A Conceptual Framework for Predicting Soil Carbon Sequestration Potential

Thesis Abstract

The increasing urgency to mitigate climate change and enhance sustainable land management has spotlighted soil carbon sequestration as a critical natural process for reducing atmospheric CO2 levels. Despite its significance, predicting soil carbon sequestration potential remains complex due to the interplay of biophysical, chemical, and land use factors. This study aims to develop a robust conceptual framework to predict soil carbon sequestration potential, thereby enhancing decision-making processes in land management and climate mitigation strategies. The specific objectives include identifying key soil and land use variables influencing sequestration, modeling the relationship between these variables and sequestration potential, and validating the developed framework with empirical data. Employing a mixed-methods research design, the study integrates qualitative and quantitative approaches. The quantitative component involves collecting soil and land use data from 150 agricultural sites across diverse biophysical zones within the region, selected through stratified random sampling. Data collection instruments encompass soil sampling for organic carbon analysis using dry combustion techniques via an elemental analyzer, remote sensing data for land cover characterization, and structured questionnaires administered to local farmers to gather land management practices. The qualitative component involves thematic analysis of semi-structured interviews with land management experts and extension officers to contextualize quantitative findings. Validity and reliability of data collection instruments are ensured through calibration of laboratory equipment, pre-testing questionnaires, and employing inter-observer reliability checks. The core analytical methods include multiple regression analysis and structural equation modeling (SEM) to elucidate relationships among variables and to develop the predictive framework. Principal component analysis (PCA) is employed to reduce dimensionality of the soil variables and land use indicators. The model specification aligns with the Theory of Planned Behavior and the Soil Functionality Model, which underpin the hypothesized relationships between land management practices, soil properties, and sequestration outcomes. Expected findings anticipate identifying critical variables—such as soil texture, organic matter content, tillage intensity, crop rotation, and cover crop practices—substantively influencing soil carbon sequestration potential. The developed model aims to quantify the relative contribution of these factors, offering a predictive tool with an accuracy rate expected to exceed 85% as validated against a subset of the data via cross-validation techniques. The framework is intended to serve as a decision support system for policymakers and practitioners aiming to optimize land management for carbon sequestration. This research significantly contributes to existing knowledge by integrating multiple soil and land use factors into a comprehensive conceptual model, filling gaps related to regional predictive tools for soil carbon sequestration. It advances theoretical understanding by empirically testing the applicability of behavioral and ecological theories within land management contexts. Furthermore, the study provides practical recommendations for land users and policymakers, including targeted land use modifications and management practices to maximize sequestration potential. The study concludes that a predictive framework based on key soil and land management variables can effectively estimate soil carbon sequestration potential across diverse agro-ecological zones. Recommendations include adopting adaptive land management practices, developing tailored incentive programs for sustainable land use, and further refining the model through longer-term monitoring to accommodate climate variability impacts. Future research should explore integrating climate change projections and socio-economic factors to enhance the predictive capacity and policy relevance of the framework, fostering sustainable land use practices that effectively contribute to climate mitigation goals.