• Asian-Australasian Journal of Animal Sciences
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• v.20 no.6
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• pp.832-841
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• 2007

Rectal Temperature;Thermoregulation;Sows;Breed;The effects of season (hot vs. warm) in a tropical humid climate, parity (primiparous vs. multiparous) and breed (Creole: CR, Large White: LW) on rectal temperature (RT) were studied for a total of 222 lactations obtained in 85 sows (43 CR and 42 LW; 56 primiparous and 166 multiparous) over a 28-d lactation, between June 2002 and April 2005. Mean daily ambient temperature was higher during the hot season than during the warm season (26.0 vs. $24.1^{\circ}C$) and relative humidity was high and similar in both seasons (89% on average). At farrowing, BW was lower (172 vs. 233 kg) and backfat thickness was higher (37 vs. 21 mm) in CR than in LW sows (p<0.01). During the hot season, the reduction of average daily feed intake (ADFI) was more pronounced in LW than in CR sows (-920 vs. -480 g/d, p<0.05). Rectal temperature was higher at 1200 than at 0700hr, which coincides with the maximum and the minimum values of daily ambient temperature. The daily RT increased ($+0.9^{\circ}C$; p<0.01) between d -3 and d 7 (d 0: farrowing day), remained constant between d 7 and d 25 and decreased (p<0.01) thereafter (i.e. $-0.6^{\circ}C$ between d 25 and d 32). The average daily RT was significantly higher during the hot than during the warm season (38.9 vs. $38.6^{\circ}C$; p<0.01). It was not affected by breed, but the difference in RT between the hot and warm seasons was more pronounced in LW than in CR sows (+0.4 vs. $+0.2^{\circ}C$; p<0.05). Parity influenced the RT response; it was greater in primiparous than in multiparous sows (38.9 vs. $38.7^{\circ}C$; p<0.01). This study suggests that thermoregulatory responses to heat stress can differ between breeds and between parities.

• Korean journal of applied entomology
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• v.17 no.2 s.35
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• pp.81-87
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• 1978

Late blight incited by Phytophthora infestans (Mont.) de Bary, is an important problem for seed potato prodcution in Korea. At the alpine Daekwanryeong area, unprotected potatoes are often defoliated within 14 days after late blight is first observed in the field. Since regular spraying can control late blight, the forecasting service is needed for timely initiation of the spraying program. Climatological data and notes on late blight incidence were recorded during 1970-1977 at the Alpine Experiment Station. The moving graph method using 7-day average mean temperature and 7-day total rainfall did not give highly accurate forecasts. Adding data on relative humidity and 7-day average minimum temperature increased the usefulness of the moving graph. Yields of late blight susceptible varietieties in sprayed plots were related to late blight occurrence and to the rainfall distribution pattern.

• Horticultural Science & Technology
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• v.31 no.4
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• pp.400-406
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• 2013

This study was conducted to investigate the growth and flowering of African marigold (Tagetes erecta L.) and salvia (Salvia splendens F. Sello ex Ruem & Schult.) seedlings before and after storage under fluorescent lamps and green LED radiation conditions with different light intensities during storage. The both seedlings were kept under a storage room controlled at $8^{\circ}C$ air temperature and $40{\pm}10%$ relative humidity conditions. Light intensities were maintained at 15 and $30{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ during storage. In lighting treatments, dry weight of African marigold at 28 days after storage was not significantly different, and decreased approximately 29% compared to pre-storage under dark treatment. There was no significant difference in the leaf area of salvia seedlings stored under dark condition compared to before storage, but the leaf area under green light radiation with higher light intensity (treatment GH) was two times greater than before storage. The survival rate after transplanting of African marigold stored under dark condition was 10%, and days to flowering increased compared to those stored under fluorescent and green light with higher light intensity (treatment FLH, GH). Comparing to before storage, growth and flowering of the both seedlings after storage were significantly promoted by the light exposure during storage. The present experimental results show that the light intensity should be decided to maintain minimum growth during lighting storage and storage quality of the seedlings such as flowering promotion and extended blooming period after lighting treatment during storage period from the above results.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.E1
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• pp.16-28
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• 2007

