soil structure and soil organic matter

Soil Structure and Soil Organic Matter: II. A Normalized Stability Indexand the Effect of MineralogyJ. Six,* E. T. Elliott, and K. Paustian

ABSTRACTSoil aggregate distribution and stability measurements have been

proposed as soil quality indicators. However, pretreatment of soil,antecedent water content and differences in sand size distributionamong soils can confound interpretation of these measurements. Wepropose a normalized stability index (NSI) which (i) compares aggre-gate distribution after slaking and rewetting to characterize wholesoil stability and eliminate confounding effects of pretreatment andantecedent water content, (ii) corrects for the confounding effect ofdifferences in sand size distribution among soils, aggregate size classesand pretreatments, and (iii) normalizes the level of disruption imposedby slaking by using a maximum level of disruption. The NSI wastested on three soils dominated by a 2:1 clay mineralogy and one soilcharacterized by a mixed (2:1 and 1:1) clay mineralogy. Each site hadnative vegetation (NV), no-tillage (NT), and conventional tillage (CT)treatments. In soils dominated by 2:1 clays, NSI decreased with in-creasing cultivation intensity (NV > NT > CT). However, NSI washigher in the soil with mixed clays compared to the other soils andwas not different along the cultivation gradient. These observationsare hypothesized to be related to the presence of Fe- and Al-oxidesand kaolinite. In conclusion, NSI appears to be a good indicator forsoil structural quality since it is sensitive to changes in agriculturalpractices and it minimizes confounding effects. A decrease of SOMlevels results in a smaller decrease of soil stability in soils dominated byoxides and 1:1 minerals compared to soils dominated by 2:1 minerals.

SOIL STRUCTURE is an important soil property to beevaluated because it mediates many biological and

physical processes in soils. For example, soil structuredetermines porosity and infiltration, hence water avail-ability to plants and soil erosion susceptibility. Sincesoil structure also influences losses of agrochemicals,sequestration of C, and N gas losses, it is importantto maintain soil structure to reduce the environmentalimpact of agricultural practices.

Aggregate stability is often used as a measurementof soil structure. Aggregate stability has been shown tobe a good indicator for erodibility (Chan and Mead,1988; Coote et al., 1988). However, aggregate stabilityis often measured on a specific aggregate size class whichis not a measurement of whole soil structure. The meanweight diameter (MWD), on the other hand is an indexthat characterizes the structure of the whole soil byintegrating the aggregate size class distribution into onenumber. The MWD has often been used to indicatethe effect of different management practices on soilstructure. For example, 2 yr of moldboard plowing andchisel plowing significantly reduced the MWD of water-stable aggregates in comparison to no-tillage (Angerset al., 1993b). Haynes and Francis (1993) reported

Natural Resource Ecology Lab., Colorado State Univ., Fort CollinsCO 80523. Received 5 Apr. 1999. *Corresponding author (johan®nrel.colostate.edu).

Published in Soil Sci. Soc. Am. J. 64:1042-1049 (2000).

an increase in MWD after 32 mo in the order perennialryegrass > annual ryegrass > perennial white clover =barley.

The use of MWD, however, is questionable if theaggregate distribution is skewed, that is, relatively non-symmetrical (Stirk, 1958). In addition, there are oftencomplications when different sites and/or managementpractices are compared for soil structural differences bymeans of the MWD. Three confounding factors havebeen identified: pretreatment of soil samples (Beare andBruce, 1993; Gollany et al., 1991), antecedent watercontent (Angers et al., 1993a; Perfect et al., 1990), andsand content (Angers et al., 1993b; Caron et al., 1992;Elliott, 1986; Gollany et al., 1991; Perfect et al., 1990).

Beare and Bruce (1993) compared four pretreatmenteffects (air-dried, capillary wetted; air-dried, tensionwetted; air-dried, slaked; field moist, capillary wetted)on water stable aggregation. They found that the fieldmoist, capillary wetted treatment had the least variabil-ity. However, this pretreatment causes aggregation tobe a function of antecedent water content (Perfect etal., 1990; Gollany et al., 1991; Angers et al., 1993a). Anegative correlation was found between the antecedentwater content and aggregate stability when soils wereprewetted to field capacity, but antecedent water con-tent and aggregate stability were positively correlatedwhen soils were analyzed at field moisture (Gollany etal., 1991). Caron et al. (1992) also observed a decreasein aggregate stability with increased water content whenmeasured on field moist samples rewetted to field capac-ity. The positive correlation between aggregation andantecedent water content is due to a decreased pressurebuild-up and aggregate disruption (slaking) upon fastwetting when the soil is at high antecedent moisturecontent. In contrast, when the soil is capillary wettedto field capacity from field moist conditions, aggregationis related to the length of antecedent drying time, be-cause inorganic cementing agents precipitate at pointsof particle contact under dry conditions (Kemper andRosenau, 1984). Consequently, aggregate stability mea-surements on field moist or field moist, capillary-wettedsamples show a seasonal variability in aggregation whichis partly induced by pure physical processes related toseasonal differences in water content. Since this physicalinduced variability is not related to the soil quality it isnot desirable in assessments of soil quality.

