soil with grassland
Structural degradation and
land use intensity
Range of soil types
National survey, mainly grazed
and mainly cut paddocks
Method development at site
and farm level
Fluvisols (high clay
content), some
Histosols
Sandy loam (Aquic
Dystric Utrochrept)
Haplustoll
Trampling effect on N2O
emissions
Testing methods for tropical
soils under a range of
management including
grassland/grazing
DE = Germany, ES = Spain, IE = Ireland, NL = Netherlands, NZ = New Zealand, VE = Venezuela, UK = United Kingdom
4.3.1 Biological factors
4.3.1.1 Sward management
The use of visual soil evaluation methods for
­arable systems is more extensive than for grassland systems (compare Tables 4.1 and 4.2),
­perhaps because soil structural degradation is
considered more likely (Six et al., 1998). However, intensively managed grassland is subject to
regular, if infrequent, ploughing and reseeding.
Newell-Price et al. (2013) sampled 150 farms
in the UK stratified by rainfall, farm type and
soil type using VSA (Shepherd, 2000) and
Peerlkamp (1967). For each farm, samples were
­collected from ‘mainly grazed’ and ‘mainly cut
for conservation’ fields. The VSA method was
limited to visual indicators for assessing soil
quality and excluded indicators of texture, potential rooting depth and surface ponding. Between 8 and 12% of fields had poor structural
conditions and 25–38% had good structural
conditions. The VSA method tended to score
lighter textured soils better because they break
more easily in the drop shatter test and earthworms were easier to find than in heavy textured
soil. The most important factors influencing
VSA score were organic matter content and
sand content. The visual methods were unable
to distinguish grazing and cut management.
Likewise, Askari and Holden (2014) found no
significant difference between predominantly
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
grazed fields and fields used for both grazing and
silage, reflecting the resilience of grassland soils.
Cui et al. (2014) evaluated the VESS method
for soils under long-term grassland management in Ireland, avoiding atypical hotspots such
as gateways, water troughs and travel lines.
All 20 sites had adequate to good soil structure
based on the interpretation of VESS score by
Ball et al. (2007), and none of the sites had poor
structure requiring immediate rectification.
­Aggregate size was identified by Cui et al. (2014)
as the most important diagnostic criterion for
VESS score under grassland management. There
is no evidence in the literature of studies under
more intensive management such as short-term
forage rotations.
Cui et al. (2014) found no simple significant
relationship between VESS score and sward age.
The best scores were observed under permanent
grassland that had never been reseeded or had
been reseeded 10–20 years previously. Sward
that is never reseeded is generally less productive
(Dubeux et al., 2007; Radrizzani et al., 2010),
supporting less grazing, but has abundant biotic
activity leading to good structure. Regularly reseeded fields will have active root systems and
smaller aggregates due to ploughing, which will
also be reflected in a good Sq score. Fields that
have not been reseeded for 10–20 years are less
productive and have few of the advantages of
permanent pasture (Allan et al., 2013).
It is not possible to state definitively that visual methods can distinguish the effects of sward
management, specifically grass silage conservation silage vs grazing and reseeding programmes.
There is evidence that if these aspects of grassland management were having an adverse effect
on soil structure, then visual methods would
be able to detect changes through time. However,
the impact would have to be severe because of
the strong influence of the grassroot system
and the cycling of organic matter in conserving
soil structure near the soil surface.
4.3.1.2
Slurry, manure and fertilizer
management
Organic nutrients normally supplement mineral fertilizers and are usually spread as manure
(solid), slurry (semi-liquid) and dirty water from
washings of animal housing and hard standing
areas of the farmyard. No studies that we know
57
of have directly assessed the impact of organic
nutrient management, but Cui et al. (2014) and
Askari and Holden (2014) did this indirectly because stocking rate is directly proportional to
the amount of slurry or manure generated on
a farm. In Ireland, where their work was conducted and for most of Europe, stocking rate is
limited by the amount of nitrogen (N) that has
to be managed in manures and slurry. MariscalSancho et al. (2011) examined the long-term
effects of spreading poultry manure and sewage
sludge on grassland experimental plots. No significant differences in soil structural quality
were found between treatments.
Cui et al. (2014) and Askari and Holden
(2014) both found no significant difference in
Sq score (using the VESS method) caused by stocking rate. This was a small sample size so it is perhaps not surprising that the direct effect of slurry
management per se cannot be seen. Cui et al.
(2014) also classified fields into three groups by
N input rate (0–43, 43–129 and >129 kg N ha−1)
but found no significant differences in structure
among them. There was an indication of poorer
structure at the greater N input rate, but this
could probably be ascribed to the overall greater
management intensity and not to this single attribute of the system.
As there have been very few studies it is
not possible to state definitively whether visual
methods can distinguish differences in nutrient
management and its impact on organic matter
cycling in the soil, which is related to soil structure. There is perhaps sufficient evidence to conclude that if nutrient management were used
to promote high productivity, visual methods
would detect any detrimental impact of overall
management intensity on soil structure.
4.3.1.3 Stocking rate
Stocking rate is closely related to both sward and
nutrient management. It is balanced with herbage growth to ensure that feed demand is met
(Fitzgerald et al., 2005). At low intensity (e.g.
range grazing) the key limitation is overgrazing,
which can result in soil surface degradation, erosion and other adverse impacts (Cao et al., 2013).
In rotational grazing systems stocking rate is
matched to animal intake requirements and to
ensure good sward quality is maintained. Studies that have used visual methods and that have
58
L.J. Munkholm and N.M. Holden
reported stocking rate data are those of Cui et al.
(2014) and Askari and Holden (2014). Askari
and Holden (2014) sampled the same sites as
Cui et al. (2014) but used a different interpretation and did not reveal any significant difference in soil structure by stocking rate per se.
field scale. The results of Cui et al. (2014) and
Askari and Holden (2014) both suggest that
as stocking rate increases, with consequent
long-term impact through trampling, a decrease in soil structural quality can start to be
detected.
4.3.2 Mechanical impacts
4.3.3 Drainage/water status
Visual assessment of mechanical impacts on
soil under grassland management has been
largely limited to assessment of surface damage
by ‘pugging score’ (Nie et al., 2001), but Ball
et al. (2012) have also used visual methods to
assess the impact of trampling on nitrous oxide
(N2O) emissions. They simulated trampling using a mechanical hoof in a field normally grazed
by sheep, and assessed the impact using VESS
(Guimarães et al., 2011). There was a significant difference in Sq score at 0–5 cm between
trampled and non-trampled plots, which indicated that VESS is sensitive to small-scale
trampling effects in the near surface soil. Even
under trampling the mean VESS score was good
(2.2), interpreted as ‘sustainable’ based on Ball
et al. (2007). As this interpretation was originally devised for arable management it might not
be ideal for grassland, but there is little reason to
believe that sub-surface structural damage was
occurring after short-term intensive trampling,
which goes some way to explaining the resilience of grasslands to mismanagement. Under
field conditions a strong effect of both cattle
trampling and machinery traffic on the soil
structural quality was found in Scotland (Douglas et al., 1992; Ball et al., 2013). Ball et al.
(2013) showed that the VESS score for the
0–25 cm layer went from moderate (2.6) before
compaction to poor (4.2 for trampling and 4–5
for machinery traffic) after 2 years of compaction treatment. This resulted in increased N2O
emissions compared with the control. It is also
worth noting that degraded grassland soil may
recover rapidly. Douglas et al. (1998) observed a
marked improvement in topsoil structural quality after 2 years of limited wheel traffic following previous compaction by conventional wheel
traffic. Sonneveld et al. (2014) present a method
for moving the assessment of land use effects on
visual soil quality from field to farm scale and a
similar approach might be useful from point to
Grassland fields were classified by Hybrid Soil
Moisture Deficit Model (HSMD model; Schulte
et al., 2005) or by drainage class by Kerebel and
Holden (2013). In the HSMD model a welldrained soil never exceeds field capacity (maximum SMD = 0 mm), a moderately drained soil
reverts to field capacity within 24 h after rainfall and a poorly drained soil can take up to 20
days to revert to field capacity. Kerebel and
Holden (2013) defined objective indicators including roughness (pugging score), vegetation
and soil indicators derived from SQSP (Ball and
Douglas, 2003). Of the soil visual indicators,
the way the soil block opened when removed
from the ground and the presence of hydromorphic features were the most important
because they tended to be most consistent
­
­between operators.
4.3.4 Management intensity
In grassland no one component of the management system will have an overriding influence on
soil structural quality because of the interaction
between land area available, grass productivity,
number of animals, nutrient management and
forage conservation. From the limited number of
studies published using VSA for grass­land, it is
evident that distinguishing the impact of specific
attributes of the management (e.g. stocking rate,
fertilizer management and sward management)
is not really possible.
Under temperate climate the study sites
presented by Askari et al. (2015), Askari and
Holden (2014) and Cui et al. (2014) were all
from low-cost, rotational grazing systems. The
analysis of Cui et al. (2014) and a reanalysis of
the data presented by Askari and Holden (2014)
both indicate that VESS Sq score is poorest under
more intensive grassland management. Nevertheless, the range of Sq scores for these studies
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
was small (the greatest mean Sq score for a site
was <3), indicating that for soil structure, management was sustaining the soil (Ball et al., 2007).
The work of Mueller et al. (2007b) using VSA,
Peerlkamp and M-SQR to analyse fen soil quality
under grassland also showed that grassland
management does not typically cause untoward
damage to soil structure. Sonneveld et al. (2014),
who worked on dairy farms in the Netherlands,
found that 81% of sites had good structural
quality, 18% moderate and 1% poor. When calculated at farm level, only one farm was classified as having moderate structure and all the rest
were good. This result is also consistent with
the view that grassland management is not normally associated with poor structural quality as
determined by visual methods.
In their assessment of visual methods for
tropical soils, Pulido Moncada et al. (2014a) included grassland sites with grazing, range grazing and no trampling, using VESS, SQSP and VSA.
They found reasonably consistent results between
the methods, and only the range soil, which was
sampled at a site with vegetation degradation,
showed significant signs of soil degradation. Overall, the evidence available suggests that visual
methods are a useful tool for low-cost monitoring of soil structure to evaluate the impact of
changes in strategic, tactical and operational
management of grassland farms, but to date
there are insufficient data to have a clear idea of
what the limits of the methods are and how they
might best be used.
4.4 Aspects Requiring Further
Development
We consider the following are important when
using and modifying visual method for arable
and grassland conditions.
4.4.1
Assessment of pores
A network of continuous macropores is beneficial for a range of crucial soil functions such as
drainage, aeration and root growth. Unfortunately, the spade methods based on block extraction and fragmentation (e.g. VSA and VESS)
have difficulty in taking this into account. Therefore, soils with a loose fragmented structure
59
such as intensely tilled soil may be overrated as
compared with a more dense soil with an extensive network of continuous macropores (e.g. direct drilled soil). In this case profile methods (e.g.
McKenzie, 1998; Peigné et al., 2013) may offer
an advantage to the spade methods. It may also
be useful to supplement with quantitative measurement of macropore properties on intact samples taken from the field as done by, for example,
Abdollahi et al. (2014). The assessment of the
smaller intra-aggregate pores can also be challenging as highlighted by, for example, Cui et al.
(2014) when using the VESS method. More
detailed and systematic description of small
visible pores is included in other methods such
as the spade method by Munkholm (2000).
These procedures may be included in VESS or
other commonly used methods.
4.4.2 Taking account of soil layering
Visual methods are excellent tools to observe
and assess layering in the soil profile as shown in
Chapter 1 of this book. However, distinct soil
layering is a challenge when using averages of
scores across distinctly different soil layers at different depths (Newell-Price et al., 2013; Cui et al.,
2014) where a sample with both excellent and
very poor structure will score as moderate with
the information about the limiting layer being
lost. A temperate climate grassland soil has a topsoil profile (Fig. 4.3a) typified by a dense thatch
of dead plant material (not included in the VESS
charts; Cui et al., 2014) making the mineral surface difficult to identify. The soil organic matter
content at the surface decreases with depth to
below the rooting depth, which is typically
deeper than the spade depth used for visual
methods. The grassland profile is usually quite
different to that of similar soil subject to tillage
(Fig. 4.3b), but much less contrast is expected
when compared with no-tilled arable soil. They
may also typically show a distinct layering (Giarola
et al., 2013). The layering issue was addressed
in the improved VESS method (Guimãraes et al.,
2011), where it was suggested that values be reported for all layers as well as a depth weighted
average. For subsoils, knowledge of limiting layers
is crucial so Ball et al. (2015) and McKenzie
(1998) recommend reporting evaluations for
individual layers.
60
L.J. Munkholm and N.M. Holden
4.4.3 Extraction and separation
of soil blocks for assessment
Both VESS (Guimarães et al., 2011) and VSA
(Shepherd, 2009) require the extraction of a
block of soil that is used for the primary assessment. Cui et al. (2014) noted that extracting a
block of soil under grassland was much more
difficult than under arable. Additionally, Giarola
et al. (2013) stated in a study on Brazilian Oxisols
that extraction of blocks was often difficult due
to dry conditions. As difficulty of extraction is a
modifier for the VESS method, this has to be
carefully interpreted for grasslands and for dry
conditions. VESS assumes the difficulty of extraction is due to structural damage, while Cui
et al. (2014) found that under grassland the dense
root mat contributed to the problem and made
sampling more time consuming. In temperate
climates grassland soils also tend to be somewhat finer textured and wetter than arable soils.
Cui et al. (2014) and Giarola et al. (2013) also
noted a methodological issue with the description of finger pressure needed to break up aggregates. Some basic knowledge of soil structure
and soil fragmentation is needed (Giarola et al.,
2013), so training of operators on soil break-up
is therefore crucial if using the VESS method.
4.4.4 Faunal activity
The activity of soil fauna, for example, earthworms is much greater in grasslands and
Fig. 4.3. (a) Grassland topsoil profile and (b) arable soil profile from similar soil types found in temperate
maritime climate (Ireland). (Photos courtesy of Mohammad Sadegh Askari.)
Visual Evaluation of Grassland and Arable Management Impacts on Soil Quality
61
(b)
Fig. 4.3. Continued.
­ o-tilled arable soils than in inversion-tilled
n
soils, with a consequent difference in the formation of structure and observable features. VSA
and the Munkholm spade method (Munkholm,
2000) use worm observations as an indicator,
whereas VESS does not. For the VSA method it
is possible for a moderately degraded grassland structure to appear to score quite well if
worm activity is not curtailed. Again, care
should be taken to recognize this possibility.
The role of earthworm burrows for subsoil
rooting was highlighted by Peigné et al. (2013).
Refining visual methods to assess faunal activity was highlighted as a research need for arable soils at the Peronne meeting in 2005
(Boizard et al., 2007) and this is still a valid
statement.
4.4.5 Need for specific methods or
interpretations for grassland soils
The distinctive features of grassland soils (layering, thatch, frequent difficulty in extraction and
separation and earthworm activity) suggest that
there is a need for a grassland specific modification or interpretation of current visual methods.
This was confirmed by Cui et al. (2014) who noted
that there were some difficulties in using the VESS
method under grassland that reflected its original development for organic rotation and tillage
sites. The method of Shepherd (2000; 2009) has a
specific version for grasslands. Some issues identified by authors who have used these approaches
for grassland soils need to be highlighted to support future research and method development.
62
L.J. Munkholm and N.M. Holden
Furthermore, there is also a need to investigate
the optimal combination of visual methods with
other simple quantitative or qualitative field
methods as emphasized by Mueller et al. (2014).
4.5
Conclusions
There is a range of well-described and tested visual methods available for soil quality ­assessment
in the topsoil and the subsoil. Extensive research
has shown the methods to be sensitive to difference in management for arable and grassland
soils and to be useful in evaluating management
impact on soil quality. Most studies have been
carried out under arable management and using
plots set up for experimental treatments, so there
is a need to test the methods for use in grasslands
and to refine them if needed and to better understand how to apply the methods for routine field
and farm scale monitoring. The recommended
choice of method depends on the purpose of the
investigation, the experience of the operator
and the time and resources available for the
task. For future research we identified a need to
take better account of soil layering in arable and
grassland soils, faunal activity, macropore characteristics, and extraction and separation of soil
blocks for topsoil spade methods.
Note
1
We are aware of the many definitions of soil quality but in this chapter we apply the Soil Science Society
of America definition ­(Karlen, 1997) but with special emphasis on the capacity of soil to sustain plant and
animal productivity.
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5 Choosing and Evaluating Soil
Improvements by Subsoiling
and Compaction Control
Richard J. Godwin1* and Gordon Spoor2
Harper Adams University, Newport, UK; 2Model Farm, Maulden, UK
1
5.1
Introduction
Soil compaction can seriously affect crop
­production, soil quality and biological activity, and
considerable time and energy are often expended in
attempts to alleviate it. Problems arise through increased mechanical impedance restricting water
availability, root development and air and water
movement, increasing the risk of anoxic conditions. Figure 5.1 illustrates how alleviating the
compaction layer or pan in a sandy loam soil has
transformed the root development of sugarbeet.
The influence of compaction on crop
production depends on the thickness, location,
macroporosity and moisture status of the compact layer, together with the prevailing weather
conditions and soil management techniques.
Compaction can also significantly influence soil
infiltration rates and the efficiency of sub-surface
drainage. These are important locally at farm
level, but also at catchment level through their
influence on soil erosion and surface flooding,
concerns likely to increase in these times of increasing extremes of weather. Visual soil assessment (VSA) has an important part to play in
identifying all such potential problems (Ball et al.,
2007 and others, see Chapter 2).
Techniques and equipment are available for
alleviating compaction, but the results are not
always successful. This has been reconfirmed in
recent work (Palmer, 2011) in south-west England on 194 fields with 420 profile pit sites on a
mixture of grass and arable fields. Only 50% of
remedial works showed significant improvements in soil structure following remediation.
The reasons for poor success rates vary, ranging
from a lack of understanding of the problem,
doubt over the type of remedial soil disturbance
required, poor equipment adjustment and recompaction of the looser weakened soil during
subsequent field operations. In some cases if
there is no real anthropic problem present anyway, the remedial operations may cause more
damage than good, disrupting natural root and
water movement pathways. Other secondary
problems can arise, including excessive soil cloddiness, unwanted uneven surfaces, loss of well
structured surface soil to depth and increased
waterlogging in situations where sub-surface
drainage (both natural and engineered) is inadequate. To maximize the chances of success,
there must first be confirmation of the presence
of a problem, identification of the type of disturbance required to alleviate it, checks to ensure
the desired disturbance is being achieved and
care during subsequent field operations to prevent recompaction. Clearly visual soil evaluation,
mainly through profile pit analysis, is important
*E-mail: r.godwin@iagre.biz
66
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
67
Fig. 5.1. Comparison of sugarbeet root development with a soil pan (left) and where the soil pan had
been disturbed (right).
at each stage of these crucial processes of soil
care and management.
Biological measures have been investigated
to alleviate compaction as alternatives to tillage.
These include stimulating increased biological
activity, particularly earthworms (Ampoorter et al.,
2011), and growing crops such as radish that
have strong tap root systems (Muller et al., 2001).
Unfortunately, to date, these methods have not
proved to be particularly successful and hence
for positive results mechanical measures are necessary. Limited and well-timed mechanical
measures can also be used to prevent severely
damaged conditions deteriorating further, an example being a severely wheel rutted field following potato harvesting. Running a tine across these
ruts at relatively wide intervals provides lateral
drainage outlets to the adjacent uncompacted
soil, thus preventing water ponding in the ruts
and runoff to the lower parts of the field thereby
decreasing the overall flood risk (Batey, 1988).
Recent studies have shown that over most
fields, conventional mechanization management
systems result in one or more passes of a tractor wheel over the entire surface per growing season, causing widespread compaction (Kroulik
et al., 2009). The damage is tending to increase
with the continuing increase in the weight of
tractors and other equipment (Chamen, 2011).
The potential benefits of alternative mechanization management systems such as lower ground
pressure systems (both tyres and tracks) and controlled traffic farming practices to reduce compaction, therefore, need to be considered.
This chapter aims to identify the basic
types of soil disturbance possible with loosening
equipment, how disturbance can be modified to
suit requirements through implement selection
and adjustment, and the extent to which field
procedures can be modified for successful operation in situations with lighter tractors and
less tractive power. Recent results from reduced
68
R.J. Godwin and G. Spoor
i­nflation pressure and controlled traffic farming
systems are reviewed and related to crop performance and environmental factors.
5.2
Identification of Compaction
Problems and Alleviation
Requirements
Compaction problems may occur at different
depths, taking diverse forms and requiring different treatments for their successful alleviation.
Their causes may be natural or the result of field
traffic from machines or animals.
Surface layer problems (<250 mm deep)
usually arise through surface trafficking and animal trampling and, on weakly structured soils,
through soil structural collapse due to heavy rainfall or waterlogging. Remedial treatments vary
from initiating a thorough soil loosening to separate and split the soil structural units, to a gentle
easing of the compacted soil mass, allowing the
natural soil structural units to separate, thereby
increasing structural porosity.
In other situations, compaction may be
relatively local in the form of a pan or discrete
compact horizon, see Fig. 5.1, formed as a result
of implement or tyre action at that depth. These
are usually below the topsoil layer and are all or
part of the ‘transition layer’ between topsoil and
subsoil (Peigné et al., 2013). In such cases, remediation may be complete pan disruption or
the creation of fissures through the compacted
zone to allow root, air and water penetration.
Deeper layer problems (>350 mm depth)
usually take the form of more massive soil conditions containing minimal or zero macroporosity.
They may be caused by very high surface loadings, excessive soil working during poorly executed
restoration operations (Batey, 2015) or through
historic natural consolidation. Remedial requirements in these situations range from reopening
the natural structural pores where present, through
to the break-up of the massive structures into
smaller soil units, as a first stage in structural redevelopment.
Reviewing these remedial requirements, two
basic types of soil disturbance are required. First,
a general loosening and soil rearrangement, and
second a gentle lifting within the affected area, to
re-establish structural porosity or to generate
cracks through the compacted zone. Both types
of disturbance may be required at different depths
and may need to be achieved under different moisture conditions. They also need to be achieved
without creating unwanted problems such as
excessive cloddiness, unfavourable surface conditions and unnecessarily loose conditions prone
to recompaction. A critical first step before any
remediation, however, must be to clearly identify
where and what the problems are.
The most direct indicators of potential
compaction problems are poor crop development, particularly root development during
periods of active growth when deep rooting
would be expected, together with unfavourable
soil conditions within the profile. Both require
profile pit examination for confirmation of the
existence of a problem (Earl et al., 2003) and, if
present, to decide using approaches such as the
numeric visual evaluation of subsoil structure
(SubVESS) (Ball et al., 2015) its nature, location and treatment requirements. Once the
situation has been established in the profile pit,
conventional penetrometers can then be used
to more rapidly determine the extent of the
problem across the whole field. This requires
identification of ‘reference’ penetrometer resistance readings of the soil conditions immediately
adjacent to the profile pit, prior to a field-wide
reconnaissance.
Alternative methods to reduce the time
needed for soil investigations with broader field
coverage include pulling a series of ‘horizontal’
penetrometers mounted at different depths on a
tine leg through the soil (Chung et al., 2004;
Verschoore et al., 2003). Sharifi et al. (2007)
demonstrated the value of these techniques as
research tools, but indicated they would require
further development for practical use. As many
tractors and harvesters are now fitted with real
time kinematic (RTK) GPS, the location of potentially compacted soils can be identified and soil
loosening equipment targeted on the damaged
zones.
The use of remotely sensed crop reflectance
(Normalised Difference Vegetation Index) data
of the previous crop (Wood et al., 2003) and
electromagnetic inductance data of soil electrical conductivity (Godwin and Miller, 2003)
can help in the identification of field zones that
may be in need of remedial treatment (Taylor
et al., 2003).
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
5.3 Basic Action of Soil Loosening
and Mole Drainage Equipment
Examples of the types of loosening tool available
for alleviating compaction are shown in Fig. 5.2.
These tools can be grouped into two categories,
namely, narrow tines and winged tines (Spoor
and Godwin, 1978). Narrow tines have a leading foot only (Fig. 5.2a, b, e and f) whose width
and height of lift vary with design. Winged tines
have, in addition to the leading foot, a pair of
wings positioned at a short distance behind the
foot tip (Fig. 5.2c and d). The lift height of the
wing varies depending upon the objectives of
their design. In terms of their basic action on the
soil, however, all tools within the same category
behave in a similar manner.
5.3.1
Narrow tine disturbance
and critical depth
The soil disturbance patterns created by narrow
tines working either relatively shallow or deep
are shown in Figs 5.3 and 5.4. At shallower
working depths the soil is moved forwards and
upwards along a succession of failure planes
that develop from the foot tip as the tine moves
forwards. These planes disrupt the soil mass
along its planes of weakness, which in structured soils would often be between the structural
units. The upward movement and disruption allows the soil to dilate, relieving compaction. As
the tine works deeper, the resistance to this upward soil movement from the soil above, usually
termed confining stress, increases to the point
where it becomes easier for the soil at depth to
flow laterally around the foot rather than move
upwards. This lateral movement, which is into
an area already occupied by soil, creates local
soil compaction, leaving a compacted zone and
often a channel. The transition depth between
the two types of disturbance is termed the critical depth, and this depth represents the maximum useful working depth of that tine for soil
loosening (Godwin and Spoor, 1977). The degree of soil disturbance generated during loosening operations is very dependent upon the
relative position of actual working depth to the
critical depth of the tine in that particular soil.
As the tine working depth approaches its critical
69
depth, the loosening effect decreases until at critical depth it becomes effectively zero. By contrast, for mole drainage, the foot (Fig. 5.2f) must
work below the critical depth to create a stable
mole channel as illustrated in Fig. 5.4b.
The critical depth for any given tine is both
soil moisture content and density dependent.
Critical depth is deeper in dry compact soils,
whereas in plastic soil conditions it will be closer
to the soil surface. In soils of varying density, looser weaker conditions above the compacted zone
will, through their effect in ­reducing the confining stress, increase the critical depth. Conversely,
strong surface conditions (visual evaluation of
soil structure, VESS Sq4 or 5 (Ball et al., 2007;
Guimarães et al., 2011) or VSA structural index 0
(Shepherd, 2009)) relative to those at working
depth will tend to reduce the critical depth.
Implement factors influencing critical depth
are the width and lift height of the leading foot,
where wider foot tips and greater lift heights increase the critical depth. It would be helpful when
planning field operations if a given tine had a
fixed critical depth. Unfortunately, due to the additional influence of the prevailing soil conditions,
this is not the case. As a guide for practical purposes, a narrow tine working in compact soil, in
the friable moisture range, that is, drier than the
plastic limit, would have a critical depth of approximately six times the foot width.
5.3.2 Winged tine disturbance
Soil disturbance with winged tines is progressive.
The leading foot creates an initial narrow tine type
disturbance, which in structured soils can include
a horizontal crack just above working depth
(Fig. 5.5a). The wings follow, positioned ideally in
this horizontal crack to minimize draught, generating additional soil disturbance as shown in
Fig. 5.5b. The lifting action of the wings also tends
to lead to the formation of tensile cracks both
within the disturbed zone (Fig. 5.5b) and in the
overall soil mass as it flows up and over the wings,
see Fig. 5.5c. Figure 5.6 illustrates the development of these soil failure planes with a winged tine
when pulled slowly into the vertical face of a
profile pit. In Fig. 5.6, the leading foot has already
entered the profile with the wings following.
­Tension cracks between soil structural units can
70
R.J. Godwin and G. Spoor
(a)
(c)
(e)
(f)
Fig. 5.2. Alternative tine designs. Top row (a) conventional subsoiler and (b) chisel tine. Middle row (c)
wide tipped-high lift height winged subsoiler, (d) narrow tipped-low lift height winged subsoiler + leading
disc. Bottom row (e) slant leg subsoiler + leading discs, (f) mole plough.
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
(a)
71
Direction of travel
Soil surface
Soil failure planes
Undisturbed soil
(b)
Soil surface
Compacted zone
Critical depth
Fig. 5.3. Side elevations (left) and cross-section (right) of a narrow tine showing the effect of the
operating depth on soil disturbance above (a) and below (b) the critical depth. (After Godwin and Spoor,
1977. Redrawn from Spoor, 2006.)
0.30 m
(a)
(i) Chisel tine
(ii) Conventional
subsoiler
(iii) Mole plough
(iv) Slant subsoiler
(b)
0.50 m
Fig. 5.4. Cross-section of the soil disturbance of a range of implements operating (a) above and (b) below
their critical depth. (After Spoor and Godwin, 1978.)
