• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.539-547
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• 1997

This study was carried out to identify how soybean seed protein concentration is influenced by climatic factors. Twelve lines selected for seed protein concentration were studied in 13 environments of North Carolina. Sensitivity of seed protein concentration, total seed protein, and seed yield to climatic variables was investigated using a linear regression model. Best response models were determined using two stepwise selection methods, Maximum R-square and Stepwise Selection. There were wide climatic effects in seed protein concentration, total protein and seed yield. The highest protein concentration environment was characterized by the most high temperature days(HTD) and the smallest variance of average daily temperature range (VADTRg), while the lowest protein concentration environment was distinguished by the fewest HTD and the largest VADTRg. For protein concentration, all lines responded positively to average maximum daily temperature(MxDT), HTD, and average daily temperature range(ADTRg) and negatively to ADRa, while they responded positively or negatively to average daily temperature(ADT), variance of average minimum daily temperature (VMnDT), and VADTRg, indicating that genotypes may greatly differ in degrees of sensitivity to each climatic variable. Eleven lines seemed to have best response models with 2 or 3 variables. Exceptionally, NC106 did not show a significant sensitivity to any climatic variable and thus did not have a best response model. This indicates that it may be considered phenotypically more stable. For total seed protein and seed yield, all the lines responded negatively to both ADTRg and VADRa, suggesting that synthesis of seed components may increase with less daily temperature range and less variation in daily rainfall.

• Journal of Korea Water Resources Association
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• v.42 no.9
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• pp.681-690
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• 2009

In this research, design precipitation was calculated by reflecting the climatic indices and its uncertainty assessment was evaluated. Climatic indices used the sea surface temperature and moisture index which observed globally. The correlation coefficients were calculated between the annual maximum precipitation and the climatic indices. and then climatic indices which have the larger correlation coefficient were selected. Therefore, the regression relationship was established by a locally weighted polynomial regression. Next, climatic indices were generated by montecarlo simulation using kernel function. Finally, the design rainfall was calculated by the locally weighted polynomial regression using generated climatic indices. At the result, the comparison of design rainfall between the reflection of the climatic indices and the frequency analysis did not indicate a significant difference. Also, this result can be used as basic data for calculation of probability precipitation to reflect climate change.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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• pp.193-202
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• 2014

This paper presents a new method of sampling the climatic data for infrared signature analysis. Historical hourly data from a stationary marine buoy of KMA(Korean Meteorological Administration) are used to select a small number of sample points (N=100) to adequately cover the range of statistics(PDF, CDF) displayed by the original data set (S=56,670). The method uses a coarse bin to subdivide the variable space ($3^5$=243 bins) to make sample points cover the original data range, and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The principal component analysis is used to calculate a joint probability of the coupled climatic variables. The selected sample data show good agreement to the original data set in statistical distribution and they will be used for statistical analysis of infrared signature and susceptibility of naval ships.

• International Journal of Industrial Entomology and Biomaterials
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• v.14 no.2
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• pp.99-105
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• 2007

The success of rearing with presently available conventional bivoltine is unpredictable in some seasons of the tropical regions due to highly fluctuating adverse climatic conditions. Thus, in order to popularize bivoltine breeds in tropical parts of India, it is very much essential to have a bivoltine breed(s), which can give stable cocoon crop under variable environments. With this objective a breeding programme was undertaken to improve the survival trait in bivoltine silkworm by introducing multivoltine genes into bivoltine through back crossing. Resultant bivoltine lines showed significantly higher survival in compared to the receptor (Bivoltine) parent and control bivoltine breed. Esterase isozyme analysis revealed similar banding pattern in the developed bivoltine and in the donor multivoltine, which predicts the introgression of multivoltine character into evolved bivoltine.