The confounding effect of sand size distribution is aresult of preferential accumulation of sand within cer-tain size fractions during sieving. Sand of the same sizeas the aggregate is most likely not within the aggregatesbut is retained on the sieve together with the aggregates

Abbreviations: CT, conventional tillage; NSI, normalized stability in-dex; NT, no-tillage; NV, native vegetation; SOM, soil organic matter.

1042

SIX ET AL.: SOIL STRUCTURE AND SOIL ORGANIC MATTER, STABILITY & MINERALOGY 1043

and therefore weighed as an aggregate. As a result,aggregation is expected to be higher (if not corrected)for a sample with a high proportion of coarse sandcompared to a sample with a high proportion of finesand. Consequently, differences in sand size distributionconfound the measurement of aggregate distributionand structural stability of the soil.

In 2:1 clay-dominated soils, SOM is a major bindingagent because polyvalent metal-organic matter com-plexes form bridges between the negatively charged 2:1clay platelets. However, SOM is not the only majorbinding agent in oxide and 1:1 clay mineral dominatedsoils. Part of the soil stability in oxide and 1:1 claydominated soils is induced by the binding capacity ofoxides and 1:1 minerals (Tisdall and Oades, 1982; Oadesand Waters, 1991). Consequently, the mineralogicalcharacteristics of a soil can influence the potential soilstability and the relationship between SOM content andsoil stability.

Since each pretreatment has its own specific advan-tages and disadvantages, Beare and Bruce (1993) sug-gested comparing the responses of soils to different pre-treatments to study the effects of environmental factorson soil structure. The objectives of this study were: (i)to develop a single index for soil stability based on twodifferent pretreatments, which takes into considerationthe confounding effects of sand size distribution, ante-cedent water content, and sample pretreatment, (ii) totest the index for applicability of detecting effects ofagricultural practices on soil structure, and (iii) to studythe effect of mineralogy on soil stability.

MATERIALS AND METHODS

SamplingSoils from four different long-term agricultural field experi-

ments were sampled to two depths (0-5 cm and 5-20 cm), inNovember 1995. The four sites are located in Sidney, NE (41°14' N , 103° 00' W), Wooster, OH (40° 48' N, 82° 00' W), W.K. Kellogg Biological Station, MI (42° 24' N, 85° 24' W; KBS),and Lexington, KY (38° 07' N, 84° 29' W). All four siteshad native vegetation (NV), no-tillage (NT), and conventionaltillage (CT) treatments. The NV was a never cultivated grass-land at Sidney and KBS. The experiment was installed in thenative grassland at Sidney, whereas the plots for the NT andCT at KBS were long-term cultivated before implementationof the treatments. At Lexington, the NV was a bluegrass (Poapratensis L.) pasture which was established in cultivated soil50 yr prior to the start of the experiment. The NV was forestand the NT and CT plots were under long-term cultivation

and then 6 yr under grass meadow prior to the start of theexperiment at Wooster. Further site characteristics are givenin Table 1.

