• 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.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 1992.12a
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• pp.15-16
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• 1992

1. 연구의 필요성 및 목적 필요성 - 기상변화에 따른 수도생육의 예측을 통한 적절한 Crop management - 수도수확량 예측을 통한 계획생산의 가능 - 최적 물관리를 위한 기초자료제공 목적 수도의 생육 및 수확량을 예측 할 수 있는 생리학적(physiological ) 모형인 SIMRIW을 우리의 기후조건과 수도품종에 적용하여 모형의 매개변수를 보정하고, 모형의 적용성을 검사하는데 있다. (중략)

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.3
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• pp.65-73
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• 2011

기후변화는 에너지 수지와 물 수지의 변화를 초래하여 육상 생물권에 영향을 미칠 것이다. 기온과 강수량의 변화와 대기중의 탄산가스 농도 변화는 작물의 생육환경을 크게 변화시킬 것이다. 본 연구에서는 FAO AquaCrop 모형을 이용하여 기온과 강수량의 변화와 대기중 탄산가스 농도의 변화가 짐바브웨의 옥수수 수확량에 미치는 영향을 분석하였다. 미래 기후 값은 HadCM3 모형 예측 값을 change factor 기법으로 상세화 하였다. 배출 시나리오는 A2와 B2를 선정하였으며 시간대는 2020s, 2050s 및 2080s의 30년 기간을 선정하였다. 기준작물 증발산량은 Penman-Monteith 식으로 산정하였다. 관개용수 공급이 충분한 것으로 가정하고 전통적인 보충관개를 실시하였을 때 기준년도 (1970s)에 비해 옥수수 증발산량은 최대 26 %, 옥수수 잠재 수확량은 최대 93 %까지 증가할 것으로 예측되었으며 물의 생산성은 최대 53 %까지 증가할 것으로 예측되었다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.31-40
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• 1997

Potential impacts for unfavourable weather conditions and the assessment of the magnitudes of their adverse effects on crop yields were studied. EPIC model was investigated for its capability on crop yield predictions for rice and soybean. Weather generationmodel was used to generate long-term climatic data. The model was verified with ohserved climate data of Suwon city. Fifty years weather data including abnormal conditions were generated and used for crop yield simulation by EPIC model. Crop yield probability function was derived from simulated crop yield data, which followed normal distribution. Probable crop yield reductions due to abnormal weather conditions were also analyzed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.284-291
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• 2010

A simulation modeling for predicting the harvesting date with high potato solids consists of development of mathematical models. The mathematical model on potato growth and its development should be obtained by using agricultural elements which analyze relations of solar radiation quantity, temperature, photon quantity, carbon dioxide exchange rate, water stress and loss, relative humidity, light intensity, and wind etc. But more reliable way to predict harvesting date against climatic change employs in vivo energy consumption for growth and induction shape in a slight environmental adaptation. Therefore, to calculate in vivo energy loss, we take a concept of estimate of the amount of basal metabolism in each tuber on the basis of $Wm={\int}^m_tf(x)dt$ and $Tp=\frac{Tm{\cdot}Wm^{Tp}}{Wm^{Tm}}$. In the validation experiments, results of measuring solid accumulation of potato harvested at simulated date agreed fairly well with the actual measured values in each regional field during the growth period of 2005-2009. The calculation method could be used to predict an appropriate harvesting date for a production of high potato solids according to weather conditions.

• Journal of Korean Society of Forest Science
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• v.80 no.4
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• pp.427-435
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• 1991

Linear programming(LP) is a well-known method in optimizing timber harvest schedules. This paper describes a linear programming formulation of korean pine stands for timber harvest scheduling problems. Simulation technique and LP were applied to optimize the time and space distribution of the sustained yield for the 10-year forest management planning horizon. Growthfunction of korean pine stands in study area was derived with the yield table. This growthfunction was contained to the simulation model in estimating of changing stand volume conditions for the planning horizon. These estimated values were served as the basic data of LP model, and LP model was formulated with the maximum of periodical harvest volume calculated by the classical yield regulation method (Paulsen-Hundeshagen formula) and the maximum of periodical harvest area calculated for the normal age distribution. The timber harvest schedule was established periodically for each subcompartment of korean pine stands in experiment forest of College of Forestry in Kangweon National University with the here developed LP model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.2
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• pp.73-80
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• 1993

