Greenhouse Gas Fluxes and Modeling from Rice Workshop

1 2 Table of Contents Table of Contents .................................................................................................................. 2 List of Figures........................................................................................................................... 3 List of Tables............................................................................................................................ 2 I. Acknowledgements ....................................................................................................... 4 II. Executive Summary ........................................................................................................ 5 III. Introduction ..................................................................................................................... 7 A. Background and Context of the Workshop ........................................................ 7 B. Objectives of the Workshop.................................................................................. 8 C. Target Audience and Participants ....................................................................... 8 D. Workshop Agenda............................................................................................... 10 IV. Plenary Sessions............................................................................................................. 10 A. Opening Session Day 1 ........................................................................................ 10 B. Plenary Session Day 2 .......................................................................................... 15 C. Plenary Session Day 3 .......................................................................................... 19 V. Parallel Sessions ............................................................................................................. 22 A. GHG Measurements Session ............................................................................... 22 B. Modeling Overview Session ................................................................................ 26 VI. Breakout Sessions on Priorities of Collaboration ......................................................... 31 VII. Outcomes and Recommendations ............................................................................ 37 A. Main Insights and Challenges ............................................................................. 37 B. Actionable Next Steps ......................................................................................... 38 VIII. Conclusion .................................................................................................................... 39 IX. Literature Cited ............................................................................................................. 40 X. Annexes ......................................................................................................................... 44 A. Summary of Models ............................................................................................. 44 B. Participant List ...................................................................................................... 49 C. Workshop Materials ............................................................................................. 53 List of Tables Table 1. Ancillary data measurements and descriptions of flux chamber method used by major rice growing regions globally. ....................................... 23 Table 2. Requirements guide for GHG measurements. Required is the minimum you need to ensure your measurements meet the standard. Preferred is what you need to optimize your conditions of measurements to minimize uncertainties. ............................................................ 24 Table 3. Features, objectives and applications of the different models. ........................ 29 Table 4. Breakout group results on data harmonization, key data gaps, and relevance to livelihoods. ....................................................................................... 33 Table 5. Key outcomes from the four Breakout Groups (BOGs), outlining available data, required actions, responsibilities, collaboration approaches, needed resources, timelines, and challenges.............................. 34 3 List of Figures Fig. 1. Participants of the workshop, representing researchers and scientists working on greenhouse gas (GHG) measurement and modeling in rice systems. 9 Fig. 2. Geographic and institutional diversity of workshop participation, with 40 organizations from 25 regions involved in the workshop. 9 Fig. 3. Dr. Reiner Wassmann presenting ASEAN climate mitigation initiatives and emerging carbon market pathways. 11 Fig. 4. Dr. Cynthia Rosenzweig presenting the evolution of AgMIP studies and their contributions to climate impact assessments in agriculture. 13 Fig. 5. Dr. Toshi Hasegawa presenting the current priorities of the AgMIP Rice Team. 14 Fig. 6. Dr. Alisher Mirzbaev presenting on national GHG inventories and private rice carbon initiatives. 16 Fig. 7. Dr. Sonali McDermid presenting on regional integrated assessments of mitigation and adaptation co-benefits for farmers. 16 Fig. 8. Panel discussions on stakeholder perspectives in national GHG inventories, UNFCCC reporting, and carbon market schemes. 17 Fig. 9. Dr. Arlene Adviento-Borbe presenting GHG measurement protocols, flux chamber methods, and emissions reporting standards. 20 Fig. 10. Dr. Tao Li presenting the role of modeling as a tool for advancing research on GHG in rice systems. 21 Fig. 11. GHG flux measurements parallel session. 26 Fig. 12. Modeling team parallel session. 28 Fig. 13. The 34 experimental sites for potential collaboration identified from a preliminary participant survey. 31 Fig. 14. Breakout group discussions on collaboration opportunities, data gaps, and the need for harmonization across sites and scales. 32 4 I. Acknowledgements The organizers of the Greenhouse Gas Fluxes and Modeling from Rice Workshop gratefully acknowledge the generous support of the Global Methane Hub (GMH) and the Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan, whose contributions made this event possible. Appreciation is also extended to the partner institutions—International Rice Research Institute (IRRI), the Agricultural Model Intercomparison and Improvement Project (AgMIP), and CGIAR Climate Action— for their collaboration in convening this workshop and for their shared commitment to advancing climate change mitigation in rice systems. The organizers further recognize the valuable contributions of more than 80 participants, experts, and institutions from across 25 regions who contributed their time, knowledge, and insights. Their active engagement and spirit of collaboration were essential in making the workshop a success and in shaping the joint agenda for advancing greenhouse gas flux measurement and modeling in rice systems. 5 II. Executive Summary Methane (CH4) emissions from paddy fields contribute significantly to agricultural greenhouse gas (GHG) emissions, posing a critical challenge for achieving global climate goals. Despite the availability of the Tier 3 method—which utilizes process-based models and high-resolution datasets to capture variability in site-specific CH4 emissions, its application has been limited to a few countries. There is still a large gap in establishing country and regional specific emissions factors and an increasing demand to measure actual emissions from the field. These requirements are not only for country-level inventories to the United Nations Framework Convention on Climate Change (UNFCCC) but also for the emerging carbon market in the agricultural sector. Significant efforts are being invested in advancing technologies and options for targeting and supporting climate mitigation initiatives in rice systems. Measurements and monitoring are critical in setting the baseline for these initiatives and in evaluating the progress made. However, their implementation remains challenging due to limited consensus in protocols for measurements, as well as the limited accessibility of tools and technologies for measurements, modeling and monitoring of emissions. Modeling is central to bridging experimental research data with actionable mitigation and adaptation strategies at scale. Current models can reliably simulate yield, water balance, and basic soil–nutrient interactions, but they struggle with trade-offs between methane reduction and nitrous oxide emissions, or with integrating soil carbon dynamics. Owing to the serious impacts of the climate crisis in crop production and human health, global assessments of GHG emissions through measurements and modeling are urgently needed to accelerate efforts in developing resilient food systems in all rice-growing countries around the world. The “Greenhouse Gas Fluxes and Modeling from Rice Workshop” was convened to address these challenges. The workshop was held from 1–5 September 2025 at the International Rice Research Institute (IRRI), Los Baños, Philippines, jointly led by IRRI, AgMIP, and the Global Methane Hub—with support from CGIAR Climate Action and Japan’s Ministry of Agriculture, Forestry and Fisheries (MAFF).The workshop gathered more than 80 participants