Application of Dexter’s soil physical quality index: an Irish case study

Open access

Abstract

Historically, due to a lack of measured soil physical data, the quality of Irish soils was relatively unknown. Herein, we investigate the physical quality of the national representative profiles of Co. Waterford. To do this, the soil physical quality (SPQ) S-Index, as described by Dexter (2004a,b,c) using the S-theory (which seeks the inflection point of a soil water retention curve [SWRC]), is used. This can be determined using simple (S-Indirect) or complex (S-Direct) soil physical data streams. Both are achievable using existing data for the County Waterford profiles, but until now, the suitability of this S-Index for Irish soils has never been tested. Indirect-S provides a generic characterisation of SPQ for a particular soil horizon, using simplified and modelled information (e.g. texture and SWRC derived from pedo-transfer functions), whereas Direct-S provides more complex site-specific information (e.g. texture and SWRC measured in the laboratory), which relates to properties measured for that exact soil horizon. Results showed a significant correlation between S-Indirect (Si) and S-Direct (Sd). Therefore, the S-Index can be used in Irish soils and presents opportunities for the use of Si at the national scale. Outlier horizons contained >6% organic carbon (OC) and bulk density (Bd) values <1 g/cm3 and were not suitable for Si estimation. In addition, the S-Index did not perform well on excessively drained soils. Overall correlations of Si. with Bd and of Si. with OC% for the dataset were detected. Future work should extend this approach to the national scale dataset in the Irish Soil Information System.

Introduction

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).

Table 1

Dexter’s SPQ Index

S–valueSPQ Index
<0.020Very poor
0.020 – 0.035Poor*
0.035 – 0.050Good
>0.050Very 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).

Sterm=n(θgsθgr)[2n1)/](1/n)2
Where qgs, qgr are gravimetric saturated and gravimetric residual water contents [see Armindo and Wendroth (2016) for a critical review, including assumptions made based on interpretation of the van Genuchten (1980) equation].

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.

Figure 1
Figure 1

Conceptual diagram of low to high-complexity data sources, and corresponding Si and Sd approaches. Si is deemed as “simple” and has been determined in the present study from the middle stream here (sand–silt–clay percentage + Bd). Sd is deemed “complex” and determined from the third stream (sand–silt–clay percentage + Bd + SWCC, curve-fitting approach). Bd = bulk density; SAWCal = soil available water calculator; Sd = S-direct; Si = S-indirect; SWCC = soil water characteristic curve.

Citation: Irish Journal of Agricultural and Food Research 56, 1; 10.1515/ijafr-2017-0005

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.

Table 2

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

GroupHeight AMSL, m*Parent materialGreat Soil GroupWorld Reference BaseHSeriesDrainage class
Regosol5AlluviumAlluvialHaplic fluvisol (humic dystric siltic)8Coolfinn (presently known as Feale)Poor
Plaggen20Irish sea tillBrown earthHaplic phaeozem (anthric albic epieutric)8ArdmoreWell
24Glaciofluvial sandBrown earthHaplic regosol (humic eutric arenic)7CurraghExcessive
Grey brown podzolic30Sandstone > limestone tillLuvisolHaplic luvisol9DungarvanWell
65Sandstone > shale > limestone tillLuvisolCutanic endostagnic luvisol (chromic)6KilmeadenWell
Brown earth70Sandstone tillBrown earthHaplic cambisol (humic eutric)5BroomhillWell
80Sandstone tillBrown earthHaplic phaeozem (anthric albic epieutric)8ClashmoreWell
90Volcanic till/bedrockBrown earthHaplic cambisol (humic epidystric oxyaquic)7KillWell
120Shale tillBrown earthHaplic cambisol (humic eutric)4ClonrocheWell
Brown podzolic20Sandstone limestone gravelBrown podzolicEntic podzol2CallaghaneExcessive
160Shale till and bedrockBrown podzolicLeptic cambisol (humic dystric)2SlievecoiltiaWell
Podzol152Sandstone rockPodzolPlacic albic podzol (endoskeltic)6DrumsligWell
165Sandstone tillPodzolPlacic albic histic podzol8AhaunModerate to imperfect
27Sandstone > limestone tillSurface-water gleyHaplic stagnosol (hypereutric)6KilladanganPoor/imperfect
30Sandstone > shale>limestone tillSurface-water gleyHaplic stagnosol (hypereutric)7WaterfordPoor
Gley45Shale > volcanic-sandstone tillSurface-water gleyHaplic stagnosol (hypereutric)7ClohernaghPoor
145Sandstone tillSurface-water gleyHaplic stagnosol (humic hypereutric)6LickeyPoor

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.

