Internationally, soil-specific farming (Rattan and Stewart, 2016) is a concept that will become increasingly important as precision farming is a reality on farms. However, soils are heterogeneous and thereby control many aspects of sustainability (e.g. environmental losses along different pathways and attenuation of such losses). Such heterogeneity influences paddock management decisions directly (Humphreys et al., 2008; Huebsch et al., 2013). Soil quality (Wilson and Maliszewska-Kordybach, 2000) is concerned with physical (the focus of the current study), chemical and biological aspects of soil across soilscapes and, therefore, defines the capacity of agricultural soil to deliver multiple functions, such as primary productivity, nutrient cycling, water filtration (Fenton et al., 2011; Jahangir et al., 2013), habitat for biodiversity and carbon sequestration (Karlen et al., 1997; Wiebe 2003; Schulte et al., 2015). This capacity can be overextended, resulting in degradation of the system by compaction (Soane and van Ouwerkerk, 1998; Vero et al., 2013), erosion (Regan et al., 2010, 2012; Sherriff et al., 2015), loss of soil organic carbon (SOC) (Grandy and Robertson, 2007) or impedance of other soil functions (Creamer et al., 2010).
In Europe, to achieve harmonisation and coordination of soil data, the recommended national soil mapping resolution is 1:250,000, which has been implemented in Ireland. This resolution is only capable of identifying problems at the regional scale. However, “reference profiles” taken during this mapping process provide detailed soil property information at a local scale. Such profiles are termed “modal profiles”. These can be classified according to diagnostic characteristics, which have developed as a result of soil genesis (Simo et al., 2014). This classification system results in the determination of soil types (soil subgroups) and landscape units in which they are typically found occurring together. These diagnostic characteristics define the drainage class of a modal profile (O’Sullivan et al., 2015), the organic matter (OM) status and so on (Simo et al., 2015).
In addition to the traditional soil classification of modal profiles, a wide range of soil chemical, physical and biological properties are measured throughout the profile. The soil physical data associated with modal profiles can be used to determine a value of soil physical quality (SPQ) within the SPQ Index as described by Dexter (2004a) using S-theory and is identified as a key metric of overall soil quality (Dexter, 2004a) (Table 1).
Dexter’s SPQ Index
|0.020 – 0.035||Poor*|
|0.035 – 0.050||Good|
SPQ = soil physical quality.
The S-Index is derived from the relationship between the gravimetric soil water content and the natural log of matric tension. This is calculated as the slope of the soil water retention curve (SWRC) (Equation 1).
Based upon the S-theory, the S-term offers a singular value, which is considered reflective of overall SPQ, and the S-term can be considered suggestive of not only physical quality but also both chemical and biological quality. The S-theory proposes that soil physical properties and behaviour are essentially controlled by soil structure, which is expressed as pore size distribution (measured by SWRC). This is traditionally measured in the laboratory using pressure plates (or equivalent devices) (American Society for Testing and Materials [ASTM], 2008). The S-term is defined as the absolute value of the slope (known as S-value) of the SWRC at its inflection point (Dexter, 2004a). Therefore, according to the S-theory, several aspects of soil physical behaviour reflect the same S-term regardless of soil type (Dexter and Czyż, 2007). Thus, the S-Index offers a simple scale that has the same physical meaning regardless of soil type and can therefore be used to compare SPQ across soil horizons, soil types and spatial scales. When a soil structure becomes degraded, the shape of the SWRC changes (Thu et al., 2007) and hence, the S-term and associated interpretation of SPQ will change accordingly. Dexter (2004a, 2004b, 2004c) presents the S-theory with an associated S-Index, which evaluates the S-term of a soil relative to threshold values indicative of SPQ status (ranging from very poor to very good –Table 1). According to Dexter (2004a), the advantage of this S-Index is that the degraded threshold value holds true across soil types. If this were true, the S-Index would have distinct advantages over other methods of quality assessment, which would differ depending on the soil type in question. The S-Index is not without its opponents, e.g. de Jong van Lier (2014) suggested that the S-theory presents no advantage over bulk density (Bd) and total porosity as a measure of soil quality, as the work involved to elucidate these parameters is much less than the production of an entire SWRC. This is true where the S-Index is being utilised in upper horizons to track the effects of tillage management over time for instance (e.g. Keller et al., 2007). The objective of this research is to test the applicability of the S-Index system by utilising all soil horizons within a profile.