(a)
(b)
(c)
Direction of travel
Tension cracks
Soil surface
Wing
Soil flow
Lift height
Fig. 5.5. Soil disturbance initiated by (a) a conventional subsoiler tine (showing horizontal crack
development); (b) a winged tine; and (c) a schematic diagram of the tension cracks developed by soil
flowing over a wing. (After Spoor and Godwin, 1990 and Spoor, 2006.)
72
R.J. Godwin and G. Spoor
Fig. 5.6. Soil failure created by a winged tine. (After Spoor and Godwin, 1978.)
be observed within the overall disturbed zone, arising from the lifting action of the wings. Wing lift
height has a significant influence on the degree of
disturbance, with disturbance increasing with increasing lift height. Working in the friable moisture range, wings also tend to cause reorientation
of the soil units at and above working depth as the
soil flows over the wing. The greater the wing lift
height, the greater the possibility of tension cracks
developing in the disturbed zone.
In fine-textured, cohesive soils, as moisture
contents increase towards the plastic state, significant disturbance and tension crack development become increasingly difficult to achieve. At
moisture contents close to or slightly above the
plastic limit, significant soil cracks can only be induced through the bending action as the soil
flows over the subsoiler wing (see Fig. 5.5c). Moisture status has less influence on soil disturbance
in loamy sands, sandy loams and silts.
5.3.3 Leg disturbance for
subsoiling vs moling
The disturbance generated by the tine leg is dependent upon whether the tine is working above
or below its critical depth (Godwin et al., 1981).
Above critical depth, the leg, which is positioned
behind the foot and wings, is always moving in
soil that has just been disturbed by the foot and
wing. Due to this the leg tends only to displace
the disturbed aggregates, increasing the surface
roughness adjacent to the leg slot. This effect is
reduced in the case of the slant leg subsoiler,
shown in Fig. 5.2e, where the leg moves at the
outer limit of the disturbed zone rather than in
the centre.
When working below critical depth, the side
area of the leg of conventional subsoilers and
mole ploughs (Fig. 5.2a and f) also influences the
soil disturbance. As the leg moves f­orward, friction and adhesion between the soil and the side
of the leg cause the soil in contact to be dragged
forward and compacted locally. This continues
until the limited friction and adhesion forces are
insufficient to drag the soil any further forward
towards the undisturbed mass. Sliding then occurs so that the leg leaves behind the ­locally compacted soil and the build-up process begins again.
The new soil now in contact with the leg is
dragged forward, opening up a more major crack
between the now stationary compacted soil and
the soil being moved forward. Figure 5.7 shows
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
73
Fig. 5.7. Leg cracks from ‘moling’ soil failure. (After Godwin et al., 1981.)
the cracking pattern developed, where the major
cracks extend from the soil s­ urface to the mole
channel at depth. These cracks are important for
effective mole drainage, as they constitute the
main pathways for free water movement from
the upper soil profile into the mole channel for
­removal.
5.4 Soil Disturbance with Multiple
Tine Arrangements
Loosening tines tend to be used in combination
rather than individually, which can allow the
soil masses disturbed by the individual tines to
interact, influencing the extent and pattern of
disturbance and the draught forces involved.
The spacing between tines operating at the
same depth has a major effect on the uniformity
of soil loosening at depth and on the levelness of
the final soil surface. This is illustrated in Fig. 5.8,
which shows the soil disturbance patterns for
conventional subsoiler tines operating at two
spacings. At the wider spacing, each tine is effectively working individually, resulting in uneven disturbance at depth and an uneven soil
surface. Reducing tine spacing to approximately
1.5× and 2× the working depth of the tine, for
the conventional and winged subsoilers, respectively, allows the soil disturbance zones to completely interact, resulting in near complete soil
breakout at depth and a final level soil surface.
In the tine arrangement shown in Fig.
5.9, the leading shallow tines loosen the soil
locally and as soil always deforms along the
path of least resistance, the failure planes from
the following winged tine, instead of developing directly to the surface, as shown in Fig. 5.6,
divert to the zone loosened by the shallow leading tine. This increases both the total area of
soil disturbed and the degree of loosening
achieved within the disturbed zone compared
with that of a single winged tine. This is illustrated in the two soil disturbance profiles shown
in Fig. 5.10 in a clay loam soil. In addition to
the increased overall soil disturbance, the soil
in the combination arrangement is also less
compact at depth.
Changing the spacing between the shallow
leading tines influences the disturbance limits. The
spacing arrangement shown in Fig. 5.9 with the resulting soil disturbance shown in Fig. 5.10 (lower)
74
R.J. Godwin and G. Spoor
Fig. 5.8. Influence of tine spacing on uniformity of soil loosening and surface level. Upper: too wide.
Lower: optimum for complete breakout. (After Spoor and Godwin, 1990.)
2.5 d
1.6 d
d
Rear view
0.6 d
Side view
Fig. 5.9. Optimal position of leading shallow tines, showing soil disturbance pattern (left). (After Spoor
and Godwin, 1978.)
has been found to be optimum to maximize
the loosening efficiency, as discussed further
in Section 5.5.
5.5 Draught Forces and Power
Requirements
The addition of both wings and leading shallow tines to subsoiling equipment has a significant effect on both the area of soil disturbed
and the draught forces. These changes in turn
affect the overall efficiency of the soil loosening operation. Spoor and Godwin (1978)
showed that, although the addition of wings
increased the draught force of an individual
tine by approximately 30%, an approximate
doubling of the area of disturbed soil more
than compensated for this increase. Adding
shallow leading tines when appropriately positioned (Fig. 5.9) working from the ‘top-down’,
had no effect on the draught force but nearly
doubled the area of soil disturbed so improving
the soil loosening efficiency. At the same time
deep tine spacing can also be increased to approximately 2.5× the depth of work and still
produce a level surface.
The draught force vs working depth relationship for a winged subsoiler tine at different
depths in soil compacted by either a loaded
combine harvester tyre or a loaded rubber
track is shown in Fig. 5.11. In both cases
‘doubling the tine working depth approximately quadrupled the draught force’. It is
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
75
Fig. 5.10. Soil disturbance from a winged (upper) and winged + shallow leading tine (lower) subsoilers.
Compare the metre rule for scale. (After Spoor and Godwin, 1978.)
40
Draught force (kN)
35
30
25
20
15
10
5
0
0
100
200
300
400
500
Depth (mm)
Fig. 5.11. Effect of depth of operation on the draught
force of a single winged subsoiler tine ­alleviating
compaction caused by: the front and rear tyres of
a 30-t combine harvester (solid line) and a rubber
tracked front axle and rear tyre of a 32-t combine
harvester (broken line). (After Ansorge, 2007.)
i­ mportant to be aware that working at depths
greater than those ­required to alleviate the
compaction problem, not only produces an
undesired soil disturbance but also causes an
unnecessarily large increase in the draught
force.
The available tractor power is the major
­factor influencing the number of tines that can
be pulled at a given working depth, as given in
Table 5.1, for typical soil conditions. Track-laying
tractors, of similar power can, however, pull
c.50% more tines at the same depth or tine depths
can be increased by up to 20%.
Often the agronomic requirement is to
loosen the soil to a uniform depth, leaving a level
surface finish without recompacting the loosened soil with the tractor wheels in the process.
With adequate tractor power this is possible using the tine spacing recommendations given in
76
R.J. Godwin and G. Spoor
Table 5.1. Approximate wheeled tractor capability for operating loosening tines. (After Spoor and Godwin,
1990.)
Tractor size
Engine power (hp/kW)
Capability
Ballasted weight (tonnes)
Working depth (m)
Number of tines (n)
150/110
7.50
250/185
12.50
350/260
17.50
0.50–0.60
0.35–0.45
0.30–0.35
0.45–0.55
0.40–0.45
0.35–0.40
0.30–0.35
0.45–0.55
0.40–0.45
0.35–0.40
0.30–0.35
1
2
3
2
3
4
5
4
5
6
7
These figures are for guide purposes only and will be affected by soil conditions.
5.6 Implement Selection,
­Adjustment and In-field Evaluation
1st Pass
5.6.1
d
2nd Pass
d
2d
4d
Fig. 5.12. A two-pass method for complete soil
loosening with winged tines. (After Spoor and
Godwin, 1990.)
Sections 5.4 and 5.5. Where this is not possible,
the simplest alternative approach, shown in
Fig. 5.12, is to mount the tines immediately b
­ ehind
the tractor wheels and loosen in two passes. During the second pass the tractor wheels operate
on undisturbed soil centrally between those of
the first pass, with the tines working at a slightly
greater depth to improve implement lateral stability. With winged tines the tractor wheel spacing would be at a distance of approximately
four times that of the depth of work.
Implement selection
The two main considerations in initial tool selection are the likely working depth range required and the type of soil disturbance to be
achieved.
As a guide to selection based on working
depth, chisel, low lift winged or slant leg type
tines (Fig. 5.2b, d and e), tend to be most suited
for working within depth ranges 0–300/350 mm.
For deeper operations, conventional subsoiler
tines and their equivalents fitted with high lift
wings (Fig. 5.2a and c) would be the normal
choice, often benefiting from the addition of
shallower working tines. Current agricultural
equipment is normally limited to a maximum
operational depth of about 0.5 m.
In minimum tillage or direct drilling situations, the main disturbance requirement would
be to re-establish the optimum porosity at shallower depths (<300 mm), requiring a gentle easing of the compacted soil with minimal surface
­disturbance. In this situation preference would
be given to low lift winged or slant legged tines.
Where more loosening is required to depths
>c.400 mm, such as rectifying damage after
root crops, the best choice would be conventional subsoiler types or their equivalents fitted
with high lift wings.
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
5.6.2
Implement adjustment
5.6.2.1
Soil disturbance
Where it is necessary to adjust the degree of
soil disturbance, decreasing the working depth,
­providing it is still below the problem area, will
increase disturbance and the rearrangement
of soil structural units, and working deeper,
­although at the expense of increased draught,
will reduce it. The degree of disturbance and
soil rearrangement can also be increased by a
separate shallower ­loosening operation or by
fitting shallower leading tines to the subsoiler,
as shown in Fig. 5.9.
When attempting to shatter or fissure compaction pans (often the transition layer) at depth,
care is needed in selecting the depth of working
below the pan. Soil conditions below the pan,
readily identifiable using SubVESS (Ball et al.,
2015), are often less dense and more readily
deformable than those in the pan itself, hence
there is a risk that the soil below the pan will
be disturbed rather than the pan. This can be
avoided in the case of low lift winged tines by ensuring they work no deeper than about 25–30 mm
below the pan. The depth tolerance level is
greater with high lift winged tools.
5.6.2.2 Surface conditions
In minimum tillage and direct drilling situations,
achieving level soil surfaces without any increase
in surface cloddiness or loss of topsoil in deep
cracks is highly desirable. Similarly in grassland,
a level surface is required while avoiding sward
tearing or bringing soil to the surface. Surface
levelness can be achieved through appropriate
adjustments to tine spacing (Fig. 5.8). On arable
land the use of low lift winged tines and careful
control of working depth can assist in minimizing
surface cloddiness and reducing the loss of surface
tilth into larger fissures.
A further problem can arise through the
­action of the subsoiler leg, which tends to move
loose clods to the surface. The leg can also tear
the sward in grassland. Both these problems can
be reduced by using narrow legs and, if necessary, by fitting a disc immediately ahead of the
leg (see Fig. 5.2d and e) to cut a slot for the leg to
follow. In grassland applications, in order to
minimize the risk of crop loss through drought,
77
soil loosening should if possible be conducted as
the soil enters a wetting phase with a moist soil
profile, using low lift winged tines. The optimum
conditions for compaction alleviation are when
soils are in the friable moisture range. As moisture contents move either side of this range, satisfactory loosening can become more difficult to
achieve, particularly as the plastic state is approached.
5.6.2.3 Compensation for moisture
content variation
Satisfactory loosening operations may not be
possible over the complete soil moisture range,
but there are adjustments available that can
widen this range. At higher moisture contents
approaching the plastic limit (the moisture content at which the soil can be rolled into a 2-mm
diameter thread without crumbling), the only
type of disturbance that can often be achieved is
some separation and movement ­between the
compacted structural units. The most feasible
way of achieving this is to generate fissures,
through a tensile soil failure at the wing tip, as
illustrated in Fig. 5.5c. The greater the lift height
of the wing at any given working depth, the
greater the chance of such fissures developing.
When moisture conditions are even higher,
possibly within a plastic state at depth, rather
than attempt fissuring, a below critical depth/
mole drainage type of soil disturbance could be
more effective. This would create vertical cracks
together with a channel for excess water ­removal
as shown in Fig. 5.7. Such an operation need not
be at traditional mole drainage spacing (c.2 m) or
depths (0.5–0.6 m), but could be closer and shallower. If shallower, a smaller diameter foot may
be required to ensure that the tool is still working
below its critical depth. In addition, the mole
channel created need not be particularly stable,
as the prime objective is to generate cracks
through the compacted layer. This operation, as
with all soil loosening operations, will only be
successful when drainage at depth is ­adequate.
Under very dry conditions, in addition to the
large increase in draught force, the major risk
during disturbance operations is the production
of loosely packed large strong soil units that are
difficult to break down during subsequent operations. Increasing the implement working depth
to generate fissures only, rather than inducing
78
R.J. Godwin and G. Spoor
significant soil rearrangement of the soil structural units and soil density reduction can reduce
this risk. Where the requirement is also to minimize the size of the large compact soil units
formed during loosening, as is common in reclamation situations such as after pipe laying, this
is best achieved by working progressively deeper
in successive passes to break the large units
whilst they are anchored in situ (Spoor, 2006).
In all situations, surface conditions should be
dry and firm enough to allow the remediation operation to proceed without excessive tractor wheel
slip and the resulting soil structural damage.
5.6.3 In-field evaluation
Whilst guidelines are available for selecting in advance the tine working depth, tine spacing and
configuration for a given operation, field variability is such that field checks are required to ensure
the desired result is being achieved. These checks
require the observance of soil flow between the
tines, the resulting surface conditions and a soil
pit excavation. The following procedure has been
found to be effective for making these checks
with the minimum time input. The stages in the
visual evaluation procedure are as follows:
1. Observe the soil flow, surface level and cloddiness, during and after a short test run at the required depth in a representative part of the field.
This provides initial evidence on surface conditions, the likely extent of disturbance at depth
and the degree of loosening/fissuring being
achieved. Where the whole soil area between
­adjacent tines lifts uniformly with a level surface, soil breakout at depth is likely to be fairly
complete as shown in Fig. 5.8.
2. Excavate a trench across two tines or more
to below their working depth, to expose the disturbance at depth and provide an open trench
into which further excavated soil can fall
(e.g. Fig. 5.10). Facing the direction of implement travel, the disturbed soil can then be pulled
away from the face with a spade or trowel to expose the limits of soil disturbance, similar to SoilPAK. The ease with which this can be done is a
good indicator of the degree of loosening and
rearrangement being achieved.
3. Following any necessary implement adjustments, repeat the short run, making surface
­ bservations as before. To avoid opening another
o
trench, checks on any new disturbance boundary at depth can be made by pushing a rod or
penetrometer into the loosened profile across the
line of travel.
4. Adjusting the subsoiler leg spacing in the
field can be physically difficult. However, using a
crowbar or fencing stake to partially take the
weight of the leg whilst sliding the tine along the
toolbar can make the operation a little easier.
5. This process is repeated until the implement
setting appears correct, after which a final trench
excavation is made for confirmation of the result.
Visual evaluation need not be conducted in
every field, provided that soil types, depth of
loosening and conditions are similar. The visual
assessment of surface level provides a simple
guide as to the appropriateness of tine spacing.
If the surface elevation appears to show distinct
heave as in Fig. 5.8 upper, then tine spacing is
too wide; an even lifting of the soil surface usually indicates a uniformity of loosening and porosity increase as shown in Fig. 5.8 lower.
Where unsatisfactory responses occur after
a loosening operation, the most common
reasons for failure include:
•
•
•
Failure to clearly define the problem and
the type of disturbance required;
Failure to check whether the work is solving the problem;
A lack of understanding or application of the
operational requirements for success.
These weaknesses need not occur as they can be
easily rectified through visual checks.
5.7
Minimizing and Alleviating
Recompaction
Studies by Soane et al. (1986) and (1987)
showed that loosened soil was prone to recompaction. In certain instances one ­sequence of
field operations, which included mouldboard
ploughing, could recompact the soil to the same
if not greater density than before loosening. To
overcome these problems the following alternatives are suggested. The first is to adopt a single
pass system incorporating deep loosening, surface cultivation and drilling similar to that currently practised in Europe for the establishment
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
of oil seed rape, where the seed is dribbled down
within the working width of the subsoiler. This
option is not suitable for c­ ereal crops as the machine width requirements are not compatible.
Whilst soil loosening after mouldboard ploughing may appear to be a solution, it is not an easy
operation to perform. The most satisfactory system is to use a mouldboard plough fitted with
‘under-buster’ tines, working 50–100 mm deep,
below the furrow bottom.
The other most practical alternatives are to
reduce the weight and inflation pressure using
low ground pressure systems, or to r­ estrict field
traffic to predetermined lanes within the field,
namely controlled traffic farming.
5.7.1
Reduced weight and inflation
pressure
The advent of rubber tracks and an improved
range of ‘ultra-flex’ (larger contact area)
tyres have aided the uptake of this option.
Figure 5.13 shows from soil bin studies the
vertical soil deformation within 0–0.6 m
depth, following the passage of the front and
rear axles of a combine harvester equipped
with either conventional tyres or rubber
tracks on the main axle. The data, obtained
from the measured displacement of markers
buried in the soil, show that at all depths the
32-t combine harvester equipped with rubber
tracks caused significantly less and shallower
soil deformation than the 30-t combine harvester equipped with radial ply tyres. The
­figure also shows that the soil deformation
caused by the 32-t rubber tracked combine is
not significantly different from that of an 11-t
combine harvester equipped with conventional
front and rear tyres. Soehne (1958) demonstrated that soil damage throughout the soil
profile was a p
­ roduct of both contact pressure
and load, where contact pressure influenced
the resulting soil pressure and hence compaction, and load influenced the depth to which it
was transmitted. Chamen (2011) predicted
that the current highly loaded machines could
generate high subsoil pressures (250 kPa) at
depths of 0.5 m. Soil compaction at depths in
excess of 0.5 m is both physically and economically difficult to repair. If equipment
manufacturers could, therefore, significantly
reduce the weight of rubber tracked machines
by the use of modern materials and improved
stress analysis techniques, further reductions
in soil damage could be made.
140
30t Combine – Tyre
Vertical soil deformation (mm)
120
32t Combine – Rubber track
100
11t Combine - Tyre
80
60
40
20
0
0
0.1
0.2
79
0.3
0.4
Depth (m)
0.5
0.6
Fig. 5.13. Vertical soil deformation caused by combine harvester tyres and rubber tracks. Inflation
pressures (front/rear): 30-t combine (1.9/1.0 bar); 11-t combine (1.2/1.5 bar) (least significant difference
(lsd) at P = 5% = 5 mm). (After Ansorge and Godwin, 2008.)
80
R.J. Godwin and G. Spoor
A further benefit arising from the reduced effect of compaction under the rubber
tracks as opposed to under the tyres is shown
in Fig. 5.11, which shows that the draught
force of a winged subsoiler if used to alleviate
the problem could be reduced by c.70%. This
is due to both the reduction in the degree of
compaction, reducing the draught force at a
given depth and the reduction in the depth of
loosening required typically, from approximately 0.5 m to 0.35 m. This in turn would
reduce the draught force from 35 kN to 10 kN
(3.5 to 1.0 t).
5.7.2
Controlled traffic farming
•
•
•
Improved crop yields;
Reduced energy requirements for tillage
and crop establishment;
Improved soil conditions and water
­infiltration.
These are achievable providing that the mechanization systems permit matching of the equipment working and wheel track widths. Field
­operation of this practice is now easier with the
use of RTK GPS guidance and auto-steer control
systems.
100
m
Kroulik et al. (2009) used global positioning system tracking devices to monitor the location of
field operations for cereal production (Fig. 5.14).
By including the tyre widths they found that
86%, 65% and 45% of the field was tracked by at
least one wheel pass during conventional tillage,
minimum tillage and direct drilling/no-till,
­respectively. This suggests that much compaction control could be gained from controlled
traffic farming practices (Tullberg et al., 2007;
­Chamen, 2011) where field operations are concentrated on predetermined traffic lanes and
equipment widths and wheel track spacing are
matched.
The potential advantages through minimizing the effect of compaction from this practice
are:
1 ha area
liquid manure application
liquid manure transport
stubble breaking
ploughing
presowing soil preparation
seeding
50
spraying rows
harvest
grains disposal
straw baller press
0
straw baller disposal
0
50
100
m
Fig. 5.14. Annual field traffic patterns for a section of a mouldboard ploughed wheat field in the Czech
Republic. (After Kroulik et al., 2009.)
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
5.7.2.1
Crop yields
Chamen (2011) reported that yield improvements
of 7% to 35% have been measured for a range of
crops in a number of different international studies. In order to determine the absolute potential of
the system, studies were i­ nitiated at Harper Adams
University, UK, in 2011 (Smith et al., 2013),
where, in a long term plot experiment, a compacted sandy loam field was chosen to determine
the relative effects of three traffic systems:
1. Random traffic farming (RTF) with ‘conventional’ inflation pressures of 1.2 and 1.5 bar in
the front and rear tractor tyres, respectively;
2. Lower ground pressure farming (LGP) with
0.7 bar inflation pressure in both the front and
rear tractor tyres;
3. Controlled traffic farming systems (CTF).
These traffic effects were investigated under three
tillage treatments, namely:
1. Deep tillage (250 mm);
2. Shallow tillage (100 mm);
3. Zero tillage.
Plot widths of 4 m were chosen for operational reasons; hence the trafficked area of
the CTF plots is high at 30% of the total area.
As CTF farmers are attempting to reduce this
to c.15%, for example, 10–12-m-wide controlled traffic systems with 1.8–2-m-wide
zones being trafficked, the adjusted estimated
yields for 15% traffic lane areas are also given
in Table 5.2.
The CTF/shallow tillage treatment (for
the 30% traffic lane area) shows a significant
(P < 0.10) 15% (1.1 t ha–1) increase in yield over
RTF/deep tillage (effectively conventional farming practice), and similarly the LGP/shallow tillage treatment shows a significant (P < 0.10) 9%
(0.64 t ha–1) increase. Reducing the trafficked
area to 15% would increase the CTF/shallow tillage yield by 19% (1.39 t ha–1) over the RTF/deep
tillage treatment.
The apparent poor performance of the zero
tillage treatments should be treated with caution
at this stage for the following reasons:
•
The traffic treatments (both area and number of
passes) followed those reported by Kroulik et al.
(2009) and were applied to a uniformly drained
and subsoiled site.
Although crop establishment in the first
year of the treatment (2012) was difficult due
to the wet autumn, the grand mean wheat
yield at 7.54 t ha –1 (Table 5.2) was typical
for the area. The data in Table 5.2 show that
the mean ­controlled traffic yields were significantly (P < 0.10) higher (0.5 t ha–1 or 7%)
than the random traffic yield, with the yield of
the low ground pressure system intermediate.
81
•
•
The yield is often lower in early years of
conversion to zero tillage and will usually
increase with time as soil structure improves (Carter, 1994);
The poor yield in the traffic lanes (4.3 t ha–1,
estimated by hand harvesting) affected performance, as the yield in the non-trafficked
zone of the zero tillage plots was estimated
at 8.15 t ha–1 (with the hand harvested data
showing a yield of 10.5 t ha–1);
Alternative purposed-designed ‘no-till’ drills
may have been better suited to the conditions that prevailed in the traffic lanes when
establishing the crop in 2012 but were not
available.
Table 5.2. Combine harvested winter wheat yield (t ha–1) for a range of tillage and traffic systems (CTF
traffic lane area is 30% of the total area). (After Smith et al., 2014.)
Deep tillage
Shallow tillage
Zero tillage
Mean
Random
traffic (RTF)
Low
ground
pressure (LGP)
Controlled
traffic (CTF)
Mean
Controlled
traffic (CTF)
(15% estimated)
7.29
7.67
6.87
7.28a
7.71
7.93
7.02
7.55ab
7.93
8.39
7.01
7.78b
7.65b
8.00b
6.97a
7.54
7.98
8.68
7.58
8.08
The 10% lsd values for the main effects of tillage and traffic are both 0.35 t ha–1 and 0.61 t ha–1 for their interaction.
Means not followed by the same letter are significantly different. The right hand column shows the estimated winter
wheat yields for controlled traffic systems with a traffic lane area of 15%.
82
R.J. Godwin and G. Spoor
These results, although only based upon 1 year’s
data, show trends similar to those found in earlier research; they provide further evidence, albeit
at a 10% probability level, on which farmers would
be confident to make management ­decisions.
5.7.2.2
likely impact on infiltration. This, together with
the ‘garden fork method’ developed by Chamen
(2014) to expose the soil structure and assess
topsoil damage, provides a further simple practical field assessment method for topsoils, as
shown in Fig. 5.16 and the referenced video.
Tillage energy
Studies by Chamen et al. (1992a; 1992b) show
that the removal of field traffic reduces the tillage
energy requirements for shallow ploughing from
107 to 47 MJ ha–1 (60% reduction), reduces the
range of secondary tillage operations from five to
three and the corresponding energy requirements
from 255 to 79 MJ ha–1 (70% ­reduction).
5.7.2.3 Water infiltration
The results of infiltration studies by Chyba
(2012), given in Fig. 5.15, show a dramatic
reduction in infiltration rate with increasing
­
numbers of wheel passes, which would seriously
increase the runoff of surface water. When these
are adjusted relative to the area and number
of passes of tractor wheels from Kroulik et al.
(2009) they give an average infiltration rate of
c.18.5 mm h–1 for a controlled traffic system
compared to c.5 mm h–1 for random traffic farming systems. This c.fourfold increase is in agreement with data collected by Chamen (2011).
Visual assessment of the traffic patterns
(number of wheel passes and area covered) in
the field will provide a ‘quick’ indication of their
5.8
Conclusions
The implements and techniques described in this
chapter for alleviating the numerous types of
compaction problem have all been well proven
with time and, providing they are adhered to,
should produce the desired result. Success is dependent upon a clear understanding of the nature
and location of the problem area and on making
appropriate checks to ensure that the desired result is being achieved. Visual soil assessments both
of the extent of surface wheeling and of soil damage have a critical role to play in this. For efficient
operation, the final soil condition must not only
have alleviated the compaction problem itself, but
also be that best suited to the requirements of subsequent operations. Careful implement adjustment
is required for this; small changes often having a
major influence in the nature of disturbance.
Particular care is needed when using narrow
tines to ensure the implement is working appropriately either above or below the critical depth (depth
of effective loosening), thus ensuring successful
soil loosening or mole drain/crack formation.
25
Infiltration rate, mm/h
20
15
10
5
0
0 Passes
1 Pass
2 Passes
Number of tractor wheel passes
3 Passes
Fig. 5.15. Effect of the number of tractor wheel passes on infiltration rate. (After Chyba, 2012.)
Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control
Fig. 5.16. Soil structure assessment using the
‘garden fork method’. (After Chamen, 2014.)
The addition of wings to individual narrow tines
increases their draught force, but this increase is
more than compensated for by an increase in the
area of soil disturbed. Winged tines in multiple tine
implements offer many advantages in terms of the
effectiveness of loosening and their flexibility in
producing a range of final overall soil conditions.
Shallower working tines, providing they are
appropriately positioned, can be fitted ahead of
deeper tines without any increase in draught.
They allow wider spacing of the deeper tines for a
given draught force. Furthermore, through their
influence in increasing the critical depth of the
deeper tines, shallower tines allow a greater
83
overall degree of loosening to depth in situations
with deeper compaction problems. Where tractor
power and traction available are limited, complete
loosening can be achieved across the field without
wheeling previously loosened soil, by adopting a
two-pass rather than a single-pass operation.
Tillage and trafficking studies indicate that
benefits in terms of improved crop yields, improved soil conditions and reduced tillage costs,
can be achieved through reducing the compaction potential of traffic by the adoption of low
ground pressure and controlled traffic systems.
The full potential of controlled traffic farming for improving both crop production and
the environmental qualities of soils has as yet
to be identified. This is because many previous
replicated experiments lacked a well-defined
­compaction-free control, a situation being rectified in a recently established long-term experiment
in the UK (Smith et al., 2013; Smith et al., 2014).
