This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

In this paper, the model for predicting yields of chinese cabbages of each cultivar (joined-up in 2015 and wrapped-up in 2016) was developed after the reflectance of hyperspectral imagery was merged as 10 nm, 25 nm and 50 nm of FWHM (full width at half maximum). Band rationing was employed to minimize the unstable reflectance of multi-temporal hyperspectral imagery. The stepwise analysis was employed to select key band ratios to predict yields in all cultivars. The key band ratios selected for each of FWHM were used to develop the yield prediction models of chinese cabbage for all cultivars (joined-up & wrapped-up) and each cultivar (joined-up, wrapped-up). Effective accumulated temperature (EAT) was added in the models to evaluate its improvement of performances. In all models, the performance of models was improved with adding of EAT. The models with EAT for each of FWHM showed the predictability of yields in all cultivars as R2≥0.80, RMSE≤694 g/plant and RE≤28.3%. Such as this result, if the yield can be predicted regardless of the cultivar, it is considered to be advantageous when predicting the yield over a wide area because it is not require a cultivar classification work as pre-processing in imagery.

본 연구는 고추의 생육특성인 초장, 엽면적, 생체중, 건물중을 조사하였고, 기상요인에 따른 수량 예측 모델 개발을 위하여 수행되었다. 생육도일온도에 따른 고추의 생체중, 건물중, 초장 및 엽면적에 대한 생장 모델(시그모이드 곡선)을 개발하였다. 고추는 정식 후 50일 전후로 초장, 엽면적, 생체중 및 건물중이 지수 함수적으로 증가하였으며, 140일 이후에는 생장요인들이 평행을 이루었다. 그리고 생육도일온도에 따른 고추의 생장을 분석 한 결과 지수 함수적으로 생장이 늘어나는 시점의 GDD는 1,000였다. 고추의 건물중에 대한 상대생장 속도를 계산하는 식은 RGR (dry weight) = 0.0562 + 0.0004 × DAT − 0.00000557 × DAT2 였다. 수확한 적과의 생체중과 건물중으로 고추의 단수를 구하였을 때, 정식 후 112일에 1,3871kg/10a였고, 건고추의 단수는 정식 후 112일에 291kg/10a이였다. 고추 작황예측 프로그램 개발을 위해서는 고추의 생산성에 관여하는 주요 요인을 분석하고, 실시간으로 계측한 생육 및 기상자료를 기반으로 하여 생육모델을 보정 및 검증해야 할 것이다.

파프리카 수확량 예측을 위한 목적으로 온실 환경과 작물의 생육 특성 및 수확량 패턴을 조사 분석하였다. 경남 거창 지역(해발고도 667m)의 유리온실에서 적색계 파프리카 ‘Cupra’와 황색계 파프리카 ‘Fiesta’를 2016년 7월 5일 파종하고, 35일 후인 8월 10일 정식하여 2017년 7월 15일까지 재배하였다. 재식밀도는 두 품종 동일하게 3.66plants/m2로 2줄기로 유인하였다. 정식 후 재배기간 동안 시설의 외부 평균 광량은 14.36MJ/m2/day였고, 온실 내부의 관리에서 24시간 평균온도 20~22oC, CO2 400~700ppm, 24시간 평균 습도 60~75% 수준으로 유지하고자 하였다. 정식 42주 후까지 신장속도는 ‘Cupra’가 7.3cm/week, ‘Fiesta’가 6.9cm/week로 ‘Cupra’가 빨랐다. 첫 착과는 ‘Cupra’가 1.0마디, ‘Fiesta’는 2.7마디에서 나타났으며, 첫 수확은 정식 후 ‘Cupra’가 14주, ‘Fiesta’가 11주로 ‘Fiesta’가 빨랐다. 재배 종료 시까지의 10a당 수확량을 비교해 보면, ‘Fiesta’가 18,848kg, ‘Cupra’가 19,307kg로 ‘Fiesta’가 2.4% 높게 나타났으며, L 사이즈인 200g 이상의 과중 비율은 ‘Cupra’가 27.7%로 ‘Fiesta’보다 7.7%로 높았다. 6월까지의 수확량에서, 착과에서 수확까지의 평균 소요일수는 ‘Cupra’가 72.6일, ‘Fiesta’가 63.8일로 ‘Cupra’가 8.8일이 더 소요되었다. 수확소요일수와 그 기간 누적된 광량과의 관계를 보면, 광량이 증가하는 2월 이후 두 품종 모두 누적광이 많을수록 수확소요일수는 짧아지는 부의 관계를 나타냈다. 1월에 가장 긴 소요일수가 요구되었는데, 이는 낮은 광량으로 생육과 착색이 지연되어 소요일수가 늘어난 것으로 판단된다. 수확량과의 관계에서는 ‘Cupra’는 광량이 증가됨에 따라 수확량이 증가되는 반면, ‘Fiesta’는 불규칙적인 패턴을 보여 품종간의 차이를 보였다.

