Exploring visual soil evaluation and examination methods on highly-weathered tropical soil

Soil & Tillage Research 195 (2019) 104360

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Exploring visual soil evaluation and examination methods on highlyweathered tropical soil

T

W.M. Cornelisa, , D. Akodia,b, E. Komutungab, C. Agabab, E. Ahumuzab, K. Oratungyeb ⁎

a b

Ghent University, Department of Environment, Soil Physics Unit – Unesco Chair on Eremology, Belgium Nat. Agricultural Research Lab. (NARL), Soil & Agrometeorology Unit, Kampala, Uganda

ARTICLE INFO

ABSTRACT

Keywords: Soil structure Soil physical quality Soil quality index VESS VSA

National and international initiatives have been undertaken in Uganda to improve soil quality and increase crop production. However, means to evaluate and examine soil quality, particularly soil physical quality, is lacking in the country. In this study, visual soil evaluation and examination (VSEE) spade and core tests, which comprise rapid and simple methods to semi-quantitatively assess soil structure, have been tested. The derived soil quality scores Sq were compared with soil quality indicators SQi derived from traditional lab-based methods of soil structure analysis. Tests were conducted and samples taken in Uganda on highly-weathered soils with sandy clay loam texture. Both the 0–15 cm topsoil and the 15–30 cm moderately compacted subsoil were considered. Test and sampling sites comprised 18 farmers’ fields (maize, Zea mays L.) that were under conventional tillage, permanent planting basins and rip lines for three years, as well as four locations in a natural forest. All VSEE approaches tested showed a significantly better Sq score in the natural forest (good quality) as compared to maize fields (fair/moderate quality), with the subsoil always showing lower quality than the topsoil. Methods based on Visual Evaluation of Soil Structure (VESS) were more responsive to differences in soil quality than the Visual Soil Assessment (VSA) approach. Statistical analysis showed that there was a good to moderate correlation between the VSEE-based Sq scores and lab-derived SQi values, with Pearson r correlation coefficients of 0.52–0.69 for bulk density, 0.66-0.78 for air capacity, 0.53–0.73 for air permeability, 0.52–0.72 for hydraulic conductivity, 0.18-0.48 for mean weight diameter under fast wetting. The correlation with an overall integrated index of soil quality SQI ranged between 0.56 and 0.77. Minimizing the potential effect of local variability by averaging Sq scores and SQi or SQI values per treatment and depth, improved the correlation, with e.g., Pearson r ranging from 0.84 to 0.95 when relating Sq to SQI. We also found a significant correlation between VESS Sq scores and the shape of the water retention curve, particularly in the wet range (r > 0.50). Our results show that in general, VSEE methods are promising alternatives to evaluate differences in physical soil quality of highlyweathered soils in a rapid, intuitive, practical and cheap way.

1. Introduction

undertaken to improve soil quality and increase crop production. Resource-poor countries such as Uganda are lacking means to evaluate and examine soil quality, particularly soil physical quality. Visual soil evaluation and examination (VSEE) methods comprise rapid and simple tests that offer a numeric semi-quantitative assessment of soil structure (e.g., Ball et al., 2017; Guimarães et al., 2017; Pulido Moncada et al., 2017). They might therefore have a great potential as an alternative for or being complementary to more expensive labour-intensive traditional quantitative methods to evaluate soil structure directly and indirectly (like determining aggregate stability, air capacity, hydraulic conductivity, air permeability, gas diffusion to mention the most widely used; Pulido Moncada et al., 2014a, b; Obour et al., 2017; Cherubin et al., 2017). Moreover, they can be used by scientists and farmers alike

Soils in Uganda are often moderately to highly structurally degraded hence showing low productivity. Soil structural degradation disturbs the water and air regime in soils, and can increase the mechanical resistance seedlings and crops are experiencing, thus impairing a range of soil functions and ecosystem services (Schjønning et al., 2015). Improving soil structure is key to build resilience against drought and water excess, and thus contributes to assuring food and water security; it is therefore imperative to fulfil the sustainable development goals (Falkenmark and Rockström, 2008; Rockström and Falkenmark, 2015). In Uganda, several national and international initiatives have been ⁎

Corresponding author. E-mail address: [email protected] (W.M. Cornelis).

https://doi.org/10.1016/j.still.2019.104360 Received 31 January 2019; Received in revised form 11 July 2019; Accepted 2 August 2019 0167-1987/ © 2019 Published by Elsevier B.V.

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(Van Leeuwen et al., 2018). VSEE methods have been widely tested and applied in some European countries and among others in Canada, China, New Zealand and Australia. Studies under more humid tropical conditions are fewer and were primarily conducted in South-America (e.g., Guimarães et al., 2013, 2017; Giarola et al., 2010, 2013; Pulido Moncada et al., 2014a, b; da Silva et al., 2014; Cherubin et al., 2017, 2018; Kraemer et al., 2017; Auler et al., 2017; Tuchtenhagen et al., 2018) where they seem to boost over the past years. At the 2018 ISTRO working group F meeting in Paris, France, one of the points of discussion was the need for studies testing VSEE methods on soils developed under tropical conditions, as was already suggested at the 2014 ISTRO meeting in Maringá, Brazil as well. To our knowledge, there are no articles reporting their evaluation in Africa, despite their particular potential as only means to evaluate soil physical quality apart from bulk density under resource-poor conditions. The lack of African studies on at least the so-called Visual Evaluation of Soil Structure (VESS; Guimarães et al., 2011) method was confirmed in a recent review by Soares Franco et al. (2019). Moreover, Van Leeuwen et al. (2018), working on podzols, histosols and fluvisols in the Netherlands recommended to deploy soil type specific visual evaluations of soil quality. This was also concluded by Pulido Moncada et al. (2015) when testing Dexter’s S index across different soil types in Belgium and Venezuela. The effect of soil texture was already recognized earlier by e.g. Peerlkamp (1959), Batey and McKenzie (2006) and Shepherd (2009). With several VSEE approaches or variants being published and used (see Emmet‐Booth et al., 2016 for a review), the question remains which one suits best the conditions and purposes in a given region of interest. Our study area, where primarily maize is grown, is dominated by highly weathered ferralitic soils which are known for their good physical properties (such as excellent porosity and good permeability), though they typically have limited plant available water capacity. Attributes related to porosity or permeability might therefore be less relevant than those related to stability of aggregates, with the former presenting soil structural form and the latter soil structural stability, two very important aspects of soil structure (Kay, 1990). As pointed out by Emmet‐Booth et al. (2016), the two major VSEE categories are spade methods and profile methods. Spade methods extract sample blocks that are typically evaluated and examined in the field on a plastic tray or sheet, by breaking up the blocks and exposing the aggregates manually or by drop tests after which aggregates are assessed by hand. Most spade methods analyse soil not deeper than 30 cm, except for a few methods that go to 40 or 50 cm depth (Emmet‐Booth et al., 2016). They offer a rapid and cheap evaluation with high spatial coverage within arable fields. Using a geostatistical approach, Leopizzi et al. (2018) demonstrated that five sample points are sufficient, whereas Ball et al. (2007) suggested at least ten points. Profile methods have their foundation in soil survey and require soil pits, typically excavated mechanically, to evaluate and examine soil profiles till 1.5 m depth. They are, however, costly, time-consuming, destructive and thus spatially restricted. They can only be applied at a very few spots within a field. Recently, Pulido Moncada et al. (2017) deployed an intact block sample variant (15 × 10 × 12 cm = 1800 cm3) allowing to evaluate and examine soil blocks in the lab. Johannes et al. (2017) used smaller 150 cm3 intact cores, i.e. those traditionally used in soil physical analysis, and suggested a specific scoring procedure that better complied with the small sample size. Use of intact soil cores taken e.g. with dedicated augers allows an evaluation of soil quality that is rapid and at any depth. In contrast, current spade methods, though rapid, only examine the top 30 cm or exceptionally the top 50 cm, while profile methods for deep soil structure evaluation are time-consuming and expensive. Use of intact cores or blocks could thus bridge rapid superficial high spatial coverage methods with time-consuming deep low coverage methods. Alternatively, a double block method in which two consecutive soil blocks are extracted and which thus covers a larger

