• 한국농림기상학회지
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• 제5권3호
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• pp.191-199
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• 2003

Optimum transplanting time for extremely early rice cultivation as an after-crop of fruit and vegetables under greenhouse conditions in the southern area was determined. Rice was transplanted on March 10, March 20, March 30, April 10 and April 20 far three years from 1998 to 2000. Meteorological computations for rice production were high for heading between early May and early July, but they were too low for heading between late July and early August. Especially the expected yield predicted with 35,000 spikelets, the average spikelets per $m^2$ for extremely early transplanting. Computation for heading between late July and early August was low by 106 kg/10a compared with that yield at heading during the same period in the field. As the transplanting date in extremely early rice cultivation was earlier) rice growth at early stages was more retarded by low temperature. Rice growth at heading stage recovered with high temperature, showing less difference for the transplanting date. Abnormal tillers occurred by 15.5∼22.2%. The contribution of 1,000 grain weight${\times}$ripened grain ratio to yield of the extremely early rice cultivation in the greenhouse was 50.6%, indicating 16% hi일or than the degree of panicle per $m^2$ on yield. The estimated optimum transplanting time on the basis of yield for the extremely early greenhouse rice cultivation ranged from March 19 to April 28, and the estimated critical transplanting date on the basis of accumulated effective temperature was March 12. Rice reduced the amount of NO$_3$-N by 97.1% and EC by 90.5% in greenhouse soil with continuous fruit/vegetables fer more than a 10-year period, and completely removed the root-knot nematodes.

• 한국농림기상학회지
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• 제3권1호
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• pp.37-43
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• 2001

Crop status monitoring and yield prediction at higher spatial resolution is a valuable tool in various decision making processes including agricultural policy making by the national and local governments. A prototype crop forecasting system was developed to project the size of rice crop across geographic areas nationwide, based on daily weather pattern. The system consists of crop models and the input data for 1,455 cultivation zone units (the smallest administrative unit of local government in South Korea called "Myun") making up the coterminous South Korea. CERES-rice, a rice crop growth simulation model, was tuned to have genetic characteristics pertinent to domestic cultivars. Daily maximum/minimum temperature, solar radiation, and precipitation surface on 1km by 1km grid spacing were prepared by a spatial interpolation of 63 point observations from the Korea Meteorological Administration network. Spatial mean weather data were derived for each Myun and transformed to the model input format. Soil characteristics and management information at each Myun were available from the Rural Development Administration. The system was applied to the forecasting of national rice production for the recent 3 years (1997 to 1999). The model was run with the past weather data as of September 15 each year, which is about a month earlier than the actual harvest date. Simulated yields of 1,455 Myuns were grouped into 162 counties by acreage-weighted summation to enable the validation, since the official production statistics from the Ministry of Agriculture and Forestry is on the county basis. Forecast yields were less sensitive to the changes in annual climate than the reported yields and there was a relatively weak correlation between the forecast and the reported yields. However, the projected size of rice crop at each county, which was obtained by multiplication of the mean yield with the acreage, was close to the reported production with the $r^2$ values higher than 0.97 in all three years.

• 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?".