the relationships between land uses, soil management practices, and soil carbon fractions in south...

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The relationships between land uses, soil management practices, and soil carbon fractions in South Eastern Australia S.M.Fazle Rabbi a, *, Matthew Tighe a , Annette Cowie a, b , Brian R. Wilson a, c , Graeme Schwenke d , Malem Mcleod d , Warwick Badgery e , Jeff Baldock f a School of Environmental and Rural Science, University of New England (UNE), Armidale, NSW 2351, Australia b Rural Climate Solutions, UNE/NSW Department of Primary Industries, Armidale, NSW 2351, Australia c NSW Ofce of Environment and Heritage, PO Box U221, Armidale, NSW 2351, Australia d NSW Department of Primary Industries, Tamworth, NSW 2340, Australia e NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia f CSIRO Land and Water/Sustainable Agriculture Flagship, Glen Osmond, SA 5064, Australia A R T I C L E I N F O Article history: Received 28 December 2013 Received in revised form 17 June 2014 Accepted 21 June 2014 Available online xxx Keywords: Soil carbon stock Soil carbon fractions Cropping Pastures Redundancy analysis A B S T R A C T This project aimed to identify land uses and soil management practices that have signicant associations with soil organic carbon (SOC) stocks (00.3 m) in New South Wales (NSW), Australia. The work presented in this paper is based on a one-off survey targeting key land uses and management practices of eastern NSW. Because of the nature of the work, the land uses and management combinations surveyed in different soils and climatic conditions were signicantly unbalanced, and separately analyzing associations after breaking the dataset into different land uses may lead to signicant increases in Type errors. Therefore, redundancy analysis (RDA) was undertaken to explore the association between explanatory variables (i.e., land uses, soil management, soil properties and environmental variables) and the variation in stocks (mass per unit area) of particulate organic carbon (POC), humic organic carbon (HOC) and resistant organic carbon (ROC) across 780 sites in eastern NSW, south eastern Australia. Results indicated that soil properties, land uses, soil management and environmental variables together could explain 52% of total variation in stocks of the SOC fractions. Specically soil properties and environmental variables explained 42.8%, whereas land uses and management practices together explained 9.2% of the total variation in SOC fractions. A forward selection RDA was also undertaken considering soil properties and environmental variables as covariates to assess the statistical signicance of land uses and management practices on stocks of POC, HOC and ROC. We found that pasture had signicant positive associations on stocks of carbon fractions. Among the soil properties and environmental variables rainfall, longitude and elevation had a signicant positive inuence while pH and bulk density had a signicantly negative inuence on the HOC, POC and ROC stocks. Using a novel multivariate technique, the current work identied the land uses and soil management that had signicant impact on carbon stocks in soil after accounting for inuences soil properties and environmental variables. ã 2014 Elsevier B.V. All rights reserved. 1. Introduction Globally, soils are viewed as a potential carbon sink that could sequester signicant quantities of atmospheric carbon dioxide. It has been recognized that clearing of native vegetation, cultivation and removal or burning of crop residues has led to reductions in soil carbon content and stocks due to reduction in carbon input in soil (Baldock et al., 2013a,b; Luo et al., 2010; Lal, 2004). In Australia, soil carbon levels appear to be continuing to decline in croplands (Sanderman et al., 2010) but there is potential to contribute to climate change mitigation through building soil carbon in depleted soils or reducing its rate of loss. It has been estimated that Australian cropped and grazing land has the potential to sequester up to 68 and 286 Mt equivalent-CO 2 per year in soil carbon (Garnaut, 2008). The carbon stock of soil could be increased by improved soil management practices and land use change towards a system that ensures high organic matter input to soil and slow decomposition. Land use change from cropping towards pasture or cropping in rotation with * Corresponding author. Tel.: +61 4121 75315; fax: +61 2677 33238. E-mail addresses: [email protected], [email protected] (S.M.F. Rabbi). http://dx.doi.org/10.1016/j.agee.2014.06.020 0167-8809/ ã 2014 Elsevier B.V. All rights reserved. Agriculture, Ecosystems and Environment 197 (2014) 4152 Contents lists available at ScienceDirect Agriculture, Ecosystems and Environment journal homepage: www.elsev ier.com/locate /agee

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Page 1: The relationships between land uses, soil management practices, and soil carbon fractions in South Eastern Australia

Agriculture, Ecosystems and Environment 197 (2014) 41–52

The relationships between land uses, soil management practices, andsoil carbon fractions in South Eastern Australia

S.M.Fazle Rabbi a,*, Matthew Tighe a, Annette Cowie a,b, Brian R. Wilson a,c,Graeme Schwenke d, Malem Mcleod d, Warwick Badgery e, Jeff Baldock f

a School of Environmental and Rural Science, University of New England (UNE), Armidale, NSW 2351, AustraliabRural Climate Solutions, UNE/NSW Department of Primary Industries, Armidale, NSW 2351, AustraliacNSW Office of Environment and Heritage, PO Box U221, Armidale, NSW 2351, AustraliadNSW Department of Primary Industries, Tamworth, NSW 2340, AustraliaeNSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, AustraliafCSIRO Land and Water/Sustainable Agriculture Flagship, Glen Osmond, SA 5064, Australia

A R T I C L E I N F O

Article history:Received 28 December 2013Received in revised form 17 June 2014Accepted 21 June 2014Available online xxx

Keywords:Soil carbon stockSoil carbon fractionsCroppingPasturesRedundancy analysis

A B S T R A C T

This project aimed to identify land uses and soil management practices that have significant associationswith soil organic carbon (SOC) stocks (0–0.3 m) in New South Wales (NSW), Australia. The workpresented in this paper is based on a one-off survey targeting key land uses and management practices ofeastern NSW. Because of the nature of the work, the land uses and management combinations surveyedin different soils and climatic conditions were significantly unbalanced, and separately analyzingassociations after breaking the dataset into different land uses may lead to significant increases in Typeerrors. Therefore, redundancy analysis (RDA) was undertaken to explore the association betweenexplanatory variables (i.e., land uses, soil management, soil properties and environmental variables) andthe variation in stocks (mass per unit area) of particulate organic carbon (POC), humic organic carbon(HOC) and resistant organic carbon (ROC) across 780 sites in eastern NSW, south eastern Australia.Results indicated that soil properties, land uses, soil management and environmental variables togethercould explain 52% of total variation in stocks of the SOC fractions. Specifically soil properties andenvironmental variables explained 42.8%, whereas land uses and management practices togetherexplained 9.2% of the total variation in SOC fractions. A forward selection RDA was also undertakenconsidering soil properties and environmental variables as covariates to assess the statistical significanceof land uses and management practices on stocks of POC, HOC and ROC. We found that pasture hadsignificant positive associations on stocks of carbon fractions. Among the soil properties andenvironmental variables rainfall, longitude and elevation had a significant positive influence whilepH and bulk density had a significantly negative influence on the HOC, POC and ROC stocks. Using a novelmultivariate technique, the current work identified the land uses and soil management that hadsignificant impact on carbon stocks in soil after accounting for influences soil properties andenvironmental variables.