An improved method was developed to determine gas-phase hydrogen peroxide($H_2O_2$) and organic hydro-peroxides (ROOH) in real-time, The analytical system for $H_2O_2$ is based on formation of hydroxybenzoic acid (OHBA), a strong fluorescent compound. OHBA is formed by a sequence of reactions, photoreduction of Fe(III)-EDTA to Fe(II)-EDTA, the Fenton reaction of Fe(II)-EDTA with $H_2O_2$, and hydroxylation of benzoic acid. By use of this analytical method rather than a previous similar method, Fenton reaction time was reduced from 2 min. to 30s. Air samples were collected by a surfaceless inlet to prevent inlet line losses. With a special arrangement of the sampling apparatus, sample delivery time was drastically reduced from ${\sim}5\;min\;to\;{\sim}20\;s$. The automated system was found to be sensitive, capable of continuous monitoring, and affordable to operate. A comparison of this method with a well-established one showed an excellent linear correlation, validating applicability of this technique to $H_2O_2$ determination. The system was applied to field measurements conducted during summertime of 2004 in Gwangju, South Korea. $H_2O_2$ was found to be a predominant species of peroxides. The diurnal variation of $H_2O_2$ displayed the maximum in early afternoon and the broad minimum throughout night. $H_2O_2$ was correlated positively with ozone, photochemical age, and temperature, however, negatively with $NO_x$ and relative humidity.

• Atmosphere
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• v.18 no.4
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• pp.459-474
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• 2008

Aerosol scattering coefficients for three different wavelengths ($\lambda$=450,550,700 nm) are measured almost continuously by a nephelometer in Seoul for a period of 13 months (February 2007-February 2008), which includes two weeks break in August 2007 for measurements at Daegwallyeong and YoungJongdo. The mean of the daily average scattering coefficients at $\lambda$=550 nm is $194.1{\pm}144.2Mm^{-1}$ and the minimum and maximum are $14.3Mm^{-1}$ and $998.1Mm^{-1}$, respectively. The scattering coefficient shows a general increasing trend with atmospheric relative humidity (RH). When the data are classified according to weather conditions, the days with no major weather events show the smallest scattering coefficient and also the lowest RH. Surprisingly haze/fog days show the largest scattering coefficient and Asian dust days comes in second. Although the variation is large within a season, winter shows the largest and autumn shows the smallest scattering coefficient. The average ${\AA}ngstr{\ddot{o}}m$ exponent is $1.40{\pm}0.32$ for the entire Seoul measurement. As expected, Asian dust days show the smallest ${\AA}ngstr{\ddot{o}}m$ exponent and haze/fog days are the next, suggesting more efficient hygroscopic growth of aerosols for this weather condition. Aerosol scattering coefficient seems to show better correspondence with CCN concentration rather than total aerosol concentration, which may indicate that CCN active aerosols are also good scattering aerosols.

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.969-977
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• 2016

Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.61-72
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• 2017

Due to the climate change, damages of human life and property caused by natural disaster have recently been increasing consistently. In South Korea, total damage by natural disasters over 20 years from 1994 to 2013 is about 1.0 million dollars. The 13% of total damage caused by heavy snow. This is smaller amount than the damage by heavy rainfall or typhoon, but still could cause severe damage in the society. In this study, the snow damage in Gangwon region was estimated using climate variables (daily maximum snow depth, relative humidity, minimum temperature) and scoio-economic variables (Farm population density, GRDP). Multiple regression analysis with enter method was applied to estimate snow damage. As the results, adjusted R-square is above 0.7 in some sub-regions and shows the good applicability although the extreme values are not predicted well. The developed model might be applied for the prompt disaster response.