In practice, an SWRC can be inferred via pedo-transfer functions (PTFs) using simple data (i.e. textural class or particle size distribution) (Moncada et al., 2014) in cases where complex data (i.e. measured SWRCs) are not available. In brief, measurement of the SWRCs can be costly and time consuming, requiring the application of pressure to a saturated soil sample, as well as the monitoring of outflow over a prolonged period, subsequent to which a fitting equation such as that of van Genuchten (1980) is applied. Conversely, PTFs use easily and rapidly measured “simple” data to estimate the van Genuchten parameters from extensive reference databases such as ROSETTA (Schaap et al., 2001). The reliability of PTFs to accurately characterise the properties of field soils has been questioned (Schaap and Leij, 1998; Khodaverdiloo et al., 2011), and increasing the data inputs has been shown to increase their reliability. A demonstration of simple versus complex physical data sources was provided by Vero et al. (2014), who found that although higher-quality soil physical data (direct approach) allow better estimation of soil hydraulic parameters, reduced resolution (indirect approach) may be sufficient for some applications, e.g. estimation of unsaturated zone solute travel time (Vero et al., 2014; Fenton et al., 2015). This type of “simple” data is very useful and is more readily available at larger scales but lacks details regarding the macro-porosity of the soil in question. Such soil structural or physical data are important when questions regarding larger scales need answering, e.g. what would the average SPQ of agricultural soils be in Ireland? Simple data may not suffice at an extremely localised scale, e.g. where the SPQ may vary within a subplot region as a result of compaction within a vehicle wheel rut (Vero et al., 2013).
Hence, S-terms can be categorised into “S-indirect (Si)” or “S-direct (Sd)”, where simple and complex data streams are utilised, respectively (Figure1). The former provides a generic characterisation (average) of a particular soil horizon, which would be applicable to similar soil horizons elsewhere or within the same subgroup (Simo et al., 2015), whereas the latter provides site-specific information, which relates to that exact soil horizon at that location on the farm (Dexter and Czyż, 2007). Therefore, this information can be used to ascertain the SPQ of a paddock at that location. This information can be used by the farmer to make assertions about past management and plan present and future management strategies.
Nationally, the most detailed soil physical and chemical dataset in Ireland is the Co. Waterford Monograph, produced as part of the National Soil Survey (NSS) (Diamond and Sills, 2011). Unlike other Irish soil datasets, the Co. Waterford Monograph contains all the data necessary to follow the simple-to complex approach, as presented in Figure 1. Therefore, this dataset allows for the calculation of both Si (generic data in Figure 1) and Sd (measured data in Figure 1) horizon-specific terms and facilitates direct comparison of Si and Sd with each other. In addition, other parameters such as organic carbon (OC) are also included to examine what soils are not suitable for assessment with the S-Index.
The overall aim of the study was to assess SPQ. The objectives of the present study were to 1) investigate the relationship between SPQ status assessed via the Si and Sd methods, 2) outline conditions that preclude the use of S-Index for the soils examined and 3) point the way forward for use of this S-Index nationally. The first objective was carried out by developing a regression comparing Si and Sd terms for all horizons from 17 soil profiles within the Co. Waterford dataset. The second objective was carried out by examining all S-terms that did not comply with this regression and examining all data available for these horizons, e.g. OC%, Bd (in grams per cubic centimetre [g/cm3]).
Materials and methods
Comparison of Si and Sd
To assess Si versus Sd, horizon-specific soil physical data obtained from 17 soil profiles (114 horizons) within the Co. Waterford NSS (Diamond and Sills, 2011) were assessed. A link to the entire dataset is available at http://gis.teagasc.ie/soils/downloads.php. General classification characteristics and the drainage classes of the 17 soils are presented in Table 2. The entire dataset was separated into the following Great Soils Groups: Brown Earths, Luvisols, Alluvial Soils, Brown Podzolics, Podzols and Surface water Gleys. The soil physical data used in the present study are as follows: Bd (grams per cubic centimetre [g/cm3], clod method), sand–silt–clay percentage (pipette method, British Standard (BS) 1796; British Standard Institution, 1989) and SWRC (0 to –1.5 hPa) (sand box and pressure plate; Richards, 1948), which were used to derive the S-term.
