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 CABI is a trading name of CAB International CABI Nosworthy Way Wallingford Oxfordshire OX10 8DE UK Tel: +44 (0)1491 832111 Fax: +44 (0)1491 833508 E-mail: info@cabi.org Website: www.cabi.org CABI 745 Atlantic Avenue 8th Floor Boston, MA 02111 USA Tel: +1 (0)617 682 9015 E-mail: cabi-nao@cabi.org © CAB International 2015. All rights reserved. No part of this publication may be reproduced in any form or by any means, electronically, mechanically, by photocopying, recording or otherwise, without the prior permission of the copyright owners. A catalogue record for this book is available from the British Library, London, UK. 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. Batey, T. (2000) Soil profile description and evaluation. In: Smith, K.A. and Mullins, C.E. (eds) Soil and 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 cost of soil erosion and conservation benefits. Science 267, 1117–1122. 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 from agroecosystems for surface water protection in Quebec and Ontario, Canada: best management practices, policies and perspectives. Canadian Journal of Soil Science 94, 109–127. Shepherd, T.G. (2009) Visual Soil Assessment. Volume 1. Pastoral Grazing and Cropping on Flat to Rolling Country, 2nd edn. Horizons Regional Council, Palmerston North, New Zealand. Shepherd, T.G., Stagnari, F., Pisante, M. and Benites, J. (2008) Visual Soil Assessment – Field Guide for Annual Crops. FAO, Rome. Weill, A. (2009) Les profils de sol agronomiques, 1st edn. Centre de référence en agriculture et agroalimentaire du Québec, Québec, Canada. 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. References Anderson, A.N., McKenzie, D.C. and Friend, J.J. (1999) SOILpak for Dryland Farmers on the Red Soil of Central Western NSW. NSW Agriculture, Orange, Australia. Askari, M.S., Cui, J. and Holden, N.M. (2013) The visual evaluation of soil structure under arable management. Soil and Tillage Research 134, 1–10. Assessing Structural Quality for Crop Performance and for Agronomy 29 Ball, B.C., Batey, T. and Munkholm, L.J. (2007) Field assessment of soil structural quality – a development of the Peerlkamp test. Soil Use and Management 23, 329–337. Ball, B.C., Guimarães, R.M.L., Batey, T. and Munkholm, L.J. (2012) Visual Evaluation of Soil Structure Score Chart. Available at: http://www.sruc.ac.uk/downloads/file/1121/visual_evaluation_of_soil_ structure_score_chart (accessed 17 June 2015). 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, 85–95. Batey, T. (2000) Soil profile description and evaluation. In: Smith, K.A. and Mullins, C.E. (eds) Soil and ­Environmental Analysis: Physical Methods, 2nd edn. Marcel Dekker Inc., New York, pp. 595–628. Batey, T. and McKenzie, D.C. (2006) Soil compaction: identification directly in the field. Soil Use and Management 22, 123–131. Boizard, H., Richard, G., Roger-Estrade, J., Dürr, C. and Boiffin, J. (2002) Cumulative effects of cropping systems on the structure of the tilled layer in northern France. Soil and Tillage Research 64, 149–164. Boizard, H., Batey, T., McKenzie, D.C., Richard, G., Roger-Estrade, J., Ball, B., Bradley, I., Cattle, S., Hasinger, G., Munkholm, L., Murphy, B., Nievergelt, J., Peigne, J. and Shepherd, G. (2007) Detailed Report. Field Meeting ‘Visual Soil Structure Assessment’, INRA Research Station, Estrées-Mons, France, 25–27 May 2005. Available at: http://iworx5.webxtra.net/~istroorg/p_frame.htm (accessed 17 June 2015). Boizard, H., Yoon, S.W., Leonard, J., Lheureux, S., Cousin, I., Roger-Estrade, J. and Richard, G. (2013) Using a morphological approach to evaluate the effect of traffic and weather conditions on the structure of a loamy soil in reduced tillage. Soil and Tillage Research 127, 34–44. Cui, J., Askari, M.S. and Holden, N.M. (2014) Visual