Using the ingenuity of farmers and their
equipment suppliers to continue to develop controlled traffic systems, it should be possible in future to achieve substantial improvements in crop
yield together with improved soil structure. In
addition, there will be the added benefits of reduced energy consumption and increased infiltration rates, the latter increasing soil-water availability and reducing runoff, erosion and flooding.
Acknowledgements
The authors would like to thank Tim Chamen,
Brian Finney, Jim Loynes, Charles Marshall,
Paula Misiewicz, Emily Smith, Letica Chico Santamarta and David White for their help in preparing this chapter, and the Journal of Biosystems
Engineering and Home Grown Cereals Authority
for the use of previously published material.
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6 Valuing the Neglected: Lessons and
Methods from an Organic, Anthropic Soil
System in the Outer Hebrides
Mary Norton Scherbatskoy,1* Anthony C. Edwards2
and Berwyn L. Williams3
1
Blackland Centre, Grimsay, North Uist, Scotland, UK; 2SRUC,
Craibstone, Aberdeen, Scotland, UK; 3formerly Macaulay
Land Use Research Institute, Aberdeen, Scotland, UK
It is too simple to say that the ‘marginal’ farms
of New England were abandoned because they
were no longer productive or desirable as living
places. They were given up for one very practical
reason: they did not lend themselves readily to
exploitation by fossil fuel technology . . . Industrial
agriculture sticks itself deeper and deeper into a
curious paradox: the larger its technology grows
in order to ‘feed the world’, the more potentially
productive ‘marginal’ land it either ruins or
causes to be abandoned.
(Wendell Berry, 1979)
6.1
Introduction
Small-scale abandoned agricultural systems can be
found worldwide: throughout Europe (MacDonald
et al., 2000; Marini et al., 2011), on American
prairie and hill farms (Manning, 1995; Berry,
2006; Salatin, 2010), and across Asia and Africa
(Reijnties et al., 1992). A wide range of formerly
productive land has been neglected – often in the
name of progress. Functioning traditional systems that for generations made best use of marginal land have been overrun by the economics of
the fossil fuel economy and the assumption that
bigger equals better. The paradox of underused
land and vanishing skills at a time of increasing
demand for food, growing climate instability and
rising fuel prices cannot easily be dismissed.
The quotation above from the American
farmer and environmental thinker Wendell Berry
resonates in the west of Scotland. Thousands of
hectares of land in the islands and coastal areas
are now derelict. Until the 1960s, these areas supported generations of crofting families through a
mosaic of productive, diverse uses – here some potatoes, there a bog, here a half-acre of corn, there a
hayfield. Today, these small fields have deteriorated significantly, damaged by overgrown drains
and unmanaged grazing by sheep; they are now
often waterlogged, with rank grasses and mosses
that conceal their history and potential. Their
unique character and management techniques
have been overlooked – even disparaged – by agriculture and science alike.
Here we present an example of how – through
fresh, even unconventional assessment – the
hidden value of such neglected lands can be revealed, providing a first step towards reclaiming
and reusing them for the benefit both of local
communities and world climate. A new study on
*E-mail: mns@uistwool.co.uk
86
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
Valuing the Neglected
regeneration of one such traditional system is
underway in North Uist (western Scotland: 57° N,
7° W), which addresses its unique character
while building on past understandings of traditional agriculture, natural peatlands and climate
effects. A new term blackland has been introduced
to identify a distinct anthropic, organic soil system; this term is derived from the Gaelic words
­talamh dubh, which originally described the narrow
belt of blacker soil bordering the sandy machair.
The term blackland has been expanded to include
the highly organic agricultural soils stretching
across the islands and onto the western Scottish
mainland, which share similar characteristics.
(It is worth noting that most of the literature has
been concerned with natural peatlands, therefore
the term ‘peaty soils’ is often used to refer to
highly organic soils such as blackland.) The West
Highland Survey (Fraser Darling, 1955) identified about one-sixth of all crofting land as being
on ‘peaty’ soil, or more than 100,000 ha. Based
on a decade of personal observation, familiarity
with the crofting system and sample soil testing,
the blackland research areas (Fig. 6.1) appear to
be representative of this type of land.
This new research contends that while
the parent material of blackland may often be
Sphagnum peat, it is no longer peat (defined as a
87
spectrum of dead plant material from partly
decomposed fibre to burnable black colloidal
­
solid). Blackland is anthropic, meaning that it has
been modified in essential aspects by long human
interaction. Such land has been managed, grazed
or cultivated for hundreds, if not thousands, of
years, and unique management systems have
evolved, which in turn have produced enduring
changes to the soil. While blackland shares many
characteristics with peatlands such as very high
organic matter content, it also differs in important
ways including acidity, hydraulic conductivity, nutrient cycling and physical structure; it is also better able to support the growth of vascular plants.
Whether other small-scale, mixed farming
systems on organic soils should be considered
‘blackland’ is unknown – in the UK, farming on
such soils is also found in Ireland, Wales, Yorkshire
and Derbyshire. However, system dynamics including nutrient cycling and other processes that
affect their use, decline and regeneration may be
very different among them, affected by factors
such as climate and geology. Incorrectly assuming similarities could obscure understanding.
Certainly, such organic soils, as reservoirs of carbon compounds, need careful consideration if
they are to be managed sustainably and loss of
carbon as CO2 kept to a minimum.
N. UIST
Atlantic dunes machair blackland
moor
blackland Minch
15 km
Fig. 6.1. Locator map of the blackland research areas: The three crofts in North Uist,
Outer Hebrides, in which field research took place are indicated: ● Grimsay (Kenary), ▲ Locheport,
■ Lochportain: The drawing represents a hypothetical West-East transect of the Uists, with the blackland
areas marked by ✦✦.
88
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
The blackland system is affected by a wide
range of factors from socio-economic context to
soil microbial activity (Fig. 6.2). This complex
system – in common with other neglected forms
of ­agriculture – will benefit from innovative
­approaches and analytical methods in order
to assess its legacy and potential. The Food and
Agriculture Organization of the United Nations
(FAO) noted the rapidity of change in organic
soils (FAO, 2011); abandonment appears to
trigger fast-moving processes that mask or impair former productivity. For these reasons, highly
organic agricultural land is best understood
through multidisciplinary methods rather than
as a set of soil samples.
This chapter presents three ‘tools for decision support’ (Section 6.3): two are novel, developed during the current research period, and
one is seldom used in Britain. All three are suitable for small-scale, field-based use by land managers and draw heavily on visual and tactile
evaluation. A summary of some characteristics
of organic soils is given below, and discussed in
the context of the agricultural and social system
known as crofting.
6.2
Background
In many terrestrial ecosystems organic matter derived from plants accumulates in or
above mineral soil horizons because of limitations on its decomposition. Aspects of climate
(influencing temperature and moisture),
geology and topography are the controlling
factors. In the northern hemisphere, the predominant influences are cool temperatures and
an excess of precipitation over transpiration.
Under these conditions the partial decomposition of organic matter can have varying
results from the development of organic rich
mineral soils to deposits of peat many metres
thick. Blackland is a product of the diverse
topography and Atlantic climate of the west
of Scotland, and the soils are justifiably said
to have been ‘built’ by generations of crofting
management.
6.2.1
Geology, slope and rainfall
In the Outer Hebrides, the rock is predominantly
of Lewisian gneiss, which is poor in essential
plant nutrients, resistant and acidic (base poor).
Intense glacial erosion has created a low undulating basement on which a mosaic of shallow
peat and thicker accumulations in depressions
develops; in parts of north-west Scotland this
pattern can cover up to 60% of the land area
(Robertson, 1968).
Slope influences hydrology and drainage
hence the height of the water table, which in
turn affects the oxygen availability in the surface
horizons. Degree of aeration is one of the key
factors controlling decomposition of plant material and accounts for many of the differences
in the structure of organic soils. On some Sphagnum bogs, the height of the natural water table
can fluctuate considerably in the surface horizons with the lower water table producing increased microbial activity and decomposition
(Williams and Wheatley, 1988).
Slope can also be a guide to a natural supply
of dissolved minerals and nutrients in drainage
waters from higher levels. Flushed areas often
have elevated levels of minerals reflected in
greater ash contents of the soil. Where particulate mineral matter has been washed over peat,
higher pH and nutrient content may result and
consequently greater retention of phosphate
and higher microbiological activity (Williams,
1984). On more level ground, rainwater may be
the only source of nutrients (including nitrogen
(N) and potassium (K)) resulting in nutrient
poor conditions.
6.2.2 Physical structure
A diplotelmic (two-layered) system exists in
areas of growing Sphagnum in which the
acrotelm (surface or growing layer) is exposed
to the weather and protects a lower layer or
­catotelm (dead but not fully decomposed moss).
These two layers form the basis for a model
of peat growth and decomposition (Ingram,
1978).
Decomposition of organic matter in the
Sphagnum system resulting from microbial activity leads to a range of organic particles varying in size from large plant fibres to amorphous
colloidal material (Williams, 1983). Chemical
properties such as cation exchange capacity
and iron content together with surface area
vary with particle size (Levesque and Dinel,
1977).
Valuing the Neglected
89
Level I: Context
Level II: Landscape
Level III: Field
Level IV: Soil
Level V: Subsurface
Fig. 6.2. Factors affecting the evaluation of blackland soil systems.
Level I: Context provides the broad societal background into which local development occurs, and
includes climate/disasters, historical events such as war, and technological changes ranging from the
internal combustion engine to birth control.
Level II: Landscape includes factors affecting regional land use such as population, traditional management
and livestock techniques, law, and local economic conditions.
Level III: Field is the local management level at which factors such as topography, drainage, vegetation,
cropping preferences, and family structure/manpower affect land use within an agricultural unit, farm or croft.
Level IV: Soil provides a general description of characteristics easily observable within 1m from the
surface, such as structure, hydrology, horizons, with the spade or auger as a tool.
Level V: Sub-surface inspection requires laboratory techniques to determine genesis (including microscopic
visual assessment e.g. pollen analysis), microbiology and molecular/chemical states.
90
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
6.2.3
Microbiological processes
The availability of nutrients to plants, particularly N and phosphorus (P), depends to a large
degree on the activity of soil organisms. Usually
organic forms of N dominate and become plant
available mainly through the activities of microorganisms (mineralization) involving:
•
•
•
•
Greater mineralization of organic nitrogen
compounds;
Greater demand for N by the microbial biomass leading to an immobilization of N;
Stimulation of nitrification;
Losses of N to the atmosphere through denitrification under wet conditions.
Mineralization can be stimulated in highly organic soils by raising the pH; these nitrogen
transformations may be critical in blackland
soils as they have proved to be in conventionally
limed blanket peat (Wheatley and Williams,
1989). It appears that blackland soils may have
a greater potential to supply nutrients for plant
growth than unimproved peatland (Williams
et al., 1985).
P is inherently deficient in organic soils but
tends to increase with mineral content. In Sphagnum peat, the microbial biomass contains a very
high proportion of the total P because of the low
content of mineral elements (Williams and Silcock,
2001).
In all soils, the microbial pool of carbon (C),
N and P is an important source of potentially
available nutrients because of its rapid turnover
(Jenkinson and Ladd, 1981).
6.2.4
Cultivation
Nineteenth-century writers in Scotland reported
on methods of reclaiming ‘the mosses’ for agriculture (Cowie, 1852). The Macaulay experimental farm in Lewis was a short-lived (1929–
1940) attempt at conversion of peaty moorland
to farming (Ogg and MacLeod, 1930). Reith and
Robertson (1971) reported on the liming and
fertilizer requirements for the growth of grass
on a deep peat derived from Sphagnum mosses in
Central Scotland. These large-scale manipulations contrast with the traditional management
employed in the crofting areas of Scotland for
hundreds of years (Fraser Darling, 1945).
6.2.5 Crofting: an agricultural
and social system
Patterns of land use, tenure and settlement in
the north and west of Scotland differ from
those in the rest of Scotland. In 1886, based
on the findings of the Napier Commission
(1884), the system of crofting was established
by an Act of Parliament as a distinctive type of
agricultural holding with heritable security of
tenure. This Act applied to the largely Gaelic
speaking areas from Caithness to the Hebrides
to Argyll (Hunter, 1976). In practice, crofting
developed as a form of small-scale (usually
<15 ha), family-based, mixed-use agriculture
that continued a pattern of cultivation dating
from the Neolithic. Today, the crofting system
covers more than 700,000 ha (including both
individual holdings and grazings held in common); the term croft refers to an individual
holding. Crofts usually have a diverse range of
physical attributes; Fraser Darling (1955)
noted that ‘earlier generations (aimed) that
crofts should as far as possible have a variety
of soil types’.
In traditional crofting, as in many other
small family farming systems worldwide, the
majority of staples and feed were home produced which meant a poor harvest of either
commodity could represent major difficulties;
being able to produce sufficient winter animal
feed was critical.
Traditional crofting systems were labour intensive and maximized the use of local ­resources,
which meant – through necessity – that they
would today be considered as achieving a high
­degree of sustainability. The dominant character
was an extremely heterogeneous pattern of land
use and production capacity over relatively small
spatial scales. Mini-­ecosystems were carefully
exploited in areas as small as 0.1 ha to gain the
maximum yield with the minimum of effort and
input, and fine-grained methods to control soil
moisture were developed. Such traditional systems – a characteristic of ‘high nature value
farming’ – can have considerable economic, social and e­ nvironmental benefits, particularly in
remote rural areas (Shucksmith and Rønningen,
2011). Pluri-activity was and remains a feature
of crofting, with more than half the annual
income being generated by a range of non-­
agricultural activities.
Valuing the Neglected
6.2.6
Maintaining soil fertility within
a mixed system
A fundamental understanding of the intrinsic land
characteristics and variability underpin most
traditional mixed agricultural systems. Maintaining a balanced flow of nutrients between the three
major croft land use components (extensive
grazing/hill, improved grazing/fodder and arable)
also has a direct impact upon the size and productivity of the arable component (Dodgson, 1994).
Smith (1994), in her study of the use of
household/byre midden material in South Uist,
considered the suitability of various amendments to differing areas; the logistics of transporting bulky material such as sand, manure
and seaweed often affected their use. Shell sand
was a traditional agricultural liming treatment
with at least two effects: it raises pH and also
leaves a mineral silicate residue that increases
ash content, bulk density and the load bearing
capacity of the surface (trafficability) (Williams
et al., 1985). The surface of the shell sand residue is a potential site for adsorption and retention of phosphate derived from feed stuffs via
animal dung. Consequently, in blackland soils,
shell sand addition has the potential to increase
the retention of phosphorus.
While precipitation, seaweed and manure/
urine supply both N and K, only manures and
composts contain significant quantities of P. This
relationship was demonstrated by Dodgson and
Olsson (1988) in a study of five traditional mainland farming systems: manure contributed between 25 and 80% of the total input of N and
60–95% of P, while seaweed supplied from 0–25%
N and 0 – <10% of P. Individual land areas must
therefore have received very different quantities,
types and frequency of amendments, which
would over time have added to the existing physical heterogeneity and complexity of the system.
6.2.7 Current situation
Land management in the Hebrides has changed
dramatically from the traditional system. By 1963
a large proportion of crofts had begun to supplement home grown animal feed with purchased
concentrates (NOSCA, 1966). Fifty years later,
crofting has nearly abandoned arable feed
­production and permanent grassland in favour
91
of a combination of rough grazing with purchase
of almost all animal feed from mainland farms
with attendant transport and carbon costs.
Today, for a variety of reasons – including
smaller populations and greatly improved transport links such that even perishables such as milk
are now all imported – the traditional system has
in many areas collapsed and recycling of nutrients through a mixed livestock cropping system is
seldom practised. Soil nutrients are now supplied
through imported fertilizers with a corresponding increase in carbon footprint and a decrease
in understanding of local soil processes. These
major changes in management have in turn produced effects on the land which must be understood in order to assess future potential. The next
section describes methods developed to evaluate
formerly productive but now derelict land.
6.3
Tools for Visual Evaluation
Highly organic agricultural soils such as blackland cannot be described by the conventional
methods used for mineral soil: the standard ‘textural triangle’ and topsoil spade tests of structure
have little meaning for soils that do not contain
sand or silt or clay in any quantity. The L and O
horizons may be very deep and complex; blackland may lie on top of deep peat, or may be less
than 200 mm over solid rock. R
­ ecent research
(Scherbatskoy, 2013) distinguished five different
soil groupings within blackland, reflecting varying history and potential (Fig. 6.3).
6.3.1 Methods
In order to begin a re-evaluation of the potential of blackland (and by extension other neglected systems) for agricultural use, holistic
methods taking into account the physical and
social contexts, landscape variability, cultivation history and soil characteristics were developed. A study of c.50 ha of representative
­crofting blackland in North Uist, Outer Hebrides was undertaken during 2011–2013. Three
crofts located in Grimsay, Locheport and Lochportain were assessed through 23 different
measurements (Table 6.1) at a total of 37 sites.
The research area corresponds approximately
92
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
__________________________________________________________________________________
GROUP VARIABLE
MEDIAN
RANK
No. in GROUP
__________________________________________________________________________________
7
(a) Modern agriculture (1960s), best grasses, shallow
vegetation
ash %
von Post
depth
cultivation
pH
P
slope °
hydrology
WT range
K
55
highest
57.9
highest
7
300
shallowest
P/M/N/B
4.9
highest
1.9
lowest
10
RGO/RO/R
200
largest
96
__________________________________________________________________________________
(b)
Modern agriculture (1960s), deeper, all possibly fertilized in 1960s
vegetation
ash %
von Post
depth
cultivation
pH
P
slope °
hydrology
WT range
K
52
30.5
8
630
P/B/N/M
4.9
7.1
5
R
190
127
7
highest
Fig. 6.3. Groupings of blackland through visual observation and other analyses.
Observation and measurement of 23 variables at 37 points produced a dataset that clustered into four
distinct groups; a fifth group (C) was constructed based on observation and statistical anomalies. All
variables (Table 6.1) were subject to a range of analyses, both bivariate including box- and scatterplots,
and multivariate including Principal Component Analysis (PCA) and hierarchical dendrograms. Vegetation scores were omitted from the Gower dendrogram, then added into the final groupings as a
cross-check and as ‘ground-truthing’. The correlation between the vegetation score and the balance of
the data was striking. Indicative variables are displayed above, selected as:
– continuous variables forming Component 1 of the PCA (explaining 33% of the variation) – ash/pH,
decomposition, soil depth;
– other continuous variables of common interest such as extractable P and K;
– discontinuous variables such as cultivation and hydrology, which observation and analyses found
to be significant.
This dataset and derived groupings provide a method of evaluation for poorly understood soil systems,
as well as an interim definition of blackland.
Valuing the Neglected
93
__________________________________________________________________________________
GROUP VARIABLE
MEDIAN
RANK
No. in GROUP
__________________________________________________________________________________
(c)
Constructed group of outliers based on observation and statistical anomalies; 4
poor vegetation, no rush, free-draining
vegetation
ash %
von Post
depth
cultivation
pH
P
slope°
hydrology
WT range
K
17
13
9
740
N/B
4.2
5.0
5
R
190
85.8
lowest
lowest
highest
lowest
__________________________________________________________________________________
9
(d) No cultivation marks; mid-range values; background condition?
vegetation
ash %
von Post
depth
cultivation
pH
P
slope °
hydrology
WT range
K
27
17
6
730
N
4.3
5
10
RO/RGO/R
160
84.6
lowest
__________________________________________________________________________________
(e)
Signs of past management; damaged? includes old beds with deep acrotelm & catotelm
vegetation
ash %
von Post
depth
cultivation
pH
P
slope °
hydrology
WT range
K
24
13.4
5.5
lowest
1600
deepest
B/N/M/P
4.3
7.9
highest
5
R/RO/RGO
130
smallest
88.4
10
__________________________________________________________________________________
Fig. 6.3. Continued.
94
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
Table 6.1. Parameters and measurements used in evaluation of blackland.
Parameters
Units
Comments
B/P/M/N
Beds, ploughed, managed, no sign
mm
Degrees
cv/cx/f
Degrees
1600 maximum
By estimation
Convex, concave, flat
From North
Soil structure
6. Acrotelm
7. Catotelm
8. O2 layers
9. Presence of worms
10. Presence of sand
11. Rooting to 200 mm
12. Colour
13. Decomposition
14. Porosity
mm
mm
no. of layers below catotelm
Y/N
Y/N
Y/N
Y/Br/Blk
von Post units
%
0–100
0–200
Hydrology
15. Character
16. Water table: range
17. Water table: median
R/G/O
mm
mm
Soil chemistry
18. pH
19. P
20. K
21. Ash
22. Smell
log H+
mg l–1
mg l–1
%
–1/0/1
Vegetation
23. Soil functionality
Blackland Vegetation score
Past use
1. Cultivation
Topography
2. Soil depth
3. Slope
4. Relief
5. Aspect
Yellow, brown, dark to black
1–10
Rainfed, groundfed, runoff
0–400
Pleasant/good, questionable, bad
The blackland system was evaluated by data collected through six parameters, displayed in decreasing order of
permanence; a total of 23 measurements were selected based on an evaluation period in 2010. Data were collected in
both transect and target sets through identical methods. Both continuous and discontinuous variables were included; all
four recognized types of measurement were represented: nominal, ordinal, interval and ratio. A range of types of data
and consequently of analytical methods was necessary due to the variability of the sampling points and the many
parameters required to arrive at a characterization of the landscape.
to types 2 and 3 of crofting land as described in
a topographical study of the Outer Hebrides
(Richards, 1998).
The Grimsay croft (Kenary) was the main
research area: 27 sites were selected for evaluation, divided among 13 transect points (at
60-m intervals along four radians) and 14 target areas (showing clear traces of earlier agriculture or other unusual characteristics). The
Locheport and Lochportain crofts were evaluated through five sites each combining both
methods, for a total of 37 points in the dataset.
Statistical ­analysis, ­including a G
­ ower distribution dendrogram, p
­ roduced strong groupings, which are illustrated in Fig. 6.3.
Three tools were used to describe complex
field situations; each produces a single numerical
score:
•
•
The Blackland Index is a means for initial
assessment of landscape potential.
The Blackland Vegetation Scoring system
uses vegetation species to indicate soil characteristics.
Valuing the Neglected
•
The von Post Humification Scale quantifies
the structure of organic soils through field
observation, and places samples within a
dynamic framework.
95
ground feels. These impressions are of course
subjective, but will help develop skills in observation and ‘reading the land’.
past use (was the land worked /managed before?).
6.3.2
Blackland Index
The Blackland Index (Scherbatskoy, 2013) is a
way of launching an initial assessment process
without specialized equipment by a person intending to manage land. It gives a basic evaluation of the potential for agricultural use of a
derelict field or area, and produces an Index
number which will help make comparisons
among fields, and indicate good candidates for
restoration. The Index is a relative, not an absolute, measurement; many of the tests depend on
the judgement of the user. However, the process
of investigating and making decisions about aspects of the land may be as useful as the actual
score and is a way to engage the user in a close
observation of his/her land.
The Blackland Index uses six key parameters
from the bio- and geosciences to take into account
the many factors that influence the potential of a
given area. It includes an initial observation period,
six sets of tests to be carried out in the field over the
period of 1 year, and one laboratory analysis. The
initial observations and steps for assessing each
parameter of the Index are summarized below.
6.3.2.1
Initial observations
Perhaps the most difficult aspect of judging the
potential for regeneration of derelict land is
knowing where to start, that is, what is a ‘field’?
In the presence of drains and absence of fences a
good deal of walking around and detection is
needed to begin to see areas of internal similarity. This process will likely have to be repeated
many times, but such an investment in observation will pay off in the long run.
Begin by forming a general impression of
the land to be evaluated. Standing outside it,
note what is striking. How much slope is there,
how many rocks and outcrops? Are there many
changes in the vegetation, or is it quite uniform?
Is there water running through it or standing on
it? Are there old drainage ditches? How green or
brown is it, at what times of year? Then on walking over it, notice how wet and how solid the
Tests: presence of cultivation marks/ditching,
local knowledge, aerial imaging
No matter how grim the current condition,
evidence of having been used confirms that the
area was found to be usable at some time in the
past 100 years. No sign of past management at
all should give rise to the suspicion that the area
has insurmountable problems.
topography.
Tests: estimation of slope and relief
Topography is a strong determining factor,
affecting not only drainage but soil depth, decomposition processes and access. Research has
indicated that both slight slope and convex relief
are factors that tend to encourage positive soil
processes through shedding water. Rockiness,
extreme slope, access and extent obviously affect
possible uses, as does the equipment available –
from spade to tractor.
structure.
Tests: spade sample; presence of
worms; ground vibration
As much as 400 mm or more of moss
may have accumulated over former cultivation
­indicating either very long disuse or problems in
shedding water; such a thick layer may make restoration very difficult. The presence of worms is a
well-known indicator of reasonable aeration and
pH in soils. Blackland soils >1.2 m in depth tend to
tremble or quiver when jumped on; thus, causing
the ground to vibrate by jumping on it serves as an
estimation of depth. Depth of blackland soils appears to inversely correlate with soil functionality.
hydrology. Tests: walking across the area during each season and noting wetness. Is the boot-sole
dry? damp/wet? covered in water over the toe-cap?
This ‘boot-sole’ test is rather crude; it does,
however, appear to work, separating the hopelessly wet from the more workable areas.
chemistry.
Tests: laboratory analyses; smell
The standard analyses of pH and of plant-­
available P and K are inexpensive and useful;
loss on ignition (LOI) is a particularly valuable
96
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
test on organic soils that may range from 20–80%
(w/w) organic matter, but increases cost. Smell is
well known as a field evaluation tool that summarizes not only aerobic status but other factors
related to soil functionality (­Shepherd, 2009); it
can be estimated as pleasant/fresh, uncertain or
unpleasant/sulfury.
vegetation.
Tests: indicator species; date of
greening
The blackland research showed that vegetation is the single most obvious and powerful
predictor of soil functionality. More nutritious
plants indicated better soils. Heavy cover of
Sphagnum with cottongrass (Eriophorum angustifolium) corresponded to saturated acidic soil conditions. Areas with good soil functionality will
be green nearly year-round; a seasonal test removes the possibility of confusing early-flowering
Site: 1a (Kenary)
grasses such as sweet vernal or Yorkshire fog
with poorer vegetation. Rushes did not appear
to be a useful predictor of soil conditions as they
will grow almost anywhere on blackland.
6.3.2.2 Scoring
Each test is scored as −1, 0 or +1, and summed
to give an overall score; the highest possible score
is +13 and the lowest is −13. An overall score
that is positive suggests that some improvement
(such as mowing) is probably worth trying; a total
negative score means that it probably isn’t. Higher
overall scores (above 5) mean that, with appropriate renovation, the land would probably be
able to grow good quality grass suitable for silage,
or some arable crops such as appropriate varieties
of oats, turnips or kale if the nutrients are sufficient (see Fig. 6.4 for examples of Index scores).
Site: 3b (Kenary)
________________________________________________________________________________________________________
RANK
SPECIES
MLURI WEIGHT SCORE RANK
SPECIES
MLURI WEIGHT SCORE
grazing
grazing
value
value
________________________________________________________________________________________________________
1 Holcus lanatus
4
Yorkshire Fog
2 Anthoxanthum oderatum 3
Sweet Vernal grass
3 Agrostis tenuis
5
Common Bent
4 Festuca ovina
3
Sheep’s fescue
5 Rumex acetosa
4
Common Sorrel
Total Vegetation Score:
(Blackland Index score 11)
x 5 = 20
x 4 = 12
x 3 = 15
x 2 = 6
x 1 = 4
= 57
1 Calluna vulgaris
1
Heather
2 Eriophorum angustifolium 1
Cotton-grass
3 Potentilla erecta
2
Tormentil
4 Anthoxanthum oderatum 3
Sweet Vernal grass
5 Festuca vivipara
3
Viviparous Fescue
Total Vegetation Score:
(Blackland Index score 0)
x 5 = 5
x 4 = 4
x 3 = 6
x 2 = 6
x 1 = 3
= 24
Fig. 6.4. The above tables illustrate the method of calculating vegetation scores for two blackland sites:
1a a formerly arable field currently in use for grass silage; 3b an old, derelict lazybed system. Photographs
of actual field conditions provide visual confirmation of the vegetation scoring results. Both photos were
taken in October 2012. MLURI = Macaulay Land Use Research Institute.
