• 농업생명과학연구
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• 제51권3호
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• pp.19-27
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• 2017

본 연구는 고온 등숙으로 인해 미질 저하가 큰 벼 조기재배 실시에 적합한 재배법 개선을 위하여 품질향상을 위한 재식방법을 설정하기 위하여 수행하였다. 재식거리 $30{\times}14cm$ 이앙구에서는 포기당 재식본수가 3, 5, 7로 증가할수록 초장은 감소하고, 경수는 증가하였으나, 재식거리 $30{\times}18cm$ 이앙구에서는 재식본수가 증가함에 따라 초장 및 경수가 증가하였다. 재식거리 및 재식본수에 따라 간장 및 수장의 차이는 적었으나, 재식본수가 적을수록 수수는 적었고 입수가 증가하였으며, 재식거리가 증가함에 따라 수수 및 입수는 증가하는 경향이었다. 이앙구 간 쌀 수량에 따른 통계적 유의성은 없었다. 완전미 수량에서는 재식거리 $30{\times}18cm$ 이앙구에서 싸라기 및 분상질립 비율이 낮아 완전미의 비율이 높은 경향이었고, 3본 이앙시 403kg/10a로 완전미 수량이 가장 높았다. 재식거리 $30{\times}18cm$ 이앙구에서는 단백질 함량이 감소하고, 취반미윤기치 값이 증가하여 식미 개선의 효과가 있었다. 이러한 연구결과를 종합해보면, 벼 조기재배에서는 재식밀도가 낮은 넓은 재식거리($30{\times}18cm$)에 개체수가 적은 3본으로 이앙하는 방법이 조기재배에서 발생하는 문제점들을 개선시킬 수 있을 것으로 판단된다.

• 한국토양비료학회지
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• 제47권6호
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• pp.398-405
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• 2014

Salt accumulation at soil surface is one of the most detrimental factors for crop production in reclaimed tidal land. This study was conducted to investigate the effect of capillary barriers beneath the soil surface on dynamics of soil salts at coarse-textured reclaimed tidal land. A field experiment was conducted at Saemangeum reclaimed tidal land for two years (2012-2013). Capillary barriers ($3.5{\times}12m$) were treated with crushed-stone, oyster shell waste, coal briquette ash, coal bottom ash, rice hull and woodchip at 40-60 cm depth from soil surface. Silage corn (Zea mays) was cultivated during the experimental period and soil salinity was monitored periodically. Soil salinity was significantly reduced with capillary barrier compared to that of control. Oyster shell waste was one of the most effective capillary barrier materials to control soil salinity at Saemangeum reclaimed tidal land. At the first growing season capillary barrier did not influence on corn growth regardless of types of the material, but plant biomass and withering rate of corn were significantly improved with capillary barrier at the second growing season. The results of this study showed that capillary barrier was effective on the control of soil salinity and improvement of corn growth, which indicated that capillary barrier treatment can be considered one of the best management practices for stable crop production at Saemangeum reclaimed tidal land.

• 한국물환경학회지
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• 제32권6호
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• pp.562-569
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• 2016

Recently, Low Impact Development (LID) is being used in Korea to control urban runoff and nonpoint source pollution. In this study, we evaluated the reduction of surface runoff from a study area, as the effect of connecting three bioretention as LID-BMP. Surface runoff and storage volume of bioretention is estimated by the Curve Number (CN) method. In this study, the storage volume of bioretention is divided by the volume of surface runoff and precipitation which directly enters the bioretention. The ratio of captured surface runoff volume to storage volume is highly influenced by the ratio of drainage area to surface area of bioretention. The high bioretention surface area-to-drainage area ratio captures more surface runoff. The ratio of 1.2 captures 51~54% of the total surface runoff, ranging from 5-30cm of bioretention depth; a ratio of 6.2 captures 81~85%. Three connected bioretentions could therefore captures much more runoff volume, ranging from $35.8{\sim}167.3m^3$, as compared to three disconnected bioretentions at their maximum amount of precipitation with non-effluent from the connecting three bioretentions. Hence, connecting LID-BMPs could improve the removal efficiencies of surface runoff volume and nonpoint source pollution.

• 원예과학기술지
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• 제30권3호
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• 한국수자원학회논문집
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• 제51권12호
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• pp.1207-1216
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• 2018

본 연구는 물리적 수리 수문모형의 적용이 제한적인 감조하천에서의 수위예측을 목적으로 하고 있으며, 이를 위해 한강 잠수교를 대상으로 딥러닝 오픈소스 소프트웨어 라이브러리인 TensorFlow를 활용하여 LSTM 모형을 구성하고 2011년부터 2017년까지의 10분 단위의 잠수교 수위, 팔당댐 방류량과 한강하구 강화대교지점의 예측조위 자료를 이용하여 모형학습(2011~2016) 및 수위예측(2017)을 수행하였다. 모형 매개변수는 민감도 분석을 통해 은닉층의 개수는 6개, 학습속도는 0.01, 학습횟수는 3000번로 결정하였으며, 모형 학습 시 학습정보의 시간적 양을 결정하는 중요한 매개변수인 시퀀스길이는 1시간, 3시간, 6시간으로 변화시키며 모의하였다. 최종적으로 선행시간에 따른 모의 예측능력을 평가하기 위해 LSTM 모형의 예측 선행시간을 6개(1 ~ 24시간)로 구분하여 실측수위와 예측수위와의 비교 분석을 수행한 결과, LSTM 모형의 최적의 성능을 내는 결과는 시퀀스길이를 1시간으로 하였을 때로 분석되었으며, 특히 선행시간 1시간에 대한 예측정확도는 RMSE는 0.065 m, NSE는 0.99로 실측수위에 매우 근접한 예측 결과를 나타내었다. 또한 시퀀스길이에 상관없이 선행시간이 길어질수록 모형의 예측 정확도는 2017년 전기간에 걸쳐 평균적으로 RMSE 0.08 m에서 0.28 m로 오차가 증가하였으며, NSE는 0.99에서 0.74로 감소하였다.