ã 2014 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

journal homepage: www.elsev ier .com/locate /agee

1. Introduction

Globally, soils are viewed as a potential carbon sink that couldsequester significant quantities of atmospheric carbon dioxide. It hasbeen recognized that clearing of native vegetation, cultivation andremoval or burning of crop residues has led to reductions in soilcarbon content and stocks due to reduction in carbon input in soil

* Corresponding author. Tel.: +61 4121 75315; fax: +61 2677 33238.E-mail addresses: [email protected], [email protected] (S.M.F. Rabbi).

http://dx.doi.org/10.1016/j.agee.2014.06.0200167-8809/ã 2014 Elsevier B.V. All rights reserved.

(Baldock et al., 2013a,b; Luo et al., 2010; Lal, 2004). In Australia, soilcarbon levels appear to be continuing to decline in croplands(Sanderman et al., 2010) but there is potential to contribute toclimate change mitigation through building soil carbon in depletedsoils or reducing its rate of loss. It has been estimated that Australiancropped and grazing land has the potential to sequester up to 68 and286 Mt equivalent-CO2 per year in soil carbon (Garnaut, 2008). Thecarbon stock ofsoilcouldbe increasedby improvedsoil managementpractices and land use change towards a system that ensures highorganic matter input to soil and slow decomposition. Land usechange from cropping towards pasture or cropping in rotation with

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42 S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52

pasture species, replacement of annual pasture with perennialpasture, improved grazing systems and afforestation are someproposed approaches to increasing soil carbon (Lal, 2004).

Research in Australia on soil carbon for the last two decades hasfocused on the change in carbon storage due to change in land useand management practices. Australia has diverse climate rangingfrom arid to humid and temperate to tropical, and the findingsregarding land use and soil management within a climatic zone inAustralia are often inconsistent with findings from other zones. Forexample, a meta-analysis of papers published between 1984 and2012 concluded that improved soil management practices includ-ing conservation tillage and inclusion of pasture in croppingsystems in Australia did not significantly affect soil carbon levels(Lam et al., 2013). However, individual long term experiments andstudies in different locations have demonstrated a significantpositive influence of zero tillage and pasture on soil carbon storage(Chan et al., 2011; Chan, 2001, 1997; Young et al., 2009, 2005), andsmall increases in soil carbon under no tillage with stubbleretention when compared with conventional tillage (Dalal et al.,2011; Armstrong et al., 2003; Dalal et al., 1995). Other studies andreviews of work in Australia have provided evidence that theretention or incorporation of crop residues may increase thecarbon input and therefore decrease the rate of carbon loss fromsoil (Page et al., 2013; Chan et al., 2011; Radford and Thornton 2011;Carter et al., 1993; Holford, 1990; Schultz, 1988). Furthermore,pasture species (Schwenke et al., 2013; Radrizzani et al., 2011;Dalal et al., 1995), P fertilizer application (Chan et al., 2010) andgrazing management (Sanjari et al., 2008) all have showninfluences on the association between carbon stocks and pasture.A review by Chan et al. (2003) however, found that these effectswere not consistent across Australian states.

Climate, topography and soil properties are strong predictors ofcarbon stocks under different land uses and management regimes(e.g., Davy and Koen, 2013; Mcleod et al., 2013; Badgery et al., 2013;Dalal and Mayer, 1986; Spain et al., 1983; Gillman, 1976). Soiltexture, iron and aluminum concentration and clay mineralogy canalso determine the extent to which SOC is protected fromdecomposition (Baldock et al., 2004; Baldock and Skjemstad,2000). It is probable that the effects of climate, topography and soilcharacteristics have contributed to the inconsistent responses tomanagement observed to date, masking responses to managementpractices in survey-based studies (Cowie et al., 2013; Mcleod et al.,2013, Schwenke et al., 2013).

Soil organic matter is a heterogeneous mixture of organicsubstances with different turnover rates and the effects of land use,soil management and climate not only influence soil carbon stocks,but are likely to influence the different fractions of carbon withinthe soil (e.g., Baldock et al., 2007; Six et al., 2000a). For example,particulate organic carbon (POC) appears to be sensitive to land usechange (Cambardella and Elliott, 1992). Chan (2001) investigated 5different sites of NSW under pasture and cropping and concludedthat the increase in POC under pasture accounted for 76% of thechanges in total organic carbon caused by land use. The organiccarbon associated with <53 mm particles, i.e. humic organiccarbon (HOC), is believed to be less sensitive to land uses changethan POC (Six et al., 1998). However, low carbon input undercropping and deterioration of soil aggregates under conventionaltillage may decrease the HOC stock in soil (John et al., 2005;Bossuyt et al., 2002). Finally, the resistant organic carbon (ROC),highly carbonised organic material such as charcoal (Baldock andSkjemstad, 2000), can vary between 0–82% of total soil carbon inAustralian soils, and can survive for >500 years (Lehmannet al.,2008; Baldock et al., 2007). Thus, analyzing these differentcomponents of SOC may provide information as to how landuse, management or climatic variables are influencing componentsof SOC, even when SOC does not appear to change.

The objective of this study was to identify land uses and soilmanagement practices that have significant associations withvariations in soil carbon fractions (i.e., POC, HOC and ROC), whenthe influenceofdifferentsoilpropertiesand environmentalvariableswere accountedfora large dataset of 780 individual sites across NSW.