from 40 organizations across 25 regions and brought together perspectives from research, stakeholders engaged in national GHG inventories, UNFCCC reporting, and the carbon market. It was delivered in a hybrid format with in-person and online participation. The workshop`s structure was composed of six plenary sessions organized to provide the general overview of the state-of-the-science in GHG measurements and modeling in rice systems and two parallel sessions that were technical focused on the overview of different methodologies in measurements and in modeling, respectively. Participants underscored the need to bring together field flux measurements, crop simulations, and socio-economic analysis to improve forward-looking impact assessments. Such integration would enable evaluation of how mitigation and adaptation strategies affect farmers’ livelihoods, food security, and resource use, while quantifying the trade-offs and synergies among socio-economic, biophysical, and environmental outcomes. The different presentations highlighted the data gap in mitigation efforts’ monitoring as current national data collection targets food security. Calibration and validation data for modeling are scarce, especially at regional and national scales. Workshop discussions emphasized that while rice models have advanced significantly—particularly through multi-model ensembles and AgMIP’s coordinated intercomparison efforts—gaps remain in simulating extreme events, traitbased responses, and GHG fluxes beyond methane. 6 The workshop identified several opportunities to strengthen rice GHG research and implementation and recommendations moving forward include among others: adopting FAIR (Findable, Accessible, Interoperable, Reusable) data principles; piloting joint measurement– modeling projects across diverse rice ecologies; aligning MRV requirements of carbon markets with scientific protocol; and fostering multi-stakeholder platforms that bridge researchers, governments, and private-sector actors. Key outputs of the workshop include:  Consensus on minimum protocols for chamber-based measurements (chamber design, sampling frequency, flux calculation, and reporting standards).  Agreement on the need for harmonized datasets and metadata standards, including soil, water, crop, and weather variables.  Roadmaps from four breakout groups (water management, methane modeling, management interventions, scaling/upscaling) outlining data needs, priority interventions, and collaborative actions.  Identification of high-potential innovations such as AWD, improved fertilizer management, and integration of socio-economic models.  Commitments to develop shared repositories and adopt common reporting templates. The participants have agreed to the following actionable next steps to confirm their overall commitment to high quality research to address global challenges in facing climate change:  Continuing communication through shared platforms (e.g., SharePoint, AgMIP frameworks) and periodic follow-up meetings to track progress.  Co-developing guidelines for GHG measurements across global rice growing areas that will support capacity building for a global network of GHG researchers and long-term flux experimental study.  Establishing a working group to coordinate model intercomparison, protocol standardization, and joint calibration efforts.  Launching pilot case studies in Asia, Africa, and Latin America to test harmonized protocols and scale findings.  Selecting case studies that can be used to conduct integrated assessments of climate change, adaptation, and mitigation using the AgMIP’s MAC-B modeling framework.  Linking workshop outcomes to policy processes, including national GHG inventories, NDC implementation, and Article 6 mechanisms. Together, these outputs and commitments signal a strong foundation for a coordinated global effort to reduce methane emissions from rice systems, while ensuring that mitigation strategies align with adaptation goals, farmer livelihoods, and sustainable development. 7 III. Introduction A. Background and Context of the Workshop GHG emission rates are inherently variable in time and space, often associated with high error estimates. Accurate estimates of GHG fluxes are increasingly needed in accounting for GHG inventories and C in-sets at the field to field and global scale benefits. It has been reported that uncertainties may reach 20-35% for inventories of anthropogenic emissions in specific sectors (IPCC, 2021). Many studies have documented that different flux calculation methods can produce substantially different GHG flux estimates for a given set of chamber data (Levy et al., 2011). These flux computations differ not only in their accuracy (close to true flux value) but also in their precision (repeatability). Crop models are widely accepted to be used to scale emissions data to regional assessments such as done in countries reporting their GHG emissions using the Tier 3 approach. They can use spatial high-resolution datasets to capture site-specific variability that may affect emissions and particularly CH4 such as climate, management practices, varieties and soils. The application of crop models for GHG inventory is limited to a few countries and this is primarily due to the demanding data requirements for their use and the limited capability of rice crop growth models simulating GHG emissions and particularly methane. Up to date, there has been no systematic intercomparison among rice crop models against observed data to quantify uncertainties associated with CH4 emission predictions, leaving gaps in understanding the mitigation potential of various strategies. The AgMIP Rice project has a long history of intercomparing crop models to assess climate change impacts and adaptation strategies. Some of these models simulate soil-plant interactions, including soil carbon dynamics, which enable CH4 emission simulations and present opportunities to enhance GHG estimates. Precise GHG emissions support robust identification of strategies to mitigate emissions and maintain high integrity of the data for reporting. Therefore, a comprehensive assessment of chamber method, trace gas flux computation and GHG modeling is critical to accurately estimate GHG emissions and mitigation interventions co-benefits at scale. In addition, AgMIP has extensive experience conducting integrated assessments that link climate, crops, livestock, economic data and models to evaluate the impacts of climate change and agricultural adaptation strategies. Over the past decade, AgMIP has implemented coordinated regional assessments across Africa and Asia, engaging scientists and policymakers to co-develop scenarios that reflect local realities and national priorities. Building on this foundation, AgMIP is now implementing the Mitigation–Adaptation Co-Benefits (MAC-B) framework to assess rice management practices in Vietnam, Bangladesh, and India. This new framework combines field data, crop and livestock modeling, and economic analysis to evaluate how practices such as the System of Rice Intensification (SRI) and Alternate Wetting and Drying (AWD) perform under current and future climates—quantifying their potential to enhance farmer livelihoods, improve resource efficiency, and reduce greenhouse gas emissions. The five-day scientific workshop held from September 1 to 5, 2025, at the International Rice Research Institute (IRRI) Headquarters aimed to address these gaps and to leverage the different opportunities with the recent advances in measurements in GHG 8 and in rice crop modeling. The document summarizes the insights from the closed interaction among the different experts in GHG measurements and modeling in defining priorities for research and for collaboration. Indeed, the workshop aimed to foster collaboration between greenhouse gas (GHG) emissions researchers, rice crop modelers, agronomists, spatial science and remote sensing experts, and to convene in addressing challenges in CH₄ data availability and collection, as well as mitigation interventions assessment at different scales. The initiative was jointly led by the International Rice Research Institute (IRRI), the Agricultural Model Intercomparison and Improvement Project (AgMIP), and the Global Methane Hub (GMH), in partnership with CGIAR Climate Action and the Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan. B. Objectives of the Workshop The workshop served as a platform for knowledge exchange, capacity building, and collaboration development. The aim was to advance best practices in chamber method applications, GHG flux analysis, and as well to attempt the development of simplified CH4 simulation methods to enable broader participation of crop models for Tier 3 approach of GHG in rice systems. This latter will also offer an opportunity for the regional integration of climate change adaptation considerations into rice modeling and integrated assessments to better understand the tradeoffs between mitigation and adaptation strategies. The specific objectives were to:  Foster collaboration between rice crop growth modelers, experimentalists, and remote sensing scientists.  Address challenges in CH4 data availability and collection  Enhance data sharing and integration between experimentalists, modelers, and remote sensing experts.  Foster joint learning on best practices for CH4 measurement and modeling.  