• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.104-105
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• 2004

[ $\bigcirc$ ] In the phenology model of ORYZA2000, the effect of photoperiod on the developmental rate was a little ignored because most crop parameters were measured with IRRI varieties which are insensitive to photoperiod, therefore it is very difficult to apply this phenology model directly to Korean varieties which are usually sensitive to photoperiod. $\bigcirc$ After introducing PPFAC and PPSE to improve the phenology model, the precision of heading date prediction was improved but not satisfied. $\bigcirc$ In the growth simulation using data from several regions, yield tended to be overestimated under high nitrogen applicated condition. $\bigcirc$ The precision of yield was much improved by introducing nitrogen use efficiency, but still different between regions because of different soil fertility or property of irrigation water between regions

• Proceedings of the Korean Society of Crop Science Conference
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• 1998.04a
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• pp.101-102
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• 1998

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.16-18
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• 2011

Evaluation of climate change on the rice productivity was conducted using crop growth simulation model, where Odae, Hwaseong, Ilpum were used as a representative cultivar of early, medium, and medium-late rice maturity type, respectively, and climate change scenario 'A1B' was applied to weather data for future climate change at 57sites. When cropping season was fixed, rice yield decreased by 4~35% as climate change which was caused by poor filled grain ratio with high temperature and low irradiation during grain-filling. When cropping season was changed, rice yield decreased by only 0~5% as climate change which was caused poor filled grain ratio with low irradiation during grain-filling period. However, this irradiation decline was less than when cropping season was fixed. Therefore, we need to develop rice cultivars resistant to low irradiation which can maintain high filled grain ratio under poor irradiation condition, and late maturity rice cultivars whose growing period is longer than the present medium-late maturity type.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.52 no.4
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• pp.393-402
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• 2007

Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.282-290
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• 2013

Biophysical and biochemical processes through which crops interact with the atmosphere have been simulated using land surface models and crop growth models. The Noah Multi Physics (MP) model and the CERES-Rice model, which are a land surface model, and a crop growth model, respectively, were used to simulate and compare rice growth and evapotranspiration (ET) in the areas near Haenam flux tower in Korea. Simulations using these models were performed from 2003 to 2012 during which flux measurements were obtained at the Haenam site. The Noah MP model failed to simulate the pattern of temporal change in leaf area index (LAI) after heading. The simulated aboveground biomass with the Noah MP model was underestimated by about 10% of the actual biomass. The ET simulated with the Noah MP model was as low as 21% of those with the CERES-Rice model. In comparison with actual ET measured at Haenam flux site, the root mean square error (RMSE) of the Noah MP model was 1.8 times larger than that of the CERES-Rice model. The Noah MP model seems to show less reliable simulation of crop growth and ET due to simplified phenology processes and assimilates partitioning compared with the CERES-Rice model. When ET was adjusted by the ratio between leaf biomass simulated using CERES-Rice model and Noah MP model, however, the RMSE of ET was reduced by 30%. This suggests that an improvement of the Noah MP model in representing rice growth in paddy fields would allow more reliable simulation of matter and energy fluxes.

• Korean Journal of Agricultural Science
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• v.43 no.4
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• pp.537-546
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• 2016

The development of models for agriculture systems, especially for crop production, has supported the prediction of crop yields under various environmental change scenarios and the selection of better crop species or cultivar. Crop models could be used as tools for supporting reasonable nutrient management approaches for agricultural land. This paper outlines the simplified structure of main crop models (crop growth model, crop-soil model, and crop-soil-environment model) frequently used in agricultural systems and shows diverse application of their simulated results. Crop growth models such as LINTUL, SUCROS, could provide simulated data for daily growth, potential production, and photosynthesis assimilate partitioning to various organs with different physiological stages, and for evaluating crop nutrient demand. Crop-Soil models (DSSAT, APSIM, WOFOST, QUEFTS) simulate growth, development, and yields of crops; soil processes describing nutrient uptake from root zone; and soil nutrient supply capability, e.g., mineralization/decomposition of soil organic matter. The crop model built for the DSSAT family software has limitations in spatial variability due to its simulation mechanism based on a single homogeneous field unit. To introduce well-performing crop models, the potential applications for crop-soil-environment models such as DSSAT, APSIM, or even a newly designed model, should first be compared. The parameterization of various crops under different cultivation conditions like those of intensive farming systems common in Korea, shortened crop growth period, should be considered as well as various resource inputs.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.39-41
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• 2003

The crop growth simulation model could be adopted to evaluate the impact not only of the long term climate change such as atmosphere $CO_2$ concentration rising and global warming but also of the predicted short term weather variability on the national crop production. There are several growth simulation models for predicting rice crop performance such as ORYZA1, CERES-Rice, Rice Clock Model, and SIMRIW.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.12-14
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• 2011

Crop growth simulation models have been developed as research and management tools. When these models are needed to incorporate new knowledge on phenology and physiology of crops, programming languages have been used for development and documentation of these models. However, researchers may have limited skill in programming languages. Furthermore, software developer may find it challenging to improve the crop models because documentation of the models are rarely available. The Unified Modeling Language (UML) can provide a simple approach for development and documentation of model. A template for implementation of the model can be obtained using the UML, which would facilitate code re-use and model improvement.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.214-227
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• 2018

An object oriented crop model was developed to perform crop growth simulation taking into account complex interaction between biotic and abiotic factors in an agricultural ecosystem. A set of classes including Atmosphere class, Plant class, Soil class, and Grower class were designed to represent weather, crop, soil, and crop management, respectively. Objects, which are instance of class, were linked to construct an integrated system for crop growth simulation. In a case study, yield of corn and soybean, which was obtained at an experiment farm in Rural Development Administration from 1984 to 1986, were compared with yield simulated using the integrated system. The integrated system had relatively low error rate of corn yield, e.g., <4%, under sole and intercropping conditions. In contrast, the system had a relatively large underestimation error for above ground biomass except for grain compared with those observed for corn and soybean. For example, estimates of biomass of corn leaf and stem was 31% lower than those of observed values. Although the integrated system consisted of simple models, the system was capable of simulating crop yield under an intercropping condition. This result suggested that an existing process-based model would be used to have more realistic simulation of crop growth once it is reengineered to be compatible to the integration system, which merits further studies for crop model improvement and implementation in object oriented paradigm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.243-251
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• 2018

The gridded simulation of crop growth, which would be useful for shareholders and policy makers, often requires specialized computation tasks for preparation of weather input data and operation of a given crop model. Here we developed an automated system to allow for crop growth simulation over a region using the DSSAT (Decision Support System for Agrotechnology Transfer) model. The system consists of modules implemented using R and shell script languages. One of the modules has a functionality to create weather input files in a plain text format for each cell. Another module written in R script was developed for GIS data processing and parallel computing. The other module that launches the crop model automatically was implemented using the shell script language. As a case study, the automated system was used to determine the maximum soybean yield for a given set of management options in Illinois state in the US. The AgMERRA dataset, which is reanalysis data for agricultural models, was used to prepare weather input files during 1981 - 2005. It took 7.38 hours to create 1,859 weather input files for one year of soybean growth simulation in Illinois using a single CPU core. In contrast, the processing time decreased considerably, e.g., 35 minutes, when 16 CPU cores were used. The automated system created a map of the maturity group and the planting date that resulted in the maximum yield in a raster data format. Our results indicated that the automated system for the DSSAT model would help spatial assessments of crop yield at a regional scale.