Aggregate SeparationField moist soil was passed through an 8-mm sieve by gently

breaking apart the soil and air dried. Two pretreatments wereapplied before wet sieving: air-dried soil was rapidly immersedin water (slaked) and air-dried soil was capillary rewetted tofield capacity plus 5% (kg/kg) and equilibrated at 4°C over-night before immersion in water (rewetted treatment). Slakingdisrupts the aggregates due to a build-up of air pressure uponrapidly wetting (Kemper and Rosenau, 1984). In contrast,when the soil is capillary rewetted before sieving, the air hastime to escape and consequently there is not a build-up of airpressure and more aggregates stay intact. In addition, thesurface tension of water increases the cohesion between soilparticles (Kemper and Rosenau, 1984) resulting in a higheraggregate yield upon rewetting. The soils were wet-sievedthrough a series of three sieves (2000, 250, and 53 (xm). Themethod used for aggregate size separation was adapted fromElliott (1986). Briefly, a 100-g subsample (air dried or capillarywetted) was submerged for 5 min on top of the 2000 u.msieve prior to sieving. Aggregates were separated by manuallymoving the sieve 3 cm up and down with 50 repetitions duringa period of 2 min. The >2000 jj,m aggregates were collectedand sieving was repeated for the fraction <2000 jJim with thenext smaller sized sieve. This procedure was repeated for everysieve size. All aggregate fractions were oven dried (50°C) andweighed. Sand size distribution (>2000 (Jim; 250-2000 (Jim;53-250 (Jim) of rewetted and slaked aggregates was determinedby sieving after dispersing the aggregates with sodium hexa-metaphosphate (5 g L"1).

Normalized Stability Index

Rationale for Normalized Stability IndexThe normalized stability index (NSI) is an index improved

from the conceptually defined aggregation index (AI) devel-oped by van Steenbergen et al. (1991). The NSI measures thestability of the soil by comparing the aggregate distributionbefore and after disruption. We chose the rewetted and slakedaggregate distribution as the initial distribution and the distri-bution after disruption, respectively. The rewetted aggregatedistribution is considered the initial aggregate distribution be-cause maximum aggregate yield is achieved after rewettingthe soil to a moisture content of field capacity plus 5% (g/kg)(Hofman and De Leenheer, 1975). The slaked aggregate distri-bution is taken as the aggregation level after disruption.

By air drying the soil, the effect of antecedent water content,as observed by Perfect et al. (1990), Gollany et al. (1991),Angers et al. (1993a), and Caron et al. (1992), is minimized.

Table 1. General characteristics of the agricultural experiment field sites.

Soil classificationSoil seriesTextureMAT, °CtMAP, mm§Crop rotationPrior vegetation, NVYear of establishment

Sidney, NE

Pachic HaplustollDurocLoam8.5440Winter wheat fallowShortgrass prairie1969

Wooster, OH

Typic FragiudalfWoosterSilt loam9.1905Continuous cornGrass meadow1962

KBS, MIt

Typic HapludalfKalamazoo and OshtemoSandy loam9.2920Corn-winter wheat-soybeanGrassland1986

Lexington, KY

Typic PaleudalfMaurySilty clay loam13.11127Continuous corn (84 kg N)Bluegrass pasture1971

t KBS, Kellogg Biological Station.I MAT, mean annual air temperature.§ MAP, mean annual precipitation.

1044 SOIL SCI. SOC. AM. J., VOL. 64, MAY-JUNE 2000

Rewetted

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1 1

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SitesFig. 1. Mean weight diameter (MWD) of rewetted soils from four

long-term agricultural experiment field sites with a native vegeta-tion (NV), no-tillage (NT), and conventional tillage (CT) treat-ment. Values followed by a different letter within a site are signifi-cantly different. Values followed by * in 5- to 20-cm depth aresignificantly different from corresponding values in 0- to 5-cmdepth. Statistical significance determined at P < 0.05 according toTukey's HSD mean separation test.

However, precipitation of inorganic binding agents is favoredupon drying and increases with time of storage (Kemper andRosenau, 1984). It has been suggested that an increase insurface acidity upon drying also increases binding betweenorganics and particles (Caron et al., 1992). By subtractingthe slaked distribution from the rewetted distribution, theincreased aggregation due to precipitation of inorganic bindingagents and increased adsorption of organics onto particles isnullified. It has been shown that 24 h soaking of soil in distilledwater does not influence the effect of air drying on aggregation(Caron et al., 1992; Kemper and Koch, 1966). Therefore, thereis no interaction between the air drying and rewetting of thesoil and subtracting the slaked from the rewetted aggregatedistribution does indeed nullify the effect of air drying.

Since sand of the same size as the aggregate size class (=aggregate-sized sand) is unlikely to be a part of an aggregate,

whereas sand with a smaller size than the aggregate cut-offsize is certainly part of the aggregate, it is necessary to correctfor the aggregate-sized sand content. This is in contrast to theAl calculated by van Steenbergen et al. (1991) which correctsfor the whole sand content of the aggregates. The NSI alsonormalizes the instability of the aggregates for the maximumdisruption level possible because a sandy soil texture has aninherently lower maximum disruption level than a clay soil.