Develop a roadmap for integrating experimental data with modeling efforts to improve methane emission predictions and mitigation assessments. C. Target Audience and Participants The workshop was intended for researchers and practitioners working on greenhouse gas (GHG) measurement and modeling in rice systems (Fig. 1). The primary audience included:  GHG emissions researchers with expertise in field measurements and flux analysis.  Crop modelers focusing on CH₄ simulation and climate-rice interactions.  Agronomists and soil scientists engaged in sustainable rice cultivation and mitigation practices.  Economists modelers conducting ex-ante impact assessments and integrated assessments  Spatial science and remote sensing experts applying geospatial tools for largescale monitoring and systems mapping. 9  Policy partners and institutional representatives supporting evidence-based decision-making in climate-smart agriculture.  Early-career researchers and technical staff seeking capacity building in GHG measurement and modeling approaches. Overall, there were 40 organizations from 25 regions involved (Fig. 2). Fig. 1. Participants of the workshop, representing researchers and scientists working on greenhouse gas (GHG) measurement and modeling in rice systems. Fig. 2. Geographic and institutional diversity of workshop participation, with 40 organizations from 25 regions involved in the workshop. 16 Fig. 6. Dr. Alisher Mirzbaev presenting on national GHG inventories and private rice carbon initiatives. Fig. 7. Dr. Sonali McDermid presenting on regional integrated assessments of mitigation and adaptation co-benefits for farmers. 17 Fig. 8. Panel discussions on stakeholder perspectives in national GHG inventories, UNFCCC reporting, and carbon market schemes. National GHG inventory systems and UNFCCC reporting processes currently rely on limited, non-targeted data collection with most efforts focused on food security rather than mitigation monitoring. Data for model calibration and validation is rarely available, especially at regional and national scales. The concept of baseline in rice production with continuous flooding field can be challenged as unintentional AWD practices are widespread. Furthermore, straw management is largely excluded, despite its major influence on GHG outcomes. Significant data gaps remain in rice systems evaluation such as:  Irrigated rice areas quantification and mapping.  Adoption rates of AWD.  The discrepancies in indicators for extension bulletin AWD definitions and farmers’ practices.  Soil organic carbon dynamics under AWD.  Straw management variability in rice systems. 18 There are however some identified opportunities that can be explored moving forward such as:  Establish systematic ground-truthing for validation of upscaling assessments.  Leverage remote sensing (RS) and AI tools for scalable, cost-effective monitoring. To be effective, this would require a data-sharing platform as well as long-term studies combined with modeling. An example shared was IRRI’s efforts to establish a framework linking farm survey data with district-level RS information, which is then integrated into GHG calculators and models for regional assessments. Currently, data collection lacks incentives, and there are no standardized protocols for cross-checking these data. From the private sector and carbon market perspective, main challenges discussed include:  The need for stratification, each requiring its own emission factor (EF).  While rice areas may be mapped and identified using ALU software, the central challenge—the “million-dollar question”—remains how to scale measurements. There are concerns about whether measurements can be standardized and made comparable.  Existing policies for model evaluation are less accessible and may not be fit for purpose. A proposal was raised on whether a different cost of carbon could be applied based on the robustness of estimation approaches. Lessons from JCM implementation by Green Carbon highlighted difficulties in applying methodologies consistently. While data collection initiatives are performed by different rice carbon developers the challenge is making these data useful for research. Suggestions included creating benefit-sharing mechanisms to cover the costs of data collection, transition, and implementation, with particular attention to ensuring local governments also benefit. In addition, several critical questions were raised, such as:  Can the measurements be standardized and made comparable across regions and contexts?  How to balance cost and accuracy in model-based estimates for carbon pricing? The presentation shared by Dr. Sonali Shukla McDermid on integrated assessments linking climate, crop, and economic models provided a different perspective on the valuation of mitigation efforts beyond the climate targets and the carbon credits estimate. She shared the work on RIA that builds on AgMIP’s history of intercomparisons and expands toward addressing greenhouse gases, farmer adoption, and regional scaling relevant to national climate commitments (NDCs). The AgMIP Mitigation and Adaptation Mitigation Co-Benefits (MAC-B) framework was developed to standardize evaluation of multi-model uncertainty and regional heterogeneity of climate change impacts. The framework:  Links climate models, multiple crop models, and the economic trade-off analysis model (TOA-MD). 19  Captures uncertainty across scenarios and sites, calibrated/validated with field data. A recent case study focusing on combined mitigation + adaptation in rice was shared using the coupled crop–soil models (ORYZA + DNDC) applied in Bangladesh (3 districts, 6 strata, ~432 farms) and evaluating AWD and SRI. Key findings suggested that integrated modeling frameworks enable assessment of both biophysical and socioeconomic trade-offs. AWD and SRI increased yields slightly, reduced methane and improved water-use efficiency. SRI delivered greater net farm return gains than AWD alone. Dr Sonali has emphasized the need of comprehensive and consistent data collection on yields, GHGs, soils, and socioeconomic conditions. The RIA models require multi-factor sensitivity tests (temperature + CO2 + precipitation) to capture uncertainty, and results are sensitive to genotype x environment x management interactions. She emphasized the approach in RIA needs to be co-developed with stakeholders to ensure alignment of strategies evaluated with farmer incentives, biodiversity, and policy goals. Governance priorities differ (local water scarcity vs national GHG targets) and the emerging carbon markets and incentives may alter adoption behavior. Furthermore, addressing uncertainty and heterogeneity is essential before scaling recommendations. The team is currently conducting a project on RIA for a case study in India (Tamil Nadu) where a new survey data has been conducted. An incoming paper with results from Vietnam will be soon published. Questions were raised on the challenges in defining strata for the study and the consideration of the biodiversity trade-offs (e.g., amphibian mortality increase with AWD) with practice change. Stratification based on farm size may be improved by capturing heterogeneity across farms and practices C. Plenary Session Day 3 The Day 3 session centered on technical presentations that highlighted and reviewed the key tools of focus for the workshop, delivered by Dr. Arlene Adviento-Borbe and Dr. Tao Li. Dr. Adviento-Borbe delivered a crash course on the history of GHG measurements and the current protocols in use. She shared as well the learning from the parallel sessions conducted during Days 1, 2 and 3 including the general principle of the flux chamber method, the GHG flux computations and emissions reporting aiming for consensus on minimum requirements and standardization (Fig. 9). 20 Fig. 9. Dr. Arlene Adviento-Borbe presenting GHG measurement protocols, flux chamber methods, and emissions reporting standards. Dr. Adviento-Borbe acknowledged the global collaboration nature of the workshop with participants from major rice regions: U.S., Colombia, Spain, China, Japan, Indonesia, Vietnam, India, Africa, and Taiwan which formed a network of researchers built on earlier collaborations (e.g., IRRI–NARS collaboration, 1993–1998) in assessing rice as a source of GHG and a sink of CO₂. She provided a brief history and the status of mitigation study in rice in the US rice sector dated back from 1980s–to present with focus on major drivers of emissions, mitigation strategies, upscaling models. She reiterated the challenges in GHG measurements such as the  GHG production mechanism in the soil that has multiple processes, highly variable in time & space.  The labor-intensive nature of GHG measurements (requires many people per field campaign)  The lack of harmonized protocols across regions. The parallel sessions on GHG fluxes discussed the diverse measurement practices across the different regions and established a classification of required and preferred approaches to unify protocol for GHG measurements. These parallel sessions discussed the comparability of the measurements and as well as how to allow cross validation for verification. The agreed protocol emphasizes mandatory requirements for producing robust estimates, and suggestions were made to reduce uncertainties and harmonize practices across rice studies (Table 2). Dr. Adviento-Borbe ended her presentation with recommendations well aligned with the workshop objectives which are:  Establish standardized chamber protocols globally.  