2. Methods and materials

2.1. Site selection

Under the national soil carbon research program (SCaRP), soilorganic carbon (SOC) content and composition under differentagricultural management practices in NSW, south eastern Australia,was sampled and quantified. A survey approach was employedwhich sampled cropping and grazing land in three major agriculturalregions within the state, viz., Central NSW, the Northern Slopes andPlains, and the Northern Tablelands. Central NSW itself is broadlycategorized into three zones of tablelands (>650 mm rainfall), slopes(650–450 mm) and plains (<450 mm). The land uses in Central NSWvary by region. Permanent pastures grazed by sheep and cattledominate on the tablelands, mixed farming on the slopes, andsegregated crop and pasture systems on the plains. Northern Slopesand Plains on average have 680 mm annual rainfall in the east and480 mm in the west and the rainfall is typically summer dominant.The main land use systems in the Northern Slopes and Plains regionare cropping with and without pasture rotation, and permanentpasture containing native or improved species. The average annualrainfall in the Northern Tablelands varies between 750 and 800 mmwhich is summer dominant. The dominant agricultural land usesacross the Northern Tablelands are either improved or native/naturalized pastures. In total 780 sites across NSW were sampled toassess the association of land use and land management with SOCstocks. Further detail on site selection and rationale can be found inCowie et al. (2012).

2.2. Sampling

A 25 m � 25 m quadrat was established at each samplinglocation, and the GPS coordinates (WGS 84) recorded in the southwest corner. When sampling cropping sites, the grid was oriented30� to the crop row to prevent sampling bias with respect to therow and inter-row area. Ten sampling points were located usingrandom coordinates within the quadrat (Sanderman et al., 2011).Soil cores were collected using a manual soil corer (metal tube50 mm in diameter). Surface litter was removed prior to coring.Intact cores were extracted to a depth of 0.3 m with 0.1 mincrement. When large rocks, trees, bedrock outcrops or subsur-face bedrock occurred within 0.3 m of the surface at any of thepredetermined sample collection locations, then the proportion ofthe sampling unit containing rocks and/or trees was recorded tocorrect calculations of soil carbon stocks.

2.3. Site history

Management data for the previous 10 year period werecollected by surveying landholders, as outlined by Sandermanet al. (2011). The survey of land management practice covered landuses, crop type, yield, tillage practice, fallowing, residue manage-ment, pasture type, stocking rate, grazing management, fertilizer(N, P, K), soil amendments and hay production.

2.4. Climate history and topographic data

The climatic history of the sites for 30 years was obtained fromthe SILO database (http://www.longpaddock.qld.gov.au/silo/index.html) based on their latitude and longitude (Jeffrey et al., 2001).

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S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52 43

The climatic variables obtained were mean annual rainfall (mm),temperature (�C) and vapor pressure (hPa) and vapor deficit wascalculated (VPD, mm). The elevation (m), slope (%), topographicwetness index (TWI) of each site were extracted from theSmoothed Digital Elevation Model of Australia (DEM-S), whichwas derived from the 1 s resolution SRTM data acquired by NASA inFebruary 2000. The climate and topographic data were consideredhenceforth as environmental variables.

2.5. Sample processing

A composite sample for each depth was created by combiningthe soil from each depth increment across the ten cores collectedfrom each site. Soil samples were air-dried at 40 �C until constantweight. The sample processing including bulk density measure-ment was as detailed in Cowie et al. (2013).

2.6. Analyzes

The total organic carbon (TOC) was analyzed using a LECO CNanalyser. The particulate organic carbon (POC), the humic organiccarbon (HOC), and the resistant organic carbon (ROC) weredetermined using and mid-infrared (MIR) prediction which werevalidated against a subset of laboratory POC, HOC and ROCmeasurements. The MIR prediction of SOC fractions is considered arapid analysis alternative to the more time consuming (andpotentially logistically impossible for very large datasets) labora-tory analysis of SOC fractions (Janik et al., 2007). Baldock et al.(2013a,b) provides details of the methodology for mid-infrared(MIR) prediction and calibration of SOC fractions, i.e. POC, HOC, and

Table 1Definitions of land uses, tillage, residue and grazing practices.

Management Definition

Land useCropping Cropped only, in last 10 years

Pasture Pasture only, in last 10 years

Irrigated cotton Cotton, mostly in rotation with cereal/grain legumrotations.

Crop/pasture (crop) Last 10 years include 5–9 years of cropping with pathe sites that were under cropping for 5 years and

Crop/pasture (pasture) Last 10 years include 5–9 years of pasture with crOrganic amendments oncropping and pasture

Crop, pasture, crop/pasture (crop) and crop/pastur

TillageConventional tillage Cultivation of soil, use of disced or tined implemeMinimum tillage Minimum tillage, weeds were mainly controlled bNo soil disturbance Pasture sites, no working of soil

Zero tillage Crops are sown by direct-drilling, weeds are mainMixed tillage More than one type of tillage practiced in last 10

ResidueNo management No residue management (pasture and some of pasResidue burnt Residue burnt

Residue grazed Residue grazed

Residue incorporated Residue incorporated in to soil using conventionaResidue baled or removed Residue baled or removed (organic amendment anResidue retained on surface Residue retained on surface(zero and minimum tiMixed management More than one type of residue management pract

GrazingNo grazing No grazing (crop dominant crop/pasture sites)

Rotational grazing Pasture sites, grazing is restricted to short periodsmonths depending on pasture condition

Set stocking Pasture sites, continuous grazing

No grazing management Pasture or cropped sites with no grazing history

Mixed grazing More than one type of grazing management pract

ROC. For MIR analysis, all soil samples were ground using a RetschMM400 grinding system for 180 s. Approximately 100 mg ground,air-dried soil was placed into 9 mm stainless steel auto-samplercups. Diffuse reflectance MIR spectra were acquired using a Nicolet6700 FTIR spectrometer (Thermo Fisher Scientific Inc., Waltham,MA, USA) equipped with a KBr beam-splitter, a DTGS detector andan AutoDiff-Automated diffuse reflectance accessory (Pike Tech-nologies, Madison, WI, USA). Spectra were acquired over 8000–400 cm�1 with a resolution of 8 cm�1. The background signalintensity was quantified by collecting 240 scans on a siliconcarbide disk before analyzing samples and was used to correct thesignal obtained for the soil samples. In total, 60 scans wereacquired and averaged to produce a reflectance spectrum for eachindividual sample, and the Omnic software (Version 8.0; ThermoFisher Scientific Inc.) was used to convert the acquired reflectancespectra into absorbance spectra (log-transform of the inverse ofreflectance). The acquired MIR spectra were truncated to 6000–1030 cm�1, baseline-corrected using a baseline-offset transforma-tion and then mean-centred before partial least square regression(PLSR) analysis (Baldock et al., 2013a).