Calculation of Normalized Stability IndexThe formula for calculation of disruption level of a size

class upon slaking (DLSi) is

- SB) - (^ -I I (p _ c s \ _ / ' p _ c s \

DLSi = -^^——^——^——^2X [1]

[1]where DLSi = disruption level for each size class I; Pio =proportion of total sample weight in size class I before disrup-tion (i.e., rewetted); Pj = proportion of total sample weightin size class I after disruption (i.e., slaked); Sio = proportionof sand with size I in aggregates of size I (= aggregate-sizedsand) before disruption; S\ = proportion of sand with size Iin aggregates of size I after disruption. All proportions areexpressed on a soil weight basis (g fraction g"1 soil). The sizeclasses used in this study were I = 1 = < 53 ixm, 1 = 2 =53-250 (Jim, I = 3 = 250-2000 u,m, I = 4 = > 2000 u.m. Thefactor before the multiplication sign in Eq. [1] calculates thedisruption level caused by slaking and corrects the proportionsof aggregate size classes for aggregate-sized sand content. Thisfactor also ensures that only weight losses are used in thecalculation of the index, that is, this factor is 0 if there is aweight gain. The factor after the multiplication sign normalizesthe weight loss to the maximum weight loss for that size class.This normalization is done because a loss of 5% from anaggregate size class with an initial proportion of 10% indicatesa lower stability than a 5% loss from a fraction with an initialproportion of 50%.

In contrast to the disruptive value calculated by vanSteenbergen et al. (1991), the DLS; is based on weight lossesand not on weight gains. We used weight losses because thisenabled us to correct for aggregate-sized sand content. Withweight gains, the correction for sand is only possible if thewhole sand content of the aggregates is used. This is a resultof the change in aggregate distribution upon disruption; weightgains occur in the smallest size classes. However, the sand isassociated with the larger size classes. If the calculations arebased on weight gains then the larger size classes are notused in the calculations and consequently neither is the sanddistribution. Only when the whole sand content of the aggre-gates is used, would the sand correction come into the calcula-tion by difference as a cumulative sand content of the largersize classes where a weight loss occurs. However, when weightlosses are used in the calculations, the weight losses occurin the larger size classes with which sand is associated; theaggregate-sized sand content can be used for the correction.

The whole soil disruption level (DL) is then calculated as

DL = [(n + 1) - I] X DLSi [2]

where n — number of aggregate size classes. The disruptionlevel is a weighted sum of the disruption for each size class Ibecause a weight loss in a smaller size class is more indicativeof instability of the soil than a weight loss in a larger size class.


The weighting factors for the disruption in different aggregatesize classes are arbitrary by ranking the aggregate size classesfrom 1 to 4. The weighting factors are not based on arithmeticor geometric means because the use of mean indexes is ques-tionable if the aggregate distribution is skewed (Stirk, 1958),which is often the case.

The maximum disruption [DLSj (max)] is calculated withthe following formula:

DLSi(max) =

X [1]- Sffl]

[3]

Pp = primary sand particle content with the same size as theaggregates size class after complete disruption of the wholesoil. Whole soil DL (max) is calculated with Eq. [2], except

i is replaced by DLSj (max).The normalized stability index (NSI) is then computed as

NSI = 1 - [DL/DL (max)] [4]The DL is divided by the DL (max) to normalize the DL forthe maximum disruption possible based on the primary sandparticle-size distribution.

We calculated the mean weight diameter (MWD) for allour soils to compare and indicate the differences between NSIand MWD in interpretation of cultivation effects on soil sta-bility.

Mineralogical AnalysesA 50-g subsample was taken from the 8-mm-sieved soil

from each replicate of the 0- to 5-cm CT samples and sievedthrough a 2-mm sieve. The 0- to 5-cm samples of CT werechosen because they represent the 0- to 20-cm soil layer dueto mixing by plowing. The 2-mm-sieved soil was treated with30% H2O2 at 60 to 70°C until there was no further reaction.For x-ray diffraction analyzes, the samples were rinsed withdeionized water and were shaken for 18 h to disperse the soil.The <20 [Jim fraction was isolated by sieving and suspendedin 250 mL deionized water. Oriented samples for x-ray diffrac-tion were made by the millipore filter transfer method (Mooreand Reynolds, 1997). A 10-mL suspension was deposited byvacuum filtration on a 0.20-|j,m Gelman GA Metricel filter,47-mm diam. The filter was then inverted and laid down ona glass slide. The sample filter-glass slide was partially driedat 50°C and the filter stripped off. X-ray analyzes were doneon air-dried and ethylene glycol treated samples with a SCIN-TAG XRD (CuKa radiation). Vermiculite was identified bya collapse of the 14 A spacing upon heating (1 h, 180°C).Samples were scanned from 5 to 45° 26.

