A model for mechanistic and system assessments of biochar effects on soils and crops and trade-offs

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• 作者：Sotirios V. Archontoulis ; Isaiah Huber ; Fernando E. Miguez ; Peter J. Thorburn ; Natalia Rogovska and David A. Laird
• 刊名：GCB Bioenergy
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：8
• 期：6
• 页码：1028-1045
• 全文大小：1237K
• ISSN：1757-1707

文摘

We developed a biochar model within the Agricultural Production Systems sIMulator (APSIM) software that integrates biochar knowledge and enables simulation of biochar effects within cropping systems. The model has algorithms that mechanistically connect biochar to soil organic carbon (SOC), soil water, bulk density (BD), pH, cation exchange capacity, and organic and mineral nitrogen. Soil moisture (SW)–temperature–nitrogen limitations on the rate of biochar decomposition were included as well as biochar-induced priming effect on SOC mineralization. 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. The biochar model was connected to APSIM's maize and wheat crop models to investigate long-term (30 years) biochar effects on US maize and Australia wheat in various soils. Results from this sensitivity analysis showed 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): SOC (11% to 86%) > N₂O emissions (−10% to 43%43%) > plant available water content (0.6% to 12.9%) > BD (−6.5% to −1.7%) > pH (−0.8% to 6.3%) > net 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%). Our 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.