Potential soil organic carbon stock and its uncertainty under variouscropping systems in Australian cropland

Zhongkui LuoA, Enli WangA,C, Jeff BaldockB, and Hongtao XingA

ACSIRO Land and Water/Sustainable Agricultural Flagship, GPO Box 1666, Canberra, ACT 2601, Australia.BCSIRO Land and Water/Sustainable Agricultural Flagship, APMB 2, Glen Osmond, SA 5064, Australia.CCorresponding author. Email: enli.wang@csiro.au

Abstract. The diversity of cropping systems and its variation could lead to great uncertainty in the estimation of soilorganic carbon (SOC) stock across time and space. Using the pre-validated Agricultural Production Systems Simulator, wesimulated the long-term (1022 years) SOC dynamics in the top 0.3m of soil at 613 reference sites under 59 representativecropping systems across Australia’s cereal-growing regions. The point simulation results were upscaled to the entire cereal-growing region using a Monte Carlo approach to quantify the spatial pattern of SOC stock and its uncertainty caused bycropping system and environment. The predicted potential SOC stocks at equilibrium state ranged from 10 to 140 t ha–1,with the majority in a range 30–70 t ha–1, averaged across all the representative cropping systems. Cropping systemaccounted for ~10% of the total variance in predicted SOC stocks. The type of cropping system that determined the carboninput into soil had significant effects on SOC sequestration potential. On average, the potential SOC stock in the top0.3m of soil was 30, 50 and 60 t ha–1 under low-, medium- and high-input cropping systems in terms of carbon input,corresponding to –2, 18 and 26 t ha–1 of SOC change. Across the entire region, theMonte Carlo simulations showed that thepotential SOC stock was 51 t ha–1, with a 95% confidence interval ranging from 38 to 64 t ha–1 under the identifiedrepresentative cropping systems. Overall, predicted SOC stock could increase by 0.99 Pg in Australian cropland under theidentified representative cropping systems with optimal management. Uncertainty varied depending on cropping system,climate and soil conditions. Detailed information on cropping system and soil and climate characteristics is needed toobtain reliable estimates of potential SOC stock at regional scale, particularly in cooler and/or wetter regions.

Additional keywords: APSIM, carbon sequestration, crop rotation, meta-model, Monte Carlo simulation, regional scale,upscaling.

Received 8 October 2013, accepted 5 March 2014, published online 26 June 2014

Introduction

Agricultural soils have large potential for sequestering soilorganic carbon (SOC) through adoption of conservationagricultural practices such as no-tillage, residue retention anddiversification of cropping systems (West and Post 2002; Lal2004; Smith 2004; Luo et al. 2010). Results from many fieldexperiments showed that SOC could be significantly affected bythe type of cropping system, with the impact varying dependingon rotation types and environmental conditions (Drinkwateret al. 1998; West and Post 2002; Luo et al. 2010; Sainju andLenssen 2011; Kou et al. 2012). A review of field experiments inAustralia by Luo et al. (2010) indicated that cropping systemand rotation type had a significant effect on SOC dynamicsbecause of their direct impact on the quality and quantity ofcarbon input to the soil. Few of these analyses, however, haveevaluated the long-term SOC dynamics and its uncertaintyassociated with variation in cropping systems at highspatiotemporal resolution. Therefore, there is a need for morereliable prediction of SOC sequestration under variouscropping systems and environmental conditions at the desiredspatiotemporal resolution and scale.

Cropping system may change significantly over space andtime depending on the individual farmer’s choice, climate andsoil conditions. In Australia’s grain-cropping regions, forexample, the spatial variation in climate and soil conditionshas resulted in diverse cropping systems and farming practices,with continual modification and/or introduction of new plantspecies and management strategies. Twenty-two crops weresuggested to be included in the carbon accounting system toaccount for >99% of the sowing area in all states of Australia(Unkovich et al. 2009). Considering the many possibilitiesof crop sequences and their varying impact on SOC, it isdifficult, if not impossible, to use an experimental approachto investigate the SOC dynamics influenced by differentcropping systems across regions. Previous modelling studiesmainly focused on the verification of models simulating SOCdynamics under specific cropping systems at plot scale(Álvaro-Fuentes et al. 2009; Luo et al. 2011; Soler et al.2011). At regional or continental scales, the effects of variousagricultural managements, including fertilisation, tillageand residue retention, have been comprehensively simulatedand predicted using several models (Grace et al. 2006, 2010;

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Soil Research, 2014, 52, 463–475http://dx.doi.org/10.1071/SR13294

Ogle et al. 2010; Zhao et al. 2013). However, extendingplot-scale modelling to analyse the influence of cropping-system change on SOC remains a challenge that will relynot only on the availability of relevant information requiredfor the modelling, but also on the capability of the modelsto simulate the biomass production of different crops and theSOC decomposition processes across environments.

