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.
Highlights
Agricultural development is the basis of human survival and an appropriate insight into the future changes in the cultivated areas is significantly important for both whole society and policy makers
By taking 1998 as the base year, value of parameter C was determined so that the most correlation occurs between actual cropping pattern and simulated cropping pattern in 2006
This paper describes the development of a dynamic model at the provincial and national levels, combined System Dynamics (SD) sub-model with crop allocation sub-model
Summary
Agricultural development is the basis of human survival and an appropriate insight into the future changes in the cultivated areas is significantly important for both whole society and policy makers. The CLUE model is one of the most widely applied models with approximately 30 applications spread over the different regions of the globe, addressing a wide range of land-use change trajectories including agricultural intensification, deforestation, land abandonment and urbanization. This model is a tool to better understand the processes that determine the changes in the spatial pattern of land use and to explore possible future changes in land use (Verburg & Overmars, 2007). A consensus has currently grown among model developers who identifying that “optimal” or
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