• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.253-258
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• 2006

An empirical model of nitrous oxide emission from agricultural soil has been applied. It is based on the relationship between $N_2O$ and three soil parameters, soil mineral N(ammonium plus nitrate) content in the topsoil(0-15cm), soil water-field pore space, and soil temperature, determined in a study on clay loam and sandy loam at the pepper field in 2004. For comparisons between estimated and observed values of $N_2O$ emissions in the pepper field, it was investigated that $N_2O$ amount in the clay loam and sandy loam were overestimated as 12.2% and less estimated as 30%, respectively. However, $N_2O$ emissions were overestimated as 27.1% in the clay loam and 14.7% in the sandy loam from $N_2O$ gas samples collected once a week at the same time analyzing soil parameters. This modelling approach, based as it is well established and widely used soil measurements, has the potential to provide flux estimates from a much wider range of agricultural sites than would be possible by direct measurement of $N_2O$ emissions.

• Applied Biological Chemistry
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• v.40 no.4
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• pp.334-341
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• 1997

In the laboratory experiment, concentration and rate of runoff of 7 pesticides were measured under the simulated rainfall. Total runoff rate of metolachlor, alachlor, chlorothalonil, chlorpyrifos, EPN, phorate and captafol were 57.0, 14.2, 13.2, 7.9, 7.2, 7.1 and 2.8%, respectively, and the average runoff concentrations were 940, 399, 55, 7.0, 9.3, 151 and 7.0 ppb, respectively. Significant relationship was observed between the runoff rate and water solubility in the laboratory experiment(r=0.923). Even though not very high, relatively significant results were obtained in other experimental conditions. Based on the results, runoff rate prediction$[Y=0.2812{\times}10exp(0.261logWS-0.366)+0.3594{\times}10exp(-0.545logKoc+1.747)+0.3594{\times}10exp(-0.362log\;Kow+1.105]$ and conversion equations were calculated to investigate the possibility of estimating runoff rate in the field by natural rain. Calculated runoff rate by conversion equation was similar to experimental result with captafol in the field while 6 times higher result was obtained by the prediction equation. Therefore, those prediction and conversion equations derived from the laboratory experiment data and physicochemical properties of the pesticides could be used for the prediction of field runoff rate of pesticides by natural rainfall.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.47-55
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• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.27-35
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• 2007

The majority of farm managers growing the garden products in greenhouse concern massively about the diagnosis and prevention of the breeding and extermination for pests. especially, the managing problem for pests turns up as main issue. In the paper, we first build a wireless sensor network with soil and environment sensors such as illumination, temperature and humidity. And then we design and implement multimedia pest predication and management system which is able to predict and manage various pest of garden products in greenhouse. The proposed system can support the database with information about the pests by building up wireless sensor network in greenhouse compared with existing high-priced PLC device as well as collect various environment information from soil, the interior of greenhouse, and the exterior of greenhouse. To verify the good capability of our system, we implemented several GUI interface corresponding desktop. web, and PDA mobile platform based on real greenhouse model. Finally, we can confirm that our system work well prediction and management of pest of garden products in greenhouse based on several platforms.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1354-1356
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• 1999

토양에 대한 연구는 오래 전부터 시작되었으며, 여러 가지 방법으로 토양의 특성을 조사하고 이를 적용하기 위한 분류법이 제안되어 왔다. 국내에서도 해방 전부터 토양에 대한 연구가 시작되었으나, 농업에 이용하기 위한 연구가 대부분을 차지하고 있으며 각각의 연구결과는 물리, 화학적으로 정량화 되어 있지 않기 때문에 환경에 따른 특성의 변화를 예측할 수 없다. 또한 접지설계 등의 전기관련 분야에 활용하기 위한 연구는 전무한 실정이다. 본 논문에서는 토양의 개념, 생성인자, 분류 및 전기적 특성 등의 개요를 알아보고, 지질학 및 지구물리학적인 방법을 통하여 국내의 암석 및 토양에 대한 비저항의 물리적, 화학적 특성을 측정하기 위한 방법을 조사하였다. 이를 바탕으로 암석과 토양에 대한 개략적인 토양비 저항도를 작성하여 접지설계 및 지하매설 금속물의 방식 설계 등에 활용하기 위한 토대를 마련하고자 한다.