The soils of the Waterford Survey, as they appear in the national representative profiles of Co. Waterford (Red Book series), used in the present study
|Group||Height AMSL, m*||Parent material||Great Soil Group||World Reference Base||H||Series||Drainage class|
|Regosol||5||Alluvium||Alluvial||Haplic fluvisol (humic dystric siltic)||8||Coolfinn (presently known as Feale)||Poor|
|Plaggen||20||Irish sea till||Brown earth||Haplic phaeozem (anthric albic epieutric)||8||Ardmore||Well|
|24||Glaciofluvial sand||Brown earth||Haplic regosol (humic eutric arenic)||7||Curragh||Excessive|
|Grey brown podzolic||30||Sandstone > limestone till||Luvisol||Haplic luvisol||9||Dungarvan||Well|
|65||Sandstone > shale > limestone till||Luvisol||Cutanic endostagnic luvisol (chromic)||6||Kilmeaden||Well|
|Brown earth||70||Sandstone till||Brown earth||Haplic cambisol (humic eutric)||5||Broomhill||Well|
|80||Sandstone till||Brown earth||Haplic phaeozem (anthric albic epieutric)||8||Clashmore||Well|
|90||Volcanic till/bedrock||Brown earth||Haplic cambisol (humic epidystric oxyaquic)||7||Kill||Well|
|120||Shale till||Brown earth||Haplic cambisol (humic eutric)||4||Clonroche||Well|
|Brown podzolic||20||Sandstone limestone gravel||Brown podzolic||Entic podzol||2||Callaghane||Excessive|
|160||Shale till and bedrock||Brown podzolic||Leptic cambisol (humic dystric)||2||Slievecoiltia||Well|
|Podzol||152||Sandstone rock||Podzol||Placic albic podzol (endoskeltic)||6||Drumslig||Well|
|165||Sandstone till||Podzol||Placic albic histic podzol||8||Ahaun||Moderate to imperfect|
|27||Sandstone > limestone till||Surface-water gley||Haplic stagnosol (hypereutric)||6||Killadangan||Poor/imperfect|
|30||Sandstone > shale>limestone till||Surface-water gley||Haplic stagnosol (hypereutric)||7||Waterford||Poor|
|Gley||45||Shale > volcanic-sandstone till||Surface-water gley||Haplic stagnosol (hypereutric)||7||Clohernagh||Poor|
|145||Sandstone till||Surface-water gley||Haplic stagnosol (humic hypereutric)||6||Lickey||Poor|
H = number of horizons with data suitable for the present study; AMSL = above mean sea level.
The retention curve retention curve (RETC) software (Schaap et al., 2001), which incorporates both SWRC fitting equations (Sd) [e.g. water retention curves have been fitted to the van Genuchten equation (van Genuchten, 1980) with the Maulem (1986) constraint (m=1 – 1/n)] and the ROSETTA (Schaap et al., 2001) PTF (Si), was used for the conversion of soil physical data into hydraulic parameters. The following parameters were obtained as outputs for all horizons (according to both indirect and direct approaches): saturated water content (qs), residual water content (qr), fitting parameters (a, n, m); tortuosity (l); and saturated conductivity (ks). For horizon-specific Si determination, ROSETTA was used to infer soil hydraulic parameters based on sand–silt–clay percentage as well as Bd data only. For Sd determination, both horizon-specific sand–silt–clay percentage, Bd and measured water retention data were used, from which soil water characteristic curves (SWCCs) were constructed. Horizon-specific Si and Sd terms were determined according to Dexter (2004a) (Equation 1) using the parameters qs, qr, a, and n as input data to the soil available water calculator (SAWCal) model (Asgarzadeh et al., 2014).
Boundary conditions for use of Si in investigated soils
Correlation of Si versus Sd using all horizons from Table 2 identified outlier horizons, and these were examined in more detail. The S-term reflects both textural and structural porosity and the contribution of each to total porosity. Imposed management will affect the structural porosity more than textural porosity, changing the slope of the inflection point on an SWCC when the curve has been plotted as gravimetric water content against the natural logarithm of the pore water suction, thereby changing the S-term. In terms of examining horizons across an entire soil profile, the ratio between textural and structural porosity becomes important and, therefore, consistency or differences in Si down the profile may indicate a horizon that needs greater inspection, i.e. clay content or Bd change.