depth than most widely used VSEE spade methods, might be another option. In a recent study, Emmet-Booth et al. (2019) deployed small two-spade deep soil pits to evaluate and examine the pit wall and coined it double spade method. VSEE methods not only differ in the way the soil is exposed and the depth of assessment, they also differ in the criteria that are employed and how soil quality is finally scored. Most common criteria are aggregate size and shape, ease of break or rupture resistance, inter- or intra-aggregate porosity, and rooting. Colour reflected by mottles, smell or earthworm number are sometimes considered as well (Ball et al., 2017). Whether all these attributes need to be assessed and some are redundant might be site specific or depend on the soil functions of interest. Most VSEE methods employ different scoring systems (per identified layer) to arrive at a semi-quantitative appreciation of soil structural quality. Some give scores to individual attributes and then calculate arithmetic mean values (e.g. coreVESS; Johannes et al., 2017), use weighting factors assigned to each attribute (Visual Soil Assessment, VSA; Shepherd, 2009) or take the most frequent score given to individual attributes (subVESS; Ball et al., 2015). Others designate an integrated score based on the different (non-scored) individual attributes presented in tabular format (e.g., VESS; Guimarães et al., 2011) or by using a flowchart like in one of the most recent variants, grassVESS (Emmet‐Booth et al., 2018). It is well known that soil structure affects soil hydraulic properties (e.g., Hillel, 1998) and thus might provide key information to improve their prediction. For example, Nguyen et al. (2015) showed that grouping soils based on categorical morphological soil structure information did improve the prediction of the water retention curve, whereas Pulido Moncada et al. (2014c) demonstrated that prediction of saturated hydraulic conductivity with a model tree was more accurate when morphological parameters, i.e. individual VSEE criteria, were included as predictor variables. Rawls and Pachepsky (2002) presented regression trees to include standard structural information from soil surveys to predict water content and −33 and −1500 kPa matric potential. Including soil structural information to predict hydraulic properties was once more recommended in a recent review on so called pedotransfer functions (PTFs) (Van Looy et al., 2017). Vice versa, including soil structural information in PTFs might allow to evaluate the effect of soil management (which in turn determines soil structural quality) on soil hydraulic properties and thus on the soil-water and air regime through appropriate models. To our knowledge, there are at present no straightforward methods enabling to statistically predict the impact of soil structural degradation or amelioration on soil hydraulic properties. Alternatively, Or et al. (2000) and Leij et al. (2002) developed a rather stochastic modelling approach with physically based coefficients to predict the changes in soil pore size distribution. Having tools at hand that link soil structure with soil hydraulic properties, might be very useful not only for farmers and their advisers but also for water managers and policy makers when assessing the impact of soilimproving cropping systems or soil structural degradation on the soilwater and air regime at a local (field) and regional level (Schipper et al., 2015). The objective of this study was to test (i) the feasibility of VSEE methods in detecting changes in structural quality of highly weathered soil resulting from differences in land use and management, (ii) if for this soil, VSEE soil quality scores (Sq) based on spade and core tests are correlated with quantitative soil physical properties used as soil quality indicators (SQi) and with a soil quality index (SQI), and thus could be an alternative for or complementary to such SQi’s and SQI, (iii) which visual criteria are most relevant under the environmental conditions of interest and which scoring system is most appropriate, and (iv) if VSEEbased soil quality scores (Sq) are correlated with soil hydraulic properties like water retention curve and hydraulic conductivity. Finding such latter correlations would be helpful in predicting the effect of land use and management on field and regional water balances with soilhydrological models. We hypothesized that though ferralitic soils are 2

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known to show inherently relatively good physical soil quality in terms of porosity and permeability, land use and management does have an effect on their soil structural quality which is reflected in VSEE-based Sq scores, SQi’s, SQI and hydraulic properties. We further hypothesized that the tested VSEE approaches and their variants are suitable for the soil type under study, with some performing better than the other.

0–15 cm and 15–30 cm — were sealed in plastic bags and transported to Belgium for laboratory analyses. They were immediately mixed and air dried by placing them on a shallow tray in a well ventilated room. The soil lumps were crushed so that the gravel, roots and organic residues could be separated, and passed through a 2 mm sieve. 2.4. Visual soil evaluation and examination

2. Material and methods

Visual soil evaluation and examination was conducted on soil blocks in the field using Visual Soil Assessment, VSA (Shepherd, 2009) and Visual Evaluation of Soil Structure, VESS (Ball et al., 2007; Guimarães et al., 2011) approaches and on undisturbed 100 cm3 soil cores in the laboratory using coreVESS (Johannes et al., 2017). The visual soil evaluations and examinations were performed by a trained person, assisted by three people.

2.1. Study area The study was conducted in the Northern moist farmland agroecological zone of Uganda, between 1° 21′N and 2° 42′N latitude, and 32° 51′E and 34° 15′E longitude at ˜1075 m above sea level. The climate is tropical with a bimodal rainfall pattern; the first rain starts in March and reaches a peak during April-May and the second starts in August and peaks during September-October. The long dry season, the driest and the hottest, runs from December to March and the short one during June-July. The average annual rainfall varies between 1000–1200 mm decreasing northwards and average annual minimum and maximum temperatures are 22.5 °C and 25.5 °C, respectively. The dominant soil in the study area is yellow to yellowish brown ferralitic sandy clay loam to sandy loam with moderate fertility. The natural vegetation is wooded savanna with scattered deciduous trees and shrubs.

