| Sumario: | Rice, while a staple food for billions worldwide, is also a significant contributor to global methane emissions due to traditional cultivation practices such as continuous flooding. To mitigate these emissions, transitioning to more sustainable irrigation practices is essential. However, scaling these low-emission techniques in projects typified by smallholder farmers is challenging due to several barriers, including the need for extensive farmer engagement, costly and resource-consuming monitoring, reporting, and verification (MRV) requirements, and a necessarily complex project documentation process. Existing manual methods for MRV are often too labour-intensive, open to human error or manipulation, and lack scientific robustness, undermining project credibility within the voluntary carbon market. These challenges can deter project developers and investors from engaging in rice-related carbon projects, limiting the expansion of better irrigation practices.
Improving project integrity and simplifying monitoring processes are essential for advancing large-scale projects supporting more sustainable rice farming practices. With progress in Artificial Intelligences (AI) and other modern forms of Machine Learning (ML), it is possible to mitigate key impediments that have hampered the wide-spread implementation of climate smart rice projects. This report will lay out some of these key applications that have the potential to revolutionise the development of rice projects in the voluntary carbon market, with a focus on the Gold Standard rice methodology launched in 2023.
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