Assessment of soil structural quality across different textures with visual and soil physical analysis methods

2. ASSESSMENT OF SOIL STRUCTURAL QUALITY
ACROSS DIFFERENT TEXTURES WITH VISUAL
AND SOIL PHYSICAL ANALYSIS METHODS
Lin Lin, Jan De Pue, Angela Katherine Martin Vivanco, Frank van der Bolt, Wim Cornelis
DEPARTMENT OF ENVIRONMENT
SOIL PHYSICS GROUP

3. INTRODUCTION
3

4. INTRODUCTION
4
Agriculture Soil
Important: support the daily life, provide the basic food
Features: well-developed structure, high organic matter content and etc.
Image from Internet

5. Problems:
Factors: intensive mechanical tillage; inappropriate crop management practices and techniques
Result: soil structure degradation has become very common, e.g. soil compaction
5
Pictures from experiment
INTRODUCTION

6. 6
Lead to a serious decline in soil health.
Increase: Bulk density; Penetration resistance
Decrease: Storage and supply of water and air; Nutrients
Impedes root development
Crops quality and yield
Reduces the water permeability: promote floods and soil erosion
Emissions of greenhouse gases: global warming
Image from Internet
Pictures from experiment
INTRODUCTION

7. INTRODUCTION
7
How to protect and improve soil structure quality?
It is crucial to identify inappropriate soil use and crop management techniques
Monitor soil quality and soil structure
Quantitative techniques Semi-quantitative techniques

8. INTRODUCTION
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Quantitative techniques include traditional lab/field-based methods
Time-consuming, labour-intensive and expensive
Ks
SWRC
SHCC
Ku
Aggregate stability
Shear strength
Penetration resistance

9. INTRODUCTION
9
Semi-quantitative techniques, such as Visual Evaluation of Soil Structure (VESS) methods
Why CoreVESS?
Field method/small scale
Blind test
Controlled conditions
Scale / moisture content
Increasingly popular
Rapid and simple test
Numeric semi-quantitative assessment
Wide range of soil properties

10. OBJECTIVE
Sq score vs SQi
(i) identify the feasibility of the CoreVESS method in detecting soil quality changes in different field
position, soil depth and texture;
(ii) assess the feasibility of the CoreVESS method by establishing relationships between Sq scores and
soil quality indicators (SQi) from lab and field analyses;
(iii) link Sq scores with soil hydraulic properties (like SWRC and SHCC);
(iv) suggest suitable thresholds for SQi’s.
Overall,
extend soil information databases by VESS
test their feasibility in discovering soil structural degradation
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11. METHODS
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12. METHODS
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Fig. Map of all the tested fields.
Fig. Textural triangle of all soil tested areas.
42 agricultural fields 7 major soil texture classes

13. METHODS
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At each site:
• Two class positions of contrasting field traffic:
Headland position (labelled as “HEAD”)
In-field position (labelled as “IN-FIELD”)
• Three depths：
the ploughed topsoil layer (~10-20 cm, “TOP”)
the compacted subsoil layer (~30-40 cm, “CSUB”)
the deeper subsoil layer (~60-70 cm, “SUB”)
• Six soil ring samples per layer
42 fields ×2 positions ×3 layers=252 analysed horizons
252 analysed horizons ×6 soil samples=1512 soil core samples
Fig. Three-tiered stepwise manner schematic.
A wide range of soil textures and structures

14. METHODS
14
Ks SWRC
SHCC
BD
AC
MacP
SOC
Texture
Traditional lab/field-based analysis methods
PR
WC

15. METHODS
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CoreVESS
Undisturbed soil core samples (250 cm3)
Equilibrated to a suction of -100 hPa (field capacity moisture content)
Gently pushed soil samples out
Manually broke down by hand
Blindly scored without knowing their origin and by one or more
operators
1. the force needed to break the samples
2. the presence of cracks or macropores and whether pores were visible
within the aggregates
3. whether the aggregates were rounded fragile or angular, and the size
range the aggregates belong to
Scoring range: from 1 to 5
Hesitation between two scores: half point.
Sq score per soil core: the arithmetic mean of individually scored
criteria.

