Our Technology Addresses Vital Challenges in Rice Methane Reduction Monitoring
Monitoring GHG emissions from rice growing is challenging
Emissions are continuous
Methane emissions from rice are diffuse and continuous in nature, necessitating continuous monitoring at scale.
Traditional MRV methods lack rigour
Current MRV practices rely on self-declared farming practices documented in paper logbooks, resulting in numerous cases of fraud.
Current MRV practices don't scale
Collecting ground data consistently and scientifically from smallholder farmers is expensive and operationally difficult.
Our technology makes MRV simple and rigorous
CarbonFarm has developed its core technology in collaboration with top academic institutions.
Satellites bring transparency at scale
Verify additionality in each paddy
CarbonFarm's proprietary Machine Learning models allow to remotely monitor a large range of farming practices including water-management and straw-management. This allows us to assess baseline and project practices and verify additionality claims at paddy level.
Establish baseline and project emissions
We use scientifically validated models to estimate GHG emissions based on observed practices, growing conditions, soil type, and more. Remote-sensing allows us to “look in the past” to establish robust baseline emissions.
Simple at scale
CarbonFarm's suite of tools simplifies operations at scale. Satellites provide an efficient and cost-effective means of collecting ‘near-real-time’ data at scale. This reduces implementation risks for project developers who can plan targeted interventions in specific areas.
Unmatched transparency and rigour
Satellites provide a rigorous, consistent and independant source of information. They reduce the bias and risks associated with self-reporting of practices. CarbonFarm provides full transparency to project developers and carbon investors, allowing to monitor project progress and impact.
We support all types of rice decarbonisation projects
We help our clients certify their rice emissions under different standards.