Assessment on the rates and potentials of soil organic carbon sequestration in agricultural lands in Japan using a process-based model and spatially explicit land-use change inventories – Part 2: Future potentials

Abstract

<p><strong>Abstract.</strong> Future potentials of the sequestration of soil organic carbon (SOC) in agricultural lands in Japan were estimated using a simulation system we recently developed to simulate SOC stock change at country-scale under varying land-use change, climate, soil, and agricultural practices, in a spatially explicit manner. Simulation was run from 1970 to 2006 with historical inventories, and subsequently to 2020 with future scenarios of agricultural activity comprised of various agricultural policy targets advocated by the Japanese government. Furthermore, the simulation was run subsequently until 2100 while forcing no temporal changes in land-use and agricultural activity to investigate duration and course of SOC stock change at country scale. <br><br> A scenario with an increased rate of organic carbon input to agricultural fields by intensified crop rotation in combination with the suppression of conversion of agricultural lands to other land-use types was found to have a greater reduction of CO<sub>2</sub> emission by enhanced soil carbon sequestration, but only under a circumstance in which the converted agricultural lands will become settlements that were considered to have a relatively lower rate of organic carbon input. The size of relative reduction of CO<sub>2</sub> emission in this scenario was comparable to that in another contrasting scenario (business-as-usual scenario of agricultural activity) in which a relatively lower rate of organic matter input to agricultural fields was assumed in combination with an increased rate of conversion of the agricultural fields to unmanaged grasslands through abandonment. <br><br> Our simulation experiment clearly demonstrated that net-net-based accounting on SOC stock change, defined as the differences between the emissions and removals during the commitment period and the emissions and removals during a previous period (base year or base period of Kyoto Protocol), can be largely influenced by variations in future climate. Whereas baseline-based accounting, defined as differences between the net emissions in the accounting period and the ex ante estimation of net business-as-usual emissions for the same period, has robustness over variations in future climate and effectiveness to factor out some of the direct human-induced effects such as changing land-use and agricultural activity. <br><br> Factors affecting uncertainties in the estimation of the country-scale potential of SOC sequestration were discussed, especially those related to estimation of the rate of organic carbon input to soils under different land-use types. Our study suggested that, in order to assist decision making of policy on agriculture, land management, and mitigation of global climate change, it is also important to take account of duration and time course of SOC sequestration, supposition on land-use change pattern in future, as well as feasibility of agricultural policy planning.</p>

Modeling European ruminant production systems: Facing the challenges of climate change

“…There are also dynamic deterministic models of soil processes, such as DNDC (Li et al, 1992) and DailyDayCent (Parton et al, 1998), which represent crop growth using empirical functions. Many of the models can be applied to a range of plant species (Yagasaki and Shirato, 2013) and are typically verified at a small number of sites, where detailed data can be readily obtained (El-Maayar and Sonnentag, 2009;Yagasaki and Shirato, 2014).…”

Coupling Remote Sensing with a Process Model for the Simulation of Rangeland Carbon Dynamics

“…However, despite its rapid and cost-effective nature, this approach is less frequently used for estimating changes in SOC stocks or C fluxes, primarily due to the scarcity of data on changes in SOC that are needed for statistical model training and verification. In contrast to purely data-driven empirical upscaling approaches, process-based models incorporate mathematical representations of underlying system processes, such as heat transfer, hydrologic flows, and C cycling (Doblas-Rodrigo et al, 2022;Khalil et al, 2020;Yagasaki & Shirato, 2014). Consequently, they possess the capability to generate processbased outcomes (e.g., SOC stock changes) and scenario-based estimates (e.g., C fluxes under different climate and management conditions) for longer term predictions.…”