Rice fields are a critical source of CH 4 and N 2 O, necessitating accurate, field-level measurements to inform effective mitigation programs. This review (Volume 1) offers a novel perspective on this topic by focusing on the measurement systems themselves, driven by one core question: To what extent have the technical capabilities and limitations of field measurement systems shaped the current scientific understanding and knowledge gaps on rice greenhouse gas (GHG) emissions? We provide a comprehensive assessment of three major field approaches: Manually Sampled Chambers (MSC), Fully Automated Chambers (FAC), and Eddy Covariance (EC). Reversing the narrative of typical literature reviews that focus primarily on scientific findings, this paper starts with the technical evolution of each method, followed by a comparative assessment based on frameworks for method selection and scientific key contributions. The current scientific consensus and global estimates are overwhelmingly derived from the highly versatile MSC approach, which has generated a vast database across different rice-growing regions and management treatments, enabling statistically robust meta-analyses. Despite limitations—such as altering the microclimate in the headspace, possibly missing diurnal or seasonal peaks, and limited spatial scalability—the MSC remains a cornerstone of rice GHG research and will continue to play a central role. The FAC system was developed as an alternative, overcoming limitations in sampling frequency and providing robust data on diurnal and seasonal emission patterns, which proved especially valuable in comparative studies on crop management impacts. Finally, we discuss the use of EC, which provides high-resolution, integrative datasets that allow for a greatly improved process-based understanding of GHG fluxes. The established FluxNet collaboration of EC researchers could serve as a blueprint to coordinate chamber-based studies, thereby building the comprehensive dataset necessary to support data-driven modeling and Machine Learning (ML) development. This retrospective assessment in Volume 1 establishes a critical framework for evaluating and selecting rice GHG measurement methods. Volume 2 of this paper will supplement this work by addressing emerging technical innovations and prospects against the backdrop of diversified research objectives.
Vo et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: