Potential soil organic carbon stock and its uncertainty under various cropping systems in Australian cropland
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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: firstname.lastname@example.org
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 Australias 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 ha1,with the majority in a range 3070 t ha1, 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 ha1 under low-, medium- and high-input cropping systems in terms of carbon input,corresponding to 2, 18 and 26 t ha1 of SOC change. Across the entire region, theMonte Carlo simulations showed that thepotential SOC stock was 51 t ha1, with a 95% confidence interval ranging from 38 to 64 t ha1 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
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 farmers choice, climate andsoil conditions. In Australias 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(lvaro-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;
Journal compilation CSIRO 2014 www.publish.csiro.au/journals/sr
CSIRO PUBLISHINGSoil Research, 2014, 52, 463475http://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 inAustralias 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/).
Information on cropping system was based on the AEZsdefined by the Grain Research and Development Corporation(GRDC) of Australia. The GRDC classifies Australias 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 farmersdifferent 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 croppasture 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 cropspastures.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 croppasture rotations to represe