The Agricultural Production Systems Simulator (APSIM)has been developed for simulation of plant and soil processesby allowing flexible specification of management options,and has the ability to simulate >30 crop and grass species asinfluenced by climate variability and management interventionsthrough an array of plant modules for simulating key,underpinning physiological processes (Wang et al. 2002;Keating et al. 2003). The credibility of APSIM to predictSOC change has also been validated under variousenvironments and agricultural managements (Huth et al.2010; Luo et al. 2011). Recently, the APSIM model has beensuccessfully used to estimate long-term SOC sequestrationpotential and the effects of agricultural management under asimplified, continuous wheat system at regional scale (Luo et al.2013; Zhao et al. 2013).

In this study, we use the APSIM model to conduct long-term simulations on SOC change in response to 59representative cropping systems at 613 reference sites inAustralia’s cereal-growing regions. One scenario of optimalfertiliser application, conservation tillage (i.e. no-till) andwhole retention of crop residue was adopted to represent theoptimal management in terms of sequestering SOC inagricultural soils. Our objectives were to: (1) estimate thepotential SOC stock (i.e. the maximum achievable SOC stockat equilibrium state under optimal management) and itsuncertainty under the representative cropping systems;(2) quantify the relative importance of cropping system andenvironmental conditions to predict the potential for SOCsequestration; and (3) investigate the uncertainty associatedwith the prediction of SOC sequestration potential in differentagro-ecological zones (AEZs) in the Australian cereal-growingregions.

Materials and methods

Soil profile and climate data

The ASPRU (Agricultural Production Systems Research Unit)database contains detailed soil-profile data for 613 referencesites distributed throughout the study region (available at www.asris.csiro.au/themes/model.html, Fig. 1). These are fullycharacterised soil profiles with information needed to run theAPSIM model, including soil bulk density, organic carbon andnutrient contents, hydraulic properties (saturation water content(SAT), drained upper limit (DUL), 15 bar lower limit (LL15)),and pH for each soil layer. Daily weather data from 1889 to2010, including daily global radiation, rainfall, and maximumand minimum temperatures, are available from the AustralianBureau of Meteorology weather stations. The climate data fromthe nearest station to each of the 613 soil sites were obtainedfrom the SILO Patched Point Dataset (www.longpaddock.qld.gov.au/silo/).

Cropping systems

Information on cropping system was based on the AEZsdefined by the Grain Research and Development Corporation(GRDC) of Australia. The GRDC classifies Australia’s grain-growing region into 18 AEZs (www.grdc.com.au/About-Us/GRDC-Agroecological-Zones) according to crop rotations,agricultural management regimes, and edaphic and climaticconditions. Five of the 18 zones were excluded in this studybased on either limited cropping areas or no available soil-profiledata. The remaining 13 zones cover almost the whole easternand western grain-growing belt, and >95% of the cropped areain Australia.

There are no detailed records available on the cropsequences in different zones, partly because of thecomplexity in spatiotemporal variations resulting from thesignificant spatiotemporal climate variability and farmers’different choices under resource limitation at farm level. Inaddition, there is little evidence that farmers maintain fixedcrop sequences from year to year. For modelling of SOCdynamics across the study regions, representative croprotations (or crop–pasture rotations) were developed based onconsultation with agronomists and agricultural consultants,and collation of the most frequently reported croppingrotations in each of the GRDC AEZs (Table 1). Each of thederived rotations represents a fixed sequence of crops–pastures.Nine crops (i.e. wheat, barley, canola, lupin, chickpea, fieldpea, faba bean, sorghum and cotton) were included in thoserotations. They are the major crops and account for the majorityof the total cropping area in Australia (Unkovich et al. 2009).Medicago sativa (known as lucerne or alfalfa) was assumed inthe crop–pasture rotations to represent the perennial pasture.Those representative cropping rotations were further dividedinto three categories: low-input, medium-input and high-inputcropping systems in terms of carbon input (IPCC 2003). Low-input cropping systems represent low residue return withremoval of residues, frequent bare-fallowing or production ofcrops yielding low residues (e.g. cotton). Medium-inputrotations are rotations with annual crops involving cerealsand legumes where all crop residues are returned to the field.High-input rotations represent crops yielding significantlygreater residues, use of green manure, cover crops, improvedvegetated fallows, and frequent use of perennial grasses in thecrop rotations (IPCC 2003; Table 1).

For most of the study region, single cropping rotations arethe dominant cropping systems. Wheat is the most widelyand frequently sown crop in Australia. As such, a simplified,continuous wheat system was constructed assuming that wheatwas sown every year across the whole study region. Althoughthis may not be practical in reality owing to disease outbreaks, itprovides a simplified system representing an annual cerealcropping system, allowing us to explore the spatiotemporalvariation of SOC dynamics.

APSIM model and simulations

The APSIM model was developed to simulate biophysicalprocesses in farming systems, and has been comprehensivelyverified and used to study productivity, nutrient cycling andenvironmental impacts of farming systems as influenced by

464 Soil Research Z. Luo et al.

climate variability and management interventions (Wang et al.2002; Keating et al. 2003; Probert et al. 2005; Luo et al. 2011).The model simulates crop growth and soil processes on a dailytime-step in response to climate (i.e. temperature, rainfall,radiation), soil water availability, and soil nutrient status (i.e.nitrogen (N) and phosphorus (P)) (Wang et al. 2003).

