Carbon farming is the collection of agricultural best practices specifically designed to maximize the capture and long-term storage of atmospheric carbon dioxide in soils and plant biomass, while simultaneously reducing greenhouse gas emissions from cultivation practices.<\/p>\n\n\n\n
Carbon farming can be viewed as a promising pathway to simultaneously address climate change mitigation, soil degradation, and farmer welfare. For example, if the entire agricultural cropland in India practices carbon farming, this will spectacularly offset about 50% of emissions from the country\u2019s annual transport-sector emissions. However, practical deployment of carbon farming is constrained by scientific challenges, inherent complexity, and fragmented understanding across disciplines. As a result, in India, for example, fewer than 1% of farmers participate in carbon credit programs.<\/p>\n\n\n\n
The success of carbon farming depends critically on the ability to accurately measure carbon outcomes, design effective farm-level interventions, ensure high levels of farmer participation, and enable efficient carbon markets. Artificial Intelligence techniques as well as game theory and mechanism design will have an important role to play across all these dimensions. Rather than acting as a single solution, AI and game theory will function as a connective layer that integrates data, models, inferences, incentives, and strategic decision-making in carbon farming systems.<\/p>\n\n\n\n