Noncrystalline components were determined with the ci-trate-ascorbate (CA) method described by Reyes and Torrent(1998). Most studies use the acid ammonium oxalate (AOD)method for determination of the noncrystalline components.We chose, however, this new method because of: (i) the similaramounts of Fe extracted by AOD and CA but higher selectiv-ity of CA (Reyes and Torrent, 1998) (ii) the simplicity of themethod and (iii) the use of nontoxic chemicals. The procedureused was: 250 mg H202 treated whole soil was weighed out incentrifuge tubes and 50 mL 0.2 M sodium citrate-0.05 Msodium ascorbate solution (pH = 6) was added. Samples wereshaken for 16 h and centrifuged. The supernatant was analyzedfor Fe, Al, and Si by atomic absorption spectrophotometry.The residue of the CA extraction was used for the determina-tion of "free" sesquioxides with dithionite (Blakemore et al.

4 -i

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Slaked0-5 cm

4 -i

SID WO KBS LX

5-20 cm

SID WO KBS

SitesFig. 2. Mean weight diameter (MWD) of slaked soils from four long-

term agricultural experiment field sites with a native vegetation(NY), no-tillage (NT), and conventional tillage (CT) treatment.Values followed by a different letter within a site are significantlydifferent. Values followed by * in 5- to 20-cm depth are significantlydifferent from corresponding values in 0- to 5-cm depth. Statisticalsignificance determined at P < 0.05 according to Tukey's HSDmean separation test.

(1987). One gram sodium dithionite and 50 mL of 0.75 Msodium citrate were added to the residue and shaken for 16 h.The suspension was centrifuged and Fe, Al, and Si concentra-tions in the supernatant were determined by atomic absorptionspectrophotometry.

Statistical AnalysesData were analyzed as a complete randomized block design

using the SAS statistical package for analysis of variance (AN-OVA-GLM, SAS Institute, 1990). Within depth, tillage treat-ment was the main factor in the model, with replicate assecondary factor. Separation of means was tested using Tu-key's honestly significant difference with a 0.05 significancelevel. Since there was no replication for the NV treatment atWooster, KBS and Lexington the NV data for these sites wasnot included in the statistical analysis.


Normalized Stability Index

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SitesFig. 3. Normalized stability index (NSI) of soils from four long-term

agricultural experiment field sites with a native vegetation (NV),no-tillage (NT), and conventional tillage (CT) treatment. Valuesfollowed by a different letter within a site are significantly different.Values followed by * in 5- to 20-cm depth are significantly differentfrom corresponding values in 0- to 5-cm depth. Statistical signifi-cance determined at P < 0.05 according to Tukey's HSD meanseparation test.

RESULTS AND DISCUSSIONMean Weight Diameter

As has been observed previously at Sidney by Elliott(1986), aggregation (MWD) of rewetted soil did notshow any clear trends across the cultivation intensitygradient (Fig. 1). In addition, there were no clear differ-ences between the two depths. In both depths, the valueswere in the same order of magnitude and no consistenteffect of cultivation was found. A large reduction inMWD was observed upon slaking (Fig. 2). In contrastto rewetted MWD, the slaked MWD was strongly influ-enced by cultivation. In both depths, the MWD (slaked)generally differed in the order NV > NT > CT. Thesimilarity in aggregation in NT and CT at KBS is proba-bly a result of the young age of the experiment. The

experiment was only established for 9 years at the timeof sampling.