The SoilN module in APSIM simulates the dynamics of bothC and N in each soil layer (Probert et al. 1998). The SOC isdivided into four conceptual pools, and the decomposition ofeach pool is treated as a first-order decay process, leading to therelease of CO2 to the atmosphere and the transfer of theremaining decomposed C to other pools. The flow of Ndepends on the C :N ratio of the receiving pool.Decomposition of surface residue is simulated by theSurfaceOM module, taking into account the degree of contactof residue with soil to modify the maximum potentialdecomposition rate of residue. APSIM allows flexiblespecification of management options such as crop androtation type, tillage, residue management, fertilisation andirrigation. The ability of APSIM to simulate SOC dynamicshas been verified under different cropping systems and

management in several studies (Probert et al. 2005; Huthet al. 2010; Luo et al. 2011).

We predicted the potential SOC stock under the identifiedcrop rotations and under the simplified continuous wheat systemin each of the GRDC zones. Here, we define the potential SOCstock as the total SOC stock in the 0–30 cm soil layer atequilibrium under optimal management, i.e. optimalapplication of fertilisers (no nutrient deficiency) and 100%residue retention. For each crop rotation, crops were sownevery year depending on rainfall and soil water content,which varies for different regions and crops in the rotation.Cultivars were assigned according to sowing date—the earlierthe sowing date, the later the maturity type of the crop cultivar.For simplification, three cultivars for each crop representingearly, middle and later maturity cultivars were selected from thelisted default cultivars in the parameter files released withAPSIM model for each zone. For perennial lucerne, however,only one cultivar (i.e. Trifecta) was used. Crop residues (stemplus leaf) after harvest were retained in the system andincorporated into soil before sowing the next crop. Lucernewas sown and removed after harvest and before sowing of

Fig. 1. Location of the 613 reference soil sites (*) and the boundaries of the 13 GRDC agro-ecological zonesin Australia.

SOC stock impacted by cropping system Soil Research 465

Table 1. Representative crop rotations in 13 GRDC agro-ecological zones and the corresponding carbon inputcategories according to the Intergovernmental Panel on Climate Change (IPCC 2003)

Zones Representative rotations IPCC categories

NSW Central and NSW Vic Slopes Lucerne� 2–chickpea–wheat–wheat–field pea–barley High-inputLucerne� 2–chickpea–wheat–wheat–field pea–wheat High-inputLucerne� 2–chickpea–wheat–wheat–field pea–(barley or wheat) High-inputCanola–wheat–wheat–barley–lupin–wheat–wheat Medium-inputCanola–wheat–wheat–barley–chickpea–wheat–wheat Medium-inputCanola–wheat–wheat–barley–field pea–wheat–wheat Medium-inputLucerne� 4–canola–wheat–wheat–lupin–wheat High-inputLucerne� 4–canola–wheat–wheat–chickpea–wheat High-inputLucerne� 4–canola–wheat–wheat–faba bean–wheat High-inputCanola–wheat–canola–wheat–canola–wheat–lupin Medium-inputCanola–wheat–canola–wheat–canola–wheat–chickpea Medium-inputCanola–wheat–canola–wheat–canola–wheat–field pea Medium-inputLucerne� 4–wheat–canola–wheat–lupin–canola–wheat High-inputLucerne� 4–wheat–canola–wheat–field pea–canola–wheat High-inputLucerne� 4–wheat–canola–wheat–faba bean–canola–wheat High-input

NSW NE/Qld SE Wheat–cotton Low-inputCotton–sorghum–chickpea–wheat Low-inputSorghum–wheat–chickpea–wheat Low-inputWheat–chickpea–wheat–sorghum–wheat Low-inputWheat–wheat–chickpea–wheat–wheat–chickpea Medium-inputWheat–canola Medium-input

NSW NW/Qld SW Wheat–wheat–wheat–fallow Low-inputSorghum–wheat–chickpea–wheat–fallow Low-inputWheat–chickpea–wheat Medium-inputWheat–faba bean–wheat Medium-inputWheat–canola–wheat–sorghum–wheat Low-inputWheat–cotton–wheat Low-inputWheat–chickpea–wheat–faba bean–wheat–canola Medium-inputSorghum–wheat–wheat–fallow Low-inputSorghum–sorghum–wheat–fallow Low-input

NSW Vic Slopes Lucerne� 2–chickpea–wheat–wheat–field pea–barley High-inputLucerne� 2–chickpea–wheat–wheat–field pea–wheat High-inputLucerne� 2–chickpea–wheat–wheat–field pea–barley/wheat High-inputCanola–wheat–wheat–barley–lupin–wheat–wheat Medium-inputCanola–wheat–wheat–barley–chickpea–wheat–wheat Medium-inputCanola–wheat–wheat–barley–field pea–wheat–wheat Medium-inputLucerne� 4–canola–wheat–wheat–lupin–wheat High-inputLucerne� 4–canola–wheat–wheat–chickpea–wheat High-inputLucerne� 4–canola–wheat–wheat–faba bean–wheat High-input

