• Journal of the Korean earth science society
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• v.39 no.4
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• pp.378-387
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• 2018

In this research, the change of Korean middle-school science education environments is investigated through analyzing eighth graders' survey data collected over the past 20 years of TIMSS. We extracted educational context variables that provide meaningful information on changes of Korean science education, and have been surveyed more than 3 study cycles up to TIMSS 2015. The selected educational context variables include school resources and school climate from the school principal's questionnaires, and teacher characteristics and instructional activities from the teacher's questionnaires. For each context variable, we analyzed its trend over TIMSS cycles, and discussed its implications in light of Korean educational policy and curriculum changes. Based on the results, we recommended several ways that help to improve science teaching and learning in light of lab assistants, computer availability, teacher learning community, and middle school Earth science curriculum.

• Korean Journal of Child Studies
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• v.27 no.6
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• pp.81-96
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• 2006

The purpose of this study was to investigate the variables that influence public kindergarten teachers' role performance. A sample of 320 public kindergarten teachers in Gyeonggi-Do was selected randomly. Teacher's career, educational background, age, self-concept, and awareness of teaching profession, class size, age of classroom students, and organizational climate were examined as possible factors affecting teachers' role performance. Results showed that all factors except class size were related to teachers' role performance. The most significant impact on public kindergarten teachers' role performance was teacher autonomy in organizational climate.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.107-123
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• 2018

The global impact of climate change on agriculture is now increasing. The purpose of this study was to investigate the effect of climate change on livestock productivity. The variables that have the greatest influence on climate change factors were examined through previous studies and expert surveys. We also used the actual productivity data of livestock farmers to investigate the relationship with climate change. In order to evaluate the climate for changes in livestock productivity, national representative data (such as bullocks, dairy, pigs, laying hens, and broilers) were surveyed in Korea. Also, to select and classify evaluation indexes, we selected climate change factor variables as prior studies and studied the weighting factor of climate variable factors. In this study, the researchers of industry, academia, and farmers in the livestock sector conducted questionnaires on the indicators of vulnerability to climate change using experts, and then weighed the selected indicators using the hierarchical analysis process (AHP). In order to verify the validity of the evaluation index, was examined using domestic climate data (temperature, precipitation, humidity, etc.). Correlation and regression analysis were performed. The empirical relationship between climate change and livestock productivity was examined through this study. As a result, we used data with high reliability of statistical analysis and found that there are significant variables.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.79-88
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• 2015

Preparations need to be made for Korean pine(Pinus koraiensis) in anticipation of climate change because Korean pine is an endemic species of South Korea and the source of timber and pine nut. Therefore, climate change adaptation policy has been established to conduct an impact assessment on the distribution of Korean pine. Our objective was to predict the distribution of Korean pine while taking into account uncertainty and afforestation conditions. We used the 5th forest types map, a forest site map and BIOCLIM variables. The climate scenarios are RCP 4.5 and RCP 8.5 for uncertainty and the climate models are 5 regional climate models (HadGEM3RA, RegCM4, SNURCM, GRIMs, WRF). The base period for this study is 1971 to 2000. The target periods are the mid-21st century (2021-2050) and the end of the 21st century (2071-2100). This study used the MaxEnt model, and 50% of the presences were randomly set as training data. The remaining 50% were used as test data, and 10 cross-validated replicates were run. The selected variables were the annual mean temperature (Bio1), the precipitation of the wettest month (Bio13) and the precipitation of the driest month (Bio14). The test data's ROC curve of Korean pine was 0.689. The distribution of Korean pine in the mid-21st century decreased from 11.9% to 37.8% on RCP 4.5 and RCP 8.5. The area of Korean pine at an artificial plantation occupied from 32.1% to 45.4% on both RCPs. The areas at the end of the 21st century declined by 53.9% on RCP 4.5 and by 86.0% on RCP 8.5. The area of Korean pine at an artificial plantation occupied 23.8% on RCP 4.5 and 7.2% on RCP 8.5. Private forests showed more of a decrease than national forests for all subsequent periods. Our results may contribute to the establishment of climate change adaptation policies for considering various adaptation options.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.219-233
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• 2013

