|Main authors:||Fernando Teixeira and Gottlieb Basch
|iSQAPERiS editor:||Jane Brandt|
|Source document:||Teixeira, F. and Basch, G. (2019) Performance of promising land managment practices to populate recommendations of SQAPP. iSQAPER Project Deliverable 6.1, 45 pp|
iSQAPER's interactive tool (SQAPP) is designed to allow onsite soil quality assessment and monitoring, providing management recommendations for improving soil quality. The complexity of this task, if based solely on measured chemical, physical and biological soil parameters, would require an amount of local information that is seldom available. Fortunately, a holistic approach to soil quality, that includes the visual perception (and the perception of other senses, like smell or touch) of signs empirically connected to soil quality, may provide the means to monitor effectively the impact of management/ land use change of the evolution of soil quality (functions).
|1. Field work: soil properties measurements and Visual Soil Assessment|
|2. Statistical analysis|
Several visual soil assessment methods exist for, depending on their goals, the observation of different soil properties (Bünemann et al. 2016). These methods (such as VESS: visual evaluation of soil structure, Guimarães et al. 2011), provide objective protocols and a scoring system that reduces the subjectiveness of the evaluation, allowing the use of semi-quantitative (ordinal scales) data treatment.
iSQAPER undertook two field campaigns to assess soil quality in the case study sites, in 2016 and 2018. The approach taken in 2018 differs from that of 2016, using an extended number of VSA indicators, consisting of a selection of VSA indicators from several other VSA approaches. For both campaigns, an ordinal scale (poor (0), moderate (1), good (2)) was used to score soil quality for each VSA indicator, but no soil quality index currently exists that groups the VSA indicators objectively. Complementary measurements of soil physical, chemical and biological properties were made during both campaigns in order to establish the association between VSA indicators and measured properties.
In this section of iSQAPERiS we examine the average impact of several innovative management practices, grouped under different categories, on soil quality assessed by means of VSA indicators and selected measured properties. The effect of different pedoclimatic zones on soil quality is addressed in »Indicator performance.
1. Field work: soil properties measurements and visual soil assessment
Soil quality assessment of innovative AMPs and controls took place in 2016 and 2018.
In the spring/summer 2016, all 132 innovative AMPs and respective control fields/plots were subjected to a visual soil assessment (VSA). VSA indicators observed at that time were 2 baseline indicators, , 6 soil indicators and measurement of 4 soil properties (Table 3) (see »Visual soil and plant quality assesment).
Table 3. Visual soil assessment observed indicators (2016).
|Type of indicators||Indicators|
|Baseline indicators||»Surface ponding (under cropping)
»Susceptibility to wind and water erosion
|VSA indicators||»Soil structure and consistency
»Soil stability (soil slaking test)
»Presence of a cultivation pan
|Measured soil properties||
In the mid-spring/summer 2018, a new VSA campaign, comprising more VSA soil indicators, and measurements of an extensive range soil properties were conducted by the CSSs on the fields under selected AMPs and respective controls (a total of 20 pairs of AMP/control). For more information on the Visual Soil Assessment (VSA) protocols see »Visual soil and plant quality assessment.
The following observations and measurements were performed and/or information gathered:
- Visual Soil Assessment (VSA) (see Table 4 for indicators observed/measured in the scope of the assessment)
- Data and parameters for water erosion modelling (RUSLE) (see Barão and Basch, 2017)
- Soil texture (clay/silt/sand)
- Stone content (%)
- Bulk density (t m3)
- Microorganisms carbon content (g kg-1 soil)
- Number of different co-occurring soil macrofauna groups
- Organic matter (%)
- Total Nitrogen
- Available P (mg kg-1)
- Exchangeable K (mg kg-1)
- pH (CaCl2)
- Electrical conductivity (dS m-1)
Table 4. Visual soil assessment observed indicators (2018)
|Type of indicators||Indicators|
|Baseline indicators||»Surface Ponding
»Susceptibility to wind and water erosion
»Soil structure and consistency
»Labile organic carbon
a) Excluded from the present study because of methodological reasons.
Topsoil compaction evaluation was performed on the fields by measuring the infiltration rate or measuring penetration resistance, scoring the results as poor, moderate or good (»Visual soil and plant quality assessment). Correlation studies are not presented because this approach, and the interchangeability in the use of the two measurements (without clearly stating what was measured) renders the results meaningless.
2. Statistical analysis
Association between measured soil properties (physical, chemical and biological) and VSA score values, between VSA indicators score values, and between VSA score values of AMPs/Control, were tested by Spearman’s rank-order correlation. To determine if the correlations were statistically significant, the respective t-values were calculated (for a significance level α=0.05), both in Excel (Microsoft Office Professional Plus 2013).
Ranking of measured soil properties was performed following 2 procedures:
- ranking in 3 levels (classification), by applying the same thresholds as used for SQAPP (for those properties with no thresholds defined we used appropriate ranking - see below);
- ranking each property by their values, in an ordinal level (except for pH and texture)).
For ranking thresholds adopted in the development of SQAPP, see »Scoring soil quality indicators. Rankings for other variables, not covered or made explicit in SQAPP, are as follows.
Ranking of soil texture was performed based on FAO-UNESCO soil texture classes, see Table 5. Scoring from 1 to 5 was attributed, respectively, from “Coarse” to “Very fine”.
Table 5. FAO soil texture classes (adapted from Jones et al. 2003)
|Code||Class||Particle size grades|
|1||Coarse||Less than 18% clay and more than 65% sand|
|2||Medium||Less than 35% clay and more than 15% sand; more than 18% clay if the sand content exceeds 65%|
|3||Medium fine||Less than 35% clay and less than 15% sand|
|4||Fine||Between 35 and 60% clay|
|5||Very fine||More than 60% clay|
Ranking of stone content was set by establishing the following criteria: less than 5% content of particles > 2mm (w/w), corresponds to a score of 2, from 5-10% to a score of 1, and >10% to a score of 0.
Ranking of macrofauna was based on the number of groups identified (0 to 14 groups). Alternatively, macrofauna was ranked based on: 0= no macrofauna groups present, 1=1 group, 2=2 or more groups.
Ranking of microorganism C was based on abundance (g/1x10-3 m3): less than 0.318, 0.318 to 0.616 and higher than 0.616, corresponding to scores of 0, 1 and 2, respectively.
Ranking of susceptibility to compaction followed the classification proposed by Jones et al. (2003), where, depending on texture class and packing density, high susceptibility scored 2, moderate susceptibility scored 1 and low susceptibility scored 0.
Note: For full references to papers quoted in this article see