Modeling soil organic carbon evolution in long-term arable experiments with AMG model
Modeling soil organic carbon evolution in long-term arable experiments with AMG model
复制标题
DOI：
10.1016/j.envsoft.2019.04.004
复制
发表时间：
2019-08-01
影响因子：
4.9
通讯作者：
Mary, Bruno
中科院分区：
文献类型：
--
作者：
Clivot, Hugues;Mouny, Jean-Christophe;Mary, Bruno
预测土壤有机碳（SOC）演变的可靠模型，需要更好地管理种植系统，以减缓气候变化和改善土壤质量的目标。在这项研究中，从60个选定的长期田间试验在法国进行的耕地系统的数据被用来评估AMG模型的修订版集成了一个新的矿化子模型。使用随机森林分析确定的SOC演变的驱动因素与AMG中考虑的驱动因素一致。该模型及其默认参数化准确地模拟了SOC库存随时间的变化，相对模型误差（RRMSE = 5.3%）与测量误差（CV = 4.3%）相当。模型性能的影响不大的植物C输入估计方法的选择，但改善了特定的网站优化SOC池分区。AMG显示出很好的潜力，在不同的气候，土壤性质和作物管理的情况下预测SOC的演变。
Reliable models predicting soil organic carbon (SOC) evolution are required to better manage cropping systems with the objectives of mitigating climate change and improving soil quality. In this study, data from 60 selected long-term field trials conducted in arable systems in France were used to evaluate a revised version of AMG model integrating a new mineralization submodel. The drivers of SOC evolution identified using Random Forest analysis were consistent with those considered in AMG. The model with its default parameterization simulated accurately the changes in SOC stocks over time, the relative model error (RRMSE = 5.3%) being comparable to the measurement error (CV = 4.3%). Model performance was little affected by the choice of plant C input estimation method, but was improved by a site specific optimization of SOC pool partitioning. AMG shows a good potential for predicting SOC evolution in scenarios varying in climate, soil properties and crop management.