Figure 2
Figure 2

S-Indirect (Si) versus S-Direct (Sd) S-term for all soil horizons. For example, an Si value of 0.035 (degradation threshold) equates to an Sd of 0.023 (below threshold). This includes all data from Table 2.

Citation: Irish Journal of Agricultural and Food Research 56, 1; 10.1515/ijafr-2017-0005

Outlier horizons

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.

Figure 3
Figure 3

OC% versus Si and Sd for all horizons, adhering to the OC% threshold of <6 and Bd threshold of >1 g/cm3. Moreover, the Si data always returns a higher S-value than the Sd equivalent. Bd = bulk density; OC = organic carbon; Sd = S-direct; Si = S-indirect.

Citation: Irish Journal of Agricultural and Food Research 56, 1; 10.1515/ijafr-2017-0005

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.

Figure 4
Figure 4

Bd versus Si taking into account all horizons with Bd >1 g/m3 and OC%<6 across all textures present within the study. This shows that Bd on its own is a good indicator of Si.

Citation: Irish Journal of Agricultural and Food Research 56, 1; 10.1515/ijafr-2017-0005

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.

Acknowledgements

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.

References

  • American Society for Testing and Materials (ASTM). 2008. “D6527. Standard Test Methods for Determination of the Soil Water Characteristic Curve for Desorption Using Hanging Column Pressure Extractor Chilled Mirror Hygrometer or Centrifuge”.

  • Armindo R.A. and Wendroth O. 2016. Physical soil structure evaluation based on hydraulic energy functions. Soil Science Society of America Journal 80: 1167–1180.

    • Crossref
    • Export Citation
  • Asgarzadeh H. Mosaddeghi M.R. and Nikbakht A.L. 2014. SAW-Cal: a user-friendly program for calculating soil available water quantities and physical quality indices. Computers and Electronic in Agriculture 109: 86–93.

    • Crossref
    • Export Citation
  • BS 1796. 1989. “Method of Test for Soils for Civil Engineering Purposes”. British Standard Institution London.

  • Creamer R.E. Brennan F. Fenton O. Healy M.G. Lalor S.T.J. Lanigan G.J. Regan J.T. Griffiths B.S. 2010. Implications of the proposed soil framework directive on agricultural systems in Atlantic North-west Europe – a review. Soil Use Management 26: 198–211.

    • Crossref
    • Export Citation
  • Daly K. and Fealy F. 2005. “Digital Soil Information System for Ireland – Scoping Study (2005-S-DS-22-M1)” Final Report.

  • de Jong van Lier O. 2014. Revisiting the S-index for soil physical quality and its use in Brazil. Revista Brasileira de Ciência do Solo 38: 1–10.

    • Crossref
    • Export Citation
  • Department of Agriculture Fisheries and Food (DAFF). 2010. “Food Harvest 2020: A Vision for Irish Agri-Food and Fisheries”.

  • Dexter A.R. 2004a. Soil physical quality: part I. Theory effects of soil texture density and organic matter and effects on root growth. Geoderma 120: 201–214.

    • Crossref
    • Export Citation
  • Dexter A.R. 2004b. Soil physical quality: part II. Friability tillage tilth and hard-setting. Geoderma 120: 215–225.

    • Crossref
    • Export Citation
  • Dexter A.R. 2004c. Soil physical quality: part III: Unsaturated hydraulic conductivity and general conclusions about S theory. Geoderma 120: 227–239.

    • Crossref
    • Export Citation
  • Dexter A.R. and Czyż E.A. 2007. Applications of S-theory in the study of soil physical degradation and its consequences. Land Degradation and Development 18: 369–381.

    • Crossref
    • Export Citation
  • Diamond J. and Sills P. 2011. “Soils of Co. Waterford. Soil Survey Bulletin No. 44. National Soil Survey of Ireland”. Teagasc Oak Park Co. Carlow page 314.