2.4.1. Visual Evaluation of Soil Structure (VESS) A soil pit (30 × 30 × 30 cm in size) was dug and two consecutive undisturbed blocks of soil (15 cm deep, 10 cm thick and 25 cm wide) were taken and put in a transparent plastic bag. Taking two blocks of soil instead of one 20 cm deep block is a modification to the original VESS method described by Ball et al. (2007) and Guimarães et al. (2011). This modification allowed to evaluate soil quality in a rapid and cheap way to a depth where soil structural differences induced by the different soil management operations were still present. Visual inspections of all soil pits prior to the evaluation showed a contrasting soil structure from ˜15 cm depth onwards, and VESS was thus performed on a block representing the top 0–15 cm layer and the 15–30 cm layer. The assessment of the soil blocks included manual break down of aggregates by hand and scores were assigned to individual criteria by comparing the appearance of the soil blocks and aggregates after hand breaking with a visual key containing descriptions and photos of each proposed soil structural quality class as described by Guimarães et al. (2011). It should be noted that the original VESS (Guimarães et al., 2011) does not assign scores to individual criteria but rather gives one overall score based on an integrated appreciation of those individual criteria. Giving, however, separate scores to the individual criteria facilitates the identification of most relevant and possibly redundant criteria statistically. Moreover, on top of the criteria from the VESS score chart suggested by Guimarães et al. (2011), i.e., difficulty to break aggregates, intra-aggregate porosity, size and appearance of aggregates, and number and distribution of roots, additional criteria like soil smell, soil colour and abundance of earthworms were scored as well. As these latter attributes did not vary much across all sites, they will not be further used. We thus employed two scoring systems. In the first, the soil quality score Sq is the arithmetic mean of the individually scored criteria and has variants depending on whether four (as in the original VESS) or less criteria are considered. An Sq score thus refers to the arithmetic mean of the four criteria from the VESS score chart, whereas e.g. Sq-r, denotes a variant without rooting or Sq-a one without considering aggregate shape and size. The second system corresponds with the integral score as in the original VESS and is denoted as Sq*. Overall, scores vary between 1 (friable, good) and 5 (very compact, poor). For each individual criterion, integer values were used. In analysing the datasets in this study, scores will be treated per layer or over the entire examined depth, i.e., from 0 to 30 cm. In the latter case, the overall score is the weighted mean of the scores given to individual layers using the depth of each layer as a weighting factor (Guimarães et al., 2011). As layer depth in our case was constant and taken at 15 cm, the overall score corresponds with the arithmetic mean of two block scores. This also holds for the scores calculated for coreVESS and VSA (Sections 2.4.2 and 2.4.3).

2.2. Experimental design VSEE methods were tested on soils under crop land and under natural forest, representing virgin soils. On crop land, maize (Zea mays L.) was grown under monoculture and under three different soil management systems. The soil management practices included permanent planting basins (PB), rip lines (RP), and conventional tillage (CT). In the PB practice, planting pits ˜35 cm long, ˜15 cm wide, ˜15 cm deep, spaced at intervals of 70–90 cm were dug using a hand hoe. Each pit was filled with 1–2 kg of manure and one bottle top of inorganic fertilizer. Maize was planted at 3–4 seeds per hole. For RP, rip lines were established with ox-drawn rippers to a depth of 20–25 cm during planting. Both PB and RP treatments complied with conservation agricultural principles with minimum soil disturbance and use of herbicide (glyphosate) to kill weeds at the early stages of land preparation. They were introduced three years prior to our study. The CT practice, which is very common in the area, consisted of ploughing multiple times (primary tillage, secondary tillage, planting and weeding) using a hand hoe to a depth of 10–15 cm. No inputs such as fertilizers and organic manures were added. Natural forests (NF) acted as a reference for the comparison of soil quality indicators and were supposed, as undisturbed ecosystems, to show optimum soil structural quality. The tests were performed at six different farmer fields (on-farm trials) per soil management practice (totalling 18 fields) where each field acted as a replicate. Per field, only one randomly selected site that deemed representative for the whole field, was selected. Under NT, four sites were chosen. 2.3. Soil sampling A total of 44 undisturbed and 44 disturbed soil samples were collected from the different sites for quantitative soil analysis at two depths. All samples were taken very close to each other and can be considered as subsamples taken from one larger sample. The undisturbed soil cores were collected at half way soil depths of 0–15 cm and 15–30 cm using 100 cm3 Kopecky rings during July-August 2016 and when soils were showing moist conditions near field capacity. The core samples were trimmed from both sides, fitted with plastic leads and transported to Belgium in dedicated boxes for further analysis. Upon arrival, analysis of the samples started. The disturbed samples — ˜500 g of bulk soil taken over a depth of

2.4.2. Visual Evaluation of Soil Structure on undisturbed soil samples (coreVESS) Visual evaluation of soil structure on undisturbed samples was 3

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conducted in the laboratory on soil cores (100 cm3) using the coreVESS method introduced by Johannes et al. (2017). It is a modification from the original VESS and has its own score chart with descriptions and pictures alike, and a same scoring range from 1 to 5. Johannes et al. (2017) suggested to only consider difficulty to break aggregates, intraaggregate porosity, and size and appearance of aggregates, but not the number and distribution of roots to allow comparison of samples taken from different depths where root distribution can be different (A. Johannes, personal communication). Rather than opting for an integral score, they proposed the arithmetic mean of individually scored attributes. Soil quality calculated from the three criteria they suggested will be denoted here as Sq. Additionally, we also scored for earthworms, number and distribution of roots, soil smell and colour, but only roots will be further considered for reasons mentioned above. When including roots as well, the notation Sq+r will be used. The evaluation and examination was done on the same intact cores used for the analysis of quantitative soil properties (see Section 2.5). Samples were removed from the cores after they had been equilibrated at −100 hPa on a sandbox (as part of the determination of the water retention curve; see Section 2.5) and then scored. A matric potential of −100 hPa has been suggested by Van den Berg et al. (1997) as corresponding to field capacity of highly weathered tropical soils. The (partly intact) remainder of the sample was then used to continue the determination of the water retention curve on pressure plates, as detailed in Section 2.5.

AC =

s

PAWC =

(1)

FC

FC

PWP

(2)