Normalized Stability IndexThe confounding effect of differences in sand size

distribution is illustrated by the differences betweenMWD (slaked) and NSI at Sidney (Fig. 2 and 3). Dueto a layer of sand below the 5-cm depth at Sidney, therewere significant differences in sand distribution betweendepths and or treatments (Table 2). The sand contentwas similar for all treatments at the 5 to 20 cm depth,but there were significant differences between the treat-ments in the 0 to 5 cm; due to mixing by plowing, CT hadthe same sand content in both depths, which resulted ina higher sand content in the 0 to 5 cm for CT comparedto NT and NV. Therefore, more aggregate sized sandwas associated with small macroaggregates (250-2000 (Jim) and microaggregates (53-250 jjim) in the 0-to 5-cm depth of CT than the NV and NT treatments(Table 2). At Sidney, slaked MWD was significantlylower in NT and CT compared to NV, but there wasno significant difference between NT and CT in bothdepths (Fig. 2). However, NSI was significantly lowerin CT compared to NT, at Sidney (Fig. 3). The slakedMWD was high in CT because aggregate-sized sand,which was between 10 and 25% of the aggregate weightin CT (Table 2), was weighted as aggregates and in-creased the slaked MWD value for CT. Slaked MWDwas therefore similar for NT and CT. However, whenwe corrected for the aggregate sized sand content withNSI then a significant difference between NT and CTwas found. This observation confirms that to comparetreatments and/or sites with differences in sand sizedistribution, a correction for aggregate-sized sand con-tent increases sensitivity for calculations of soil struc-tural stability.

In addition, the difference in sand distribution be-tween slaked and rewetted pretreatments was clearlyindicated in the data presented in Table 2. Therefore,the correction for aggregate-sized sand content was de-sirable when comparing rewetted and slaked aggregatedistribution. Since an increase in aggregate-sized sandcontent upon slaking was observed (Table 2), the calcu-lated disruption level upon slaking would be less whenaggregate proportions in rewetted and slaked soils werenot corrected for aggregate-sized sand content thanwhen they were corrected for aggregate-sized sandcontent.

The NSI was similar for both depths (0-5 and5-20 cm) in the CT treatments at all sites (Fig. 3). Thiswas a consequence of mixing during plowing which re-sulted in similar soil properties over the whole plowdepth. Both rewetted MWD and slaked MWD, on theother hand, showed differences in stability betweendepths in CT at some of the sites.

The lower stability value (NSI) in the surface layer(0-5 cm) compared to the subsurface layer (5-20 cm)in NV and NT (Fig. 3) indicates that surface relatedprocesses have an influence on aggregate stability (Paus-tian et al., 1997). Wet-dry cycles, freeze-thaw cycles,


and raindrop impact lead to aggregate destabilizationand disruption (Kay, 1990). The soil surface is the mostsusceptible to wetting and drying, raindrop effect andfreeze-thaw cycles. Therefore, it is not surprising thatthe aggregate stability tended to be higher in the subsur-face layer compared to the surface layer in NV and NT.In addition, every plowing event in CT brings up depthprotected aggregates to the surface layer where theyare exposed to wet-dry and/or freeze-thaw cycles, andraindrop impact (Paustian et al., 1997). This repeatedexposure of aggregates decreases the amount of stableaggregates in CT. In the soils dominated by 2:1 clays,the NSI differed generally in the order: NV > NT >CT (Fig. 3), which indicates that the NSI is a goodindicator for detrimental effects of agricultural practiceson soil structure.

In the surface layer, NSI was significantly differentbetween NT and CT at Sidney and Wooster, but not atKBS and Lexington (Fig. 3). The similarity of NSI forNT and CT at KBS is probably a result of the young ageof the experiment (9 yr old). Total carbon, particulateorganic matter C and aggregate distribution were alsonot different between NT and CT at this site (Six et al.,1999, 2000). The highest soil stability for NT and CTwas observed at Lexington. The similar NSI value forall management treatments at Lexington indicates thatthe soil stability was not strongly affected by cultivation.However, total C and particulate organic matter C weresignificantly lower in CT compared to NT at Lexington(Six et al., 1999). Therefore, the soil stability of this soildoes not seem to be related to SOM content as is oftenobserved in temperate soils. We suggest that the specificmineralogy of the soil in Lexington could be the causeof this higher stability (see below).

Before we can use the NSI as an effective indicatorfor soil quality across sites and ecosystems, however,we have to understand the effect of driving variableson NSI (Elliott, 1997). Driving variables are factors con-trolling soil quality that are themselves not influencedby soil quality. For example, soil organic matter is nota driving variable of soil quality because it affects aggre-gate stability or NSI, but it is also related to soil qualityitself and is influenced by aggregate stability. Mean an-nual temperature, mean annual precipitation, clay con-

tent and clay type, on the other hand, are driving vari-ables that influence soil structure independently fromthe quality of the soil and are not affected by aggregatestability. Therefore, the statistical relationships betweenthese driving variables and NSI must be developed be-fore we can account for the effect of driving variableson the potential NSI for a soil and use the NSI as aneffective indicator. If a direct comparison between man-agement treatments is not possible then a valid judge-ment of the soil quality for a specific soil can only bemade in comparison to the potential soil structural qual-ity for that specific soil.