  • Dillon E.J. Hennessy T. Buckley C. Donnellan T. Hanrahan K. Moran B. and Ryan M. 2014. The sustainable intensification of the Irish dairy sector. 88th Annual Conference of the Agricultural Economics Society AgroParisTech Paris France. 9th–11th April 2014.

  • Emmet-Booth J. Forristal D. Fenton O. and Holden N.M. 2016a. A simple procedure for estimating soil porosity. Geophysical Research Abstracts 18. EGU General Assembly 2016.

  • Emmet-Booth J. Forristal D. Fenton O. Ball B. and Holden N.M. 2016b. A review of visual soil evaluation techniques for soil structure. Soil Use Management 31: 623–634.

  • European Commission (EC). 2006. “Thematic Strategy for Soil Protection COM(2006)231 Final”. European Commission Brussels.

  • Fenton O. Healy M.G. Henry T. Khalil M.I. Grant J. Baily A. and Richards K.G.. 2011. Exploring the relationship between groundwater geochemical factors and denitrification potentials on a dairy farm in southeast Ireland. Ecological Engineering 37: 1304–1313.

    • Crossref
    • Export Citation
  • Fenton O. Vero S. Ibrahim T.G. Murphy P.N.C. Sherriff S. and O’hUallachain D. 2015. Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates. Journal of Contaminated Hydrology 182: 16–24.

    • Crossref
    • Export Citation
  • Ghiberto P.J. Imhoff S. Libardi P.L. da Silva A P. Tormena C.A. and Pilatti M.A. 2015. Soil physical quality of Mollisols quantified by a global index. Scientia Agricola 72: 167–174.

    • Crossref
    • Export Citation
  • Grandy A.S. and Robertson G.P. 2007. Land-use intensity effects on soil organic carbon accumulation rates and mechanisms. Ecosystems 10: 58–73.

  • Håkansson I and Lipiec J. 2000. A review of the usefulness of relative bulk density values in studies of soil structure and compaction. Soil and Tillage Research 53: 71–85.

    • Crossref
    • Export Citation
  • Huebsch M. Horan B. Blum P. Richards K. Grant J. and Fenton O. 2013. Impact of agronomic practices of an intensive dairy farm on nitrogen concentrations in a karst aquifer in Ireland. Agriculture Ecosystems and Environment 179: 187–199.

    • Crossref
    • Export Citation
  • Humphreys J. Casey I.A. Darmody P. O’Connell K. Fenton O. and Watson C.J. 2008. Quantities of mineral N in soil and concentrations of nitrate-N in groundwater in four grassland-based systems of dairy production on a clay-loam soil in a moist temperate climate. Grass and Forage Science 63: 481–494.

    • Crossref
    • Export Citation
  • Ippisch O. Vogel H.J. and Bastian P. 2006. Validity limits for the van Genuchten-Maulem model and implications for parameter estimation and numerical simulation. Advances in Water Resources 29: 1780–1789.

    • Crossref
    • Export Citation
  • Jahangir M.M.R. Johnston P. Barrett M. Khalil M.I. Groffman P. M. Boeckx P. Fenton O. Murphy J.B. and Richards K. 2013. Denitrification and indirect N2O emissions in groundwater: Hydrologic and biogeochemical influences. Journal of Contaminant Hydrology 152: 72–80.

  • Karlen D.L. MAausbach M.J. Doran J.W. Cline R.G. Harris R.F. and Schuman G.E. 1997. Soil quality: a concept definition and framework for evaluation. Soil Science Society of America Journal 61: 4–10.

    • Crossref
    • Export Citation
  • Keller T. Arvidsson J. and Dexter A.R. 2007. Soil structures produced by tillage as affected by soil water content and the physical quality of soil. Soil and Tillage Research 92: 45–52.

    • Crossref
    • Export Citation
  • Khodaverdiloo H. Homaee M. van Genuchten M.Th. and Dashtaki S.G. 2011. Deriving and validating pedotransfer functions for some calcareous soils. Journal of Hydrology 399: 93–99.

    • Crossref
    • Export Citation
  • Kiely G. Leahy P. Lewis C. Xu X. Zhang C. He Y. Dao L. Golden N. Zi T. and Albertson J. 2014. “Strive Report 118: Interactions of Soil Hydrology Land use and Climate Change and their Impact on Soil Quality (SoilH)”.