Qld Central Sorghum–sorghum Medium-inputWheat–chickpea–wheat–sorghum Low-inputWheat–chickpea–wheat Medium-inputCotton–sorghum Low-inputWheat–wheat–wheat–fallow Low-input

SA Midnorth–Lower Yorke Eyre Faba bean–canola–wheat–wheat–barley Medium-inputLupin–canola–wheat–wheat–barley Medium-inputField pea–canola–wheat–wheat–barley Medium-inputLucerne–canola–wheat–barley–lucerne–faba bean–wheat–barley High-inputLucerne–canola–wheat–barley–lucerne–lupin–wheat–barley High-inputLucerne–canola–wheat–barley–lucerne–field pea–wheat–barley High-input

SA Vic Bordertown–Wimmera Lupin–canola–cereal Medium-inputFaba bean–canola–cereal Medium-inputField pea–canola–cereal Medium-input

SA Vic Mallee Faba bean–canola–wheat–wheat–barley Medium-inputLupin–canola–wheat–wheat–barley Medium-inputField pea–canola–wheat–wheat–barley Medium-input

466 Soil Research Z. Luo et al.

annual crops in the corresponding rotations, respectively.Harvest to the height of 10 cm was assumed wheneverlucerne reached the flowering stage.

For the continuous wheat system and the identifiedrepresentative crop rotations in the corresponding zone, theAPSIM model was run for 1022 years for each soil site tosatisfy the condition for SOC to reach the equilibrium state (Luoet al. 2013). Climate data from 1889 to 2010 (the period ofclimate record) were used for the first 122 years of simulations,and the data from 1911 to 2010 were repeated nine times for thefollowing 900 years of simulations.

Soil organic carbon stock in the top 30 cm soil profile in eachof the 1022-year simulations was output every year for each soilsite and each cropping system. The average SOC stock in the last100 years of simulations was used as the potential SOC stock atequilibrium state, i.e. Ce (Luo et al. 2013). The absolute changein SOC (Cc) after 1022 years of simulation was calculated as thedifference between the potential at equilibrium state and theinitial SOC at the start of the simulation (C0), i.e. Cc =Ce – C0.

Relative importance of cropping system

Soil and climate conditions (i.e. environment) and croppingsystem (i.e. management) are the two sources of variation in theSOC estimates in each GRDC AEZ in this simulation. In eachGRDC AEZ, the total variance of the SOC estimates (VT) can becalculated as the sum of the variance among environments (VE)and the variance among management within environments (VM;Sokal and Rohlf 1995):

VT ¼ VE þ VM ð1Þand

VE ¼ 1nc � 1

Xns

i¼ 1

1nc

Xnc

j¼ 1

qi; j � G

!2

ð2Þ

and

VM ¼ 1nsðnc � 1Þ

Xns

h¼ 1

Xnc

k¼ 1

qh; k � 1nc

Xnc

m¼ 1

qh;m

!2

ð3Þ

where ns and nc are the number of soils and cropping systems,respectively; q is the SOC estimate (i.e. Cc or Ce) in a typical soilunder a typical cropping system with i and h denoting the ithand hth soil, respectively, and j, k and m the jth, kth and mthcropping system, respectively; G is the grand mean of allestimates. The relative importance (Ir) of cropping system inVT was calculated as:

I r ¼ VM

VT� 100%: ð4Þ

The greater the value of Ir, the more important the croppingsystem.

Upscaling

The SOC estimates at the 613 sites and 59 cropping systemswere extrapolated to the whole cereal-growing region ofAustralia to assess the spatial pattern. To do this, all of thedata was first grouped into IPCC categories in terms of carboninput depending on the type of cropping system (IPCC 2003;Table 1). For the medium-input group, continuous wheat wasexcluded and treated as a reference group. The probabilitydensity distributions of both Ce and Cc were comparedbetween different groups. Using the same soil profile andclimate data as used in this study, Luo et al. (2013)developed a simple meta-model (summary model) driven byseveral primary drivers, including plant-available water capacityof soil, pH, total radiation, rainfall and average temperature.The meta-model could explain >70% of the variation of theAPSIM-simulated potential SOC stock under a continuous

Table 1. (continued )

Zones Representative rotations IPCC categories

Lucerne–canola–wheat–barley–lucerne–faba bean–wheat–barley High-inputLucerne–canola–wheat–barley–lucerne–lupin–wheat–barley High-inputLucerne–canola–wheat–barley–lucerne–field pea–wheat–barley High-input

Vic High Rainfall Lucerne� 3–wheat� 7 High-inputLucerne� 7–wheat� 3 High-input

WA Central Wheat–barley–canola–lucerne High-inputWA Eastern Wheat–barley–wheat–lucerne High-input

Wheat–barley–wheat–fallow Low-inputWheat–barley–wheat–canola Medium-input

WA Mallee Wheat–barley–wheat–lupin Medium-inputWheat–barley–wheat–canola Medium-inputWheat–barley–wheat–(lupin or canola) Medium-input

WA Northern Wheat–lupin–wheat–canola Medium-inputWheat–wheat–wheat–lucerne High-inputWheat–wheat–wheat–fallow Low-inputWheat–wheat–wheat–canola Medium-input

WA Sandplain Wheat–lupin–wheat–canola Medium-inputAll zones Continuous wheat Medium-input

SOC stock impacted by cropping system Soil Research 467

wheat system. The model was then used to predict potential SOCstock in the whole cereal-growing region of Australia at a spatialresolution of 0.018 by 0.018 (latitude� longitude) for thecontinuous wheat system.