The independent prediction and validation of POC, HOC andROC have been carried out by Baldock et al. (2013a) using samplesfrom NSW, Queensland (QLD) and Victoria (VIC). Thus, the MIRpredictions used in the current manuscript contain a subset of thispreviously calibrated and validated dataset. Briefly, regressionpredictions were generated using a subset of 312 samples takenfrom NSW, QLD and VIC, for which POC, HOC and ROC wereestimated using standard laboratory approaches. A standard 67%calibration, 33% validation procedure was used, and the regressionapplied to the remainder of the sites in this study. Validation

Code

CORPAS

e/sorghum in last 10 years. Some sites have no- crop between cotton IC

sture in remaining years (crop dominant). This category also includes with 5 years under pasture.

CPC

opping in remaining years (pasture dominant). CPPe (pasture) sites that received organic amendments OA

nts for soil preparation and weed control Til_CTy herbicides Til_MT

Til_NDly controlled by herbicides Til_ZTyears Til_MX

ture or crop dominant crop/pasture sites) RM_NMRM_RBRM_RG

l tillage practices RM_RId crop/pasture (crop) sites) RM_RRlled sites) RM_RSiced in last 10 years RM_MX

GM_NG of several days, followed by long rest periods, generally of several GM_RG

GM_SSGM_NGM

iced in last 10 years GM_MX

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44 S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52

coefficients of regression using this approach were 0.87, 0.89 and0.86 for POC, HOC and ROC respectively. More information on thelaboratory procedures, calibration, validation, additional valida-tion metrics and the production of the rapid MIR predicted carbonfractions can be found in Baldock et al. (2013a,b). In addition to theabove, following data exploration the MIR fractionation data wasconsidered acceptable for use in this study. In particular, asinorganic carbon comprised an insignificant amount of carbon inthe majority of samples, the sum of the three MIR predictedfractions was compared to the actual measured TOC for allsamples. The comparison provided a correlation coefficient of 0.81,which we took as an additional partial check that the MIRpredictions were approximating the stock of carbon at each site.Following this check the relative proportion of each SOC fractionswere adjusted against the measured soil carbon stocks using theapproach of Schwenke et al. (2013) (Eq. (1)). With the weakassumption that noise within the predictions was random and notpreferentially attributed to one land use or set of environmentalvariables, this approach of normalizing predictions against totalSOC was considered the best to maintain any explainable patternwhile reducing the effect of any potential outliers or anomalies thatexist due to the rapid nature of the MIR analyses.

Adjusted SOC fraction

¼ Stock of a predicted fractionTotal stock of predicted carbon fractions

� �� �

� Measured TOC stock (1)

Total soil carbon stocks were expressed as total organic carbonto 0.3 m depth, in Mg ha�1. To account for possible impacts ofmanagement on bulk density, stock of TOC (henceforth, soil carbonstock), and stocks of carbon fractions (i.e., POC, HOC and ROCstocks) were also expressed on the basis of equivalent soil mass.The 10th percentile of 0–0.3 m soil was determined separately forsoils from each order of the Australian soil classification system(Isbell, 2002). The gravimetric content of clay, soil pH andconcentrations of Si and Al were determined by applying predictive

Table 2Definitions of pasture type, soil conditioners, irrigation, fallow period and fertilizer ap

Management Definition

Pasture typeAnnual pasture Annual pasture, either grass or legume dominant

Mixed annual/perennialpasture

Mixed annual/perennial pasture, either grass or legume do

Mixed pasture Crop dominant and pasture dominant crop/pasture sites whpasture

No pasture Cropped and crop dominant crop/pasture sites

Perennial pasture Perennial pasture either grass or legume dominant

Soil conditioners (i.e., agricultural lime, gypsum)No soil conditioners Sites where soil conditioners were not applied (pasture anSoil conditioners Sites where soil conditioners were applied

IrrigationNo irrigation Sites where irrigation was not applied

Irrigation Sites where irrigation was applied (irrigated cotton sites)

Mixed irrigation Sites where occasional irrigation was applied

Fallow periodLong fallow Sites where long fallow was practiced

No long fallow Sites where long fallow was not practiced

Mixed Long fallow Sites where occasional long fallow practiced

Fertilizer applicationNitrogen Nitrogen fertilizer was applied in cropped, irrigated cotton,

in cropped and irrigated cotton sites. Nitrogen fertilizer wPhosphorus Phosphorus fertilizer was applied in all land uses. ApplicaPotassium Potassium fertilizer was applied in all land uses. Applicati

algorithms developed by Janik et al. (1995); Janik and Skjemstad(1995) to the MIR spectra acquired for all samples.

2.7. Data coding and variable selection

Land uses and soil management (i.e., crop intensity, crop yield,tillage, residue, grazing management, pasture type, irrigation, soilconditioners and long fallow period) history of 10 years prior tosampling was recorded under the SCaRP project. In the currentstudy, data were recoded as nominal variables to summarize eachof the management practices in a site over the 10 years. The sitesthat were under continuous pasture for the last 10 years werecoded as pasture and in the case of 10 years continuous croppingthe sites were coded as cropping. Land uses were also coded as cropdominant and pasture dominant crop/pasture sites depending onthe numbers of years under crop or pasture in last 10 years. Siteswith application of organic amendments and irrigated cotton werecoded separately. A description and codes of land uses and soilmanagement practices is presented in Tables 1 and 2.

N, P and K fertilizer rates were expressed as the average ofapplication rates of each type of fertilizer over 10 years.Gravimetric content of clay, Si and Al (mg g�1) and average bulkdensities of 0–30 cm soil were used in the present study. Latitude,longitude, elevation, 30 years mean annual rainfall, temperature,vapor pressure deficit, aspect, slope, and topographic wetnessindex were used in the statistical data analysis.

2.8. Statistical analysis

Due to the unbalanced nature of many combinations ofvariables in the dataset both within different land use classificationand spatially, an exploratory pattern association type approachwas taken for data analysis. This was done primarily to reduce thepotential of model selection bias in which a trend may be identifiedin a dataset and then further examined within the same dataset,inappropriately strengthening inferences made about potential

plication.

