• Korean Journal of Environment and Ecology
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• v.30 no.4
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• pp.724-729
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• 2016

Environmental factors that affect the singing of cicadas have not been studied extensively, especially those affecting the cicadas' singing during the nighttime. Therefore, the objective of this study is to identify the effects of tropical night and light pollution on the cicadas' singing in a downtown area. The study sites were an apartment complex in Seocho-gu, Seoul, and the Chiaksan National Park in Wonju-si. The study subjects were Hyalessa fuscata and Cryptotympana atrata, which are the dominant species in Korea during summer. Cicada songs were recorded 24 hours a day, every day. The recording period was between July and August, lasting 25 days at the Seoul site and 14 days at the Chiaksan National Park. Temperature, precipitation, humidity, and amount of sunshine were selected as the environmental factors that potentially affect the cicadas' singing. Statistical analyses included correlations of meteorological factors with the cicadas' singing per hour, per 24 hours, and at nighttime (21:00~04:00). The results showed that: 1) H. fuscata began singing during the dawn hours, and the singing increased in intensity early in the morning. C. atrata's singing reached its peak in the morning and afternoon, ceased during sunset hours, thereby exhibiting a difference in the singing pattern of the two species. 2) The frequency of singing by H. fuscata decreased when C. atrata began to sing intensively in numbers, thereby exhibiting interspecific influence. 3) The results of the correlation analysis between meteorological factors and the singing of H. fuscata and C. atrata showed that both species tended to sing more when the temperature was higher and sang less on rainy days. 4) When limited to nighttime only, C. atrata showed a tendency of singing when the nighttime temperature was high ($24-30^{\circ}C$, average $27^{\circ}C$), whereas H. fuscata did not show a correlation with meteorological factors. However, since H. fuscata sang during the night in areas with artificial lighting, it was concluded that its singing was due to light pollution.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.2
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• pp.142-149
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• 1995

This study was conducted to investigate the relationships between yearly variation of climatic components and yearly variations of productivity in monoculture cotton. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components from the four varieties(Kinggus, Yongdang local. 113-4, 380) were collected from 1978 to 1992 in Mokpo area. The meteorological data gathered at the Mokpo Weather Station for the same period were used to find out the relationships between climatic components and productivity. Yearly variation of the amount of precipitation and number of stormy days in July are large with coefficients of the variations(C.V)84.89 and 97.05%, respectively, while yearly variation, of the average temperature, maximum temperature, minimum temperature from May to Sep. are relatively small. Seed cotton yield before frost in Sep. and Oct. very greatly with C.V. of 68.77, 78.52%, respectively. Number of boll bearing branches and lint percentage show more or less small in C.V. with 11.77 and 19.13%, respectively and flowering date and boll opening date show still less variation. Correlation coefficients between precipitation in May and number of boll bearing branches, duration of sunshine in July and number of bolls per plant, maximum temperature in July and total seed cotton before the frost in Sep., Oct., and Nov. evaporation in Aug. are positively sig-nificant at the 1% level. There are highly significantly positive correlated relationships among yield(total seed cotton) and yield components. Total seed cotton yield(Y) can be predicted by multiple regression equation with independent variables of climatic factors in July such as monthly averages of average temperature($X_1$), maximum temperature($X_2$) and minimum temperature($X_3$), monthly amount of precipitation ($X_4$), evaporation($X_5$), monthly average of relative humidity($X_6$), monthly hours with sunshine($X_7$) and number of rainy days($X_8$). The equation is estimatedas Y =-1080.8515 + 144.7133$X_1$+15.8722$X_2$ + 164.9367$X_3$ + 0.0802$X_4$ + 0.5932$X_5$ + 11.3373$X_6$ + 3.4683$X_7$- 9.0846$X_8$. Also, total seed cotton yield(Y) can be predicted by the same method with climatic components in Aug., Y =2835.2497 + 57.9134$X_1$ - 46.9055$X_2$ - 41.5886X$_3$ + 1.2559$X_5$ - 21.9687$X_6$ - 3.3763$X_7$- 4.1080$X_8$- 17.5586$X_9$. And the error between observed and theoretical yield were less with approached linear regression.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s02
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• pp.13-33
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• 1989

