WechatIMG67.jpeg

Hello

My current research mainly focuses on disentangling the compound effect of Genetics x Environment x Management (GxExM) on agricultural productivity from regional to global using process-based crop model and earth system model, and further investigating the agricultural feedback to climate change. My research aims at facilitating a more sustainable agricultural system to alleviate both food insecurity and climate shocks.

About me

I'm a second year postdoctoral associate in Dr. Ying Sun's lab. Prior to joining Dr. Sun’s lab, I obtained my PhD degree in Environmental Engineering at the University of Connecticut, advised by Dr. Guiling Wang. My PhD research covers climate-ecosystem interaction, agricultural prediction, WEF Nexus, and machine learning application. The Hydro-Groundwater-Crop modeling framework I developed integrates three physical models - CREST, MODFLOW and DSSAT. It was successfully applied in Ethiopia in making forecasts at site, regional and national levels to help farmers and decision makers in enhancing water and food security. I also hold a Masters degree in Hydrology and Water Resources from China Institute of Water Resources and Hydropower Research.

During my leisure time, I’m a big fan of indoor and outdoor sports like tennis, squash, hiking, kayaking, skating and swimming. I enjoy traveling to different countries for their culture and view.
 

WechatIMG68.jpeg

Research Projects:

Photo_Meijian.jpg

Disentangling GxExM impact on agricultural productivity from historical evidence


Background: Global agricultural productivity has been sharply increasing for nearly a century. As the global population is projected to continue increasing along with the rising pressure from achieving the 2 degree global warming goal, understanding the role of different GxExM factors on historical agricultural productivity could provide insight to guide future activity in tackling food and climate crises.


Taking advantage of the current state-of-the-art crop model DSSAT, I further developed the model to make it feasible of simulating crop yield with time-varying genetic and management input. Results indicate that the improvement in agronomic management practices in the past decades has the most significant contribution to crop yield increase.