Archontoulis SV, Huber I, Miguez FE, Thorburn PJ, Rogosvka N, Laird DA, 2016. A model for mechanistic and system assessments of biochar effects in soils and crops and trade-offs. GCB-Bioenergy, doi:10.1111/gcbb.12314
To develop a mechanistic biochar simulation model to address complex interactions between biochar types, soils, crops, climate and management
We developed the biochar model within APSIM by synthesizing broad literature information. The model has algorithms that mechanistically connect biochar to soil organic carbon (SOC), soil water, bulk density, pH, cation exchange capacity, and organic and mineral nitrogen (Fig. 1). Soil moisture– temperature–nitrogen limitations on the rate of biochar decomposition were included as well as biochar-induced priming effect on fresh and soil organic matter mineralization (Fig. 1). The model has 10 parameters that capture the diversity of biochar types, 15 parameters that address biochar-soil interactions and 4 constants. The range of values and their sensitivity is reported together with model performance again field data (Fig. 2).
We used the model to investigate long-term (30 years) biochar effects on US maize and Australia wheat in various soils. Results indicated that the effect of biochar was the largest in a sandy soil (Australian wheat) and the smallest in clay loam soil (US maize). On average across cropping systems and soils the order of sensitivity and the magnitude of the response of biochar to various soil-plant processes was (from high to low; Fig. 3): SOC (11% to 86%) > N2O emissions (–10% to 43%) > plant available water content (0.6% to 12.9%) > BD (–6.5% to –1.7%) > pH (–0.8% to 6.3%) > net soil N mineralization (–19% to 10%) > CO2 emissions (–2.0% to 4.3%) > water filled pore space (–3.7% to 3.4%) > grain yield (–3.3% to 1.8%) > biomass (–1.6% to 1.4%). This analysis showed that biochar has a larger impact on environmental outcomes rather than agricultural production. The mechanistic model has the potential to optimize biochar application strategies to enhance environmental and agronomic outcomes but more work is needed to ﬁll knowledge gaps identiﬁed in this work. The biochar model is currently undergoes extensive calibration using a variety of lab and field datasets.
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