Simulation of Agricultural Land Use Cover Changes Using Mathematical Programming Based Multi-agent system (MP-MAS) in Babolsar

Subject Areas : Agricultural Economics Research

kamal Ataie s ¹ , ahmad ali keykha ² , Mahmoud Ahmadpour ³ , Saman Ziaee ⁴ , Farhad HoseiAli ⁵

1 - دانشجوی دکتری
2 - Associated Professor of Agricultural economics of university of Zabol
3 - Assistance Professor of Agricultural Economics of university of Zabol
4 - Assistance Professor of agricultural economics of university of Zabol
5 - Assistance Professor of Civil of Shahid Rajaee Teacher Training University of Tehran

Received: 2017-08-31 Accepted : 2020-01-12 Published : 2019-12-22

Keywords: Agent-based modeling, Babolsar, Land Use Cover Change, Mathematical Programming Based Multi-agent system (MP-MAS), Positive Mathematical programming Planning,

Abstract :

According to debates and concerns about environmental changes, land use coverage changes have been taken seriously in recent decades. One of the most popular approaches to the simulation is Agent-Based Modelling (ABM). ABM is a set of computational models for simulating the actions and reactions of autonomous agents. The purpose of this research in 2016, is to simulate the status of agricultural land use cover changes in Babolsar over the next 30 years using the mathematical programming based-multi agent system (MP-MAS). The results showed that rain fed products would eliminate from the cultivation pattern of the region, and the development of mechanized Rice cultivation will occur. The area under cultivation of the horticultural products of the region will grow from 3,959 hectares to over 5585 hectares. The results will help managers with a strategic insight at the future of agricultural land use cover changes as they can simulate feedback on their policies and economic decisions.

References:

- Arnold, R. T., Troost, C. & Berger, T. (2015). Quantifying the economic importance of irrigation water reuse in a Chilean watershed using an integrated agent-based model, Water Resources Research, 51: 648-66.

- Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton, 248p.

- Bannwarth, M., Grovermann, C., Schreinemachers, P., Ingwersen, J., Lamers, M., Berger, T. & Streck, T. (2016). Non-hazardous pesticide concentrations in surface waters: An integrated approach simulating application thresholds and resulting farm income effects. Journal of Environmental Management, 165: 298 - 312.

- Becu, N., Perez, P., Walker, B., Barreteau, O. & Page, C. L. (2003). Agent-based simulation of a small catchment water management in northern Thailand. Description of the Cath scape model, Ecological Modeling, 170: 319-331.

- Belcher, K. W., Boehm, M. M. & Fulton, M. E. (2004). Agro-ecosystem sustainability: a system simulation model approach. Agricultural Systems, 79 (2): 225-241.

- Berger, T., (2001). Agent-based Spatial Models Applied to Agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics 25: 245-260.

- Berger, T., Troost, C., Wossen, T., Latynskiy, E., Tesfaye, K., & Gbegbelegbe, S., (2017). Can smallholder farmers adapt to climate variability, and how effective are policy interventions? Agent-based simulation results for Ethiopia. Agricultural Economics, 48: 693-706.

- Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems, National Academy of Science, 99: 7280-7287.

- Casti, J. L. (1997). Would-be worlds: How Simulation is Changing the Frontiers of Science. John Wiley and Sons Inc., New York, 242p.

- Clarke, D., Smith, M., & El-Askari, K. (1998). CropWat for Windows: User Guide, Southampton University Press, Cairo, 43p.

- Conte, R., Hegselmann, R., & Terna, P. (1997). Simulation Social Phenomena. American Behavioral Scientist, 42 (10): 1485-1487.

- Crooks, A., Castle, C. and Batty, M. (2008). Key challenges in agent-based modeling for geo-spatial Simulation. Computers, Environment and Urban Systems, 32: 417-430.

- Epstein, J. M. & Axtell, R. L. (1996). Growing Artificial Societies: Social Science from the Bottom Up, MIT Press, Cambridge, 208p.