where θs is the volumetric water content at saturation, θFC is that at field capacity taken at −100 hPa for our highly weathered soils (Van den Berg et al., 1997), and θPWP that at permanent wilting point taken at −15,000 hPa. Air permeability Ka was determined on the intact 100 cm3 soil cores after their equilibration to −100 hPa matric potential (see supra), using a steady-state in-house made air permeameter proposed by Grover (1955) and used by Pulido Moncada et al. (2017) in a similar study. Saturated hydraulic conductivity Ks was determined on the intact 100 cm3 cores equilibrated earlier to −100 hPa matric potential (see supra) and after they were gradually prewetted in a tray overnight to remove the entrapped air. To this end, a laboratory permeameter from Eijkelkamp Soil & Water (type 09.02.01.05) was used in constant head mode and with upward water flow within the soil samples. Aggregate stability was determined on both air-dried and premoistened aggregates using methods proposed by Le Bissonnais (1996). Air-dried soil materials were passed through a 5 mm mesh and 5 g of aggregates measuring 3–5 mm were used for different treatments, i.e., fast wetting, slow wetting (using samples brought to equilibrium at −3 hPa of matric potential) and mechanical breakdown by shaking. The aggregates were put in an oven at 40 °C for 24 h to bring them under the same moisture conditions. For the fast wetting treatment, aggregates were immersed in water and transferred to a 50 μm sieve. In case of slow wetting, aggregates were brought to equilibrium at −3 hPa of matric potential before transferring them to the sieve. To mechanically breakdown aggregates, they were first immersed in ethanol and then agitated upon which they were transferred to the sieve. The aggregate stability for each breakdown mechanism was expressed by calculating the mean weight diameter (MWD), which is the sum of the mass fraction of soil remaining on each sieve after sieving multiplied by the mean aperture of the adjacent meshes. Soil organic carbon (SOC) was determined by the wet oxidation method of Walkley and Black (1934). The obtained results of SOC were converted to 100% by multiplying by 1.3 since the method only determines 75% of SOC. A soil quality index was calculated using the SQi’s. For the sake of simplicity in this explorative study, we used linear scoring of ‘more is better’ as e.g. used by Askari and Holden (2014) to transfer the values of the quantitative soil properties to unit-less scores SQi′:

2.4.3. Visual Soil Assessment (VSA) Two soil blocks were extracted similarly as for VESS (Section 2.4.1). They were dropped from a height of 1 m, followed by visual assessment of key indicators such as soil texture, soil structure, soil porosity, number and colour of soil mottles, soil colour, number of earthworms, soil smell, soil erosion, surface cover, and surface crusting and surface ponding. Evaluating potential rooting depth according to the VSA manual, would require digging holes substantially deeper than 30 cm. For practical reasons, this was not done here. Each of the above mentioned indicators was evaluated by assigning a visual score of 0 (poor), 1 (moderate), 2 (good) or an in-between score (0.5 = moderately poor and 1.5 = moderately good) by comparing the soil quality observed with the description of the indicator and the photographs in the field guide manual by Shepherd (2009). The scores assigned were multiplied by a weighting factor of 1, 2, or 3 suggested by Shepherd (2009) and based on the relative importance of each indicator in the assessment of soil quality. An overall final score Sq-r, i.e. without potential rooting depth, was then obtained by summing those products.

SQi =

2.5. Quantitative soil properties used as soil physical quality indicators

SQi SQimin SQimax SQimin

(3)

and then a (non-weighted) additive method to derive the SQI

Several quantitative soil properties were used as soil physical quality indicators (SQi). They were determined using standard procedures. Bulk density (BD) was measured on the intact 100 cm3 soil cores at −100 hPa matric potential. Air capacity (AC) and plant-available water capacity (PAWC) were derived from the soil-water retention curve established using the sandbox and pressure plate method as outlined in Cornelis et al. (2005). The sand box was used to determine water contents by weighing the core sample once equilibrium at matric potentials of −10, −30, −50, −70 and −100 hPa was reached. The core sample was then used to derive air permeability at −100 hPa (see below), saturated hydraulic conductivity (see below), brought back to −100 hPa on the sand box and then scored according to coreVESS (see Section 2.4.2). Non-manipulated subsamples were used to determine water content required to calculate BD and water content after having been equilibrated at −330 hPa, while disturbed samples were used to determine water content at −1000 and −15,000 hPa. The latter three water retention points were derived by using the pressure plates. AC and PAWC were defined as:

n

SQI = i=1

SQi i n

(4)

where the subscripts ‘min’ and ‘max’ refer to the minimum and maximum value for the specific SQi in our dataset, the prime ′ denotes the normalisation to a unit-less value, and n is the number of SQi’s to calculate SQI. Since our dataset was rather small (N = 44), finding appropriate weights for SQi using principal component or redundancy analysis is not recommended. We only used those physical SQi’s that were significantly correlated with Sq scores (n = 5; see Section 3.2). Note that BD was converted to porosity enabling to apply ‘more is better’ to all SQi’s. Linear scoring rather than (sigmoid) non-linear scoring avoids the use of threshold values needed when using the model presented in Cherubin et al. (2016); these thresholds were not known a priori for our soil of interest. The non-linear scoring applied by Askari and Holden (2014) did not significantly affect the results and was thus not used either. 4

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2.6. Statistical analysis

Table 1 Sq scores per layer of evaluation from different VSEE approaches. NF is natural forest, CT is conventional ploughing, PB is permanent beds, is RL rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered in case of VESS and soil quality score calculated as the weighted sum of the individual scores given to the criteria considered in case of VSA, Sq-r is Sq without accounting for roots, Sq* is the integral Sq score. Values shown are mean values of four replicates for NF and six for PB, RL and CT, with standard deviations between parentheses.

All statistical analyses were performed using R and STATA programs at a 0.05 significance level. The normality of the data was checked using Quantile-Quantile plots and confirmed by a Shapiro-Wilk test. The data were then subjected to a comparison of means test using analysis of variance (ANOVA). As a pre-requisite for ANOVA (F-test), Levene’s test was used to check for homogeneity of variance between groups. Possible pair-wise comparison of the means was conducted using Fisher’s Least Significant Difference (LSD) method. In relating Sq scores between the VSEE methods and with SQi’s and SQI, a one-to-one comparison was employed, with the number of observations N being 44. To minimize the effects of local or withintreatment variance, Sq scores and SQi or SQI values were additionally averaged per treatment and depth, reducing the number of observations to 8. To meet the normality requirement, one outlier showing the highest soil quality was omitted further reducing the number of observations to 7. Pearson’s correlation coefficient was used to examine the strength of linear relationships between the scores and values. The significance of the correlation was checked using a t-test.

Sq coreVESS Sq VESS Sq-r VSA Sq* VESS

0–15 cm 15–30 cm 0–15 cm 15–30 cm 0–15 cm 15–30 cm 0–15 cm 15–30 cm

NF

CT

PB

RL

1.0 (0.00) 1.5 (0.58) 1.0 (0.00) 1.9 (0.38) 40.5 (0.00) 36.5 (0.71) 1.7 (0.50) 2.0 (0.00)

2.2 (0.18) 3.1 (0.34) 2.4 (0.41) 3.2 (0.29) 33.7 (0.61) 33.2 (2.27) 2.7 (0.52) 2.8 (0.75)

2.3 (0.37) 2.8 (0.72) 2.7 (0.29) 3.0 (0.66) 33.4 (2.78) 32.7 (1.94) 2.7 (0.52) 3.2 (0.41)

