Workshop: Tackling Climate Change with Machine Learning

Optimization of Agricultural Management for Soil Carbon Sequestration based on Deep ReinforcementLearning and Large-Scale Simulations

Jing Wu


Soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop the optimal management practices for maximization of the sequestered carbon as well as the crop yield. This project aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, we propose to build a simulator to model and simulate the complex soil-water-plant-atmosphere interaction. By formulating the management decision as an RL problem, we can leverage the state-of-the-art algorithms to train management policies through extensive interactions with the simulated environment. The trained policies are expected to maximize the stored organic carbon while maximizing the crop yield in the presence of uncertain weather conditions. The whole system will be tested using data of soil and crops in both mid-west of the United States and the central region of Portugal. The proposed research will impact food security and climate change, two of the most significant challenges currently facing humanity.