Here, we adopted the approach of Luo et al. (2013) toextrapolate the point simulations to spatial distribution maps,by centring a 28 by 28 grid cell on each of the reference sites andcreating a continuous region by overlapping those grid cells.Each grid cell was further divided into 0.018 by 0.018 pixels (seedetails in Luo et al. 2013). We also adopted their predictions ofpotential SOC stock under the continuous wheat system (Luoet al. 2013), i.e. the reference cropping system, to estimate thepotential SOC stock for each of the pixels. We then used thecontinuous SOC map for continuous wheat and the pointestimates of relative change in SOC predicted under differentrepresentative cropping systems compared with the referencewheat cropping system at the soil sites to generate spatial mapsfor the SOC potential for each of the IPCC groups.

First, the whole study region was gridded by overlaying thegrid cells with GRDC AEZs. Some areas covered in Luo et al.(2013) are not covered by the GRDC zones, and were assumedto belong to the nearest GRDC zone. Second, in each GRDCzone, the relative change of Ce under each of the three IPCCgroups compared with continuous wheat were then calculated,and probability density functions (PDF) of the relative changeswere generated based on the site simulation results. For a givenzone, if a certain IPCC group was not available (say, high-inputcropping systems in the zone of Qld Central, Table 1), the valuesof the relative change in SOC in all other zones (where they areavailable) were used to construct the PDF for that IPCC group inthat specific zone. Third, the PDFs were used together with theSOC predictions for continuous wheat to estimate Ce under eachof the three IPCC groups in each pixel (0.018 by 0.018) throughMonte Carlo simulations.

Uncertainty analysis

As the number of simulations varied significantly in each GRDCzone for each cropping system, the Monte Carlo approach wasused to estimate the range of potential SOC stock at equilibriumstate (Ce) as caused by possible variations in cropping systemand environment in each zone. We randomly drew 2000 MonteCarlo replicates from the PDFs constructed for cropping systemsunder each of the IPCC categories in each of the GRDC zones toensure the convergence of the samples. The estimate of Ce wasconducted in following steps.

First, an estimate for a pixel was computed for each MonteCarlo replicate i using the following equations:

yi; j; k ¼ w� rij; k ; i ¼ 1; 2; . . .; 2000;

j ¼ 1; 2; 3; k ¼ 1; 2; . . .; 13ð5Þ

and

rij; k � Nðmj; k ; s2j; kÞ ð6Þ

where w is the Ce under the reference wheat system in thecorresponding pixel adopted from Luo et al. (2013); and rij, k isthe relative value of Ce for IPCC group j compared with thereference wheat system, sampled from a normally distributed

population with the mean of mj, k and variance of s2j, k for the jth

IPCC group of cropping system in the kth GRDC zone (i.e. PDFderived in last section, the relevant data being log-transformed ifnecessary to ensure normality).

Second, the average of the estimates for each IPCC groupwas calculated as the average of the m (i.e. 2000) Monte Carloreplicates:

yj; k ¼1m

Xm

i¼ 1

yi; j; k ð7Þ

Third, the 95% confidence interval was calculated as the 2.5and 97.5 percentiles of these 2000 estimates to indicate theuncertainty of the estimates. For each estimate, the absolutechange of SOC at equilibrium state (Cc), i.e. the differencebetween Ce and the initial SOC stock, was also estimated basedon the estimation of initial SOC stock by Luo et al. (2013). The95% confidence interval for Cc was calculated following thesame method as Ce. All the analyses were conducted using R2.15.2 (R Core Team 2012) including fields, lattice, maptools,raster, sp and other default packages.

Results

Averaged across all of the representative cropping systems (e.g.all input categories), the predicted potential SOC stocks rangedfrom 10 to 140 t ha–1, with the majority in a range 30–70 t ha–1

(Fig. 2a). The predicted SOC net change from the current state(as represented in the APSRU database) ranged from –30 to+120 t ha–1, with most in the range –10 to +50 t ha–1 (Fig. 2b).These values represent the maximum SOC stocks or changesthat can be achieved with the defined cropping systems acrossthe grain-growing regions. Of the total variance in predictedpotential SOC stocks and net change, cropping systemaccounted for 10% and 9%, respectively, on average(Table 2). In Vic High Rainfall AEZ, the contribution ofcropping system to the total variance of potential SOC stockswas the highest (38%). For SOC change, the highest contributionof cropping system (28%) occurred in NSW NW/Qld SW.