2.1 (0.83) 2.5 (0.35) 2.4 (0.74) 3.1 (0.26) 34.6 (1.88) 32.5 (1.90) 2.3 (0.52) 3.0 (0.63)

working under different soil environments observed that it typically takes over three years before soil-improving systems manifest real effects (Araya et al., 2016; Moreira et al., 2016). On the other hand, results show that on cropland, the physical quality as reflected by all VSEE scores was fair/moderate, which might be due to the inherently good physical quality of highly weathered soils (WRB, 2015). Under NF, all methods suggested good soil structural quality. Table 1 shows the Sq scores per layer for the four different soil management practices. Subsoils always showed lower soil quality (higher Sq scores), which resulted from soil compaction by repeated tillage on cropland and lower SOC content in the subsoil. BD clearly increased with depth (not shown), which was most pronounced in NF showing a mean BD of 1.07 Mg m−3 in the topsoil and 1.29 Mg m−3 in the subsoil. In the other treatments, mean BD of the topsoil varied between 1.34 and 1.39 Mg m−3, while for the subsoil from 1.41 to 1.43 Mg m−3. Under NF, SOC content was on average 2.2 times higher in the upper layer (26.1 g kg−1) compared to the lower (11.8 g kg−1). The trends observed for all VSEE methods are similar to those for BD, with VESS scoring based on an arithmetic mean of individual scores (Sq) showing the best correspondence (not shown). NF showed highest differences in the VSEE Sq scores between top and subsoil, which was

3. Results and discussion 3.1. Feasibility of VSEE methods in detecting changes in structural quality The overall VSEE Sq scores over the entire examined soil depth, i.e., 0–30 cm, are presented in Fig. 1 for the four different soil management practices. Also indicated in Fig. 1 are the threshold values corresponding to the major soil quality categories used in the different VSEE methods. It should be noted that by omitting potential rooting depth in VSA, which has a weighting factor of 3 in the original VSA manual, the maximal total VSA Sq score is 48 instead of 54 and accordingly, the threshold between good and moderate reduces from 37 to 33. As could be expected, NF resulted in the highest soil quality (p < 0.05), but no significant differences were observed across the three cropping systems. A similar trend was observed for all VSEE methods (and scoring) tested. This indicates that though all VSEE tests were able to assess differences in soil quality resulting from different land use, the soil-improving systems PB and RL did, after three years, not result in a significant improvement in the quality of the ferralitic soils under study, in comparison with CT. Several other authors

Fig. 1. Overall Sq scores over the entire examined depth (0–30 cm) from different VSEE approaches (coreVESS Sq, a; VESS Sq, b; VSA Sq, c; VESS Sq*, d), with indication of the soil quality categories and their thresholds (horizontal dashed lines): 2 and 3 is the threshold between good and fair, and fair and poor, respectively, in VESS-based methods, and 33 between good and moderate in VSA. Note that by omitting potential rooting depth in VSA (which has a weighting factor of 3 in the original VSA manual), the maximal total VSA score is 48 instead of 54 and accordingly, the threshold between good and moderate reduces from 37 to 33. NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered in case of VESS and soil quality score calculated as the weighted sum of the individual scores given to the criteria considered in case of VSA, Sq-r is Sq without accounting for roots, Sq* is the integral Sq score. Different letters denote significantly different values at p = 0.05. Bars shown are mean values of four replicates for NF and six for PB, RL and CT. Error bars are standard deviations.

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Fig. 2. Comparison of normalized Sq and Sq* scores across different VSEE tests (N = 44). NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered in case of VESS and soil quality score calculated as the weighted sum of the individual scores given to the criteria considered in case of VSA, Sq-r is Sq without accounting for roots, Sq* is the integral Sq score. Full line is regression line, dashed line is 1:1 line, r values shown are Pearson correlation coefficients. Note that several symbols do overlap.

reflected as well in the BD values. To compare Sq and/or Sq* scores across different VSEE tests, their values were first normalized. A normalized value of 0 corresponds with 5 in case of VESS methods and with 0 in case of VSA, whereas 1 represents a score of 1 and of 48, respectively. Fig. 2a shows that VESS Sq and coreVESS Sq relate very well (r = 0.92) though the latter seems to show a slight overestimation of soil quality as compared with VESS Sq. Comparison of the two VESS scoring systems illustrates that Sq and Sq* (Fig. 2b) show relatively large differences, though their Pearson r (r = 0.66) was still significant. Likewise, Sq values based on the original coreVESS and VESS procedures, i.e. coreVESS Sq and VESS Sq* showed substantial deviation (not shown, r = 0.58). Fig. 2b indicates that VESS Sq* showed less variation as it results in integer values (without digits) per assessment instead of real numbers. Soils with slightly different soil quality can thus receive a similar score. This might reduce its responsiveness to changes in soil quality as compared with the approach based on individual scoring of attributes. However, averaging Sq* scores per field or treatment as done in practice might overcome this setback. VSA Sq-r and VESS Sq or VESS Sq* show a high correlation (r = 0.76 and r = 0.78, respectively) though VSA overestimated soil quality relative to VESS, except for the highest soil quality (Fig. 2c, d). It should be noted that VSA considers much more attributes than VESS; these attributes, like soil colour, mottling, erosion surface cover, crusting and ponding, all received maximum scores in our study. This also resulted in a lower variation in VSA Sq-r and thus probably a lower responsiveness, as these attributes, along with soil texture, are not necessarily related to soil management of our highly-weathered soils. If the major purpose of using VSEE methods is to evaluate the effect of soil

management on soil physical quality, omitting criteria less affected by management might increase the responsiveness of the method. Strikingly, there was an almost exact correspondence between the thresholds suggested for both VSA and VESS. It is also clear from Fig. 2 that overall, the variation in Sq values across sites and depths was rather substantial. To investigate the effect of root counting in both VESS and coreVESS, rooting number and distribution was in- and excluded from Sq scoring (Fig. 3). Including roots in coreVESS (Sq+r) resulted in a better match with VESS Sq (compare with Fig. 2a) as coreVESS scores became slightly higher. Likewise, excluding roots from VESS (Sq-r) improved the agreement with coreVESS Sq. It should be noted that Johannes et al. (2017) did not include the evaluation of roots in developing coreVESS because of their dynamic behaviour and their dependency on sample depth, in contrast with VESS where samples are always taken at the same depth, i.e. the top 20 cm. Overall, when considering the same criteria and when using the same procedure to calculate scores (by taking the arithmetic mean of individual scores), coreVESS and VESS show almost the same results suggesting that sample size does not matter, at least within a given Sq layer. The effect of different sampling size and preparation (i.e., prewetting to a preset suction) between the three VSEE methods was evaluated by comparing individual attributes. Fig. 4a shows that strength-related attributes like ‘difficulty to break’ yielded similar scores (r = 0.94) on soil blocks (VESS) and prewetted cores (coreVESS). However, ‘structure’ scored after dropping the soil block (VSA) and ‘difficulty to break’ the block by hand (VESS) showed much less correspondence (r = 0.59; Fig. 4b). Similar results were obtained when comparing VSA ‘structure’ 6

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Fig. 3. Comparison of normalized Sq, Sq+r and Sq-r scores across different VESS tests (N = 44). NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered, Sq-r is Sq without accounting for roots in VESS, Sq+r is Sq which accounts for roots in coreVESS. Full line is regression line, dashed line is 1:1 line, r values shown are Pearson correlation coefficients. Note that several symbols do overlap.

with VESS ‘aggregate size and shape’ (not shown). Scores given to ‘difficulty to break’ were almost similar to scores given to ‘aggregate size and shape’, rendering one of both possibly redundant for the highly-weathered soils under study (not shown). Consequently, omitting ‘aggregate size and shape’ in the Sq calculation (Sq-a) did not alter the score (r = 1.00). Porosity examined in cores and blocks (coreVESS vs. VESS) differed more than when comparing blocks in the field (VSA vs. VESS) (Fig. 4c, d). The relative low number of data points as seen in Fig. 4 results from the many overlaps between both methods. As could be expected from Fig. 3, when comparing VESS and coreVESS in terms of rooting, there were almost no differences (r = 0.96; Fig. 4e).