- FAO, (1994). Integrated Approach to the planning and management of Land Resources. Report of the UN Secretary-General on the Implementation of chapter 10 of Agenda 21 (UNCED) to the Commission on Sustainable Development, Third edition, FAO/AGL, 28 November 1994, Rome.

- FAO, (2013). Statistical Yearbook 2012, Rome: FAO.

- Happe, K., Schnicke, H., Sahrbacher, C. & Kellermann, K. (2009). Will they stay or will they go? Simulating the dynamics of single-holder farms in a dualistic farm structure in Slovakia, Agricultural Economics, 57 (4): 497-511.

- Hazell, P. B. R. & Norton, R. D., (1986). Mathematical Programming for Economic Analysis in Agriculture. MacMillan Publishing Company, New York, 600p.

- Henseler, M., Wirsig, A., Herrmann, S., Krimly, T. & Dabbert, S. (2009). Modeling the impact of global change on regional agricultural land use through an activity-based nonlinear programming approach, Agricultural Systems, 100 (13): 31-42.

- Holden, S. & Shiferaw, B. (2004). Land degradation, drought and food security in a less favored area in the Ethiopian highlands: a bio-economic model with market imperfections, Agricultural Economics, 30 (1): 31-49.

- Jennings, N. R. (2000). On agent-based software engineering. Artificial Intelligence, 117: 277-296.

- Krol, M. S. & Bronstert, A. (2007). Regional integrated modeling of climate change impacts on natural resources and resource usage in semi-arid Northeast Brazil, Environmental Modeling and Software, 22 (2): 259-268.

- Kuyvenhoven, A., Ruben, R. & Kruseman, G. (1998). Technology, market policies and institutional reform for sustainable land use in southern Mali. Agricultural Economics, 19: 53-62.

- Lambin, E. F. (1997). Modeling and monitoring land-cover change processes in tropical regions, Progress in Physical Geography, 21(3): 375-393.

- Le, Q. B., Park, S. J., Vlek, P. L. G. & Cremers, A. B. (2008). Land-Use Dynamic Simulator (LUDAS): a multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification, Ecological Informatics, 3 (2): 135-153.

- Letcher, R. A., Croke, B. F. W., Merritt, W. S. & Jakeman, A. J. (2006). An integrated modeling toolbox for water resources assessment and management in highland catchments: sensitivity analysis and testing. Agricultural Systems, 89 (1): 132-164.

- Lighmann-Zielinska, A. & Jankowski, P. (2010). Exploring normative scenarios of land use development decision with an agent-based simulation laboratory, Computers, Environment and Urban Systems, 34: 409-423.

- Maes, P. (1994). Modeling adaptive autonomous agents. Artificial Life, 1: 135-162.

- Malleson, N., Heppenstall A., & See, L. (2010). Crime reduction through simulation: An agent-based model of burglary, Computers, Environment and Urban Systems, 34: 236-250.

- Marohn, C., Schreinemachers, P., Quang, D. V., Berger, T., Siripalangkanont, P., Nguyen, T. T. & Cadisch, G. (2013). A software coupling approach to assess low-cost soil conservation strategies for highland agriculture in Vietnam, Environmental Modelling & Software, 45: 116 – 128.

- Matthews, R. (2006). The People and Landscape Model (PALM): towards full integration of human decision-making and biophysical simulation models, Ecological Modeling, 194 (4): 329-343.

- Parker D.C., Manson S. M., Janssen M. A., Hoffmann M. J. & Deadman P., (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review, Annals of the association of American Geographers, 93(2): 314-337.

- Quang, D. V., Schreinemachers, P., & Berger, T., (2014). Ex-ante assessment of soil conservation methods in the uplands of Vietnam: An agent-based modeling approach, Agricultural Systems, 123: 108 – 119.

- Reidsma, P., Ewert, F., Lansink, A.O. & Leemans, R. (2010). Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses, Agronomy, 32 (1): 91-102.