An assessment of how the S-Index behaved across soil drainage classes was also conducted. For Irish soils, Simo et al. (2014) and Schulte et al. (2015) have established indicative soil drainage classes using diagnostic features identified as part of the Irish soil classification. Poorly draining soils were defined as those showing mottling within 40 cm of the surface. Poorly draining soils may also contain an argic horizon (i.e. where a 20% increase in clay content is found in a lower horizon compared to the above horizon), which should denote a change in Si, compared with an overlying layer. Poorly draining soils may also have a spodic horizon (leaching of iron (Fe)/(aluminium (Al) from upper horizons to lower horizons, whereby Fe/Al is precipitated, in extreme cases resulting in an Fe pan (should denote a Si change). Both argic or spodic horizons may cause stagnation.
Soils with >40 cm of an organic layer are classified as peat. Peat soils are mainly composed of organic materials in which particle size distribution of the mineral fraction has little textural significance (therefore, not suitable for Si allocation). Moderately drained soils display mottling at depth (40–80 cm depth) but lack OM accumulation. However, an argic or spodic horizon may be present (should denote a Si change). Imperfectly drained soils also show mottling at the same depth (40–80 cm) but with the presence of some OM accumulation as well as an argic or spodic horizon (should denote a Sichange). Well-drained soils are those that show no evidence of waterlogging and have no argic or spodic horizon (should denote similar Si terms through profile) present. Excessively drained soils are distinguished by texture alone, whereby the presence of loamy sand or sandy textural classes is dominant (should denote similar Si terms through profile). These categories represent a spectrum of drainage capacity, with the poorer-drained soils remaining at or above field capacity for several days following a rainfall event, and the better-drained soils rapidly returning to below field capacity within days or even hours. All diagnostic features of the soil profile outlined herein were compiled and collated with horizon-specific Si data. Si terms for each horizon were checked, looking for consistency or differences in Si relative to the horizon above it.
Results and discussion
Si versus Sd
Regarding the Si and Sd terms for all horizons examined in this study, the linear relationship is shown in Figure 2 (n=114, Sd=0.635 × (Si) +0.0009 (R2=0.60, P<0.05). Although Si is determined by published PTFs for the parameters of the van Genuchten (1980) equation, it is a good approximation of trends in quality of the soils examined. In all cases, the Sd term placed the soil horizon and overall soil profile (taking average of all horizons) at a lower SPQ than the Si equivalent. The range of S-terms achieved covered the entire SPQ index, as presented in Table 1, namely, from very poor to very good. The upper horizons typically have higher S-terms, while lower terms are observed deeper in the soil profile. While low terms at depth may, in some instances, reflect compaction as a result of management practices, it cannot be assumed to indicate anthropogenic degradation. Lower horizons will naturally exhibit greater Bd than upper horizons, with implications for the calculated S-terms.
Examination of outlier horizons showed discrepancies for OC% and Bd. Dexter (2004a) examined mean clay content (percentage) versus Si, showing the degradation threshold to occur with clay contents >40%. In Ireland, very few soil horizons exhibit clay contents >40% (Tuohy et al., 2016), with no examples >50%. Soil horizons that exceed 6% SOC (OC% >6, w/w) are considered humose to peaty in nature (Jones et al., 2011). Therefore, other physical properties must be also considered for the vast majority of soils to drive Si into the degraded zone (<0.035, Table 1). Using PTFs, Dexter (2004a) shows the Si equivalents where soil textural class and Bd values are known. Here, values of Bd >1 g/cm3 were only considered. Other authors have also only included soil horizons with Bd values >1 g/cm3 (e.g. Ghiberto et al., 2015). Therefore, soil horizons with Bd <1 g/cm3 and an OC% >6 should be removed from the present study and future studies of Irish soils when assessing the SPQ. In addition, excessively drained soils and peat soils were found to be outliers. The former is due to a lack of differentiation across horizons typified in an excessively draining soil. The peat drainage class is associated with Bd values less than that required to infer S-values (minimum Bd <0.5 g/cm3; Dexter, 2004a), and as discussed previously, are influenced more strongly by OM content than by textural and structural porosity.