3.2. Correlation between Sq scores and soil physical properties used as SQi The overall VESS and VSA Sq scores exhibited a good to moderate correlation with most individual soil physical properties used as SQi when performing a one-to-one comparison of all scores and values (Figs. 5 and 6; results for coreVESS Sq are not shown). There was no significant correlation with plant available water capacity (r ≤ 0.25), MWDs (r ≤ 0.31) and MWDm (r ≤ 0.14). Strongest relationships were found with BD (r = 0.61–0.69), Ka (r = 0.67-0.73), Ks (r = 0.66–0.72) and AC (r = 0.72–0.78). Soil OC (r = 0.34–0.49), MacPOR (r = 0.47–0.55) and MWDf (r = 0.29–0.48) showed intermediate (yet significant) correlations. VESS Sq performed always better than

Fig. 4. Comparison of normalized Sq scores given to individual attributes across different VESS tests (N = 44). NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered. Full line is regression line, dashed line is 1:1 line, r values shown are Pearson correlation coefficients. Note that several symbols do overlap. 7

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Fig. 5. Relation between VESS Sq scores and soil quality indicators SQi’s. SOC is soil organic carbon, BD is bulk density, AC is air capacity, Ka is air permeability, Ks is saturated hydraulic conductivity, MWDf is mean weight diameter of aggregates after fast wetting. NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered. Full line is regression line, dashed lines are soil quality thresholds, r values shown are Pearson correlation coefficients. Note that some symbols do overlap.

coreVESS Sq and VSA Sq. Evaluating root number and density in coreVESS did improve the correlations with most SQi’s. Scoring soil quality in an integrated manner like in the original VESS (Sq*) resulted in poorer correlations (not shown), with r values of 0.52 for BD, 0.53 for Ka, 0.52 for Ks, 0.66 for AC, 0.36 for SOC, 0.43 for MacPOR and 0.18 for MWDf. As mentioned above, using integral scores per layer (Sq*) results in integer values per assessment, instead of real numbers. Scoring systems based on averaging individual criterion scores (Sq) thus seem to better capture subtle differences in soil quality as compared to a system with integral scoring. Moreover, scoring the criteria individually allows to appreciate them differently – criteria that might be better linked to the SQi, while integral scores only allow to give one overall appreciation. On the other hand, though local spatial variability was minimized in as much as possible by taking VSEE and SQi samples very close to each other, it can never be excluded. This might have contributed to the relatively large scatter observed in the plots. In a study by Johannes et al. (2017), local variability was held responsible for the poor correlations between SQi values and Sq* scores. When evaluating soil quality within a field or treatment, five (Leopizzi et al., 2018) to at least ten (Ball et al., 2007) replicates are therefore suggested to overcome local or within field/treatment spatial variability. When using averaged Sq scores and SQi values per treatment and depth in our study, very good relations were observed between both (plots not shown). Pearson r values between VESS Sq, coreVESS Sq or VSA Sq-r on the one hand and SQi on the other ranged between 0.89 and 0.93 for BD, 0.74 and 0.92 for Ka, 0.76 and 0.98 for Ks, and 0.93 and 0.98 for AC. However, for MacPOR it reduced to 0.40-0.64, and for SOC and MWDf, the correlation was not significant (r ≤ 0.25). For VESS Sq*, r values were 0.89,

0.80, 0.90, 0.93, 0.47 and r ≤ 0.26, respectively. The correlation of Sq and Sq* with PAWC, MWDs and MWDm remained low. It should be noted that by averaging the scores and values, the number of observations reduced from 44 to 7, after having removed one outlier that corresponded with highest soil physical quality (NF, 0–15 cm depth). Averaging scores or values of replicates like done in practice when evaluating the soil quality of a field or treatment might thus take away the above concern of not-capturing subtle differences. The poor relation between Sq and PAWC might not be surprising given its strong dependence on soil texture and SOC rather than on soil structure (Hillel, 1998). Botula et al. (2013) showed for example that for highly-weathered tropical soils, water retention curves beyond field capacity can primarily be predicted from clay content, cation exchange capacity and dithionite-citrate-bicarbonate extractable iron and aluminium. The same authors found no difference in water content at field capacity when using repacked or intact cores taken from highlyweathered tropical soils (Botula et al., 2012), which demonstrates that structure does not affect the water content at field capacity of these soils. When testing aggregate stability, methods of fast wetting (resulting in MWDf in our study) enable better discrimination between soils when they are relatively stable (Le Bissonnais, 1996) as in our study, than methods of slow wetting or mechanical breakdown by shaking (MWDs, MWDm). Fast wetting encourages slaking by compression of entrapped air and better represents heavy rain storms (Le Bissonnais, 1996), which is typical in our study area. Though many studies have shown that SOC is a key indicator for physical soil quality (e.g., Shukla et al., 2006), our results show that SOC was not the best predictor for soil structural quality. Though we found a significant correlation between SOC and Sq scores, these 8

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Fig. 6. Relation between VSA Sq scores and soil quality indicators SQi’s. SOC is soil organic carbon, BD is bulk density, AC is air capacity, Ka is air permeability, Ks is saturated hydraulic conductivity, MWDf is mean weight diameter of aggregates after fast wetting. NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VSA is Visual Soil Assessment, Sq-r is soil quality score calculated as the weighted sum of the individual scores given to the criteria considered, but without considering roots. Full line is regression line, dashed lines are soil quality thresholds, r values shown are Pearson correlation coefficients. NS means not significant. Note that some symbols do overlap.

