Machine learning based reduced-order models to predict spatiotemporal dynamics of soil carbon and biomass yield of different bioenergy crops

作者：Sagar Gautam, Umakant Mishra, Corinne D. Scown · 发表于：Carbon Capture Science & Technology · 年份：2025 · DOI：10.1016/j.ccst.2025.100440 · 被引用次数：3 · 研究领域：Bioenergy crop production and management、Biofuel production and bioconversion、Forest Biomass Utilization and Management

Agroecosystem models are commonly used to predict the effects of management practices and environmental changes on biomass yields, and soil organic carbon (SOC) dynamics under different bioenergy crops. However, the computational and data requirements of these models limit their scalability across multiple scenarios and timescales, particularly when trying to capture the range of outcomes under different scenarios. Machine learning (ML) offers a way to streamline these complex processes, enabling efficient modeling with substantially less computational resources. In this study, we combined ML model with an agroecosystem model Daily Century (DAYCENT) to project baseline (2009–2018) and future (2021–2100) biomass yields and SOC changes for three bioenergy crops:Miscanthus, sorghum, and switchgrassacross U.S. agricultural lands . These projections were made under the Shared Socio-economic Pathway 8.5 (SSP5 8.5), using data from the coupled model intercomparison project phase six models. The ML-based reduced order model, trained on field observations and DAYCENT outputs, accurately predicted baseline biomass yields with R 2 values ranging from 0.96 to 0.98, and SOC changes with R 2 values between 0.93 and 0.98 across the three bioenergy crops. Under a SSP5 8.5 scenario, Miscanthus and sorghum exhibited lower sensitivity to precipitation and temperature impacts in terms of biomass yield and SOC changes compared to switchgrass . Sorghum and Miscanthus are projected to see an increa...