  • Maulem Y. 1986. Hydraulic conductivity of unsaturated soils: prediction and formulas. In: “Methods of Soil Analysis Part 1: Physical and Mineralogical Methods” 2nd Edition (ed. A. Klute) American Society of Agronomy Monograph 9: pages 799–823.

  • Moncada M.P. Ball B.C. Gabriels D. Lobo D. and Cornelis W.M. 2014. Evaluation of soil physical quality index S for some tropical and temperate medium-textured soils. Soil Science Society of America Journal 79: 9–19.

  • O’Sullivan L. Creamer R.E. Fealy R. Lanigan G. Simo I. Fenton O. Carfrae J. and Schulte R.P.O. 2015. Functional land management for managing soil-based ecosystem services: a case-study of the trade-off between primary productivity and carbon storage in response to the intervention of drainage systems in Ireland. Land Use Policy 47: 42–54.

    • Crossref
    • Export Citation
  • Rattan L. and Stewart B.A. 2016. “Soil-Specific Farming: Precision Agriculture”. Taylor and Francis Group LLC CRC Press.

  • Regan J.T. Rodgers M. Healy M.G. Kirwan L. and Fenton O. 2010. Determining phosphorus and sediment release rates from five Irish tillage soils. Journal of Environmental Quality 39: 185-192.

    • Crossref
    • Export Citation
  • Regan J.T. Fenton O. and Healy M.G. 2012. A review of phosphorus and sediment release from Irish tillage soils the methods used to quantify losses and the current state of mitigation practice. Proceedings of the Royal Irish Academy. Biology and Environment 112: 157–183.

  • Reynolds W.D. Drury C.F. Tan C.S. Fox C.A. and Yang X.M. 2009. Use of indicators and pore volume-function characteristics to quantify soil physical quality. Geoderma 152: 252–263.

    • Crossref
    • Export Citation
  • Richards L.A. 1948. Porous plate apparatus for measuring moisture retention and transmission by soil. Soil Science 66: 105–110.

    • Crossref
    • Export Citation
  • Ruane E.M. Treacy M. McNamara K. and Humphreys J. 2014. Farm-gate phosphorus balances and soil phosphorus concentrations on intensive dairy farms in the south-west of Ireland. Irish Journal of Agricultural and Food Research 53: 105–119.

  • Santos G.G. Da Silva E.M.; Marchão R.L.; Da Silveira P.M.; Bruand A.; James F. and Becquer T. 2011. Analysis of physical quality of soil using the water retention curve: validity of the S-index. Comptes Rendus Geoscience 343: 295–301.

    • Crossref
    • Export Citation
  • Schaap M.G. and Leij F.J. 1998. Database-related accuracy and uncertainty of pedotransfer functions. Soil Science 163: 765–779.

    • Crossref
    • Export Citation
  • Schaap M.G. Leij F.J. and van Genuchten M.Th. 2001. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251: 163–176.

    • Crossref
    • Export Citation
  • Schulte R.P.O. Simo I. Creamer R.E. and Holden N.M. 2015. A note on the Hybrid Soil Moisture Deficit Model v2.0. Irish Journal of Agricultural and Food Research 54: 126–131.

    • Crossref
    • Export Citation
  • Sherriff S.C. Rowan J.S. Melland A.R. Jordan P. Fenton O. and Ó hUallacháin D. 2015. Identifying the controls of soil loss in agricultural catchments using ex situ turbidity-based suspended sediment monitoring. Hydrology and Earth System Science 12: 2707–2740.

    • Crossref
    • Export Citation
  • Simo I. Creamer R.E. Reidy B. Hannam J.A. Fealy R. Hamilton B. Jahns G. Massey P McDonald E. Schulte R.P.O. Sills P. and Spaargaren O. 2014. “Irish SIS Final Technical Report 10: Soil Profile Handbook” Associated datasets and digital information objects connected to this resource are available at: Secure Archive for Environmental Research Data (SAFER) managed by Environmental Protection Agency Ireland.

  • Simo I. Schulte R.P.O. Corstanje R. Hannam J.A. and Creamer R.E. 2015. Validating digital soil maps using soil taxonomic distance: a case study of Ireland. Geoderma Regional 5: 188–197.