Agroclimate should be analyzed and evaluated accurately to make better use of available chimatic resources for the establishment of optimum cropping systems. Introducing of appropriate cultivars and their cultivation techniques into classified agroclimatic zone could contribute to the stability and costs of crop production. To classify the agroclimatic zones, such climatic factors as temperature, precipitation, sunshine, humidity and wind were considered as major influencing factors on the crop growth and yield. For the classification of rice agroclimatic zones, precipitation and drought index during transplanting time, the first occurrence of effective growth temperature (above 15$^{\circ}C$) and its duration, the probability of low temperature occurrence, variation in temperature and sunshine hours, and climatic productivity index were used in the analysis. The agroclimatic zones for rice crop were classified into 19 zones as follows; (1) Taebaek Alpine Zone, (2) Taebaek Semi-Alpine Zone, (3) Sobaek Mountainous Zone, (4) Noryeong Sobaek Mountainous Zone, (5) Yeongnam Inland Mountainous Zone, (6) Northern Central Inland Zone, (7) Central Inland Zone, (8) Western Soebaek Inland Zone, (9) Noryeong Eastern and Western Inland Zone, (10) Honam Inland Zone, (ll) Yeongnam Basin Zone, (12) Yeongnam Inland Zone, (13) Western Central Plain Zone, (14) Southern Charyeong Plain Zone, (15) South Western Coastal Zone, (16) Southern Coastal Zone, (17) Northern Eastern Coastal Zone, (18) Central Eastern Coastal Zone, and (19) South Eastern Coastal Zone. The classification of agroclimatic zones for cropping systems was based on the rice agroclimatic zones considering zonal climatic factors for both summer and winter crops and traditional cropping systems. The agroclimatic zones were identified for cropping systems as follows: (I) Alpine Zone, (II) Mountainous Zone, (III) Central Northern Inland Zone, (IV) Central Northern West Coastal Zone, (V) Cental Southern West Coastal Zone, (VI) Gyeongbuk Inland Zone, (VII) Southern Inland Zone, (VIII) Southern Coastal Zone, and (IX) Eastern Coastal Zone. The agroclimatic zonal characteristics of climatic disasters under rice cultivation were identified: as frequent drought zones of (11) Yeongnam Basin Zone, (17) North Eastern Coastal Zone with the frequency of low temperature occurrence below 13$^{\circ}C$ at root setting stage above 9.1％, and (2) Taebaek Semi-Alpine Zone with cold injury during reproductive stages, as the thphoon and intensive precipitation zones of (10) Hanam Inland Zone, (15) Southern West Coastal Zone, (16) Southern Coastal Zone with more than 4 times of damage in a year and with typhoon path and heavy precipitation intensity concerned. Especially the three east coastal zones, (17), (18), and (19), were subjected to wind and flood damages 2 to 3 times a year as well as subjected to drought and cold temperature injury.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.20-28
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• 1999

This study was conducted to reveal the effect of some meterological factors on the weight of cone and seed per cone in Korean white pine (Pinus koraiensis SIEB. et ZUCC.). The weight of cone and seed per cone for 7 years from 1992 to 1998 on 45(1992) year-old trees and some meterological factors for 9 years from 1990 to 1998 were surveyed in Hongcheon-Gun region, Gangweon-Do. Simple correlations and multiple regression between weight of cone and seed per cone and some meterological factors were analyzed. The results obtained from the above experiments were as follows: 1. Positive correlations were found between weight of cone and monthly mean temperature of February in flower bud differentiation year. number of annual hoarfrost days of the cone production year monthly mean temperature of may in the cone production year, as July respectively. There were negative correlations between weight of cone and monthly mean temperature of august in the flowering year, wind speed of April in the flower bud differentiation year, number of clear days of december in the flowering year, number of annual cloudy days of the flowering year, number of precipitation days of june in the flowering year, number of annual precipitation days of the flowering year, number of annual cloudy days of March in the cone production year. number of annual cloudy days from march to October in the flowering year as well as number of precipitation from march to october in the flowering year. 2. Positive correlation between weight of seed per cone and number of hours with sunshine duration of June in the flowering year, the percentage of sunshine duration of June in the flowering year, number of clear days of June in the flowering year. monthly mean temperature of may in the cone production year. as well as monthly mean temperature of July in the cone production year were found. Negative correlations were recognized between weight of seed per cone and monthly mean temperature of January in the flowering year, monthly mean temperature of august in the flowering year, wind speed of april in the flower bud differentiation year, number of annual cloudy days of the flowering year, number of precipitation days of June in the flowering year, number of annual cloudy days from March to October in the flowering year as well as number of precipitation from march to October in the flowering year.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.3
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• pp.268-275
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• 1982

This study was conducted to obtain basic informations for breeding and improving cultural practices of sesame (Sesamum indicum L.) through investigation of several agronomic characters of 82 major varieties plants in April 20, May 15 and June 20. Stem diameter and plant height were largest in early planting(April 20). On the contrary, they were smallest in late planting(June 15). Therefore, the later planting, the poorer the vegetative growth on the basis of stem diameter and plant height. Number of capsules per plant and number of grains per capsule were 88 and 54, respectively, in the early planting, while they were decreased to 25 capsules and 40 grains in late planting. The percentage of ripeness and 1000 grain weight 78 and 2.3g in the April 20planting while they were 58% and 2.1g in June 20 planting indicating the later planting, the lower the ripeness. Grain yield per m1 in April 20 and June 20 planting was 112 and 18g respectively. Consequently, grain yield significantly decreased as planting delayed. On the basis of the vegetative and reproductive growth, varieties could be classified into early, conventional, late and indetermediate planting adapted groups. The major yield components were highly and postively correlated with accmulated sunshine hours and temperatures confirming that grain yield was closely related with sunshine and temperature. Among the varieties tested, Gurye and Local 135 had more than 150 capsules. Haenam and Woogang had more than 75 grains per capsule. Since grain yield oflate planted sesame were significantly different among the varieties, breeding of sesame for after barly cropping would be more effective under late cultural condition of after barly.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.89-96
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• 2014