Consolidate datasets to define global emission ranges. 21  Improve reporting consistency (SI units, GWP, yield-scale).  Enhance data collection efficiency (automation, coordinated campaigns).  Integrate GHG with crop & soil models for better prediction.  Broaden international collaboration for policy-relevant reporting. Dr. Tao Li highlighted the role of modeling as a research tool that can be used to  Extrapolate experimental results.  Interpret field studies.  Generate insights to optimize systems and designs for adaptation, mitigation, productivity, and yield. (Fig. 10) Fig. 10. Dr. Tao Li presenting the role of modeling as a tool for advancing research on GHG in rice systems. Current models used for climate change adaptation can predict yields while fewer can directly quantify GHG emissions. He identified Gaps and Challenges and opportunities for rice modeling as below: Gaps and challenges  Inconsistent GHG responses to elevated CO₂ across studies and timescales.  Model uncertainties stem from both structure and input data quality.  Lack of modules for certain mitigation options (e.g., biochar, inhibitors).  GHG measurements are often localized, limiting scalability.  Data-related issues are the hardest to resolve and significantly affect quantification. 22 Opportunities for Improvement  Collaboration and data sharing across modeling groups.  Standardization of protocols, modules, and MRV alignment with NDCs.  Development of shared calibration protocols to avoid overor undercalibration. Discussion points after this presentation were critical including:  Water management representation -There is a concern in models capability to adequately capture positive yield effects of AWD and drainage events. Most models can simulate water balance (evapotranspiration, drainage, runoff), but accuracy may vary. Root growth and soil physical change effects are partially handled by some models; there are areas for improvement to consider in rice modeling.  Model improvement and data sharing - For improved modeling of water depth effects on methane, nitrous oxide emissions, lodging impacts, and mitigation opportunities like mid-season drainage, standardized calibration protocols are key to avoid overfitting and improve model comparability. The adoption of AIready data for standardization and the sharing of protocols for model calibration and the validated model post-calibration may offer opportunity for continuous collaboration between modelers and experimentalists. Modeling offers opportunities to standardize GHG measurements datasets, making them Findable, Accessible, Interoperable and Reusable but as well to capture the variability of rice systems and address challenges of heterogeneity in GHG emissions due to differences in environments, management, and varieties. The discussion on data for modeling identified the potential to build an inventory of datasets linked to 1) searchable metadata, and 2) structured into portable, harmonized inputs usable not only for modeling but also for broader applications and analyses. The implementation of these required, however, an open discussion on data sharing and ownership, and acknowledgement in co-authorship. It was suggested to consider the AgMIP’s concept of FAIRER data principles: Findable, Accessible, Interoperable, Reusable, Ethical, and Responsible use. FAIRER could serve as a guiding framework, going beyond the standard FAIR principles for the onwards collaboration among the workshop participants. V. Parallel Sessions A. GHG Measurements Session Following intensive review of literature on GHG emissions in paddy rice systems, the organizing team of the workshop chose scientists and researchers who are actively conducting GHG emissions studies in major rice regions of the world (Table 1). The team invited nine scientists/professors to speak about chamber methods for GHG flux measurements from eight (8) regions representing Asia, Americas, Europe, and Africa (Fig. 11). Additionally, the team invited two scientists to speak on analytics and tools that improve computation of GHG emissions from chamber method. Participants who are involved in GHG emission data assessment, inventories and sustainability program 23 were also invited to share their knowledge and experiences in using GHG data for their applications. Annex B. Table 2 summarizes the information about speakers and participants of the meeting and the table below shows the specifications of the different GHG measurements approaches shared during the sessions. Table 1. Ancillary data measurements and descriptions of flux chamber method used by major rice growing regions globally. Country Chamber design Gas sampling Gas storage Gas analyses Flux calculation Ancillary data Japan  Rectangular - transplanted  Base  Water sealing  Mid-morning  20-30 min  3-4 gas samples  >3 reps  Pre-evacuated glass container  GC  External calibration  Linear Temperature Indonesia  Rectangular – transplanted  Base  Water sealing  Mid-morning  20-40 min  4 gas samples  3 reps  Pre-evacuated glass container  GC  External calibration  Linear Eh, pH, plant height, tiller number, grain yield, weight of biomass India  Rectangular – transplanted  Base  Water sealing  Mid-morning  20-60 min  4 gas samples  3 reps  N2 flushed, preevacuated glass container  GC  External calibration  Linear  Non-linear Temperature, water depth, MC, WFPS, grain yield, LAI, Eh, pH, EC, N uptake China  Rectangular – transplanted  Round – CO2  Base  Water sealing  Mid-morning  30 min  4 gas samples  automated  Pre-evacuated glass container  GC  Linear Temperature Taiwan  Rectangular – transplanted  Base  Water sealing  Mid-morning  5 min  4 gas samples  Smart chamber  Not applicable  LICOR-8710  LICOR-8720  Linear Temperature, water depth, Eh, soil texture, grain yield Vietnam  Rectangular – transplanted  Base  Water sealing  Water holes  Mid-morning  30 min  4 gas samples  Pre-evacuated glass container  GC  LICOR-8710  Linear Temperature, floodwater depth, MC, grain yield, nutrient uptake, Eh, pH, OC, N US  Round – drill seeded  Base  Silicon band  Water holes  Mid-morning  60 min  4-5 gas samples  3-10 reps  Pre-evacuated Exetainer glass vial  Double Si seal  GC  External calibration  3 levels  Linear  Non-linear (HMR) Temperature, water depth, MC, yield, grain quality, soil properties, nutrients Colombia  Round – drill seeded  Base  Silicon band  Water holes  Mid-morning  60 min  4-5 gas samples  Pre-evacuated glass container  GC  External calibration  5 levels  Linear Temperature, floodwater, MC, yield, grain quality, EC, pH, soil texture, N Spain  Rectangular – drill seeded  Base  Water sealing  10:00 – 15:00  10-30 min  4-5 gas samples  3 reps  Pre-evacuated glass container  GC  Photoacoustic gas analyzer  Linear Temperature, Eh, EC, pH, OC, NPK, soil texture, yield, C:N ratio GC= gas chromatograph, LAI = leaf area index, WFPS = water filled pore spaces, MC = moisture content, OC = organic C, N = nitrogen. 24 Generally, the chamber method involves the installation of a chamber base (rectangular or cylindrical) over the soil surface, which is closed with a chamber cover. Many of the studies on soil-atmosphere exchange of soil trace gases from paddy rice have used chamber method because it is common and the most suitable for the study of the effects of different factors. It is as well less expensive for field installation and operations, compared to micrometeorological techniques (e.g. eddy covariance or gradient techniques). Many researchers have long acknowledged the occurrence of the “chamber effect” due to the disruption of natural conditions and the diffusion of gas after chamber deployment in case of inattention, resulting in an overor underestimation of the actual trace gas flux. Studies have demonstrated that chamber data can suffer from negatively biased flux estimates when addressing chamber effects. Additionally, there is wide variation in the application of chamber types and techniques (Table 1), so the magnitude of flux estimation is also expected to vary widely across studies. Given this uncertainty and variation, the standardization to improve the chamber method and computation of GHG fluxes may require an innovative approach than a simple attempt of unification. Also, the sensitivity of the chamber effects to variations of climate, soil and crop properties which influence the interpretation of treatment effects within a given study is minimized when datasets are processed and analyzed using a similar approach across rice cropping regions. Standardizing the chamber method and flux computation when feasible will widen the implementation of the robust chamber method protocol in rice studies. Following an exhaustive review of the many chamber methods across rice regions the group reached a strong consensus on minimum required metrics, sampling principles, flux calculation methods, and reporting standards (Table 2). These agreements are intended to strengthen data integrity, reduce variability across studies, and make results more useful for both scientific modeling and climate policy applications. Table 2. Requirements guide for GHG