A simplified physiologically-based dynamic model, SIMRIW was selected for predicting the growth and yield of rice. The applicability of the model to the rice cultivars and weather conditions in the Republic of Korea was evaluated. Parameters of the model were calibrated using actual rice yields in Suweon region and an optimization scheme, Constrained Rosenbrock Algorithm. The simulated results from the calibrated model were in good agreement with the field data. The model with parameters calibrated for Suweon was applied to other five regions for the evaluation of transferability, but the simulated results fell short of satisfaction. However, the model is found to be applied to real-time prediction of the growth and yield of rice crop, which is believed to be useful for timely rice crop management, agricultural policy making, and optimal irrigation water management.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1883-1886
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• 2010

농업가뭄은 강수의 부족으로 인하여 농업용 저수지의 저수량 저하로 농작물 생육 및 수확량의 직접적인 영향을 미치는 것으로, 농업가뭄평가는 강수량뿐만 아니라 작물의 생육시기별 필요수량과 용수공급능력을 모두 고려할 수 있어야 한다. 농업가뭄관리의 주요 대상인 논벼는 기본적으로 수원공과 관개지역 사이의 물수지를 판단함으로써 농업가뭄의 위험을 정의할 수 있다. 본 연구에서는 가뭄관리가 필요한 농업용 저수지 관개지구의 농업가뭄 평가를 위해 논 물수지 분석 모형과 저수지 물수지 분석 모형을 구성하고, 용수수급해석의 결과로부터 가뭄의 크기를 객관화하고 가뭄의 단계를 평가할 수 있도록 빈도개념을 적용한 저수지가뭄지수 (Reservoir Drought Index, RDI)를 이용하여 농업가뭄을 분석 평가하였다. 또한 농업용 저수지의 저수량 모의치와 실시간 저수위를 이용하여 경험적으로 농업용 저수지의 유입량을 자동으로 보정하여 장기적으로 최적화할 수 있는 방안을 제시하였으며, 과거 유효강수량을 시기별로 나누어 빈도분석을 통해 농업가뭄대응을 위한 가뭄 기상시나리오를 사용하여 향후 가뭄의 여러 가지 패턴에 따른 농업가뭄을 전망하였다. 이러한 다양한 시나리오를 통해 실제 물 관리 및 가뭄대책 업무에 반영하고 농업가뭄대응책 수립 및 농업수자원관리의 의사결정을 수립하는데 기초자료가 될 것으로 판단된다.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.83-92
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• 2023

This study was established to investigate the site environment of mixed forests in Korea and to estimate the growth and yield of stands using national forest resources inventory data. The growth of mixed forests was derived by applying the Chapman-Richards model with diameter at breast height (DBH), height, and cross-sectional area at breast height (BA), and the yield of mixed forests was derived by applying stepwise regression analysis with factors such as cross-sectional area at breast height, site index (SI), age, and standing tree density per ha. Mixed forests were found to be growing in various locations. By climate zone, more than half of them were distributed in the temperate central region. By altitude, about 62% were distributed at 101-400 m. The fitness indexes (FI) for the growth model of mixed forests, which is the independent variable of stand age, were 0.32 for the DBH estimation, 0.22 for the height estimation, and 0.18 for the basal area at breast height estimation, which were somewhat low. However, considering the graph and residual between the estimated and measured values of the estimation equation, the use of this estimation model is not expected to cause any particular problems. The yield prediction model of mixed forests was derived as follows: Stand volume =-162.6859+6.3434 ∙ BA+9.9214 ∙ SI+0.7271 ∙ Age, which is a step- by-step input of basal area at breast height (BA), site index (SI), and age among several growth factors, and the determination coefficient (R²) of the equation was about 96%. Using our optimal growth and yield prediction model, a makeshift stand yield table was created. This table of mixed forests was also used to derive the rotation of the highest production in volume.