Modelling carbon dioxide gas emission from Notopterus chitala fish ponds by stella software

Nguyen Van So, Le Anh Tuan
Author affiliations


  • Nguyen Van So Department of Natural Resources and Environment of Hau Giang province, no 3, Xo Viet Nghe Tinh street, ward V, Vi Thanh city, Hau Giang province, Viet Nam
  • Le Anh Tuan College of Environment and Natural Resources, Can Tho University, Campus II, 3/2 street, Xuan Khanh ward, Ninh Kieu district, Can Tho city, Viet Nam



Carbon dioxide, Greenhouse gas emission, Notopterus chitala fish ponds, Stella model, Water quality


The rapid development of the areas of ​​Notopterus chitala fish ponds in Hau Giang province in recent years has raised a question about greenhouse gas emissions, in the form of total carbon dioxide equivalents (CO2e). There are many parameters that affect greenhouse gas emissions in a fish pond, such as amount of feed, dissolved oxygen (DO), chemical oxygen demand (COD) in the water, pH, water temperature, windy velocity and sunlight reaching the pond surface. In this study, a System Thinking, Experimental Learning Laboratory with Animation, shortly called as Stella is applied as a visual programming language for system dynamics modelling in order to find the relationship between simulated CO2 and measured CO2 in Notopterus chitala fish pond. Three ponds were used for measuring average pH, temperature, feeds, DO, COD and phytoplankton inside the ponds while windy speed and light intensity data were collected from a Weather Station nearby. The results of model calibration and validation showed that the Stella 8.0 can be used as predictable tool for the change in time of CO2 emission during 240 days of fishing. Model can help fishing farmers to adjust the quantity of feeds and control the water quality in their Notopterus chitala fish ponds to reduce greenhouse gas emissions appropriately.


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How to Cite

V. S. Nguyen and A. T. Le, “Modelling carbon dioxide gas emission from Notopterus chitala fish ponds by stella software”, Vietnam J. Sci. Technol., vol. 61, no. 6, pp. 1038–1049, Dec. 2023.