Code

PT_APminant PT_MAP

ere a crop year with ‘no pasture’ and pasture year with annual/perennial PT_MX

PT_NPPT_PP

d pasture dominant crop/pasture sites) Cn_NOCn_YES

Ir_NOIr_YESIr_MX

Fa_YESFa_NOFa_MX

crop/pasture and organic amendment sites. Application rates were highas also used in some pasture sites

N

tion rates were highest in irrigated cotton sites Pon rates were high in irrigated cotton and organic amendment sites K

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S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52 45

cause and effect relationships using data not specifically designedto focus on such relationships. Redundancy discriminant analysis(RDA) is a linear canonical (i.e., constraining) ordination techniquethat is designed to detect the pattern of variation in responsevariables that can be associated with potential explanatoryvariables (Jongman et al., 1995). RDA was performed usingCANOCO 5.0 (Microcomputer Power, Ithaca, USA) to find theeffects of explanatory variables (i.e., land uses, soil management,soil properties and environmental variables) on the variation instocks of particulate organic carbon (POC), humic organic carbon(HOC) and resistant organic carbon (ROC) in 780 sites in NSW. Landuse, tillage, residue management, grazing, soil conditioners,irrigation and fallow period were used as nominal variables,which were converted into a set of dummy variables (1–10) inCANOCO 5.0. During the RDA analysis stocks of carbon fractionswere centred and standardized as described by ter Braak andSmilauer (2012). The statistical significance of the relationshipbetween stocks of carbon fractions and explanatory variables wasevaluated using Monte Carlo permutation tests in CANOCO 5.0 (terBraak and Smilauer, 2012). The association between stocks ofcarbon fractions and explanatory variables that had p-value < 0.05were considered statistically significant.

Constrained-partial RDA was also undertaken considering soilproperties and environmental variables as covariates. Constrained-partial RDA is a technique in which effects of explanatory variableson response variables are separated from the effect of thecovariates (Jongman et al., 1995). Since constrained-partial RDAordination shows simple independent effects of each explanatoryvariable, a forward selection of explanatory variables was used toremove the effect of a selected variable from the remainingvariables (ter Braak and Smilauer, 2012). The forward selection of

-1.0

-1.0

1.0

N

Temperature

VPD

Aspect

TWI

Bulk Density

Clay

pH

S

Til_ZT

Til_CT

Til_MX

Til_MT

RM_RBRM_MX

RM_RS

RM_RIPT_MX

PT_NP

PT_APGM_MX

GM_NG

GM_NGM

F_MX Ir_YE S

Ir_MX

Cn_YES

CPCCOR

IC

Axis 1 (

Axis

2 (

Eig

envalu

e=0.0

7)

Fig. 1. Constrained-RDA biplot showing the relationship between stocks of carbon fractEnvironmental and soil variables: vapor pressure deficit = VPD; aluminium, and silicon

explanatory variables shows how much of the total variation isexplained by a selected variable. The contribution of a selectedvariable is calculated as: (explained variation/total variation)� 100. When the constrained-partial RDA model was significant, aforward stepwise procedure was carried out to select a reducedmodel as described by Blanchet et al. (2008). Using this approachvariable selection ends when the adjusted R2 value of selectedvariables exceeds the adjusted R2 value for all explanatoryvariables. The p-values were adjusted using false discovery rateswhich is well suited to the forward selection approach (Benjaminiand Gavrilov, 2009) and the correlation coefficients of stocks ofcarbon fractions with soil management and environmentalvariables were also extracted during the forward selection RDA(ter Braak and Smilauer, 2012). A forward selection was also carriedout after performing constrained RDA with soil properties,environmental variables, land uses and soil management, to selectthe soil properties and environmental variables that havesignificant influence on stocks of carbon fractions. Before theforward selection step, we checked for collinearity betweenvariables using the variance inflation factor (VIF). ter Braak andSmilauer (2012) suggest that VIF < 20 indicates the absence ofstrong collinearity problems. Among the variables selected byforward selection “no soil conditioners” had collinearity with otherselected variables (i.e., pasture, soil conditioners application, crop/pasture (pasture), organic amendments) and was removed fromthe ordination diagram. The VIF of other selected variables variedbetween 1.0 and 2.5. The RDA ordination was interpreted by biplotrules (Jongman et al., 1995).

The arrows of stocks of carbon fractions and quantitativeenvironmental and soil management variables indicated thedirection in which the values of quantitative variables increased.

0.8

PK

Rainfall

Elevation

Slop e

i

Al

Latitud e

Longitud e

Til_ND

RM_RG

RM_RR

RM_NM

PT_MAP

PT_PP

GM_SS

GM_RG

F_YES

F_NO

Ir_NO

Cn_NO

CPP

OA

PAS POC

HOC

ROC

Eigenv alue=0. 42)

ions with land uses, soil management, soil properties and environmental variables. concentration = Al, Si.

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46 S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52

The soil management variables and arrows of stocks of carbonfractions jointly approximate correlation between managementand stocks of carbon fractions (e.g., Jongman et al., 1995).

3. Results

Large variation in stocks of soil carbon, POC, HOC and ROC wereobserved under different land uses and soil management. Carbonstock in 0–0.3 m soil under different land uses varied from 11–94Mg C ha�1 on the equivalent soil mass basis. The ranges of POC,HOC and ROC stocks varied between 0.1–17, 5–58 and 3–34 Mg Cha�1 on the equivalent soil mass basis. The constrained-RDAshowed that land uses, soil management, soil properties andenvironmental parameters explained 52% of total variation in thestocks of carbon fractions (Fig. 1). Soil properties and environmen-tal variables explained 42.8% of total variation, whereas 9.2% oftotal variation was explained by land uses and soil managementpractices. Since soil properties and environmental variablestogether accounted for 42.8% of variation in stocks of carbonfractions, the soil properties and environmental variables weretreated as covariates to further assess the association of land usesand soil management with stocks of carbon fractions. Theconstrained-partial RDA biplot, after eliminating effects of soil

-0.6

-0.4

1.0

T

Til_ND

RM_RS

RM_RR

RM_NM

PT_MAP

PT_PP

GMGM

GM_RG

F_YE S

FI

O

PAS

POC

HOC

ROC

Axi s 1 (Eige

Axis

2 (

Eig

en

valu

e=0.0

1)

Fig. 2. Constrained-partial RDA biplot showing the relationship between stocks of carbproperties and environmental variables.

properties and environmental variables, showed that the stocks ofPOC, HOC and ROC tended to be higher under pasture, residueremoved (which included site with organic amendments), no soildisturbance, mixed annual-perennial pasture, perennial pasture,set stocking and rotational grazing sites (Fig. 2). A forwardselection of land uses and soil management variables indicatedthat pasture and nitrogen fertilizer application could account for6.2% of the variation in stocks of carbon fractions (Fig. 3 andTable 3). Pasture had a positive correlation, whereas application ofnitrogen fertilizer had negative correlation with stocks of carbonfractions (Table 4).