We aimed to evaluate the effects of climatic elements on potato yield and create a model with climatic elements for estimating the potato yield, using the results of the regional adjustment tests of potato. We used 86 data of the yield data of a potato variety, Sumi, from 17 regions over 11 years. According to the results, the climatic elements showed significant level of correlation coefficient with marketable yield appeared to be almost every climatic elements except wind velocity, which was daily average air temperature (Tave), daily minimum air temperature (Tmin), daily maximum air temperature(Tmax), daily range of air temperature (Tm-m), precipitation (Prec.), relative humidity (R.H.), sunshine hours (S.H.) and days of rain over 0.1 mm (D.R.) depending on the periods of days after planting or before harvest. The correlations between these climatic elements and marketable yield of potato were stepwised using SAS, statistical program, and we selected a model to predict the yield of marketable potato, which was $y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$. The correlation coefficient between the yield derived from the model and the real yield of marketable yield was 0.588 (DF 85).

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.733-740
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• 2017

This review introduces the empirical approach of rice yield forecasting and compares it with remote sensing approach. The empirical approach, was based on the results of the rice growth and yield monitoring experiment in 17 sites, estimated rice yield by recombination of yield components. The number of spikelet per unit area was from results of experiment sites and grain filling rate was estimated from linear regression with sunshine hours. The estimation results were relatively accurate from 2010 to 2016. The smallest error was 1 kg / 10a and the largest error was 19 kg / 10a. The largest error was caused by the typhoon. The empirical approach did not fully reflect the spatial variation caused by disasters such as typhoon or pest. On the other hand, remote sensing could explain spatial variation caused by disasters. Therefore, if there are not any disaster in rice field, both approaches are valid and remote sensing will be more accurate when any local disaster occurs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.3
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• pp.215-221
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• 1988

The hop yield prediction model developed based on meteorological elements in Hoeongseong was Y=6,042.846-17.665 $X_1$-0.919 $X_2$-96.538 $X_3$-138.105 $X_4$＋86.910 $X_{5}$ ＋ $X_{6}$ with MS $E_{p}$ of 25.258, $R_{p}$$^{2}$ of 0.9991, R $a_{p}$$^{2}$ of 0.9962 and $C_{p}$ of 7.00. The minimum air temperature at early growing stage ( $X_1$), the total precipitation at cone ripening stage ( $X_2$), the maximum air temperature at flower bud differentiation stage ( $X_3$) and the maximum air temperature at flowering stage ( $X_4$) influenced on hop yield as decrement weather elements. The average air temperature at early growing stage ( $X_{5}$ ) and the total sunshine hours at cone development stage ( $X_{6}$ ) influenced on hop yield as increment weather elements.lements.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.480-488
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• 2018

The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.31 no.3
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• pp.318-325
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• 1986

The studies were performed to obtain the basic informations on the influence of weather condition for grain yield and yield components in barley. The data of Olbori tested in 9 sites for 12 years were used in the studies. Milled grain yield was decreased in paddy field after rice harvested comparing to the upland condition, and yield potential was differed by test sites with the most stable yield in Gyeongnam. The coefficients of variation analyzed for milled grain yield by years were 12.2-42.6% with the differences between high-yield and low-yield year. Heading date was earlier in high-yield year and southern part compared to the low-yield year and middle part of the Korean peninsular showing the negative correlation between grain yield and heading date. High-yield year showed longer in culm length, shorter in spike length, almost same in number of grains per spike, and lower in 1,000grain weight compared to the low-yield year. Correlation analyzed between number of spikes and grain yield showed positive relationship. Temperatures affected to the grain yield analyzed by high in vegitative growth stage, low in alternative growth stage, and almost same in reproductive growth stage in high-yield year comparing to the low-yield year, however no remarkable differences of temperatures affected were detected in over wintering stage between high-yield and low-yield year. Precipitation amount in high-yield year was lesser in sowing time, more in seedling time, and lesser in over wintering time than those of the low-yield year. Correlation between rainfall amount in the early of April and grain yield showed significant negative correlation with the remarkable affects to the grain yield. Sunshine hours in high-yield year were longer in sowing time, shorter in over wintering time, and after the over wintering time to harvesting time was longer than those of the low-yie-ld year.