I am a Principal Scientist, based in Brisbane, Australia. My specialization includes ecosystem modeling, remote sensing, spatial analysis, life cycle assessment, big data, and machine learning for agricultural systems. I was a Fulbright Scholar and a U.S. National Science Foundation Graduate Fellow at the Natural Resource Ecology Laboratory at Colorado State University, a leading institution in the U.S. for soil carbon and sustainability research. I served as one of the main developers of the DayCent ecosystem model at Colorado State University (CSU). Throughout my career, I have developed multiple decision-support applications using process-based ecosystem models. These include Comet Farm (USDA), Soil Metrics (Indigo Ag), Ag-EcoSOpt (Shell Oil Company), DayCent-AU soil carbon model (Queensland University of Technology QUT), ForeSiteTM (Ukko Ag), and the California Resiliency Index (CaRI) at California State University (CHICO). I also possess exemplary leadership experience, having led integrated environmental assessment projects at the Shell Technology Centre in Houston (USA), CSU, QUT, and CHICO. Additionally, I have contributed to the development of the Australian Carbon Credit Methodology Determination 2021, the official Australian soil carbon credit calculators, and the development of the grazingDNDC model at Regrow.
- B.Sc. (Agricultural Science), 2008, Hue University of Agriculture and Forestry (HUAF), Vietnam.
- Ph.D. (Soil and Crop Sciences), 2018, Colorado State University (CSU), Fort Collins, CO, USA.
- 2024 Developing the open-source web-based model called the California Resiliency Index (CaRI), a regenerative conservation planning tool.
- 2023 Principal scientist, leading the development of grazingDNDC model for use in Regrow’s MRV platform and MRV solution for small holder rice systems.
- 2022 Principal investigator of the National Soil Carbon Innovation Challenge – Development and Demonstration Grants, by Department of Industry, Science and Resources.
- 2022 Principal investigator of the National Soil Carbon Innovation Challenge – Feasibility Study Grants, by Department of Industry, Science and Resources.
- 2022 Developer of the 2021 soil carbon credit calculator for the Clean Energy Regulator.
- 2021 Chief investigator of the Meat & Livestock Australia’s pasture dieback project.
- 2021 Technical reviewer for the 2021 Carbon Credits (Carbon Farming Initiative—Estimation of Soil Organic Carbon Sequestration using Measurement and Models) Methodology Determination for the Clean Energy Regulator, Australian Government.
- 2021 Developer of the 2014 soil carbon credit calculator for the Clean Energy Regulator.
- 2021 Lecturer for School of Biology and Environmental Science, QUT; guest lecturer for Faculty of Land Resources and Agricultural Environment, Hue University, Vietnam.
- 2020 Consultant for Soil Metrics, Indigo Agriculture (the largest participant in the US agricultural carbon market).
- 2019-20 Developer of a multi-product landscape life-cycle assessment framework, a joint project between Colorado State University (CSU) and U.S. Department of Energy’s Argonne National Laboratory.
- Model development – Nguyen, T.H. (2023). Development of the grazingDNDC module for Regrow Ag’s MRV platform. Link{:target="_blank"}
- Model development – Nguyen, T.H. (2021). Development of the automated baseline spinup module for the Soil Metrics Global Greenhouse Inventory Tool. Link{:target="_blank"}
- Model development – Nguyen, T.H. (2018). Development of Ecosystem Services and Land Use Change Optimization tool (ESLUCO) and the Agricultural Ecosystem Service Optimization (Ag-EcoSOpt) tool for Shell Oil Company to optimise land use change and feedstock production for biofuels.
- Model development – Nguyen, T.H. (2018). Development of the Agroforestry module for the COMET-Farm™ platform. Link{:target="_blank"}
- Journal paper – Nguyen, T.H., Field, J.L., Kwon, H., Hawkins, T.R., Paustian, K., Wang, M.Q., (2022). A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential. Journal of Cleaner Production, 354, 131691. Link{:target="_blank"}
- Journal paper – Nguyen, T.H., Nong, D., Paustian, K., (2019). Surrogate-based multi-objective optimization of management options for agricultural landscapes using artificial neural networks. Ecol. Model., 400, 1–13. Link{:target="_blank"}
- Journal paper – Nguyen, T.H., Granger, J., Pandya, D., Paustian, K., (2019). High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries. Appl. Energy, 238, 1484–1496. Link{:target="_blank"}
- Journal paper – Nguyen, T.H., Cook, M., Field, J.L., Khuc, Q.V., Paustian, K., (2018). High-resolution trade-off analysis and optimization of ecosystem services and disservices in agricultural landscapes. Environ. Model. Softw., 107, 105–118. Link{:target="_blank"}
- Journal paper – Nguyen, T.H., Williams, S., Paustian, K., (2017). Impact of ecosystem carbon stock change on greenhouse gas emissions and carbon payback periods of cassava-based ethanol in Vietnam. Biomass Bioenergy, 100, 126–137. Link{:target="_blank"}
This outline details the tasks involved in conducting a carbon project following Verra's VM0042 Methodology (Quantification Approach 1: Measure-Model) and utilizing DayCent biogeochemical model (or other process-based models). The project will require model validation using Verra's VMD0053 methodology and a subsequent validation report. Staff training on model usage and model management are also included.
1.1. Review project goals, objectives, and scope to ensure alignment with DayCent modeling capabilities.
1.2. Evaluate the available versions of the DayCent model based on project-specific requirements and select the most suitable version for the project.
1.3. Identify the need for model development and/or addition of new parameter sets to support the project.
1.4. Evaluate relevant data sources, including soil, climate, and historical management practices available within the project areas, necessary for DayCent model inputs.
