| Research Abstract |
(1) In this research project, I finalized the nation-wide, two-level database on land use, natural-and socioeconomic conditions. Using this database, I applied both a single-level (conventional) multivariate regression model and a two-level multivariate regression model (the level 1 : municipalities, the level 2 : prefectures). The results shows that the topographic factors, the farm management conditions and the urbanization factors were the core factors that decided the share of farmland and its change. I carried out regression analysis at prefecture level, in which the criterion variables are the random terms at prefectural level (the outputs form the multilevel analysis) and the explanatory variables are socioeconomic factors at the same level. It became clear that the factors such as the leadership of agricultural cooperatives, the development level of labor market, the investment level for agricultural infrastructure and the strength of social bonds of rural community would significantly influence the farmland share. In this way, the multi-level modeling approach would bring us more appropriate insight on the mechanism of land-use change than the conventional regression approach.
(2) Iprepared the other two-level database of Kyoto prefecture (time series data at level 1, rural community at level 2). I could extract the patters of farmland change of 1600 communities in Kyoto Prefecture for 30 years (1970-2000) by applying the multilevel model.
(3) On the other hand, I applied Agent-based approach for modeling agricultural land use at community level to develop a prototype model. By the model, we can describe the effects of various land use policies such as rice price, adjustment of rice production, land improvement project and so on. The linkage between the factor analysis by the multilevel model and the simulation model by the multi-agent system is the most promising task to develop "the Regionally Sensitive Global Land-use Model".
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