measurements. Required is the minimum you need to ensure your measurements meet the standard. Preferred is what you need to optimize your conditions of measurements to minimize uncertainties. Parameter Required Preferred Key considerations Chamber size area (24-250 L) Yes Volume to surface area ratio should be sufficient to avoid dilution effect, minimize warming, adequate air mixing and made of non-permeable and inert material. (Parkin and Venterea, 2010, Healy et al., 1996) Chamber base (0.03 to 0.15 m soil depth insertion) Yes Assure good chamber-to-soil seal, minimize disturbance to root system, shelter effects (rain, fertilizer, amendments), account for the porosity of the topsoil and minimize microenvironment perturbations. (Pavelka et al. 2018). Installed base at least 24 hr prior to sampling (Bahn et al., 2009) Fan Yes Better mixing of the enclosed air, avoid dead space in corners for rectangular (Livingston and Hutchinson, 1995, Livingston et al., 2006, Maier et al. 2022, Pavelka et al., 2018) Boardwalk Yes Avoid soil disturbance, pumping effect, plant damages, and compacts soil (Maier et al., 2022) Glass sample container Yes Use of Exetainer vials Ability to hold vacuum and pressure and preserve gas concentration >1 week (Glatzel and Well, 2008, Rochette and Eriksen-Hamel, 2008, Laughlin and Stevens, 2003, Rochette and Bertrand, 2003) Gas overpressure for transport and storage Yes The use of butyl rubber stopper and Exetainer vials are recommended for long-term storage and transport (Glatzel and Well, 2008, Rochette and Eriksen-Hamel, 2008) Syringe needle size Yes 2225-gauge needle provides minimal pressure losses (Parkin and Venterea, 2010) Time 0 Yes Measured gas concentration represents pre-deployment soil-chamber vertical GHG profile (Rochette, 2011) Overhead ambient gas sampling Yes Minimize elevated ambient gas concentrations during gas sampling (Rochette, 2011, Parking and Venterea, 2010) 25 Time of gas sampling Yes Diurnal GHG emission measurements provide daily emission estimates yielding the smallest average bias (Wu et al., 2021). It is recommended to do measurements every 4 hr with minimum 4 measurements during the day (Pavelka et al., 2018, Darenova et al., 2014) Separate chamber for N2O flux This practice is implemented in Indonesia for low range of N2O emissions (IAERI) Number of chambers per plot ≥3 or depends on project Number of chambers needed adequately estimate the mean and variance of gas fluxes within a site (Davidson et al., 2002, Livingston and Hutchinson, 1995) Vent tubes and dimensions Yes Proper vent tube dimensions transmit changes in external pressure to chamber headspace, thus minimizing suppression by deployment on gas fluxes (Hutchingson and Livingston, 2001, Parkin and Venterea, 2010, Xu et al., 2006, Davidson et al., 2002). Dimensions of vent tubes for selected wind speeds and enclosure volumes are described by Parkin and Venterea, 2010) Duration of chamber closure Depending on the project Duration should be based on expected flux and precision of gas analyses. 20-60 min for GC is recommended (Maier et al., 2022, Parkin and Venterea, 2010, Rochette and Hutchinson, 2015, de Klein and Harvey, 2012) Number of gas samples ≥4 (min of 3 if linear) Gas samples withdrawn at regular intervals during closure of <60 min. At least 3 time points are required for flux calculation. Use of fitting models and uncertainty in dC/dt requires >2 samples (Parkin and Venterea, 2010, Pedersen et al., 2010) End of the season flux Depending on research or field condition and management Short-lived CH4 peaks occurred after dry event due to champagne effect or physical release of entrapped gas during soil drying before harvest. Can contribute to 10-15% of total growing season emissions (Adviento-Borbe et al., 2015, van der Gon and Neue, 1995) Gas sampling during drain events (AWD, Furrow) Yes Short-lived CH4 peaks occurred after dry event due to champagne effect or physical release of entrapped gas during dry cycle (Adviento-Borbe et al., 2015, van der Gon and Neue, 1995) Frequency of response curve for GC calibration Depending on stability of GC Quality control needed in separation of N2O and CO2 with similar retention times, check non-linearity of ECD at high N2O concentrations using multipoint calibration. Include check standard every 10 samples to account for instrument drift, check calibration curve using control values and CV of 1 to 3% (Harvey et al., 2020, de Klein & Harvey, 2012, Pavelka et al., 2018). Parameters for GC calibration and check Yes Instrument metrics for GC calibration depend on GC brand, this includes span calibration, column cleaning, pneumatic line checks, sample loop. Best sensitivity is achieved in P5 gas for ECD detection (N2O (Maier et al., 2022, Pavelka et al., 2018) Regular standard check per samples analyzed Yes To avoid bias in analytical response, include standard checks every 10 randomized samples; ), about 20% of analyzed samples are adequate number of standard gases for calibrating instrument to cope with temporal drift (Parkin and Venterea, 2010, Pavelka et al., 2018). Linear flux equation Yes GHG fluxes are computed from rate of change of gas concentration over time of closure, when rate of change is constant, linear regression is appropriate (Lundegardh, 1927, Venterea et al., 2009). Dasig model is software to compute linear increase of gas content over time (Adviento-Borbe et al., 2025) Non-linear flux equation Yes GHG fluxes are computed from rate of change of gas concentration over time of closure, when rate of change is not constant, non-linear regression is appropriate. HMR model provides non-linear flux estimates and Dasig software incorporated the HMR equation (Pedersen et al., 2010, Adviento-Borbe et al., 2025). Parameters for linearity Yes Use R2 value that close to 1, variance associated with slope is considered, use at least three datapoints, and implement significant test for slope and minimum detection limit of ambient air (Parkin and Venterea, 2010, Maier et al., 2022) Zero flux Yes Consider zero flux when gas concentrations are above minimum detection limit of chamber and gas analyzers (Adviento-Borbe et al., 2025, Parkin et al., 2012). Negative flux Yes Consider negative flux when R2 is high, gas concentrations are above minimum detection limit (Adviento-Borbe et al., 2025). Linear interpolation of missing days of samples Depending on project objectives Daily GHG emissions are computed using trapezoidal time integration either seasonal or annual estimates (Adviento-Borbe et al., 2025). Fallow emissions Depending on project objective Total GHG emissions during the non-growing season are relevant in the assessment of annual emissions and impacts of straw, tillage and cropping practice on GHG emissions from next cropping (Fitzgerald et al., 2000, Adviento-Borbe et al., 2013) IPCC conversions using 100-yr and 20-yr time horizon Yes Emission metric that is crucial tool in setting effective exchange rates between non-CO2 gases with arbitrary choice of time horizon. (Abernethy and Jackson, 2022, Allen et al., 2016) Data reporting Depending on project objectives Consider unit as elemental or compound form of GHG emissions for modeling and flux estimates i.e. CH4/m2/hr; CH4 kg/ha/d or CH4-C/m2/hr; CH4-C kg/ha/d. Yield-scaled and area-scaled GWP units express both yield and emission potential of mitigating practice (Adviento-Borbe et al, 2013) Ancillary data Yes Include measurement of grain yield expressed at 14% moisture content and milling quality. The next steps agreed during the parallel sessions were as follow:  Technical report development - Summarize chamber commonalities and methodological approaches; provide evidence-based recommendations.  Prepare a step-by-step data handling protocol for flux processing to be included in the technical report as handout series.  Develop annex comparing DASIG, GET, and HMR models, including their compliance with the MIRSA guidelines, advantages and limitations. 32 Fig. 14. Breakout group discussions on collaboration opportunities, data gaps, and the need for harmonization across sites and scales. 33 Table 4. Breakout group results on data harmonization, key data gaps, and relevance to livelihoods. BOG Data Harmonization Key Data Gaps Relevance to Livelihoods BOG 1 – Simulating Rice Productivity under Water Management Scenarios Standardize AWD definitions and thresholds; align protocols for water, yield, and GHG simulations across countries; integrate irrigation and grain quality parameters. Lack of consistent data on irrigation volumes, soil water content, rainfall, soil type, and grain quality; variable AWD thresholds complicate modeling. Better water management (e.g., AWD) can reduce water use, maintain yields, lower GHGs, and enable access to carbon credits, supporting farmer resilience and income. BOG 2 – Modeling and Measuring CH₄ Emissions from Paddy Fields Establish common evaluation metrics (R², RMSE); harmonize minimum datasets for soil, water, crop, residues; use shared repositories with metadata and quality standards. Missing soil data (bulk density, organic matter, C/N content), inconsistent water and weather data, limited residue management data, lack of standardized AWD definitions. Improved methane models support credible MRV frameworks, enabling participation in carbon markets and climate policies that could benefit farmers financially. BOG 3 – Management Interventions and Their Impact on Emissions Standardize monitoring systems and model calibration for interventions (water, fertilizer, varieties); compile inventory of models and their applications. Data mismatches between interventions and model requirements; limited data for crop varieties, organic amendments, and soil microbiomes; lack of regional flux networks. Adoption of improved management practices (AWD, biochar, efficient fertilizers, better varieties) can increase yield, reduce emissions, and improve grain quality, enhancing farmer livelihoods. BOG 4 – Scaling from Field to Region: Data Integration and Upscaling Promote standardized data formats/units; use AI/ML to integrate remote sensing, climate, soil, and socioeconomic datasets; link GHG fluxes to management practices. Spatial and temporal mismatches (field vs. national data, highfrequency needs); coarse soil data; limited flux network coverage; inconsistent management datasets. Scaling integrated assessments helps design region-specific policies that balance productivity, profitability, and sustainability, ensuring that farmers benefit from both yield gains and climate incentives. 