    • Crossref
    • Export Citation
  • Soane B.D. and van Ouwerkerk C. 1998. Soil compaction: a global threat to sustainable land use. Advances in GeoEcology 31: 517–525.

  • Thu T.M. Rahardjo H. and Leong E.-C. 2007. Soil-water characteristic curve and consolidation behaviour for a compacted silt. Canadian Geotechnical Journal 44: 266–275.

    • Crossref
    • Export Citation
  • Tuohy P. Humphreys J. Holden N.A. and Fenton O. 2016. Runoff and subsurface drain response from mole and gravel mole drainage across episodic rainfall events. Agriculture Water Management 169: 129–139.

  • van Genuchten M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44: 892–898.

    • Crossref
    • Export Citation
  • Vereecken H. Weynants M. Javaux M. Pachepsky Y. Schaap M.G. and van Genuchten M.Th. 2010. Using pedotransfer functions to estimate the van Genuchten–Maulem soil hydraulic properties: a review. Vadose Zone Journal 9: 795–820.

    • Crossref
    • Export Citation
  • Vero S.V. Antille D.L. Lalor S.T.J. and Holden N.M. 2013. Field evaluation of soil moisture deficit thresholds for limits to trafficability with slurry spreading equipment on grassland. Soil Use and Management 30: 69–77.

  • Vero S.V. Ibrahim T.G. Creamer R.E. Grant J. Healy M.G. Henry T. Kramers G. Richards K.G. and Fenton O. 2014. Consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates. Journal of Contaminant Hydrology 170: 53–67.

    • Crossref
    • Export Citation
  • Wiebe K. 2003. “Linking Land Quality Agricultural Productivity and Food Security”. USDA. Economic Research Service. Agricultural Economic Report Number 823.

  • Wilson M.J. and Maliszewska-Kordybach B. 2000. NATO advanced research workshop on soil quality in relation to sustainable development of agriculture and environmental security in Central and Eastern Europe. In: “Soil Quality Sustainable Agriculture and Environmental Security in Central and Eastern Europe” Kluwer Academic Publishers Dordrecht [Netherlands].

Footnotes

*Soils with values <0.035 are considered degraded.
*It is important to include landscape position as this is important for soil drainage classification.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • American Society for Testing and Materials (ASTM). 2008. “D6527. Standard Test Methods for Determination of the Soil Water Characteristic Curve for Desorption Using Hanging Column Pressure Extractor Chilled Mirror Hygrometer or Centrifuge”.

  • Armindo R.A. and Wendroth O. 2016. Physical soil structure evaluation based on hydraulic energy functions. Soil Science Society of America Journal 80: 1167–1180.

    • Crossref
    • Export Citation
  • Asgarzadeh H. Mosaddeghi M.R. and Nikbakht A.L. 2014. SAW-Cal: a user-friendly program for calculating soil available water quantities and physical quality indices. Computers and Electronic in Agriculture 109: 86–93.

    • Crossref
    • Export Citation
  • BS 1796. 1989. “Method of Test for Soils for Civil Engineering Purposes”. British Standard Institution London.

  • Creamer R.E. Brennan F. Fenton O. Healy M.G. Lalor S.T.J. Lanigan G.J. Regan J.T. Griffiths B.S. 2010. Implications of the proposed soil framework directive on agricultural systems in Atlantic North-west Europe – a review. Soil Use Management 26: 198–211.

    • Crossref
    • Export Citation
  • Daly K. and Fealy F. 2005. “Digital Soil Information System for Ireland – Scoping Study (2005-S-DS-22-M1)” Final Report.

  • de Jong van Lier O. 2014. Revisiting the S-index for soil physical quality and its use in Brazil. Revista Brasileira de Ciência do Solo 38: 1–10.

    • Crossref
    • Export Citation
  • Department of Agriculture Fisheries and Food (DAFF). 2010. “Food Harvest 2020: A Vision for Irish Agri-Food and Fisheries”.

  • Dexter A.R. 2004a. Soil physical quality: part I. Theory effects of soil texture density and organic matter and effects on root growth. Geoderma 120: 201–214.

    • Crossref
    • Export Citation
  • Dexter A.R. 2004b. Soil physical quality: part II. Friability tillage tilth and hard-setting. Geoderma 120: 215–225.

    • Crossref
    • Export Citation
  • Dexter A.R. 2004c. Soil physical quality: part III: Unsaturated hydraulic conductivity and general conclusions about S theory. Geoderma 120: 227–239.

