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It is well recognized that aggregation of the inhomogeneous data for soils, management, and weather within a land unit for crop simulation plays a key role in the scaling-up task of crop simulation at regional scales. While the other spatial data could be obtained at a desired level, weather data might not be available in mountainous regions where the production system is comprised of many small farms. This study was carried out to develop a scaling-up scheme of a plot-based crop model to an area-based rice production forecasting system suitable for the Korean situation, with an emphasis on the weather data preparation. Daily meteorological surfaces on a 1 × 1 km grid spacing were generated by spatial interpolation of 63 point observations from the Korea Meteorological Administration network. Potential effects of elevation on daily temperature were implemented by applying a seasonal lapse rate function developed for the terrain. A land cover classification scheme based on a satellite data analysis was used to identify those pixels corresponding to paddy rice fields, and an empirically determined land cover effect (cooling) was applied to those pixels to obtain the final temperature surface over rice paddies. The rice paddy pixels falling within a given cultivation zone units (CZU) were extracted from the temperature surface to calculate the average weather representing that CZU. The other weather variables were interpolated by an inverse distance weighting. By this procedure, the spatially explicit climatic data set for crop simulation (daily maximum/minimum temperature, solar radiation, and precipitation) was prepared for 1455 CZU's making up the coterminous South Korea. Soil characteristics and management information representing each CZU were available from the Rural Development Administration of Korea. CERES-rice, a rice crop growth simulation model, was calibrated to have genetic characteristics pertinent to domestic cultivars in Korea. The model was run to predict rice production of 162 crop-reporting districts for the 3 years (1997-1999) during which official production statistics are available. The simulation was done with the past weather data as of September 15 each year, which is about a month earlier than the actual harvest date. Simulated yields of the 1455 CZUs were aggregated into the corresponding 162 crop reporting districts by acreage-weighted summation, and compared with the reported yield and production. Forecast yields were less sensitive to the inter-annual climate variation than the reported yields, and there was a relatively weak correlation between the forecast and the reported yields. Projected production of rice for each crop- reporting district, which is the product of yield and acreage, was close to the reported production with the r2 values over 0.97 in all 3 years.
