• Journal of Animal Science and Technology
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• 제61권3호
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• pp.122-137
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• 2019

Methane, one of the important greenhouse gas, has a higher global warming potential than that of carbon dioxide. Agriculture, especially livestock, is considered as the biggest sector in producing anthropogenic methane. Among livestock, ruminants are the highest emitters of enteric methane. Methanogenesis, a continuous process in the rumen, carried out by archaea either with a hydrogenotrophic pathway that converts hydrogen and carbon dioxide to methane or with methylotrophic pathway, which the substrate for methanogenesis is methyl groups. For accurate estimation of methane from ruminants, three methods have been successfully used in various experiments under different environmental conditions such as respiration chamber, sulfur hexafluoride tracer technique, and the automated head-chamber or GreenFeed system. Methane production and emission from ruminants are increasing day by day with an increase of ruminants which help to meet up the nutrient demands of the increasing human population throughout the world. Several mitigation strategies have been taken separately for methane abatement from ruminant productions such as animal intervention, diet selection, dietary feed additives, probiotics, defaunation, supplementation of fats, oils, organic acids, plant secondary metabolites, etc. However, sustainable mitigation strategies are not established yet. A cumulative approach of accurate enteric methane measurement and existing mitigation strategies with more focusing on the biological reduction of methane emission by direct-fed microbials could be the sustainable methane mitigation approaches.

• 유기물자원화
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• 제23권3호
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• pp.78-84
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• 2015

본 연구에서는 표면 메탄농도와 지구통계기법(거리역산가중기법)을 기초로 표면 메탄발산량을 평가하고자 하였다. 실험결과 표면 메탄농도는 표면 메탄발산량과 높은 상관성이 있는 것으로 나타나, 표면 메탄농도를 기초로 표면 메탄발산량을 산정하는 것이 가능한 것으로 나타났다. 또한 지구통계기법 적용 결과 측정 면적의 12.85%가 총 메탄발산량의 42.21%를 나타내어 챔버 방법으로 메탄발산량을 정확하게 평가하기 위해서는 지구통계기법을 반드시 적용해야 하는 것으로 사료된다.

• 농촌계획
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• 제27권3호
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• pp.21-33
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• 2021

Rice straw management and winter crop cultivation are crucial components for the accurate estimation of paddy methane emissions. Field-based extensive investigation of paddy organic matter management requires enormous efforts however it becomes more feasible as drone technology advances. The objectives of this study were to identify paddy fields of straw application and winter crop cultivation using drone images and to apply for the estimation of yearly methane emission. Total 35 sites of over 150ha in area were selected nationwide as the study areas. Drone images of the study sites were taken twice during summer and winter in 2018 through 2019: Summer images were used to identify paddy cultivation areas, while winter images for straw and winter crop practices. Drone-image-based identification results were used to estimate paddy methane emission and compared with conventional method. As the result, mean areas for paddy, straw application and winter crop cultivation were 118.9ha, 12.0ha, and 11.3ha, respectively. Overall rice straw application rate were greater in Gyeonggi-do(20%) and Chungcheongnam-do(12%), while winter crop cultivation was greatest in Gyeongsangnam-do(30%) and Jeolla-do(27%). Yearly mean methane emission was estimated to be 226.2kg CH₄/ha/yr in this study and about 32% less when compared to 331.8kg CH₄/ha/yr estimated with the conventional method. This was primarily because of the lower rice straw application rate observed in this study, which was less than quarter the rate of 55.62% used for the conventional method. This indicates the necessity to use more accurate statistics of rice straw application as well as winter crop practices into paddy methane emission estimation. Thus it is recommended to further study to link drone technology with satellite image analysis in order to identify organic management practices at a paddy field level over extensive agricultural area.

• 한국자원공학회지
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• 제55권6호
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• pp.649-659
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• 2018

본 연구는 매립장 매립가스 발전소를 대상으로 발전소 운영 데이터들을 수집 후, 딥러닝(Deep Learning) 기법을 적용하여 향후 메탄가스 농도를 예측하였다. 2017년 1월부터 11월까지 88일치에 대해서 23개 포집공에서 메탄가스 농도, 이산화탄소 농도, 황화수소 농도, 산소 농도, 밸브 개방정도, 기온, 습도 데이터를 수집하였다. 수집 데이터로 딥러닝 모델을 학습한 후 실제 데이터와 비교하였다. 추정 결과 23개 포집공 모두에서 매우 정확한 추정결과를 보였다.

• 한국농공학회논문집
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• 제62권3호
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• pp.39-49
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• 2020

Rice paddy cultivation is one of the major sources in methane (CH₄) emission of which accurate assessment would be a prerequisite for agricultural greenhouse gas management. Biomass treatment in paddy fields is an important factor that affects CH₄ emissions and thus needs to be taken into account. The objectives of this study were to apply drone images to investigate organic matter practices and to incorporate into the estimation of CH₄ emissions from paddy fields. Three study areas were selected by one from each of the three different regions of Yeongnam, Honam and Jungbu, which are the most active region in paddy cultivation. The eBee drone was used to take images of the study sites twice a year; Jul mid-season for identifying rice cultivation area; Jan for investigating rice straw management and winter crop cultivation. Based on biomass management practices, different emissions factors were assigned on an individual paddy field and CH₄ emmisions were estimated by multiplying respective areas. The ratios of rice straw application and winter crop cultivation were 1.4% and 37.2% in Hapcheon, 1.3% and 19.8% in Gimje, and 0.0% and 0.5% in Dangjin, respectively. The CH₄ emissions estimates for respective sites were 0.40 ton CH₄/year/ha, 0.34 ton CH₄/year/ha, and 0.29 ton CH₄/year/ha. On average, estimated CH₄ emissions of this study were 28.5% less than the current Tier 2 CH₄ emission estimation method.

• 한국재난정보학회 논문집
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• 제15권4호
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• pp.560-569
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• 2019

연구목적: 온실가스는 전 세계적인 재난인 지구 온난화의 주요 원인 중 하나이다.본 연구에서는 도로부문 온실가스 배출량 산정을 기존의 방법보다 정밀하게 산정하는 목적을 가진다. 연구방법: 기존에는 온실가스 배출량 산정에는 전체 차량의 평균속도를 이용한다. 본 연구에서는 개별차량의 속도를 이용해 온실가스 배출량 산정을 진행하여 기존의 방법과 비교분석을 진행한다. 연구결과: 기존의 배출량 산정 방법이 이산화탄소의 경우 약15%가 과소측정 되었음이 확인되었으며 아산화질소의 경우에 약 1%가 과대측정이 되었고 메탄의 경우 약 1%가 과소 측정 되었음이 확인되었다. 결론: 기존의 온실가스 배출량 산정 방법은 2000년 이전에 개발되어 가용자료의 한계에 맞추어 개발되었다. 하지만 기술의 발전으로 가용 자료의 질이 높아진 현재는 새로운 배출량 산정 방법이 필요하다. 따라서 본 연구에서는 진보된 자료에 맞는 좀 더 정밀한 온실가스 배출량 산정을 진행 할 수 있는 방법인 개별차량의 속도기반 온실가스 배출량 산정 방법을 제시한다.