We employed the ecological niche modeling framework using GARP (Genetic Algorithm for Ruleset Production) to model the current and future geographic distribution of Pinus densiflora based on environmental predictor variable datasets such as climate data including the RCP 8.5 emission climate change scenario, geographic and topographic characteristics, soil and geological properties, and MODIS enhanced vegetation index (EVI) at 4 $km^2$ resolution. National Forest Inventory (NFI) derived occurrence and abundance records from about 4,000 survey sites across the whole country were used for response variables. The current and future potential geographic distribution of Pinus densiflora, one of the tree species dominating the present Korean forest was modeled and mapped. Future models under RCP 8.5 scenarios for Pinus densiflora suggest large areas predicted under current climate conditions may be contracted by 2090 showing range shifts northward and to higher altitudes. Area Under Curve (AUC) values of the modeled result was 0.67. Overall, the results of this study were successful in showing the current distribution of major tree species and projecting their future changes. However, there are still many possible limitations and uncertainties arising from the select of the presence-absence data and the environmental predictor variables for model input. Nevertheless, ecological niche modeling can be a useful tool for exploring and mapping the potential response of the tree species to climate change. The final models in this study may be used to identify potential distribution of the tree species based on the future climate scenarios, which can help forest managers to decide where to allocate effort in the management of forest ecosystem under climate change in Korea.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.165-178
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• 2014

National and local governmental adaptation plan for climate change will become mandatory in 2015. In order to establish the plan, assessment of vulnerability to climate change needs to be preceded. LCCGIS, a toolkit for vulnerability assessment, has been widely used by many local governments. However, assessment results by LCCGIS are not yet reliable because most of the vulnerability indices applied to LCCGIS　have the same value for almost all administrative units in Korea. In this study, proxy variables for hard-collectable indices were introduced, and the results were compared with those without any proxy variables. Vulnerability assessment could be conducted subjectively due to uncertainty. Thus, determination of objective indices, understanding the available data, and changes of indices in local conditions were organized. Results from this study are expected to make vulnerability assessment reliable and contribute to assessing vulnerability to climate change reflecting on local governmental characteristics.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.427-437
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• 2019

Background: Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results: MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions: The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts.

• Journal of Ecology and Environment
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• v.42 no.4
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• pp.298-307
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• 2018

Background: Subalpine ecosystems at high altitudes and latitudes are particularly sensitive to climate change. In South Korea, the prediction of the species richness of subalpine plant species under future climate change is not well studied. Thus, this study aims to assess the potential impact of climate change on species richness of subalpine plant species (14 species) in the 17 mountain national parks (MNPs) of South Korea under climate change scenarios' representative concentration pathways (RCP) 4.5 and RCP 8.5 using maximum entropy (MaxEnt) and Migclim for the years 2050 and 2070. Results: Altogether, 723 species occurrence points of 14 species and six selected variables were used in modeling. The models developed for all species showed excellent performance (AUC > 0.89 and TSS > 0.70). The results predicted a significant loss of species richness in all MNPs. Under RCP 4.5, the range of reduction was predicted to be 15.38-94.02% by 2050 and 21.42-96.64% by 2070. Similarly, under RCP 8.5, it will decline 15.38-97.9% by 2050 and 23.07-100% by 2070. The reduction was relatively high in the MNPs located in the central regions (Songnisan and Gyeryongsan), eastern region (Juwangsan), and southern regions (Mudeungsan, Wolchulsan, Hallasan, and Jirisan) compared to the northern and northeastern regions (Odaesan, Seoraksan, Chiaksan, and Taebaeksan). Conclusions: This result indicates that the MNPs at low altitudes and latitudes have a large effect on the climate change in subalpine plant species. This study suggested that subalpine species are highly threatened due to climate change and that immediate actions are required to conserve subalpine species and to minimize the effect of climate change.

• Journal of Animal Science and Technology
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• v.63 no.1
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• pp.77-90
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• 2021

This study aimed to assess the impact of abnormal climate events on the production of Italian ryegrass (IRG), such as autumn low-temperature, severe winter cold and spring droughts in the central inland, southern inland and southern coastal regions. Seasonal climatic variables, including temperature, precipitation, wind speed, relative humidity, and sunshine duration, were used to set the abnormal climate events using principal component analysis, and the abnormal climate events were distinguished from normal using Euclidean-distance cluster analysis. Furthermore, to estimate the impact caused by abnormal climate events, the dry matter yield (DMY) of IRG between abnormal and normal climate events was compared using a t-test with 5% significance level. As a result, the impact to the DMY of IRG by abnormal climate events in the central inland of Korea was significantly large in order of severe winter cold, spring drought, and autumn low-temperature. In the southern inland regions, severe winter cold was also the most serious abnormal event. These results indicate that the severe cold is critical to IRG in inland regions. Meanwhile, in the southern coastal regions, where severe cold weather is rare, the spring drought was the most serious abnormal climate event. In particular, since 2005, the frequency of spring droughts has tended to increase. In consideration of the trend and frequency of spring drought events, it is likely that drought becomes a NEW NORMAL during spring in Korea. This study was carried out to assess the impact of seasonal abnormal climate events on the DMY of IRG, and it can be helpful to make a guideline for its vulnerability.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.4
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• pp.462-470
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• 2012

The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030.