Research project NG| Optimization of Agricultural Management for Soil Carbon Sequestration

The project "Optimization of agricultural management for carbon sequestration using deep reinforcement learning and large-scale simulations" aims to develop an intelligent agricultural management system with deep reinforcement learning (RL) and large-scale soil and crop simulations.

Global climate change is one of the biggest challenges facing humanity. Leveraging the carbon sink capacity of global soils has great potential to mitigate such change. The project entitled “Optimization of Agricultural Management for Soil Carbon Sequestration Using Deep Reinforcement Learning and Large-Scale Simulations” aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, a simulator will be built for modelling, quantifying and predicting the complex soil-water-plant-atmosphere interactions, using high-performance computing platforms. This is needed because soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop management practices for optimal carbon sequestration and crop yield solutions.