Valuing the Neglected
6.3.3
Blackland Vegetation
Scoring (BVS)
Blackland Vegetation Scoring (BVS) will be useful as a survey tool within a given ecosystem to
evaluate the agricultural potential of unmanaged/
derelict areas; it can be used when accurate field
identification of plant species is possible, that
is, with good botanical knowledge available. Its
results appear to correlate with those of the
Blackland Index, but it is less subject to personal
interpretation, and more likely to give similar results when used by different observers. During the
blackland research, it was also used to ‘groundtruth’ other findings for plausibility. BVS is useful
for comparing defined areas within a given ecosystem, but it seems unlikely to provide meaningful
comparisons between dissimilar regions.
The BVS system is based on the general
principle that plant species reflect soil conditions. Plant species distribution and abundance
in established vegetation are precise and multi-­
variant indicators of underlying soil characteristics because plants grow in the same spot for
their lifetimes, and each species is adapted to
(and limited by) the conditions and available
­nutrients in the soil.
BVS links two recognized systems of ecological observation, the Ellenberg Indicator Values
and the Macaulay Land Use Research Institute
(MLURI) Table of Grazing Values, into a single
number expressing the potential of the area for
growth of desirable grasses and crops.
•
•
Ellenberg’s Indicator Values are the known
range of requirements for nutrients, pH,
moisture and light for native plant species
in northern Europe, based on data collected
by Ellenberg in Germany in the 1970s and
adapted for the UK by Hill et al. (1993). Detailed information on local soil conditions
can be determined by recording the plant
species on an area of ground but this involves calculations using up to six indicator
values for each species and is not a convenient comparative tool.
MLURI Grazing Values were used to rank the
usefulness of vegetation to grazing sheep
(based on the work of Klapp). A single value,
ranked from −1 (dangerous) to 9 (most nutritious), was assigned to a range of grasses,
forbs and shrubs as part of the MLURI land
97
Capability Classification for Agriculture
(Bibby, 1982). Although simple to use, these
single species values were not designed to
characterize entire plant communities.
The novel BVS system was developed, in collaboration with Dr Barbra Harvie (University of Edinburgh), combining the precision of Ellenberg
Indicator Values with the simplicity of MLURI
grazing values to produce an easy-to-use tool
for evaluating the potential of an unmanaged/
derelict area for regeneration or improvement.
During the research period all 37 sites
were assessed using the new method. The resulting vegetation scores corresponded with
calculations made using Ellenberg Indicator
Values. Fields with higher Blackland Vegetation
Scores (Fig. 6.4) were associated with plant species that had higher pH and nutrient requirements and lower moisture tolerance (based on
Ellenberg Indicator Values). The results were
corroborated by field measurements of soil pH,
nutrients, water table and structure, validating
the BVS system as a suitable method for assessing the soils of landscapes such as those in the
study area.
To use the BVS system:
•
•
•
•
Select a small area (approximately 2 m r­ adius)
representative of the larger area that is to
be assessed;
List the five most common (rough estimate)
plant species by degree of cover, then rank
the populations from 5 (most common) to 1
(least common);
For each of the five species look up the
MLURI value then multiply this by the rank
given to the species;
Add these five values to produce a single
score.
Given accurate botanical identification of the
entire assemblage, total scores are quick and
easy to calculate. Generating a single condition
score is useful both for statistical treatment and
as a shorthand notation for practical use.
6.3.4 von Post Humification Scale
The von Post scale (Table 6.2) replaces the soil
textural triangle and spade sample tests as a
baseline for description of highly organic soils.
98
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
Table 6.2. Von Post Humification Scale. (From FAO, 2011.)
Symbol Description
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
Completely undecomposed peat that, when squeezed, releases almost clear water. Plant
remains easily identifiable. No amorphous material present
Almost entirely undecomposed peat that, when squeezed, releases clear or yellowish water.
Plant remains still easily identifiable. No amorphous material present
Very slightly decomposed peat that, when squeezed, releases muddy brown water, but from
which no peat passes between the fingers. Plant remains still identifiable, and no amorphous
material present.
Slightly decomposed peat that, when squeezed, releases very muddy brown water. No peat is
passed between the fingers but plant remains are slightly pasty and have lost some of their
identifiable features.
Moderately decomposed peat that, when squeezed, releases very muddy water with a very
small amount of amorphous granular peat escaping between the fingers. The structure of
the plant remains quite indistinct although it is still possible to recognize certain features.
The residue is very pasty.
Moderately highly decomposed peat with a very indistinct plant structure. When squeezed,
about one-third of the peat escapes between the fingers. The residue is very pasty but shows
the plant structure more distinctly than before squeezing.
Highly decomposed peat. Contains a lot of amorphous material with very faintly recognizable
plant structure. When squeezed, about one-half of the peat escapes between the fingers.
The water, if any is released, is very dark and almost pasty.
Very highly decomposed peat with a large quantity of amorphous material and very indistinct
plant structure. When squeezed, about two-thirds of the peat escapes between the fingers.
A small quantity of pasty water may be released. The plant material remaining in the hand
consists of residues such as roots and fibres that resist decomposition.
Practically fully decomposed peat in which there is hardly any recognizable plant structure.
When squeezed, it is a fairly uniform paste.
Completely decomposed peat with no discernible plant structure. When squeezed, all the wet
peat escapes between the fingers.
The von Post scale is a field test to rank organic soils by degree of humification (decomposition). The 10 steps
correspond to percentage of decomposition, i.e. 1 = 10% or undecomposed plant material, and 10 = 100% decomposed
or colloidal, such as burnable black peat of the highest caloric value.
It provides a rapid, sensitive and multivariate
method for characterization in the field, and
has been shown to relate soil structure to other
measurements of functionality. Where the Blackland Index encourages observation across a
range of parameters, the von Post scale summarizes their effects on the soil, providing a first
approximation of the soil condition of a derelict
blackland field.
This scale was devised by Lennart von Post
(von Post and Granlund, 1926) during his work
on the Soil Survey of Sweden for measurement
of degree of decomposition of dead plant matter such as Sphagnum moss. Using parameters
such as fibre integrity, colour and viscosity of
exudate, and presence of colloidal particles, it
creates a descriptive framework across a wide
range of organic soils, and assigns a numerical
value from 1 (undecomposed) to 10 (colloidal).
The US Department of Agriculture (USDA)/FAO
compressed von Post’s 10 steps into three levels
(­fibric, hemic, sapric) thereby reducing its diagnostic usefulness at field scale.
6.3.4.1 Test
The von Post scale is simple to use: a handful of
wet soil (small enough to cover with the fingers
of one hand) is squeezed very hard, until as
much material as possible has extruded through
the fingers. Colour and viscosity of exudate, proportion and condition of remaining fibre and
other qualities are noted; a score is assigned according to Table 6.2.
This elegantly simple test clearly described
the continuum found in the research areas, from
Valuing the Neglected
a sponge-like catotelm with nearly unlimited hydraulic conductivity to impermeable black peat.
Although Sphagnum when left to itself passes
from fibre to colloidal paste with no intermediate
crumb stage, the von Post score correlates
strongly with other characteristics of good soil
functionality. It showed strong statistical relationships with soil depth, colour, K, pH, ash, hydrology and smell, emphasizing its predictive value
as a multivariate indicator for field analysis. It
may also indicate topographical or hydrological
conditions that support or impair land restoration. In the research areas, desirable plants
(good grasses) grew most strongly in areas with
von Post scores between 7 and 9.
6.3.5
Evaluation
These three systems – Blackland Index, Blackland Vegetation Scoring, von Post Humification
Scale – represent a range of approaches to the
problem of creating ‘feasible tools of decision
support’ for small-scale agriculture. They are
­designed for in situ judging of highly organic,
formerly productive but now derelict land,
especially within a variable landscape mosaic.
These methods are intended to aid land managers in initial judging or re-evaluation of land
capability, but they also take a fresh approach
to the problem of meaningful description of
highly organic soils, and are able to distinguish
quite small differences in potential. In each
case, the tool is suitable for a first approximation of the condition of a blackland field: the
von Post scale may appeal to the soil scientist,
Blackland Vegetation Scoring to the ecologist
and the Blackland Index to the ordinary person, but all three appear to be internally consistent, that is, giving similar ranking to similar
land areas. None requires special equipment;
each generates a single score to enable comparison and ranking of different areas within a
croft, farm or ecological region, as well as being
convenient for statistical treatment.
6.4
Return to Use
Land management affects nutrient availability
and other factors within the system. Agricultural soils in general can be ‘improved’ by
99
changes being made to the physical (e.g. drainage) and the chemical (liming, balancing nutrient availability through fertilizer additions);
these can also produce substantial long-term
modifications to soil physical, chemical and biological attributes. The rate of change and ‘memory effect’ of such anthropogenic forcings may
vary from a decade to centuries (Richter, 2007).
While in many lowland agricultural areas physical issues associated with cultivation have been
‘overcome’ through the tremendous increase in
mechanical power and fertilization available,
these have very limited applicability for upland
and marginal areas such as blackland.
Current research distinguishes five stages
that may lead to successful regeneration of derelict agricultural systems such as blackland; each
stage requires about 1 year to complete.
1. Analysis of soil and of landscape history. Specialized methods of landscape evaluation have
been developed for blackland (see Section 6.3);
other derelict areas would benefit from a similar
process to develop suitable analytical tools.
2. Restoring appropriate water management to
prevent further deterioration, whether by drainage in an Atlantic climate or supply methods in a
continental one. In the blackland research areas,
attention to drainage and water management is
paramount; in the Atlantic climate, precipitation almost always exceeds transpiration, and
waterlogging progresses rapidly, undoing in decades what generations had achieved.
3. Surface treatment, whether to increase aeration in organic soils by removing litter/moss,
or to add organic matter in exhausted mineral
soils. Preventing the build-up of organic ­matter
through mowing, grazing and other methods
of stripping off Sphagnum moss is as essential
in blackland areas as restoring organic matter
to the soil is in others. In the research areas,
mowing produced the most rapid improvement.
4. Amendments to pH, mineral content, nutrients as required or possible. Blackland soil
chemistry and structure were traditionally altered through the use of shell sand, manure
and seaweed. Such local resources are still easily available, costing only the effort required to
use them. In addition to less expense, they may
present advantages as compared with artificial
fertilizers such as improved trafficability or soil
conditioning (Knox et al., 2013).
100
M.N. Scherbatskoy, A.C. Edwards and B.L. Williams
5. Cultivation and use with an eye on traditional methods. Traditional methods were effective to build soil structure and maintain
­fertility in a cost-effective manner, using and
adapting available technology. Traditional systems can be ancient (Reijntjes et al., 1992),
persisting because they enabled communities
to survive on local resources. Fenton (1994)
emphasized that the survival of local techniques was not a sign of backwardness, but of
adaptation to difficult circumstances.
Returning derelict land to productive use has important implications for carbon capture. Predicted benefits of management are often equivocal, as uptake in one pathway may lead to losses
in another (Worral et al., 2010). Bringing back
land formerly abandoned from agriculture was
suggested by Powlson et al. (2011) as a suitable
strategy for carbon sequestration. Research on
fen peats in Poland noted the loss of carbon on
drainage, but the resulting ‘muck soils’ became
stable and supported cropping (Okruzko, 1968;
Okruzko and Ilnicki, 2003). The small-scale mosaic of blackland areas makes them suitable for
carbon sequestration techniques such as tree (or
at least scrub) planting, desirable as a wind break
for arable areas and as shelter for stock. The most
important climate effect in reuse of derelict land
may lie in reducing ‘carbon footprint’ through a
return to local production of food and feed, as
well as the strengthening of local communities.
6.5
Conclusion
If our goal is to enable best use of neglected
and marginal land, then land evaluation
needs to go beyond simple factual questions
and become more holistic. As Sir Albert Howard (1947) wrote, ‘Many of the things which
matter on the land . . . cannot be weighed or
measured. Nevertheless, their presence is
everything; their absence spells failure.’
Looking to the future we should ask, is it
correct to continue to obliterate successful
food-producing strategies in the name of efficiency (and profit)? Given that world climate is
likely to become more unstable, an understanding of the traditional agricultural methods that
created useful soils in ecosystems throughout
the world will be valuable. Relearning the forgotten secrets of soil management on varied and
marginal land may become essential in an age
of uncertainty.
First, land exists in significant quantity,
well-suited to the grazing animals which alone
on rough terrain can turn sunlight into food.
Second, the flexibility of a diverse landscape
mosaic engenders system resilience: in a dry
year, one area is productive, in a wet year, another. Third, the habit of care, thrift and adaptation ingrained in populations on the edge
may have lessons for us all in a future era of extreme conditions.
We have discussed the rapid decline of
Hebridean blackland as an example of a traditional system undermined by assumptions
from industrial agriculture. However, once
understood, it may also provide lessons in system resilience and a way forward. The lesson
of the blackland evaluation is not of a new
uniform approach (such as the Green Revolution) but of a method which first observes,
then understands and finally adapts systems
that worked for centuries.
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7 Evaluating Land Quality for Carbon
Storage, Greenhouse Gas Emissions
and Nutrient Leaching
Joanna M. Cloy,1* Bruce C. Ball1 and T. Graham Shepherd2
1
SRUC, Edinburgh, Scotland, UK; 2BioAgriNomics Ltd,
Palmerston North, New Zealand
7.1
Introduction
Recently the importance of good soil structure in
mitigating climate change and environmental
contamination has been recognized because soil
structure influences the storage of carbon (C)
sources and sinks of greenhouse gases (GHGs)
and cycling of nutrients, which are key soil system processes. This is because the maintenance
of soil structure by aggregation, particle transport and formation of soil habitats operates across
many spatial scales to regulate water drainage,
water retention, air transfer to roots for favourable gas exchange and mineralization of nutrients for release to crop roots (Kibblewhite et al.,
2008; Ball et al., 2013a). For the functions being
considered, the most important aspect of soil
structure is the soil pore network, which determines the movement of gases, liquids and associated solutes, as well as particulates and organisms,
through the soil matrix (Haygarth and Ritz, 2009;
Sakrabani et al., 2012). Good soil structure also
sustains a favourable rooting medium for plants;
if roots can’t get to the nutrients the plants can’t
use them.
Overall, the maintenance of good soil structure is linked to favourable soil quality for agricultural production. This chapter will first discuss
how soil properties influence soil C storage, GHG
emissions and nutrient leaching and how these
subsequently influence land quality. Next, visual
methods for evaluating soils relative to their potential for C storage, GHG emissions and nutrient
leaching will be described using both measured
and modelled data. Finally, future directions for
research are summarized.
7.2 Soil Properties Regulating
Carbon Storage, Greenhouse Gas
Emissions and Nutrient Leaching and
their Relationship with Soil Structure
Key soil properties for the three functions of
regulating C storage, the exchange of GHGs and
nutrient leaching are summarized in Table 7.1.
These properties can be categorized as dynamic,
unstable and subject to change or static, stable
and essentially unchanged over time. Dynamic
properties related to water and air flow in the soil
are more suitable for assessing the functionality
of soil under different management practices
(Cavalieri et al., 2009). Some of these soil properties can be directly and semi-quantitatively estimated using visual evaluation (see Section 7.3).
Soil properties can also exert a direct or indirect
*E-mail: joanna.cloy@sruc.ac.uk
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
103
104
J.M. Cloy, B.C. Ball and T.G. Shepherd
Table 7.1. Summary of soil properties which influence the three soil functions: carbon (C) storage,
greenhouse gas (GHG) emissions and nutrient leaching.
Soil property
Structure
Organic matter content
Inorganic and organic
nitrogen content
pH
Texture
Mineralogy
Porosity
Drainage
Compaction status
Temperature
Moisture/water content
Land use/managementa
Dynamic property
that changes over
time
Static property
that doesn’t
change over
time
√
√
√
Indirect effect on C
storage, GHG
emissions and
nutrient leaching
√
√
√
√
√
√
√
√
√
√
√
√
√
Direct effect on C
storage, GHG
emissions and
nutrient leaching
√
√
√
√
√
√
√
√
Land use and management activities are important because they can damage soil structure and change dynamic soil
properties. Soil properties are classified as dynamic or static and having a direct or indirect effect on these functions.
Soil properties which themselves are influenced by soil structure are shown in italics.
a
influence on the three soil functions. The soil
quality focused on here is soil structure. Texture,
organic matter (OM), drainage and compaction
status have both a direct effect and an indirect
effect on the three functions – the latter via their
influence on soil structure.
7.2.1 Soil carbon storage
and soil structure
Soil organic carbon (SOC) plays a critical role in
supporting the productive capacity of soils and
their ability to store atmospherically derived carbon dioxide (CO2) from photosynthesis. Plant
material can be consumed by animals or become
humified soil organic matter (SOM) through the
action of microorganisms (CAST, 2011). Storage of C as SOC is controlled by the soil environment and the quality of the OM in which the C
resides. Formation of recalcitrant materials is
one mechanism of protection (Ball, 2013).
Roots comprise c.40% C and the greater the
plant root and shoot mass, the greater the C input from root systems, additional surface litter
and dung from any grazing animals (Shepherd,
2009). Estimates of total belowground C input
into soils by cereals and grasses are c.1500 and
c.1750 kg C ha−1 year−1, respectively (Kuzyakov
and Domanski, 2000).
The global soil C inventory is estimated to
be 2500 Pg C to 1 m depth, comprising 1500 Pg
of SOC and 950 Pg of soil inorganic C. The global SOC pool holds approximately four times
more C than that stored in vegetation and double
that in the atmosphere (IPCC, 2013). SOC levels
are determined by the balance of net OM inputs
(e.g. vegetation, crop residues, organic amendments) and net losses of C from the soil through
decomposition, dissolved organic C (DOC) export
and soil erosion (Cloy et al., 2012; Cloy and
Smith, 2015).
Decomposition rates and turnover of C in
soils are controlled by water regimes, temperature, litter addition and rooting as well as the distribution of finer particles of C within the soil
matrix and interactions with reactive minerals
(e.g. clay surfaces) (Oades, 1988; CAST, 2011).
Soil OM has a dominant effect on soil structure
through its role in the formation of stable aggregates (e.g. via bonding of polysaccharides to clay
minerals) (Oades, 1988). Aggregations of clay
particles and OM are responsible for stabilizing
soil structure and protecting C from microbial
decomposition through occlusion within aggregates or small micropores (Oades, 1988).
Soil C storage, GHG emissions and Nutrient Leaching
A unique set of factors such as climate, waterlogging and low nutrient status and low pH
are responsible for C storage and accumulation
in organic soils such as peats (CAST, 2011). Soil
C will only accumulate in mineral soils (such as
agricultural soils) under a set of ‘ideal’ conditions and if their maximum soil C storage capacity has not been reached (Krull et al., 2001).
Soils will gain SOC if the rate of C addition is
greater than the rate of C loss through decomposition and DOC export. If these rates are the
same, the SOC levels are at steady state, that is, C
inputs = outputs and total SOC is neither increasing nor decreasing. Soils will lose SOC if
rates of C input are less than rates of C loss
(Shepherd, 2009).
Land use and management are particularly important anthropic controls on soil C
storage, with SOC levels generally following the
order forest > grassland > arable land (Sakrabani
et al., 2012). Disruption of soil aggregates and
deterioration of soil structure through management practices and mechanical stress such as
tillage, trampling and erosion accelerates microbial oxidation of SOC and makes it more
available as DOC (Oades, 1988; Hillel and
Rosenzweig, 2009). Intensive use of nitrogen
(N) fertilizers to increase crop yields is generally
thought to increase SOC sequestration by increasing crop residue inputs, but Khan et al.
(2007) observed a decline in SOC levels after
40–50 years of synthetic N fertilizer, despite
increasing residue incorporation. Manure additions can result in greater and longer-lasting C
sequestration than the addition of equivalent
amounts of N as mineral fertilizer (Diacono and
Montemurro, 2010), but initial soil C contents
are important. A decline in SOC levels for soils
from a long-term experiment receiving annual
manure applications was attributed to a continuing decline in native SOM originating from
vegetation that preceded cultivation (­Christensen
and Johnston, 1997).
High levels of inorganic water-soluble N
and phosphorus (P) are reported to prevent the
formation of stable forms of soil C (such as
humus) due to the inhibition of the fungi and
bacteria essential to C sequestration (Mulvaney
et al., 2009; Czarnecki et al., 2013; Jones,
2014). Good farm management practices including rotational grazing management, maintaining good residual pastoral levels and cover
105
crops, and avoiding high applications of mineral fertilizers can increase soil C levels significantly, particularly in the subsoil (Ampt and
Doornbos, 2010; Jones, 2011). Differences in
grassland soil C storage for soils receiving low
amounts of NPK, slow-release or organic fertilizers vs soils receiving high amounts of mineral
fertilizer are illustrated in Fig. 7.1 (Shepherd,
2009).
Poor soil quality and fertility are associated
with a decline in OM. Key benefits of increasing
SOM are improved resistance to compaction
(characterized by compression of soil aggregates
and reduction in pore size and continuity), nutrient conservation and improved water infiltration and retention due to better soil structure
(Kibblewhite et al., 2008). Soil C sequestration
and enhancing the photosynthetic capacity of
plants to absorb atmospheric CO2 play a major
role in regulating the emission of GHGs.
7.2.2 Soil greenhouse gas exchange
and soil structure
The ‘greenhouse effect’ is the enhanced warming of the Earth’s surface and lower atmosphere
due to additional emissions of GHGs as a result
of human activity and the subsequent increase
in adsorption of infrared radiation. The most
important individual GHG is CO2, but substantial contributions to global warming are made
by trace gases, including methane (CH4) and nitrous oxide (N2O), both of which come in part
from soil emissions. The Global Warming Potential (GWP) of a GHG is a measure of its contribution to atmospheric global warming over a fixed
period of time relative to that of CO2, which is
assigned a GWP of one. One kilogram of CH4
has a GWP 25 times greater than 1 kg of CO2,
over a 100-year period, while the GWP of 1 kg
of N2O is nearly 300 times greater (Cloy and
Smith, 2015).
Soil porosity, temperature, SOM content,
soil mineralogy, pH and soil N content influence
the emission and exchange of GHGs at a range
of scales (Table 7.1). Climatic conditions, particularly rainfall and temperature, also regulate
GHG emissions from soil directly through their
influence on microbial activity. Soil structure
and properties such as texture and drainage
106
J.M. Cloy, B.C. Ball and T.G. Shepherd
Fig. 7.1. Soils under dairy grazing. (a) Soil profile with increasing carbon (C) levels, dark soil colour, good
potential rooting depth and pasture colour and growth, moderately good earthworm numbers and root length
and root density. (b) Soil profile with steady-state C levels, light soil colour, moderate earthworm numbers,
potential rooting depth, pasture colour and growth and moderately poor root length and root density. The
difference between (a) and (b) soil profiles was related to long-term differences in management and land use.
NPK, slow-release bio-fertilizers (fertilizers containing living microorganisms that promote rhizosphere activity
and plant growth) were applied in (a), whereas highly soluble, synthetic fertilizers and mineral N were applied at
higher rates in (b). (From Shepherd, 2009.)
Soil C storage, GHG emissions and Nutrient Leaching
107
Fig. 7.1. Continued.
affect emissions indirectly by their influence on
other properties mainly soil moisture content
and distribution, compaction status, pore size
and continuity, and the distribution of organic
residues (Ball, 2013). Soil porosity or more
specifically water-filled pore space (WFPS) – the
proportion of porosity filled with water – has a
major bearing on the generation and release of
GHGs. As WFPS increases to saturation, CO2
and N2O, and finally CH4 are emitted (Fig. 7.2).
As macropores, mesopores and pore continuity decrease due to compaction, saturation is
reached more quickly and lasts longer so that
more GHGs are emitted than from well-structured
and well-­aerated soils with good porosity and
inter-pore drainage (Shepherd, 2009). Clayey
soils are often poorly drained and are therefore
more likely to emit GHGs (Shepherd, 2009).
Structure can override the influence of texture
in regulating gas exchange mainly because of
its substantial influence on soil water content
and pore continuity in soils of the same type
(Ball et al., 2013a). Poorly drained soils generally emit greater amounts of GHGs than welldrained soils (Cloy and Smith, 2015). The role
of soil structure will now be discussed for CO2,
N2O and CH4 in relation to their production
in soils.
108
J.M. Cloy, B.C. Ball and T.G. Shepherd
100
Emission of CH41
Water-filled pore space (m3 100 m–3)
90
80
Maximum emission of N2O
by denitrification 2
e
or
70
ity
A
VS
60
=
0
e
or
sc
it
s
ro
po
A
VS
s
ro
po
=
1.5
GOOD CONDITION VS = 2
Soils have many macropores
and coarse micropores
between and within
aggregates associated with
good soil structure
c
ys
MODERATE CONDITION VS = 1 POOR CONDITION VS = 0
Soil macropores and coarse
No soil macropores
micropores between and
and coarse micropores
within aggregates have
are visually apparent
declined significantly but are
within compact, massive
present in parts of the soil on
structureless clods. The clod
close examination. The soil
surface is smooth with few
shows a moderate amount of
cracks or holes, and can have
sharp angles
consoildation
PLATE 63. Visual scoring (VS) of soil porosity under cropping
Emission of N2O by nitrification 3
Maximum aerobic microbial activity and emission of CO2 by respiration 3
Pasture
4 yrs maize
5 yrs barley
11 yrs maize
23 yrs barley
30 yrs barley
11 yrs maize then 10 yrs pasture
Decline in CO2 and N2O emissions
50
40
25
30
35
40
45
50
Volumetric water content (m3 100 m–3)
55
60
Fig. 7.2. Water-filled pore space (WFPS) and water content at which greenhouse gases are emitted in a
Kairanga silty clay soil under pasture and at varying degrees of structural degradation under increasing
periods of continuous cropping and conventional cultivation. 1 MacDonald et al. (1996); 2 Dobbie et al.
(1999); 3 Linn and Doran (1984). (From Shepherd, 2009.)
7.2.2.1 CO2
The main mechanism for CO2 release is microbial decomposition of plant material and SOM
via aerobic decay processes in aerated soils. Both
CO2 and CH4 are released during slow anaerobic
decay processes in waterlogged soils (Cloy and
Smith, 2015):
Aerobic decay: CH2O + O2 → CO2 + H2O
Anaerobic decay: 2CH2O → CH4 + CO2
Spatial location of OM within the soil matrix determines physical accessibility to decomposers.
Soil structure influences this accessibility and is
crucial for decomposition. Physical protection
of OM is achieved through aggregation and adsorption of OM on mineral surfaces.
Decomposition rates are also modified by
temperature and moisture. Moist well aerated
soils with loose, well-aggregated structures,
such as those found in sandy loam soils, favour
organic carbon (OC) mineralization and CO2
­exchange (Ball et al., 2013a).
Soil CO2 emissions decrease substantially
after heavy rainfall because high WFPS and poor
gas diffusivity and air-filled porosity restrict respiration and increase anaerobic conditions (Ball
et al., 2013a). Soil CO2 emissions increase ­linearly
with increasing water content to a maximum of
approximately 60% WFPS before decreasing
(see Fig. 7.2) (Linn and Doran, 1984; Shepherd,
2009). For soils with different clay contents,
Franzluebbers (1999) found that cumulative C
mineralization during a 24-day incubation at
25°C increased with increasing WFPS to a maximum of 0.53–0.66 m3 m−3. Clay content had no
effect on the level of WFPS required to achieve
maximum C mineralization under compressed
and uncompressed conditions (Fig. 7.3). Unlike
cumulative C mineralization, net N mineralization (i.e. conversion of organic forms of N to inorganic N) decreased strongly when the
‘­optimum’ WFPS level of 0.53–0.66 m3 m−3 was
exceeded and net N mineralization appro­ached
zero at values near 0.8–0.9 m3 m−3 (Fig. 7.3)
(Franzluebbers, 1999).
Soil C storage, GHG emissions and Nutrient Leaching
600
60
10% clay
40
0
20
Natural
Compressed
0
14% clay
400
40
200
20
0
0
20% clay
400
40
20
200
0
0
23% clay
400
40
200
20
0
0
28% clay
400
40
200
20
0
0.0
0.5
0.0
0.5
Net N mineralization (mg kg–1 soil 24 d–1)
Cumulative C mineralization (mg kg–1 soil 24 d–1)
400
200
109
0
1.0
Water-filled pore space (m3 m–3)
Fig. 7.3. Cumulative carbon (C) mineralization and net nitrogen (N) mineralization as affected by bulk
density, clay content and water-filled pore space. Observations are means of three soils. Error bars are
least significant difference at P ≤ 0.05. (From Franzluebbers, 1999.)
