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Model Simulated Land Use/Cover Changes in Thailand -Results from AGENT-LUC Model
2.2 Model Description
The overall framework of the model is given below, in Fig. 2. The model consists of four sun-models- the bio-physical crop yield sub-model, the agricultural income sub-model, the urban land use sub-model and the land use decision sub-model. All these four sub-models interact and have feedback loops, to determine the new course of action by the agent at the next time step. The model structure is sequential. The model calculations were carried out on a land unit basis, consisting of 1 km square grids.
The bio-physical crop yield sub-model calculates the potential productivity of the land unit for the given conditions of soil, topography, water availability and climatic parameters. The distribution of water availability takes into account the soil conditions, amount of rain-received, and the existence of irrigation facilities. The main assumption of this sub-model is that these are a strong linkage between the climate and crop distributions. (Leemans, et. al., 1993). The crop yield estimates are derived by modifying the approach described in the EPIC model (EPIC, 1990). The central concept of this approach is the growing period and the photosynthetic efficiency of the crops. The biomass and yield calculations are carried out on a day-to day-basis and the final yield takes into effect the fluctuations in water and nutrient availability.
The agricultural income sub-model estimates the income per land unit from various sources including the yield-related revenue and the cost of production. The model also accounts for the initial cost incurred in land conversion from other uses to agricultural lands. The other incomes considered are the non-yield-on-farm income and the off-farm income. These factors influence the decision making process, in case of fluctuating crop-yield incomes from a given area.
Urban land use is the other major land use that is primarily influenced by the activities of the human beings. Here, we estimate the urban land requirements as its competes with the agricultural areas due to increasing population pressures and the rise in the economic levels of the region. The model takes into account the location value of the land-unit in assessing the new areas that will be urbanized. The model assumes that all the extra land needed for the urban areas in a given year is fulfilled in the next year. The model provides information on the urban land demand and supply, on a spatial basis.
The final step in the simulation is the land use decision model, which uses the estimated income, urban land needs & the existing landuse in the land unit under consideration as its input to predict the land use. The "agent" is the decision maker in this model, where in the agent arrives at a decision taking into account the prevailing conditions in the respective grids. In addition to the economic factor, the demographic condition (age distribution and educational levels) and the land use history are considered to help in arriving at a reasonable estimate for the changes in the land use patterns. The decision includes the consideration of risk when arriving at the crop combinations in the respective grids. Also, the model accounts for the external influences to shifts in the agricultural patterns, by considering the export quantities of specific crops, like cassava in Thailand. As of now, these external influences are exogenous variables and are not calculated within the model.
3. Study Area
The study area chosen to check the application of this model is the Royal Kingdom of Thailand, because land use/cover in Thailand has undergone dramatic modifications in the last century. Cultivated land area has shown an astounding increase by nearly ten-times, a net increase in area by about 16.4 million hectares, during the period 1880-1980 and has since risen by 10% till 1990. The model run is for the period of 1980-1990. This period was chosen to check the model simulation (on a year-on-year basis), because of two reasons - one that it being a recent period in history helps us to get a substantial amount of information on some of the causes for changes including quite detailed data at the sub-provincial level. The second reason is that this has been a period of rapid changes in the country's economic structure and the model-run would help us to understand the pitfalls and the better points in our assumptions.
Fig.2 Conceptual Framework of the Model
4.Conclusions
Our aim in developing the "agent-based" model was to mimic the changes by including all the major forces that drive land use changes as well as the basic bio-physical characteristics at the lowest level of interaction (land-lot-size), to help in constructing the possible land use change scenarios. Also, it would help to evaluate our understanding of the land use change mechanisms. The results from the first sub-model, the agricultural productivity sub-model, shows that the yield values are very much within the acceptable range of estimation and the approach has a high potential in estimating yields.
The income sub-model depends heavily on the initial data and the tuning of the model according to it. Historical data can be used to develop scenarios of land use changes and the model can also be validated with such data. In addition to it, the use of remote sensing images can be made to compare the estimated land cover from the model, with the measured values. In this case, care must be taken to maintain the spatial resolution at acceptable levels of comparison.
The entire modeling approach is based on the GIS platform. The use of GIS platform and its tools has helped in analyzing the micro-information (spatial) with in the boundaries of the available macro-level (non-spatial) data. The results of the model application to simulating land use changes within the national boundaries of the Royal Kingdom of Thailand, the case study region, will be presented at the conference.
References
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