Soil organic carbon (SOC) in agricultural soils is vital for soil fertility for sustainable agricultural production and climate change resilience. Process-based farming system models are widely used to predict SOC dynamics in agricultural soils, but their application at regional scales is largely limited by computational requirements, data availability, and uncertainties in model predictions. Here we present an approach of combining a farming system model and a simplified surrogate model that emulates and mimics the behaviour of complex process-based models to predict SOC change (Delta SOC) and its uncertainty in Australian dryland cropping regions under anticipated climate change. We first calibrated and validated the farming system model APSIM for simulating Delta SOC (0-30 cm soil) using data from 90 farming-system trials at 28 sites across the study regions. Next we conducted a comprehensive simulation across the region using the validated APSIM model to predict Delta SOC over the period 2009-2070. Then simple surrogate models were developed based on the APSIM outputs. The surrogate models were able to explain > 96% of the variation in APSIM-predicted Delta SOC. Last the surrogate models were applied across the regions at the resolution of 1 km. In our simulations, Australian dryland cropping soils under farmers' common management practices and future climate conditions were a net carbon source (0.66 Mg C ha(-1) with the 95% confidence interval ranging from -5.79 to 8.38 Mg C ha(-1)) during the 62-year period. Across the regions, simulated Delta SOC exhibited great spatial variability ranging from -108.8 to 9.89 Mg C ha(-1) at the resolution of 1 km, showing significant (P < 0.05) negative correlation with baseline SOC level, temperature and rainfall, and positive correlation with pasture frequency (the duration of pasture in the rotation divided by the whole duration of the rotation) and nitrogen application rate. The uncertainty in Delta SOC and the underlying drivers were also assessed. This study presented a novel approach to efficiently predict future SOC dynamics and their uncertainty at fine resolutions, facilitating the development of site-specific management strategies for soil carbon sequestration.
1.CSIRO Agr & Food, Canberra, ACT 2601, Australia 2.CSIRO Agr & Food, Armidale, NSW 2350, Australia 3.Life Cycle Strategies, Fitzroy, Vic 3065, Australia 4.Wagga Wagga Agr Inst, NSW Dept Primary Ind, Wagga Wagga, NSW 2650, Australia 5.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia 6.Univ New South Wales, ARC Ctr Excellence Climate Syst Sci, Sydney, NSW 2052, Australia 7.NSW Dept Primary Ind, Trevenna Rd, Armidale, NSW 2351, Australia 8.Univ New England, Armidale, NSW 2351, Australia 9.CSIRO Agr & Food, Glen Osmond, SA 5064, Australia 10.Orange Agr Inst, NSW Dept Primary Ind, 1447 Forest Rd, Orange, NSW 2800, Australia 11.CSIRO Land & Water, Canberra, ACT 2601, Australia 12.CSIRO Land & Water, Brisbane, Qld 4001, Australia
Recommended Citation:
Luo, Zhongkui,Eady, Sandra,Sharma, Bharat,et al. Mapping future soil carbon change and its uncertainty in croplands using simple surrogates of a complex farming system model[J]. GEODERMA,2019-01-01,337:311-321