With the continuous wheat system, the simulated potentialSOC stock showed a similar pattern to those averaged across allrepresentative cropping systems across regions (Fig. 2a v.Fig. 2c, Fig. 2b v. Fig. 2d). With the continuous wheatsystem, the SOC potential has a slightly higher average (andmedian) and a relatively narrower range (Fig. 2a v. Fig. 2c,Fig. 2b v. Fig. 2d).

Cropping system in terms of carbon input had a significanteffect (F(3,4339) = 937, P< 0.001) on potential SOC stock atequilibrium (Ce). The Ce was significantly decreased andincreased under low- and high-input cropping systems,respectively, compared with medium-input cropping systemsunder which, as expected, Ce was comparable to that under thereference continuous wheat system, which also belonged to themedium-input group (Figs 2 and 3). On average, Ce was 52, 29,50 and 60 t ha–1 under the reference wheat, low-input, medium-input and high-input systems, respectively. Cropping systemalso had significant effect on the absolute change in SOC (Cc)(F(3,4339) = 751.1, P < 0.001), and showed a similar pattern to itseffect on Ce (Fig. 2). On average, Cc showed an increase by 18,

468 Soil Research Z. Luo et al.

17 and 25 t ha–1 under the reference wheat, medium-inputand high-input systems, respectively (Fig. 2d, f, h). Under thelow-input cropping systems, on the contrary, Cc showed adecrease by 2 t ha–1 (Fig. 2j).

Between GRDC AEZs, the same IPCC group of croppingsystems could induce significant differences in Ce (Fig. 3). Forexample, Ce under medium-input systems was significantlyhigher than under low-input systems in WA Eastern, but theywere similar in Qld Central. Additionally, the within groupvariability of Ce was also greatly different between GRDCAEZs. These results demonstrated that it is necessary toconsider the between-zone variance of the effect of croppingsystem when upscaling the point-scale results to regional scale.

Across the entire study area, the potential SOC stock was51 t ha–1, with a 95% confidence interval ranging from 38 to64 t ha–1 under identified representative cropping systems (i.e.all input; Fig. 4a–c), which is comparable with that under themedium-input cropping systems (Fig. 4g–i). Under high-inputcropping systems, the potential SOC stock was increased to59 t ha–1 with a 95% confidence interval ranging from 46 to

0

0.005

0.010

0.015

0.020

0.025(a) (b)

(c) (d )

(e ) (f)

(g ) (h )

(i ) (j)

All input All input

Wheat Wheat

0

0.005

0.010

0.015

0.020

0.025

0.030

00.0050.0100.0150.0200.0250.0300.035

Den

sity

00.0050.0100.0150.0200.0250.0300.035

0

0 20 40 60 80 100 120 140–40 –20 0 20 40 60 80 100

0.02

0.04

0.06Low-input

Soil organic carbon content (t ha–1)

Low-input

High-input High-input

Medium-inputMedium-input

Fig. 2. Probability density distribution (left-hand side) of soil organic carbon stock and change at equilibrium state(right-hand side) in the 0–30 cm soil layer under (a and b) all representative cropping systems, (c and d) continuouswheat, (e and f) high-input, (g and h) medium-input, (i and j) and low-input rotations based on the 1022-year simulationsat the 613 reference sites. Vertical grey solid and dashed lines show the mean and median of the distribution, respectively.

Table 2. Number of soils (ns) and cropping systems (nc) in each GRDCagro-ecological zone (AEZ) of Australia, and the relative importance (Ir)of cropping systems in total variance in predicted potential of soil

organic carbon sequestration (Ce) and change (Cc)

GRDC AEZ ns nc Ir (%)Ce Cc

Qld Central 9 6 16 15NSW NW/Qld SW 27 10 27 28NSW NE/Qld SE 137 7 5 4NSW Central 22 13 10 17NSW Vic Slopes 68 7 7 4SA Midnorth–Lower Yorke Eyre 110 4 1 2SA Vic Mallee 30 4 <1 <1SA Vic Bordertown–Wimmera 90 3 12 9Vic High Rainfall 5 2 38 17WA Sandplain 24 3 1 <1WA Northern 59 4 4 4WA Eastern 7 4 14 12WA Central 25 2 <1 <1

Average – – 10 9

SOC stock impacted by cropping system Soil Research 469

72 t ha–1 (Fig. 4d–f). Under low-input cropping systems, thepotential SOC stock was decreased to 38 t ha–1 with a 95%confidence interval ranging from 31 to 46 t ha–1 (Fig. 4j–l).Spatially, SOC stock exhibited relatively similar patternsacross, low-, medium- and high-input cropping systems(Fig. 4). Generally, SOC stock decreased from south-west tonorth-east in Western Australia (WA), and decreased fromsouth-east to north-west in other parts of study area. In theregions with higher SOC stock, however, there was greateruncertainty (i.e. wider range of the 95% confidence interval).This resulted in increasing uncertainty in the prediction fromnorth-east to south-east in WA, and from north-west to south-east in the other parts of the study area (Fig. 4).