Most agricultural soils contain low SOC
concentrations relative to unmanaged natural
soils because of higher rates of mineralization
accelerated by soil temperature and moisture regimes, lower input of biomass C and higher
losses caused by accelerated erosion and leaching. The use of high yielding plant varieties, fertilizers, irrigation and residue management can
reduce CO2 losses and enhance uptake within
managed areas (Lal, 2010). Several researchers
have discovered the quick release of CO2 in the
1 or 2 days immediately after ploughing (Reicosky,
1997) or even the hours after ploughing (Vinten
et al., 2002) due to the flush of microbial CO2
released from the large voids created by the
­
ploughing. It has been proposed that no-till and
reduced till practices cause an accumulation of
OC (Lal, 2010), but Powlson et al. (2014) suggested that no-till may be beneficial for soil
quality and adaptation of agriculture to climate
change but that its role in mitigation has been
widely overstated. The estimation of vegetative
cover at the soil surface and the distribution of
roots and residues within the topsoil permitted
by VSE methods are relevant to CO2.
7.2.2.2 N2O
Approximately 65% of all atmospheric emissions of N2O are from soils (Cloy and Smith,
2015) derived mainly from microbial nitrification and denitrification, which are controlled by
soil mineral N content, soil temperature and pH,
water and WFPS (Lilly, 1997; Shepherd, 2009).
A small soil sink (uptake of N2O in soil) has been
suggested, and attributed to reduction of atmospheric N2O to molecular N, N2 during denitrification. Nitrification is the microbial oxidation of
ammonium (NH4+) to nitrite (NO2−) and thence to
nitrate (NO3−). The nitrification process is fundamentally an aerobic one, for which the presence
of molecular oxygen (O2) is essential.
As for CO2, emission of N2O by nitrification
increases linearly with increasing soil water
content to a maximum of 60% WFPS and then
decreases (see Fig. 7.2) (Shepherd, 2009).
110
J.M. Cloy, B.C. Ball and T.G. Shepherd
The other main microbial process producing
N2O is denitrification, involving the reduction of
NO3−, in the absence of O2. Anaerobic zones within
the soil profile then occur, and within these zones
NO3− is the chemical species that most readily
acts as an electron acceptor, and so it becomes
reduced by a succession of enzymes, to NO2−, NO,
N2O and finally N2 (Cloy and Smith, 2015):
NO3− → NO2− → NO → N2O → N2
While the WFPS needs to be 60–65% for substantial emissions of N2O to occur (i.e. critical
WFPS), the highest emissions occur by denitrification when the WFPS is between 70 and 90%
with lowest at WFPS <50% (see Fig. 7.2). The
critical WFPS is a major driver of GHG emissions
and in finer textured soils the critical WFPS and
the subsequent degree of saturation required to
generate GHGs decreases so that these soils tend
to emit more GHGs than coarser textured soils.
The level of N2O emissions varies according
to soil properties listed in Table 7.1, but the application of fertilizer N has the biggest impact. In soils,
soil matric potential, volumetric water content,
relative gas diffusivity and WFPS are indicators
of soil aeration status (Ball, 2013). Aeration influences WFPS and the air-filled pore network,
thereby influencing N2O production and emission. Blockage of the air-filled pores by water
near the surface of compacted soils can dramatically increase N2O emission and decrease CO2
emission (Ball, 2013). The fraction of the total
gaseous products of denitrification that is actually emitted as N2O depends heavily on soil structure and soil wetness. On the one hand, small
anaerobic microsites may form in an otherwise
well aerated soil profile, caused by a localized region of high respiration, and any N2O formed in
the microsite will have a high probability of escaping before being reduced to N2. On the other
hand, the soil may contain large, virtually saturated anaerobic clods, into which NO3− ions may
diffuse in solution. Any N2O produced well within
the clod can only escape after diffusing to the
surface, and is much more likely to be reduced to
N2 before this occurs (Cloy and Smith, 2015).
On grazed land, the deposition of excreted N
is spatially very variable. A small patch of soil surface may receive N as urine at a rate equivalent to
some hundreds of kilograms per hectare, whereas
land between patches receives none. Also, the
treading of the soil surface in wet conditions by
animal hooves can create localized compacted
zones in which water collects and the soil becomes
anaerobic. Such factors lead to the occurrence of
‘hotspots’ of microbial activity and N2O emissions
that vary significantly in size; thus the average
overall emission rate from grazed grassland
may be two to three times greater than that from
N-­fertilized grass grown as a crop to be cut for
winter feed (Cloy and Smith, 2015). The estimation of surface damage to soil and vegetation in
areas of restricted macroporosity and of general
greying and mottling, indicative of anaerobic zones,
permitted by VSE methods are relevant to N2O.
7.2.2.3 CH4
Production and emission of CH4 only occur in
very wet mineral soils after organic fertilizer is
applied or in organic soils. The microbial breakdown of organic compounds in strictly anaerobic conditions, a process called methanogenesis
is responsible for CH4 formation in soils. A very
low redox potential is required for this process,
and CH4 production does not begin until reduction of O2, NO3−, iron(iii), manganese(iv) and sulfate (all of which maintain the potential at higher
levels) is complete. Such low-redox conditions are
predominantly found in soils where prolonged
waterlogging is a normal feature, for example,
natural wetlands and flooded rice fields.
The CH4 formed in soils can migrate to the
surface and be emitted into the atmosphere. Diffusion can take place in solution from the point of
formation in an anaerobic layer, upward to water
layers containing O2, where much of the CH4 is
oxidized and only a fraction outgasses to the atmosphere. If sufficient CH4 gas is produced, bubbles form in the water layer and force their way to
the surface before significant oxidation can occur
(Cloy and Smith, 2015).
In well-aerated agricultural soils, uptake of atmospheric CH4 is more common. Moist well-aerated
soil conditions favour CH4 oxidation by methanotrophic bacteria, being optimal in soils of intermediate textures and moderate water contents, which
permit a reasonable rate of diffusion through the
soil matrix and thus allow oxidation to take place.
Soil bulk density and water content, and their
consequent effects on gas movement and penetration in the soil profile, have a major impact on
the rate of oxidation of atmospheric CH4 in soils
(Cloy and Smith, 2015). Good aeration conditions
Soil C storage, GHG emissions and Nutrient Leaching
provided by the presence of continuous macro­
pores (detectable from measurements of air
­permeability) are important for access of the atmospheric CH4 to oxidation sites (Ball et al., 1997).
However, excessive soil disturbance and excessive
use of mineral N fertilizer can reduce the capacity
of soils to take up and oxidize atmospheric CH4 as
they can reduce the activity of methanotrophic
bacteria (Shepherd, 2009). The estimation of the
distribution and degree of decomposition of residues, compaction status and of macroporosity
and its restriction permitted by VSE methods are
relevant to CH4.
7.2.3
Soil nutrient leaching
and soil structure
Poor soil quality and fertility are associated with
low nutrient retention and subsequent leaching
into groundwater and waterways. The loss of
nutrients such N, P, potassium (K), sulfur (S),
calcium (Ca), magnesium (Mg) and sodium (Na)
has major implications on land quality because
it further affects soil health and agricultural
productivity adversely and can lead to environmental problems such as accelerated GHG emissions and eutrophication (Siemens et al., 2004;
Shepherd, 2009).
Only 40–50% of applied fertilizer N may
­actually be utilized by plants (Mengel, 1992;
Shepherd, 2009). The remaining N is either included in the organic and inorganic soil N pools
where it may be utilized by plants in subsequent
years, or lost to the environment. Apart from the
losses to the atmosphere as N2O and N2 (see
Section 7.2.2.2), and ammonia (NH3) via volatilization, N is leached into the groundwater and
lost as runoff into waterways (Shepherd, 2009).
The potential for nutrient loss via leaching into
groundwater and waterways is influenced by
soil cation exchange capacity (CEC) and anion
exchange capacity (AEC). Soil CEC and AEC provide measures of the ability of soil to adsorb and
exchange nutrient cations (e.g. NH4+, Ca2+) and
anions (e.g. NO3−, phosphate (PO43−)), respectively.
Clayey soils have much higher CECs than sandy
soils and usually retain more nutrients and OM
(Shepherd, 2009). The concentration and composition of SOM, particularly DOC, are also important drivers of nutrient loss, particularly
metal cations. Nutrients held within or on OM
111
surfaces can remain and be retained in the solid
phase or become associated with and mobilized
alongside DOC in the solution phase. Nutrients
associated with soil mineral colloid solutions,
particularly P, can also be mobilized in this way
(Siemens et al., 2004).
Nutrients dissolved or adsorbed on particles
in soil water are transported down the soil profile
through soil pores or across the soil surface as
runoff. Soil matrix pore size, largely determined
by soil structure, has a major influence on nutrient transport as DOC and nutrients in macro- and
mesopores are subjected to convective transport
by seepage and preferential flow (Kalbitz et al.,
2000; Shepherd, 2009). The drainage status, hydraulic conductivity and infiltration capacity of
soils are properties that change under different
soil types and poor management practices such as
compaction. These properties have an important
influence on nutrient leaching as they determine
the ability of water and solutes to move through
soil and be soaked up by the soil. The intensive use
of well-drained, sandy and coarse loamy soils in
the UK was found to produce soil structural damage and enhanced surface water runoff from
fields that should naturally absorb winter rain
(Palmer and Smith, 2013). Nutrient leaching has
generally been found to be greater in sandy soils
with low water holding capacity and high hydraulic conductivity than in clayey soils with high
water holding capacity and low hydraulic conductivity (Palmer and Smith, 2013; Pulido et al.,
2014) but reactive mineral colloids, prevalent in
clayey soils, can lead to increased leaching of nutrients, particularly P (Siemens et al., 2004). Silty
textured soils are prone to capping, where the surface forms a hard crust, preventing water from
infiltrating and resulting in water running off the
surface (Palmer and Smith, 2013). Land topography and slope also influence nutrient loss.
Well-structured soils on flat land with a higher infiltration and permeability are more susceptible to
nutrient leaching than poorly structured soils
with a slower infiltration and permeability. On the
other hand, poorly structured soils with a slower
infiltration and permeability on undulating and
rolling land are more susceptible to nutrient loss
by runoff than well-structured soils (Shepherd,
2009). Also, in poorly structured soils with poor
water storage and infiltration capacity there is
less opportunity for N uptake by plants, utilization by soil microbes (e.g. denitrification of NO3−)
112
J.M. Cloy, B.C. Ball and T.G. Shepherd
and microbial or chemical immobilization to
­remove N from the soil solution (Lilly, 1997;
Shepherd, 2009). Overall, good soil structure and
functional field drainage systems are key properties for achieving soil nutrient retention, but also
for good water quality and minimizing flood risk.
In general the susceptibility of soils to nutrient
and DOC losses increases when soil structure is
degraded but soil texture and OM content, which
also influence drainage and related properties,
may override the influence of soil structure.
Enhanced nutrient use efficiency by plants
reduces leaching of nutrients to water courses.
Soils with high root density and deep rooting
plants have increased capability for nutrient use,
reducing the likelihood of leaching compared
with soils with shallow, sparse root systems
(Shepherd, 2009). Linkages between plants and
soil microbes may play a major role in controlling N transformations. For example, fungal-­
dominated microbial communities were found
to enhance N retention and reduce N loss in
extensively managed grassland (de Vries and
­Bardgett, 2012). Use of agricultural practices
that encourage N to remain in the root zone long
enough for plant uptake will help to prevent N
pollution (Lilly, 1997). Under certain environmental conditions, nitrification (conversion of
NH4+ to NO3−, see Section 7.2.2.2) occurs rapidly. In
soils subject to leaching, nitrification inhibitors
can be applied to slow nitrification and delay N
losses (Lilly, 1997; Cameron et al., 2013). Aeration equipment can also be used to improve
structure and thence soil infiltration and nutrient
movement (Lilly, 1997).
7.3 Estimation of Soil C Storage,
GHG Emissions and Nutrient
Leaching using Visual Techniques
Quantitative indicators of flow and macroporosity have been shown to relate to visual evaluation scores and clearly show the relevance of
such scores to properties governing GHG emissions and nutrient leaching. ­Shepherd (2003)
found that the VSA structure score related well
to saturated hydraulic conductivity and that
the VSA porosity score related well to macroporosity in a range of soils from New Zealand
(Fig. 7.4).
Guimarães et al. (2013) showed that air
permeability correlated negatively and significantly (P < 0.01) with visual evaluation of soil
structure (VESS) on two contrasting soil types
and cite that air permeability (Ka) values <1 μm2
can be used as a reference for impermeable soils
that can restrict aeration. Such values occurred
mostly between VESS Sq3 and Sq5. McQueen
and Shepherd (2002) also reported air permeability to be a good indicator of soil structural
degradation, particularly at a water potential
of −10 kPa.
Examples of soil properties that can be observed visually to assess potential for functions of
soil C storage, GHG emissions and nutrient leaching are provided below. Indicators for the above
functions have been produced using modified
scorecards of the visual soil assessment (VSA)
technique for scoring soil quality and plant performance using soil and plant indicators.
7.3.1 Soil C storage
For soil C storage, indicators for the scorecards
are: (i) textural group; (ii) clay mineralogy;
(iii) soil colour; (iv) earthworm numbers; (v) potential rooting depth; and (vi) root length and
density (Shepherd, 2009). Other indirect, nonsoil visual indicators required include crop/­
pasture growth, colour and growth relative to
urine patches (for pasture), the amount and
form of fertilizer and N applied, and method of
cultivation (for cropping) (Shepherd, 2009).
Measured changes in C storage and the VSA Soil
C Index of a soil under dairying in the Manawatu Region of New Zealand demonstrated the
close relationship between measured and observed values (Table 7.2). Total SOC declined initially over time reaching a steady state (neither
gaining nor losing C) with a VSA Soil C Index of
21 (Shepherd, 2009).
Grassland compaction can impair the ability
of soil to store C and to allow water infiltration.
Newell-Price et al. (2013) conducted a survey of
grassland soil compaction in England and Wales
using both the VSA technique and regular physical measurements of soil compaction. They
found that the most important factors influencing VSA ranking scores, alongside compaction status, were SOM content (positive
Soil C storage, GHG emissions and Nutrient Leaching
(a)
160
y = 0.49e 2.62x
120
Ksat (mm h–1)
113
r2 = 0.86; P<0.001
80
40
0
0
0.5
1.5
1
VSA structure score
2
Macroporosity (m3 100 m–3)
(b) 40
y = 1.41e 1.37x
30
r2 = 0.78; P<0.001
20
10
0
0
0.5
1
1.5
2
VSA porosity score
Fig. 7.4. Relationship between (a) VSA field structure and saturated hydraulic conductivity (Ksat), and
(b) VSA porosity scores and macroporosity (>30 μm) measured in core samples in a range of soils in
New Zealand. (From Shepherd, 2003.)
relationship) and soil sand content (positive
relationship), indicating the potential for these
visual techniques to estimate SOC content.
The visual property most indicative of C
storage that the VSA and VESS techniques
make use of is soil colour. Munsell soil colour
charts developed in the early 1900s are frequently used to make colour assessments. Soil
OM (and ­therefore SOC) contents can be roughly
estimated using soil colour. Generally the darker
brown the soil, the higher the OM content
(see Fig. 7.2) but the role of soil texture, moisture, carbonate and mineral contents on soil
colour should be included (Escadafal et al.,
1989). For example, Fe oxides and hydroxides
have ­characteristic colours: hematite alpha-Fe2O3
114
J.M. Cloy, B.C. Ball and T.G. Shepherd
Table 7.2. Changes in soil carbon (C) storage versus the VSA Soil C Index scores in the top 10 cm of a
fine clayey soila under dairying over time.
Year
Total organic C (g kg−1)
Bulk density (Mg m−3)
Total organic C (t ha–1)
Soil C Indexb
1982
1988
1989
1992
1997
56.0c
55.0d
52.4d, e
51.0f
49.9g
1.02
1.03
1.03
1.00
1.03
57.12
56.65
53.97
51.00
51.40
31.5
31.5
24.5
21
21
Kairanga silty clay loam soil (Eutric Gleysol, FAO classification; fine, mixed, mesic, Typic Endoaquept, Soil Survey Staff,
2014) formed from quartzo-feldspathic alluvium; bShepherd (2009); cShepherd (1992); dSparling et al. (1992);
e
Shepherd et al. (2001); fMcQueen and Shepherd (2002); gSaggar et al. (2001).
a
(brownish red), magnetite Fe3O4 (blackish grey), in Fig. 7.5. There is a quick and significant
wustite FeO (greyish blue), goethite alpha-­ ­decline in total C from 90.8 t C ha–1 in the upper
FeOOH (bright yellowish brown) and lepidocro- 20 cm to 69.8 t C ha–1 in just 4 years under
cite gamma-FeOOH (­o range) and ferrihydrite cropping. Soil colour becomes lighter with inFe2O3 (reddish brown) (Schwertmann, 1993). creasing C loss and, if we assume that 1 t of OC
Colour chips in Munsell colour charts can be oxidizes to 3.67 t of CO2, the loss of 31.6 t C ha–1
used to visually estimate a soil’s SOM con- after 11 years of conventionally cultivated maize
tent. For example, Wills et al. (2007) used results in the emission of approximately 116 t
Munsell colours to show that SOC could be CO2 ha–1 (Fig. 7.5). The loss of 49.6 t C ha–1 after
predicted from field measurements and that 35 years of continuous barley produces 182 t
separating samples by land use improved pre- CO2 ha–1.
dictions. Limitations to the use of the MunAs well as cropping systems and type of
sell charts were individual perceptions of cultivation, the degree of soil cover by vegeta­colour, soil type and water content. For re- tion also influences soil decomposition and CO2
assurance, it is good practice for farmers to emissions (CAST, 2011). This can be estimated
consider getting SOM contents measured at visually by the VSA or as surface condition
the same time as soil fertility testing, bearing (e.g. Soil Quality Scoring Procedure of Ball and
in mind that SOM contents can change very Douglas, 2003).
slowly. Sonneveld et al. (2014) used Munsell
colour chart and SOM content data from a
7.3.2 GHG emissions
national soil survey database for soil series in
a dairy farming area of the Netherlands to
develop a scheme for visually scoring soil col- For GHG emissions (principally N2O) the indicaour at farm level. Colour scores using their tors for the scorecards are: (i) textural group;
visual soil examination and an evaluation (ii) porosity; and (iii) soil mottles and colour
method that was based on the VSA approach (Shepherd, 2009). Other indirect, non-soil
were defined as 0 for Munsell value >4, 1 for ­visual indicators include pasture/crop quality;
Munsell value = 4 or 3.5 and 2 for Munsell ­pasture/crop yield; colour and growth relative
value ≤3, where 0 reflected poor condition to urine patches (for pasture); the amount and
and lower SOM content than 1 (moderate form of N applied; and method of cultivation
condition) and 2 (good condition).
(for cropping). For pastures, stocking rate is
Greater pasture growth might be expected also a useful indicator for the N deposited in the
to be associated with greater removal of CO2 form of animal urine and dung, which are
from the atmosphere. Soil C loss, associated major sources of N2O (Shepherd, 2009). Paswith changes in soil colour and CO2 emissions ture quality can be used as a visual indicator of
from soils that were initially under pasture before N2O and CH4 emissions because the efficiency
a switch to continuous maize and barley crop- and function of rumen microorganisms are reping using conventional cultivation are shown duced when pasture quality is poor (e.g. high
Soil C storage, GHG emissions and Nutrient Leaching
100
Carbon content (t/ha)
90
Dark grey soil
(10YR 4/1)
P
So
il co
lou 1
r b
wit
h in
eco
cre
me
asin
s lig
g lo
hte
r
ss
of c
arb
on
Greyish brown soil
(10YR 4.5/2)
144 t CO2/ha
emitted
Greyish brown soils
(10YR 5/2)
Dark greyish brown soil
(10YR 4/2)
5B
59 t CO2/ha
emitted
80
70
4M
60
11M
116 t CO2/ha
emitted
50
182 t CO2/ha
emitted
23B
Decline in organic C under barley
40
30
115
30B
Decline in organic C under maize
0
5
10
15
1 Soil colour according to the Munsell notation
20
25
30
35B
35
37B
40
Time/years
Fig. 7.5. Soil carbon loss, associated soil colour and CO2 emissions from soils that were under pasture
(P) before a switch to continuous maize (M) and barley (B) cropping using conventional cultivation. Data
point labels indicate crop and number of years since the experiment started. Soil colour descriptions
according to Munsell notation are also labelled. (From Shepherd, 2009.)
levels of NO3−-N, low sugar and therefore energy
levels) (McAllister et al., 1996; Shepherd, 2009).
Poor quality pasture results in high emissions
of CH4 (and NH3) from livestock and excretion
of N-rich urine. Poor growth and chlorosis-­
induced yellow pasture between urine patches
with strong growth indicates poor quality
pasture with reduced plant N uptake (Bolan
and Kemp, 2003) and the subsequent release
of N as N2O and leached NO−3 -N (Shepherd,
2009).
Although gaseous exchange is not related
directly to the topsoil appearance, assessment of
soil structure changes with depth using VESS is
important in identifying layers active in the production and transmission of gases or layers that
restrict gas exchange or are likely to be anaerobic (Ball, 2013; Ball et al., 2013a). These zones
are where further measurements of soil properties related to aeration status and mineral N
might be assessed (Ball et al., 2013b).
Organic carrot production involves considerable tractor traffic for the many management
operations, including mechanical and hand
weeding. Ball (2013) studied soil damage under
a former tractor ‘tramline’ route at an organic
carrot production site in east Scotland. Residual
compaction damage resulted in very poor soil
structure with a VESS score of Sq5 because the
soil consisted mostly of very large, compact clods
with minimal macroporosity. High soil moisture
contents, combined with the presence of straw
used to protect the former carrot crop, resulted
in anaerobic conditions shown by the grey-blue
appearance of the soil below the straw layer.
This was confirmed by the large N2O emissions
from cores taken at 15–20-cm depth.
No-tillage is effective in many countries for
controlling erosion by preserving soil structure.
The aspect of erosion control is particularly
important in Brazil where the influence of structural changes due to surface compaction on GHG
emissions was investigated. Intact cores of Oxisol clays taken from a southern Brazilian field
site under long-term no-tillage were used to assess VESS Sq score and CO2 and N2O fluxes along
a transect aligned so that the looser areas within
the crop rows and the compacted areas between
the rows (interrows) were alternately sampled
(see Fig. 7.6). VESS scores and physical properties were more favourable in the crop rows than
in the compacted interrows and these changes
were found to affect soil CO2 and N2O emissions
(da Silva et al., 2014).
Soil structural damage from animal treading is expected to increase soil N2O emissions
and to limit C storage, thereby impairing the C
balance of pasture dairy farming and long-term
sustainability of dairy production from pasture.
Interactions with N fertilizer application rate
116
J.M. Cloy, B.C. Ball and T.G. Shepherd
5
Row
Interrow
(a)
Bulk density (g cm–3)
4
Sq
3
2
1
0
0
5
10
15
20
25
30
35
1.30
1.20
1.10
1.00
10
15
20
25
30
35
40
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
40
0
5
50 (d)
(c)
0.33
40
Ka (μm2)
0.28
0.23
0.18
30
20
10
0.13
0.08
1.40
0.90
40
0
5
10
15
20
25
30
35
0
40
10000 (e)
CO2 flux (mg C m–2 h–1)
Air-filled porosity (m3 m–3)
0.38
N2O flux (g ha day–1 log scale)
1.50 (b)
1000
100
10
1
0
5
10
15
20
25
30
35
40
Sampling point
0
500
450
400
350
300
250
200
150
100
50
0
(f)
0
Sampling point
Fig. 7.6. Variation in Oxisol structural quality (VESS Sq score) and CO2 and N2O fluxes with sampling
point along a transect according to crop row or interrow position in a long-term no-tillage experiment in
Southern Brazil. (From da Silva et al., 2014.)
and type are likely. N uptake can appear poor at
high N application rates. To investigate this, Ball
et al. (unpublished data) measured soil structural and pasture quality using visual techniques (VESS and VSA), alongside other key soil
data, to identify N2O emission potential at farms
from an area of intensive dairy production near
Palmerston North, New Zealand. Soil sampling
site details and results are listed in Table 7.3.
Sites 1 to 6 were located on Kairanga silty clay
loam soils (Typic Endoaquepts, Soil Survey Staff,
2014), with two each on pasture receiving low,
medium and high N applications. Sites 7 and 8
were located on the Manawatu fine sandy loam
(Dystric Fluventic Eutrochrept, Soil Survey Staff,
2014), a flood plain soil vulnerable to damage.
Farms were chosen according to three levels of N
input. At each level, fields containing soils of
poor and good quality were identified. The VSA
scorecards were used to estimate the likelihood
and relative magnitude of N2O flux at each site
from visual estimates of soil and pasture quality.
The likely magnitude of N2O fluxes were confirmed using a simple model of N2O emissions
based on measurements of soil mineral N, WFPS
and soil temperature (Conen et al., 2000). Poor
quality soils were more common at high N inputs
(Table 7.3). Nevertheless some poor structures
as a result of treading damage were identified at
low N inputs. The high N input, poorly structured soils were deemed most likely to emit high
levels of N2O due to their likely high WFPS even
at low soil water contents, high soil temperature,
low porosity and air permeability, poor aeration
status near the soil surface and high exposed soil
surface area due to poaching (Ball et al., unpublished data).
Soils with stable structure resist compaction
damage and have satisfactory macroporosity
permitting good water drainage and aeration
while retaining sufficient moisture for good crop
growth (Ball, 2013; Ball et al., 2013a). Soils with
Soil C storage, GHG emissions and Nutrient Leaching
117
Table 7.3. Details of field sites, N application, structural quality, water-filled pore space (WFPS), air
permeability, mineral nitrogen (N) contents, soil temperature and estimated greenhouse gas (GHG) index
on two soil types under pasture (unpublished data).
N statusb
Sitea (kg ha–1 year–1)
Soil
structure
(VSA and
VESS)
1
2
3
4
5
6
7
8
Poor
Mod. good
Poor
Mod. good
Poor
Mod. poor
Mod. poor
Mod. good
Low−45
Low−35
Mod high – 115
Mod high – 250
High – 435
High – 435
High – 435
High – 435
Soil
Soil
Soil
Air
NH4+-N
NO−3-N temperature GHG
WFPS permeability content
content
at 5 cm
emission
(%)
(μm2)
(mg kg–1) (mg kg–1) depth (°C) index
67
64
59
54
56
54
47
38
43
137
52
106
68
138
17
20
4
0.3
9.1
2.6
6.4
5.9
20.1
12.5
24
25
11.4
13.8
8.7
6.8
16.5
9.9
20.3
22.4
22.4
22.4
23.4
23.4
23.4
22.5
Mod.–high
Moderate
High
Moderate
High
High
Mod.–high
Moderate
Soils 1–6 are Kairanga silty clay loams and soils 7–8 are Manawatu fine sandy loams.
N was applied as a foliar spray at sites 1 and 2, and as solid urea at remaining sites.
a
b
less stable structure are prone to compaction and
low macroporosity (<10% m3 m–3 soil) and high
WFPS (>65%) so that poor water drainage and
aeration may result. The relationship between
soil WFPS and the VSA visual assessment of soil
porosity has been proposed as an immediate and
effective guide to the susceptibility of a soil to
emit GHGs. Figure 7.2 in Section 7.2.2 illustrates
the WFPS and water content at which GHGs
are emitted from the Kairanga series soils, New
­Zealand. It demonstrates that moderately well-­
structured soil with a VSA soil porosity score of
1.5 requires a water content of approximately
42% (v/v) to ensure >70% of the soil pores are
water-filled and therefore able to generate significant emissions of N2O. In contrast, a severely
compacted soil after 11 years of poorly managed
maize cropping with a VSA porosity score of
0 requires a water content of only 33% (v/v) to
reach 70% WFPS to increase N2O emissions
significantly. The severely compacted soil will
therefore produce more GHGs than the well-­
structured soil because of the greater number of
days during the year when the soil water content
is at or above 70% WFPS.
Tractor compaction and animal trampling
at a grassland site in south-west Scotland with
imperfectly drained clay loam soils were also
found to decrease structural quality measured
using VESS during two consecutive autumns.
This impairment of quality was found to increase soil N2O emissions (Ball et al., 2013a).
