This study aimed to develop a comprehensive spatial assessment of soil carbon sequestration potential for supporting future decisions on soil carbon stock monitoring and management. It provides a practical and effective framework that supports targeted policymaking for soil carbon management and climate mitigation.
Abstract
Firstly, the spatial distribution of soil organic carbon (SOC) and mineral associated organic carbon (MAOC) was mapped at the 500 m resolution at the northern half of Ireland using machine learning tools with environmental covariates and soil geochemistry.
Random forest models achieved robust estimations of SOC and MAOC, with R2 values of 0.69 and 0.73 respectively.
Afterwards, carbon sequestration potential was assessed using two complementary approaches:
(1) quantile regression to estimate MAOC saturation and deficit, and
(2) a data-driven method using agroclimatic-landcover units to estimate achievable SOC levels.
These approaches revealed contrasting spatial patterns, reflecting different carbon storage mechanisms constrained by mineralogy versus climate-land cover conditions.
Hence, we integrated both methods into a comprehensive framework to support decision-making for carbon farming.
Classified by below or above the mean value of estimated SOCseq and MAOCseq, a four-classification framework of high/low SOCseq by high/low MAOCseq was established and mapped to delineate areas in each class with distinct sequestration potential characteristics and corresponding management strategies.
These included dual saturation areas (Low SOCseq–Low MAOCseq: 34.87%) requiring protection strategies and dual high potential areas (High SOCseq–High MAOCseq: 32.93%) ideal for carbon farming projects, with other areas having specific limitations in either MAOC (High SOCseq–Low MAOCseq: 20.24%) or SOC (Low SOCseq–High MAOCseq: 11.95%) accumulation.
This provides a practical and effective framework that supports targeted policymaking for soil carbon management and climate mitigation.