    • Crossref
    • Export Citation
  • Dexter A.R. and Czyż E.A. 2007. Applications of S-theory in the study of soil physical degradation and its consequences. Land Degradation and Development 18: 369–381.

    • Crossref
    • Export Citation
  • Diamond J. and Sills P. 2011. “Soils of Co. Waterford. Soil Survey Bulletin No. 44. National Soil Survey of Ireland”. Teagasc Oak Park Co. Carlow page 314.

  • Dillon E.J. Hennessy T. Buckley C. Donnellan T. Hanrahan K. Moran B. and Ryan M. 2014. The sustainable intensification of the Irish dairy sector. 88th Annual Conference of the Agricultural Economics Society AgroParisTech Paris France. 9th–11th April 2014.

  • Emmet-Booth J. Forristal D. Fenton O. and Holden N.M. 2016a. A simple procedure for estimating soil porosity. Geophysical Research Abstracts 18. EGU General Assembly 2016.

  • Emmet-Booth J. Forristal D. Fenton O. Ball B. and Holden N.M. 2016b. A review of visual soil evaluation techniques for soil structure. Soil Use Management 31: 623–634.

  • European Commission (EC). 2006. “Thematic Strategy for Soil Protection COM(2006)231 Final”. European Commission Brussels.

  • Fenton O. Healy M.G. Henry T. Khalil M.I. Grant J. Baily A. and Richards K.G.. 2011. Exploring the relationship between groundwater geochemical factors and denitrification potentials on a dairy farm in southeast Ireland. Ecological Engineering 37: 1304–1313.

    • Crossref
    • Export Citation
  • Fenton O. Vero S. Ibrahim T.G. Murphy P.N.C. Sherriff S. and O’hUallachain D. 2015. Consequences of using different soil texture determination methodologies for soil physical quality and unsaturated zone time lag estimates. Journal of Contaminated Hydrology 182: 16–24.

    • Crossref
    • Export Citation
  • Ghiberto P.J. Imhoff S. Libardi P.L. da Silva A P. Tormena C.A. and Pilatti M.A. 2015. Soil physical quality of Mollisols quantified by a global index. Scientia Agricola 72: 167–174.

    • Crossref
    • Export Citation
  • Grandy A.S. and Robertson G.P. 2007. Land-use intensity effects on soil organic carbon accumulation rates and mechanisms. Ecosystems 10: 58–73.

  • Håkansson I and Lipiec J. 2000. A review of the usefulness of relative bulk density values in studies of soil structure and compaction. Soil and Tillage Research 53: 71–85.

    • Crossref
    • Export Citation
  • Huebsch M. Horan B. Blum P. Richards K. Grant J. and Fenton O. 2013. Impact of agronomic practices of an intensive dairy farm on nitrogen concentrations in a karst aquifer in Ireland. Agriculture Ecosystems and Environment 179: 187–199.

    • Crossref
    • Export Citation
  • Humphreys J. Casey I.A. Darmody P. O’Connell K. Fenton O. and Watson C.J. 2008. Quantities of mineral N in soil and concentrations of nitrate-N in groundwater in four grassland-based systems of dairy production on a clay-loam soil in a moist temperate climate. Grass and Forage Science 63: 481–494.

    • Crossref
    • Export Citation
  • Ippisch O. Vogel H.J. and Bastian P. 2006. Validity limits for the van Genuchten-Maulem model and implications for parameter estimation and numerical simulation. Advances in Water Resources 29: 1780–1789.

    • Crossref
    • Export Citation
  • Jahangir M.M.R. Johnston P. Barrett M. Khalil M.I. Groffman P. M. Boeckx P. Fenton O. Murphy J.B. and Richards K. 2013. Denitrification and indirect N2O emissions in groundwater: Hydrologic and biogeochemical influences. Journal of Contaminant Hydrology 152: 72–80.

  • Karlen D.L. MAausbach M.J. Doran J.W. Cline R.G. Harris R.F. and Schuman G.E. 1997. Soil quality: a concept definition and framework for evaluation. Soil Science Society of America Journal 61: 4–10.

    • Crossref
    • Export Citation
  • Keller T. Arvidsson J. and Dexter A.R. 2007. Soil structures produced by tillage as affected by soil water content and the physical quality of soil. Soil and Tillage Research 92: 45–52.