7.3.3 Nutrient leaching
For nutrient leaching the indicators for the scorecards are: (i) textural group; (ii) soil structure;
and (iii) potential rooting depth (Shepherd,
2009). Other indirect, non-soil visual indicators
include root length and density; root development and soil erosion (for cropping); pasture/
crop quality; pasture colour and growth relative
to urine patches; the amount and form of fertilizer and N applied; and rainfall. For pastures,
stocking rate is also a useful indicator for nutrients deposited in the form of animal urine and
dung (Shepherd, 2009). Moderate to good relationships were found between soil visual
evaluation scores (VSA and VESS) and hydraulic
conductivity measurements in soils with contrasting textures and land uses (Pulido Moncada
et al., 2014). The potential for nutrient loss on a
dry-stock farm adjacent to Lake Taupo in New
Zealand on highly permeable, coarse textured
pumice soils in a moderately high rainfall area
(1480 mm year–1) with medium CEC, was assessed to be low according to the VSA scoring
system. The assessment was in close agreement
with the low levels of N measured in two streams
running through the farm into the lake. For
each stream, measured total NH4+-N levels were
0.01 g m−3, total Kjeldahl N levels were 0.2 g m−3
and measured total NO3−-N + NO2−-N levels
ranged from 1.5–2.2 g m−3 for water samples
taken in July 2006.
118
J.M. Cloy, B.C. Ball and T.G. Shepherd
Soil erosion and leaching can be prevented
by protecting the soil surface and improving soil
structure. This can be done using vegetation and
crop residues, high-residue crops, winter cover
crops, no-till or conservation tillage methods,
windbreaks, maintaining good soil fertility (especially levels of the soil flocculent Ca), avoiding soil compaction and adding OM (Shepherd,
2003). Maintaining crop residues not only ­reduces
erosion but encourages maximum water infiltration and storage. Estimations of soil leaching
based on amounts of fertilizer applied may be
limited to N because the relationship ­between
surplus nutrients and leaching to surface waters
is more direct for N than for P (van Beek et al.,
2003). Assessments of and the use of visual soil
techniques to estimate nutrient leaching are
not well documented but the use and potential
ability of visual field examinations for assessing soil structural quality has been evaluated
through comparison with soil physical and hydraulic properties related to soil function (Pulido
Moncada et al., 2014).
7.4
Future Directions
Soil C storage, GHG emissions and nutrient
leaching are key soil functions that are affected
by agricultural activities such as changes in the
structure of soils, reduction in soil fertility and
processes such as soil erosion and destruction of
humus (Sakrabani et al., 2012). Soil structure is
variable and increasingly vulnerable to compaction and erosion damage as agriculture intensifies and the climate changes (Ball et al., 2013b).
As the climate warms and rainfall patterns
change, there is a growing risk that soil GHG
emissions to the atmosphere will increase, in turn
causing further climate change as well as reducing the soil’s productive capacity. Suboptimal
agricultural soils with C deficits or unstable
structure offer an opportunity to absorb CO2
from the atmosphere and store it as OM in the
coming decades and off-set GHG emissions and
reduce nutrient leaching (Hillel and Rosenzweig,
2009; Shepherd, 2009). Changes in rainfall
­intensity and amount, vegetative cover and patterns of land use will affect soil erosion and
­nutrient leaching (Hillel and Rosenzweig, 2009).
Structural stability is improved by addition of
OM with a significant labile fraction, which also
contributes to the overall capture of OC in the
soil (Ball, 2013).
Farmer involvement is crucial in conserving
soil quality. Repeated and consistent use of quick
visual tools such as the VSA and VESS techniques to survey agricultural land and identify
problems in advance should be promoted. Farmers need encouragement to adopt improved management practices that protect soil structure,
particularly cost-effective C strategies such as
adopting agroecological management (Lal, 2010).
Good farm management strategies that ensure
good soil structure, aeration, available moisture
and nutrient status will increase soil health,
earthworm numbers, microbial biomass and activity, and enhance plant root systems and plant
photosynthetic rate and capacity (Shepherd,
2009). It is important to check that soil visual
techniques and soil quality scoring are reflected
in crop yields and any variability in N-fertilizer
management that is often visible in the crop
(­Mueller et al., 2013). Visual soil evaluation can
help farmers to identify areas for amendment
with OM or tillage in order to improve the three
functions: soil C storage, GHG emissions and nutrient leaching. The role of soil physics in conserving N in soil is becoming increasingly important as fertilizer prices increase and as climate
change results in soil conditions more conducive
to N loss (Ball, 2013). Ball (2013) suggested that
mitigation of soil N2O emissions could involve
increasing porosity and reducing moisture content through tillage and drainage. Soil chemistry
and biology also play important roles and interpretation of detailed soil analyses to exploit biologically active and slow release fertilizers is
­important. This allows mitigation of the ongoing
consequences of soil deterioration and NO3− pollution of ground and surface waters through
management of N fertilization by site-specific assessment of soil N availability (Khan et al., 2007).
Scientists need increasingly to use visual evaluation to target areas for measurements to quantify the three functions and their drivers.
Detailed quantification of soil structure is
conducted using measurements or observations of a soil’s component aggregates and
pores using micromorphology or CT scanning.
Future research directions could include the
comparison of soil assessments conducted using quick visual tools with these more sophisticated methods for describing soil structure
Soil C storage, GHG emissions and Nutrient Leaching
(Garbout et al., 2013; Munkholm et al., 2013),
which can then be linked to measured soil C
storage, GHG emission and nutrient leaching
processes.
7.5
Conclusions
Agricultural and environmental protection of
soils through the maintenance of good soil quality
and the implementation of good farm management practices are key improvements as they can
reduce degradation of C stores, losses of GHGs and
nutrient leaching. Poor soil quality is associated
with a decline in OM, high emissions of N2O, low
uptake of CH4 and poor nutrient retention. Improving and maintaining the physical condition
119
of the soil is an effective means of mitigating GHG
emissions. Soils with good structure resist erosion
and nutrient loss. Virtually all soils need at least a
moderately good structure through the soil profile
in order to function effectively as a growing medium. In particular, soil structure influences the
main soil and plant root functions: aeration,
drainage and root development. Without structure, soils will suffer from anaerobism, waterlogging and nutrient lock-up and, ultimately, crops
will fail. Assessment of agricultural land in terms
of soil quality and soil structure using quick visual
tools such as the VSA and VESS techniques provide an indication of the potential for soils to store
C, release GHGs and lose nutrients, and are therefore critical for helping farmers identify problems
as well as combat environmental change.
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van Beek, C.L., Brouwer, L. and Oenema, O. (2003) The use of farmgate balances and soil surface balances as estimator for nitrogen leaching to surface water. Nutrient Cycling in Agroecosystems 67, 233–244.
Vinten, A.J.A., Ball, B.C., O’Sullivan, M.F. and Henshall, J.K. (2002) The effects of cultivation method, fertilizer input and previous sward type on organic C and N storage and gaseous losses under spring and
winter barley following long-term leys. Journal of Agricultural Science 139, 231–243.
Wills, S.A., Burras, C.L. and Sandor, J.A. (2007) Prediction of soil organic carbon content using field and
laboratory measurements of soil color. Soil Science Society of America Journal 71, 380–388.
8 Soil Structure under Adverse
Weather/Climate Conditions
Rachel M.L. Guimarães,1* Owen Fenton,2
Brian W. Murphy3 and Cássio A. Tormena4
1
Department of Agronomy, Federal University of Technology – Paraná,
Brazil; 2Teagasc, Environmental Research Centre, Johnstown Castle,
Co. Wexford, Ireland; 3Honorary Scientific Fellow with the New South Wales
Office of Environment and Heritage, Cowra, Australia; 4Department
of Agronomy, Universidade Estadual de Maringá, Paraná, Brazil
8.1
Introduction
The use of the Earth’s natural resources in a sustainable manner has increased in importance
over the past few decades. This is particularly
true for soil, with soil degradation likely to continue being a serious problem throughout the
21st century, due to its impact on food security
and environment quality (Eswaran et al., 2001).
Soil degrades by losing its actual or potential
productivity or its function as a result of natural
or anthropogenic factors (Lal, 1997). Soil degradative processes include physical, chemical
and biological processes. The most important of
the physical processes is the deterioration of soil
structure leading to crusting, compaction, erosion, anaerobism, salinization, acidification, decrease in cation exchange capability, leaching,
volatilization, nutrient imbalance, reduction in
soil biodiversity and a decrease in soil organic
carbon. The main degradative processes are
­deforestation, inappropriate land use, cropping
systems and management (tillage, drainage, irrigation) along with socio-economic pressures
such as market forces (Lal, 1997). Degradation
of land resources affects one in three people
on Earth in some way (Gurtner et al., 2011; von
Braun et al., 2013).
Soil degradation reduces the productivity
potential of the land due to a loss in soil fertility
and has a negative impact on the environment
due to sediment deposition and pollution. Currently, the reduction in productivity incurred
through soil degradation is being masked by production increases brought about by the application
of high levels of fertilizers and the development
of new technologies. Nevertheless, through time
the loss of soil organic matter combined with soil
physical degradation by overgrazing and burning can lead to desertification. Approximately
one-third of the global land surface is subject to
desertification (Eswaran et al., 2001). Semi-arid
and arid regions are threatened by global warming-induced problems of soil degradation and
desertification risk (Lobell et al., 2008).
The combination of extreme rainfall events
and increased temperatures predicted by climate
change models, in conjunction with higher production demand, will require the soil to respond
to increased demand for nutrients and, especially, water (Cruse, 2012) (see also Chapter 3).
Genetic improvements can produce plants that
*E-mail: rachelguimaraes@utfpr.edu.br
122
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
Soil Structure under Adverse Weather/Climate Conditions
123
resources available to plants, limiting their productivity (Fig. 8.1).
Compaction, mostly caused by machinery
traffic, decreases water and nutrient availability
and changes the biochemical environment, which
can lead to intensive denitrification and nitrous
oxide emission contributing to the greenhouse
effect. Subsoil compaction is an increasing threat
to future agriculture due to the use of increasingly heavier machinery and overexploitation
of soil (Jones et al., 2003; Ball, 2013; Ball et al.,
2015).
Climate change can contribute to the faster
decomposition of soil organic matter, which can
affect crop productivity and directly influence
the balance of carbon and greenhouse gas emissions (Brevik, 2012). Decrease of soil organic
matter is a worldwide problem and typically occurs due to various types of human activity
8.2 Climate Change
(Curry and Good, 1992), such as rotations dominated by annual cash crops, removal of crop
Global warming is ongoing. The global mean tem- residues, tillage, overgrazing and a lack of cover
perature has risen 0.85°C over the period 1880 to crops during crop rotations.
Adverse combinations of soil and climate
2012, with a decadal increase of 0.12°C from
1951–2012 (IPCC, 2013). In general terms, it is can impose limitations on plant productivity. Cliestimated that global mean surface air tempera- matic factors, which include moisture and therture will likely increase within the range of 0.3 to mal regimes, are the most important at a global
0.7°C between 2016 and 2035 (Girvetz et al., scale, with a 10% reduction in yield with every
2009). However, future warming is unlikely to be 1°C increase in growing season temperature obspatially homogeneous, with differences expected served for some crops (Peng et al., 2004; Brown,
between regions. With this warming an increase 2009; Asseng et al., 2015). Plant growth is conin average precipitation is also projected. How- trolled by temperature and soil moisture, which
ever, this global increase in precipitation will not dictate both the growing season and the uptake
happen uniformly across the globe and through of soil nutrients. These regimes also define the
seasons, with increases in precipitation extremes boundaries of extreme climatic events such as
predicted for most tropical and mid- and high-­ droughts and wetness. Internal soil property faclatitude areas. At the same time there is likely to tors also contribute towards soil productivity, a
be less rainfall in mid-continental areas, leading deep soil profile containing the appropriate
chemical, biological and physical properties is
to a greater likelihood of droughts.
Climate change is expected to limit agro- required to allow the plant’s roots to exploit a
nomic productivity, mainly through erosion, large volume of soil for structural support and
compaction and loss of organic matter (Mueller the uptake of nutrients and water. Deviation
et al., 2014). Erosion is expected to increase in from the appropriate conditions, such as salinmany areas under climate change because of ization or an increase in sodium, exchangeable
the widespread reduction in rainfall and in- aluminium or acidity for example, can have an
creased evaporation which reduces the potential adverse effect on plant growth. In marginal rainfor ground cover, and increases in rainfall ero- fall areas in China overgrazing and population
sivity due to a greater frequency of more intense pressure for cropping have caused degradation
storms. Reduced vegetation cover and drier sur- through wind and water erosion. The loess platface soils also increase the wind erosion hazard eau of Central China is especially susceptible to
(Nearing et al., 2004). Erosion, by reducing water water erosion. Salinization also affects some
storage, fertility and depth of soil, decreases the areas with high groundwater tables and high
are adapted to overcome these environmental
stresses thereby limiting their threat to crop
yields. However, yield and product quality are
linearly related to transpired water (Sinclair,
2011) and nutrient uptake. Therefore, to meet
the plant water demand, soils need to be able to
retain and release water to plant roots. Soil degradation impedes these processes increasing the
effect of environmental stresses.
This chapter will discuss how soil structure
can be affected by climate or weather conditions
and how it can be adapted to mitigate the effects
of extreme weather events. Furthermore, we
show how visual methods can be useful in detecting changes in the soil structure to prevent
soil degradation, reducing the risk of productivity loss and of environmental functions.
124
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
Fig. 8.1. Picture showing laminar and furrow erosion. Note the loss of cropping area due to erosion.
(Photo: Clodomir Ascari.)
evaporation rates or areas where flooding style
irrigation is applied on soils with impeded drainage (Lada Project Team, 2010).
Several reviews of climate change in Europe
and its effect on soil (e.g. Jones et al., 2009)
have been produced. In northern Europe climate
change may increase the turnover of soil organic matter. In southern areas disadvantages
will predominate, with an increase in water
shortage and extreme weather events. These effects may further push current trends of intensification of agriculture in northern and western
Europe and extensification in the Mediterranean
and south-eastern parts of Europe (Olesen and
Bindi, 2002). Any loss in soil carbon (EU soils
presently contain c.71 Gt of organic carbon with
60% in peat soils) will inevitably lead to changes
in soil structural and physical quality. For Europe any increase in temperature with a corresponding reduction in soil moisture will speed up
organic carbon decomposition while increasing
CO2 emissions. Climate change projections for
Europe show losses of soil carbon for most parts,
but research shows that these losses may be reversible if adaptation measures in the agricultural sector are implemented (Smith et al., 2005).
An estimated 115 million ha in the EU are subject to water-driven soil erosion (12% of land
area) and this is again likely to increase under
changes to rainfall patterns and intensity, for example, a predicted increase of 7% in rainfall
could lead to a 26% increase in water erosion in
the UK, where it has been observed that heavy
rainfall events during the summer months are
­increasing (Maran et al., 2008). In Ireland, forecasts predict the same amount of rainfall but
a greater likelihood of high intensity events
throughout the year. Approximately 42 million
ha of land in Europe are prone to wind erosion,
of which 1 million ha are classified as severely
affected. It is expected that, with increased aridity,
finer-textured soils will become more vulnerable
to wind erosion and this will increase in soils
with a decreasing level of soil organic matter.
Climate change in Australia also has a large
potential to impact soil structure. The decreasing
rainfall, increasing temperatures (NSW ­DECCW,
2010; Cleugh et al., 2011) and increased evaporation demand will be likely to reduce plant
growth and ground cover and also reduce the
input of biomass into the soil, while the higher
temperatures will increase soil organic matter
Soil Structure under Adverse Weather/Climate Conditions
decomposition rates. The content of soil organic
carbon in ­
Australian soils to 30 cm depth of
c.24.97 Gt (3.5% of the world wide soil carbon
store) (Viscarra R
­ ossel et al., 2014) may decrease
based on the predicted climate change scenarios
under current land use and land management
practices. Given that many of Australia’s soils are
dependent on soil organic matter for maintaining
soil structure, this has the potential to result in
more soil structure problems such as surface crusting, compaction and wind and water erosion, a potential that will be enhanced by the possibility of
reduced levels of ground cover. Soil organic matter
also acts as a buffer against soil acidification in
lighter textured soils and this will be reduced. Accompanying the reduced rainfall is the prediction
that there will be an increase in the number and
intensity of storms, particularly in summer months
over much of south-east Australia, especially in
the cropping zones (NSW DECCW, 2010). This increased storm activity will put greater stress on
structure at the soil surface, especially where ground
cover is reduced. It has been estimated that this
increased storm activity will also result in increased
rainfall erosivity in many areas of New South
Wales (Lu and Yu, 2002; Yang and Lu, 2015).
In Asia, climate scenarios indicate that
temperature increases by 2 to 6°C towards the
end of the century, with the highest increases in
­northern Asia in Siberia (Cruz et al., 2007). The
consistent prediction is that rainfall will be more
intense and the number of rain days reduced.
This will not only result in higher runoff, but also
put stress on surface soils and soil structure. The
lower rainfall and higher temperatures for central
Asia are of concern for soil organic matter levels,
erosion potential and surface soil structure.
For South and Central America, rainfall is
increasing in south-eastern South America and
decreasing in Central America. Warming has
been recorded throughout Central and South
America, except for a cooling along the Chilean
coast. The changes observed so far have been attributed mainly to deforestation and land degradation. The associated increase in precipitation
has affected soil stability in fragile ecosystems,
such as the Amazon forest and the tropical
Andes. Food production is threatened in regions
where precipitation is decreasing and temperature is increasing, such as in the north-east of
Brazil (Magrin et al., 2014).
Observed climate trends in North America
include an increased occurrence of severe hot
125
weather events over much of the USA, decreases
in frost days and increases in heavy precipitation. Droughts and floods have been attributed
to these changes in climate (e.g. earlier peak flow
of snowmelt runoff and declines in the amount
of water stored in spring snowpack in snowdominated streams and areas of western USA
and Canada) (Romero-Lankao et al., 2014).
Africa is the continent that emits the least
CO2, but is the most likely to suffer the consequences of climate change (Osman-Elasha, 2009).
A lack of consistent long-term data collection for
many regions of Africa makes observation of historical climate trends and projections ­difficult.
IPCC (2013) decadal analyses of temperature
indicate an increased warming trend for the
­African content over the past 50–100 years. By
the end of the 21st century mean annual temperature increase is likely to exceed 2°C relative
to the late 20th-century level, with the predicted
rise in Africa occurring more rapidly than that of
the global land average, especially in arid regions
(IPCC, 2013; James and Washington, 2013).
Precipitation is likely to be reduced over northern Africa and parts of southern Africa by the
end of the 21st century. Droogers et al. (2012)
estimated that by 2050 22% of water shortages
in north Africa will be attributable to this climate
change. Supplies of seasonal melt water from the
Atlas mountains are also likely to be affected by
warming and reduced precipitation (García-Ruiz
et al., 2011), while all countries of the Zambezi
river basin are likely to experience water shortages due in part to climate change.
These projections are very likely to reduce
cereal and high-value perennial crop productivity, this reduction is likely to have an adverse
effect on food security (IPCC, 2013). The IPCC
2013 report indicates that conservation agriculture is already being adopted in some regions of
Africa and that an increased adoption of these
methods throughout the continent will be key
to minimizing climate change effects through
adaptation and management.
8.3
Soil Structure under
Intensive Rainfall
In some climates, such as the tropics, rainfall intensities, and consequently energy, are much
higher, leading to a greater erosion potential. The
rainfall erosivity can be up to 10 to 20 times
126
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
greater in tropical areas than in the more humid
areas (e.g. Yu, 1995; Lu et al., 2001; Nachtergaele et al., 2011). Exposing the soil surface to
these higher levels of energy and erosivity makes
them more susceptible to structural degradation.
The impact of extreme rainfall events on land
degradation is likely to increase as the frequency
of these events increases (Nearing et al., 2004;
Soil and Water Conservation Society, 2006).
Regions with a tropical climate usually experience high levels of weathering, which can
lead to soils that are structurally fragile, reducing
their resistance to erosion. The reduced resistance
to erosion is often a consequence of lower levels
of soil organic matter and reduced aggregate
stability. With climate change, soil will interact
with periods of intensive rainfall more frequently
(e.g. in Ireland there is a predicted increase of
10% in winter rainfall by 2050, with simultaneous
reductions in summer of 12–17%). This, combined with a poorly structured soil may result in
severe land degradation including surface sealing caused by raindrop impact on the soil surface
and water erosion (Fig. 8.2a and b).
Land that has never been cropped and contains natural vegetation tends to have a more
stable soil structure. When the vegetation is
cleared for agriculture and the soil is tilled, the
soil structure is more exposed and susceptible to
damage, often resulting in lower soil organic matter levels and lower aggregate stability, depending
on the use and management (Lal, 1997).
In regions with intensive rainfall it is important to maintain a high water infiltration
rate to avoid increased runoff and reduced
amounts of water storage in the soil. Stable soil
aggregation has an important role in such regions as it increases stability and durability
against the impact of the raindrops. Besides soil
loss, a lack of soil aggregation can lead to clay
dispersion that can block soil pores, decreasing
the water infiltration rate in the top few centimetres of soil and contributing to runoff (Fig. 8.2)
(Ibrahim et al., 2013). Maintaining vegetative
cover and/or the addition of organic residues to
increase soil organic matter are vital to maintaining soil aggregate stability. Where soils are
sodic and highly unstable to wetting, it may be
necessary to add ameliorants such as gypsum to
ensure structural stability (Watts and Dick,
2014). Identifying the drivers of soil structural
stability whether they are sodicity, soil organic
(a)
5 cm
(b)
Fig 8.2. (a) Soil degradation due to water erosion/
sealing. (Photo: Craig David Rogers.) (b) Thin
horizontal layers of pale sand grains within the
cultivated layer resulting from disintegration of
aggregates into their component particles. (Photo:
John Regan.)
matter or iron and aluminium sesquioxides has
important implications for managing the soils.
Crop rotations that include plants with aggressive root systems can provide soil coverage to
prevent the direct impact of rain on the soil, and
also contribute to the formation of biopores,
which are crucial for increasing water infiltration rate. In this context, cropping systems that
leave a high residual biomass are important for
soil coverage and for maintaining high levels of
organic carbon in the soil.
8.3.1 Erosion and soil quality
screening toolkit
A screening toolkit (Table 8.1) compiling erosion risk indicators and certain visual indicators
of soil quality for use by farmers and specialist
Table 8.1. Proposed indicators, proposed assessment method for indicator scores and steps to be taken to assign risk class. (From Regan, 2012.)
Indicator
Soil texture
Slope angle
Ponding
VSA ponding (Shepherd, 2009)
Potential rooting
depth
VSA potential rooting depth
(Shepherd, 2009)
%SOMa
% loss on ignition
AARb
AAR amount (low, moderate, high)
Land use
Defra (2005) land use risk categories
Step 2: Adjust erosion risk class if necessary
Observation of rilling or gullying is used to upgrade sites classed as lower and moderate risk to high risk
Step 3: Add up modified indicator scores for each of the following
2 (soil dominated by friable, fine aggregates with no significant clodding) = good
1 (soil contains significant proportion (50%) of both coarse clods and friable fine aggregates) =
moderate
0 (soil dominated by coarse clods with very few finer aggregates) = poor
Modified version of scores are as follows: 2 =+2, <2 and ≥1 = 0, <1 = −2
2 (no surface ponding of water evident after 1 day following heavy rainfall on soils that were at, or
near, saturation) = good
1 (moderate surface ponding occurs for 2 days after heavy rainfall on soils that were at, or near,
saturation) = moderate
0 (significant surface ponding occurs for 4 days or more after heavy rainfall on soils that were at, or
near, saturation) = poor
Modified version of scores are as follows: 2 = +1, <2 and ≥1 = 0, <1 = −1
2 (>800 mm) = good
1.5 (600–800 mm) = moderately good
1 (400–600 mm) = moderate
0.5 (200–400 mm) = moderately poor
0 (<200 mm) = poor
Modified version of scores are as follows: 2 = +1, <2 and ≥1 = 0, <1 = −1
>3.4% soil considered not to be vulnerable or depleted
<3.4% (c.2% SOC) = soil may be vulnerable or depleted
Modified version of scores are as follows: >3.4% = +1, <3.4% = −1
<850 mm = low rainfall risk
850–1200 mm = moderate rainfall risk
>1200 mm = high rainfall risk
Modified version of scores are as follows: <850 mm = +1, 850–1200 mm = 0, >1200 mm = −1
Avoid erosion susceptible land uses, such as late sown winter cereals, potatoes, and field vegetables,
on very high or high risk sites unless precautions are taken to limit erosion. Land uses such as early
sown winter cereals, OSRc and spring sown cereals, can be carried out with care on these sites
SOM, soil organic matter; bAAR, average annual rainfall; cOSR, oilseed rape.
127
a
Step 1: Select texture and slope and assign erosion risk class
Heavy, medium or light sandy and silty
For each texture class there are different intervals (see text)
Soil Structure under Adverse Weather/Climate Conditions
Indicator
Erosion features
Indicator
Soil structure
Proposed assessment method
Defra (2005) hand texturing method
Measure slope as accurately as possible
using a clinometer; record position in
landscape
Proposed assessment method
VSA soil erosion (Shepherd, 2009)
Proposed assessment method
VSA drop shatter test (Shepherd, 2009)
128
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
advisors in assessing sites for likelihood of erosion and reduced soil quality was developed by
Regan (2012) based on a number of tillage site
assessments in Ireland (Regan et al., 2010;
Regan et al., 2012; Regan et al., 2014). The toolkit, based on the principles of risk assessment
and classification of the Department for Environment, Food and Rural Affairs (Defra, 2005)
was developed by comparing results from quick
and easy onsite visual assessments and more detailed quantitative assessments with observed
erosion features. Many methods were considered
and those included in the final erosion-specific
toolkit are shown in Table 8.1. Indicators proposed for inclusion in the toolkit include: soil texture, slope, erosion features, structure, ponding,
potential rooting depth (PRD), % soil organic
matter (SOM), average annual rainfall (AAR)
and current land use. However, this is not to say
that alternative methods could not be more applicable to different geographical areas or soil
types (e.g. The Muencheberg Soil Quality Rating
(SQR) material of Mueller (2014)), or equivalents
for North America (see Wischmeier and Smith,
1978). Therefore there is a need to validate this
toolkit developed for Irish tillage scenarios, where
episodic rainfall events occur frequently and annual rainfall is typically >1000 mm, in different
geographical areas.
Soil organic matter is an important indicator of soil quality and productivity influencing
soil erodibility (Jankauskas et al., 2007) and is
an important parameter for loss of soil quality
and likelihood of erosion in the screening toolkit. In Ireland and the UK, soils with SOM levels
above 3.4% (c.2% SOC) are considered to be neither depleted in SOM nor vulnerable to erosion
(DAFF, 2009). Land use is included in the screening toolkit, so that high and very high erosion
risk crops can be identified by the farmer and the
necessary precautions taken with specialist advice to ensure that soil erosion is kept to a minimum, taking into consideration the erosion risk
class of the field. If the precautions taken are ineffective and erosion persists, then the land use
should change. The AAR in areas where tillage
land is mainly concentrated (i.e. the midlands,
south and east of Ireland) varies from c.657 mm
to c.1400 mm. As such, the risk of erosion occurring in a farmer’s field is strongly influenced
by location. Previously, Unwin (2001) observed
that erosion problems in England were worse in
areas where the AAR exceeds 800 mm. A similar AAR threshold is proposed for use in Ireland
to upgrade or downgrade a field’s erosion risk
class in the screening toolkit (Table 8.1). Locationspecific AAR can be obtained by the farmer from
his/her specialist advisor. Other toolkits available
in the world are commonly based on visual soil
assessment (VSA) (Shepherd, 2009) or assessment of existing damage to the landscape, for
example that of Okoba and Sterk (2006), or remote sensing (Ypsilantis, 2011).
The Defra (2005) soil erosion assessment
(see also ThinkSoils booklet (Environment
Agency, 2007)) utilizes field slope and soil texture primarily to determine a field’s erosion risk
class in the UK. Therefore in the screening toolkit, step 1 of the risk assessment starts with
these two indicators, but a risk adjustment can
be made in step 2 by considering erosion features. In the Defra system there are three textural classes, that is, heavy, medium or light
sandy or silty. Each of these classes can be further split by the particular slope in the field. For a
heavy textured soil a corresponding slope of
0–11° or >11° gives a Defra risk class of ‘Lower’
or ‘High’, respectively. For Medium textured a
slope of 0–3°, 3–7° and >7° would give ‘Lower’,
‘Moderate’ or ‘High’. For light sandy or silty texture a slope of 0–2°, 2–3°, 3–7° or >7° would
give ‘Lower’, ‘Moderate’, ‘High’ and ‘Very high’,
respectively. Using the erosion indicator if rills
are observed in the field in most years or if the
field is known to flood at least 1 in 3 years, then
the risk can be upgraded (lower or moderate) to
high risk. To refine the risk class further in the
toolkit, step 3 utilizes a total combined modified
indicator score gathered from each of the other
indicators (soil structure, ponding, potential
rooting depth, % soil organic carbon, average
annual rainfall). The modified indicator scores
and their equivalents are itemized in Table 8.1 at
the end of each indicator section.
