DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment

Taghikhah, F; Borevitz, J; Costanza, R; Voinov, A

DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment

Taghikhah, F

Borevitz, J Costanza, R Voinov, A

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Ecological Modelling, 2022, 468
Issue Date:: 2022-06-01

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Field	Value	Language
dc.contributor.author	Taghikhah, F https://orcid.org/0000-0002-0851-6816
dc.contributor.author	Borevitz, J
dc.contributor.author	Costanza, R
dc.contributor.author	Voinov, A https://orcid.org/0000-0002-2985-4574
dc.date.accessioned	2023-04-19T01:39:27Z
dc.date.available	2023-04-19T01:39:27Z
dc.date.issued	2022-06-01
dc.identifier.citation	Ecological Modelling, 2022, 468
dc.identifier.issn	0304-3800
dc.identifier.issn	1872-7026
dc.identifier.uri	http://hdl.handle.net/10453/169980
dc.description.abstract	Threats to sustainable food production are accelerating due to climate change, population growth, depletion of natural capital, and global market instability. This causes significant risks to farmers, consumers, and financial and policy institutions. Understanding agro-ecosystems, and how varying management styles can impact their performance is critical to future wellbeing. To better understand and manage agricultural production, we have developed a dynamic simulation model that accounts for the core natural capital components of agro-ecosystems, including climate, soil, carbon, water, nitrogen, phosphorus, microorganisms, erosion, crops, farm animals and plants. Dynamic Agro-Ecosystem Simulation (DAESim) model can be used to simulate dynamics of soil health and project it into the future to assess vulnerabilities and resilience. This knowledge can inform and guide investment decisions by financial institutions, insurance companies, farmers, and governmental agencies. Here, we describe the basic model structure, sensitivity, and calibration results. We then run a few scenarios to demonstrate the model's ability to analyze alternative agro-ecosystem management options.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Ecological Modelling
dc.relation.isbasedon	10.1016/j.ecolmodel.2022.109930
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Ecology
dc.title	DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment
dc.type	Journal Article
utslib.citation.volume	468
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/School of Communication
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-19T01:39:25Z
pubs.publication-status	Published
pubs.volume	468

Abstract:

Threats to sustainable food production are accelerating due to climate change, population growth, depletion of natural capital, and global market instability. This causes significant risks to farmers, consumers, and financial and policy institutions. Understanding agro-ecosystems, and how varying management styles can impact their performance is critical to future wellbeing. To better understand and manage agricultural production, we have developed a dynamic simulation model that accounts for the core natural capital components of agro-ecosystems, including climate, soil, carbon, water, nitrogen, phosphorus, microorganisms, erosion, crops, farm animals and plants. Dynamic Agro-Ecosystem Simulation (DAESim) model can be used to simulate dynamics of soil health and project it into the future to assess vulnerabilities and resilience. This knowledge can inform and guide investment decisions by financial institutions, insurance companies, farmers, and governmental agencies. Here, we describe the basic model structure, sensitivity, and calibration results. We then run a few scenarios to demonstrate the model's ability to analyze alternative agro-ecosystem management options.

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http://hdl.handle.net/10453/169980