34 Table 5. Key outcomes from the four Breakout Groups (BOGs), outlining available data, required actions, responsibilities, collaboration approaches, needed resources, timelines, and challenges. BOG What data we have? What needs to be done? Who will do it? How we can work together? Required resources/support Timelines Anticipated challenges BOG 1 – Rice Productivity under Water Managem ent Ponding height, water table, daily weather, irrigation frequency (but often missing volumes), soil texture (0–15 cm), NPK data, yields, biomass, phenology. Develop standardized water management datasets (AWD thresholds, irrigation, grain quality); harmonize protocols; integrate irrigation use into models. Modelers (AgMIP) prepare templates; data providers supply datasets; rapporteur ensures quality check. Shared repositories; direct communication between modelers and providers; coauthorship and credit agreements. Data management tools, repository hosting, training in data use and models. Short-term: draft terms of use/templates; medium-term: iterative harmonization. Missing/incomplete data; laborintensive templates; institutional restrictions; data security risks. BOG 2 – Modeling and Measuring CH₄ Emissions Flux measurements (chambers, eddy covariance); soil property data (inconsistent); limited water/weather monitoring; residue management records at some sites. Harmonize AWD definitions; create minimum dataset (soil, water, weather, crop, residues); crossvalidate models; build shared repository. Flux groups, modelers (DSSAT, DNDC), regional teams for data inventories. Open data exchange with metadata; regional partnerships; modeling group integration; public– private collaboration. Sustained funding; coordination staff; IT support for repository and quality control. Short-term: curate/share datasets; Mediumterm: crossvalidation and integration pilots; Long-term: shared repositories and AWD standardization. Soil heterogeneity; inconsistent protocols; institutional restrictions; uncertainty in microbial impacts. BOG 3 – Managem ent Interventio ns & Emissions Data stronger for water and fertilizer interventions; limited for crop varieties, organic amendments, microbiomes; uneven flux network coverage. Inventory models/datase ts; metaanalysis of intervention impacts; standardize monitoring; benchmark model Regional leaders assigned; Toshi to develop metadata template; participants provide feedback and data. Metadata-driven sharing; open-data journals; phased focus (water/fertilizer first, broader later). Institutional support for data access; funding for coordination; journal access for reviews. 2-week metadata feedback; phased benchmarking and intervention focus. Institutional restrictions; fragmented datasets; harmonizing units/definitions; regional coordination challenges. 35 performance. BOG 4 – Scaling Field to Region Remote sensing (MODIS, Landsat, Sentinel-2), climate (MERRA-2, ERA5, NCEP), soil (SoilGrids, WoSIS), crop models (DNDC, ORYZA, Daycent, RiceGrow). Regional data in India, Bangladesh, Vietnam. Conduct regional integrated assessments; select sentinel sites; link management data with GHG fluxes; integrate economic and biophysical models. Site leaders (India, Vietnam, Bangladesh); facilitators for climate, crop, socioeconomi c modeling. Collaborative proposals; sentinel site categorization; phased scaling (pilot → multimodel → remote sensing). AgMIP IT revival; capacity building; funding for surveys/workshops/s prints; highperformance computing. Start with India pilot; expand to Vietnam/Banglades h; later include other regions. Data heterogeneity; high monitoring cost; institutional agreements; balancing resolution vs feasibility. 36 All BOGs highlighted missing or inconsistent datasets (soil, water, crop, management, and socio-economic data). There was consensus on the urgent need for standardized data formats, metadata, and harmonized protocols across regions. The participants emphasized the importance of benchmarking models, integrating multiple modeling tools, and aligning simulations with available datasets to reduce uncertainties and improve reliability. For mitigation interventions, Alternate Wetting and Drying (AWD) emerged as a focal practice, requiring clearer definitions, thresholds, and standardization for both scientific use and for practical implementation such as in carbon credit methodology. Establishing durable data-sharing communities, supported by principles of reciprocity, co-authorship, and transparency, was prioritized. Experiences from networks such as Fluxnet and AgMIP were cited as models. The team has then identified the following items as priorities:  Develop a global benchmark database for rice systems that combines biophysical, management, and socioeconomic datasets.  Standardize protocols for AWD implementation, GHG measurement, and datasharing frameworks.  Mobilize funding for sustained data collection, data management coordinators, and collaborative model intercomparisons.  Establish metadata-driven mapping of datasets to identify regional strengths, gaps, and priorities.  Pilot regional integrated assessments (starting with India) to demonstrate scalable approaches, followed by expansion to other countries.  Strengthen cross-disciplinary collaboration between agronomists, modelers, soil scientists, and socioeconomic researchers to ensure both scientific rigor and policy relevance. At the end of the sessions, the steering committee of the workshop has defined the below plan to sustain the work accomplished during the five days:  The workshop will generate a technical document compiling the different team’s practices that will serve as references for context dependent best practices in GHG measurements in rice.  The workshop participants agreed to establish a community of practices in GHG measurements and modeling that will have regular/ monthly meeting. This will be an initial platform for knowledge sharing until a more formal platform is established. The team will explore the establishment as well of an AgMIP emissions data subgroup.  With the planned position papers on policy, GHG measurements and modeling, the organizing committee of the workshop will develop an integrated project proposal that will aim to leverage the workshop participants network and expertise for capacity building in measurements and modeling and for the establishment of a framework of data, modeling and regional integrated assessment to improve baseline estimate in rice growing areas globally and for 37 mitigation interventions targeting guided by robust estimate of gains and co benefits at scale. VII. Outcomes and Recommendations The plenary and parallel sessions of the workshop have confirmed the timeliness of the initiative to address the challenges in mitigation research and efforts in rice systems. The different sessions have articulated the challenges and the existing opportunities for reducing methane emissions in rice from the standards of measurements at field level to the monitoring and assessments of co-benefits at global scale for system sustainability and resilience to climate change. A. Main Insights and Challenges The planned workshop outcomes include documentation on the best practices in chamber method applications and GHG flux analysis, rice model intercomparison and improvement, and policy considerations. These documents will articulate the main insights and challenges shared during the workshop that will serve as reference for researchers and practitioners in rice-growing regions across the United States and Latin America, Europe, Southeast Asia, South Asia, East Asia, and Sub-Saharan Africa. These insights and challenges can be categorized as below: Policy and Mitigation Context  Rice is recognized as a significant contributor to GHG emissions, yet no ASEAN country has set specific mitigation targets for rice systems; existing commitments are usually conditional on international financing.  Achieving the Global Methane Pledge (30% reduction by 2030) would require large-scale adoption of mitigation interventions such as for AWD estimated roughly up to 60% of rice area.  