evaluation of soil structure under grassland management. Soil Use and Management 30, 1–9. Field, D.J., McKenzie, D.C. and Koppi, A.J. (1997) Development of an improved Vertisol stability test for SOILpak. Australian Journal of Soil Research 35, 843–852. Garbout, A., Munkholm, L.J. and Hansen, S.B. (2013) Tillage effects on topsoil structural quality assessed using X-ray CT, soil cores and visual soil evaluation. Soil and Tillage Research 128, 104–109. Gautronneau, Y. and Manichon, H. (1987) Guide méthodique du profil cultural. CEREF-ISARA/GEARA-­ INAPG, France. Available at: http://profilcultural.isara.fr/ (accessed 8 July 2015). Giarola, N.F.B., da Silva, A.P., Tormena, C.A., Ball, B. and Rosa, J.A. (2010) Visual soil structure quality assessment on Oxisols under no-tillage system. Scientia Agricola 67, 479–482. Giarola, N.F.B., da Silva, A.P., Tormena, C.A., Guimarães, R.M.L. and Ball, B.C. (2013) On the visual evaluation of soil structure: the Brazilian experience on Oxisols under no-tillage. Soil and Tillage Research 127, 60–64. Guimarães, R.M.L., Ball, B.C. and Tormena, C.A. (2011) Improvements in the visual evaluation of soil structure. Soil Use and Management 27, 395–403. Guimarães, R.M.L., Ball, B.C., Tormena, C.A., Giarola, N.F.B. and da Silva, A.P. (2013) Relating visual evaluation of soil structure to other physical properties in soils of contrasting texture and management. Soil and Tillage Research 127, 92–99. Hartemink, A.E. (2008) Soils are back on the global agenda. Soil Use and Management 24, 327–330. Jones, R.J.A., Spoor, G. and Thomasson, A.J. (2003) Vulnerability of subsoils in Europe to compaction: a preliminary analysis. Soil and Tillage Research 73, 131–143. Kay, B.D. (1990) Rates of change of soil structure under different cropping systems. Advances in Soil ­Science 12, 1–52. McKenzie, D.C. (1998) SOILpak for Cotton Growers, 3rd edn. NSW Agriculture, Orange, Australia. McKenzie, D.C. (2001a) 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. McKenzie, D.C. (2001b) Rapid assessment of soil compaction damage. II. Relationships between the SOILpak score, strength and aeration measurements, clod shrinkage parameters and image analysis data on a Vertisol. Australian Journal of Soil Research 39, 127–141. McKenzie, D.C. and McBratney A.B. (2001) Cotton root growth in a compacted Vertisol (Grey Vertosol). I. Prediction using strength measuring devices and ‘limiting water ranges’. Australian Journal of Soil ­Research 39, 1157–1168. McKenzie, D.C., Shaw, A.J., Rochester, I.J., Hulugalle, N.R. and Wright, P.R. (2003) Soil and Nutrient Management for Irrigated Cotton. NSW Agriculture, Orange, Australia. Manichon, H. (1982) Influence des systèmes de culture sur le profil cultural: élaboration d’une méthode de diagnostic basée sur l’observation morphologique. Thèse, Institut National Agronomique Paris-­Grignon, Paris, France. 30 T. Batey, R.M.L. Guimarães, J. Peigné and H. Boizard Mueller, L., Kay, B.D., Hu, C., Li, Y., Schindler, U., Behrendt, A., Shepherd, T.G. and Ball, B.C. (2009) Visual assessment of soil structure: evaluation of methodologies on sites in Canada, China and Germany. Part I: comparing visual methods and linking them with soil physical data and grain yield of cereals. Soil and Tillage Research 103, 178–187. Mueller, L., Shepherd, T.G., Schindler, U., Ball, B.C., Munkholm, L.J., Henning, V., Smolentseva, E., ­Rukhovic, O., Lukin, S. and Hu, C. (2013) Evaluation of soil structure in the framework of an overall soil quality rating. Soil and Tillage Research 127, 74–84. Munkholm, L.J., Ball, B.C. and Batey, T. (2013) Applications of visual soil evaluation. Soil and Tillage ­Research 127, 1–2. Newell-Price, J.P., Whittingham, M.J., Chambers, B.J. and Peel, S. (2013) Visual soil evaluation in relation to measured soil physical properties in a survey of grassland soil