• 대기
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• 제23권2호
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• pp.237-243
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• 2013

A set of scripts for GrADS (Grid Analysis and Display System) and R was implemented to produce agricultural climate information using the future climate scenarios based on the Representative Concentration Pathways. The GrADS script was used to calculate agricultural climate indices including growing degree days and cooling degree days. The script generated agricultural climate maps of these indices, which are compatible with common Geographic Information System (GIS) applications. To perform a statistical analysis using the agricultural climate maps, a script for R, which is open source statistical software, was used. Because a large number of spatial climate data were produced, parallel processing packages such as SNOW, doSNOW, and foreach were used to perform a simple statistical analysis in the R script. The parallel script of R had speedup on workstations with multi-CPU cores.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Quantitative Bio-Science
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• 제37권2호
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• pp.91-101
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• 2018

This study has analyzed applicable characteristics on big data of the meteorology and climate depending on press releases in the media. As a result, more than half of them were conducted by governmental departments and institutions (26.9%) and meteorological administration (25.0%). Most articles were written by journalists, especially the highest portion stems from straight articles focusing on delivering simple information. For each field, the number of cases had listed in order of rank to be exposed to the media; information service, business management, farming, livestock, and fishing industries, and disaster management, but others did rank far behind; insurance, construction, hydrology and energy. Application of big data about meteorology and climate differed depending on the seasonal change, it was directly related to temperature information during spring, to weather phenomenon such as monsoon and heat wave during summer, to meteorology and climate information during fall, and to weather phenomenon such as cold wave and heavy snow during winter.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• 한국재난정보학회 논문집
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• 제15권3호
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• pp.367-379
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• 2019

연구목적: 본 연구는 기후변화에 따른 재난의 특성을 분석하여 기후위험에 대비하기 위한 관리체계를 제시함을 목적으로 한다. 연구방법: 최근 국내외 자연재난으로 인한 피해의 추이를 분석하고 기후변화에 따른 재난의 특성을 파악함으로써 기후위험을 위한 관리체계를 설계한다. 연구결과: 기후변화에 따른 위험의 불확실성과 다양한 규모의 재난을 고려할 때, 위험의 평가에서부터 목표 설정, 계획 수립, 모니터링 및 평가, 학습과 조정 등의 핵심과정을 포함하는 포괄적 기후위험 관리체계가 요구되며, 이는 이해관계자 참여를 바탕으로 지속적으로 반복되는 체계를 의미한다. 결론: 본 연구에서 제시한 포괄적 기후위험 관리체계를 효과적으로 추진하기 위해 시범사업을 통해 관리체계를 수정 및 보완하고, 필요한 제도적 여건을 마련해야 한다.

• 한국농공학회논문집
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• 제57권2호
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2751-2771
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• 2022

In the post covid-19 era, organizations will experience a new environment. Advances in technologies such as AI and big data, and new experiences such as online meetings and lectures, will increase the use of online communication. Businesses will increasingly engage in online-based information sharing, virtual team operations, and online meetings. This study focuses on meeting climate and satisfaction, to improve the performance of online meetings. Existing studies on meeting climate presuppose off-line situations. Offline and online communication methods and meeting formats are different. This paper proposes new climate types to develop an appropriate climate for online-based meetings. To apply these climates in online meetings, a measurement scale was developed and the impact on online meeting satisfaction was verified. As a result of the study, it was found that the creativity-oriented meeting climate was the most important, and relation-oriented and participation-oriented meeting climates also had a significant effect, while the direction-oriented and task-oriented climates were relatively less important. This study develops new variables and measurements for online meeting climates, and explains their importance. Companies will be able to leverage the appropriate climates for online meetings to improve performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• 수산경영론집
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• 제45권3호
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• pp.71-84
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• 2014

This study indicates that 84.5% of fishermen have perceived climate change and 74.9% of fishermen have responded that frequency and intensity of the impacts of climate change are increasing. The results of regression analysis have shown that the level of fishermen experiencing the impacts of climate change differs according to individual's characteristics including age, length of experience, sea area (fishing area) and types of fisheries. About half of the respondents have shown that they are not taking any actions against the effects of climate change. The main reasons are that they either have lack of knowledge on how to respond to the impacts of climate change or have the perception that climate change is irresistible. The majority of respondents have responded that they are not aware of the government's climate change policy and emphasized that it is necessary to have effective countermeasures strengthening the provision of information about climate change policy. The result of perception survey have highlighted that it is essential for the government and the fishermen to share relevant information and to consider method of cooperation.

• 한국유기농업학회지
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• 제30권2호
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• pp.241-253
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• 2022

The negative impact of climate change on the agricultural sector is rapidly increasing, and it is urgent to prepare policies at the government level to mitigate it. In the case of Rwanda's agricultural sector, which lacks the government's budget and farmers' capital, efficient and effective policy implementation is of paramount importance. To this end, rather than establishing related policies in the public sector from the top down, it is necessary to establish a bottom-up customized policy that is reflected in policy establishment by identifying the characteristics and behaviors of farmers who actually participate in adaptation activities. In this study, the effects of farmers' characteristics and farmers' perception status/adaptation status to climate change on the selection of adaptation methods for climate change were analyzed. 357 rice farmers randomly selected from Eastern Rwanda were surveyed to explore the information related to farmers' perception to climate change and adaptation methods as well as basic information of the farm. Research shows that the probability of selecting a variety of adaptation methods rather than not responding to climate change increases the younger the age, the higher the education level, and the easier access to climate information and credit. As a policy proposals, it is judged that public support such as strengthening agricultural technology support services, including more detailed guidance for elderly and low-educated farmers, and improving access to farm loan services by agricultural financial institutions is needed. In addition, it is necessary to adjust the planting time and cultivation method, provide timely information related to climate change, and provide crop variety improvement services to farmers.