Modeling the spatial distribution of crop cultivated areas at a large regional scale combining system dynamics and a modified Dyna-CLUE: A case from Iran

Iman Mesgari, Mohammad Saeed Jabalameli

Abstract


Agricultural land use pattern is affected by many factors at different scales and effects that are separated by time and space. This will lead to simulation models that optimize or project the cropping pattern changes and incorporate complexities in terms of details and dynamics. Combining System Dynamics (SD) and a modified Conversion of Land Use and its Effects (CLUE) modelling framework, this paper suggests a new dynamic approach for assessing the demand of different crops at country-level and for predicting the spatial distribution of cultivated areas at provincial scale. As example, a case study is presented for Iran, where we have simulated a scenario of future cropping pattern changes during 2015–2040.The results indicated a change in the spatial distribution of cultivated areas during the next years. An increase in the proportion of rice is expected in northern Iran, whereas the proportion of wheat is increasing in the mountainous western areas. Wheat and barley crops are expected to become dominant within the cropping system throughout the country regions.


Keywords


integrated modeling; land use; cropping pattern; system dynamics

Full Text:

PDF

References


Akıncı H, Özalp AY, Turgut B, 2013. Agricultural land use suitability analysis using GIS and AHP technique. Comput Electron Agric 97: 71-82. https://doi.org/10.1016/j.compag.2013.07.006

Balassa, B, 1965. Trade liberalisation and "revealed" comparative advantage. The Manchester School 33 (2): 99-123. https://doi.org/10.1111/j.1467-9957.1965.tb00050.x

Barlas Y, 1996. Formal aspects of model validity and validation in system dynamics. Syst Dynam Rev 12 (3): 183-210. https://doi.org/10.1002/(SICI)1099-1727(199623)12:3<183::AID-SDR103>3.0.CO;2-4

Britz W, Verburg PH, Leip A, 2011. Modelling of land cover and agricultural change in Europe: Combining the CLUE and CAPRI-Spat approaches. Agric Ecosyst Environ 142 (1): 40-50. https://doi.org/10.1016/j.agee.2010.03.008

Castellazzi MS, Matthews J, Angevin F, Sausse C, Wood G, Burgess PJ, Brown I, Conrad KF, Perry JN, 2010. Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale. Environ Model Softw 25 (12): 1881-1889. https://doi.org/10.1016/j.envsoft.2010.04.006

Doglioni A, Primativo F, Laucelli D, Monno V, Khu ST, Giustolisi O, 2009. An integrated modelling approach for the assessment of land use change effects on wastewater infrastructures. Environ Model Softw 24 (12): 1522-1528. https://doi.org/10.1016/j.envsoft.2009.06.006

Forrester JW, 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Bus Rev 36 (4): 37-66.

Li FJ, Dong SC, Li F, 2012. A system dynamics model for analyzing the eco-agriculture system with policy recommendations. Ecol Model 227: 34-45. https://doi.org/10.1016/j.ecolmodel.2011.12.005

Luo G, Yin C, Chen X, Xu W, Lu L, 2010. Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China. Ecol Complex 7 (2): 198-207. https://doi.org/10.1016/j.ecocom.2010.02.001

Mesgari I, Jabalameli MS, Barzinpour F, 2017. System dynamics modeling for national agricultural system with policy recommendations: application to Iran. Pak J Agric Sci 54 (2): 457-466.

Parker P, Letcher R, Jakeman A, Beck MB, Harris G, Argent RM, Hare M, Pahl-wostl C, Voinov A, Janssen M, Sullivan P, 2002. Progress in integrated assessment and modelling. Environ Model Softw 17 (3): 209-217. https://doi.org/10.1016/S1364-8152(01)00059-7

Peng M, Chen H, Zhou M, 2014. Modelling and simulating the dynamic environmental factors in post-seismic relief operation. J Simul 8 (2): 164-178. https://doi.org/10.1057/jos.2013.27

Pilehforooshha P, Karimi M,Taleai M, 2014. A GIS-based agricultural land-use allocation model coupling increase and decrease in land demand. Agr Syst 130: 116-125. https://doi.org/10.1016/j.agsy.2014.07.001

Priya S, Shibasaki R, 2001. National spatial crop yield simulation using GIS-based crop production model. Ecol Model 136 (2): 113-129. https://doi.org/10.1016/S0304-3800(00)00364-1

Qudrat-Ullah H, 2012. On the validation of system dynamics type simulation models. Telecom Sys 51 (2-3): 159-166. https://doi.org/10.1007/s11235-011-9425-4

Rosegrant MW, Msangi S, Ringler C, Sulser TB, Zhu T, Cline SA, 2008. International model for policy analysis of agricultural commodities and trade (IMPACT): Model description. International Food Policy Research Institute, Washington DC.

Senge PM, 1994. The fifth discipline: The art & practice of the learning organization. Dell Publishing Group Inc, NY.

Schaldach R, Alcamo J,2006. Coupled simulation of regional land use change and soil carbon sequestration: a case study for the state of Hesse in Germany. Environ Model Softw 21 (10): 1430-1446. https://doi.org/10.1016/j.envsoft.2005.07.005

Schreinemachers P, Berger T, 2011. An agent-based simulation model of human–environment interactions in agricultural systems. Environ Model Softw 26 (7): 845-859. https://doi.org/10.1016/j.envsoft.2011.02.004

Sharon A, de Weck OL, Dori D, 2011. Project management vs. systems engineering management: A practitioners' view on integrating the project and product domains. Syst Eng 14 (4): 427-440. https://doi.org/10.1002/sys.20187

Sterman JD, 2000. Business dynamics: systems thinking and modeling for a complex world. No. HD30. 2 S7835. McGraw-Hill Education, Boston.

Verburg, PH, Overmars KP, 2007. Dynamic simulation of land-use change trajectories with the CLUE–s model. In: Modelling land-use change: Progress and Applications, Chapter 18; Koomen E et al. (eds), pp: 321-335. Springer.

Verburg PH, Overmars KP, 2009. Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecol 24 (9): 1167-1181. https://doi.org/10.1007/s10980-009-9355-7

Verburg PH, Soepboer W, Limpiada R, Espaldon MVO, Sharifa M, Veldkamp A, 2002. Land use change modelling at the regional scale: the CLUE-S model. Environ Manage 30 (3): 391-405. https://doi.org/10.1007/s00267-002-2630-x

Wang J, Chen J, Ju W, Li M, 2010. IA-SDSS: A GIS-based land use decision support system with consideration of carbon sequestration. Environ Model Softw 25 (4): 539-553. https://doi.org/10.1016/j.envsoft.2009.09.010

Zarghami M, Akbariyeh S, 2012. System dynamics modeling for complex urban water systems: Application to the city of Tabriz, Iran. Resour Conserv Recy 60: 99-106. https://doi.org/10.1016/j.resconrec.2011.11.008

Zhang Y, Li C, Zhou X, Moore B, 2002. A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture. Ecol Model 151 (1): 75-108. https://doi.org/10.1016/S0304-3800(01)00527-0

Zhong TY, Zhang XY, Huang XJ, 2009. Simulation of farmer decision on land use conversions using decision tree method in Jiangsu Province, China. Span J Agric Res 7 (3): 687-698. https://doi.org/10.5424/sjar/2009073-454

Zhou M, Pan Y, Chen Z, Li B, 2016. Enterprise behaviour under Cap-and-Trade conditions: an experimental study with system dynamic models. J Simul 10 (1): 12-23 https://doi.org/10.1057/jos.2014.36




DOI: 10.5424/sjar/2017154-10630