16. RESULTS
16

17. RESULTS
(I) Feasibility of CoreVESS method in discovering soil structural changes
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Fig. Overall CoreVESS Sq scores from three layers/two positions/seven texture classes.

18. RESULTS
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Fig. Soil quality indicators value per layer.
Fig. Soil quality indicators value per field position.

19. RESULTS
(II) Relationship between Sq scores and soil quality indicators
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Scores of (2,3] and (3,4] had the largest weight: fair to poor soil structural quality.
Sq score of 3 as the threshold: Acceptable (<=3) and Degraded (>3) structure.
Fig. Percentage (%) of soil samples in each soil quality assessment category (CoreVESS Sq scores).

20. RESULTS
Sq PR (MPa) BD (Mg m-3) AC (m3 m-3) MacP (m3 m-3) Ks (cm d-1) Sq CoreVESS
[1,3] 2.95 (1.6) b 1.418 (0.124) b 0.071 (0.04) a 0.022 (0.02) a 158.5 (181.6) a 2.5 (0.4) b
(3,5] 4.23 (2.2) a 1.508 (0.113) a 0.059 (0.04) b 0.017 (0.01) b 99.9 (147.8) b 3.6 (0.4) a
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Sq SOC (g kg-1) Sand (%) Silt (%) Clay (%)
[1,3] 10.1 (5.6) a 52.6 (29.5) a 36.2 (25.0) a 11.1 (9.3) a
(3,5] 9.3 (4.8) a 56.7 (28.3) a 33.6 (25.1) a 9.7 (6.5) a
Table. Soil quality indicator values with CoreVESS Sq score classifications (Acceptable and Degraded
soil structure) over all soil samples.
Table. Soil organic matter content and texture values with CoreVESS Sq score classifications over all
soil samples.

21. RESULTS
(III) Relationship between CoreVESS Sq scores and soil hydraulic properties
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Ratio =
𝑊𝐶𝐷𝑒𝑔𝑟𝑎𝑑𝑒𝑑 𝑠𝑜𝑖𝑙 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒
𝑊𝐶𝐴𝑐𝑐𝑒𝑝𝑡𝑎𝑏𝑙𝑒 𝑠𝑜𝑖𝑙 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒
Fig. Soil water retention and hydraulic conductivity curves with CoreVESS Sq scores classifications
(Acceptable and Degraded soil structure) overall soil samples.
Note: [1,3] is referred to as Acceptable soil structure, (3,5] is Degraded soil structure.
WC Ratio is between: 0.93~0.95
HC Ratio is between: 0.51~0.7

22. RESULTS
(IV) Correlation between CoreVESS Sq scores and soil physical quality properties used as SQi
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Fig. Relationship between CoreVESS Sq scores and soil quality indicators (SQi) / soil quality index (SQI) averaged
per soil texture class and layer (N = 21).

23. RESULTS
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Fig. Broken-stick regression between CoreVESS Sq score and soil properties.

24. RESULTS
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Fig. Broken-stick regression between CoreVESS Sq score and water contents at different suctions.

25. CONCLUSIONS
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26. TAKE HOME MESSAGE
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1. Visual evaluation method performs well over a wide range of different textures
2. VESS soil quality scores were not significantly affected by soil texture
3. Topsoil and deeper subsoil show acceptable quality, upper subsoil was degraded
4. Soil quality indicator and integrated index were significantly related to Sq score
5. Acceptable (Sq =< 3) and degraded (Sq>3) soil structure were significantly different
Overall,
• VESS methods: efficient, cheap and labour-saving way
• soil structural quality: from fair to poor
• More attention should be paid to soil management and evaluation, especially to prevent
or remediate soil compaction in the subsoil and at headland zones

27. Lin Lin
PhD student
Ghent University
Department of Environment
Soil Physics Group
Lin.Lin@UGent.be
linlinfighting@outlook.com
Thanks for your attention!