• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.468-468
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• 2011

작물생육모형은 다양한 환경조건 하에서 주요 작물에 대한 생육 및 생산량의 종합적인 모의가 가능하며, DSSAT(Decision Support System for Agrotechnology Transfer)의 경우 지난 20여 년간 많은 연구자들에 의해서 작물생육을 모의하는데 사용되어왔다. 또한, 하수재이용은 물 부족을 겪는 많은 국가에서 주요한 대체수자원으로 간주되고 있으며, 생활용수, 공업용수, 농업용수 등의 다양한 형태로 이용되고 있다. 본 연구에서는 DSSAT을 이용하여 하수 처리수의 농업용수 재이용에 따른 논벼의 수확량을 모의하고 분석하였다. 하수처리수의 농업용수 재이용에 따른 논벼 수확량 분석을 위하여 기상, 토양, 관개량 및 관개수 수질 등의 입력자료를 구축하였다. DSSAT을 이용한 논벼 수확량의 모의치와 수원시 하수처리장 인근에 조성된 시험포장에서 실측한 4년간(2006년~2009년)의 수확량 자료와의 비교를 통해 작물생육모형의 적용성을 평가하였다. DSSAT을 이용한 논벼 수확량의 모의치는 실제 수확량과 높은 상관관계를 보여줌으로서 하수재이용에 따른 논벼 수확량 분석에서 작물생육모형이 적용 가능한 것으로 나타났다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.166-174
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• 2018

Potato(Solanum tuberosum L.) is one of the major food crop in the world following rice, wheat, and maize. It is thus important to project yield predict of potato under climate change conditions for assessment of food security. A crop growth modelling is widely used to simulate crop growth condition and total yield of various crops under a given climate condition. The decision support system for agrotechnology transfer (DSSAT) cropping system model, which was developed by U.S. which package integrating several models of 27 different crops, have been used to project crop yield for the impact assessment of climate change on crop production. In this study, we simulated potato yield using RCP 8.5 climate change scenario data, as inputs to the DSSAT model in five regions of Korea. The genetic coefficients of potato cultivar for 'superior', which is one of the most widely cultivated potato variety in Korea were determined. The GenCalc program, which is a submodule of the DSSAT package, was used to determine the genetic coefficients for the superior cultivar. The values of genetic coefficients were validated using results of 39 experiments performed over seven years in five regions. As a case study, the potato yield was projected that total yields of potato across five regions would increase by 26% in 2050s but decrease by 17% in 2090s, compared with 2010s. These results suggested that the needs for cultivation and irrigation technologies would be considerably large for planning and implementation of climate change adaptation for potato production in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.68-81
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• 2021

The agricultural ecosystem is one of the major sources of greenhouse gas (GHG) emissions. In order to search for climate change adaptation options which mitigate GHG emissions while maintaining crop yield, it is advantageous to integrate multiple models at a high spatial resolution. The objective of this study was to develop a tool to support integrated assessment of climate change impact b y coupling the DSSAT model and the DNDC model. DNDC Regional Input File Tool(DRIFT) was developed to prepare input data for the regional mode of DNDC model using input data and output data of the DSSAT model. In a case study, GHG emissions under the climate change conditions were simulated using the input data prepared b y the DRIFT. The time to prepare the input data was increased b y increasing the number of grid points. Most of the process took a relatively short time, while it took most of the time to convert the daily flood depth data of the DSSAT model to the flood period of the DNDC model. Still, processing a large amount of data would require a long time, which could be reduced by parallelizing some calculation processes. Expanding the DRIFT to other models would help reduce the time required to prepare input data for the models.

• 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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.93-93
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• 2017

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.4
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• pp.67-74
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• 2011

The objectives of this study were to assess the rice yields and evaluate fertilizer reduction effect of reclaimed wastewater irrigation in paddy fields using the Decision Support System for Agrotechnology Transfer (DSSAT) v4.5 model. The experimental plots were designed, which was located near the Suwon wastewater treatment plant in Gyeonggi-do, Korea. The rice yield, irrigation amount, irrigation water quality and soil data were monitored and collected between 2006 and 2009. The DSSAT model was calibrated and validated with observed data. The methods that were used to evaluate this model were the root mean square error (RMSE), normalized root mean square error (nRMSE), and index of agreement (d). The values of RMSE, nRMSE, and d ranged from 145 to $789\;kg\;ha^{-1}$, 3.0 to 13.3 %, and 0.90 to 0.95 for the calibration period, respectively and represented from 91 to $538\;kg\;ha^{-1}$, 2.0 to 10.4 %, 0.94 to 0.98 for the validation period, respectively. Overall, this model showed good agreement with observed data of rice yields irrigated with reclaimed wastewater. The fertilizer reduction effect in paddy field of reclaimed wastewater irrigation was assessed about 60 % in 2008 and 40 % in 2009.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.04a
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• pp.35-35
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• 2018

• 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 Crop Science Conference
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• 2017.10a
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• pp.112-112
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• 2017

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.256-266
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• 2022

Empirical models including the Angstrom-Prescott (AP) model have been used to estimate solar radiation at sites, which would support a wide use of crop models. The objective of this study was to estimate two sets of solar radiation estimates using the AP coefficients derived for climate zone (AP_Frere) and specific site (AP_Choi), respectively. The daily solar radiation was estimated at 18 sites in Korea where long-term measurements of solar radiation were available. In the present study, daily solar radiation and sunshine duration were collected for the period from 2012 to 2021. Daily weather data including maximum and minimum temperatures and rainfall were also obtained to prepare input data to a process-based crop model, CERES-Rice model included in Decision Support System for Agrotechnology Transfer (DSSAT). It was found that the daily estimates of solar radiation using the climate zone specific coefficient, SFrere, had significantly less error than those using site-specific coefficients S_Choi (p<0.05). The cumulative values of S_Frere for the period from march to September also had less error at 55% of study sites than those of S_Choi. Still, the use of S_Frere and S_Choi as inputs to the CERES-Rice model resulted in slight differences between the outcomes of crop growth simulations, which had no significant difference between these outputs. These results suggested that the AP coefficients for the temperate climate zone would be preferable for the estimation of solar radiation. This merits further evaluation studies to compare the AP model with other sophisticated approaches such as models based on satellite data.