본 연구는 배추의 작황 예측프로그램을 개발하기 위한 생육조사로 정식시기를 봄과 가을에 2주 간격으로 3회씩 각각 정식하여, 생체중, 건물중, 엽장, 엽폭, 엽수, 엽면적등을 정식후 2주간격으로 조사하였다. 정식 후 일수에 따른 생체중과 건물중의 변화와 GDD에 따른 생체중, 건물중, 엽면적 그리고 엽수의 변화에 대하여 회귀분석하였다. 정식 후 일수에 따른 봄배추와 가을배추의 생장을 S자형 곡선으로 분석한 결과 생체중의 회귀식은 각각 FW=4451.5/[1+exp{-(DAT-34.1)/3.6}](R2=0.992)과 각각 FW=7182.0/[1+exp(-(DAT-53.8)/11.6)](R2=0.979) 였다. 그리고 GDD에 따른 봄배추의 생체중의 모델은 각각 FW=4411.2/[1+exp{-(GDD-585.2)/128.6}] (R2=0.992) 및 FW=13718/[1+exp{-(GDD-1278.6)/219.5}] (R2=0.981)였다. 봄배추와 가을배추의 단위면적당 생산량은 각각 11348.3kg/10a와 1,5128.2kg/10a로 노지재배의 단수와는 차이를 보인 반면에 봄배추의 경우 시설재배의 단수 1,1147.3kg/10a와 유사한 결과를 보였다. 차후에 노지재 배를 통해, 배추의 생산성에 관여하는 주요 요인을 분석하고, 실시간으로 계측한 생육 및 기상자료를 기반으로 하여 보다 정확한 예측프로그램으로 보정 및 검증해야 할 것이다.

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model (R2) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD –8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as 9,790±120 (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables

최근 지구온난화에 따른 이상기상 발생 빈도가 증가하고 있으며 배추 등 일부 채소작물의 저온 및 고온 등으로 인하여 생산량에 문제가 발생하고 있다. 이러한 이상기상 조건 발생시 사전에 생산량을 예측하면 수급을 조절하는데 효과적이라 판단된다. 따라서 본 실험은 기상이변에 따른 봄배추의 생육량을 추정하기 위하여 정식시기와 질소시비량을 달리하여 생육인자간 상관계수를 도출하였다. 그 결과, 정식시기별 최종 생육은 4월 15일과 4월 22일 정식 처리에서 건물중이 각각 168g과 139g으로 타 시기에 비해 높았으며, 질소처리에 따른 차이는 없었다. 기후인자 온도, 일사량, GDD, 그리고 생육인자 엽수, 지상부생체중, 지상부건물중 등의 편상관분석 결과, 유의성이 높은 것으로 나타났다. GDD와 엽수, GDD와 지상부 건물중의 분포를 측정한 결과, 질소시비 수준에 따른 차이는 없었으며, 3차함수로 다항회귀식을 구한 결과, 엽수(y)=-0.0000004x3+0.00042+0.0225x+5.4045(R2=0.9818), 지상부건물중(y)=-0.0000008x3+0.0012-0.0958x+0.3426(R2=0.9584)로 나타났다. 따라서 봄배추 생육기간중에 GDD 측정만으로도 봄배추의 지상부 생산량을 추정할 수 있을 것으로 판단되었다.

모형의 구조, 모델링에 사용되는 자료, 매개변수 등에 포함된 다양한 불확실성 원인들은 수문모의 및 예측결과에 있어 불확실성을 야기한다. 본 연구에서는 강우-유출 및 강우-유사유출 모의가 가능한 분포형 강우-유사-유출 모형을 용담댐 상류유역인 천천유역에 적용하여 수문곡선 및 유사량곡선의 재현성을 평가하고, 다중최적화기법인 MOSCEM을 이용하여 강우-유출 모듈, 강우-유사유출 모듈의 매개변수를 독립적으로 보정한 경우(Case I과 II), 그리고 두 모듈

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity (ECa) , slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with δi~leq2 were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and ECa were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.