A final refinement to risk class allocation for
the field in question is provided by step 4 as follows: if after adding all modified indicator scores
together the outcome is >0, the Defra risk class
in step 2 must be downgraded (e.g. ‘High’ becomes ‘Moderate’). In this case soil quality may
not be an issue. If this score is <0 the Defra risk
class in step 2 can be upgraded (‘Lower’ becomes
‘Moderate’, ‘Moderate’ becomes ‘High’, etc.). In
this case soil quality may be a problem.
Soil Structure under Adverse Weather/Climate Conditions
Soils with visual indicator (VSA) scores of 2
were in good condition and indicated that the
soil erodibility was lower, and hence the erosion
risk class was downgraded to a lower class. In
contrast, soils with visual indicator scores <1
were in poor condition and therefore required
upgrading to a higher risk class. Soil structure is
the critical parameter here and its appraisal was
supported by assessment of PRD, ponding, SOM and
AAR so it was weighted to be double the values
of the other indicators used in the methodology
for upgrading/downgrading the Defra risk class.
In Ireland, cropped areas vulnerable to erosion (Fig. 8.3) included sites at Bunclody, Fermoy
(see Fig. 8.4) and Tullow in good condition (VSA
structure scores of 2), dominated by friable, fine
aggregates with no significant content of clods
and sites on tramlines where traffic gave soil in
moderate-to-moderately good condition (VSA
structure scores of 1–1.75) containing a mix of
Bunclody
UTL (VS = 1.5)
UC (VS = 2)
Tullow
UC (VS = 2)
Fermoy
UC (VS = 2)
UTL (VS = 1.75)
UTL (VS = 0)
UTL (VS = 1.5)
Clonmel
UC (VS = 1.5)
Letterkenny
UC (VS = 1.5)
129
UTL (VS = 0)
Duleek
UC (VS = 1.0)
UTL (VS = 1.0)
UC, Undercrop; UTL, Under tramline; VS, Visual scoring
Fig. 8.3. An example of visual scoring of soil structure at different tillage sites between and within
tramlines used for crop spraying in Ireland using the erosion toolkit developed by Regan (2012).
130
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
Fig. 8.4. Fine and very fine sand deposited by erosion at tramline ends at the Fermoy site (see Fig. 8.3
for comparable visual scoring of structure). (Photo: John Regan, 2012.)
coarse clods and friable fine aggregates. Tramline areas of the Clonmel and Letterkenny soils
were in poor condition (VSA structure scores
of 0) (Shepherd, 2009), dominated by very firm,
angular or sub-angular coarse clods with very
few finer aggregates or pores.
8.4
Wet Weather Conditions
and Soil Compaction
Wet conditions have affected many countries in
the last few years, triggering floods and landslides.
Areas of Alaska, Canada, south-eastern Brazil,
Spain, Norway, north-western China and eastern
Russia have been subjected to extreme precipitation events leading to above average rainfall
(Blunden and Arndt, 2014). These events along
with the wetter winters and springs projected for
areas such as northern areas of the USA, the UK
and central Europe indicate that soils will be wetter for longer periods and a greater risk of compaction damage to soils is possible.
Wet soils can be very vulnerable to compaction because their structures are weakened and
the aggregates are lubricated and slide easily into
a compacted structure. The presence of dense
stable root structure and other organic matter
helps resist compaction damage particularly in
vulnerable cultivated soils (See Chapter 4).
Compacted soil having a higher bulk density
and lower porosity directly impedes growth, reduces drainage and inhibits gas exchange. This
can result in less access to nutrients and reduced
nutrient cycling. In dry periods, the restricted
root growth can result in drought stress. Finer
textured and imperfectly drained soils are the
most vulnerable. The challenge of managing wet
land makes it harder to farm competitively, even
free draining soils will have compaction and trafficability challenges in wet periods (Fig. 8.5).
When soils are wet, their ability to support animal
or machinery traffic is compromised (Fig 8.6);
this causes a number of problems.
The main problems are reduced trafficability:
where the soil can simply be too wet to sustain
continuous machinery (or animal) traffic due
to a danger of sinkage or excessive surface
damage. For grain crops, reduced trafficability
in wet soil can delay grain harvesting, reducing
grain quality. Other consequences include crop
yield losses, delay in tillage operations and
seeding of the next crop, as is seen in the maize–­
soybean production system in Brazil (Fig. 8.6).
Soil Structure under Adverse Weather/Climate Conditions
131
Fig. 8.5. Soil structure under compaction in wet conditions (a). Note how the soil has lost its aggregates
and is very compacted (b). Some blue patches can be observed (c) (Oxisol, 70% clay, Brazil). (Photos:
Suelen Mazon.)
132
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
Fig. 8.5. Continued.
Fig. 8.6. (a) Compacted topsoil (0–20 cm), sparse vegetation cover prone to erosion/shallow rooting.
Poor crop livestock system management, area under clayey Oxisol. (Photo: Rachel M.L. Guimarães.)
(b) Trampling on an undrained clay loam soil on a dairy farm in south Ireland. (Photo: Owen Fenton and
Patrick Tuohy.)
Soil Structure under Adverse Weather/Climate Conditions
133
Fig. 8.6. Continued.
Other problems are surface damage where ruts
from wheeled machinery and poaching from
animal traffic can reduce yields and reduce the
amount of productive grasses in the sward. The
soil near the surface can also be damaged resulting in reduced surface water drainage, increased
runoff and potentially longer term impact on
grass growth. The VSA method was found to be
sensitive to differences in soil treatment, for example, under wheel tracks by Murphy et al.
(2013), and the Soil Quality Scoring Procedure
of Ball and Douglas (2003) is capable of assessing arable and grassland soils and is useful in assessing impact of compaction on crop growth.
An often unseen and potentially ignored problem is subsurface damage (compaction): when
soils are wet or moderately wet, animal or machinery traffic can consolidate or compact the
soil, which can reduce water drainage and impede grass or crop growth.
Strategies to decrease the risk of compaction damage and to improve soil degraded by
compaction are discussed in Chapter 5. The appearance of soil exposed to waterlogging damage is shown in Chapter 1.
Degradation of land resources often results
in waterlogging or secondary salinization.
In such cases, soil colour is a good indicator of
soil quality because it can provide an indirect
measure of other more useful properties that are
not so easily and accurately assessed (Shepherd,
2009). Change in soil colour can be a useful indicator of soil drainage class and the degree of
oxidation or reduction in the soil. The number
and colour of soil mottles present in a soil horizon are important measurements related to soil
aeration and hence waterlogging. As oxygen depletion increases, orange, and ultimately grey
mottles predominate (Batey, 2000). The abundance of grey mottles indicates the soil is poorly
drained (Shepherd, 2009), and is prone to waterlogging during wet periods. White or grey colours mainly in soil surface and poor crop growth
can be visually indicative of soil salinization
(­Department of Natural Resources, 1997).
8.5
Periods of Droughts
Precipitation has declined in the tropics and subtropics since 1970 (IPCC, 2013). For example,
southern Africa, the north-east of South America,
and the south-east and central USA recorded
severe droughts in recent years (Blunden and
134
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
Arndt, 2014). This increases the risk of low
productivity for all agricultural sectors, as water
is a critical component for animal and plant production. Soil structure is responsible for storing
and releasing water to plants and plays a major
role in allowing a soil to withstand the effects of
weather or climate change.
As soil moisture content decreases soil resistance to root penetration increases – these
two properties of soil are dynamic and interdependent (da Silva and Kay, 1997). Even in
compacted soils, each period of rainfall that increases water content to at least field capacity
allows roots to expand and search for water and
nutrients. However, the problem is when soil
dries and increased strength restricts root movement. Thus maintaining low resistance to penetration and favourable porosity by ensuring good
soil structure will allow roots to explore a greater
volume of soil and improve the chances of getting water from deeper layers as the top layer
slowly dries.
Managing soil structure in regions where
extended dry periods are known to occur requires
careful management of water resources and the
vegetative cover. To maximize the amounts of
water and nutrients available to the plants, useful strategies emphasize the interaction between
soil structure and moisture content. Soil structure should ideally be non-limiting throughout
the soil profile, especially in the subsoil, so that
the plants can access water and nutrients in the
subsoil as the water becomes scarcer in the dry
season. The water needs to be able to infiltrate the
soil when rainfall occurs and the plant roots
need to be able to grow with few limitations
throughout the subsoil. The SOILpak and the
numeric visual evaluation of subsoil structure
(SubVESS) methods are important visual techniques for detection of subsoil compaction. The
SOILpak method was especially developed with
emphasis on ensuring soil quality and productivity in dry areas (see McKenzie et al., Chapter 3,
this volume).
8.6 Extreme Temperature
Global temperature increases of c.4°C above
late-20th century levels have been projected,
however it is thought that only a 2°C or more
temperature rise would negatively impact production of wheat, rice and maize grown in tropical and temperate regions. This means that
­climate change without adaptation, in conjunction
with increased food demand, would be a great
risk to food security (Quasem, 2011). As nighttime temperatures increase, photosynthates are
consumed due to increased respiration resulting
in losses in productivity (Loka and Oosterhuis,
2010).
Extreme temperatures can affect the germination of seeds, root growth and soil biological processes. Recently, temperature extremes
have periodically devastated crops. Record temperatures and droughts experienced in Europe
(2003), Russia (2010) and the southern USA
(2011) demonstrate situations projected to
occur with increased frequency (Cruse, 2012).
During 2013 parts of South America, Australia,
New Zealand, Russia, South Korea and Japan
all observed various maximum and average
temperature records, while southern China
also experienced heat waves (Blunden and
Andt, 2014).
Bare soil under direct sunlight can reach
temperatures in excess of 50°C. Conversely, soils
under very cold winters can reach temperatures
well below the freezing point of water. Insulation
of the main body of the soil from temperature
extremes is a function of vegetation cover at the
surface, and of soil structural quality. Ensuring
vegetative cover is the main management strategy for preventing temperature extremes affecting the soil. However, a well-structured soil with
a high amount of pore space is also an effective
insulation layer at the soil surface. The higher
the bulk density, the higher the thermal conductivity of the soil. Therefore, maintaining a
soil with low bulk density, high soil organic matter and high porosity at the soil surface is an effective method for minimizing the effects of both
high and low temperature extremes on the soil
(Stewart, 1989). The scheme developed by
Murphy et al. (2013) provides some guidelines
for categorizing a wider range of surface soils
and identifying what methods maybe applicable
and what soil features are important to consider
in applying visual soil evaluation (VSE) methods.
Ball and Douglas (2003) also describe a method
for describing the soil surface.
Higher evaporation rates often occur in association with higher temperatures and, if the
Soil Structure under Adverse Weather/Climate Conditions
r­ ainfall is inadequate to maintain a vegetative
cover under grazing stress, then lands can become degraded. This degradation process is generally referred to as desertification. A rigorous
definition of desertification is not possible because desertification is a set of land degradation processes that includes water and wind
erosion, loss of vegetation cover and ground
cover, n
­ utrient decline, surface sealing and loss
of soil organic matter. These processes are associated with climates with an extended annual
dry season and a high risk of drought. There
may also be increasing population pressure on
the land. As a consequence, there is not a single
sustainable land management practice to reverse and control desertification, but a range of
practices depending on the local biophysical
features of the ecosystems and socioeconomic
and cultural systems (Le Houérou, 2002;
Squires, 2002; Jones et al., 2013).
8.7
The Further Role of VSE
Evaluation of soil degradation on a global scale is
difficult due to the vast area, the need for
­specialist surveyors and the associated cost. Further to this, several methods are utilized across
different regions, making comparisons between
studies difficult (Lal, 1997). In a climate change
context it is important to continuously monitor
soil structure. Quantitative measurements based
on a single soil property to q
­ uantify soil quality
can provide relevant information, however, these
methodologies are expensive, time consuming
and logistically difficult to implement, while not
providing as broad an overall picture as visual assessment procedures (Murphy et al., 2013).
More widespread use of visual methodologies to evaluate soil structure could be useful,
as most of the methods have a very low cost,
are repeatable, can be recorded as photographs
and, since no samples need to be returned to
the laboratory for analysis, they are logistically
easier for assessing a large or remote area.
These characteristics make them a good choice
for inclusion in soil degradation monitoring
programmes.
There are many important aspects of soils
that are easily observed or can be detected
quickly through the manipulation of the soil
by hand. Soil colour and general structural
135
f­ eatures such as the existence of a surface crust,
large cloddy structure or a very fine self-mulching structure are immediately observed. Soil
texture can be detected by the feel of the soil and
the response of soil to physical deformation or
wetting. What is important, and can be difficult,
is the interpretation of the soil features and the
implications they have for plant growth.
Regular visual assessments of soil at risk of
being exposed to adverse weather conditions, either currently or in the near future, as predicted
by climate change scenarios, allows the early
identification of signs of degradation and the
early implementation of remedial action before
significant damage occurs. Monitoring compaction is vitally important especially as it can be
the primary cause of erosion and waterlogging.
In systems where intensive/prolonged rainfall
events are projected to become more frequent
there is an increased risk of extensive structural
damage by machinery. Visual signs, such as
mottling, smell and colour, which are used in
methods such as visual evaluation of soil structure (VESS), VSA, SubVESS and the erosion and
soil quality screening toolkit can indicate poor
water infiltration rates or waterlogging (Fig. 8.5a)
and anaerobic activity. Regular visual and tactile monitoring of aggregate shape, strength and
porosity can elucidate problems with compaction, since aggregates become more angular in
appearance and more resistant to breakup as
compressive forces act upon them. An example
of this can be seen in Fig. 8.7, which shows
VESS samples of a field before and after the application of a cover crop to improve soil quality.
Before the cover crop aggregates appear large
and angular due to compressive forces and successive soybean cropping (Fig. 8.7a). However,
after the introduction of a grass cover crop the
aggregates in the top 10 cm of soil look smaller
and contain more pores (Fig. 8.7b), demonstrating how a visual evaluation method can indicate
damage and track the effect of management
practices on soil quality.
Several studies have shown that visual assessments of soil structure can indicate an increase or decrease in soil quality over periods
of a crop cycle during years of the same use
and/or soil management (Boizard et al., 2013;
­Guimarães et al., 2013; Munkholm et al., 2013;
Murphy et al., 2013; Cui et al., 2014; Abdollahi
et al., 2015).
136
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
Fig. 8.7. For the same soil (Oxisol, 70% clay), VESS images of (a) compacted soil due to cropping
soybean after soybean; and (b) after including a grass as a cover crop for one cycle. Note the improvement on soil structure in the top 10 cm depth. Note that in (a) most aggregates in the top 10 cm are Sq 4
and after the grass this improved to Sq 2. For more information about this method, see Chapter 2.
(Photo: Rachel M.L. Guimarães.)
Selection of the method used to evaluate
soil degradation depends on the crop and depth
of the soil layer of interest. Topsoil ­visual assessments such as VESS (Guimarães et al., 2011)
and VSA (Shepherd, 2009) can be used to identify soil degeneration in the upper 30 cm of soil.
SubVESS is useful for identifying subsoil compaction and is more suitable for crops with an
effective root system extending below 30 cm and
for soils susceptible to deep compaction through
heavy machinery and harvesting under wet
conditions. SOILpak (McKenzie) and le profil cultural (Peigné et al., 2013) can reveal soil quality
for the whole profile; the latter shows the spatial
variability of soil structure (see Chapter 2). Most
of these methods have been developed under
temperate conditions, although they have been
shown to work well on tropical soils (Giarola
et al., 2013; G
­ uimarães et al., 2013; Moncada
et al., 2014; Ball et al., 2015). SOILpak and VSA
have been tested in a wide range of soils (see
Batey et al., Chapter 2, this volume for more
­details of these methods). Le profil cultural has
also been d
­ eveloped under temperate conditions;
however, studies such as ­Tavares Filho et al.
(1999) have shown its adaptability to tropical
soil conditions. Figure 8.8 shows the use of the
le profil cultural and SubVESS methods to detect
compacted layers, demonstrating the value of
these methods in tracking soil degradation
(­Tavares Filho et al., 1999; ­Peigné et al., 2013;
Ball et al., 2015).
8.8
Conclusion
Under current climate change projections, a global increase in frequency of adverse ­weather
conditions is predicted, which could compromise soil quality and in turn put at risk food and
energy security. Evaluations of soil structure
can quickly and simply indicate crucial resultant
changes in the soil’s capacity to provide a suitable environment for plant growth. This is important for making management decisions for
adapting soils so that they remain functional
Soil Structure under Adverse Weather/Climate Conditions
137
(a)
20 cm
Fig. 8.8. Profiles in compacted area under sugarcane, through different methods. (a) Le profil cultural and
(b) SubVESS. The soil of this area is a sandy Oxisol (70% sand). (a) A soil profile of 1.30 m and (b) a
profile of 1.80 m depth. (Photos: Craig David Rogers.)
when exposed to adverse conditions. Visual
methods that allow fast, simple and reliable assessment of soil structure, such as VESS, VSA or
the erosion toolkit will play a crucial role in
monitoring soil d
­ egradation, especially in extensive and/or less developed countries, where
widespread use of expensive equipment and
training can be limited.
Acknowledgements
We would like to acknowledge Dr Mark Healy
(Civil Engineering, NUI Galway) and Dr John
Regan, who were co-developers of the erosion
toolkit. We would also like to thank Dr Clodomir
Ascari, Dr Craig David Rogers and Suelen ­Mazon
for providing photographs for this ­chapter.
138
R.M.L. Guimarães, O. Fenton, B.W. Murphy and C.A. Tormena
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pp. 113–123.
9 The Expanding Discipline and Role
of Visual Soil Evaluation
1
Bruce C. Ball1* and Lars J. Munkholm2
SRUC, Edinburgh, Scotland, UK; 2Aarhus University, Tjele, Denmark
9.1
Introduction
Drawing on the conclusions of previous chapters, our objective here is to show that methods
of visual soil evaluation (VSE) are key aids to the
management of soils. They can identify and
quantify soil degradation, particularly compaction. These methods can be used to monitor soil
quality and thus to maintain its cropping potential. We also identify the future roles of VSE in
soils and the environment and suggest improvements in the methods to support these roles.
The prominence of the role of soils for food
security and environmental sustainability is likely
to increase as the area of land available shrinks
and the quality of what is left decreases. Soil is
basically a non-renewable resource and, with
limited scope to bring new land into cultivation,
degradation needs to be decreased or negated by
conservation and by restoration of prior degraded
land (Lal, 2013). New technologies such as genetic modification are restricted in their ability to
increase crop yields by limitations in soil water
and/or nitrogen supply (Sinclair and Rufty, 2012)
and the constraints of photosynthetic efficiency.
Accurate assessment of the physical condition of soils is important for making decisions on
soil management needed to identify and fine-tune
soil-based technology for site-specific situations
(Lal, 2013). Increasing awareness of soils and of
the need to protect them from degradation is also
important for the future of humanity (European
Union, 2012). Visual soil evaluation offers the
possibility of increasing awareness by improving
and rationalizing the examination of soils with
the main aim of discovering degradation and of
helping to identify where improvements are required. VSE can be used by a wide range of people
including farmers, advisors, researchers and
those with limited knowledge of soils. Farmers in
many countries already use VSE methods as key
aids to soil management for example in the UK,
Ireland, New Zealand, Australia, Denmark,
France, Germany and Brazil. In the UK it is currently in use to help maintain soils and water in
good agricultural and environmental condition
(GAEC) through initiatives such as the Farm Soils
Plan (2005) and Think Soils (2007).
9.2 The scale and Scope of
VSE and the Relationship
with Crop Yield
The structures in the main VSE systems are
summarized into classes of good, moderate and
poor quality as shown in Table 9.1 (see also
Table 2.1 in Chapter 2).
*E-mail: bruce.ball@sruc.ac.uk
142
© CAB International 2015. Visual Soil Evaluation: Realizing Potential Crop Production
with Minimum Environmental Impact (eds B.C. Ball and L.J. Munkholm)
The Expanding Discipline and Role of Visual Soil Evaluation
143
Table 9.1. Soil characteristics of the different categories of semi-quantitative soil structural quality scales
in the principal visual soil evaluation methods. Details of these methods are given in Chapter 2.
Soil structural characteristics
Method
Good
Visual soil
Mostly fine, friable
assessment
aggregates
(VSA) (spade)
Visual evaluation of Friable. Rounded,
soil structure
porous aggregates
(VESS) (spade)
2 mm–7 cm. Easily
crumbles
SOILpak (profile)
Le profil cultural
(profile)
Primary aggregates
<5 mm wide and
porous. Polyhedral
shape
Aggregates with open,
non-compacted
structure
Moderate
Poor
Mixture of coarse, firm
clods and friable, fine
aggregates
Firm. Mixture of aggregate
sizes, sub-angular with
low porosity and distinct
faunal macropores.
Root clustering
Primary aggregates
5–50 mm wide of mixed
shapes, some with
angular corners
Mixture of massive clods
and more porous
aggregates, little fine
soil
Mostly large, very firm angular
or sub-angular clods with
very few finer aggregates
Very compact. Mostly very
large, angular clods or
massive. Few macropores.
Often anaerobic
The scope of most soil quality assessment
methods is broader than soil structure alone
making them multi-attribute systems. In the
visual soil assessment (VSA) system from New
Zealand, attributes include soil surface conditions,
earthworm counts, degree of erosion, cropping
indicators and production costs (Shepherd, 2009).
The Muencheberg Soil Quality Rating (M-SQR)
system uses topsoil structure from systems such
as VSA and includes rooting depth, profile available water, wetness, slope and relief, as well as
hazard indicators specific to the site such as contamination, acidification, salinization, drought
and flooding (Mueller et al., 2013). They demonstrate that their approach to assessing soil
quality for the potential for cropping applies at
scales from within fields to global. ­Mueller et al.
(2013) also showed a clear relationship of
M-SQR scores with yield from a range of sites
(see McKenzie et al., Chapter 3, this volume).
They identified that drainage status was one of
the most important factors influencing soil
structural scores. Drainage status can be assessed ­using visual soil indicators and vegetation
indicators (Kerebel and Holden, 2013). Another
multi-­
attribute system originally designed for
cotton crops in Australia is SOILpak, which uses
structural form, structural stability in water,
structural resilience, texture, stoniness, water
repellence, waterlogging, salinity, and nutrient
Aggregates >50 mm wide,
platy or conchoidal.
Structural components
have sharp corners
Dominated by massive
structure and clods of fused
together aggregates
and biological status (McKenzie, 2013). It can be
used for the evaluation of whole farms and helps
farm businesses to deal with the soil-related
issues of profitability. Further details of these
systems are given in Chapters 2 and 3.
Good relationships between visual evaluation of soil structure (VESS) or VSA structure
scores and yields have been found at the within-field scale. For example, Munkholm et al.
(2013) working on maize in Canada found a
positive correlation between topsoil structure
and crop yield as shown in Fig. 9.1. Maize yields
decreased linearly with increasing VESS Sq values (R2 = 0.35**). The relatively good agreement
between visual methods and crop yield is in
agreement with previous studies (see McKenzie
et al., Chapter 3, this volume). They concluded
that where VSE techniques reveal soil limitations
restricting crop yields, it is important first to interpret the quality scores via comparison with
crop growth thresholds and then to respond by
considering soil management options for soil improvement from a broad range of techniques.
9.3
Improving and Harmonizing
VSE Methods
VSE methods have the advantage of being useful
at a wide range of scales and can be used by
144
B.C. Ball and L.J. Munkholm
13
Maize yield, Mg ha–1
12
Yield = 13.2–1.3 * VESS score R2=0.35**
11
10
9
8
5
Poor
4
3
VESS score
1
2
Good
Fig. 9.1. Correlation between corn yield and visual evaluation of soil structure (VESS) score (June
assessment). Structural quality improves from score 5 to 1. (From Munkholm et al., 2013.)
many different users. Their strengths and weaknesses are discussed in Chapter 2. More comprehensive, simple descriptions of the methods
would help to improve objectivity and would enable better training on opening up access holes,
preparation of surfaces for evaluation and on the
techniques of evaluation. Methods were usually
developed for specific soil types or land uses and
often need further work to extend beyond these.
Many are difficult to apply in very sandy or very
clayey soils and where there are many stones.
Most methods stress the importance of detection
of layers of compact or anaerobic soil and can include the influence of texture (see Weill and
Munkholm, Chapter 1, this volume).
All methods, particularly spade methods,
tend to identify fine, loose structures as ‘good’
such as in a freshly cultivated soil. However, this
may give an optimistic scoring for a loose, fragmented structure resulting from intensive tillage.
No assessment of the stability of the loosened
structure is made, a test which is included in the
SOILpak technique. Thus, it was suggested that
VESS needs a better description of porosity to reflect the importance of its contribution to drainage, aeration and root growth (see Munkholm
and Holden, Chapter 4, this volume). These aspects are identified well by the profile method, le
profil cultural. In both Chapters 1 and 2, it was
suggested that the scope of VSE could be extended by increasing emphasis on rooting, on
evidence of aeration status and on biological
and faunal activity.
At present VSE methods have different
means of ranking the soils with, commonly,
scores ranging from 1 to 3 or 1 to 5. Increasing
numbers indicate better structure in some systems and worse structure in others. There is a
need for a common scale, or at least harmonization into good, moderate and poor as shown in
Table 9.1. Improvements in these aspects are
particularly important when interpretations of
the qualities made relate to management. The
value of a simple test for rapid assessment with a
moderately trained eye so that decisions can be
made quickly during soil management operations is equally important to a detailed assessment of spatial variability of structure required
for research applications.
The use and replication of VSE methods
may be restricted when describing the soils of
small plots due to soil disturbance, particularly
The Expanding Discipline and Role of Visual Soil Evaluation
the profile methods. The increasing awareness
of the vulnerability of subsoils to compaction
damage has led to the development of methods
that include assessment of compacted subsoil
layers (Peigné et al., 2013; Ball et al., 2015).
Generally the digging of pits and use of subsoil
assessment methods are required when subsoil
damage is suspected and the transition layer
between topsoil and subsoil needs to be examined. The detection and location of compact
soil layers in the soil profile are important so
that they can be loosened with minimum effort
and maximum benefit (see Godwin and Spoor,
Chapter 5, this volume). Where soil surface
monitoring is sufficient, for example, for e­ rosion
assessment, there is a potential to integrate VSE
with recent advances in remote sensing that
offer the spatial assessment of soil structure
and moisture content (Anderson and Croft,
2009). Other technological advances include
the application of mobile phone apps for image
analysis or transmission and viewing of three-­
dimensional images of soil aggregates and
profile faces to give a more objective assessment
of structural form.
9.4
Expanding the Role of VSE
9.4.1 Sustainability, environmental
conservation and climate change
Soil sustainability has been defined as ‘managing soil and crop cultural practices so as not to
degrade or impair environmental quality on- or
off-site, and without eventually reducing yield
potential as a result of the chosen practice
through exhaustion of either on-site resources
or non-renewable inputs’ (Soil Science Society
of America, 2008). Soil degradation is the loss
of soil quality, caused by erosion, crusting, compaction, runoff, waterlogging, sealing, organic
matter loss, and biological and chemical degradation (Hatfield and Morton, 2013) (see Guimarães
et al., Chapter 8, this volume). Poor soil quality is
also associated with high emissions of nitrous
oxide, low uptake of methane and poor nutrient
retention (see Cloy et al., Chapter 7, this volume).
In addition, degradation and improper management moves soil towards marginality (Hatfield
and Morton, 2013).
145
The sustainability of an agricultural system
also involves stability of GHG emissions, nutrient
losses and carbon (C) storage in relation to management and stocking rates; the substantial influence of soil structure on these functions was
revealed by Cloy et al., Chapter 7, this volume.