correlations were poorer than with other SQi’s. It also contrasts with findings of e.g. Pulido Moncada et al. (2014a), Cherubin et al. (2018) and Cui et al. (2014) in tropical Venezuela and Colombia, and temperate Ireland, respectively. This might result from differences in the soil organic matter quality as reflected by differences in chemical fractions (humic vs. fulvic) and physical fractions (heavy vs. light fractions) (Pulido Moncada et al., 2018) in these studies as compared to ours. Poor quality of soil organic matter can reduce its effect on overall soil physical quality presented by the SQi’s. Relatively strong correlations of Sq with BD and Ks as observed in other studies on tropical soils (Pulido Moncada et al., 2014a; Cherubin et al., 2017) were found in this study. Our correlations were even stronger, though this might be explained by the low variability in clay content in our study in comparison with that in previous studies. Working with highly-weathered soils with different texture, Guimarães et al. (2017) did, however, not find any relation between Sq and BD. We also found a good correlation of Sq with Ka, similarly as Pulido Moncada et al. (2014a, c) for tropical soils in Venezuela. The good correlations between Sq, and Ks or Ka show that transmission of water and air are facilitated by a combination of porosity and roundness of the aggregates. Soils with a poor structure or angular aggregates tend to show more resistance to water flow than well-structured soils with rounded aggregates (Hu et al., 2009). In contrast with Pulido Moncada et al. (2014a), we also did find a good correlation with AC. This suggests that the visual soil structure of the soils under study is not only related to water and air movement parameters (as observed by Pulido Moncada et al., 2014a), but also to water retention parameters. For temperate soils, strong relations of Sq scores with SQi’s have been reported in several studies: with BD or total porosity (Mueller

et al., 2009; Guimarães et al., 2013; Pulido Moncada et al., 2014b; Obour et al., 2017), with Ka (Guimarães et al., 2013; Pulido Moncada et al., 2014b; Obour et al., 2017), with Ks (Pulido Moncada et al., 2014b) and with AC (Pulido Moncada et al., 2014b). The strong correlations with particularly these soil properties widely used as proxy for soil structure and thus as indicators for soil physical quality confirm once more the usefulness of VSEE methods, moreover as they integrate several soil physical attributes in assessing soil structure and evaluating soil physical quality. Individual Sq scores showed lower correlations with (individual) SQi’s than overall scores that integrate various facets of soil structure. Table 2 illustrates this for coreVESS and VESS Sq scores. Similar observations were made with the other VSEE methods. Visual porosity seems to be the most important attribute to explain variation in SQi values in case of coreVESS, whereas for VESS, number and distribution of roots seems crucial. This latter attribute also appears to highly explain variation in SQi. These observations show that physical soil properties and related SQi’s cannot be grasped by one single aspect of soil structure, but that they depend on several facets. In Figs. 5 and 6, we also present threshold values that are widely suggested in literature, for each SQi. For tropical soils, Sys et al. (1993) suggested 8 g SOC kg−1 as critical. For BD, a value above 1.45 Mg m-3 would indicate soil structural degradation by soil compaction for the soil in our study (i.e., after having corrected for clay content; Jones et al., 2003; Huber et al., 2008). Soils are supposedly unstable and sensitive to crusting when MWD after fast, slow or mechanical wetting is smaller than 0.8 mm (Le Bissonnais, 1996). It is remarkable to observe that the VSEE thresholds suggested by Ball et al. (2007) and Shepherd (2009) to guide management of temperate soils coincide very 9

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Table 2 Pearson correlation coefficient between quantitative soil properties used as soil physical quality indicator SQi and coreVESS and VESS Sq scores (N = 44). VESS is Visual Evaluation of Soil Structure, Sq is soil quality score per individual criterion or the arithmetic mean of the four criteria, SOC is soil organic carbon, BD is bulk density. Ka is air permeability, Ks is saturated hydraulic conductivity, PAWC is plant available water capacity, MacPOR is macroporosity, AC is air capacity, MWDf is mean weight diameter of aggregates after fast wetting. coreVESS Sq

SOC BD log Ka log Ks PAWC Porosity MacP AC MWDf

VESS Sq

Difficulty to break aggregates

Porosity

Aggregate shape & size

Number and distribution of roots

Mean

Difficulty to break aggregates

Porosity

Aggregate shape & size

Number and distribution of roots

Mean

−0.25 0.59** −0.56** −0.56** −0.26 −0.60** −0.36* −0.71** −0.33*

−0.37* 0.66** −0.62** −0.42** −0.14 −0.66** −0.59** −0.67** −0.34*

−0.33* 0.56** −0.56** −0.46** −0.26 −0.57** −0.39** −0.65** −0.29

−0.28 0.62** −0.56** −0.58** −0.12 −0.63** −0.53** −0.74** −0.55**

−0.34* 0.64** −0.70** −0.66** −0.23 −0.65** −0.47** −0.72** −0.34*

−0.36* 0.60** −0.64** −0.60** −0.31* −0.60** −0.35* −0.68** −0.31*

−0.44** 0.58** −0.60** −0.58** −0.22 −0.58** −0.47** −0.63** −0.37*

−0.38* 0.60** −0.67** −0.62** −0.23 −0.61** −0.42** −0.64** −0.36*

−0.32* 0.66** −0.67** −0.71** −0.15 −0.67** −0.56** −0.78** −0.57**

−0.41** 0.69** −0.73** −0.72** −0.25 −0.70** −0.52** −0.78** −0.48**

* and ** indicate significance at 0.05 and 0.01 level, respectively.

Fig. 7. Relation between VSEE Sq scores and soil quality index SQI (N = 44). NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered in case of VESS and soil quality score calculated as the weighted sum of the individual scores given to the criteria considered in case of VSA, Sq +r is Sq which accounts for roots in coreVESS, Sq-r is Sq without accounting for roots in VSA, Sq* is the integral Sq score. Full line is regression line, r values shown are Pearson correlation coefficients. Note that some symbols do overlap.

Limiting values for AC, Ka and Ks are 0.1 m3 m−3, 1 μm and 1.0 × 10−6 m s−1, respectively (Ball et al., 1988; Huber et al., 2008; Reynolds et al., 2009). In our highly porous soils, there should be no aeration deficits, neither limitations for water and air transmission. This explains why, in contrast with SOC, BD and MWDf, the thresholds for these SQi’s do by far not coincide with the VESS Sq = 3 or VSA Sq = 33 threshold. Finally, as VSEE Sq scores integrate several aspects of soil structural quality, we compared them with an integrated index (SQI) calculated from quantitative soil physical properties used as SQi (Fig. 7). It should

well with the SOC, BD and MWD thresholds on the regression line. Except for SOC, the thresholds for BD and MWD presented in Figs. 6 and 7 are for temperate soils and thus need to be interpreted with care here. A similar match between VSEE Sq and SQi thresholds was also found by Cherubin et al. (2016) when using VESS on highly-weathered soils in Brazil. This made them to conclude that Sq scores offer a good first approximation of soil structural quality and the threshold Sq score of 3 is useful as a provisional threshold to help farmers and consultants making better soils management decisions. Our study seems to confirm this (see also Fig. 1). 10

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Fig. 8. Relation between VSEE Sq scores and soil quality index SQI averaged per treatment and depth (N = 7; the outlier was excluded from the regression). NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, VSA is Visual Soil Assessment, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered in case of VESS and soil quality score calculated as the weighted sum of the individual scores given to the criteria considered in case of VSA, Sq+r is Sq which accounts for roots in coreVESS, Sq-r is Sq without accounting for roots in VSA, Sq* is the integral Sq score. Full line is regression line, r values shown are Pearson correlation coefficients.

