Process simulation and optimization of agro-systems by DNDC model

TL;DR

This paper presents a simulation-based optimization framework using the DNDC model to enhance crop yields and reduce greenhouse gas emissions in agriculture, addressing global food security and climate change.

Contribution

It integrates DNDC with gPROMS for fertilizer optimization, demonstrating significant yield improvements and emission reductions in a case study.

Findings

Crop yield increased by 18% with optimized fertilizer use.

GHG emissions reduced by 10% through optimization.

Validated the effectiveness of simulation-based fertilizer planning.

Abstract

Many people are still facing hunger and the global food shortages is still an urgent problem. Meanwhile, global warming is still severe. Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of agriculture system. We simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model. A set of empirical equations of DNDC model were selected and implemented in gPROMS for obtaining the optimal solution of fertilizer usage. A case study shows that the optimized framework improves crop yield by 18%, when 72.42kg N/ha urea was used. Meanwhile the GHG emission of the system was reduced by 10%. The results show the necessity of optimal planning and usage of fertilizer in agriculture system.