Journal of Scientific Research and Reports

Over the last three decades, a plethora of soil carbon models have been developed, but a fundamental gap still persists in the formulation of a comprehensive model that incorporates long-term carbon dynamics from wetland rice ecosystems. Rice-based cropping systems predominating in the Indo-Gangetic plains, extends an area of 9.64 Mha and accounts for 17.5 % of greenhouse gas (GHG) emissions in India. With atmospheric CO₂ concentration approaching 420 ppm, carbon storage in wetlands becomes critical for improving the soil properties. Soil carbon modelling, which employs techniques such as machine learning, is emerging as a critical tool for connecting disparate data sources for quantifying sequestration capacity, generating carbon distribution maps and optimizing agricultural operations. Integration of two well-known models viz., PAPRAN (Production of Annual Pasture limited by Rainfall and Nitrogen model) and Long-term model help to understand the breakdown of organic matter pools and track the carbon and nitrogen movement through these pools. This combined approach is able to adjust with different soil and climatic conditions, works well in various farming systems and provides insights into carbon and nitrogen interactions on crop yields. Hence, this review attempts a critical exploration on the possibilities and necessity of a combined approach in carbon modelling in rice-based cropping systems for achieving sustainable ecosystem and climate-resilient agriculture.