Mineralogical EffectThe effect of mineralogy is illustrated in the compari-

son between the Lexington soil and the three other soils(Fig. 3). The Lexington soil is characterized by a claymineralogy dominated by kaolinite and vermiculitewhereas the other soils are dominated by chlorite and/orillite (Table 3). In addition, and of even more impor-tance for soil stability, the Lexington soil contains 2 to16 times more Fe and Al extracted by citrate-ascorbate(Feca, Alca) and dithionite (Fed, Ald) compared to theother soils (Table 4). Compared to literature values ofsesquioxide concentrations in Oxisols (Colombo andTorrent, 1991; Pinheiro-Dick and Schwertmann, 1996;Reyes and Torrent, 1998), the Feca and Alca concentra-tions are similar, whereas the Fed and Ald are ratherlow in the Lexington soil. This indicates that, eventhough the Lexington soil is not an Oxisol (Table 1), itcontains a substantial amount of amorphous and poorlycrystalline oxides.

Both the presence of Fe- and Al-oxides and kaoliniteare important factors for the stability of a soil. A positivecorrelation between Fe- and Al-oxide content and ag-gregate stability has been found by Kemper and Koch(1966). Selective extractions with oxalate, citrate-bicarbonate-dithionite, and dithionite resulted in abreak down of aggregates (Colombo and Torrent, 1991;Pinheiro-Dick and Schwertmann, 1996). Arduino et al.(1989) concluded that the aggregating effect of oxalateextractable Fe was mostly exerted on sand sized parti-cles. A reduction of slaking in dry clay discs with the

Table 2. Sand size distribution at Sidney, NE. (NV = native vegetation, NT = no-tillage, CT = conventional tillage).

Depth Treatment

0-5 cm NV

NT

CT

5-30 cm NV

NT

CT

% sandin whole soil16,4

14.3

35.5

31.1

33.2

32.5

± 2.8t

± 0.4

± 2.2

±4.4

± 2.1

± 1.8

% sand of same size as aggregate size

Pretreatment

RewettedSlakedRewettedSlakedRewettedSlakedRewettedSlakedRewettedSlakedRewettedSlaked

>2000 (Jim

0.00.1 ± 0.10.1 ± 0.1

NA|0.1 ± 0.1

NA

0.1 ± 0.00.2 ± 0.20.4 ± 0.4

NA0.0NA

250-2000 |xm2.22.51.31.9

10.211.17.47.0

0.10.10.10.21.72.6

3.03.1

6.2 ± 1.09.4 ± 3.17.9 ± 1.6

10.4 ± 4.2

53-250 fun

4.0 ± 0.99.4 ± 1.62.4 ± 0.9

11.9 ± 1.910.9 ± 1.324.9 ± 2.2

9.5 ± 1.412.7 ± 2.08.0 ± 0.5

20.8 ± 1.57.0 ± 0.5

24.9 ± 1.3

t Standard deviation.I Not analyzed due to lack of material.


Table 3. Mineralogical composition of soils at Sidney, NE, Woos-ter, OH, Kellogg Biological Station (KBS), MI, and Lexing-ton, KY.

Table 4. Amounts of Fe, AI, and Si sequentially extracted bycitrate-ascorbate (Feca, Alca, SiM) and citrate-dithionite (FetoAI*, Sid).

Site Minerals present

Sidney, NE Illite, (hepta)-chlorite (cronstedtite), feldspar, quartzWooster, OH Chlorite, illite, feldspar, quartzKBS, MI Chlorite, illite, feldspar, quartzLexington, KY Kaolinite, vermiculite, illite, feldspar, quartz

addition of Fe- and Al-oxides has also been observed(El-Swaify and Emerson, 1975).

Evidence for the flocculation capacity of kaolinite hasbeen reported. Biihmann et al. (1996) found that 2:1clay minerals were on average slightly more easily disag-gregated than kaolinite. In addition, Seta and Karatha-nasis (1996) found a significant negative correlation be-tween kaolinite content and water dispersibility of soilaggregates. The high flocculation capacity of kaolinite iscaused by electrostatic interaction between the positivecharges on the edges of the clay platelets and the nega-tive charges in the body of the crystal (Dixon, 1989; El-Swaify, 1980; Schofield and Samson, 1954; Tama andEl-Swaify, 1978); both charges co-exist at prevailingfield pH (El-Swaify, 1980).