• Radioisotope journal
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• v.8 no.4
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• pp.32-38
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• 1993

• Journal of the Korean Geographical Society
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• v.50 no.2
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• pp.165-183
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• 2015

It is well known that, since the 15th century, the amount of soil loss in our country due to change in land use by human has increased more rapidly than ever before. However we cannot answer the question 'How long can the soil persist under the current rates of soil loss?', because it was difficult to quantify the soil production rate. With the advancement of accelerated mass spectrometry, the attempt to quantify rate of soil production and derive soil production function succeeded, and recently it was also applied into the Daegwanryeong Plateau. Here we introduce the principles for quantifying soil production and deriving soil production function using terrestrial cosmogenic nuclides, and then compare the soil production rates from the plateau with soil loss data after the late 20th century, and finally estimate how long the soil can persist. Averaged soil production rate since the Holocene derived from the plateau is revealed as ${\sim}0.05[mm\;yr^{-1}]$, and, however, the recent soil loss rate of intensively used farmlands at the same region is up to sixty times greater than the soil production rate. Thus, if current land use system is maintained, top soils on the cultivated lands over hillslopes especially in upland areas are expected to disappear within several decades at the earliest.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1423-1431
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• 2018

IoT(Internet of Things) is applied to technologies such as agriculture and dairy farming, making it possible to cultivate crops easily and easily in cities.In particular, IoT technology that intelligently judge and control the growth environment of cultivated crops in the agricultural field is being developed. In this paper, we propose a method of predicting the growth environment of plants by learning the moisture supply cycle of plants using the intelligent object internet. The proposed system finds the moisture level of the soil moisture by mapping learning and finds the rules that require moisture supply based on the measured moisture level. Based on these rules, we predicted the moisture supply cycle and output it using media, so that it is convenient for users to use. In addition, in order to reduce the error of the value measured by the sensor, the information of each plant is exchanged with each other, so that the accuracy of the prediction is improved while compensating the value when there is an error. In order to evaluate the performance of the growth environment prediction system, the experiment was conducted in summer and winter and it was verified that the accuracy was high.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.584-584
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• 2015

최근 기후변화로 인한 국지성 집중호우와 태풍 등으로 홍수피해가 급증하고 있음에 따라 침수지역에 대한 공간적인 분석과 사전 예측으로 피해를 최소화하려는 노력이 필요하다. 따라서 본 연구에서는 소유역 별 평균화된 매개변수로 홍수량을 산정하는 집중형 모형이 아닌 분포형 모형을 적용하여 남강댐 유역의 유출량 산정 및 침수예측을 분석하였다. 분포형 모형은 격자체계를 기반으로 유역에 각 격자별 공간적 특성이 반영된 매개변수를 적용하므로 유역의 특성을 효과적으로 반영하므로 집중형 모형보다 정확한 해석이 가능하다. DEM, 토양도, 토지피복도 등의 격자크기 $240{\times}240$의 지형공간 자료를 ArcGIS를 이용하여 남강댐유역의 Flow direction, 경사도, 하도경사, 불투수율, 유효공극률, 조도계수, 토양심도, 수리전도도, 토양흡인수두 등의 수문매개변수를 추출하였다. 강우 자료의 경우 티센(Thiessen)법에 의해 선정된 남강댐유역 주변의 장수, 거창, 진주, 합천, 산청, 남원 강우관측소의 100년빈도 확률강우량 산정하여 24시간 확률강우를 3분위 Huff 분포시킨 후 강우의 공간적 통계특성을 반영하는 크리깅(Kriging)기법으로 적용하여 강우보간을 실시하였다. 침수예측을 위해 $Vflo^{TM}$모형을 이용해 48시간의 강우모의시간 홍수수문곡선 유도 및 홍수량 산정하였으며, 시간에 따른 침수 시뮬레이션하여 침수예측도를 작성하였다. 작성 시 침수심의 정도에 따라 5개의 구간으로 분류해 침수위험지역을 확인 할 수 있도록 도식화하였다. 본 연구에서는 남강댐유역의 침수위험지역을 개략적으로 예측할 수 있었으며, 추후 연구에서는 보다 조밀한 격자크기와 강우를 이용하여 분석한다면 향후 피난 정보 제공과 홍수재해지도 작성, 홍수방지 시설물 건설 또는 홍수보험계획 등에 응용이 될 것으로 판단된다.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.10
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• pp.551-557
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• 2016

Fusing satellite images and site-specific observations have potential to improve a predictive quality of environmental properties. However, the effect of the utilization of satellite images to predict soil properties in a wetland is still poorly understood. For the reason, block kriging and regression kriging were applied to a natural wetland, Water Conservation Area-2A in Florida, to compare the accuracy improvement of continuous models predicting total phosphorus in soils. Field observations were used to develop the soil total phosphorus prediction models. Additionally, the spectral data and derived indices from Landsat ETM+, which has 30 m spatial resolution, were used as independent variables for the regression kriging model. The block kriging model showed $R^2$ of 0.59 and the regression kriging model showed $R^2$ of 0.49. Although the block kriging performed better than the regession kriging, both models showed similar spatial patterns. Moreover, regression kriging utilizing a Landsat ETM+ image facilitated to capture unique and complex landscape features of the study area.