The absolute change in SOC stock showed similar patternsto the potential SOC stock (Fig. 5). On average, the absolutechange in SOC was +7 t ha–1, with a 95% confidence intervalranging from –6 to +20 t ha–1 under identified representativecropping systems across the entire region (Fig. 5a–c), which iscomparable to that under the medium-input cropping systems(Fig. 5g–i). Under high-input cropping systems, its 95%

confidence interval ranged from +2 to +28 t ha–1 with a meanof +15 t ha–1 (Fig. 5d–f ). Under low-input cropping systems, theabsolute change in SOC was –6 t ha–1 with a 95% confidenceinterval ranging from –13 to +2 t ha–1 (Fig. 5j–l). Regionally, thesoils showed net loss in the north-east of WA and the north-westof the rest of the study region, and net gains in south-west ofWAand the south-east of the rest of the region regardless of croppingsystem (Fig. 5). The greatest gains (>100 t ha–1) occurred on theedge of the study area near the border between New SouthWalesand Victoria belonging to the Vic High Rainfall and NSW VicSlopes of GRDC AEZs (Figs 1 and 5). In Qld Central, SOCshowed the greatest loss of >10 t ha–1. In most other regions,SOC change could be either negative or positive depending oncropping system and environmental conditions (Fig. 5).

Discussion

The simulation results in this study demonstrate that the type ofcropping system has a significant effect on potential SOC stock.Pooling all of the representative cropping systems (all inputs in

Relative change

WA Central

0.6 0.8 1.0 1.2 1.4

WA Eastern

WA Northern

WA Sandplain

Vic High Rainfall

SA Midnorth−Lower Yorke Eyre

SA Vic Bordertown−Wimmera

SA Vic Mallee

NSW Vic Slopes

NSW Central

NSW NE/Qld SE

NSW NW/Qld SW

Qld Central

All region

All inputLow-inputMedium-input

High-input

Fig. 3. Relative change in soil organic carbon sequestration potential in the 0–30 cm soil layer under allrepresentative cropping systems (i.e. all input), low-input, medium-input and high-input croppingsystems compared with the reference continuous wheat system in 13 GRDC agro-ecological zones.Values are means� standard deviation. Vertical dotted line shows the baseline of the referencewheat system.

470 Soil Research Z. Luo et al.

Figs 2 and 3) at national level, the average SOC stock atequilibrium state is predicted to be ~50 t ha–1 in Australia’scropland, corresponding to net SOC gains of 15 t ha–1 fromthe current state (as represented in the APSRU soil data). This

result confirms that the agricultural soils of Australia may be anet sink of atmospheric carbon dioxide if conservationagricultural practices (Dalal and Chan 2001; Sanderman et al.2010; Luo et al. 2013) such as sufficient nutrient supply and

Lower limit

All

inpu

tH

igh-

inpu

tM

ediu

m-in

put

Low

-inpu

t

(a) (b) (c)

(d) (e) (f )

(g) (h) (i )

( j) (k)

10 30

Soil organic carbon content (t ha–1)

50 70 90 110 130

(l)

Average Upper limit

Fig. 4. Regional pattern of the potential soil organic carbon stock (Ce, t ha–1) at the equilibrium state in the 0–30 cm soil layer under all representative

cropping systems (i.e. all input), high-, medium- and low-input systems (from upper to lower panels) in Australia’s cereal-growing regions. Upper, average,and lower limits of the 95% confidence intervals are shown from left to right panels.

SOC stock impacted by cropping system Soil Research 471

100% residue retention are adopted. However, the effect ofcropping system must be considered for more reliablepredictions. Under low-input cropping systems, for example,average SOC stock at equilibrium state is predicted to be

~30 t ha–1 (20 t ha–1 less than pooling all inputs) and todecrease by 2 t ha–1 compared with current state (i.e. netloss), even though optimal management is applied. Underhigh-input cropping systems, SOC stock is 10 t ha–1 higher

Lower limit

All

inpu

tH

igh-

inpu

tM

ediu

m-in

put

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-inpu

t

(a) (b) (c)

(d) (e) (f )

(g) (h) (i )

( j) (k)

–40 –10 10 30 50 70 90 120

Soil organic carbon change (t ha–1)

(l)

Average Upper limit

Fig. 5. Regional pattern of soil organic carbon change (Cc, t ha–1) at the equilibrium state in the 0–30 cm soil layer under all representative cropping

systems (i.e. all input), high-, medium- and low-input systems (from upper to lower panels) in Australia’s cereal-growing regions. Upper, average, and lowerlimits of the 95% confidence intervals are shown from left to right panels.

472 Soil Research Z. Luo et al.

than under all inputs and medium-input cropping systems. Theseresults suggest that adopting cropping systems with highercarbon input can increase the efficiency of SOC sequestrationin agricultural soils (West and Post 2002; Gaiser et al. 2009;Luo et al. 2010), but the effects of increased SOC on othergreenhouse gas emissions, including methane and nitrousoxide (Schulze et al. 2010; Stocker et al. 2013), and theeconomic implications (Grace et al. 2012) also need to beconsidered.