    • Crossref
    • Export Citation
  • Khodaverdiloo H. Homaee M. van Genuchten M.Th. and Dashtaki S.G. 2011. Deriving and validating pedotransfer functions for some calcareous soils. Journal of Hydrology 399: 93–99.

    • Crossref
    • Export Citation
  • Kiely G. Leahy P. Lewis C. Xu X. Zhang C. He Y. Dao L. Golden N. Zi T. and Albertson J. 2014. “Strive Report 118: Interactions of Soil Hydrology Land use and Climate Change and their Impact on Soil Quality (SoilH)”.

  • Maulem Y. 1986. Hydraulic conductivity of unsaturated soils: prediction and formulas. In: “Methods of Soil Analysis Part 1: Physical and Mineralogical Methods” 2nd Edition (ed. A. Klute) American Society of Agronomy Monograph 9: pages 799–823.

  • Moncada M.P. Ball B.C. Gabriels D. Lobo D. and Cornelis W.M. 2014. Evaluation of soil physical quality index S for some tropical and temperate medium-textured soils. Soil Science Society of America Journal 79: 9–19.

  • O’Sullivan L. Creamer R.E. Fealy R. Lanigan G. Simo I. Fenton O. Carfrae J. and Schulte R.P.O. 2015. Functional land management for managing soil-based ecosystem services: a case-study of the trade-off between primary productivity and carbon storage in response to the intervention of drainage systems in Ireland. Land Use Policy 47: 42–54.

    • Crossref
    • Export Citation
  • Rattan L. and Stewart B.A. 2016. “Soil-Specific Farming: Precision Agriculture”. Taylor and Francis Group LLC CRC Press.

  • Regan J.T. Rodgers M. Healy M.G. Kirwan L. and Fenton O. 2010. Determining phosphorus and sediment release rates from five Irish tillage soils. Journal of Environmental Quality 39: 185-192.

    • Crossref
    • Export Citation
  • Regan J.T. Fenton O. and Healy M.G. 2012. A review of phosphorus and sediment release from Irish tillage soils the methods used to quantify losses and the current state of mitigation practice. Proceedings of the Royal Irish Academy. Biology and Environment 112: 157–183.

  • Reynolds W.D. Drury C.F. Tan C.S. Fox C.A. and Yang X.M. 2009. Use of indicators and pore volume-function characteristics to quantify soil physical quality. Geoderma 152: 252–263.

    • Crossref
    • Export Citation
  • Richards L.A. 1948. Porous plate apparatus for measuring moisture retention and transmission by soil. Soil Science 66: 105–110.

    • Crossref
    • Export Citation
  • Ruane E.M. Treacy M. McNamara K. and Humphreys J. 2014. Farm-gate phosphorus balances and soil phosphorus concentrations on intensive dairy farms in the south-west of Ireland. Irish Journal of Agricultural and Food Research 53: 105–119.

  • Santos G.G. Da Silva E.M.; Marchão R.L.; Da Silveira P.M.; Bruand A.; James F. and Becquer T. 2011. Analysis of physical quality of soil using the water retention curve: validity of the S-index. Comptes Rendus Geoscience 343: 295–301.

    • Crossref
    • Export Citation
  • Schaap M.G. and Leij F.J. 1998. Database-related accuracy and uncertainty of pedotransfer functions. Soil Science 163: 765–779.

    • Crossref
    • Export Citation
  • Schaap M.G. Leij F.J. and van Genuchten M.Th. 2001. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251: 163–176.

    • Crossref
    • Export Citation
  • Schulte R.P.O. Simo I. Creamer R.E. and Holden N.M. 2015. A note on the Hybrid Soil Moisture Deficit Model v2.0. Irish Journal of Agricultural and Food Research 54: 126–131.

    • Crossref
    • Export Citation
  • Sherriff S.C. Rowan J.S. Melland A.R. Jordan P. Fenton O. and Ó hUallacháin D. 2015. Identifying the controls of soil loss in agricultural catchments using ex situ turbidity-based suspended sediment monitoring. Hydrology and Earth System Science 12: 2707–2740.

