• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.65-66
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• 2017

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.2
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• pp.135-141
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• 1999

The information on agricultural weather over the Korean Peninsula was produced, using the Bio-sphere-Atmosphere Transfer Scheme(BATS). We modified the input and output portions of BATS such that the whole processes can be excuted as a package. Based on this approach, automatic production system of agricultural meteorology information was developed. The results of this model was compared with the observed data. The differences between the model results and observed data for surface temperature, rooting zone temperature and top soil temperature were 2.0$^{\circ}$C, 2.0$^{\circ}$C and 2.6$^{\circ}$C, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.47-54
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• 2012

Agriculture is dependable to weather condition and its change so that it is necessary to understand the impacts of climatic change. The aim of this study is to analyze the change of consumptive use of water and rice yield due to climate change using CERES-Rice. In this study, the weather data of three emission scenario of A1B, A2 and B1 created from CGCM (Coupled General Circulation Model) were used from 2011 to 2100, and downscaled daily weather data were simulated using LARS-WG (Long Ashton Research Station Weather Generator). The input data for cultivated condition for simulating CERSE (Crop-Environment Resource Synthesis)-Rice were created referring to standard cultivation method of paddy rice in Korea. The results showed that consumptive uses of water for paddy rice were projected decreasing to 4.8 % (2025s), 9.1 % (2055s), 12.6 % (2085s) comparing to the baseline value of 403.5 mm in A2 scenario. The rice yield of baseline was 450.7 kg/10a and projected increasing to -0.4 % (2025s), 3.9 % (2055s), 17.5 % (2085s) in A1B scenario. The results demonstrated relationships between consumptive use of water and rice yields due to climate change and can be used for the agricultural water resources development planning and cultivation method of paddy rice for the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.85-91
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• 2020

In order to calculate the Crop Water Stress Index (CWSI), it is necessary to collect weather data (air temperature, humidity, wind speed and solar radiation) and canopy temperature. However, it is not always available to have necessary data sets for CWSI calculation. Therefore, this study was aimed to develop an easy and simple CWSI equation (CWSI_EE) using only two data, air and canopy temperatures. Infrared sensors and weather sensors were installed on apple and peach trees and nearby a study area and every ten-minute data were collected from June to October in 2018 and 2019, respectively. A relationship between air-canopy temperature difference and CWSI was statistically analyzed and used to develop CWSI_EE using the three dimensional Gaussian model. The performance of CWSI_EE against original CWSI showed R² and NSE to 0.780 and 0.710 for apple trees and R² and NSE to 0.884 and 0.866 for peach trees. This study found that the level of crop water stress could be easily calculated using CWSI_EE with only air and canopy temperature data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.137-149
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• 2019

Accurate estimation of reference evapotranspiration (RET) is important to quantify crop evapotranspiration for sustainable water resource management in hydrological, agricultural, and environmental fields. It is estimated by different methods from direct measurements with lysimeters, or by many empirical equations suggested by numerous modeling using local climatic variables. The potential to use some such equations depends on the availability of the necessary meteorological parameters for calculating the RET in specific climatic conditions. The objective of this study was to determine the proper RET equations using limited climatic data and to analyze the temporal and spatial trends of the RET in South Korea. We evaluated the FAO-56 Penman-Monteith equation (FAO-56 PM) by comparing several simple RET equations and observed small fan evaporation. In this study, the modified Penman equation, Hargreaves equation, and FAO Penman-Monteith equation with missing solar radiation (PM-Rs) data were tested to estimate the RET. Nine weather stations were considered with limited climatic data across South Korea from 1973 - 2017, and the RET equations were calculated for each weather station as well as the analysis of the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The FAO-56 PM recommended by the Food Agriculture Organization (FAO) showed good performance even though missing solar radiation, relative humidity, and wind speed data and could still be adapted to the limited data conditions. As a result, the RET was increased, and the evapotranspiration rate was increased more in coastal areas than inland.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2002.11a
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• pp.85-88
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• 2002

기상학적으로 안개는 지상에서 발생하는 응결 현상으로, 시정이 1km 이하일 때로 정의된다. 안개 발생은 기후 인자의 영향을 많이 받는다. 따라서 각 지역마다의 발생 특성을 따로 통계해야 할 필요가 있다. 특히 항공 교통의 장애가 되는 위험 요소로서의 역할이 중시되어 각 비행장마다 발생 특성이 따로 통계 분석되고 이용되어 왔다.(중략)

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.193-199
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• 2000

In order to provide basic information for the estimation of reference crop evapotranspiration in the greenhouse, an lysimeter experiment was performed. Kenturky Blue Grass was used as a reference crop. Relationships between measured reference crop evapotranspiration and weather factors were analyzed. A multi-regression model was developed and tested.