1.5. Based on project scopes and available resources, identify a plan for literature review and data acquisition and corresponding realistic timelines.
2.1. Provide access to DayCent model to project team members.
2.2. Develop internal training materials.
2.3. Conduct comprehensive training on DayCent model theory, input requirements, and modeling procedures to project team members.
2.4. Provide hands-on training on model setup, calibration, and validation processes.
2.5. Evaluate staff proficiency through practical exercises.
3.1. Establish a version control system to track model updates and changes.
3.2. Establish protocols for model usage, updates, and version control.
3.3. Maintain detailed documentation for all model versions, updates, and changes.
4.1. Assist in protocol development for VM0042 compliance
4.1.1. Review VM0042 protocol requirements and guidelines.
4.1.2. Collaborate with project team members to develop protocols for data collection, analysis, and reporting.
4.2. Design data collection protocol
4.2.1. Define data collection methods and procedures to ensure quality and consistency.
4.2.2. Establish protocols for data management and storage to facilitate analysis and reporting.
4.3. Oversee data collection progress and quality
4.3.1. Communicate with ground data collection team to monitor progress and address any issues or concerns.
4.3.2. Provide guidance and support to ensure data collection activities align with protocol requirements and project objectives.
4.4. Establish a quality assurance and quality control (QAQC) process to validate and verify data accuracy.
5.1. Based on project scopes, conduct literature review to identify potential data for model calibration and validation (cal/val).
5.2. Establish criteria filters for literature search for different combinations of Practice Categories/Crop Functional Group/ Emission Sources.
5.3. Coordinate with stakeholders for data acquisition, translation.
5.4. Identify the final dataset for data extraction and establish a data extraction protocol (tools and procedures) for model cal/val.
5.5. Extract data into a cal/val database.
5.6. QAQC the extracted data.
5.7. Develop gap filling procedures to fill missing or unreported data.
6.1. Based on the cal/val database, parameterize the model for crop/site/practice-specific combinations.
6.2. Identify the need for ground-data collection to improve model parameterization if required.
6.3. Set up the model runs for cal/val sites.
7.1. Conduct sensitivity analysis to identify the most influential parameters and processes based on the project domain (locations, crops, practice categories, emissions sources).
7.2. Conduct sensitivity analysis to identify the most influential site inputs for uncertainty estimation and propagation.
7.3. Develop calibration protocols based on project goals and objectives.
7.4. Develop procedures for random splitting of cal/val dataset for unbiased calibration and validation.
7.5. Calibrate the model parameters using observed data from the calibration dataset.
7.6. Write report on model calibration.
8.1. Format model input/outputs and validation data according to VMD0053 methodology requirements.
8.2. Implement validation tests outlined in VMD0053 standards, including proper statistical analyses.
8.3. Document validation results, including any discrepancies or issues encountered during the process.
8.4. Compile findings and conclusions from the validation process into an internal report, including recommendations for model improvements or adjustments based on validation results.
8.5. Iterate on model calibration based on validation results until satisfactory results are achieved.
9.1. Quantify model input uncertainty associated with physical data collection, model structure and other sources.
9.2. Develop methods to propagate input uncertainty through the model simulations.
9.3. Calculate overall uncertainty estimates for model predictions.
10.1. Summarize model performance, calibration results, and uncertainty assessments.
10.2. Generate comprehensive model validation report for Verra submission, ensuring compliance with VMD0053 guidelines for validation report formatting, content, and calculations.
11.1. Assess the need for additional model development to support specific project requirements. The model may not perform optimally for specific crops or practices that necessitate more extensive model development to support their functionality. Daycent's internal equations may require recalibration or the establishment of new equations due to inadequate calibration using data from a specific region, potentially leading to inaccuracies in its outputs.
11.2. Develop new model components or modify existing ones to improve model performance.
12.1. Develop a monitoring plan to ensure ongoing data collection and verification.
12.2. Monitor project implementation and track progress towards project goals.
12.3. Develop MRV protocols specific to the project scope.
- Note: smallholder farming systems' unique characteristics may pose challenges to conventional MRV protocols, particularly those employing remote sensing. To ensure effective monitoring, reporting, and verification (MRV) for projects involving smallholder farmers, developing customized and robust MRV protocols is crucial. These custom protocols should be designed to address specific challenges and accurately assess the impacts and outcomes of the project.
13.1. Provide technical support for verification activities using remote sensing or on-site management data verification.
13.2. Assist in evaluating project implementation and ensuring compliance with VCS requirements.
14.1. Prior to project activities (ex-ante), model the impacts of different combinations of regenerative practices in the project area, taking into account land management history to determine suitable carbon projects using uncalibrated/calibrated models.
15.1. Ensure appropriate format of the collected data to support model setup.
15.2. Parameterize model for simulating the project area using collected data (management and soil samples).
15.3. Model Simulations for Baseline Scenarios:
15.3.1. Develop baseline practice scenarios that represent the project area prior to the project activities.
15.3.2. Run model simulations for baseline scenarios to estimate baseline emissions (ex-post and counterfactual).
15.4. Model Simulations for Project Scenarios:
15.4.1. Develop project scenarios that represent the project activities and interventions.
15.4.2. Run model simulations for project scenarios to estimate project emissions.
15.5. Calculate emissions reductions achieved by the project activities following VM0042 protocols.
15.6. Assist with monitoring and verification report development based on modeling results.
16.1. Respond to comments and requests from Verra's VVB and Independent Model Expert (IME) during the model validation report review process.
16.2. Make necessary revisions to the model validation reports and model calibration/validation based on IME feedback.
17.1. Respond to comments and requests from VVB during the verification of ERRs and from Verra during the verification approval request.
17.2. Assist in making necessary revisions to the verification reports and other supporting documentation based on Verra feedback.