Two profiles from the Waterford dataset provide good examples of the conditions that are not suitable for S-value designation. The Coolfinn (Feale) series (Table 2) exhibits Si values of >0.25. This is an alluvial soil that displays soil horizons (stratifications) of different textures and Bd values, due to its formation by the flooding of river banks. Peaty layers are common and the Bd ranges from 1 to 0.3 g/cm3. The OC% is always >6 and not suitable for S-terms. Such soils should not be considered for S-theory determination. Alluvial soils represent 7.79% of the County of Waterford and 4.34% of Irish soils. Another outlier example from the Waterford dataset is the podzol of the Drumslig series, in which the upper horizons (A1-A2) have high OC% of 9.9 and 6.6, with associated high Si terms (0.1 and 0.08, respectively). A correlation between OC% and Si is presented in Figure 3, where only the data adhering to both criteria (Bd <1 g/ cm3 and an OC% >6) were observed. This shows that once the appropriate data ranges are used (i.e. Bd and OC% ranges, along with the removal of excessively drained profiles), other correlations within the datasets may be elucidated.
When the suitable range of OC% and Bd is adhered to, the S-Index correlates well with other soil parameters, e.g. Si versus Bd (regardless of texture) (Figure 4). Results from this correlation would indicate that a soil horizon would need to be >1.8 g/cm3 to become physically degraded soil (negatively affects a soil’s ability to perform soil functions), with respect to the 0.035 degradation S-term (at or below) proposed by Dexter (2004a) (Table 1). This could be further developed using a greater dataset and divided into regressions based on individual textural classes. This also has practical implications in the field as Bd (which is easily taken in the field using sample rings of known volume) could be used as a proxy for SPQ.
Further research and conclusions
Utilisation of simple and complex horizon data from 17 representative profiles of the Co. Waterford Soil Survey Monograph produced a significant correlation between Si and Sd. This means that use of Si on a national basis shows potential to track the SPQ of Irish soils at the presently mapped scale. Outlier horizons showed that soil horizons with OC >6% and Bd values <1 g/cm3, or excessively drained sandy profiles, are not suitable for Si estimation. Removal of such horizons and profiles allowed for good correlations among Si, OC% and Bd (regardless of texture). Future work should extend this approach to a national scale dataset and examine the relationship of these estimates to site-specific visual examination approaches (Emmet-Booth et al., 2016b).
It is important to note that the Si consistently scored a higher SPQ value than the Sd equivalent and could result in an overestimation of the soil quality of Irish soils; therefore, results must take this into consideration. Overall, however, the examination of Si values compared with Sd indicates that the use of simple data can potentially be applied to the Irish Soil Information System data to allow the status of Irish soils in relation to their S-term to be described. Further research is currently under way that will apply the S-term to that national level dataset utilising the boundary conditions identified in this work. While the boundary conditions identified here may limit the potential to develop this research from a mapping perspective, future work will explore to what extent the findings of this research are applicable at a soil subgroup classification level. Moreover, at this scale, the use of the S-Index in tandem with other soil parameters will be explored to determine their ranges/influence for the SPQ of Irish soils, e.g. the ranges of soil OC for soils of each category of SPQ in Table 1. This could allow a more nuanced approach towards developing thresholds of soil quality on a country-specific basis, as opposed to applying a universal critical value. At a local level, defining reference S-terms at a soil subgroup level represents a mechanism to potentially allow farmers to better understand their management influence by comparing their S-term against such a reference S-term. Other methods to assess SPQ will also be explored in future research (Armindo and Wendroth, 2016).
In the European Union (EU), despite the withdrawal of the Soil Framework Directive in 2014, the European Commission (EC) remains committed to the objective of the protection of soils, as outlined in the EU Soil Thematic Strategy [COM(2006)231] (EC, 2006). Currently, soil quality is only considered in measures that contribute indirectly to the protection of soils, including policies related to agriculture such as the Water Framework Directive (2000/60/EC) or the Good Agriculture and Environmental Conditions under Pillar 1 of the EU Common Agricultural Policy. The withdrawal of the EU Soil Framework Directive, coupled with the renewed commitment to soil protection for soil quality, means that space now exists for the development of future policy alternatives. Following the application of this index at a national level for Ireland, development of the index across EU jurisdictions could potentially facilitate a harmonised assessment of the overall status of SPQ of European soils.
Funding was provided as part of Department of Agriculture, Food and the Marine (DAFM) Soil Quality Assessment and Research (SQUARE) Research Stimulus Fund No. 6582.Task 1 output.
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