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This lab focuses on soil carbon sequestration and greenhouse gas monitoring in agricultural systems. Through field measurements, MRV methodology development, and spatial analysis using GIS and remote sensing, the research aims to quantify carbon stocks and emissions. The goal is to support sustainable agriculture and climate change mitigation through science-based soil carbon management strategies.
Establishment of MRV for Agricultural Soil Carbon Sequestration in Taiwan
Establishment of MRV for Agricultural Soil Carbon Sequestration in Taiwan
Key areas of expertise include the development of measurement, reporting, and verification (MRV) methodologies, soil organic carbon (SOC) analysis, and the design of robust sampling strategies tailored to field conditions.
Geographic Information Systems (GIS) are employed to assess spatial variability in soil carbon and GHG concentrations across sampling sites. Satellite imagery is integrated to enhance spatial interpretation and inform decision-making processes.
Development of a Taiwan-Specific Soil Carbon Sequestration Model
Development of a Taiwan-Specific Soil Carbon Sequestration Model
This research focuses on the application of modeling approaches within MRV (Measurement, Reporting, and Verification) methodologies, particularly in agricultural soils. It utilizes process-based models such as DNDC (DeNitrification-DeComposition) and RothC (Rothamsted Carbon Model) to simulate soil organic carbon dynamics and greenhouse gas (GHG) emissions under various land management practices and environmental conditions.
By comparing short-term and long-term scenarios, the research evaluates the carbon sequestration potential of farmlands and assesses the climate impacts of agricultural activities. Current work centers on cropland systems in Taiwan, with emphasis on soil carbon modeling and methane emissions from paddy fields.
Cost-Effective Methods for Soil Carbon Measurement
Cost-Effective Methods for Soil Carbon Measurement
The research focuses on developing rapid and non-destructive soil analysis techniques. Traditional soil testing methods are often time-consuming, costly, and labor-intensive. To address these challenges, this study explores the use of mid-infrared (MIR) spectroscopy as a substitute for parts of conventional analysis. By collecting soil spectral data and pairing it with laboratory-derived reference values such as pH, cation exchange capacity (CEC), and total organic carbon (TOC), the research applies machine learning and deep learning algorithms to build predictive models.
Effects of Alternative Wetting and Drying Irrigation and Brittle Rice Straw Return on the Distribution of Carbon, Phosphorus and Iron Species
Effects of Alternative Wetting and Drying Irrigation and Brittle Rice Straw Return on the Distribution of Carbon, Phosphorus and Iron Species
Soils were incubated with two types of rice straw, brittle and non-brittle, under two different water management practices: continuous flooding and alternate wetting and drying (AWD). This approach simulated field conditions to investigate how these treatments influence the forms and stability of carbon, phosphorus, and iron in soils after incubation.
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