They also found the relationship between VSE
and these functions – particularly soil carbon sequestration and nutrient leaching – to be a
promising area for application of VSE. VSE also
has the potential to detect differences in structure caused by nutrient management as well as
by compaction (see Munkholm and Holden,
Chapter 4, this volume). Promoting and maintaining the physical condition of the soil is an
effective means of increasing C storage and reducing greenhouse gas (GHG) emissions. Good
soil structure can also reduce nutrient loss (see
Cloy et al., Chapter 7, this volume).
The major influences of climate change
on soils are discussed (see Guimarães et al.,
Chapter 8, this volume). Soil losses by erosion
are set to increase with climate change as the
hydrological cycle becomes more vigorous
and the erosivity of rainfall and wind increase.
An eroded landscape is scarred and barren
(Fig. 9.2). Erosion results in a loss or displacement of topsoil, a reduction of soil organic
matter and nutrient contents, and loss of topsoil depth (Cruse et al., 2013) that can directly
influence crop yield (Fig. 9.3). We need to
adapt visual methods when dealing with soil
under adverse conditions or climates that are
often drier, more tropical and more pedologically diverse than the areas for which they
were originally developed. Guimarães et al.
(Chapter 8, this volume) propose a set of soil
toolkit indicators for use in screening sites for
erosion risk. VSE can potentially be used to
monitor the risk of overgrazing (see Munkholm and Holden, Chapter 4, this volume) and
desertification. For example, Mueller et al.
(2014) recognized the critical importance of
conservation of the agricultural landscapes in
central Asia and suggest the need for novel
measurement methods, including VSE, to
monitor land and water resources in order to
prevent degradation and accelerated desertification.
Compaction is a global problem that is increasing because of organic matter depletion,
the use of progressively larger field machinery
146
B.C. Ball and L.J. Munkholm
Fig. 9.2. Eroded soil in Madagascar. (Photo courtesy of M. Brouwers.)
in intensive agriculture and climate change
(see Guimarães et al., Chapter 8, this volume).
The extremes of wet weather recently found in
the UK can make soil structure worse in tractor
compacted or animal trampled treatments than
in looser soil (Fig. 9.4). The importance of the
compaction problem has been a key driving
force for development and modification of most
VSE methods, with several that can detect and
describe subsoil compaction as a consequence of
the use of heavy machinery (see Batey et al.,
Chapter 2, and Godwin and Spoor, Chapter 5,
this volume).
9.4.2
Soil monitoring and resilience
VSE provides an important diagnostic tool for
soil monitoring, helped by the use of digital
photography to maintain a record for comparison between sampling occasions. There is often
a need for a reference ‘good’ soil such as under
a fence line, permanent low input pasture or
under woodland. These soils need to be in good,
natural condition; some undisturbed, natural
soils in Australia, for example, are in very poor
condition due to naturally occurring instability
caused by sodicity and/or excessive exchangeable magnesium. The value of VSE in providing
an early warning of change or decline in soil
quality from a baseline or reference point is important (see Batey et al., Chapter 2, this volume).
Repeating VSE measurements at the same site
over several years enables medium- to long-­
term monitoring of soil quality. In Fig. 9.4,
VESS data are shown for five occasions from
­autumn 2011 to autumn 2013. Soil conditions
were extremely wet in autumn and winter
2012. In this period the trampled and tractor
compacted treatments were the most affected
by soil management but showed signs of recovery in autumn 2013.
Soil resilience is considered to be vital for
coping with extreme weather conditions. Soil
structural resilience in Australian soils was
taken as a measure of the ability of soil to regain
a desirable soil structural form through swelling
and shrinkage induced by wetting and drying
cycles (see McKenzie et al., Chapter 3, this volume). This is important for maintaining yield
under extreme weather conditions and can be
assessed by observing soil shrinkage patterns
when the soil is dry.
The Expanding Discipline and Role of Visual Soil Evaluation
147
10000
Loess-derived
Corn yield (kg/ha)
9000
Till-derived
8000
7000
6000
0
10
20
30
50
40
Thickness of a horizon (cm)
Fig. 9.3. Effect of thickness of the A horizon on corn yields for loess- and till-derived soils. (From Fenton
et al., 2005.)
Mean VESS score for whole slice (1 to 5)
1
Animal trampling
Tractor compaction
2
No compaction
3
4
4
4
/1
/1
08
4
05
3
/1
/1
02
3
11
3
/1
/1
08
3
05
2
/1
02
2
/1
/1
11
08
2
/1
2
/1
05
02
11
/1
1
5
Fig. 9.4. Mean visual evaluation of soil structure (VESS) scores (structural quality improves from score
5 to 1) for soil slices from 0–25 cm depth. Treatments are tractor compaction and animal trampling, located
at SRUC Dumfries, Scotland from October 2011 to October 2014 on an eutric gleysol. (Unpublished data
from Paul Hargreaves, SRUC.)
148
B.C. Ball and L.J. Munkholm
VESS and VSA both give indicative limiting
values for when soil structural improvements are
required (Table 9.1). In this way the sustainability
of the system in terms of soil resistance and resilience can be identified. Where anthropic influences
gradually degrade the soil, reducing resilience,
the soil can become more resistant to further
degradation and it is important to protect and
improve what remains by increasing porosity
and promoting soil biological activity (Shaxson,
2006). The addition of organic matter to increase
structural stability is also important, contributing to carbon capture and to increased nutrient
and water retention that can reduce soil leaching and GHG emissions (see Cloy et al., Chapter 7,
this volume). VSE can prove useful in detecting
any improvement over time in degraded soil
structure. In soil compacted during sugarbeet
harvest and under reduced tillage, le profil cultural was used to detect the changes in compacted zones and the slow recovery of structural
porosity over several years (Boizard et al., 2013).
Tenywa et al. (2013) stress the importance of integrating methods and results from farmers’
traditional knowledge with science in order to
cope with the ‘real-life complexity’ of improving
soil resilience.
9.4.3
Improvement of arable
and grassland soils
VSE methods were shown to be sensitive to differences in management of arable and grassland
soils and to be useful in evaluating management
impact on soil quality (see Munkholm and Holden,
Chapter 4, this volume). The visible changes of
some degraded soils of suboptimal structural
quality as a result of improvement are shown in
Fig. 9.5. The additions of organic material either
directly as residues or by establishing a grass
sward are important for improving soils (Fig. 9.5a
and c). VSE is also useful for estimating the distribution and degree of degradation of residues
to monitor conditions for mineralization and
retention of nutrients. Some VSE techniques
were developed specifically to enable organic
farmers to monitor and maintain good physical
fertility (Munkholm, 2000; Ball and Douglas,
2003). These principles are important in agroecological or conservation agriculture. Such
types of agriculture are likely to be increasingly
important in a resource-poor future (Tudge,
2004); they rely on concepts of land husbandry
or soil care where soil examination is a regular,
integral part of farming (Batey, 1988) improving the linkage between the farmer and the soil.
The VSA management system of Shepherd
(2009) for arable and grassland specializes in
linking soil conditions, plant nutrition, animal
health and farm productivity with ‘smart fertilizers’ and agroecological farm management
practices.
In Batey et al., Chapter 2, this volume, the
main motivation for development of VSE methods
was recognized as the need to identify compaction and drainage problems. This perhaps resulted from Batey (1988) and others identifying
in the 1970s that compaction and drainage are
the two properties interacting with structure
that are of most concern to current farming
practices. Improving and maintaining the drainage status to ensure favourable moisture conditions by, for example, using weather forecasting
to time tractor traffic and tillage would help to
improve structure (Fig. 9.5b and d) and to reduce
compaction damage by machinery.
In remediating compaction damage the importance of detection of layering within the topsoil and of any damage in the transition layer
between topsoil and subsoil are both facilitated
by VSE techniques. Based on these, VSE can be
used for guidance on the need for improvements
during restoration and for analysing the often
complex patterns of soil movement that result
from the use of various forms of soil loosening
equipment (see Godwin and Spoor, Chapter 5,
this volume). Where VSE techniques reveal soil
limitations restricting crop yields, it is important
to interpret the quality scores by comparison
with any thresholds of limits for crop growth
(see McKenzie et al., Chapter 3, this volume).
Management options include compaction control and prevention (see also Godwin and Spoor,
Chapter 5, this volume), addition of soil ameliorants, maximization of the value of soil biological
processes, permaculture and agroforestry, terracing for erosion prevention and use of raised
beds to reduce waterlogging damage.
The use of VSE techniques that extend into
the subsoil such as le profil cultural and subsoil
visual evaluation of soil structure (SubVESS) are
important to detect any transition layer below
the topsoil, which will enable identification of the
The Expanding Discipline and Role of Visual Soil Evaluation
agronomic potential of the soil and of the extent
and depth of any loosening required (Peigné et al.,
2013) (see Batey et al., Chapter 2, this volume).
This is especially important in no-tillage systems,
where regular assessment of subsoil structure
allows detection and remediation of compaction
that could otherwise accumulate and restrict
crop growth.
149
9.4.4 Improvement of marginal
and urban soils
Marginal soils, such as those subject to progressive erosion, can be renewed by enhancing
soil biological function with the aim of improving soil quality (Hatfield and Morton, 2013).
Such soils, for example, abandoned organic
Fig. 9.5. Arable or grassland soils before (left) and after (right) improvement. Improvement was by
establishment of a grass sward (a) and (c), reduced wheel traffic (b) and improved drainage (d).
150
B.C. Ball and L.J. Munkholm
Fig. 9.5. Continued.
soils, ­particularly benefit from VSE techniques
because the highly diverse fertility and quality in
relation to previous management, topography
and shelter allow use of a wide range of visual
indicators of soil, drainage, vegetation and
­topography for in situ ­
judging of land (see
Scherbatskoy et al., Chapter 6, this volume).
Other marginal soils i­nclude rangelands that
are coming under increased pressure for conversion to crop production. These are often
The Expanding Discipline and Role of Visual Soil Evaluation
soils of low resilience and resistance (­Herrick
et al., 2012). Such land that is suboptimal for
agriculture is deficient in C and has potential
to be improved and thereby to enhance C storage, offset GHG emissions and reduce nutrient
leaching (see Cloy et al., Chapter 7, this volume).
VSE can help not only to monitor changes
with land use, but also to develop a flexible
resilience-based land classification system
­
(Herrick et al., 2012).
At the time of writing, about 15% of the
world’s food is grown in urban areas, which
­include garden allotments, backyards, rooftops,
balconies, vacant spaces, parks and urban fringe
agriculture (Gerster-Bentaya, 2013). Such production is increasingly important for the food
system as the number of people living in cities
increases. Greater support is needed to address
the specific challenges of urban agriculture based
on soil science.
Such small-scale agricultural systems
­require research to improve understanding of
local resources, their efficient use and climate–
environment interactions in which VSE has an
important role in empowering local land users.
The habit of care, thrift and adaptation i­ ngrained
in populations on marginal land to ensure their
survival may provide lessons to survive in an age
of uncertainty (see Scherbatskoy et al., Chapter 6,
this volume).
9.4.5
Soil science
Visual soil structure assessment can be used to
facilitate the greater role of soil science in the
transition post ‘peak’ oil, phosphorus and water
by encouraging farmers to become more efficient with their use of inputs (McKenzie, 2013).
Of these peaks, Brown (2012) considered that the
depletion of underground water resources poses
a greater threat to future food security than the
slowing of the rate of supply of oil. Pragmatic
new systems need to evolve for soil assessment
and management that are interlinked with
­proposed new global networks of land care –
­associated, for example, with the new ‘soil security’ approach described by McBratney et al.
(2014) (see McKenzie et al., Chapter 3, this volume). VSE will prove useful for such systems by
providing monitoring of soil degradation and
improvement (Fig. 9.5).
151
VSE needs further integration with measured soil properties. For example, in tea production in Africa, farmers usually assess soil quality
in terms of visual and tactile properties such as
organic matter, fertility and soil compaction that
have been shown to match up well with quantitative estimates based on soil analysis (Minh,
2007). Combination of VSE with a location optimized minimum dataset (MDS) of measured
properties could give a good overall indication of
soil structural quality in an approach similar to
that of Mueller et al. (2013). VSE results can be
used to determine an appropriate MDS (Pulido
Moncada et al., 2014; Askari and Holden,
2015).
As resources dwindle the practice of soil science may need to become more holistic, with
greater interdisciplinary cooperation (Hillel,
2009) and integration of the traditional knowledge and innovative thinking of farmers to help
improve food security (Venkateswarlu et al.,
2013). The increase in tolerance and connection
required for such approaches can be achieved by
development of a shared awareness of the land
by all those associated with soil from farmer and
farm worker to research scientist (Ball, 2013)
and by providing an important visual aid to foster
discussion and exchange of ideas. This is also
important for training of students and advisors.
Ball (2013) stressed the importance of integration of new agricultural methods with old, traditional methods and their development to adapt
to local circumstances, especially where workers
are poor, partially skilled or partially educated.
VSE is clearly valuable for illustrating the range
of such complexity, for encouraging dialogue
and for recording any improved soil condition.
For the public, increasing urbanization means
that many children are unaware of the origins
of their food (Hillel, 2009). VSE affords the opportunity to demonstrate the soil and how it
supports crop growth and recycling. Community
engagement is likely to become more important
as greater recycling of resources is required and
there is more pressure on farmers to produce
food for direct consumption rather than to feed
animals for meat production.
VSE is an immediate, efficient and inclusive
assessment of quality. Our imperative is to conserve soil quality to ensure ‘beauty that is soil
deep . . . that can feed us again and again, if we
care for it’ (Ball, 2015).
152
B.C. Ball and L.J. Munkholm
9.5
Conclusions
Enhancing soil quality has a substantial role to
play in tackling food insecurity, global change
and environmental degradation. For food security, the required future increases in crop yields
will involve further intensification of land use
practices, particularly in existing farming areas
where soil degradation has occurred, yield gaps
are wide and restoration is required. For global
change, soil and cropping resilience will need
to improve to mitigate the predicted increase in
upcoming emergencies such as extreme rainfall
events and droughts. All demand as a priority
reduction in the rates of soil degradation and restoration of degraded soils by soil management.
VSE is gaining wide acceptance in many
countries as a key tool of soil management. It is
important for monitoring degradation by compaction (both in topsoil and subsoil) and by erosion and can be readily extended to monitor
other forms of degradation such as overgrazing
and desertification. VSE is already used with
crop indicators to enable the reduction of farm
inputs of fertilizers and pesticides. It can help to
develop the survival strategies offered by smallscale, mixed-use agriculture throughout the
world by incorporating further indicators of
land use, drainage and topography. Integration
of VSE within systems of soil assessment and
management from the field scale to global networks will bring benefits by showing the importance of soil care for sustainable production and
for linking farmers, soil scientists and agronomists within political processes. However, to succeed, this demands the rapid development of a
large, accredited workforce of land managers
with thorough training in visual soil examination and evaluation and associated topics. This
can be achieved by continued encouragement
and support from organizations such as the
International Soil Tillage Research Organization, National Soil Science societies, the Food
and Agriculture Organization of the United Nations (FAO) and the United Nations (UN). Soil scientists with expertise in VSE also have much to
offer in working with agronomists, land managers and environmental specialists to ensure
best use of the soil for sustainable production
and environmental protection.
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Index
adverse weather 122–123, 136–137
climate change 123–125
compaction 130–133
drought 133–134
erosion 123–125
rainfall 125–126
soil quality screening toolkit 126–130
temperature 134–135
visual soil evaluation (VSE) 134–136,
145–146
aeration 11
agronomic profile method 20–24, 27, 135–136
arable management impacts 49–51, 59–62
biological factors 51–52
mechanical factors 52–54
grassland management impacts 54–55, 59–62,
148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
anion exchange capacity (AEC) 111
arable management impacts 49–51, 59–62,
148–149
biological factors 51–52
mechanical factors 52–54
biological activity 11
macroporosity 12
plant residue turnover 12–13
blackland (marginal organic soils) 86–88, 100
crofting 90, 91
cultivation 90
geology 88
microbiological processes 90
physical structure 88
return to use 99–100
soil fertility 91
visual soil evaluation (VSE) 91–95, 149–151
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
evaluation 99
von Post humification scale 95, 97–99
carbon storage 103, 118–119
land use 105
soil properties 103–104
structure 104–105
visual soil assessment (VSA) 112–114
cation exchange capacity (CEC) 111, 117
clayey soil 2
root development 10–11
structure evaluation 2
compacted soil 3–4
good condition soil 2–3
naturally massive structures 9
climate change 123–125, 136–137
compaction 130–133
drought 133–134
erosion 123–125
rainfall 125–126
soil quality screening toolkit 126–130
temperature 134–135
visual soil evaluation (VSE) 134–136,
145–146
clods 22–24
carbon dioxide (CO2) 105–109, 114–115
155
156
Index
colour
carbon storage 113–114
compaction 11
compaction 66–68, 82–83, 115, 123
alleviation requirements 68
identification 1–2, 13, 68
biological activity 11–13
colour 11
root development 10–11
structure evaluation 3–4, 6–7
tillage 8–9
natural recovery 4, 7
recompaction alleviation 78–79
controlled traffic farming 80–82
reducing weight and pressure 79–80
subsoiling 82–83
critical depth 69
draught forces 74–75
implement adjustment 77–78
in-field evaluation 78
leg disturbance 72–73
mole drainage 69, 72–73
multiple tine arrangements 73–74
narrow tine 69
power requirements 75–76
selection 76
winged tine 69–72
tillage
conventional tillage systems 8–9
minimum / no tillage systems 9
wet weather conditions 130–133, 145–146
controlled traffic farming 80
crop yields 81–82
energy 82
water infiltration 82
cotton 39–40
crofting 90, 91
crop yield 123, 142–143
controlled traffic farming 80–82
food security 31–32
gaps 32–33, 45
analysis 33–35
land management frameworks 37–44
sustainable intensification 32–33
drought 133–134
earthworms 24, 60–61
compaction 12
erosion 1–2, 123–125, 145–146
rainfall 125–126
soil quality screening toolkit 126–130
evaluation 2
clayey soil 2
compacted soil 3–4
good condition soil 2–3
naturally massive structures 9
complete profile observation 7
layer identification 8–9
methods 15–16
agronomic profile method 20–24, 27,
49–62, 135–136
comparisons 27
Muencheberg Soil Quality Rating
(M-SQR) 40–41, 54–62, 128,
142–143, 148–149
SOILpak 2, 7, 9, 11, 19–20, 24–28,
39–40, 41, 49–51, 53–62,
134 , 136, 142–144, 148–149
visual evaluation of soil structure
(VESS) 2, 7, 9, 11, 16–17, 25,
49–62, 59–62, 68, 112–117,
134–136, 142–144, 146–149
visual soil assessment (VSA) 2, 11,
17–18, 25, 49–62, 112–118,
128–130, 133, 135–136,
142–143, 148–149
See also visual soil evaluation (VSE)
natural structural limitations 9
cemented layers 9
glacial soils (tills) 10
naturally massive structures 9
root development 10–11
sandy soil 4
cemented layers 9
compacted soil 6–7
good condition soil 4–6
food security 31–32
degradation 122–123, 136–137
climate change 123–125
compaction 130–133
drought 133–134
erosion 123–125
rainfall 125–126
soil quality screening toolkit 126–130
temperature 134–135
visual soil evaluation (VSE) 134–136, 145–146
dissolved organic carbon (DOC) 104–105, 111–112
drainage 58
global warming potential (GWP) 105
grassland management impacts 54–55, 59–62,
148–149
biological factors
nutrient management 57
stocking rate 57–58
sward management 56–57
drainage 58
management intensity 58–59
mechanical impacts 58
Index
greenhouse gases 103, 118–119
carbon dioxide (CO2) 105–109, 114–115
methane (CH4) 105–107, 110–111,
114–115
nitrous oxide (N2O) 105–107, 109–110,
114–117
soil properties 103–104
structure 105–107
visual soil assessment (VSA) 112, 114–117
Hebrides 86–88, 100
crofting 90, 91
cultivation 90
geology 88
microbiological processes 90
physical structure 88
return to use 99–100
soil fertility 91
visual soil evaluation (VSE) 91–95,
149–151
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
evaluation 99
von Post humification scale 95, 97–99
hydrological cycles 36–37
land management frameworks 37–44
landscape function analysis (LFA) 41–44
layering 59
le profil cultural See agronomic profile method
macroporosity
biological origin 12
detection 20
marginal land 86–88, 100
crofting 90, 91
cultivation 90
geology 88
microbiological processes 90
physical structure 88
return to use 99–100
soil fertility 91
visual soil evaluation (VSE) 91–95, 149–151
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
evaluation 99
von Post humification scale 95, 97–99
methane (CH4) 105–107, 110–111, 114–115
mole drainage 69, 72–73
Muencheberg Soil Quality Rating (M-SQR)
40–41, 128
crop yield 142–143
157
grassland management impacts 54–55, 59–62,
148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
nitrous oxide (N2O) 105–107, 109–110, 114–117
no-tillage 9, 16, 21, 24, 44, 53, 81, 149
nutrient
availability 90
leaching 103, 118–119
soil properties 103–104
structure 111–112
visual soil assessment (VSA) 112,
117–118
management 57, 91
organic production 44, 52, 61
organic matter (OM) 118–119, 123, 124–125
carbon storage 104–105, 113–114
nutrient leaching 112
organic soils 86–88, 100
crofting 90, 91
cultivation 90
geology 88
microbiological processes 90
physical structure 88
return to use 99–100
soil fertility 91
visual soil evaluation (VSE) 91–95, 149–151
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
evaluation 99
von Post humification scale 95, 97–99
plant residues 12–13
porosity 12, 59
water-filled pore space (WFPS) 107–110,
116–117
rainfall 123, 124–126
reduced tillage 44, 148
root development 6, 10–11
sandy soil 2
root development 11
structure evaluation 4
cemented layers 9
compacted soil 6–7
good condition soil 4–6
158
soil organic carbon (SOC) 103, 118–119
land use 105
soil properties 103–104
structure 104–105
visual soil assessment (VSA) 112–114
soil quality scoring procedure (SQSP)
55, 58, 59
soil quality screening toolkit 126–130
SOILpak 19–20, 27, 28, 134, 136, 144
arable management impacts 49–51
compaction 2, 7
crop yield 142–143
drought 134
grassland management impacts 54–55,
59–62, 148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
yield gap analysis 39–40, 41
soil resilience 40, 146–148
soil security 41, 45, 151
Sphagnum mosses 86–88, 100
crofting 90, 91
cultivation 90
geology 88
microbiological processes 90
physical structure 88
return to use 99–100
soil fertility 91
visual soil evaluation (VSE) 91–95,
149–151
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
evaluation 99
von Post humification scale
95, 97–99
stocking rate 57–58
structure 1–2
adverse weather 122–123, 136–137
climate change 123–125
compaction 130–133
drought 133–134
erosion 123–130
temperature 134–135
visual soil evaluation (VSE) 134–136,
145–146
arable management impacts 49–51,
59–62
biological factors 51–52
mechanical factors 52–54
carbon storage 103, 118–119
land use 105
soil properties 103–104
structure 104–105
visual soil assessment (VSA) 112–114
Index
evaluation See evaluation
grassland management impacts 54–55, 59–62,
148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
greenhouse gases 103, 118–119
CO2 105–109, 114–115
methane (CH4) 105–107, 110–111,
114–115
nitrous oxide (N2O) 105–107, 109–110,
114–117
soil properties 103–104
structure 105–107
visual soil assessment (VSA) 112, 114–117
hydrological cycles 36–37
nutrient leaching 103, 118–119
soil properties 103–104
structure 111–112
visual soil assessment (VSA) 112,
117–118
tillage 1
arable management 52–54
recompaction alleviation 78–82
soil structure 8–9, 24–25, 115
subsoiling 69–83
subsoil See compaction, evaluation
subsoiling 82–83
critical depth 69
draught forces 74–75
implement adjustment
moisture content variation 77–78
soil disturbance 77
surface conditions 77
in-field evaluation 78
leg disturbance 72–73
mole drainage 69, 72–73
multiple tine arrangements 73–74
narrow tine 69
power requirements 75–76
selection 76
winged tine 69–72
subsoil visual evaluation of soil structure
(SubVESS) 7, 9, 11, 24–28, 51, 53, 68,
134, 135–136
sustainable intensification 32–33
sward management 56–57
temperature 134–135
tillage 1
recompaction alleviation 78–79
controlled traffic farming 80–82
reducing weight and pressure 79–80
soil structure
conventional tillage systems 8–9
Index
minimum tillage 9
no-tillage systems 9, 24–25, 115
subsoiling 82–83
critical depth 69
draught forces 74–75
implement adjustment 77–78
in-field evaluation 78
leg disturbance 72–73
mole drainage 69, 72–73
multiple tine arrangements 73–74
narrow tine 69
power requirements 75–76
selection 76
winged tine 69–72
visual soil evaluation (VSE)
arable management 52–54
topsoil
clayey soil 2–4
sandy soil 4–7
urban soils 151
visual evaluation of soil structure (VESS) 16–17, 25,
135–136
arable management impacts 49–51
biological factors 51–52
mechanical factors 52–54
carbon storage 113–114
compaction 2, 7, 9, 11
crop yield 142–143
development of 59–62, 144
grassland management impacts 54–55, 59–62,
148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
greenhouse gas emissions 112, 114–117
nutrient leaching 112, 117–118
soil monitoring 146–148
SubVESS 7, 9, 11, 24–28, 51, 53, 68,
134–136
visual soil assessment (VSA) 2, 11, 17–18, 25, 133,
135–136
arable management impacts 49–51,
59–62
biological factors 51–52
mechanical factors 52–54
carbon storage 112–114
crop yield 142–143
development of 59–62
grassland management impacts 54–55,
59–62, 148–149
biological factors 56–58
drainage 58
159
management intensity 58–59
mechanical impacts 58
greenhouse gas emissions 112, 114–117
nutrient leaching 112, 117–118
soil monitoring 148
soil quality screening toolkit 128–130
visual soil evaluation (VSE) 33, 35, 45, 49, 142,
151–152
agronomic profile method 20–24, 27,
135–136
arable management impacts 49–54,
59–62
grassland management impacts 54–55,
56–62, 148–149
arable management impacts 49–51,
59–62
biological factors 51–52
mechanical factors 52–54
blackland 91–95
blackland index 94, 95–96, 99
blackland vegetation scoring (BVS)
94, 97, 99
von Post humification scale 95, 97–99
climate change 135–136, 145–146
comparisons of VSE methods 27
crop yield 142–143
development of 59–62
faunal activity 60–61
grassland specific methods 61–62
layering 59
porosity 59
soil block extraction 60
grassland management impacts 54–55, 59–62,
148–149
biological factors 56–58
drainage 58
management intensity 58–59
mechanical impacts 58
harmonising methods 143–145
land management frameworks 37–44
landscape function analysis (LFA) 41–44
Muencheberg Soil Quality Rating (M-SQR)
40–41, 128
crop yield 142–143
grassland management impacts 54–55,
56–62, 148–149
soil management inputs 43–44
soil monitoring 146–148
soil structure assessment 35–36
SOILpak 19–20, 27, 28, 134, 136, 144
arable management impacts 49–51
compaction 2, 7
crop yield 142–143
drought 134
grassland management impacts 54–62,
148–149
yield gap analysis 39–40, 41
160
visual soil evaluation (VSE) (Continued)
training 44–45
visual evaluation of soil structure (VESS)
16–17, 25, 135–136
arable management impacts 49–54
carbon storage 113–114
compaction 2, 7, 9, 11
crop yield 142–143
development of 59–62, 144
grassland management impacts 54–62,
148–149
greenhouse gas emissions 112, 114–117
nutrient leaching 112, 117–118
soil monitoring 146–148
SubVESS 7, 9, 11, 24–28, 51, 53, 68,
134–136
visual soil assessment (VSA) 2, 11, 17–18, 25,
133, 135–136
arable management impacts 49–54,
59–62
carbon storage 112–114
crop yield 142–143
development of 59–62
grassland management impacts 54–62,
148–149
greenhouse gas emissions 112, 114–117
nutrient leaching 112, 117–118
Index
soil monitoring 148
soil quality screening toolkit 128–130
von Post humification scale 95, 97–99
von Post humification scale 95, 97–99
water
compaction 130–133
drainage 58
hydrological cycles 36–37
least limiting water range (LLWR) 36
storage 33–35, 123
water-filled pore space (WFPS) 107–110,
116–117
water-filled pore space (WFPS) 107–110, 116–117
yield 123, 142–143
controlled traffic farming 80–82
food security 31–32
gaps 32–33, 45
analysis 33–35
land management frameworks 37–44
sustainable intensification 32–33
zero tillage (see no-tillage)