    • Crossref
    • Export Citation
  • Simo I. Creamer R.E. Reidy B. Hannam J.A. Fealy R. Hamilton B. Jahns G. Massey P McDonald E. Schulte R.P.O. Sills P. and Spaargaren O. 2014. “Irish SIS Final Technical Report 10: Soil Profile Handbook” Associated datasets and digital information objects connected to this resource are available at: Secure Archive for Environmental Research Data (SAFER) managed by Environmental Protection Agency Ireland.

  • Simo I. Schulte R.P.O. Corstanje R. Hannam J.A. and Creamer R.E. 2015. Validating digital soil maps using soil taxonomic distance: a case study of Ireland. Geoderma Regional 5: 188–197.

    • Crossref
    • Export Citation
  • Soane B.D. and van Ouwerkerk C. 1998. Soil compaction: a global threat to sustainable land use. Advances in GeoEcology 31: 517–525.

  • Thu T.M. Rahardjo H. and Leong E.-C. 2007. Soil-water characteristic curve and consolidation behaviour for a compacted silt. Canadian Geotechnical Journal 44: 266–275.

    • Crossref
    • Export Citation
  • Tuohy P. Humphreys J. Holden N.A. and Fenton O. 2016. Runoff and subsurface drain response from mole and gravel mole drainage across episodic rainfall events. Agriculture Water Management 169: 129–139.

  • van Genuchten M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44: 892–898.

    • Crossref
    • Export Citation
  • Vereecken H. Weynants M. Javaux M. Pachepsky Y. Schaap M.G. and van Genuchten M.Th. 2010. Using pedotransfer functions to estimate the van Genuchten–Maulem soil hydraulic properties: a review. Vadose Zone Journal 9: 795–820.

    • Crossref
    • Export Citation
  • Vero S.V. Antille D.L. Lalor S.T.J. and Holden N.M. 2013. Field evaluation of soil moisture deficit thresholds for limits to trafficability with slurry spreading equipment on grassland. Soil Use and Management 30: 69–77.

  • Vero S.V. Ibrahim T.G. Creamer R.E. Grant J. Healy M.G. Henry T. Kramers G. Richards K.G. and Fenton O. 2014. Consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates. Journal of Contaminant Hydrology 170: 53–67.

    • Crossref
    • Export Citation
  • Wiebe K. 2003. “Linking Land Quality Agricultural Productivity and Food Security”. USDA. Economic Research Service. Agricultural Economic Report Number 823.

  • Wilson M.J. and Maliszewska-Kordybach B. 2000. NATO advanced research workshop on soil quality in relation to sustainable development of agriculture and environmental security in Central and Eastern Europe. In: “Soil Quality Sustainable Agriculture and Environmental Security in Central and Eastern Europe” Kluwer Academic Publishers Dordrecht [Netherlands].

Search
Journal information
Impact Factor
IMPACT FACTOR 2018: 0.645
5-year IMPACT FACTOR: 1.101

CiteScore 2018: 0.82

SCImago Journal Rank (SJR) 2018: 0.258
Source Normalized Impact per Paper (SNIP) 2018: 0.447

Figures
  • View in gallery

    Conceptual diagram of low to high-complexity data sources, and corresponding Si and Sd approaches. Si is deemed as “simple” and has been determined in the present study from the middle stream here (sand–silt–clay percentage + Bd). Sd is deemed “complex” and determined from the third stream (sand–silt–clay percentage + Bd + SWCC, curve-fitting approach). Bd = bulk density; SAWCal = soil available water calculator; Sd = S-direct; Si = S-indirect; SWCC = soil water characteristic curve.

  • View in gallery

    S-Indirect (Si) versus S-Direct (Sd) S-term for all soil horizons. For example, an Si value of 0.035 (degradation threshold) equates to an Sd of 0.023 (below threshold). This includes all data from Table 2.

  • View in gallery

    OC% versus Si and Sd for all horizons, adhering to the OC% threshold of <6 and Bd threshold of >1 g/cm3. Moreover, the Si data always returns a higher S-value than the Sd equivalent. Bd = bulk density; OC = organic carbon; Sd = S-direct; Si = S-indirect.

  • View in gallery

    Bd versus Si taking into account all horizons with Bd >1 g/m3 and OC%<6 across all textures present within the study. This shows that Bd on its own is a good indicator of Si.

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 840 511 40
PDF Downloads 188 119 12