Evaluating Agricultural Soil GHG Emission Models: A Critical Analysis of Identification and Future Challenges

This study focuses on the increasing global challenge of the need for efficacy assessment of techniques and the critical problem of GHG emissions from agrosystems. It addresses their processes, input variables, and simulation results in different cropping systems to present a comprehensive analysis of four well- known process models: DNDC, DayCent, STICS, and APSIM. These may include model structure, management practices, weather conditions, and the level of data available at the time the simulation was built that influence the model's accuracy in the study. Development of appropriate measurement and reduction tools in agriculture is quite essential because, as the IPCC noted, agriculture plays a vital role in global anthropogenic emissions of greenhouse gases. This study puts forward an integrated hybrid framework, which includes both process-based models and geospatial techniques to increase the precision of emission assessments over varied agricultural systems. The proposed method showed promising improvement in the scalability and robustness of models to enable effective tailoring of mitigation strategies toward sustainable agricultural practices. This paper aims to enhance model accuracy and applicability by offering a methodology focusing on increasing the model's applicability area, unifying the data analysis, and improving the model's algorithms. This project aims to facilitate sustainable food production methods and climate change adaptation by providing better tools and methods to stakeholders by tackling the problem of identifying and managing soil GHG emissions.