Organic amendments and crop/pasture (pasture) tended to havelarge HOC and ROC stocks but had an association with lower POCstock. Since the soil properties and environmental variablesexplained 42.8% of total variation, a forward selection was alsoperformed to select the soil properties and environmental variablesthat had the most significant associations with stocks of carbonfractions. The forward selection RDA biplot showed that rainfall, Sicontent, soil pH, bulk density, latitude and longitude accounted for42.3% of the total variation in stocks of carbon fractions (Fig. 4).Among the selected variables rainfall, pasture and longitude hadpositive, whereas soil pH and bulk density had a negative associationwith stocks of POC, HOC and ROC (Tables 5 and 6).

0.6

N

P

K

il_ZT

Til_CT

Til_MX

Til_MT

RM_RB

RM_MX

RM_RI

RM_RG

PT_MX

PT_NP

PT_AP

_MX

GM_NG

_SS

GM_NGM

F_MX

_NO

Ir_YE S

Ir_MX

r_NO

Cn_ YESCPC

CPP

COR

A

IC

nvalue=0 .05)

on fractions with land uses and soil management after removing the effects of soil

Page 7: The relationships between land uses, soil management practices, and soil carbon fractions in South Eastern Australia

-0.4 0.6

-0.2

1.0

N

P

RM_RR

Cn_YE S

Cn_ NOCPP

OA

PASPOC

HOC

ROC

Axis 1 (E igenvalue=0.04)

Axis

2 (

Eig

en

valu

e=0.0

05)

Fig. 3. Forward selection-partial RDA biplot showing the relationship between stocks of carbon fractions with land uses and soil management variables.

S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52 47

4. Discussion

The constrained RDA, a multivariate technique, was used toidentify variation of stocks of carbon fractions in NSW soils across arange of climatic, topographic and soil properties. Land uses andsoil management practices that are associated with higher or lowerstocks of carbon fractions were identified, once the potentiallyconfounding influences of environmental variables wereaccounted for. In this study the MIR predicted fractions of carbonstocks were used, due to the method's general rapidity andfeasibility when dealing with large datasets. For this study it wasassumed that the pattern of SOC fractions as determined by MIRwas a good representation of actual SOC fractions, an assumption

Table 3Statistical significance of land uses and soil management variables selected using the f

Land use/Management Explains(%)

Pasture 5.1

Nitrogen fertilizer application rate 1.1

Application of soil conditioners 0.9

Organic amendments 0.9

Crop/pasture (pasture) 0.9

Residue removal 0.5

Phosphorus fertilizer application rate 0.4

* Statistical significance at p < 0.05.

that was supported by the quality control measures undertaken.However, it needs to be acknowledged that this rapid analysistechnique must by necessity sacrifice some precision, and theremay be scope for improvement in predictions, particularly in theprediction of POC (see for example Janik et al., 2007). The belowinterpretations and discussion is thus based on the patterns foundin the MIR predicted fractionation data.

4.1. Pastures had the largest stocks of carbon fractions

The pasture was significantly associated with higher levels ofthe stocks of all three carbon fractions compared with cropping orcrop/pasture soils. Since the soils under pasture had not been

orward selection procedure.

Contribution(%)

Pseudo-F p-value

39.4 40.4 0.02*

8.2 8.5 0.02*

6.8 7.1 0.03*

7 7.4 0.04*

6.8 7.2 0.04*

3.8 4.1 0.213.4 3.6 0.21

Page 8: The relationships between land uses, soil management practices, and soil carbon fractions in South Eastern Australia

Table 4Correlation coefficients between stocks of soil carbon fraction and statisticallysignificant land uses and soil management variables selected using the forwardselection procedure.

Land use/management POCa HOCa ROCa

Pasture 0.19 0.08 0.10Nitrogen fertilizer application rate �0.13 �0.07 �0.08Application of soil conditioners �0.11 �0.06 �0.11Organic amendments �0.04 0.03 0.05Crop/pasture (pasture) �0.05 0.01 0.01

a Values represent correlation coefficients between explanatory and responsevariables after centering and standardization of response variables.

48 S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52

mechanically disturbed in the last 10 years, the variationsexplained by pasture also included the effects of no soildisturbance. The trend of high stocks of carbon fractions inpastures compared to cropping was consistent with many otherlocation specific studies (e.g., Davy and Koen, 2013; Chan et al.,2011). The reasons are well known, ranging from retention ofcarbon in aggregates and micro-aggregates through to slowdecomposition of pasture roots. The longer period of activegrowth, greater below ground biomass allocation and diversity inspecies composition may also contribute to high stocks of carbon inpastures. Soils under pasture are well aggregated and tend tosequester more carbon than intensively tilled croplands because ofincreased return of shoot residues, reduced soil disturbance andcontribution from pasture root system (Percival et al., 2000). Theinteraction between soil and soil organic matter in pastures can

-1.0

-1.0

1.0

Rainfall

Elevation

Al

Latitud e

Longitude

RM_RR

Cn_NO

PASPOC

HOC

ROC

Axis 1 (

Ax

is 2

(E

igen

va

lue

=0

.07

)

Fig. 4. Forward selection RDA biplot showing the relationship between stocks of carb

promote long term SOC sequestration within micro-aggregates(Blanco-Canqui and Lal, 2004). Although the carbon input and itsdistribution in soil profile from pasture roots depends on pasturespecies and management, the relative contribution of root towardssoil carbon is considered higher than shoot contribution (Rasseet al., 2005; Gale et al., 2000). Most pasture species have extensiveroot systems which can contribute carbon in soil either asrhizodeposition or directly as dead roots (Crawford et al., 2000,1997). Soil aggregates can form around decomposing root residuesand hence root fragments can be occluded in aggregates (Rasseet al., 2005; Golchin et al., 1998). The turnover time of occludedcarbon is higher than the organic carbon that is not present insideaggregates (Christensen, 2001). Moreover, plant roots containhigher lignin and other recalcitrant macromolecules includingsuberin, which reduce the decomposition rate of roots in soilcompared to shoots (Rasse et al., 2005). The active pasture rootscan also promote aggregate formation and carbon stabilization.Active root growth can promote microbial activity by producingroot exudates which could enhance stable aggregate formation inthe rhizosphere. The enmeshment, penetration force, and wetting-drying cycles that are exerted by growing roots can also stabilizeaggregates (Denef and Six, 2006; Czarnes et al., 2000). Therefore,carbon input from pasture roots, decomposition of dead pastureroots, and formation of stable aggregates by both active and deadroots might explain the higher stocks of carbon fractions in pasturesites studied in the current study.