compaction in England and Wales. Soil and Tillage Research 127, 65–73. Peerlkamp, P.K. (1959) A visual method of soil structure evaluation. Meded. v.d. Landbouwhogeschool en Opzoekingsstations van de Staat te Gent. 24, 216–221. Peigné, J., Vian, J.F., Cannavaciuolo, M., Lefevre, V., Gautronneau, Y., et al. (2013) Assessment of soil structure in the transition layer between topsoil and subsoil using the profil cultural method. Soil and Tillage Research 127, 13–25. Pulido Moncada, M., Gabriels, D., Lobo, D., Rey, J.C. and Cornelis, W.M. (2014a) Visual field assessment of soil structural quality in tropical soils. Soil and Tillage Research 139, 8–18. Pulido Moncada, M., Penning, L.H., Timm, L.C., Gabriels, D. and Cornelis, W.M. (2014b) Visual examinations and soil physical and hydraulic properties for assessing soil structural quality of soils with contrasting textures and land uses. Soil and Tillage Research 140, 20–28. Reicosky, D.C. (1997) Tillage-induced CO2 emissions from soil. Nutrient Cycling from Agroecosystems 49, 273–285. Roger-Estrade, J., Richard, G., Caneill, J., Boizard, H., Coquet, Y., Défossez, P. and Manichon, H. (2004) 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. Shepherd, T.G. (2000) Visual Soil Assessment. Volume 1. Field Guide for Cropping and Pastoral Grazing on Flat to Rolling Country. Horizons mw and Landcare Research, Palmerston North, New Zealand. Shepherd, T.G. (2003) Assessing soil quality using visual soil assessment. In: Currie, L.D. and Hanly, J.A. (eds) Tools for Nutrient and Pollutant Management: Applications to Agriculture and Environmental Quality. Occasional Report No. 17. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand. Shepherd, T.G. (2009) Visual Soil Assessment. Volume 1. Field Guide for Pastoral Grazing and Cropping on Flat to Rolling Country, 2nd edn. Horizons Regional Council, Palmerston North, New Zealand. Shepherd, T.G. and Janssen, H.J. (2000) Visual Soil Assessment. Volume 3. Field Guide for Hill Country Land Uses. Horizons Regional Council, Palmerston North, New Zealand. Shepherd, T.G. and Park, S.C. (2003) Visual soil assessment: a management tool for dairy farmers. In: Brookes, I.M. (ed.) Proceedings of the 1st Dairy Conference Dexcel’s Ruakura Dairy Farmers’ Conference. Continuing Massey University, Rotorua, New Zealand. Shepherd, T.G., Bird, L.J., Jessen, M.R., Bloomer, D.J., Cameron, D.J., Park, S.C. and Stephens, P.R. (2001) Visual Soil Assessment of soil quality – trial by workshops. In: Currie, L.D. and Loganathan, P. (eds) Precision Tools for Improving Land Management. Occasional Report No. 14. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand. Shepherd, T.G., Sparling, G.P. and Todd, M.D. (2002) Visual soil assessment: can we see what we measure? In: Stephens, P.R. and Callaghan, J. (eds) Proceedings Soil Quality and Sustainable Land Management Conference. Landcare Research, Palmerston North, New Zealand. Van den Akker, J.H.H., Arvidson, J. and Horn, R. (2003) Introduction to the special issue on experiences with the impact and prevention of subsoil compaction in the European Union. Soil and Tillage ­Research 73, 1–8. 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. References Abdollahi, L., Hansen, E.M., Rickson, R.J. and Munkholm, L.J. (2015) Overall assessment of soil quality on humid sandy loams: effects of location, rotation and tillage. Soil and Tillage Research 145, 29–36. Affholder, F., Poeydebat, C., Corbeels, M., Scopel, E. and Tittonell, P. (2013) The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Research 143, 106–118. Alvarez, M.F., Osterrieth, M.L. and del Rio, J.L. 