In addition to the stabilizing effects of Fe- and Al-oxides and kaolinite by themselves, interactions be-tween the two components have been reported (Setaand Karathanasis, 1996; El-Swaify, 1980). Iron oxidedeposits on kaolinite platelets have been observed byKitagawa (1983) and Fordham and Norrish (1983). Thisadsorption of Fe- and Al-oxides on the few negativecharged sites of kaolinite reduces the CEC of kaoliniteand increases the positive charge property of the kaolin-ite (Dixon, 1989). Therefore, this interaction betweenFe- and Al-oxides and kaolinite is synergetic and in-creases the aggregation potential of kaolinite. Schofieldand Samson (1954) observed also electrostatic interac-tions between negatively charged 2:1 minerals and posi-tively charged kaolinite edge faces by x-ray examination.Since vermiculite (which is present in the Lexington soil(Table 3) has the highest CEC (or negative chargedsites) of all clay minerals (Alexiades and Jackson, 1965),it seems that such an interaction would occur betweenvermiculite and kaolinite.

The electrostatic interactions between kaolinite, ox-ides, and vermiculite seem to result in a soil stabilitynot as dependent on SOM content as soils dominated by2:1 clays. Due to the binding of particles by electrostaticinteractions, SOM does not have to function as the criti-cal binding agent. This is supported by the observationthat C concentrations do not increase with increasingaggregate size in kaolinitic soils (Elliott et al., 1991;Feller et al., 1996; Six et al., 2000). Similar C concentra-tions across aggregate size classes is in contrast to soilsdominated by 2:1 minerals where SOM forms bridgesbetween negative charged clay minerals within aggre-gates and consequently leads to increased C concentra-tions with increasing aggregate size (Elliott, 1986; Sixet al., 2000). The similar soil stability but different SOMlevels among different management systems in the Lex-ington soil indicates a partly decoupling of aggregation

Site Fee, AU SI, Fed Ald Sid

gkg~Sidney, NE 0.47ct 0.37c 0.42a 3.23c 0.54c 1.64aWooster, OH 4.3Sb 0.77b O.lTb 7.44b 0.88c 0.53bKBS, MI* 2.74bc 0.68bc O.OSc 4.23c 0.44c 0.41bLexington, KY 7.69a 1.94a 0.14b 14.18a 1.39a 0.35b

t Values within a column followed by a different lowercase are significantlydifferent (P > 0.05) according to Tukey's HSD mean separation test.

t KBS, Kellogg Biological Station.

from SOM. However, Six et al. (1999) presented datafor the Lexington soil which suggests that increasedaggregate turnover under CT increased SOM turnoverdue to the reduced protection of SOM by aggregates.Therefore, increased management intensity seems toincrease aggregate turnover and decrease SOM levelswithout a concomitant decrease in soil stability in ka-olinitic soils. In conclusion, soils dominated by 2:1 min-erals show positive feedbacks between SOM and aggre-gation, but the feedback from SOM to aggregation isdiminished by 1:1 minerals and oxides in kaolinitic soils.

CONCLUSIONSThe NSI integrates two pretreatments that involve

air drying to help avoid the confounding effect of ante-cedent water content and nullifies the effect of dryingby subtracting the two pretreatments. The NSI also nor-malizes for differences in sand size distribution betweensites and/or management treatments and pretreatments.The NSI generally decreased with increasing cultivationintensity (NV > NT > CT).

The higher soil stability at Lexington compared tothe other sites and the smaller effect of managementpractices on soil stability at Lexington were a result ofthe presence of kaolinite and oxides. We conclude thatthe feedback of SOM on aggregation, as observed insoils dominated by 2:1 minerals is reduced in soils withoxides and 1:1 clay minerals due to the electrostaticinteractions between these mineral components.

ACKNOWLEDGMENTSThanks to Clay Combrink and Dan Reuss for the laboratory

assistance. The assistance provided by Sally Sutton and GeneKelly with the mineralogical analysis is greatly appreciated.We acknowledge the assistance provided by Drew Lyon (Uni-versity of Nebraska, Panhandle Research and Extension Cen-ter, Scottsbluff, NE), Edmund Perfect and Robert Blevins(University of Kentucky, Lexington, KY), H.P. Collins andG.P. Robertson (University of Michigan, W.K. Kellogg Bio-logical Station, Hickory Corners, MI), and W.A. Dick (OhioState University, Wooster, OH). This research was supportedby a grant (DEB-9419854) from the National Science Foun-dation.


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