One of the most important reasons that soil carbon is maintainedor increased under pastures is absence of soil disturbance. Generally,

1.0

N

P

Bulk Density

Clay

pH

Si

RM_RS

Cn_YE S

CPP

OA

IC

Eigenvalue = 0 .4)

on fractions with land uses, soil management, soil and environmental variables.

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Table 5Statistical significance of environmental, land uses and soil management variables selected using the forward selection procedure.

Variables Explains(%)

Contribution(%)

Pseudo-F p-value

Rainfall 21.4 39.1 209 0.02*

Si content 8.2 14.9 88.8 0.02*

Pasture 5.7 10.4 67.1 0.02*

Soil pH 4.7 8.5 59.3 0.01*

Latitude 4.1 7.4 60.6 0.01*

Bulk density 2.7 4.9 35.7 0.01*

Irrigated cotton 1.1 2 14.9 0.01*

Longitude 1.2 2.2 17.0 0.01*

Elevation 0.9 1.7 14.0 0.01*

Nitrogen fertilizer application rate 0.5 0.9 7.4 0.04*

Application of soil conditioners 0.4 0.8 6.4 0.02*

Organic amendments 0.3 0.6 5.3 0.07*

Al content 0.4 0.8 6.4 0.03*

Crop/pasture (pasture) 0.5 0.9 7.5 0.02*

Clay content 0.3 0.6 4.8 0.04*

Phosphorus fertilizer application rate 0.2 0.4 3.8 0.13*

Residue removal 0.2 0.4 3.7 0.10Residue retained on surface 0.2 0.4 3.8 0.09

* Statistical significance at p < 0.05.

S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52 49

tillage leads to aggregate breakdown and liberation of occludedcarbon from soil aggregates (Six et al., 2000b). The liberation of SOChinders the formation of SOC rich stable micro-aggregates which inturn affects SOC stabilization. Past research has demonstrated thatinter-aggregate SOC, free light fraction, occluded SOC and mineralassociated SOC are sensitive to land use change and cultivation(Llorente et al., 2011; Mueller and Koegel-Knabner, 2009; Paul et al.,2008a,b; John et al., 2005; Bossuyt et al., 2004; Six et al., 1998).Therefore, pastures without soil disturbance have the potential toincrease carbon stocks in soil. The mechanisms that are responsibleforhigher stocks ofcarbon fractions inpasturescouldalsobeactive incropped sites with zero tillage. However, zero tillage sites in thecurrent study were only found under dryland cropping systems withrelatively low rainfall. Given the verylarge influenceof rainfallon soilcarbon fractions, this may account for the lack of any observed effectof zero tillage on soil carbon fractions.

4.2. Nitrogen fertilizer application had a negative effect on stocks ofcarbon fractions

Application of nitrogen fertilizer and soil conditioners had anegative influence on stocks of carbon fractions. Generally, high

Table 6Correlation coefficients of stocks of soil carbon fractions with statisticallysignificant soil properties, environmental variables, land uses and soil managementvariables selected using the forward selection procedure.

Variables POCa HOCa ROCa

Rainfall 0.55 0.42 0.41Si content 0.20 �0.33 �0.15Pasture 0.56 0.24 0.23Soil pH �0.46 �0.20 �0.01Latitude �0.10 0.01 0.16Bulk density �0.07 �0.19 �0.36Irrigated cotton �0.20 �0.17 �0.10Longitude 0.42 0.35 0.45Elevation 0.63 0.37 0.32Nitrogen fertilizer application rate �0.27 �0.11 0.06Application of soil conditioners �0.03 �0.06 �0.08Organic amendments �0.07 �0.01 0.08Al content �0.20 0.29 0.11Crop/pasture (pasture) �0.11 �0.01 �0.05Clay content �0.44 0.00 0.02Phosphorus fertilizer application rate �0.05 0.02 0.01

a Values represent correlation coefficients between explanatory and responsevariables after centering and standardization of response variables.

nitrogen fertilizer application based on the nitrogen requirementof crop produces high plant biomass and yield but the amount ofcarbon return to soil is dependent on tillage and crop residuemanagement (e.g., Luo et al., 2010; Khan et al., 2007). The croppedsites of the current study received nitrogen fertilizer andcultivation varied from zero to conventional tillage. Contrary tocropped sites, the pasture sites did not receive nitrogen fertilizerand had higher stocks of carbon fractions. Since we werecomparing cultivated vs. pasture sites, application of nitrogenfertilizer appeared to have a significant negative association withstocks of carbon fractions. In this instance this effect is confounded,and any interpretation of such requires caveats of a preliminaryand exploratory nature. However, there exists a possible actualmechanism that may actually account for such a change.Application of inorganic nitrogen fertilizer could increase theconcentration of available nitrogen in cropped soils, which may inturn increase microbial decomposition and loss of organic matter(Khan et al., 2007). In contrast to our results, Dalal et al. (1995)found non-significant association between nitrogen fertilizerapplication and organic carbon concentrations in conventionaland no-tilled wheat rotations in Vertosols. However, based on alimited amount of published data, Luo et al. (2010) showed thatnitrogen application could increase soil carbon but the change wasdependent on availability of soil water. Thus, there exists a lack ofconsensus on the effect of nitrogen fertilizer application on stocksof soil carbon in Australia, which our analysis could not clarify.Similar to nitrogen fertilizer, soil conditioners were also appliedmostly in cropped sites to maintain soil pH for crop production. Butthe deterioration of soil aggregates by tillage increases the rate ofSOC decomposition, which counterbalances the effect of increasingcrop production on soil carbon. This may be the reason for thenegative association of soil conditioners with stocks of carbonfractions.

4.3. Pasture dominant crop/pasture rotation had a positive effect onstocks of carbon fraction

The pasture dominant crop/pasture land use tended to havelarge HOC and ROC but small POC stocks in soil. Chan (1997)reported that conversion of pasture into cropping land usesignificantly decreased POC stock in Vertosols. In crop/pasturesites the cultivation of soil during the crop phase could enhance thedecomposition rate of pasture-derived POC. The decompositionproduct of POC could be adsorbed on <53 mm particles and thus

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50 S.M.F. Rabbi et al. / Agriculture, Ecosystems and Environment 197 (2014) 41–52

increase the HOC stocks under crop/pasture (pasture) land use. Theredistribution of soil carbon from POC to HOC in cultivated soilswas also reported by Cambardella and Elliott (1992). Theassociation of high ROC stock with this land use may be relatedto historical burning episodes of pasture and crop residues of thestudied crop/pasture (pasture) sites. It has been reported that inAustralian soils high level of resistant organic matter (i.e., charcoal)is related with historical burning episodes (Lehmann et al., 2008;Skjemstad et al., 2002). As carbon in the HOC fraction is consideredto be more stabilised against microbial decomposition due toformation of organo-mineral complexes (Baldock and Skjemstad,2000), the pasture dominant crop/pasture land use appears to havepotential to alter SOC fractions in a way that encourages stableforms of carbon in the soil to persist.

