Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

中文翻译：

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微生物显式过程和精致的多年生植物性状改善了模拟生态系统碳动态

在全球范围内，土壤容纳了大约一半的生态系统碳，并且可以作为源或汇，具体取决于气候、植被、管理和干扰制度。了解土壤碳动态如何受到这些因素的影响对于评估拟议的自然气候解决方案和有关净生态系统碳平衡的政策至关重要。土壤微生物在碳通量和稳定性方面发挥着关键作用。然而，生物地球化学模型通常不专门解决微生物显性过程。在这里，我们将微生物显式过程纳入 DayCent 生物地球化学模型中，以更好地代表大型多年生草类以及土壤碳形成和稳定的机制。我们还利用最近的模型改进来更好地代表多年生草的结构复杂性和生活史特征。具体来说，本研究重点关注：1）代表多年生物候学和更精细的植物化学的植物子模型，对通过凋落物输入进行土壤有机质（SOM）循环的下游影响；2）影响微生物路径的活和死土壤微生物库碳到物理保护和未保护的池中，3) 土壤分解计算中的米氏动力学而不是一级动力学，4) 分解和活微生物池之间的反馈。我们使用净生态系统生产、生态系统呼吸、土壤呼吸、微生物生物量和土壤的观测结果评估了植物子模型和两个 SOM 循环子模型（米氏 (MM) 和一阶 (FO)）的性能来自美国中西部长期生物能源研究地块的碳。MM 子模型比 FO 子模型更好地代表了土壤碳通量的季节性动态，后者始终高估了冬季土壤呼吸。虽然两个 SOM 子模型都针对总的、物理保护的和物理未保护的土壤碳测量进行了类似的校准，但这些模型在未来土壤碳对干扰和气候的响应方面有所不同，尤其是在受保护的池中。将土壤过程的微生物显性机制添加到生态系统模型中将改善生态系统碳平衡的模型预测，但需要更多的数据和研究来验证干扰和气候变化响应以及土壤库分配。