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http://dx.doi.org/10.5532/KJAFM.2021.23.2.122

Comparison of the Weather Station Networks Used for the Estimation of the Cultivar Parameters of the CERES-Rice Model in Korea  

Hyun, Shinwoo (Department of Agriculture, Forestry and Bioresources, Seoul National University)
Kim, Tae Kyung (Department of Agriculture, Forestry and Bioresources, Seoul National University)
Kim, Kwang Soo (Department of Agriculture, Forestry and Bioresources, Seoul National University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.2, 2021 , pp. 122-133 More about this Journal
Abstract
Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.
Keywords
Parameter calibration; ASOS; AWS; GLUE; Cultivar parameter;
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