The effect of cropping system on potential SOC stockexhibits great variability between AEZs. Compared with SOCunder the continuous wheat system as the baseline, the SOCstock under representative cropping systems is much lower innorthern zones (i.e. Qld Central) than that in southern zones(i.e. NSW Central, Fig. 3). This may attributable to the differentspecies in the crop sequences, which results in different carboninput relative to wheat. For example, sowing frequency of peas(low biomass production and thus carbon input relative to wheat)in northern zones is much higher than that in southern zones(Table 1). In addition, the within-zone variation of the SOCstock under the same carbon input category is also differentbetween zones. This suggests the importance of soil and climateconditions for regulating the efficiency of cropping systems tosequester SOC (Gaiser et al. 2009; Franzluebbers et al. 2012).Compared with soil and climate conditions, the variation inpredicted potential SOC stock and change induced by croppingsystem is relative small (Table 2). Consequently, the diversity ofcrop sequence and its interaction with soil and climate must beconsidered to reduce the uncertainty in SOC predictions at aregional scale.

Our results suggest that soils in most of Australia’s southerngrain-growing regions may be able to sequester carbon under thetypical cropping systems currently practiced provided bestmanagement practices are adopted, i.e. optimal application offertilisers and 100% residue retention. The regional calculationshows that compared with initial SOC stock (as represented inthe APSRU soil data), SOC stock is increased by 0.99 Pg, onaverage, with a 95% confidence interval from –0.89 to 2.86 Pg.This value is similar to the value of 1.04 Pg estimated by asimple reversed calculation by comparing SOC content inuncultivated soils with that in cultivated soils (Dalal andChan 2001). For this estimate, however, we assume theoptimal agricultural practices with no nutrient deficiency and100% residue retention across Australia’s cereal-growingregions. In reality, the rate of N applied is likely to be lowerthan the optimal rate (Wang et al. 2008; Luo et al. 2013), andcrop growth may be limited by other nutrients, such as P andsulfur. To sequester SOC, additions of multiple nutrients arerequired in order to match the stoichiometry of the soilorganisms and soil organic matter (Hessen et al. 2004;Kirkby et al. 2013). Therefore, the current predictions mayoverestimate the potential capacity of SOC sequestrationacross the grain regions. Additionally, we lack information onthe proportions of growing area of different cropping systems ineach GRDC AEZ, and assume an equal probability of growingfor each cropping system in each zone. The assumption of equalprobability of growing for each cropping system would inducebias of the estimation as the great variability of potential SOCstocks under different cropping systems (Fig. 3).

At the national scale, however, there is significant uncertaintyin potential SOC stock relating to cropping systems, temperatureand rainfall regimes across the study area. In the northernregions of the study area where the climate is warmer, SOCstock is relatively low, with most SOC stock ranging from 10 to40 t ha–1 regardless of cropping system. At the south-easternedge of the studied regions with higher rainfall and lowertemperature, the agricultural soils are predicted to be able toaccumulate >100 t ha–1 of SOC during the 1022-year simulationsunder optimal management (100% residue retention and no Ndeficiency), particularly under high-input cropping systems.Zhao et al. (2013) found a similar pattern of SOC changebased on 100-year simulations of a continuous wheat systemand demonstrated that initial SOC stock is the predominantfactor controlling the SOC change, with lower initial SOCstock leading to greater SOC accumulation (Goidts et al.2009). In addition, both lower temperature and higher rainfallcan benefit SOC accumulation, because lower temperature canreduce the decomposition rate and higher rainfall can promotecarbon input. In an experimental study, Chan et al. (2010)found that improved pasture (high-input system) in high-rainfall regions could sequester SOC at a rate of0.72 t ha–1 year–1, and this sequestration could be maintainedfor decades. Based on analysis of data from three long-termexperiments at Wagga Wagga, Chan et al. (2011) furtherindicated that improved pasture could increase SOC stock bya rate of 0.5–0.7 t ha–1 year–1, which could be expected for along period, particularly when initial SOC stock is well belowthe maximum level at steady-state. Considering the hot and dryclimate at Wagga Wagga, it is reasonable to have a higherprediction of SOC accumulation in the cooler and wetter south-eastern area of the study region, as estimated in this study.However, there is a much higher uncertainty in the highestimates of SOC accumulation in those cooler and/or wetterregions, which mainly resulted from the variation in soilconditions and cropping systems. To sequester SOC in coolerand/or wetter regions, cropping system must be carefullychosen. In general, the simulation results suggest thatadopting a cropping system of higher carbon input in coolerand/or wetter regions would be more efficient for sequesteringcarbon in agricultural soils.

Acknowledgements

This study was supported by grants from the Australian Government throughthe Department of Agriculture, Fisheries and Forestry (DAFF) and theGrains Research and Development Corporation (GRDC). We gratefullyacknowledge the contributions from the following people who have helpedon the derivation of the input data layers and representative croppingrotations across the study region: Maureen Cribb, Zvi Hochman, DavidJacquier, Garry O’Leary, Deli Liu, John Kirkegaard, Andrew Moore, Harmvan Rees, Cameron Weeks and Jeremy Whish. We thank Mark Farrelland Julianne Lilley for their comments and suggestions on the early versionof the paper.

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