4.4. Application of organic amendments had a positive effect on stocksof carbon fraction

Sites with organic amendments were associated with a patternof high HOC and ROC stocks. It is commonly expected that carbonstocks would be higher in organic farming than conventionalfarming systems (Teasdale et al., 2007; Marriott and Wander, 2006;Wells et al., 2000). Cowie et al. (2013) did not observe a significantpositive increase in organic amendment sites compared toconventional farming sites but these authors showed that organicamendments increased fungal to bacterial ratio and enzymaticactivities of soils. Thus, the addition of organic amendments couldpotentially reduce bacterial induced C loss, which may result in achange in C dynamics with the more stable SOC fractionsdominating. This needs confirmation with additional detailedsurveys or experiments focusing on such a process and pattern.

4.5. Grazing did not have an effect on stocks of carbon fraction

Although it has been documented in literature that grazingmanagement has an influence on carbon stocks of soil, we foundthe effect of rotational grazing or set stocking did not havesignificant association with higher fractions of carbon stocks insoil. Studies show that generally, grazing at appropriate stockingrate maintains soil carbon stock due to positive effects on pasturegrowth and turnover of both above- and below-ground pasturebiomass (Conant et al., 2001; Lecain et al., 2000; McNaughton et al.,1996), and grazing will not decrease soil carbon stocks as long aspasture management maintains adequate ground cover (Conantand Paustian, 2002). Although P fertilizer was applied in many ofthe pasture sites examined, the effect of P application was notsignificant. This is similar to recent findings of Wilson andLonergan (2013) and Schwenke et al. (2013); who found nosignificant influence of P fertilizer on increasing carbon stocks ofsoil under pastures in northern NSW. Thus, the large stocks of allcarbon fractions under pasture are probably mostly related to theabsence of soil disturbance, low soil erosion, and high carbon inputinto soil from pasture species.

4.6. Soil and environmental variables had the largest effect on stocks ofcarbon fractions

While land uses and soil management explained 9.2% of totalvariation in stocks of carbon fractions, the influence of soilproperties and environmental variables on stocks of carbonfractions was considerably larger (42.8% of total variation). Therainfall, longitude and elevation positively correlated with stocksof carbon fractions, as might be expected as the amount ofprecipitation and soil water availability for plant growth increaseswith these variables (Davy and Koen, 2013). The negativerelationship between bulk density and stocks of carbon fractions

is probably due to the negative influence high bulk density has onroot growth of plants, while concurrently limiting aeration andwater availability (Davy and Koen, 2013; Haynes and Naidu, 1998).The negative association between high soil pH and stocks of carbonfractions is most likely due to the relatively lower pH of soils underpasture compared to cropping and crop/pasture sites. Siliconcontent had a negative correlation with HOC and ROC stocks.Silicon is a major component of sand and aluminosilicate clays ofsoil. As the sand particles have low surface area and do not possesselectrostatic charges, the capacity of sand to adsorb carbon islimited (Brady and Weil, 2008). Thus, soil properties andenvironmental variables appeared to have a strong associationwith the SOC fractions, and by implication, appear to be significantdrivers of influencing carbon stocks in soils, with land use and soilmanagement practices having a secondary level of influence. Thestrong association in our data between environmental variablesand carbon dynamics is a trend that has recently been detected forother production systems globally (Delgado-Baquerizo et al.,2013), and suggests that relatively small changes in externalfactors such as rainfall may either mask or amplify any effects onSOC from land use or management practices.

5. Conclusion

We observed that pastures in NSW had potential to increasestocks of carbon fractions in soil. Other land management practicesincluding residue and grazing management did not have signifi-cant influence on stocks of carbon fractions. The variables such asthose related to climate, and soil properties related to texture, soildensity and pH, had strong associations with SOC fractionscompared with land use or management practices. This suggeststhat any attempt to influence SOC fractions via active managementneeds to consider the overriding influence of the local environ-ment, as well as what changes to these external drivers may meanin terms of the potential to manipulate SOC. The work presented inthis paper is based on a one-off survey targeting key land uses andmanagement practices of eastern New South Wales, Australia.Because of the nature of the work, the land uses and managementcombinations surveyed in different soils and climatic conditionswere significantly unbalanced, and separately analyzing associa-tions after breaking the dataset into different land uses may lead tosignificant increases in Type errors. Several of the trends andpotential associations identified within this large dataset requirenot only comparison with existing, spatially explicit regional work,but further large scale sampling based work. Such further largescale work requires surveys designed to address the imbalancednature of land use and climatic variable combinations that areinherent within the current large dataset, and would by necessityand practicality need to address a subset of the potentialassociations identified within this initial study.

Acknowledgements

This study was funded by the Department of Agriculture,Fisheries and Forestry (DAFF), Australia ‘Filling the Research Gap’program. The Department of Agriculture, Fisheries and Forestry(DAFF) and the Grains Research and Development Corporation(GRDC), Australia provided research funding for the originaldatabase generated through the Soil Carbon Research Program(SCaRP). Institutional funding was provided by the NSW Depart-ment of Primary Industries (NSW DPI), the University of NewEngland, and the NSW Department of Environment and Heritage.Thanks to the NSW DPI and Landcare staff and landholders whoidentified sampling locations and to all surveyed landholders whoprovided access to their properties and supplied information aboutmanagement history. We gratefully acknowledge Vanessa

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Lonergan for project management, Dale Higgins, Gary Sparke, JohnLemon, Dacre King for soil sampling; Derek Purdy, Chris Fyfe, JanCarruthers, Leanne Lisle, Gary Cluley, and Phoebe Barnes forsample processing, laboratory analysis and data entry. We wouldalso like to acknowledge CSIRO Adelaide who provided the MIRpredictions, DEM model interpolations, and data-drill climateinformation. The authors would also like to acknowledge thesignificant contribution to the Central NSW project from AaronSimmons, Brian Murphy, Andrew Rawson, Geoff Millar, KarlAndersson, Elizabeth Warden, Kim Broadfoot, Ian Toole, DavePerovic, Ian Packer, John Young and Sue Orgill.

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