• The Journal of Fisheries Business Administration
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• v.44 no.3
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• pp.77-84
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• 2013

While some steps in laver aquaculture production can be controlled artificially to a certain extent, the culturing process is largely affected by natural factors, such as the characteristics of seawater, climatic and oceanographic conditions, etc. This study aims to find a direct relationship between climatic and oceanographic factors (water temperature, air temperature, salinity, rainfall, sunshine duration and wind speed) and laver aquaculture production in Wando region, the biggest aquaculture production area of laver, located in the southwest coast of Korea using a multiple regression analysis. Despite the small sample size of a dependent variable, the goodness of model fit appeared acceptable. In addition, the R-squared value was 0.951, which means that the variables were very explanatory. Model results indicated that duration of sunshine, temperature, and rainfall during the farming period from the end of September to the end of April would be important factors affecting significantly to the laver aquaculture production.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.89-99
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• 2007

Uncertainty analysis is used to eliminate the climatic variables of input nodes and construct the model of an optimal type from COMBINE-GRNNM-GA(Type-1), which have been developed in this issue(2007). The input variable which has the lowest smoothing factor during the training performance, is eliminated from the original COMBINE-GRNNM-GA (Type-1). And, the modified COMBINE-GRNNM-GA(Type-1) is retrained to find the new and lowest smoothing factor of the each climatic variable. The input variable which has the lowest smoothing factor, implies the least useful climatic variable for the model output. Furthermore, The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. The optimal COMBINE-GRNNM-GA(Type-1) is developed to estimate and calculate the PE which is missed or ungaged and the $ET_r$ which is not measured with the least cost and endeavor Finally, the PE and $ET_r$. maps can be constructed to give the reference data for drought and irrigation and drainage networks system analysis using the optimal COMBINE-GRNNM-GA(Type-1) in South Korea.

• Journal of Ecology and Environment
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• v.36 no.3
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• pp.193-204
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• 2013

Altitudinal patterns of plant species richness and the effects of area, the mid-domain effect, climatic variables, net primary productivity and latitude on observed richness patterns along the ridge of the Baekdudaegan Mountains, South Korea were studied. Data were collected from 1,100 plots along a 200 to 1,900 m altitudinal gradient on the ridge. A total of 802 plant species from 97 families and 342 genera were recorded. Common and rare species accounted for 91% and 9%, respectively, of the total plant species. The altitudinal patterns of species richness for total, common and rare plants showed distinctly hump-shaped patterns, although the absolute altitudes of the richness peaks varied somewhat among plant groups. The mid-domain effect was the most powerful explanatory variable for total and common species richness, whereas climatic variables were better predictors for rare plant richness. No effect of latitude on species richness was observed. Our study suggests that the mid-domain effect is a better predictor for wide-ranging species such as common species, whereas climatic variables are more important factors for range-restricted species such as rare species. The mechanisms underlying these richness patterns may reflect fundamental differences in the biology and ecology of different plant groups.

• Journal of Forest and Environmental Science
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• v.39 no.2
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• pp.105-117
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• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.3
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• pp.223-236
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• 2016

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.233-242
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• 2018

This study explored the richness patterns of vascular plant species and evaluated the effects of the climatic and habitat variables on the observed patterns along elevational gradients on the Mt. Baekhwa, South Korea. Plant data were recorded from 70 plots and a total of 187 plant species with 78 woody and 109 herbaceous species were recorded along two study transects, the Banyasa and Bohyunsa transects, on the Mt. Baekhwa. A total of 154 plant species with 66 woody and 88 herbaceous species and 131 plant species with 58 woody and 73 herbaceous species were recorded along the Banyasa and Bohyunsa transects, respectively. We used simple ordinary least squares regression model, multi-model inference and variation partitioning to analyze the relative contribution of climatic and habitat variables on the elevational richness patterns. Species richness pattern for vascular plants along the Banyasa transect monotonically decreased with elevation, whereas plant species richness showed reversed hump-shaped pattern along the Bohyunsa transect. Although the elevational patterns of species richness for vascular plants were different between the both transects, habitat variables are more important predictors than climatic variables for the elevational patterns of plant species richness along our study transects on the Mt. Baekhwa. These results indicate that elevational diversity patterns of vascular plants may be different even between nearby elevational transects in a mountain ecosystem but the diversity patterns may be controlled by same drivers.