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(2011) Restoring Disturbed Landscapes: Putting Principles into Practice. Island Press, Washington, DC. Trouse, A.C. Jr (1978) Root tolerance to soil impediments. In: Jung, J.A. (ed.) Crop Tolerance to Suboptimal Land Conditions. ASA, CSSA, SSSA, Madison, Wisconsin, pp. 193–232. van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P. and Hochman, Z. (2013) Yield gap analysis with local to global relevance – a review. Field Crops Research 143, 4–17. 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. References Abdollahi, L. and Munkholm, L.J. 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Available at: http://www.teagasc.ie/publications/2011/1386/ cattle-201106.pdf (last accessed 18 June 2015). Vero, S.E., Antille, D.L., Lalor, S.T.J. and Holden, N.M. (2014) Field evaluation of soil moisture deficit thresholds for limits to trafficability with slurry spreading equipment on grassland. Soil Use and Management 30, 69–77. 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. References Ampoorter, E., De Schrijver, A., De Frenne, P., Hermy, M. and Verheyen, K. (2011) Experimental assessment of ecological restoration options for compacted forest soils. Ecological Engineering 37, 1734–1746. Ansorge, D. (2007) Soil reaction to heavily loaded rubber tracks and tyres. PhD thesis, Cranfield University, Silsoe, UK. Ansorge, D. and Godwin, R.J. 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(2011) Improvements in visual evaluation of soil structure. Soil Use and Management 27, 395–403. Kroulik, M., Kumhala, F., Hula, J. and Honzik, I. (2009) The evaluation of agricultural machines field trafficking intensity for different soil tillage technologies. Soil and Tillage Research 105, 171–175. Muller, M.M.L., Ceccon, G. and Rosolem, C.A. (2001) Influência da compactação do solo em subsuperfície sobre o crescimento aéreo e radicular de plantas de adubação verde de inverno. Revista Brasiliera de Ciencia do Solo 25,531–538. Palmer, R.C. (2011) Assessment of effectiveness of attempts to improve soil structure in three East Devon Catchments in 2009–11. Report to Environment Agency, Bristol, UK. Peigne, 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 Research 127, 13–25. Sharifi, A., Godwin, R.J., O’Dogherty, M.J. and Dresser, M.L. (2007) Evaluating the performance of a soil compaction sensor. Soil Use and Management 23, 171–177. Shepherd, G. (2009) Visual Soil Assessment. V ­ olume 1. Field Guide for Pastoral Grazing and Cropping on Flat to Rolling Country, 2nd edn. Horizons Regional Council, Palmerston North, New Zealand. Smith, E.K., Misiewicz, P.A., White, D.R., Chaney, K. and Godwin, R.J. (2013) An investigation into the effect of traffic and tillage on soil properties and crop yields. ASABE Paper No. 1597846, St. Joseph, Michigan. Smith, E.K., Misiewicz, P.A., Girardello, V., Arslan, S., Chaney, K., White, D.R. and Godwin, R.J. (2014) ­Effects of traffic and tillage on crop yield (winter wheat Triticum aestivum) and the physical properties of a sandy loam soil. ASABE Paper No. 1912652, St. Joseph, Michigan. Soane, G.C., Godwin, R.J. and Spoor, G. (1986) Influence of deep loosening techniques and subsequent wheel traffic on soil structure. Soil and Tillage Research 8, 231–237. Soane, G.C., Godwin, R.J., Marks, M.J. and Spoor, G. (1987) Crop and soil response to subsoil loosening, deep incorporation of phosphorus and potassium fertiliser and subsequent soil management on a range of soil types. Part 2: soil structural conditions. Soil Use and Management 3(3), 123–130. Choosing and Evaluating Soil Improvements by Subsoiling and Compaction Control 85 Soehne, W. (1958) Fundamentals of pressure distribution and soil compaction under tractor tires. Agricultural Engineering 39, 276–281, 290. Spoor, G. (2006) Alleviation of soil compaction: requirements, equipment and techniques. Soil Use and Management 22, 113–122. Spoor, G. and Godwin, R.J. (1978) Experimental investigation into the deep loosening of soil by rigid tines. Journal of Agricultural Engineering Research 23(3), 