Carbon markets are emerging with multiple schemes (compliance, voluntary, bilateral, ODA, financial institutions, scope 3), but report on success of these remains very limited.  It is essential to quantify the impacts of GHG mitigation strategies on farmers’ livelihoods and broader policy-relevant outcomes such as poverty and food security, while assessing the trade-offs among socio-economic, biophysical, and environmental dimensions. Scientific Approaches for GHG Measurements and Modeling  Measurement practices remain diverse, creating inconsistencies.  Agreement was reached on the need for a unified protocol to ensure robust estimates and reduce uncertainties.  Rice models are generally ready for climate change applications but require further development of emission modules and standardized calibration.  Modeling offers pathways to standardize and harmonize datasets, improving usability and comparability.  Water management processes need better representation, particularly for AWD and drainage effects on yield and GHG emissions. 38  AgMIP and its rice team provide tools to integrate climate, socio-economic, and crop models for adaptation and mitigation co-benefits.  Modeling supports uncertainty analysis, extrapolation of experimental results, and scaling from field to regional and global levels.  There is strong potential for standardized protocols, ensemble modeling, and integration of emerging technologies (ML/AI, digital twins, genomics). Data Gaps  Current data collection is designed for food security, not mitigation.  The baseline concept is flawed: unintentional AWD adoption and straw management variability are not accounted for.  National inventories lack systematic, reliable, and validated data for calibration/validation.  Data availability and quality remains the largest barrier - sharing, standardization, and coordinated calibration/validation efforts are crucial.  Examples like IRRI’s farm survey + RS framework show pathways to link farm-level data with district/regional assessments.  Data sharing remains a sensitive issue due to ownership and access concerns.  AgMIP’s FAIRER principles (Findable, Accessible, Interoperable, Reusable, Ethical, Responsible reuse) provide a stronger framework than the basic FAIR approach. Scaling assessment  The limited understanding and quantification of variability of rice ecosystems and the heterogeneity of management practices, and varieties that introduce wide variability in emissions and mitigation potential. This has impact on the Baseline validity in rice that is currently undermined by widespread unreported AWD and straw management practices.  Limited robustness of Monitoring and Verification tool: Tier 1 and 2 approaches carry large uncertainties, requiring transparent reporting of these and shared understanding of their limits.  Data scarcity as there is no clear incentives, protocols, or benefit-sharing mechanisms for data collection and sharing among stakeholders either for national actors or private entities.  Knowledge gaps in research and in modeling such for instance representation of ratoon crops, grain quality (e.g., arsenic, starch, and chalkiness), SOC variability, and pest/disease interactions. B. Actionable Next Steps The different discussions raised from the different sessions have suggested the below recommendations for the team to leverage and to address moving forwards:  Implement a unified GHG measurement protocol across teams to reduce inconsistencies. These can contribute to addressing verification challenges by standardizing emission factors and stratification methods.  Create data-sharing platforms with clear benefit-sharing mechanisms and opportunities for governments, researchers, and local stakeholders. Use of such platform can be used to update baselines as these need to be redefined to 39 reflect actual practices (e.g., unintentional AWD, straw management). It is important to move beyond FAIR to FAIRER for this platform embedding ethical and responsible reuse into all data initiatives. And as data became available, opportunities to leverage advanced technologies in RS, AI/ML, genomics and other will offer opportunities to scale mitigation and adaptation interventions with confidence and sustainable impact.  Promote long-term studies combining measurements and modeling that will ensure closer integration between modelers and experimentalists which can improve both field experiment design and model performance.  Expand capacity building in GHG flux measurement, crop modeling, and integrated assessment approaches. A structured training and mentorship program will help harmonize methods across regions, strengthen technical expertise, and support the next generation of researchers in applying these tools to policy-relevant questions.  These initiatives can encourage cooperation across regions, disciplines and sectors that will ensure data access, availability and trust with future efforts focusing data-driven model intercomparison, and MRV/NDC alignment for broader, policy-relevant applications. VIII. Conclusion The 5-day workshop achieved its main objective: establishing a community of practice that can be used as a platform for knowledge exchange, capacity building, and collaboration development. By bringing together GHG measurement experts and modelers, the workshop has created an opportunity for dialogue between these two disciplines and established the key gaps that may offer opportunities for effective collaboration, such as the development of a common ontology and the establishment of minimum data requirements. As a first example, a data template was developed to evaluate data availability for model applications that will be used to create a repository of metadata of available data that the participants of the workshop will be ready to share for crop model evaluations. The roadmap below was identified for the integration of both efforts in measurements and in modeling GHG quantification for baseline, predictions, and mitigation from field to regional assessments. Agreed Actions  Technical report on narratives for commonalties among practices  Position papers on Measurements, Modeling, and Impact and Policy  Regular monthly meeting for sustaining impact  Activities plan in a year’s time - East Asia AgMIP Nov 2026, Global early 2027, IRC 2027 GMH Commitment  Support for a core group to keep and bring people together  Catalyze existing opportunities and funding  Seed fund for proposal development Roles and Responsibilities 40  Dr. Hasegawa for the modeling - Modeling different pathways for low emissions rice  Roberto for the RIA - Co-roasting fundingBuilding the scientist network towards shared goals - Google docs for proposal development and seed funding for proposal development  Ando for the GHG data - Robust GHG data, sharing data and capacity building sustaining the discussion Priority Objectives  Publication and sharing of the workshop outputs  Capacity building for GHG measurements and modeling  Proactive engagement in initiatives to support national capacity for mitigation data collection, harmonization and different countries GHG inventory for UNFCCC reporting aiming for robust baseline update across rice growing regions and targeted interventions for mitigation and adaptation co benefits IX. 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Enclosure-based measurement of trace gas exchange: applications and sources of error. In. P.A. Matson and R.C. Harriss (eds.) Biogenic Trace Gases: Measuring Emissions from Soil and Water. Methods in Ecology. Blackwell Science Cambridge University Press. pp 14-51. Livingston, G.P., G.L. Hutchinson and K. Spartalian. 2006 Trace gas emission in chambers: A nonsteady-state diffusion model. Soil Sci. Soc. Am. J. 70:1459-1469. Parkin, T.B. and Venterea, R.T. 2010. Sampling Protocols. Chapter 3. Chamber-Based Trace Gas Flux Measurements. IN Sampling Protocols. R.F. Follett, editor. p. 3-1 to 3-39. Available at: www.ars.usda.gov/research/GRACEnet Parkin, T.B., Venterea, R.T., Hargreaves, S.K., 2012. Calculating the detection limits of chamberbased soil greenhouse gas flux measurements. J. Environ. Qual. 41, 705–715. Pavelka, M., Acosta, M., Kiese, R., Altimir, N., Brummer, C., Crill, P., Darenova, E., FuB, R., Gielen, B., Graf, A., Klemedtsson, L., Lohila, A., Longdoz, B., Lindroth, A., Nilsson, M., Jimenez, S., Merbold, L., Montagnani, L., Peich, M., Philatie, M., Pumpanen, J., Ortiz, P., Silvennoinen, H., 48 RiceGrow Liujun Xiao liujunxiao@nja u.edu.cn Nanjing Agricultural University https://doi.org/10.101 6/j.njas.2009.12.003; 10.1016/j.agrformet. 