be noted that our SQI used those SQi’s that represent soil structural quality only as per our objective, rather than including a range of SQi’s that exemplifies physical, chemical and biological soil properties in relation to soil threats, soil functions or ecosystem services (Bünemann et al., 2018) as with a conventional SQI. Only those physical SQi that showed a significant correlation with Sq scores (i.e., those shown in Fig. 5) and that characterise a different aspect of soil structure were selected. The used SQi’s were thus log Ka, log Ks, porosity, AC and MWDf. Note that instead of BD, porosity, which is linearly related to it, was used, allowing to employ a ‘more is better’ approach for all SQi’s. In comparison with individual SQi values, correlations with SQI were even better now, with r ranging between 0.68 and 0.77. Including roots with coreVESS did substantially improve the correlation (to r = 0.75). For Sq*, r was 0.56. When further minimizing the effect of local variability by using averaged Sq scores and SQI values, the relationships between both was good to very good (Fig. 8), with r values now ranging between 0.84 and 0.95. These findings suggest once more that integrated aspects of soil structural quality are highly captured by visual evaluation and examination of soil structure.

than the water being retained at a given suction, we calculated the slope at different segments between saturation and −100 hPa. The slope of segment 1 is the difference between water content at saturation and that at −10 hPa over the difference in the log of the corresponding matric potentials. Segment 2 is between −10 and −30 hPa, 3 between −30 and −50 hPa, 4 between −50 and −70 hPa, and 5 between −70 and −100 hPa. Fig. 9 shows a fair (r = −0.52, −0.50 and −0.39, respectively) yet highly significant (P < 0.01) correlation between VESS Sq and the slopes in segment 1, 2 and 5. For segment 3 and 4, correlations were not significant (p > 0.05). Similar trends were observed with coreVESS and VSA scores. This might suggest that the attributes being scored in VSEE and their integration capture information on the size distribution of primarily wide and narrow macropores. Indeed, steep slopes in segment 1 and 2 would suggest that a relative larger number of highlysized pores comes with a high overall pore visibility, and easy breakable and rounded aggregates. Holden (1995) e.g., found that shape and surface roughness of peds did affect pore characteristics and thus soilwater retention. VSEE scores also seem to contain more information on the shape of the water retention curve than bulk density, a parameter often used to represent soil structure when developing pedotransfer functions. In segment 2 and 5, Pearson r between slope and BD was -0.37 and -0.19, respectively. In segment 1 and thus very near saturation, however, BD was much better related to the slope, with r = −0.80. Though not impressive, these correlations are promising and should be further explored with much larger datasets. The small dataset (N = 44) we used here only enables to give a first indication of possible relations between VSEE-based soil structure information and hydraulic properties. Since soil quality assessment, including soil structural

3.3. Correlation between Sq scores and soil hydraulic properties Two essential hydraulic properties to simulate water transport in soils are the water retention curve and the hydraulic conductivity. A good relation between Sq scores and Ks (e.g., r = −0.72 for VESS Sq) was already demonstrated in Section 4.2. As mentioned earlier and demonstrated in this study, soil structure only affects the rather wet part of the water retention curve, that represents the distribution of wide and narrow macropores (see e.g. also Assouline, 2006; Głąb, 2014). As structure affects the shape of the water retention curve rather 11

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Fig. 9. Relation between VESS Sq scores and shape of water retention curve expressed in slope segments. NF is natural forest, CT is conventional ploughing, PB is permanent beds, RL is rip lines, VESS is Visual Evaluation of Soil Structure, Sq is soil quality score calculated as the arithmetic mean of the individual scores given to the criteria considered, SWRC is soil-water retention curve, BD is bulk density. Full line is regression line, r values shown are Pearson correlation coefficients. NS means not significant. Note that some symbols do overlap.

quality, is increasingly a central part of studies that serve a range of purposes including sustainable land management, environmental risk assessments, environmental change monitoring and land restoration (Bünemann et al., 2018), and with VSEE being given a prominent role in such studies to complement quantitative methods of soil quality assessment, more VSEE data will become available in the future. At the ISTRO 2018 conference held in Paris e.g., a substantial number of studies being presented did apply VSEE methods. If demonstrated to be good predictors, VSEE scores would thus have the capability to assess the effect of soil management or soil degradation and remediation on hydraulic properties and thus on the water and air regime in soil.

with SQi thresholds (relevant for highly-weathered soils). As we found that including ‘number and distribution of roots’ improved coreVESS results, we suggest to include it and thus take samples at moments when roots are present, such as after harvesting, at least if soil wetness conditions are favourable. Similarly, we suggest to include it in VSA if evaluation of roots till at least 0.8 m as prescribed in the current guidelines would show not feasible. Inclusion of roots resulted in coreVESS Sq scores very similar to VESS Sq scores, showing that there is no size effect when using spade blocks or 100 cm3 cores. Evaluation of ‘aggregate shape and size’ in VESS seemed, at least for the highlyweathered soils under the prevailing environmental conditions in our study and when scoring attributes individually, to provide redundant information. When using an integral score like in the original VESS, its use remains however fundamental. All VSEE methods showed Sq scores that were reasonably correlated with ‘manageable’ structure-related SQi’s, like bulk density, air capacity, air permeability, saturated hydraulic conductivity and the mean weight diameter after having exposed aggregates to fast wetting. They were not correlated with plant available water capacity. Correlations between Sq scores and an overall soil physical quality index SQI based on the above SQi’s, were good to very good, particularly when averaging scores and values over the different treatments and depths. Considering individual Sq scores in VESS and then calculating a mean value rather than using an integral score is another recommendation, at

4. Conclusions This study showed that the VSEE methods tested, i.e. VSA, VESS and coreVESS and their variants, were all feasible to detect changes in structural quality of highly-weathered soil resulting from differences in land use. The VESS and coreVESS approach was responsive to such differences, which was less the case for VSA. The latter evaluates several properties that are inherent to soil (e.g., soil texture) or that were not affected by land use and management in case of our highlyweathered tropical soil (e.g., water ponding). Anyway, we found all approaches to match rather well to each other. A VESS and VSA Sq score of 3 and 33, respectively, converged well 12

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least when using VESS for scientific purposes. This variant showed better correlations with quantitative soil properties used as soil quality indicators SQi. Evaluators often found this less confusing as well. It should be noted that yes-no charts (Emmet‐Booth et al., 2018) that also lead to integral scores were not tested here. Whether weights should be given when calculating mean Sq scores could be further explored. On the other hand, the scoring system of the original VESS resulting in one integral score (Sq*) remains useful in practice where anyway average values per field or treatment are considered. We found promising relations between Sq scores and soil hydraulic properties such as saturated hydraulic conductivity and the shape of the water retention curve in the wet part. This finding might be useful in developing tools to evaluate the effect of soil management on field and regional water balances with soil-hydrological models. Finally, we believe that VSEE methods could not only be complementary, but even an alternative for quantitative soil physical properties to evaluate soil structural quality, particularly in those regions of the world with limited access to soil physical analysis facilities.

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