- Roetter, R.P., Berg, M.M., Laborte, A.G., Hengsdijk, H., Wolf, J., Ittersum, M.K. van, Keulen, H., Agustin, E.O., Son, T.T., Lai, N.X. & Guanghuo, W. (2007). Combining farm and regional level modeling for integrated resource management in East and South-east Asia, Environmental Modeling and Software, 22 (2): 149-157.

- Schreinemachers, P. & Berger, T. (2011). An agent-based simulation model of human environment interactions in agricultural systems, Environmental Modeling and Software, 26: 845-859.

- Schreinemachers, P., Berger, T. & Aune, J.B. (2007). Simulating soil fertility and poverty dynamics in Uganda: A bio-economic multi-agent systems approach, Ecological Economics, 64(2): 387-401.

- Schreinemachers, P., Berger, T., Sirijinda, A., & Praneetvatakul, S. (2009). The diffusion of greenhouse agriculture in northern Thailand: Combining econometrics and agent-based modeling, Agricultural Economics, 57(4): 513-536.

- Schreinemachers, P., Potchanasin, C., Berger, T. & Roygrong, S., (2010). Agent-based modeling for ex ante assessment of tree crop innovations: Litchis in northern Thailand, Agricultural Economics, 41: 519-536.

- Schreinemachers, P., Sirijinda, A., Potchanasin, C., Berger, T. & Praneetvatakul, S. (2009). An Agent-Based Land Use Model of the Mae Sa Watershed Area, Thailand. University of Hohenheim Press. Germany and Kasetsart University, Bangkok, 71p.

- Schuler, J., & Sattler. C. (2010). The estimation of agricultural policy effects on soil erosion-An application for the bio-economic model MODAM. Land Use Policy, 27(1): 61-69.

- Smith, M. (1992). CROPWAT, a computer program for irrigation planning and management, FAO Irrigation and Drainage, 46-101.

- Srbljinovic, A. & Skunca, O. (2004). An introduction to agent based modeling and simulation of social processes. Interdisciplinary Description of Complex Systems, 1 (2): 1-8.

- Stephenne, N., & Lambin, E. (2001). A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU). Agriculture, Ecosystems and Environment, 85(1): 145-161.

- The World Bank, (2010). World Development Report 2010: Development and Climate Change. Washington DC: The International Bank for Reconstruction and Development, the World Bank.

- Troost, C., Walter, T. & Berger, T. (2015). Climate, energy and environmental policies in agriculture: Simulating likely farmer responses in Southwest Germany, Land Use Policy 46: 50 - 64

- Turner, B.L., D. Skole, S. Sanderson, G. Fischer, L. Fresco & R. Leemans. (1995). Land-Use and Land-Cover Change. Science Research Plan, IGBP Report, 35 (7): 151-172.

- Valbuena, D., Verburg, P. H., Bregt, A. K., & Ligtenberg, A. (2010). An agent-based approach to model land-use change at a regional scale, Landscape Ecology, 25: 185-199.

- Van Oel, P. R., Krol, M.S., Hoekstra, A. Y. & Taddei, R. R. (2010). Feedback mechanisms between water availability and water use in a semi-arid river basin: a spatially explicit multi-agent simulation approach. Environmental Modeling and Software, 25 (4): 433-443.

- Vatn, A., Bakken, L., Bleken, M.A., Baadshaug, O.H., Fykse, H., Haugen, L. E., Lundekvam, H., Morken, J., Romstad, E., Rørstad, P. K., Skjelvag, A.O. & Sogn, T. (2006). A methodology for integrated economic and environmental analysis of pollution from agriculture, Agricultural Systems, 88 (2-3): 270-293.

- Wossen, T., Berger, T., Swamikannuh, N., & Ramilan, T., (2014). Climate variability, consumption risk and poverty in semi-arid Northern Ghana: Adaptation options for poor farm households. Environmental Development 12: 2-15.