2025.110452 Yield prediction;Climate change impact assessment;Adaptation strategy assessment;Cultivar/genot ype comparison;Nutrient management simulation;Water manatement simulation;Greenhouse gas (GHG) eimissions estimation ;Nutrient cycling modeling;Carbon sequestration modeling Phenological development;Leaf and canopy photosynthesis;Respiratio n;Biomass accumulation and partitioning;Architecture of below-ground organs;Architecture of above-ground organs;Nutrient uptake and partitioning;Yield formation;Product quality;Transpiration CO2;H2O;CH 4;N2O Soil water movement;Nutrient mineralization ;Nutrient immobilization;Nutrient leaching;Organic matter decomposition;CH4 production ;CH4 oxidation;Denitrification and nitrification RicePSM Lloyd T. (Ted) Wilson [email protected] amu.edu Texas A&M University https://doi.org/10.101 6/S0308521X(97)00070-X Yield prediction;Climate change impact assessment;Adaptation strategy assessment;Cultivar/genot ype comparison;Pest and disease dynamics; Phenological development;Leaf and canopy photosynthesis;Respiratio n;Biomass accumulation and partitioning;Architecture of below-ground organs;Architecture of above-ground organs;Nutrient uptake and partitioning;Yield formation;Product quality;Transpiration; CO2; Soil water movement; RiceSM Xianguan Chen 1069335668@ qq.com Fujian Agriculture and Forestry University Scheduled for publication this year. Yield prediction;Climate change impact assessment;Adaptation strategy assessment;Nutrient management simulation;Greenhouse gas (GHG) eimissions estimation ;Nutrient cycling modeling;Carbon sequestration modeling Phenological development;Leaf and canopy photosynthesis;Respiratio n;Biomass accumulation and partitioning;Architecture of below-ground organs;Architecture of above-ground organs;Nutrient uptake and partitioning;Yield formation;Transpiration CO2;N2O Soil water movement;Soil temperature dynamics;Nutrient mineralization ;Nutrient immobilization;Nutrient leaching;Organic matter decomposition;Denitrific ation and nitrification;Nutrient volatilization 49 B. List of Participants Table 2. Complete list of speakers and participants of the workshop. No Fullname Email Address Affiliation 1 Agnes Padre [email protected] Global Methane Hub, Philippines 2 Aimee Evangelista [email protected] Climate Change Commission Office Philippines 3 Alisher Mirzabaev [email protected] International Rice Research Institute, Philippines 4 Anaida Ferrer [email protected] International Rice Research Institute, Philippines 5 Ando Radanielson [email protected] International Rice Research Institute, Philippines 6 Andriamananjara Andry [email protected] University of Antananarivo, Madagascar 7 Angelica Ariel Mawili-Carpio [email protected] University of the Philippines Los Baños, Philippines 8 Anggri Hervani [email protected] Ministry of Agriculture, Indonesia 9 Ankita Paul [email protected] International Rice Research Institute, India 10 Anton Urfels [email protected] International Rice Research Institute, Philippines 11 Arti Bhatia [email protected]; [email protected] ICAR-Indian Agricultural Research Institute, India 12 Aung Zaw Oo [email protected] Japan International Research Center for Agricultural Sciences, Japan 13 Austin Pearce [email protected] Field To Market: The Alliance for Sustainable Agriculture, USA 14 Benjamin Runkle [email protected] The University of Arkansas, USA 15 Bjoern Ole Sander [email protected] International Rice Research Institute, Philippines 16 Caesar Arloo Centeno [email protected] International Rice Research Institute, Philippines 17 Catalina Trujillo [email protected] Alliance of Bioversity International / CIAT, Colombia 18 Chen Xianguan [email protected] Fujian Agriculture and Forestry University, China 19 Chia-Kang Huang [email protected] Taiwan Agricultural Researcher Institute, Taiwan 20 Chi-Chieh Hu [email protected] Kaohsiung District Agricultural Research and Extension Station, Taiwan 21 Cristhian Delgado [email protected] University of Otago, New Zealand 22 Dalmo Vieira [email protected] Arkansas State University, USA 23 Emmali Manalo [email protected] International Rice Research Institute, Philippines 24 Erik Mencos Contreras [email protected] Columbia University, USA 25 Evangeline Sibayan [email protected] Creattura Company Ltd, Philippines 50 26 Feng Zhou [email protected] Peking University, China 27 Francis Rubianes [email protected] International Rice Research Institute, Philippines 28 Fulu Tao [email protected] Chinese Academy of Sciences, China 29 Hannah Jose [email protected] International Rice Research Institute, Philippines 30 Jean Martial Johnson [email protected] International Rice Research Institute, Philippines 31 Katherine Nelson [email protected] International Rice Research Institute, Vietnam 32 Kazuki Saito [email protected] International Rice Research Institute, Philippines 33 Kazunori Minamikawa [email protected] Japan International Research Center for Agricultural Sciences, Japan 34 Kelvin Mashisia Shikuku [email protected] International Livestock Research Institute, Kenya 35 Kevin Karl [email protected] Columbia University / NASA-GISS, USA 36 Kofi Konadu Boateng [email protected] Global Methane Hub, Ghana 37 Kristine Pascual [email protected] Philippine Rice Research Institute, Philippines 38 Laura Natalia Arenas Calle [email protected] Cornell University, USA 39 Linh Huynh [email protected] International Rice Research Institute, Philippines 40 Liping Feng [email protected] BeiDaHuang Group, China 41 Liujun Xiao [email protected] Nanjing Agricultural University, China 42 Lixin Qiu [email protected] BeiDaHuang Group, China 43 Li-Yu Daisy Liu [email protected] National Taiwan University, Taiwan 44 Lolita Adriano [email protected] International Rice Research Institute, Philippines 45 Longlong Xia [email protected] Chinese Academy of Sciences, China 46 Maduabuchi Paul Iboko [email protected] AfricaRice, Cote d'Ivoire 47 Mai Văn Trịnh [email protected] Institute For Agricultural Environment, Vietnam 48 Maite Martínez Eixarch [email protected] Institute of Agrifood Research and Technology, Spain 49 Maria Anthea Faltado [email protected] International Rice Research Institute, Philippines 50 Maria Camila Rebolledo [email protected] International Center for Tropical Agriculture, Colombia 51 Mary Ann Burac [email protected] International Rice Research Institute, Philippines 52 Meijian Yang [email protected] Columbia University, USA 53 Myriam Adam [email protected] Centre de Cooperation International en Recherché Agronomique pour le Development, Cambodia 51 54 Naoya Takeda [email protected] Queensland University of Technology, Australia 55 Nataraja Subash Pillai [email protected] ICAR-Indian Agricultural Research Institute, India 56 Olivyn Angeles [email protected] International Rice Research Institute, Philippines 57 Pauline Chivenge [email protected] International Rice Research Institute, Tanzania 58 Reiner Wassmann [email protected] International Rice Research Institute, Philippines 59 Reti Ranniku [email protected] University of Arkansas, USA 60 Roberto Valdivia [email protected] Oregon State University / AgMIP, USA 61 Rubenito Lampayan [email protected] University of the Philippines Los Baños, Philippines 62 Rusty Bautista [email protected] RiceTec, Inc., USA 63 Ryan Romasanta [email protected] International Rice Research Institute, Philippines 64 S.M. Mofijul Islam [email protected] Soil Science Division, Bangladesh 65 Saz Hamsha [email protected] Green Carbon Inc., Philippines 66 Sonam Rinchen Sherpa [email protected] Centro Internacional de Mejoramiento de Maíz y Trigo, Nepal 67 Soora Naresh Kumar [email protected] Indian Agriculture Research Institute, India 68 Sukamal Sarkar [email protected] Ramakrishna Mission Vivekananda Educational and Research Institute, India 69 Swamikannu Nedumaran [email protected] International Crops Research Institute for the SemiArid Tropics, India 70 Tao Li [email protected] International Rice Research Institute, Philippines 71 Toshihiro Hasegawa [email protected] National Agriculture and Food Research Organization, Japan 72 Tsai-Wei Chiang [email protected] National Taiwan University, Taiwan 73 Upendra Singh [email protected] International Fertilizer Development Center, USA 74 Virender Kumar [email protected] International Rice Research Institute, Philippines 75 Vo Thi Bach Thuong [email protected] International Rice Research Institute, Philippines 76 William Salas [email protected] Regrow Agriculture, USA 77 Winnie Jelagat Kimuta [email protected] Kenyatta University, Kenya 78 Xiaobo Qin [email protected] Chinese Academy of Agricultural Sciences, China 79 Xuhui Wang [email protected] Peking University, China 80 Yujing Gao [email protected] The University of Maryland Center for Environmental Science, USA 52 Virtual Participants No Fullname Email Address Affiliation 1 Achim Dobermann [email protected] International Fertilizer Association, France 2 Anna McClung [email protected] Global Methane Hub 3 Chloe Lai [email protected] University of Southern Queensland, Australia 4 Christophe Pradal [email protected] Centre de Cooperation International en Recherché Agronomique pour le Development, France 5 Cynthia Rosenzweig [email protected] Columbia University / NASA-GISS, USA 6 Hao Liang [email protected]; [email protected] Hohai University, China 7 Hiroe Yoshida [email protected] National Agriculture and Food Research Organization, Japan 8 Miranti Ariani [email protected] Indonesian Agricultural Environment Standardization Institute, Indonesia 9 Sonali Mcdermid [email protected] New York University, USA 10 Soren O. Pedersen [email protected] University of Aarhus, Denmark 11 Tamon Fumoto [email protected] National Agriculture and Food Research Organization, Japan 12 Tanh Nguyen [email protected] An Giang University, Vietnam 13 Ted Wilson [email protected] Texas A&M University, USA 14 Xuefei Li [email protected] United Nations Environment Programe 15 Yan Zhu [email protected] Nanjing Agricultural University, China 16 Yubin Yang [email protected] Texas A&M University, USA 53 C. Workshop Materials Program and PowerPoint Presentations (LINK)