243–258. Spoor, G. and Godwin R.J. (1990) Soil loosening: requirements, implements and techniques. HGCA ­Research Review No 19. Home Grown Cereals Authority, London. Taylor, J.C., Wood, G.A., Earl, R. and Godwin, R.J. (2003) Soil factors and their influence on within-field crop variability, Part ll: field observation of soil variation. Biosystems Engineering 84(4), 441–453. Tullberg, J.N., Yule, D.F. and McGarry, D. (2007) Controlled traffic farming - from research to adoption in ­Australia. Soil and Tillage Research 97, 272–281. Verschoore, R., Pietrers, J.G., Seps, T., Spriet, Y. and Vangeyet, J. (2003) Development of a sensor for continuous soil resistance measurement. Proceedings of European Conference of Precision Agriculture, Berlin, Germany. Wood, G.A., Taylor, J.C. and Godwin, R.J. (2003) Calibration methodology for mapping within-field crop variability using remote sensing. Biosystems Engineering 84(4), 409–423. 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. 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(2001) Does nitrogen addition to raised bogs influence peat phosphorus pools? Biogeochemistry 53, 307–321. Williams, B.L. and Wheatley, R.E. (1988) Nitrogen mineralization and water-table height in oligotrophic peat. Biology and Fertility of Soils 6,141–147. Williams, B.L., Boggie, R., Cooper, J. and Mitchell, J.W. (1985) Changes in some physical and chemical characteristics of peat following reseeding and grazing. Irish Journal of Agricultural Research 24, 229–236. Worral, F., Bell, M.J. and Bhogal, A. (2010) Assessing the probability of carbon and greenhouse gas benefit from the management of peat soils. Science of the Total Environment 408, 2657–2666. 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. 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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 References Abdollahi, L., Hansen, E.M., Rickson, R.J. and Munkholm, L.J. (2015) Overall assessment of soil quality on humid sandy loams: effects of location, rotation and tillage. 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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. References Anderson, K. and Croft, H. (2009) Remote sensing of soil surface properties. Progress in Physical Geography 33, 457–473. Askari, M.S. and Holden, N.M. (2015) Quantitative soil quality indexing of temperate arable management systems. Soil and Tillage Research 150, 57–67. Ball, B.C. (2013) Spiritual aspects of sustainable soil management. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 257–284. Ball, B.C. (2015) The Landscape Below: Soil, Soul and Agriculture. Wild Goose Publications, Glasgow, Scotland, UK. Ball, B.C. and Douglas, J.T. (2003) A simple procedure for assessing soil structural, rooting and surface conditions. Soil Use and Management 19, 50–56. 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. Batey, T. (1988) Soil Husbandry. Soil and Land Use Consultants Ltd, Aberdeen, Scotland, UK. Boizard, H., Won Yoon, S., Lheureux, S., Cousin, I., Roger-Estrade, J. and Richard, G. (2013) Using a morphological approach to evaluate the effect of traffic and weather conditions on the structure of a loamy soil in reduced tillage. Soil and Tillage Research 127, 34–44. Brown, L.R. (2012) Full Planet, Empty Plates: The New Geopolitics of Food Security. Norton, New York. Cruse, R.M., Lee, S., Fenton, T.E., Wang, E. and Laflen, J. (2013) Soil renewal and sustainability. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 477–500. European Union (2012) The State of Soils in Europe. Reference report by the Joint Research Centre, Institute for Environment and Sustainability, ISPRA, Italy. Farm Soils Plan (2005) FSP Farm Soils Plan: Protecting Soils and Income in Scotland. Scottish Government, Edinburgh, Scotland, UK. The Expanding Discipline and Role of Visual Soil Evaluation 153 Fenton, T.E., Kazemi, M. and Lauterbach-Barrett, M.A. (2005) Erosional impact on organic matter content and productivity of selected Iowa soils. Soil and Tillage Research 81, 163–171. Gerster-Bentaya, M. (2013) Nutrition-sensitive urban agriculture. Food Security 5, 723–737. Hatfield, J.L. and Morton, L.W. (2013) Marginality principle. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 19–55. Herrick, J.E., Brown, J.R., Bestelmeyer, B.T., Andrews, S.S., Baldi, G. et al. (2012) Revolutionary land use change in the 21st century: is (rangeland) science relevant? Rangeland Ecology and Management 65, 590–598. Hillel, D. (2009) The mission of soil science in a changing world. Journal of Plant Nutrition and Soil Science 172, 5–9. Kerebel, A. and Holden, N.M. (2013) Allocation of grass fields to hybrid soil moisture deficit model drainage classes using visual indicators. Soil and Tillage Research 127, 45–59. Lal, R. (2013) Principles of soil management. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 1–18. McBratney, A., Field, D.J. and Koch, A. (2014) The dimensions of soil security. Geoderma 213, 203–213. McKenzie, D.C. (2013) Visual soil examination techniques as part of a soil appraisal framework for farm evaluation in Australia. Soil and Tillage Research 127, 26–33. Minh, V. (2007) Quantitative and qualitative soil quality measurements in Vietnam. African Journal of Agricultural Research 2, 455–462. Mueller, L., Saparov, A. and Lischeid, G. (eds) (2014) Novel Management and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia. Springer International, Switzerland. Mueller, L., Shepherd, G., Schindler, U., Ball, B.C., Munkholm, L.J., Hennings, V., Smolentseva, E., Rukhovic, O., Lukin, S. and Hu, C. (2013) Evaluation of soil structure in the framework of an overall soil quality rating. Soil and Tillage Research 127, 74–84. Munkholm, L.J. (2000) The Spade Analysis – A Modification of the Qualitative Spade Diagnosis for Scientific Use. DIAS-report No. 28. Plant Production, Danish Institute of Agricultural Sciences, Tjele, Denmark. Munkholm, L.J., Heck, R.J. and Deen, B. (2013) Long-term rotation and tillage effects on soil structure and crop yield. Soil and Tillage Research 127, 85–91. Peigné, J., Vian, J.-F., Cannavacciulo, 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 Research 127, 13–25. Pulido Moncada, M., Gabriels, D. and Cornelis, W.M. (2014) Data-driven analysis of soil quality indicators using limited data. Geoderma 235–236, 271–278. Shaxson, T.F. (2006) Re-thinking the conservation of carbon, water and soil: a different perspective? Agronomy for Sustainable Development 26, 9–19. Shepherd, T.G. (2009) Visual Soil Assessment. Volume 1. Field Guide for Pastoral Grazing and Cropping on Flat to Rolling Country, 2nd edn. Horizons Regional Council, Palmerston North, New Zealand. Sinclair, T.R. and Rufty, T.W. (2012) Nitrogen and water resources commonly limit yield increases, not necessarily plant genetics. Global Food Security 1, 94–98. Soil Science Society of America (2008) Glossary of Soil Science Terms. Soil Science Society of America, Madison, Wisconsin. Tenywa, M.M., Zake, J.Y.K. and Lal, R. (2013) Building upon traditional knowledge to enhance resilience of soils in Sub-Saharan Africa. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 109–140. Think Soils Manual (2007) Soil Assessment to Avoid Erosion and Runoff. Environment Agency, Bristol, UK. Tudge, C. (2004) So Shall We Reap. Penguin Books, London. Venkateswarlu, B., Srinivasarao, Ch. and Venkateswarlu, J. (2013) Traditional knowledge for sustainable management of soils. In: Lal, R. and Stewart, B.A. (eds) Principles of Sustainable Soil Management in Agroecosystems. CRC Press, Boca Raton, Florida, pp. 303–335. 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)
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